From c8d15e296358095ce804f1a5825b2cdc9f551f7c Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Tue, 16 Jan 2018 14:58:48 -0800 Subject: [PATCH 001/423] Updating the docker login command. The email flag is deprecated. (#16171) PiperOrigin-RevId: 181769938 --- tensorflow/tools/docker/parameterized_docker_build.sh | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh index e7de7df856..1214b6b0a3 100755 --- a/tensorflow/tools/docker/parameterized_docker_build.sh +++ b/tensorflow/tools/docker/parameterized_docker_build.sh @@ -408,9 +408,8 @@ fi # Optional: set TF_DOCKER_BUILD_PUSH_WITH_CREDENTIALS to push image if [[ ! -z "${TF_DOCKER_BUILD_PUSH_WITH_CREDENTIALS}" ]]; then - docker login --username "${TF_DOCKER_USERNAME}" \ - --email "${TF_DOCKER_EMAIL}" \ - --password "${TF_DOCKER_PASSWORD}" + docker login -u "${TF_DOCKER_USERNAME}" \ + -p "${TF_DOCKER_PASSWORD}" if [[ $? != "0" ]]; then die "FAIL: Unable to login. Invalid credentials." -- GitLab From cc0922c1af35a850077966d7fe95dfd5c208c4c4 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Thu, 18 Jan 2018 10:27:07 -0800 Subject: [PATCH 002/423] Fixing a typo for the argument to docker push. (#16204) --- tensorflow/tools/docker/parameterized_docker_build.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh index 1214b6b0a3..fa867b65db 100755 --- a/tensorflow/tools/docker/parameterized_docker_build.sh +++ b/tensorflow/tools/docker/parameterized_docker_build.sh @@ -414,7 +414,7 @@ if [[ ! -z "${TF_DOCKER_BUILD_PUSH_WITH_CREDENTIALS}" ]]; then if [[ $? != "0" ]]; then die "FAIL: Unable to login. Invalid credentials." fi - docker push $1 + docker push "${FINAL_IMG}" if [[ $? == "0" ]]; then docker logout echo "Successfully pushed Docker image ${FINAL_IMG}" -- GitLab From b2637597c5f23bbd3f5a71f9ec91b65898ea896f Mon Sep 17 00:00:00 2001 From: Yilei Yang Date: Thu, 14 Dec 2017 11:12:52 -0800 Subject: [PATCH 003/423] Continue to allow flag access before explicit parse. Made tf.flags.FLAGS a wrapper of absl.flags.FLAGS, when the flag is access, parse flags implicitly with sys.argv if not yet. PiperOrigin-RevId: 179068530 --- tensorflow/python/BUILD | 5 ++- tensorflow/python/platform/flags.py | 55 +++++++++++++++++++++++ tensorflow/python/platform/flags_test.py | 57 +++++++++++++++++++++++- 3 files changed, 114 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 5255b69418..375a5a0720 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -171,7 +171,10 @@ tf_py_test( name = "flags_test", size = "small", srcs = ["platform/flags_test.py"], - additional_deps = [":platform"], + additional_deps = [ + ":client_testlib", + ":platform", + ], ) tf_py_test( diff --git a/tensorflow/python/platform/flags.py b/tensorflow/python/platform/flags.py index abd6f3d855..6225db7744 100644 --- a/tensorflow/python/platform/flags.py +++ b/tensorflow/python/platform/flags.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import logging as _logging +import sys as _sys # go/tf-wildcard-import from absl.flags import * # pylint: disable=wildcard-import @@ -59,6 +60,58 @@ def _wrap_define_function(original_function): return tf_decorator.make_decorator(original_function, wrapper) +class _FlagValuesWrapper(object): + """Wrapper class for absl.flags.FLAGS. + + The difference is that tf.flags.FLAGS implicitly parses flags with sys.argv + when accessing the FLAGS values before it's explicitly parsed, + while absl.flags.FLAGS raises an exception. + """ + + def __init__(self, flags_object): + self.__dict__['__wrapped'] = flags_object + + def __getattribute__(self, name): + if name == '__dict__': + return super(_FlagValuesWrapper, self).__getattribute__(name) + return self.__dict__['__wrapped'].__getattribute__(name) + + def __getattr__(self, name): + wrapped = self.__dict__['__wrapped'] + # To maintain backwards compatibility, implicitly parse flags when reading + # a flag. + if not wrapped.is_parsed(): + wrapped(_sys.argv) + return wrapped.__getattr__(name) + + def __setattr__(self, name, value): + return self.__dict__['__wrapped'].__setattr__(name, value) + + def __delattr__(self, name): + return self.__dict__['__wrapped'].__delattr__(name) + + def __dir__(self): + return self.__dict__['__wrapped'].__dir__() + + def __getitem__(self, name): + return self.__dict__['__wrapped'].__getitem__(name) + + def __setitem__(self, name, flag): + return self.__dict__['__wrapped'].__setitem__(name, flag) + + def __len__(self): + return self.__dict__['__wrapped'].__len__() + + def __iter__(self): + return self.__dict__['__wrapped'].__iter__() + + def __str__(self): + return self.__dict__['__wrapped'].__str__() + + def __call__(self, *args, **kwargs): + return self.__dict__['__wrapped'].__call__(*args, **kwargs) + + # pylint: disable=invalid-name,used-before-assignment # absl.flags APIs use `default` as the name of the default value argument. # Allow the following functions continue to accept `default_value`. @@ -68,3 +121,5 @@ DEFINE_bool = DEFINE_boolean DEFINE_float = _wrap_define_function(DEFINE_float) DEFINE_integer = _wrap_define_function(DEFINE_integer) # pylint: enable=invalid-name,used-before-assignment + +FLAGS = _FlagValuesWrapper(FLAGS) # pylint: disable=used-before-assignment diff --git a/tensorflow/python/platform/flags_test.py b/tensorflow/python/platform/flags_test.py index e8200142dd..bd3c8e3995 100644 --- a/tensorflow/python/platform/flags_test.py +++ b/tensorflow/python/platform/flags_test.py @@ -17,11 +17,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import sys import unittest from absl import flags as absl_flags from tensorflow.python.platform import flags +from tensorflow.python.platform import test flags.DEFINE_string( @@ -48,8 +50,59 @@ flags.DEFINE_boolean( class FlagsTest(unittest.TestCase): - def test_global_flags_object(self): - self.assertIs(flags.FLAGS, absl_flags.FLAGS) + def setUp(self): + self.original_flags = flags.FlagValues() + self.wrapped_flags = flags._FlagValuesWrapper(self.original_flags) + flags.DEFINE_string( + 'test', 'default', 'test flag', flag_values=self.wrapped_flags) + + def test_attribute_overrides(self): + # Test that methods defined in absl.flags.FlagValues are the same as the + # wrapped ones. + self.assertEqual(flags.FLAGS.is_parsed, absl_flags.FLAGS.is_parsed) + + def test_getattr(self): + self.assertFalse(self.wrapped_flags.is_parsed()) + with test.mock.patch.object(sys, 'argv', new=['program', '--test=new']): + self.assertEqual('new', self.wrapped_flags.test) + self.assertTrue(self.wrapped_flags.is_parsed()) + + def test_setattr(self): + self.assertEqual('default', self.wrapped_flags.test) + self.wrapped_flags.test = 'new' + self.assertEqual('new', self.wrapped_flags.test) + + def test_delattr(self): + del self.wrapped_flags.test + self.assertNotIn('test', self.wrapped_flags) + with self.assertRaises(AttributeError): + _ = self.wrapped_flags.test + + def test_dir(self): + self.assertEqual(['test'], dir(self.wrapped_flags)) + + def test_getitem(self): + self.assertIs(self.original_flags['test'], self.wrapped_flags['test']) + + def test_setitem(self): + flag = flags.Flag(flags.ArgumentParser(), flags.ArgumentSerializer(), + 'fruit', 'apple', 'the fruit type') + self.wrapped_flags['fruit'] = flag + self.assertIs(self.original_flags['fruit'], self.wrapped_flags['fruit']) + self.assertEqual('apple', self.wrapped_flags.fruit) + + def test_len(self): + self.assertEqual(1, len(self.wrapped_flags)) + + def test_iter(self): + self.assertEqual(['test'], list(self.wrapped_flags)) + + def test_str(self): + self.assertEqual(str(self.wrapped_flags), str(self.original_flags)) + + def test_call(self): + self.wrapped_flags(['program', '--test=new']) + self.assertEqual('new', self.wrapped_flags.test) def test_keyword_arguments(self): test_cases = ( -- GitLab From a1e777e379170ca10f422d78c75d9c709eb692c1 Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Thu, 18 Jan 2018 15:50:16 -0800 Subject: [PATCH 004/423] Update tensorboard dependency to minimum of 0.4.0 This should address https://github.com/tensorflow/tensorboard/issues/877. PiperOrigin-RevId: 182451796 --- 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 0c5f42b123..02310730fe 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -36,7 +36,7 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.12.1', 'six >= 1.10.0', 'protobuf >= 3.4.0', - 'tensorflow-tensorboard', + 'tensorflow-tensorboard >= 0.4.0', ] project_name = 'tensorflow' -- GitLab From 474929984808916ee89e26c8007081aa0975fe80 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 10 Jan 2018 18:32:40 -0800 Subject: [PATCH 005/423] Added the "Getting Started with TensorFlow for ML Beginners" chapter to Get Started home page. PiperOrigin-RevId: 181548668 --- tensorflow/docs_src/get_started/index.md | 32 +++++++++---------- tensorflow/docs_src/get_started/leftnav_files | 5 +++ 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index d0cb69d211..b7bd1286e3 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -1,35 +1,35 @@ # Getting Started TensorFlow is a tool for machine learning. While it contains a wide range of -functionality, it is mainly designed for deep neural network models. +functionality, TensorFlow is mainly designed for deep neural network models. -The fastest way to build a fully-featured model trained on your data is to use -TensorFlow's high-level API. In the following examples, we will use the -high-level API on the classic [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set). -We will train a model that predicts what species a flower is based on its -characteristics, and along the way get a quick introduction to the basic tasks -in TensorFlow using Estimators. +TensorFlow provides many APIs. This section focuses on the high-level APIs. +If you are new to TensorFlow, begin by reading one of the following documents: -This tutorial is divided into the following parts: + * @{$get_started/get_started_for_beginners}, which is aimed at readers + new to machine learning. + * @{$get_started/premade_estimators}, which is aimed at readers who have + experience in machine learning. - * @{$get_started/premade_estimators}, which shows you - how to quickly setup prebuilt models to train on in-memory data. - * @{$get_started/checkpoints}, which shows you how to save training progress, +Then, read the following documents, which demonstrate the key features +in the high-level APIs: + + * @{$get_started/checkpoints}, which explains how to save training progress and resume where you left off. * @{$get_started/feature_columns}, which shows how an Estimator can handle a variety of input data types without changes to the model. - * @{$get_started/datasets_quickstart}, which is a minimal introduction to - the TensorFlow's input pipelines. + * @{$get_started/datasets_quickstart}, which introduces TensorFlow's + input pipelines. * @{$get_started/custom_estimators}, which demonstrates how to build and train models you design yourself. For more advanced users: * The @{$low_level_intro$Low Level Introduction} demonstrates how to use - tensorflow outside of the Estimator framework, for debugging and + TensorFlow outside of the Estimator framework, for debugging and experimentation. - * The remainder of the @{$programmers_guide$Programmer's Guide} contains - in-depth guides to various major components of TensorFlow. + * 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 668daae9cb..437791d6a3 100644 --- a/tensorflow/docs_src/get_started/leftnav_files +++ b/tensorflow/docs_src/get_started/leftnav_files @@ -1,5 +1,10 @@ index.md + +### Getting Started +get_started_for_beginners.md premade_estimators.md + +### Details checkpoints.md feature_columns.md datasets_quickstart.md -- GitLab From 0c97ad742c176b3854c03067afc42eac20810fe9 Mon Sep 17 00:00:00 2001 From: Austin Anderson Date: Wed, 24 Jan 2018 12:52:02 -0800 Subject: [PATCH 006/423] Remove note about Ubuntu 14 incompatibility due to future change plans --- RELEASE.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 3bb612848d..eb7323dac9 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -2,8 +2,6 @@ ## Breaking Changes * Prebuilt binaries are now built against CUDA 9 and cuDNN 7. -* Our Linux binaries are built using ubuntu 16 containers, potentially - introducing glibc incompatibility issues with ubuntu 14. * Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs. -- GitLab From c75a6087c19ed7203081af0b0f0bdb215aedfc00 Mon Sep 17 00:00:00 2001 From: Austin Anderson Date: Wed, 24 Jan 2018 13:24:18 -0800 Subject: [PATCH 007/423] Update version names to 1.5.0 from 1.5.0-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 | 22 +++++++++---------- tensorflow/docs_src/install/install_mac.md | 10 ++++----- .../docs_src/install/install_sources.md | 14 ++++++------ tensorflow/tools/pip_package/setup.py | 2 +- 8 files changed, 38 insertions(+), 38 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index c2fad5dbd8..f28d89125e 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 "" #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 ba1a4118ae..14add7c77e 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 87cc647317..d2af9d9843 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 49dc3cf47f..d87fedcf77 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.5.0-rc1 + 1.5.0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.5.0-rc1 + 1.5.0 @@ -123,12 +123,12 @@ instead: org.tensorflow libtensorflow - 1.5.0-rc1 + 1.5.0 org.tensorflow libtensorflow_jni_gpu - 1.5.0-rc1 + 1.5.0 ``` @@ -147,7 +147,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.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_64-1.5.0-rc1.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.5.0.zip). 3. Extract this .zip file. @@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.5.0-rc1.jar HelloTF.java
+
javac -cp libtensorflow-1.5.0.jar HelloTF.java
### Running @@ -239,11 +239,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.5.0-rc1.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.5.0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.5.0-rc1.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.5.0.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 f94bd4e5e6..ac8836748b 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -188,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv: Virtualenv environment: 
(tensorflow)$ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl If you encounter installation problems, see [Common Installation Problems](#common_installation_problems). @@ -293,7 +293,7 @@ take the following steps: 
      $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
If this step fails, see @@ -480,7 +480,7 @@ 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-1.5.0rc1-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl @@ -648,14 +648,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -667,14 +667,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -686,14 +686,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl
@@ -705,14 +705,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-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 1851275571..ae4d760a2c 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -115,7 +115,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -238,7 +238,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -347,7 +347,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl @@ -520,7 +520,7 @@ This section documents the relevant values for Mac OS installations. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl
@@ -528,7 +528,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index f2af2919df..52e1bc79c3 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -355,10 +355,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.5.0rc1 on Linux: +for TensorFlow 1.5.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0rc1-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0-py2-none-any.whl
## Validate your installation @@ -457,8 +457,8 @@ Stack Overflow and specify the `tensorflow` tag. - - + + @@ -474,7 +474,7 @@ Stack Overflow and specify the `tensorflow` tag. **Mac**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.5.0-rc1CPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.0N/AN/A
tensorflow_gpu-1.5.0-rc1GPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.079
tensorflow-1.5.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.0N/AN/A
tensorflow_gpu-1.5.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.079
tensorflow-1.4.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.5.4N/AN/A
tensorflow_gpu-1.4.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.5.468
tensorflow-1.3.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.4.5N/AN/A
- + @@ -487,8 +487,8 @@ Stack Overflow and specify the `tensorflow` tag. **Windows**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.5.0-rc1CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
tensorflow-1.5.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
tensorflow-1.4.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.5.4N/AN/A
tensorflow-1.3.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.4.5N/AN/A
tensorflow-1.2.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.4.5N/AN/A
- - + + diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 0c5f42b123..7654b8df1b 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -29,7 +29,7 @@ from setuptools.dist import Distribution # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.5.0-rc1' +_VERSION = '1.5.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From 1df1544aeb8a6311c98a0d9ee9b6946e035fdbeb Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 24 Jan 2018 15:23:12 -0800 Subject: [PATCH 008/423] Make tensor_util_test.py work with the C API enabled. Some of the shape inference error messages have changed. We also need to wrap an InvalidArgumentError as a ValueError for backwards compat. PiperOrigin-RevId: 183150857 --- .../python/framework/tensor_util_test.py | 15 +++++++++++++-- tensorflow/python/framework/ops.py | 18 +++++++++++------- 2 files changed, 24 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/framework/python/framework/tensor_util_test.py b/tensorflow/contrib/framework/python/framework/tensor_util_test.py index 2effe8eb26..8cdb340f2d 100644 --- a/tensorflow/contrib/framework/python/framework/tensor_util_test.py +++ b/tensorflow/contrib/framework/python/framework/tensor_util_test.py @@ -30,6 +30,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl 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 variables as variables_lib from tensorflow.python.platform import test @@ -77,6 +78,7 @@ class AssertScalarIntTest(test.TestCase): [3, 4], dtype=dtypes.int32)) +@test_util.with_c_api class WithShapeTest(test.TestCase): def _assert_with_shape(self, tensor, expected_value, expected_shape, @@ -213,16 +215,25 @@ class WithShapeTest(test.TestCase): tensor_partial_shape.set_shape([None, 2]) for incompatible_shape in [[0], [1]]: + if ops._USE_C_API: + error_message = "Shapes must be equal rank, but are 2 and 1" + else: + error_message = r"Shapes \(\?, 2\) and \([01],\) are not compatible" self.assertRaisesRegexp( - ValueError, r"Shapes \(\?, 2\) and \([01],\) are not compatible", + ValueError, error_message, tensor_util.with_shape, incompatible_shape, tensor_partial_shape) for incompatible_shape in [[1, 2, 1]]: self.assertRaisesRegexp(ValueError, "Dimensions must be equal", tensor_util.with_shape, incompatible_shape, tensor_partial_shape) for incompatible_shape in [[2, 1]]: + if ops._USE_C_API: + error_message = (r"Dimension 1 in both shapes must be equal, but are " + r"2 and 1. Shapes are \[\?,2\] and \[2,1\].") + else: + error_message = r"Shapes \(\?, 2\) and \(2, 1\) are not compatible" self.assertRaisesRegexp( - ValueError, r"Shapes \(\?, 2\) and \(2, 1\) are not compatible", + ValueError, error_message, tensor_util.with_shape, incompatible_shape, tensor_partial_shape) compatible_shape = [2, 2] diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index a1c2e07e94..b107670275 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -481,13 +481,17 @@ class Tensor(_TensorLike): dim_list.append(-1) else: dim_list.append(dim.value) - with errors.raise_exception_on_not_ok_status() as status: - c_api.TF_GraphSetTensorShape_wrapper( - self._op._graph._c_graph, # pylint: disable=protected-access - self._as_tf_output(), - dim_list, - unknown_shape, - status) + try: + with errors.raise_exception_on_not_ok_status() as status: + c_api.TF_GraphSetTensorShape_wrapper( + self._op._graph._c_graph, # pylint: disable=protected-access + self._as_tf_output(), + dim_list, + unknown_shape, + status) + except errors.InvalidArgumentError as e: + # Convert to ValueError for backwards compatibility. + raise ValueError(str(e)) @property def value_index(self): -- GitLab From 7bf8ccdb4ef5b0b28c1cf0d5084e07ffbf0e2703 Mon Sep 17 00:00:00 2001 From: Yutaka Leon Date: Wed, 24 Jan 2018 15:45:55 -0800 Subject: [PATCH 009/423] Set export_outs in KMeans' EstimatorSpec. PiperOrigin-RevId: 183154542 --- .../contrib/factorization/python/ops/kmeans.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py index 9a5413fc3f..4d0f9b2424 100644 --- a/tensorflow/contrib/factorization/python/ops/kmeans.py +++ b/tensorflow/contrib/factorization/python/ops/kmeans.py @@ -25,6 +25,7 @@ import time from tensorflow.contrib.factorization.python.ops import clustering_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_output from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -32,6 +33,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics from tensorflow.python.ops import state_ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.saved_model import signature_constants from tensorflow.python.summary import summary from tensorflow.python.training import session_run_hook from tensorflow.python.training import training_util @@ -207,6 +209,15 @@ class _ModelFn(object): training_hooks.append( _LossRelativeChangeHook(loss, self._relative_tolerance)) + export_outputs = { + KMeansClustering.ALL_DISTANCES: + export_output.PredictOutput(all_distances[0]), + KMeansClustering.CLUSTER_INDEX: + export_output.PredictOutput(model_predictions[0]), + signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: + export_output.PredictOutput(model_predictions[0]) + } + return model_fn_lib.EstimatorSpec( mode=mode, predictions={ @@ -216,7 +227,8 @@ class _ModelFn(object): loss=loss, train_op=training_op, eval_metric_ops={KMeansClustering.SCORE: metrics.mean(loss)}, - training_hooks=training_hooks) + training_hooks=training_hooks, + export_outputs=export_outputs) # TODO(agarwal,ands): support sharded input. -- GitLab From ffa63e57bdd703ae051ae849af5b5a272fca2223 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 24 Jan 2018 16:10:12 -0800 Subject: [PATCH 010/423] [TF:XLA] Replace most of HloProfilePrinter by a protocol buffer This change replaces the meat of HloProfilePrinter with a protobuf HloProfilePrinterData. The original plan was to serialize HloProfilePrinter into C++ source code and put that in a .cc file along with the string for the xla::ProgramShape. However, since we now directly serialize xla::ProgramShape into a .o file, for consistency I think we should do the same thing for HloProfilePrinter (instead of adding yet another output file to tfcompile). The change itself is fairly simple, it is large mostly due to the mass renaming I had to do. PiperOrigin-RevId: 183158192 --- .../tf2xla/xla_compiled_cpu_function.cc | 2 +- .../tf2xla/xla_compiled_cpu_function.h | 20 +-- .../tf2xla/xla_jit_compiled_cpu_function.cc | 6 +- tensorflow/compiler/xla/service/BUILD | 6 + .../compiler/xla/service/cpu/cpu_compiler.cc | 10 +- .../xla/service/cpu/cpu_executable.cc | 4 +- .../compiler/xla/service/cpu/cpu_executable.h | 2 +- .../service/cpu/parallel_cpu_executable.cc | 4 +- .../xla/service/cpu/parallel_cpu_executable.h | 2 +- tensorflow/compiler/xla/service/executable.cc | 2 +- tensorflow/compiler/xla/service/executable.h | 21 ++-- .../compiler/xla/service/gpu/gpu_compiler.cc | 4 +- .../xla/service/gpu/gpu_executable.cc | 4 +- .../compiler/xla/service/gpu/gpu_executable.h | 2 +- .../xla/service/hlo_execution_profile.cc | 118 ++++++++---------- .../xla/service/hlo_execution_profile.h | 15 ++- .../xla/service/hlo_execution_profile_test.cc | 4 +- .../xla/service/hlo_profile_printer.cc | 45 ++++--- .../xla/service/hlo_profile_printer.h | 79 +----------- .../service/hlo_profile_printer_data.proto | 60 +++++++++ tensorflow/compiler/xla/service/service.cc | 2 +- .../xla/tests/xla_hlo_profile_test.cc | 3 +- tensorflow/compiler/xla/util.h | 8 +- 23 files changed, 208 insertions(+), 215 deletions(-) create mode 100644 tensorflow/compiler/xla/service/hlo_profile_printer_data.proto diff --git a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc index 79da701fd2..672e19bd93 100644 --- a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc +++ b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc @@ -29,7 +29,7 @@ XlaCompiledCpuFunction::XlaCompiledCpuFunction(const StaticData& static_data, arg_names_(static_data.arg_names), result_names_(static_data.result_names), program_shape_(static_data.program_shape), - hlo_profile_printer_(static_data.hlo_profile_printer) { + hlo_profile_printer_data_(static_data.hlo_profile_printer_data) { // Allocate arg and temp buffers. if (alloc_mode == AllocMode::ARGS_RESULTS_PROFILES_AND_TEMPS) { alloc_args_ = tensorflow::tfcompile::runtime::MallocContiguousBuffers( diff --git a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h index e0ae3ed9a8..48a8c083ca 100644 --- a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h +++ b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h @@ -26,7 +26,7 @@ limitations under the License. // never use this functionality. namespace xla { class ProgramShape; -class HloProfilePrinter; +class HloProfilePrinterData; } namespace tensorflow { @@ -77,12 +77,14 @@ class XlaCompiledCpuFunction { // [Optional] Arg and result shapes. const xla::ProgramShape* program_shape = nullptr; - // [Optional] Profile printer. Null if profiling is disabled. - const xla::HloProfilePrinter* hlo_profile_printer = nullptr; + // [Optional] Profile printer data. Null if profiling is disabled. + const xla::HloProfilePrinterData* hlo_profile_printer_data = nullptr; // [Optional] The number of profile counters expected in the profile counter // buffer by the generated code and hlo_profile_printer. 0 if profiling is - // disabled. + // disabled. This information is already present in + // hlo_profile_printer_data but xla::HloProfilePrinterData is forward + // declared so we don't have access to that information here. int64 profile_counters_size = 0; }; @@ -205,10 +207,12 @@ class XlaCompiledCpuFunction { // program shape isn't available. const xla::ProgramShape* ProgramShape() const { return program_shape_; } - bool hlo_profiling_enabled() const { return hlo_profile_printer_ != nullptr; } - const xla::HloProfilePrinter& hlo_profile_printer() const { + bool hlo_profiling_enabled() const { + return hlo_profile_printer_data_ != nullptr; + } + const xla::HloProfilePrinterData& hlo_profile_printer_data() const { assert(hlo_profiling_enabled()); - return *hlo_profile_printer_; + return *hlo_profile_printer_data_; } private: @@ -234,7 +238,7 @@ class XlaCompiledCpuFunction { const char** arg_names_ = nullptr; const char** result_names_ = nullptr; const xla::ProgramShape* program_shape_ = nullptr; - const xla::HloProfilePrinter* hlo_profile_printer_ = nullptr; + const xla::HloProfilePrinterData* hlo_profile_printer_data_ = nullptr; }; } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc index 584417bc72..1fe6e69ff2 100644 --- a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc +++ b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc @@ -182,10 +182,10 @@ XlaJitCompiledCpuFunction::Compile( jit->static_data_.program_shape = jit->program_shape_.get(); if (cpu_executable->hlo_profiling_enabled()) { - jit->static_data_.hlo_profile_printer = - &cpu_executable->hlo_profile_printer(); + jit->static_data_.hlo_profile_printer_data = + &cpu_executable->hlo_profile_printer_data(); jit->static_data_.profile_counters_size = - cpu_executable->hlo_profile_printer().profile_counters_size(); + cpu_executable->hlo_profile_printer_data().profile_counters_size(); } return std::move(jit_unique_ptr); diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index d0e2dc88ea..2c5f3ea1dd 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -29,6 +29,11 @@ xla_proto_library( deps = ["//tensorflow/compiler/xla:xla_data_proto"], ) +xla_proto_library( + name = "hlo_profile_printer_data", + srcs = ["hlo_profile_printer_data.proto"], +) + # Filegroup used to collect source files for dependency checking. filegroup( name = "c_srcs", @@ -2267,6 +2272,7 @@ cc_library( srcs = ["hlo_profile_printer.cc"], hdrs = ["hlo_profile_printer.h"], deps = [ + ":hlo_profile_printer_data", ":human_readable_profile_builder", "//tensorflow/compiler/xla:types", ], diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index f0507982b3..33af77e1a8 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -485,7 +485,7 @@ StatusOr> CpuCompiler::RunBackend( std::unordered_map instruction_to_profile_idx; std::unordered_map computation_to_profile_idx; std::unique_ptr hlo_profile_index_map; - std::unique_ptr hlo_profile_printer; + std::unique_ptr hlo_profile_printer_data; if (module->config().hlo_profiling_enabled()) { hlo_profile_index_map = MakeUnique(*module); @@ -505,8 +505,8 @@ StatusOr> CpuCompiler::RunBackend( HloCostAnalysis cost_analysis(shape_size_bytes); TF_RETURN_IF_ERROR(entry_computation->Accept(&cost_analysis)); - hlo_profile_printer = - CreateHloProfilePrinter(*hlo_profile_index_map, cost_analysis); + hlo_profile_printer_data = + CreateHloProfilePrinterData(*hlo_profile_index_map, cost_analysis); computation_to_profile_idx = hlo_profile_index_map->computation_to_profile_idx(); } @@ -619,7 +619,7 @@ StatusOr> CpuCompiler::RunBackend( cpu_executable.reset(new ParallelCpuExecutable( std::move(jit), std::move(assignment), std::move(module), std::move(function_names), std::move(aligned_constants), - std::move(hlo_profile_printer), std::move(hlo_profile_index_map))); + std::move(hlo_profile_printer_data), std::move(hlo_profile_index_map))); if (embed_ir_in_executable) { static_cast(*cpu_executable) @@ -698,7 +698,7 @@ StatusOr> CpuCompiler::RunBackend( jit->AddModule(std::move(llvm_module)); cpu_executable.reset(new CpuExecutable( std::move(jit), std::move(assignment), std::move(module), function_name, - std::move(hlo_profile_printer), std::move(hlo_profile_index_map))); + std::move(hlo_profile_printer_data), std::move(hlo_profile_index_map))); if (embed_ir_in_executable) { static_cast(*cpu_executable) diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc index f335bd1bbc..802d0a6fb4 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc @@ -55,9 +55,9 @@ CpuExecutable::CpuExecutable( std::unique_ptr assignment, std::unique_ptr hlo_module, const string& entry_function_name, - std::unique_ptr hlo_profile_printer, + std::unique_ptr hlo_profile_printer_data, std::unique_ptr hlo_profile_index_map) - : Executable(std::move(hlo_module), std::move(hlo_profile_printer), + : Executable(std::move(hlo_module), std::move(hlo_profile_printer_data), std::move(hlo_profile_index_map)), jit_(std::move(jit)), assignment_(std::move(assignment)) { diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.h b/tensorflow/compiler/xla/service/cpu/cpu_executable.h index 50443a5995..267b89a10b 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.h @@ -51,7 +51,7 @@ class CpuExecutable : public Executable { std::unique_ptr assignment, std::unique_ptr hlo_module, const string& entry_function_name, - std::unique_ptr hlo_profile_printer, + std::unique_ptr hlo_profile_printer_data, std::unique_ptr hlo_profile_index_map); ~CpuExecutable() override {} diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc index d1b88b27f0..cd997f0789 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc @@ -61,9 +61,9 @@ ParallelCpuExecutable::ParallelCpuExecutable( std::unique_ptr> function_names, std::unordered_map> aligned_constants, - std::unique_ptr hlo_profile_printer, + std::unique_ptr hlo_profile_printer_data, std::unique_ptr hlo_profile_index_map) - : Executable(std::move(hlo_module), std::move(hlo_profile_printer), + : Executable(std::move(hlo_module), std::move(hlo_profile_printer_data), std::move(hlo_profile_index_map)), jit_(std::move(jit)), assignment_(std::move(assignment)), diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h index 90ac94ef92..c393e9b8ea 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h +++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.h @@ -55,7 +55,7 @@ class ParallelCpuExecutable : public Executable { std::unordered_map> aligned_constants, - std::unique_ptr hlo_profile_printer, + std::unique_ptr hlo_profile_printer_data, std::unique_ptr hlo_profile_index_map); ~ParallelCpuExecutable() override {} diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc index 21e7fbea29..90481c7a88 100644 --- a/tensorflow/compiler/xla/service/executable.cc +++ b/tensorflow/compiler/xla/service/executable.cc @@ -73,7 +73,7 @@ StatusOr> Executable::ExecuteOnStreamWrapper( std::unique_ptr profile_ptr = module_config().debug_options().xla_hlo_profile() && hlo_profiling_enabled() - ? MakeUnique(&hlo_profile_printer(), + ? MakeUnique(&hlo_profile_printer_data(), &hlo_profile_index_map()) : nullptr; diff --git a/tensorflow/compiler/xla/service/executable.h b/tensorflow/compiler/xla/service/executable.h index 5ecfdffe21..0aee535ee7 100644 --- a/tensorflow/compiler/xla/service/executable.h +++ b/tensorflow/compiler/xla/service/executable.h @@ -44,13 +44,14 @@ namespace xla { // interface that is used for launching compiled programs across platforms. class Executable { public: - explicit Executable(std::unique_ptr hlo_module, - std::unique_ptr hlo_profile_printer, - std::unique_ptr hlo_profile_index_map) + explicit Executable( + std::unique_ptr hlo_module, + std::unique_ptr hlo_profile_printer_data, + std::unique_ptr hlo_profile_index_map) : hlo_module_(std::move(hlo_module)), - hlo_profile_printer_(std::move(hlo_profile_printer)), + hlo_profile_printer_data_(std::move(hlo_profile_printer_data)), hlo_profile_index_map_(std::move(hlo_profile_index_map)) { - CHECK_EQ(hlo_profile_printer_.get() == nullptr, + CHECK_EQ(hlo_profile_printer_data_.get() == nullptr, hlo_profile_index_map_.get() == nullptr); } virtual ~Executable() {} @@ -116,9 +117,9 @@ class Executable { "Equality test on this executable is not implemented."); } - const HloProfilePrinter& hlo_profile_printer() const { + const HloProfilePrinterData& hlo_profile_printer_data() const { CHECK(hlo_profiling_enabled()); - return *hlo_profile_printer_; + return *hlo_profile_printer_data_; } const HloProfileIndexMap& hlo_profile_index_map() const { @@ -129,7 +130,9 @@ class Executable { // Returns whether this executable was compiled with HLO profilings support // enabled. If not, the caller should not expect an hlo_execution_profile // passed to ExecuteOnStream above to be populated during execution. - bool hlo_profiling_enabled() const { return hlo_profile_printer_ != nullptr; } + bool hlo_profiling_enabled() const { + return hlo_profile_printer_data_ != nullptr; + } const HloModule& module() const { return *hlo_module_; } @@ -179,7 +182,7 @@ class Executable { // execution. int64 execution_count_ = 0; - std::unique_ptr hlo_profile_printer_; + std::unique_ptr hlo_profile_printer_data_; std::unique_ptr hlo_profile_index_map_; }; diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index 21798ed606..0cca3ca092 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -593,14 +593,14 @@ StatusOr> GpuCompiler::RunBackend( XLA_VLOG_LINES(2, thunk_schedule->ToString()); std::unique_ptr profile_index_map; - std::unique_ptr profile_printer; + std::unique_ptr profile_printer; if (module->config().hlo_profiling_enabled()) { HloCostAnalysis cost_analysis(ShapeSizeBytesFunction()); TF_RETURN_IF_ERROR(module->entry_computation()->Accept(&cost_analysis)); profile_index_map = MakeUnique(*module); profile_printer = - CreateHloProfilePrinter(*profile_index_map, cost_analysis); + CreateHloProfilePrinterData(*profile_index_map, cost_analysis); } auto* gpu_executable = new GpuExecutable( diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 51d164cdf4..f5d67b9ea9 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -116,9 +116,9 @@ GpuExecutable::GpuExecutable( std::unique_ptr thunk_schedule, std::unique_ptr hlo_module, std::unique_ptr assignment, - std::unique_ptr hlo_profile_printer, + std::unique_ptr hlo_profile_printer_data, std::unique_ptr hlo_profile_index_map) - : Executable(std::move(hlo_module), std::move(hlo_profile_printer), + : Executable(std::move(hlo_module), std::move(hlo_profile_printer_data), std::move(hlo_profile_index_map)), ptx_(ptx), cubin_(cubin), diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.h b/tensorflow/compiler/xla/service/gpu/gpu_executable.h index 00da64dfad..b19cfd43de 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.h @@ -54,7 +54,7 @@ class GpuExecutable : public Executable { std::unique_ptr thunk_schedule, std::unique_ptr hlo_module, std::unique_ptr assignment, - std::unique_ptr hlo_profile_printer, + std::unique_ptr hlo_profile_printer_data, std::unique_ptr hlo_profile_index_map); // This should be called after set_ir_module_string. diff --git a/tensorflow/compiler/xla/service/hlo_execution_profile.cc b/tensorflow/compiler/xla/service/hlo_execution_profile.cc index 849aac0b12..f0df93b61d 100644 --- a/tensorflow/compiler/xla/service/hlo_execution_profile.cc +++ b/tensorflow/compiler/xla/service/hlo_execution_profile.cc @@ -40,83 +40,75 @@ HloProfileIndexMap::HloProfileIndexMap(const HloModule& module) { } } -std::unique_ptr CreateHloProfilePrinter( +std::unique_ptr CreateHloProfilePrinterData( const HloProfileIndexMap& hlo_profile_index_map, const HloCostAnalysis& cost_analysis) { - using HloComputationInfo = HloProfilePrinter::HloComputationInfo; - using HloInstructionInfo = HloProfilePrinter::HloInstructionInfo; - - HloComputationInfo* computation_infos = - new HloComputationInfo[hlo_profile_index_map.computation_count()]; - - // There are two "indices" in play here. The first one is the index of the - // HloComputationInfo or HloInstructionInfo in the array that contains said - // HloComputationInfo or HloInstructionInfo. The second index is the index of - // the HloComputationInfo or HloInstructionInfo in the profile counters array, - // as decided by hlo_profile_index_map. The latter index is always referred - // to as "profile_index". - - size_t computation_index_in_static_data = 0; - size_t max_profile_index = hlo_profile_index_map.total_count(); - for (const auto& pair : hlo_profile_index_map.computation_to_profile_idx()) { - CHECK_LT(pair.second, max_profile_index); + using HloComputationInfo = HloProfilePrinterData::HloComputationInfo; + using HloInstructionInfo = HloProfilePrinterData::HloInstructionInfo; + + size_t profile_counters_size = hlo_profile_index_map.total_count(); + + std::unique_ptr profile_printer_data = + MakeUnique(); + profile_printer_data->set_profile_counters_size(profile_counters_size); + profile_printer_data->mutable_computation_infos()->Reserve( + hlo_profile_index_map.computation_count()); + + const auto& computation_to_profile_idx_map = + hlo_profile_index_map.computation_to_profile_idx(); + + // computation_to_profile_idx_map's order is not deterministic so create a + // deterministic computation_and_profile_idx_list so that we end up with a + // deterministic HloProfilePrinterData protobuf. + + std::vector> + computation_and_profile_idx_list(computation_to_profile_idx_map.begin(), + computation_to_profile_idx_map.end()); + + // The profile indices were computed deterministically in + // HloProfileIndexMap::HloProfileIndexMap. + c_sort(computation_and_profile_idx_list, + [](const std::pair& left, + const std::pair& right) { + return left.second < right.second; + }); + + for (const auto& pair : computation_and_profile_idx_list) { + CHECK_LT(pair.second, profile_counters_size); const HloComputation* computation = pair.first; - size_t current_computation_index = computation_index_in_static_data++; HloComputationInfo* computation_info = - &computation_infos[current_computation_index]; + profile_printer_data->add_computation_infos(); - computation_info->name = strdup(computation->name().c_str()); - computation_info->profile_index = pair.second; - computation_info->instructions = - new HloInstructionInfo[computation->instruction_count()]; - computation_info->instructions_size = computation->instruction_count(); + computation_info->set_name(computation->name()); + computation_info->set_profile_index(pair.second); + computation_info->mutable_instruction_infos()->Reserve( + computation->instruction_count()); - size_t instruction_index_in_static_data = 0; for (const HloInstruction* hlo : computation->instructions()) { - HloProfilePrinter::HloInstructionInfo* instruction_info = - &computation_info->instructions[instruction_index_in_static_data++]; - instruction_info->long_name = strdup(hlo->ToString().c_str()); - instruction_info->short_name = strdup( - hlo->ToString(HloPrintOptions().set_compact_operands(true)).c_str()); - instruction_info->category = strdup(hlo->ToCategory().c_str()); - instruction_info->flop_count = cost_analysis.flop_count(*hlo); - instruction_info->transcendental_count = - cost_analysis.transcendental_count(*hlo); - instruction_info->bytes_accessed = cost_analysis.bytes_accessed(*hlo); - instruction_info->optimal_seconds = cost_analysis.optimal_seconds(*hlo); - instruction_info->profile_index = - hlo_profile_index_map.GetProfileIndexFor(*hlo); - CHECK_LT(instruction_info->profile_index, max_profile_index); + HloInstructionInfo* instruction_info = + computation_info->add_instruction_infos(); + instruction_info->set_long_name(hlo->ToString()); + instruction_info->set_short_name( + hlo->ToString(HloPrintOptions().set_compact_operands(true))); + instruction_info->set_category(hlo->ToCategory()); + instruction_info->set_flop_count(cost_analysis.flop_count(*hlo)); + instruction_info->set_transcendental_count( + cost_analysis.transcendental_count(*hlo)); + instruction_info->set_bytes_accessed(cost_analysis.bytes_accessed(*hlo)); + instruction_info->set_optimal_seconds( + cost_analysis.optimal_seconds(*hlo)); + instruction_info->set_profile_index( + hlo_profile_index_map.GetProfileIndexFor(*hlo)); } } - auto deleter = [](HloProfilePrinter::HloComputationInfo* computation_infos, - int64 computation_infos_size) { - for (int64 i = 0; i < computation_infos_size; i++) { - HloInstructionInfo* instruction_infos = computation_infos[i].instructions; - for (int64 j = 0; j < computation_infos[i].instructions_size; j++) { - // We can't make instruction_infos[j].long_name etc. non-const pointers - // since they may point into static storage, so we have a const_cast - // here. - free(const_cast(instruction_infos[j].long_name)); - free(const_cast(instruction_infos[j].short_name)); - free(const_cast(instruction_infos[j].category)); - } - delete[] instruction_infos; - free(const_cast(computation_infos[i].name)); - } - delete[] computation_infos; - }; - - return MakeUnique( - computation_infos, hlo_profile_index_map.computation_count(), - /*profile_counters_size=*/max_profile_index, deleter); + return profile_printer_data; } HloExecutionProfile::HloExecutionProfile( - const HloProfilePrinter* hlo_profile_printer, + const HloProfilePrinterData* hlo_profile_printer_data, const HloProfileIndexMap* hlo_profile_index_map) - : hlo_profile_printer_(*hlo_profile_printer), + : hlo_profile_printer_data_(*hlo_profile_printer_data), hlo_profile_index_map_(*hlo_profile_index_map), profile_counters_( /*count*/ hlo_profile_index_map_.total_count(), diff --git a/tensorflow/compiler/xla/service/hlo_execution_profile.h b/tensorflow/compiler/xla/service/hlo_execution_profile.h index 1a6b069609..6fb91b9bef 100644 --- a/tensorflow/compiler/xla/service/hlo_execution_profile.h +++ b/tensorflow/compiler/xla/service/hlo_execution_profile.h @@ -77,8 +77,8 @@ class HloProfileIndexMap { std::unordered_map computation_to_profile_idx_; }; -// Create an instance of `HloProfilePrinter` that owns its memory. -std::unique_ptr CreateHloProfilePrinter( +// Create an instance of `HloProfilePrinterData`. +std::unique_ptr CreateHloProfilePrinterData( const HloProfileIndexMap& hlo_profile_index_map, const HloCostAnalysis& cost_analysis); @@ -90,7 +90,7 @@ class HloExecutionProfile { public: using DeviceDescription = perftools::gputools::DeviceDescription; - HloExecutionProfile(const HloProfilePrinter* hlo_profile_printer, + HloExecutionProfile(const HloProfilePrinterData* hlo_profile_printer_data, const HloProfileIndexMap* hlo_profile_index_map); // Record how many cycles this HLO took to execute. @@ -117,11 +117,10 @@ class HloExecutionProfile { // debugging; e.g. emits cycle counts, execution time at the nominal device // frequency, and the effective throughput given the provided cost_analysis // for the operations in a given computation. Returns an empty string if it - // wasn't possible to generate a printable version. cost_analysis should be a - // clean analysis that can be used to visit the computation. + // wasn't possible to generate a printable version. string ToString(const DeviceDescription& device_description) const { - return hlo_profile_printer_.ToString(profile_counters_.data(), - device_description.clock_rate_ghz()); + return PrintHloProfile(hlo_profile_printer_data_, profile_counters_.data(), + device_description.clock_rate_ghz()); } std::vector* mutable_profile_counters() { return &profile_counters_; } @@ -130,7 +129,7 @@ class HloExecutionProfile { } private: - const HloProfilePrinter& hlo_profile_printer_; + const HloProfilePrinterData& hlo_profile_printer_data_; const HloProfileIndexMap& hlo_profile_index_map_; // Stores per-Hlo profile counters. This is the only thing that changes when diff --git a/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc b/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc index b1e6729e2b..a0cb28246d 100644 --- a/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc +++ b/tensorflow/compiler/xla/service/hlo_execution_profile_test.cc @@ -73,8 +73,8 @@ TEST_F(HloExecutionProfileTest, Basic) { HloCostAnalysis cost_analysis(shape_size_function); HloProfileIndexMap profile_index_map(*hlo_module); - std::unique_ptr profile_printer = - CreateHloProfilePrinter(profile_index_map, cost_analysis); + std::unique_ptr profile_printer = + CreateHloProfilePrinterData(profile_index_map, cost_analysis); HloExecutionProfile execution_profile(profile_printer.get(), &profile_index_map); diff --git a/tensorflow/compiler/xla/service/hlo_profile_printer.cc b/tensorflow/compiler/xla/service/hlo_profile_printer.cc index e944ad1513..dcc2279301 100644 --- a/tensorflow/compiler/xla/service/hlo_profile_printer.cc +++ b/tensorflow/compiler/xla/service/hlo_profile_printer.cc @@ -18,20 +18,20 @@ limitations under the License. #include "tensorflow/compiler/xla/service/human_readable_profile_builder.h" namespace xla { -string HloProfilePrinter::ToString(const int64* counters, - double clock_rate_ghz) const { +string PrintHloProfile(const HloProfilePrinterData& hlo_profile_printer_data, + const int64* counters, double clock_rate_ghz) { + using HloComputationInfo = HloProfilePrinterData::HloComputationInfo; + using HloInstructionInfo = HloProfilePrinterData::HloInstructionInfo; + string result; - for (int computation_idx = 0; computation_idx < computation_infos_size_; - computation_idx++) { - const HloComputationInfo& computation = computation_infos_[computation_idx]; - const HloInstructionInfo* instructions_begin = computation.instructions; - const HloInstructionInfo* instructions_end = - computation.instructions + computation.instructions_size; + for (const HloComputationInfo& computation_info : + hlo_profile_printer_data.computation_infos()) { + const auto& instruction_infos = computation_info.instruction_infos(); bool any_instruction_profiled = - std::any_of(instructions_begin, instructions_end, + std::any_of(instruction_infos.begin(), instruction_infos.end(), [&](const HloInstructionInfo& instruction_info) { - return counters[instruction_info.profile_index] != 0; + return counters[instruction_info.profile_index()] != 0; }); if (!any_instruction_profiled) { @@ -41,16 +41,19 @@ string HloProfilePrinter::ToString(const int64* counters, // Once we start using this in AOT for real, we will probably need a more // minimal version of HumanReadableProfileBuilder. HumanReadableProfileBuilder builder( - computation.name, counters[computation.profile_index], clock_rate_ghz); + computation_info.name(), counters[computation_info.profile_index()], + clock_rate_ghz); - for (const auto* instruction = instructions_begin; - instruction != instructions_end; instruction++) { + for (const auto& instruction_info : instruction_infos) { builder.AddOp( - /*op_name=*/instruction->long_name, - /*short_name=*/instruction->short_name, instruction->category, - counters[instruction->profile_index], instruction->flop_count, - instruction->transcendental_count, instruction->bytes_accessed, - instruction->optimal_seconds); + /*op_name=*/instruction_info.long_name(), + /*short_name=*/instruction_info.short_name(), + instruction_info.category(), + counters[instruction_info.profile_index()], + instruction_info.flop_count(), + instruction_info.transcendental_count(), + instruction_info.bytes_accessed(), + instruction_info.optimal_seconds()); } result += builder.ToString(); @@ -58,10 +61,4 @@ string HloProfilePrinter::ToString(const int64* counters, return result; } - -HloProfilePrinter::~HloProfilePrinter() { - if (deleter_) { - deleter_(computation_infos_, computation_infos_size_); - } -} } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_profile_printer.h b/tensorflow/compiler/xla/service/hlo_profile_printer.h index 35152e744d..b72325c755 100644 --- a/tensorflow/compiler/xla/service/hlo_profile_printer.h +++ b/tensorflow/compiler/xla/service/hlo_profile_printer.h @@ -20,84 +20,13 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/service/hlo_profile_printer_data.pb.h" #include "tensorflow/compiler/xla/types.h" namespace xla { -// Instances of this class can pretty-print profile counters gathered from -// running an XLA computation without having access to the backing module. -class HloProfilePrinter { - public: - // Holds meta information about an HloInstruction. - // - // The pointer-typed fields can be owning or non-owning -- this decision is - // manifested as the deleter_ function in the containing HloProfilePrinter. - struct HloInstructionInfo { - // Textual information for pretty printing. - const char* long_name; - const char* short_name; - const char* category; - - // Metrics computed by HloCostAnalysis. - float flop_count; - float transcendental_count; - float bytes_accessed; - float optimal_seconds; - - // The index into the profile counters array for the HloInstruction - // corresponding to this HloInstructionInfo. - int64 profile_index; - }; - - // Holds meta information about an HloComputation. - // - // The pointer-typed fields can be owning or non-owning -- this decision is - // manifested as the deleter_ function in the containing HloProfilePrinter. - struct HloComputationInfo { - const char* name; - - // The index into the profile counters array for the HloInstruction - // corresponding to this HloComputationInfo. - int64 profile_index; - - HloInstructionInfo* instructions; - int64 instructions_size; - }; - - HloProfilePrinter( - HloComputationInfo* computation_infos, int64 computation_infos_size, - int64 profile_counters_size, - std::function deleter = nullptr) - : computation_infos_(computation_infos), - computation_infos_size_(computation_infos_size), - profile_counters_size_(profile_counters_size), - deleter_(std::move(deleter)) {} - - HloProfilePrinter(HloProfilePrinter&& other) { - std::swap(other.computation_infos_, computation_infos_); - std::swap(other.computation_infos_size_, computation_infos_size_); - std::swap(other.deleter_, deleter_); - } - - HloProfilePrinter(const HloProfilePrinter&) = delete; - HloProfilePrinter& operator=(const HloProfilePrinter&) = delete; - - // Converts the profile counter sequence `counters` to a human readable string - // representation. - string ToString(const int64* counters, double clock_rate_ghz) const; - - // Returns the size of the profile buffer expected by this printer. - int64 profile_counters_size() const { return profile_counters_size_; } - - ~HloProfilePrinter(); - - private: - // The `computation_infos_` field can be owning or non-owning -- this decision - // is manifested as the deleter_ function. - HloComputationInfo* computation_infos_ = nullptr; - int64 computation_infos_size_ = 0; - int64 profile_counters_size_ = 0; - std::function deleter_; -}; +// Pretty-print an array of profile counters using hlo_profile_printer_data. +string PrintHloProfile(const HloProfilePrinterData& hlo_profile_printer_data, + const int64* counters, double clock_rate_ghz); } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PROFILE_PRINTER_H_ diff --git a/tensorflow/compiler/xla/service/hlo_profile_printer_data.proto b/tensorflow/compiler/xla/service/hlo_profile_printer_data.proto new file mode 100644 index 0000000000..9f22b733fe --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_profile_printer_data.proto @@ -0,0 +1,60 @@ +/* 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. +==============================================================================*/ + +syntax = "proto3"; + +package xla; + +option cc_enable_arenas = true; + +// Describes how to pretty-print a profile counter array gathered for a specific +// HloModule. +message HloProfilePrinterData { + // Pretty-printer information about an HloInstruction. + message HloInstructionInfo { + string long_name = 1; + string short_name = 2; + string category = 3; + + // Metrics computed by HloCostAnalysis. + float flop_count = 4; + float transcendental_count = 5; + float bytes_accessed = 6; + float optimal_seconds = 7; + + // The index into the profile counters array for the HloInstruction + // corresponding to this HloInstructionInfo. + int64 profile_index = 8; + } + + // Pretty-printer information about an HloComputation. + message HloComputationInfo { + string name = 1; + + // The index into the profile counters array for the HloComputation + // corresponding to this HloComputationInfo. + int64 profile_index = 2; + + // HloInstructionInfos for every HloInstruction in the HloComputation for + // corresponding to this HloComputattionInfo. + repeated HloInstructionInfo instruction_infos = 3; + } + + // HloComputationInfos for every HloComputation in the HloModule. + repeated HloComputationInfo computation_infos = 1; + + // The size of the profile counters array we will pretty-print. + int64 profile_counters_size = 2; +} diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index e230d25f1e..926ebbe314 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -569,7 +569,7 @@ Service::ExecuteParallelAndRegisterResult( se::Stream* stream = index_to_profiled_stream.second; Executable* executable = executables[device]; const HloModule& module = executable->module(); - HloExecutionProfile hlo_profile(&executable->hlo_profile_printer(), + HloExecutionProfile hlo_profile(&executable->hlo_profile_printer_data(), &executable->hlo_profile_index_map()); TF_RETURN_IF_ERROR( executable->PopulateExecutionProfile(&hlo_profile, stream->parent())); diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 146fbadcb6..1d2f436194 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -110,7 +110,8 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, Executable* executable = local_executable->executable(); HloExecutionProfile hlo_execution_profile( - &executable->hlo_profile_printer(), &executable->hlo_profile_index_map()); + &executable->hlo_profile_printer_data(), + &executable->hlo_profile_index_map()); TF_ASSERT_OK_AND_ASSIGN( Backend::StreamPtr stream_ptr, diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index bb2db2010c..1d7dd34449 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -398,13 +398,11 @@ std::vector> CommonFactors( // Removes illegal characters from filenames. string SanitizeFileName(string file_name); -// Simple wrapper around std::all_of. template bool c_all_of(Container container, Predicate predicate) { return std::all_of(std::begin(container), std::end(container), predicate); } -// Simple wrapper around std::transform. template OutputIterator c_transform(InputContainer input_container, @@ -414,7 +412,6 @@ OutputIterator c_transform(InputContainer input_container, output_iterator, unary_op); } -// Simple wrapper around std::copy_if. template OutputIterator c_copy_if(InputContainer input_container, OutputIterator output_iterator, @@ -423,6 +420,11 @@ OutputIterator c_copy_if(InputContainer input_container, output_iterator, predicate); } +template +void c_sort(InputContainer& input_container, Comparator comparator) { + std::sort(input_container.begin(), input_container.end(), comparator); +} + } // namespace xla #define XLA_LOG_LINES(SEV, STRING) \ -- GitLab From e76a7a7c8bccb1fb67559160c9a06ba3a722fd54 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 24 Jan 2018 16:10:37 -0800 Subject: [PATCH 011/423] [TF:XLA:CPU] Fix an issue with explicitly vectorized reductions The explicitly vectorized reduction kernel assumes that the input and the output layouts are "same" (not identical which is not possible, but have the same ordering of the un-reduced dimensions). This change makes IrEmitter check this precondition. PiperOrigin-RevId: 183158249 --- .../compiler/xla/service/cpu/ir_emitter.cc | 51 +++++++ tensorflow/compiler/xla/tests/BUILD | 13 ++ .../compiler/xla/tests/reduce_hlo_test.cc | 132 ++++++++++++++++++ 3 files changed, 196 insertions(+) create mode 100644 tensorflow/compiler/xla/tests/reduce_hlo_test.cc diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index b03a9f9aa5..71e8133189 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -62,6 +62,7 @@ limitations under the License. #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" @@ -1271,6 +1272,52 @@ Status IrEmitter::HandleParameter(HloInstruction* parameter) { return Status::OK(); } +// Returns true if the relative order of the unreduced dimensions stays the same +// through the reduce operation. +static bool ReductionPreservesLayout(const HloInstruction& reduce) { + DCHECK_EQ(reduce.opcode(), HloOpcode::kReduce); + + // Maps dimensions that were not reduced from their dimension numbers in the + // source shape to their dimensions numbers in the destination shape. + // + // So if we reduce f32[A,B,C,D] on dimensions 1 and 2, this map contains + // [0->0, 3->1]. + gtl::FlatMap unreduced_dim_map; + + gtl::FlatSet reduced_dims(reduce.dimensions().begin(), + reduce.dimensions().end()); + + const Shape& operand_shape = reduce.operand(0)->shape(); + const Shape& result_shape = reduce.shape(); + + int64 delta = 0; + for (int64 i = 0; i < operand_shape.dimensions_size(); i++) { + if (reduced_dims.count(i)) { + delta++; + } else { + InsertOrDie(&unreduced_dim_map, i, i - delta); + } + } + + // Iterate dimensions minor to major and check that the corresponding + // dimensions in the source and target shapes are equivalent. + int64 result_dim_idx = 0; + for (int64 operand_dim_idx = 0; + operand_dim_idx < operand_shape.dimensions_size(); operand_dim_idx++) { + int64 operand_dim = operand_shape.layout().minor_to_major(operand_dim_idx); + if (!reduced_dims.count(operand_dim)) { + if (FindOrDie(unreduced_dim_map, operand_dim) != + result_shape.layout().minor_to_major(result_dim_idx++)) { + return false; + } + } + } + + CHECK_EQ(result_dim_idx, result_shape.dimensions_size()); + + return true; +} + IrEmitter::ReductionGenerator IrEmitter::MatchReductionGenerator( HloComputation* function, string* failure_reason) const { CHECK_EQ(function->num_parameters(), 2); @@ -1540,6 +1587,10 @@ StatusOr IrEmitter::EmitVectorizedReduce( HloInstruction* reduce, HloInstruction* arg, HloInstruction* init_value, gtl::ArraySlice dimensions, HloComputation* function, string* failure_reason) { + if (!ReductionPreservesLayout(*reduce)) { + return false; + } + ReductionGenerator reduction_generator = MatchReductionGenerator(function, failure_reason); if (!reduction_generator) { diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 6ab406d6c3..bc15bd9593 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -1072,6 +1072,19 @@ xla_test( ], ) +xla_test( + name = "reduce_hlo_test", + srcs = ["reduce_hlo_test.cc"], + deps = [ + ":client_library_test_base", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", + "//tensorflow/core:lib", + "//tensorflow/core:test", + ], +) + xla_test( name = "call_test", srcs = ["call_test.cc"], diff --git a/tensorflow/compiler/xla/tests/reduce_hlo_test.cc b/tensorflow/compiler/xla/tests/reduce_hlo_test.cc new file mode 100644 index 0000000000..c0a2c0ca4c --- /dev/null +++ b/tensorflow/compiler/xla/tests/reduce_hlo_test.cc @@ -0,0 +1,132 @@ +/* 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 + +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/types.h" + +// Tests the Reduce HLO in ways that can't be done using the ComputationBuilder +// API. + +namespace xla { +namespace { + +namespace str_util = tensorflow::str_util; +namespace strings = tensorflow::strings; + +struct ReduceLayout { + std::array input_minor_to_major; + std::array output_minor_to_major; + + string ToString() const { + return strings::StrCat(str_util::Join(input_minor_to_major, "x"), "_", + str_util::Join(output_minor_to_major, "x")); + } +}; + +string PrintReduceLayout( + ::testing::TestParamInfo reduce_layout_param) { + return reduce_layout_param.param.ToString(); +} + +void PrintTo(const ReduceLayout& reduce_layout, ::std::ostream* os) { + *os << reduce_layout.ToString(); +} + +class ReduceWithLayoutTest + : public HloTestBase, + public ::testing::WithParamInterface {}; + +StatusOr> GetParsedModule() { + const char* const hlo_string = R"( +HloModule BadReduce + +Sum { + x.1 = f32[] parameter(0) + y.1 = f32[] parameter(1) + ROOT add.1 = f32[] add(x.1, y.1) +} + +ENTRY reduce.1 { + parameter = f32[2,2,2,3]{3,2,1,0} parameter(0) + init_value = f32[] constant(0) + reduce = f32[2,2,3]{2,1,0} reduce(parameter, init_value), dimensions={1}, to_apply=Sum + ROOT copy = f32[2,2,3]{2,1,0} copy(reduce) +} +)"; + + return tools::Parse(hlo_string); +} + +// TODO(b/72454718): XLA:GPU does not support executing code compiled without +// optimizations. +XLA_TEST_P(ReduceWithLayoutTest, DISABLED_ON_GPU(Reduce)) { + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, GetParsedModule()); + HloInstruction* reduce_instruction = + module->entry_computation()->root_instruction()->mutable_operand(0); + ASSERT_EQ(reduce_instruction->opcode(), HloOpcode::kReduce); + + const ReduceLayout& reduce_layout = GetParam(); + + Shape* reduce_output_shape = reduce_instruction->mutable_shape(); + *reduce_output_shape->mutable_layout() = + LayoutUtil::MakeLayout(reduce_layout.output_minor_to_major); + + Shape* reduce_input_shape = + reduce_instruction->mutable_operand(0)->mutable_shape(); + *reduce_input_shape->mutable_layout() = + LayoutUtil::MakeLayout(reduce_layout.input_minor_to_major); + + std::unique_ptr reduce_input = + Literal::CreateR4({{ /*i0=0*/ + {/*i1=0*/ + {-0.246092796, -0.179497838, -0.161181688}, + {-0.151643038, -0.240213156, -0.198156}}, + {/*i1=1*/ + {-0.14222312, -0.162200093, -0.193907976}, + {-0.239411, -0.198166847, -0.172471642}}}, + { /*i0=1*/ + {/*i1=0*/ + {-0.22965157, -0.218723893, -0.129257083}, + {-0.188762426, -0.16123569, -0.181166649}}, + {/*i1=1*/ + {-0.241772294, -0.245131493, -0.160247207}, + {-0.179881215, -0.23383224, -0.121976733}}}}); + + EXPECT_TRUE(RunAndCompareNoHloPasses(std::move(module), ErrorSpec(1e-5))); +} + +INSTANTIATE_TEST_CASE_P(ReduceWithLayoutTest_Instantiation, + ReduceWithLayoutTest, + ::testing::Values( // + ReduceLayout{{3, 2, 1, 0}, {0, 1, 2}}, // + ReduceLayout{{3, 2, 1, 0}, {0, 2, 1}}, // + ReduceLayout{{3, 2, 1, 0}, {1, 2, 0}}, // + ReduceLayout{{3, 2, 1, 0}, {1, 0, 2}}, // + ReduceLayout{{3, 2, 1, 0}, {2, 0, 1}}, // + ReduceLayout{{3, 2, 1, 0}, {2, 1, 0}}, // + ReduceLayout{{3, 1, 2, 0}, {1, 2, 0}}, // + ReduceLayout{{1, 2, 3, 0}, {1, 0, 2}}, // + ReduceLayout{{0, 2, 1, 3}, {2, 0, 1}}), // + PrintReduceLayout); + +} // namespace +} // namespace xla -- GitLab From dc0511e51a8f2af8b0f053d7f04352f0b0be58fa Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 16:23:09 -0800 Subject: [PATCH 012/423] Basic AddN support in toco PiperOrigin-RevId: 183160197 --- tensorflow/contrib/lite/toco/BUILD | 1 + .../contrib/lite/toco/export_tensorflow.cc | 15 ++++++ .../convert_trivial_addn_to_add.cc | 51 +++++++++++++++++++ .../graph_transformations.h | 1 + .../propagate_fixed_sizes.cc | 25 +++++++++ .../contrib/lite/toco/import_tensorflow.cc | 15 ++++++ tensorflow/contrib/lite/toco/model.h | 11 ++++ .../contrib/lite/toco/tflite/operator.cc | 2 + tensorflow/contrib/lite/toco/toco_tooling.cc | 1 + tensorflow/contrib/lite/toco/tooling_util.cc | 11 ++++ 10 files changed, 133 insertions(+) create mode 100644 tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_addn_to_add.cc diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index ad8f0e4a47..041e248790 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -172,6 +172,7 @@ cc_library( "graph_transformations/convert_expanddims_to_reshape.cc", "graph_transformations/convert_pure_conv_to_depthwise.cc", "graph_transformations/convert_reorder_axes.cc", + "graph_transformations/convert_trivial_addn_to_add.cc", "graph_transformations/convert_trivial_transpose_to_reshape.cc", "graph_transformations/create_im2col_arrays.cc", "graph_transformations/dequantize.cc", diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index 4fc01dbc20..529df3cd2e 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -519,6 +519,18 @@ void ConvertAddOperator(const Model& model, const AddOperator& src_op, (*add_op->mutable_attr())["T"].set_type(DT_FLOAT); } +void ConvertAddNOperator(const Model& model, const AddNOperator& src_op, + GraphDef* tensorflow_graph) { + auto* add_op = tensorflow_graph->add_node(); + add_op->set_op("AddN"); + add_op->set_name(src_op.outputs[0]); + for (const auto& input : src_op.inputs) { + *add_op->add_input() = input; + } + (*add_op->mutable_attr())["N"].set_i(src_op.inputs.size()); + (*add_op->mutable_attr())["T"].set_type(DT_FLOAT); +} + void ConvertMulOperator(const Model& model, const MulOperator& src_op, GraphDef* tensorflow_graph) { auto* add_op = tensorflow_graph->add_node(); @@ -1406,6 +1418,9 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kAdd) { ConvertAddOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kAddN) { + ConvertAddNOperator(model, static_cast(src_op), + tensorflow_graph); } else if (src_op.type == OperatorType::kMul) { ConvertMulOperator(model, static_cast(src_op), tensorflow_graph); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_addn_to_add.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_addn_to_add.cc new file mode 100644 index 0000000000..dcaaddbf3b --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_addn_to_add.cc @@ -0,0 +1,51 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +// This pass will convert an AddN operator with only 2 inputs into a regular Add +// operator, to which more optimizations may apply. +bool ConvertTrivialAddNToAdd::Run(Model* model, std::size_t op_index) { + auto addn_it = model->operators.begin() + op_index; + if (addn_it->get()->type != OperatorType::kAddN) { + return false; + } + AddNOperator* addn_op = static_cast(addn_it->get()); + CHECK_GE(addn_op->inputs.size(), 2); + CHECK_EQ(addn_op->outputs.size(), 1); + + // We only reduce AddN with N=2 to a regular Add. + if (addn_op->inputs.size() != 2) { + return false; + } + + // Copy inputs & outputs to regular Add. + auto* add_op = new AddOperator; + add_op->inputs.push_back(addn_op->inputs[0]); + add_op->inputs.push_back(addn_op->inputs[1]); + add_op->outputs = addn_op->outputs; + + // Replace the AddN operator in the graph. + const auto add_it = model->operators.emplace(addn_it, add_op); + addn_it = add_it + 1; + CHECK_EQ(addn_it->get(), addn_op); + model->operators.erase(addn_it); + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h index 4ac2265be9..e11bebcd4e 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -114,6 +114,7 @@ void RunGraphTransformations(Model* model, const string& message, // List of all graph transformations DECLARE_GRAPH_TRANSFORMATION(ConvertExpandDimsToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertPureConvToDepthwise) +DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialAddNToAdd) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTransposeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertReorderAxes) DECLARE_GRAPH_TRANSFORMATION(EnsureBiasVectors) 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 ff0a3bd881..4fb3b6ae7a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -406,6 +406,28 @@ void ProcessSimpleBinaryOperator(Model* model, Operator* op) { &output_array); } +void ProcessAddNOperator(Model* model, Operator* op) { + // Yield until all input dims have been resolved. + // + // TODO(myenik): Since AddN does not support broadcasting, maybe we could + // actually use this to improve shape propagation by propagating the shape of + // one input to all other inputs once it is resolved instead of just the + // output, since all inputs must be the same size and shape for a well-formed + // graph. + for (const auto& input : op->inputs) { + const auto& input_array = model->GetArray(input); + if (!input_array.has_shape()) { + return; + } + } + + // AddN does not support broadcasting, all inputs must be the same shape, so + // we just take the first input shape and apply it to the output. + const auto& input0_array = model->GetArray(op->inputs[0]); + auto& output_array = model->GetArray(op->outputs[0]); + output_array.copy_shape(input0_array.shape()); +} + bool KeepDims(const Operator& op) { switch (op.type) { case OperatorType::kTensorFlowMin: @@ -1282,6 +1304,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kTensorFlowGreaterEqual: ProcessSimpleBinaryOperator(model, op); break; + case OperatorType::kAddN: + ProcessAddNOperator(model, op); + break; case OperatorType::kConv: ProcessConvOperator(model, static_cast(op)); break; diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index e8f318cd43..ca378af4c5 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -696,6 +696,19 @@ void ConvertAddOperator(const NodeDef& node, model->operators.emplace_back(op); } +void ConvertAddNOperator(const NodeDef& node, + const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "AddN"); + const int num_inputs = GetInputsCount(node, tf_import_flags); + auto* op = new AddNOperator; + for (int i = 0; i < num_inputs; ++i) { + op->inputs.push_back(node.input(i)); + } + op->outputs.push_back(node.name()); + model->operators.emplace_back(op); +} + void ConvertMulOperator(const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -1862,6 +1875,8 @@ std::unique_ptr ImportTensorFlowGraphDef( ConvertSquareOperator(node, tf_import_flags, model); } else if (node.op() == "Add") { ConvertAddOperator(node, tf_import_flags, model); + } else if (node.op() == "AddN") { + ConvertAddNOperator(node, tf_import_flags, model); } else if (node.op() == "Mul") { ConvertMulOperator(node, tf_import_flags, model); } else if (node.op() == "Sub") { diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index b1b9b718bb..d1af371fd4 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -32,6 +32,7 @@ enum class OperatorType { kNone, // General-purpose neural network operators. kAdd, + kAddN, kAveragePool, kBatchNormalization, kConv, @@ -559,6 +560,16 @@ struct AddOperator : Operator { AddOperator() : Operator(OperatorType::kAdd) {} }; +// Element-wise addition operator for N inputs. +// +// Inputs: +// inputs[i]: The i-th array to add together to form the output. +// +// TensorFlow equivalent: AddN +struct AddNOperator : Operator { + AddNOperator() : Operator(OperatorType::kAddN) {} +}; + // Concatenation operator: concatenates its inputs // along the axis. // diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index d75d1fcc5b..298f49025f 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -807,6 +807,8 @@ std::vector> BuildOperatorList() { // There operators are supported by Toco, but not by TF Lite, and has no // attributes. + ops.emplace_back( + new SimpleOperator("ADDN", OperatorType::kAddN)); ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg)); ops.emplace_back(new SimpleOperator( "RSQRT", OperatorType::kTensorFlowRsqrt)); diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index f2753c84e9..720c33777d 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -52,6 +52,7 @@ void MakeGeneralGraphTransformationsSet( GraphTransformationsSet* transformations) { CHECK(transformations->empty()); transformations->Add(new ConvertExpandDimsToReshape); + transformations->Add(new ConvertTrivialAddNToAdd); transformations->Add(new ConvertTrivialTransposeToReshape); transformations->Add(new ConvertReorderAxes); transformations->Add(new ResolveReshapeAttributes); diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 8543ba4742..99a54a300b 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -197,6 +197,7 @@ const char* OperatorTypeName(OperatorType type) { case OperatorType::k##c: \ return #c; HANDLE_OPERATORTYPENAME_CASE(Add) + HANDLE_OPERATORTYPENAME_CASE(AddN) HANDLE_OPERATORTYPENAME_CASE(AveragePool) HANDLE_OPERATORTYPENAME_CASE(BatchNormalization) HANDLE_OPERATORTYPENAME_CASE(Conv) @@ -1396,6 +1397,16 @@ bool EstimateArithmeticOpsCount(const Model& model, int64* result) { total += RequiredBufferSizeForShape(output_array.shape()); break; } + case OperatorType::kAddN: { + const auto& output_array = model.GetArray(op->outputs[0]); + if (!output_array.has_shape()) { + return false; + } + // AddN cost is roughly the same cost as N-1 Adds. + const int num_adds = op->inputs.size() - 1; + total += num_adds * RequiredBufferSizeForShape(output_array.shape()); + break; + } case OperatorType::kLogistic: case OperatorType::kSoftmax: case OperatorType::kTanh: { -- GitLab From 7b44779ff2031e73ea46ecc7cf2a73405d22a57a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 16:28:20 -0800 Subject: [PATCH 013/423] Prepare internal kernels for BroadcastMul and BroadcastAdd PiperOrigin-RevId: 183160907 --- .../internal/optimized/optimized_ops.h | 48 ++++++++++++--- .../internal/reference/reference_ops.h | 60 ++++++++++++++----- 2 files changed, 86 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index c35b9da938..8163c76cfd 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -1538,9 +1538,10 @@ void Add(const int32* input1_data, const Dims<4>& input1_dims, // 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 +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"); @@ -1563,15 +1564,30 @@ void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, 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)] = ActivationFunction( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] + - input2_data[SubscriptToIndex(desc2, c, x, y, b)]); + 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, @@ -1772,9 +1788,10 @@ void Mul(const int32* input1_data, const Dims<4>& input1_dims, // 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 +template void BroadcastMul(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("BroadcastMul"); @@ -1797,15 +1814,30 @@ void BroadcastMul(const T* input1_data, const Dims<4>& input1_dims, 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)] = ActivationFunction( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] * - input2_data[SubscriptToIndex(desc2, c, x, y, b)]); + 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 BroadcastMul(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); + + BroadcastMul(input1_data, input1_dims, input2_data, input2_dims, + output_activation_min, output_activation_max, output_data, + output_dims); +} + inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, const uint8* input2_data, const Dims<4>& input2_dims, int32 input2_offset, diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 64651d8348..31bade26f9 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -889,10 +889,11 @@ inline void Add(int left_shift, const uint8* input1_data, // 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 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) { +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; @@ -914,15 +915,30 @@ void BroadcastAdd(const float* input1_data, const Dims<4>& input1_dims, 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)] = ActivationFunction( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] + - input2_data[SubscriptToIndex(desc2, c, x, y, b)]); + 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, @@ -1053,10 +1069,11 @@ void Mul(const float* 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 BroadcastMul(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) { +template +void BroadcastMul(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("BroadcastMul"); NdArrayDesc<4> desc1; @@ -1078,15 +1095,30 @@ void BroadcastMul(const float* input1_data, const Dims<4>& input1_dims, 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)] = ActivationFunction( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] * - input2_data[SubscriptToIndex(desc2, c, x, y, b)]); + 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 BroadcastMul(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); + + BroadcastMul(input1_data, input1_dims, input2_data, input2_dims, + output_activation_min, output_activation_max, output_data, + output_dims); +} + inline void BroadcastMul(const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, const uint8* input2_data, const Dims<4>& input2_dims, int32 input2_offset, -- GitLab From 0454180a47bdbe7174a2f51ecbe76b2aed2ce719 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Wed, 24 Jan 2018 16:41:50 -0800 Subject: [PATCH 014/423] Defer logging infeed error messages for a short time to see if the main session returns. PiperOrigin-RevId: 183162866 --- .../contrib/tpu/python/tpu/tpu_estimator.py | 457 ++++++++++-------- 1 file changed, 249 insertions(+), 208 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index b6d685b3fc..2ae3a26a85 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # =================================================================== - """TPUEstimator class.""" from __future__ import absolute_import @@ -24,6 +23,7 @@ from contextlib import contextmanager import copy import threading import time +import traceback import six from six.moves import queue as Queue # pylint: disable=redefined-builtin @@ -60,7 +60,6 @@ from tensorflow.python.training import session_run_hook from tensorflow.python.training import training from tensorflow.python.training import training_util - _INITIAL_LOSS = 1e7 _ZERO_LOSS = 0. _TPU_ESTIMATOR = 'tpu_estimator' @@ -86,8 +85,7 @@ def _create_global_step(graph): initializer=init_ops.zeros_initializer(), trainable=False, use_resource=True, - collections=[ops.GraphKeys.GLOBAL_VARIABLES, - ops.GraphKeys.GLOBAL_STEP]) + collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP]) def _create_or_get_iterations_per_loop(): @@ -100,8 +98,8 @@ def _create_or_get_iterations_per_loop(): raise RuntimeError('Multiple iterations_per_loop_var in collection.') with ops.colocate_with(training_util.get_global_step()): - with variable_scope.variable_scope(_TPU_ESTIMATOR, - reuse=variable_scope.AUTO_REUSE): + with variable_scope.variable_scope( + _TPU_ESTIMATOR, reuse=variable_scope.AUTO_REUSE): return variable_scope.get_variable( _ITERATIONS_PER_LOOP_VAR, initializer=init_ops.zeros_initializer(), @@ -242,9 +240,9 @@ class _TPUContext(object): return self._eval_batch_size return None - global_batch_size = (self._train_batch_size if - mode == model_fn_lib.ModeKeys.TRAIN - else self._eval_batch_size) + global_batch_size = ( + self._train_batch_size + if mode == model_fn_lib.ModeKeys.TRAIN else self._eval_batch_size) # On TPU if self.is_input_sharded_per_core(): return global_batch_size // self.num_cores @@ -291,8 +289,9 @@ class _TPUContext(object): # The tpu job is determined by the run_config. Right now, this method is # required as tpu_config is not part of the RunConfig. mode = self._assert_mode() - master = (run_config.evaluation_master if mode == model_fn_lib.ModeKeys.EVAL - else run_config.master) + master = ( + run_config.evaluation_master + if mode == model_fn_lib.ModeKeys.EVAL else run_config.master) if master in _LOCAL_MASTERS: return None @@ -319,6 +318,7 @@ class _TPUContext(object): def tpu_host_placement_function(self): """Returns the TPU host place function.""" master = self.master_job + def _placement_function(_sentinal=None, core_id=None, host_id=None): # pylint: disable=invalid-name assert _sentinal is None if core_id is not None and host_id is not None: @@ -333,19 +333,23 @@ class _TPUContext(object): if core_id is not None: host_id = core_id / 8 return '/job:%s/task:%d/device:CPU:0' % (master, host_id) + return _placement_function @property def tpu_device_placement_function(self): master = self.master_job job_device = '' if master is None else ('/job:%s' % master) + def _placement_function(i): return '%s/task:%d/device:TPU:%d' % (job_device, i / 8, i % 8) + return _placement_function @property def tpu_ordinal_function(self): """Returns the TPU ordinal fn.""" + def _tpu_ordinal_function(index): """Return the TPU ordinal associated with a shard. @@ -358,6 +362,7 @@ class _TPUContext(object): The ordinal of the TPU device the shard's infeed should be placed on. """ return index % 8 + return _tpu_ordinal_function @@ -371,14 +376,16 @@ class _SIGNAL(object): STOP = -2 -class TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ - 'mode', - 'predictions', - 'loss', - 'train_op', - 'eval_metrics', - 'export_outputs', - 'scaffold_fn'])): +class TPUEstimatorSpec( + collections.namedtuple('TPUEstimatorSpec', [ + 'mode', + 'predictions', + 'loss', + 'train_op', + 'eval_metrics', + 'export_outputs', + 'scaffold_fn' + ])): """Ops and objects returned from a `model_fn` and passed to `TPUEstimator`. See `EstimatorSpec` for `mode`, 'predictions, 'loss', 'train_op', and @@ -416,111 +423,116 @@ class TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ """Creates a validated `TPUEstimatorSpec` instance.""" if eval_metrics is not None: _EvalMetrics.validate(eval_metrics) - 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) + 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) def as_estimator_spec(self): """Creates an equivalent `EstimatorSpec` used by CPU train/eval.""" eval_metric_ops = _EvalMetrics.to_metric_metric_ops_for_cpu( self.eval_metrics) scaffold = self.scaffold_fn() if self.scaffold_fn else None - return model_fn_lib.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, - scaffold=scaffold) + return model_fn_lib.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, + scaffold=scaffold) + + +class _OpQueueContext(object): + """Manages work queue and thread for a infeed/outfeed thread.""" + + def __init__(self, name, target, args): + self._name = name + self._queue = Queue.Queue() + args = (self,) + args + self._thread = threading.Thread(name=name, target=target, args=args) + self._thread.daemon = True + self._thread.start() + + def stop(self): + self._queue.put(_SIGNAL.STOP) + + def send_next_batch_signal(self, iterations): + self._queue.put(iterations) + + def read_iteration_counts(self): + while True: + signal = self._queue.get(block=True) + logging.debug('%s read signal %s', self._name, signal) + if signal == _SIGNAL.STOP: + logging.info('%s received signal, stopping.', self._name) + return + yield signal + def join(self): + logging.info('Shutting down %s thread.' % self._name) + self.stop() + self._thread.join() -class _InfeedOutfeedThreadBaseController(object): - """This wraps the infeed/outfeed thread and stops when Estimator finishes.""" - def __init__(self, thd): - self._signal_queue = Queue.Queue() - thd.daemon = True - thd.start() - self._thd = thd +class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): + """A Session hook setting up the TPU initialization, infeed, and outfeed. - def block_and_get_signal(self): - return self._signal_queue.get() + This hook does two major things: + 1. initialize and shutdown TPU system. + 2. launch and join the threads for infeed enqueue and (optional) outfeed + dequeue. + """ - def send_next_batch_signal(self, signal=_SIGNAL.NEXT_BATCH): - self._signal_queue.put(signal) + def __init__(self, ctx, enqueue_ops, dequeue_ops=None): + self._master_job = ctx.master_job + self._enqueue_ops = enqueue_ops + self._dequeue_ops = dequeue_ops + self._initial_infeed_sleep_secs = ( + ctx.config.tpu_config.initial_infeed_sleep_secs) + self._session_cancel_timer = None - def join(self): - self._signal_queue.put(_SIGNAL.STOP) - self._thd.join() + self._feed_error = None + self._finished = False + def begin(self): + logging.info('TPU job name %s', self._master_job) + self._iterations_per_loop_var = _create_or_get_iterations_per_loop() + self._init_op = [tpu.initialize_system(job=self._master_job)] + self._finalize_op = [tpu.shutdown_system(job=self._master_job)] -class _OutfeedThreadController(_InfeedOutfeedThreadBaseController): - """This wraps the outfeed thread and stops when Estimator finishes.""" + def _log_error(self, session, error): + """Log an infeed or outfeed error. - def __init__(self, session, dequeue_ops): - super(_OutfeedThreadController, self).__init__( - threading.Thread(target=self._execute_dequeue_ops, - args=(session, dequeue_ops))) + 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. - def _execute_dequeue_ops(self, session, dequeue_ops): - count = 0 - while True: - signal = self.block_and_get_signal() - if signal == _SIGNAL.STOP: - logging.info('Stop outfeed thread.') - return + Args: + session: `tf.Session`, session to be terminated + error: exception that triggered logging. + """ + 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) - iterations = signal - for i in range(iterations): - logging.debug('Outfeed dequeue for iteration (%d, %d)', count, i) - session.run(dequeue_ops) - count += 1 + self._feed_error = traceback.format_exc() - def join(self): - logging.info('Waiting for Outfeed Thread to exit.') - super(_OutfeedThreadController, self).join() - - -class _InfeedThreadController(_InfeedOutfeedThreadBaseController): - """This wraps the infeed thread and stops when Estimator finishes.""" - - def __init__(self, session, enqueue_ops, initial_infeed_sleep_secs): - super(_InfeedThreadController, self).__init__( - threading.Thread( - target=self._input_thread_fn_for_loading, - args=(session, enqueue_ops, initial_infeed_sleep_secs))) - - def _input_thread_fn_for_loading(self, session, enqueue_ops, - initial_infeed_sleep_secs): - count = 0 - if initial_infeed_sleep_secs: - logging.info('Infeed thread sleeping for %d seconds.', - initial_infeed_sleep_secs) - time.sleep(initial_infeed_sleep_secs) - logging.info('Infeed thread starting after sleep') - try: - while True: - signal = self._signal_queue.get() - if signal == _SIGNAL.STOP: - logging.info('Stop Infeed input thread.') - return - - if _WRAP_INPUT_FN_INTO_WHILE_LOOP: - # Enqueue batches for next loop. - session.run(enqueue_ops) - else: - iterations = signal - for i in range(iterations): - logging.debug('Infeed enqueue for iteration (%d, %d)', count, i) - session.run(enqueue_ops) - count += 1 + # If we've already encountered a feed error, don't schedule another + # cancellation op. + if self._session_cancel_timer: + return - except Exception: # pylint: disable=broad-except + 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 @@ -535,77 +547,94 @@ class _InfeedThreadController(_InfeedOutfeedThreadBaseController): # 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. - logging.error( - 'Failed running infeed, closing session.\n' - 'You may see an exception from your main session after this. ' - 'Sleep for 2 minutes before close Session from infeed thread to ' - 'allow the main thread returning an error first, if any.', - exc_info=1 - ) - time.sleep(120) - logging.error('Closing the failed session.') - session.close() - - def join(self): - logging.info('Waiting for Infeed Thread to exit.') - super(_InfeedThreadController, self).join() - - -class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): - """A Session hook setting up the TPU initialization, infeed, and outfeed. - - This hook does two major things: - 1. initialize and shutdown TPU system. - 2. launch and join the threads for infeed enqueue and (optional) outfeed - dequeue. - """ + time.sleep(5) + + # If the main session is still running, the infeed/outfeed errors are + # legitimate, and should be logged. + if not self._finished: + 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: + logging.info('%s thread sleeping for %d seconds.', self._name, + self._initial_infeed_sleep_secs) + time.sleep(self._initial_infeed_sleep_secs) + logging.info('%s thread starting after sleep', self._name) - def __init__(self, ctx, enqueue_ops, dequeue_ops=None): - self._master_job = ctx.master_job - self._enqueue_ops = enqueue_ops - self._dequeue_ops = dequeue_ops - self._initial_infeed_sleep_secs = ( - ctx.config.tpu_config.initial_infeed_sleep_secs) + try: + if _WRAP_INPUT_FN_INTO_WHILE_LOOP: + for _ in queue_ctx.read_iteration_counts(): + session.run(self._enqueue_ops) + else: + for count, steps in enumerate(queue_ctx.read_iteration_counts()): + for i in xrange(steps): + logging.debug('Infeed enqueue for iteration (%d, %d)', count, i) + session.run(self._enqueue_ops) + logging.debug('Infeed thread finished, shutting down.') + except Exception as e: # pylint: disable=broad-except + self._log_error(session, e) - def begin(self): - logging.info('TPU job name %s', self._master_job) - self._iterations_per_loop_var = _create_or_get_iterations_per_loop() - self._init_op = [tpu.initialize_system(job=self._master_job)] - self._finalize_op = [tpu.shutdown_system(job=self._master_job)] + def _run_outfeed(self, queue_ctx, session): + logging.info('Starting outfeed thread controller.') + try: + 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) + except Exception as e: # pylint: disable=broad-except + self._log_error(session, e) def after_create_session(self, session, coord): logging.info('Init TPU system') - session.run(self._init_op, - options=config_pb2.RunOptions(timeout_in_ms=5*60*1000)) + session.run( + self._init_op, + options=config_pb2.RunOptions(timeout_in_ms=5 * 60 * 1000)) logging.info('Start infeed thread controller') - self._infeed_thd_controller = _InfeedThreadController( - session, self._enqueue_ops, self._initial_infeed_sleep_secs) + self._infeed_controller = _OpQueueContext( + name='InfeedController', target=self._run_infeed, args=(session,)) if self._dequeue_ops is not None: logging.info('Start outfeed thread controller') - self._outfeed_thd_controller = _OutfeedThreadController( - session, self._dequeue_ops) + self._outfeed_controller = _OpQueueContext( + name='OutfeedController', target=self._run_outfeed, args=(session,)) def before_run(self, run_context): + if self._feed_error: + logging.warning('Feed error occurred, terminating session.') + run_context.request_stop() + return + iterations = run_context.session.run(self._iterations_per_loop_var) logging.info('Enqueue next (%d) batch(es) of data to infeed.', iterations) + self._infeed_controller.send_next_batch_signal(iterations) - self._infeed_thd_controller.send_next_batch_signal(iterations) if self._dequeue_ops is not None: # TODO(xiejw): Refactor the outfeed dequeue into tf.while_loop. - logging.info( - 'Dequeue next (%d) batch(es) of data from outfeed.', iterations) - self._outfeed_thd_controller.send_next_batch_signal(iterations) + logging.info('Dequeue next (%d) batch(es) of data from outfeed.', + iterations) + 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_thd_controller.join() + self._infeed_controller.join() if self._dequeue_ops is not None: logging.info('Stop output thread controller') - self._outfeed_thd_controller.join() + self._outfeed_controller.join() logging.info('Shutdown TPU system.') session.run(self._finalize_op) @@ -676,8 +705,8 @@ class _TPUStopAtStepHook(session_run_hook.SessionRunHook): run_context.request_stop() else: iterations = self._next_iterations(global_step, self._last_step) - self._iterations_per_loop_var.load(iterations, - session=run_context.session) + self._iterations_per_loop_var.load( + iterations, session=run_context.session) class _SetEvalIterationsHook(session_run_hook.SessionRunHook): @@ -698,8 +727,8 @@ class _SetEvalIterationsHook(session_run_hook.SessionRunHook): self._iterations_per_loop_var.load(self._num_steps, session=session) -def generate_per_core_enqueue_ops_fn_for_host( - ctx, input_fn, inputs_structure_recorder): +def generate_per_core_enqueue_ops_fn_for_host(ctx, input_fn, + inputs_structure_recorder): """Generates infeed enqueue ops for per-core input_fn on a single host.""" captured_infeed_queue = _CapturedObject() @@ -729,9 +758,9 @@ def generate_per_core_enqueue_ops_fn_for_host( per_host_sharded_inputs) per_host_enqueue_ops = infeed_queue.generate_enqueue_ops( - per_host_sharded_inputs, - tpu_ordinal_function=ctx.tpu_ordinal_function) + per_host_sharded_inputs, tpu_ordinal_function=ctx.tpu_ordinal_function) return per_host_enqueue_ops + return enqueue_ops_fn, captured_infeed_queue @@ -748,8 +777,7 @@ def generate_per_host_enqueue_ops_fn_for_host( features, labels = inputs else: features, labels = inputs, None - inputs_structure_recorder.validate_and_record_structure( - features, labels) + inputs_structure_recorder.validate_and_record_structure(features, labels) unsharded_tensor_list = ( inputs_structure_recorder.flatten_features_and_labels( features, labels)) @@ -763,9 +791,9 @@ def generate_per_host_enqueue_ops_fn_for_host( per_host_enqueue_ops = ( infeed_queue.split_inputs_and_generate_enqueue_ops( - unsharded_tensor_list, - placement_function=lambda x: device)) + unsharded_tensor_list, placement_function=lambda x: device)) return per_host_enqueue_ops + return enqueue_ops_fn, captured_infeed_queue @@ -815,6 +843,7 @@ class _InputPipeline(object): def validate_and_record_structure(self, features, labels): """Validates and records the structure of features` and `labels`.""" + def _extract_key_names(tensor_or_dict): if tensor_or_dict is None: return [] @@ -842,8 +871,8 @@ class _InputPipeline(object): flattened_inputs = [] if self._feature_names: # We need a fixed ordering for enqueueing and dequeueing. - flattened_inputs.extend([features[name] - for name in self._feature_names]) + flattened_inputs.extend( + [features[name] for name in self._feature_names]) else: flattened_inputs.append(features) @@ -870,11 +899,11 @@ class _InputPipeline(object): ValueError: If the number of expected tensors from `flattened_inputs` mismatches the recorded structure. """ - expected_num_features = (len(self._feature_names) if self._feature_names - else 1) + expected_num_features = ( + len(self._feature_names) if self._feature_names else 1) if self._has_labels: - expected_num_labels = (len(self._label_names) if self._label_names - else 1) + expected_num_labels = ( + len(self._label_names) if self._label_names else 1) else: expected_num_labels = 0 @@ -895,8 +924,8 @@ class _InputPipeline(object): if expected_num_labels == 0: unflattened_label = None elif self._label_names: - unflattened_label = dict(zip(self._label_names, - flattened_inputs[expected_num_features:])) + unflattened_label = dict( + zip(self._label_names, flattened_inputs[expected_num_features:])) else: # Single tensor case. unflattened_label = flattened_inputs[expected_num_features] @@ -961,8 +990,9 @@ class _InputPipeline(object): self._ctx, self._input_fn, self._inputs_structure_recorder)) if _WRAP_INPUT_FN_INTO_WHILE_LOOP: - enqueue_ops.append(_wrap_computation_in_while_loop( - device=host_device, op_fn=enqueue_ops_fn)) + enqueue_ops.append( + _wrap_computation_in_while_loop( + device=host_device, op_fn=enqueue_ops_fn)) else: enqueue_ops.append(enqueue_ops_fn()) # Infeed_queue_getter must be called after enqueue_ops_fn is called. @@ -979,8 +1009,9 @@ class _InputPipeline(object): self._batch_axis, host_device)) if _WRAP_INPUT_FN_INTO_WHILE_LOOP: - enqueue_ops.append(_wrap_computation_in_while_loop( - device=host_device, op_fn=enqueue_ops_fn)) + enqueue_ops.append( + _wrap_computation_in_while_loop( + device=host_device, op_fn=enqueue_ops_fn)) else: enqueue_ops.append(enqueue_ops_fn()) infeed_queues.append(captured_infeed_queue.get()) @@ -1066,6 +1097,7 @@ class _ModelFnWrapper(object): with ops.control_dependencies([train_op]): return array_ops.identity(loss) + return train_step, captured_scaffold_fn def convert_to_single_tpu_eval_step(self, dequeue_fn): @@ -1114,6 +1146,7 @@ class _ModelFnWrapper(object): with ops.control_dependencies([outfeed_ops]): return math_ops.add(total_loss, loss) + return eval_step, eval_metrics, captured_scaffold_fn def _call_model_fn(self, features, labels): @@ -1138,10 +1171,9 @@ class _ModelFnWrapper(object): kwargs['params'] = params if 'params' not in model_fn_args: - raise ValueError( - 'model_fn ({}) does not include params argument, ' - 'required by TPUEstimator to pass batch size as ' - 'params[\'batch_size\']'.format(self._model_fn)) + raise ValueError('model_fn ({}) does not include params argument, ' + 'required by TPUEstimator to pass batch size as ' + 'params[\'batch_size\']'.format(self._model_fn)) batch_size_for_model_fn = self._ctx.batch_size_for_model_fn if batch_size_for_model_fn is not None: @@ -1348,8 +1380,9 @@ class ExamplesPerSecondHook(basic_session_run_hooks.StepCounterHook): def _log_and_record(self, elapsed_steps, elapsed_time, global_step): examples_per_sec = self._batch_size * elapsed_steps / elapsed_time if self._summary_writer is not None: - example_summary = Summary(value=[Summary.Value( - tag='examples_sec', simple_value=examples_per_sec)]) + example_summary = Summary(value=[ + Summary.Value(tag='examples_sec', simple_value=examples_per_sec) + ]) self._summary_writer.add_summary(example_summary, global_step) logging.info('examples/sec: %g', examples_per_sec) @@ -1488,9 +1521,8 @@ class TPUEstimator(estimator_lib.Estimator): '`config` must be provided with type `tpu_config.RunConfig`') if params is not None and any(k in params for k in _RESERVED_PARAMS_KEYS): - raise ValueError( - '{} are reserved keys but existed in params {}.'.format( - _RESERVED_PARAMS_KEYS, params)) + raise ValueError('{} are reserved keys but existed in params {}.'.format( + _RESERVED_PARAMS_KEYS, params)) if use_tpu: if train_batch_size is None: @@ -1571,8 +1603,9 @@ class TPUEstimator(estimator_lib.Estimator): if max_steps is not None: util_lib.check_positive_integer(max_steps, 'Train max_steps') - return [_TPUStopAtStepHook(self._iterations_per_training_loop, steps, - max_steps)] + return [ + _TPUStopAtStepHook(self._iterations_per_training_loop, steps, max_steps) + ] def _convert_eval_steps_to_hooks(self, steps): with self._ctx.with_mode(model_fn_lib.ModeKeys.EVAL) as ctx: @@ -1640,6 +1673,7 @@ class TPUEstimator(estimator_lib.Estimator): # `features` in `model_fn` signature. def _input_fn(): return input_fn(**kwargs) + return _input_fn def _augment_model_fn(self, model_fn, batch_axis): @@ -1695,9 +1729,10 @@ class TPUEstimator(estimator_lib.Estimator): total_loss, eval_metric_ops, scaffold = _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, - math_ops.cast(iterations_per_loop_var, dtype=total_loss.dtype)) + mean_loss = math_ops.div(total_loss, + math_ops.cast( + iterations_per_loop_var, + dtype=total_loss.dtype)) # Creates a dummy metric update_op for all metrics. Estimator expects # all metrics in eval_metric_ops have update_op and calls them one by @@ -1725,6 +1760,7 @@ class TPUEstimator(estimator_lib.Estimator): evaluation_hooks=hooks, eval_metric_ops=eval_metric_ops, scaffold=scaffold) + return _model_fn @@ -1737,15 +1773,16 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): model_fn_wrapper.convert_to_single_tpu_eval_step(dequeue_fn)) def multi_tpu_eval_steps_on_single_shard(): - return training_loop.repeat(iterations_per_loop_var, - single_tpu_eval_step, - [_ZERO_LOSS], - name='loop') + return training_loop.repeat( + iterations_per_loop_var, + single_tpu_eval_step, [_ZERO_LOSS], + name='loop') - (loss,) = tpu.shard(multi_tpu_eval_steps_on_single_shard, - inputs=[], - num_shards=num_cores, - outputs_from_all_shards=False) + (loss,) = tpu.shard( + multi_tpu_eval_steps_on_single_shard, + inputs=[], + num_shards=num_cores, + outputs_from_all_shards=False) scaffold = _get_scaffold(captured_scaffold_fn) return loss, eval_metric_ops, scaffold @@ -1762,14 +1799,14 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): def multi_tpu_train_steps_on_single_shard(): return training_loop.repeat( iterations_per_loop_var, - single_tpu_train_step, - [_INITIAL_LOSS], + single_tpu_train_step, [_INITIAL_LOSS], name=b'loop') - (loss,) = tpu.shard(multi_tpu_train_steps_on_single_shard, - inputs=[], - num_shards=num_cores, - outputs_from_all_shards=False) + (loss,) = tpu.shard( + multi_tpu_train_steps_on_single_shard, + inputs=[], + num_shards=num_cores, + outputs_from_all_shards=False) scaffold = _get_scaffold(captured_scaffold_fn) return loss, scaffold @@ -1777,6 +1814,7 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): def _wrap_computation_in_while_loop(device, op_fn): """Wraps the ops generated by `op_fn` in tf.while_loop.""" + def computation(i): with ops.control_dependencies(op_fn()): return i + 1 @@ -1788,7 +1826,8 @@ def _wrap_computation_in_while_loop(device, op_fn): iterations = array_ops.identity(iterations_per_loop_var) return control_flow_ops.while_loop( lambda i: i < iterations, - computation, [constant_op.constant(0)], parallel_iterations=1) + computation, [constant_op.constant(0)], + parallel_iterations=1) def _validate_tpu_training_graph(): @@ -1801,8 +1840,9 @@ def _validate_tpu_training_graph(): # Check if there is atleast one CrossReplicaSum operation in the graph # This should be introduced by using the CrossShardOptimizer wrapper - cross_replica_sum_ops = [o for o in operations - if o.type == _CROSS_REPLICA_SUM_OP] + cross_replica_sum_ops = [ + o for o in operations if o.type == _CROSS_REPLICA_SUM_OP + ] if not cross_replica_sum_ops: raise ValueError( 'CrossShardOptimizer must be used for model training on TPUs.') @@ -1849,9 +1889,11 @@ def _get_scaffold(captured_scaffold_fn): if scaffold: wrapped_finalize = scaffold.finalize + def _finalize(): with _CapturingContext('Inside Scaffold.finalize'): wrapped_finalize() + scaffold.finalize = _finalize return scaffold @@ -1866,9 +1908,8 @@ class _CapturingContext(control_flow_ops.ControlFlowContext): def AddOp(self, op): # pylint: disable=invalid-name for c in op.inputs: if tpu._TPU_REPLICATE_ATTR in c.op.node_def.attr: # pylint: disable=protected-access - raise ValueError( - '{}: Op {} depends on TPU computation {}, ' - 'which is not allowed.'.format(self._message, op, c)) + raise ValueError('{}: Op {} depends on TPU computation {}, ' + 'which is not allowed.'.format(self._message, op, c)) def __enter__(self): # pylint: disable=protected-access -- GitLab From 5e3ed99469d32e494869b8d044620b2ef8e96a40 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 17:11:42 -0800 Subject: [PATCH 015/423] Dedup control dependencies where the node already depends on the source as a non-control input. PiperOrigin-RevId: 183166819 --- .../grappler/optimizers/dependency_optimizer.cc | 6 +----- tensorflow/python/framework/function_test.py | 17 +++++++++++------ 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc index 1f68ecbade..d2da125236 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc @@ -58,11 +58,7 @@ void PruneControlInputs(NodeDef* node) { int pos = 0; while (pos < node->input_size()) { const string& input = node->input(pos); - // TODO(rmlarsen): Remove control inputs that also appears as a regular - // inputs. Currently, doing so breaks testControlFlowStrictness in - // python/framework/function_test. - // if (!inputs.insert(NodeName(input)).second && IsControlInput(input)) { - if (IsControlInput(input) && !inputs.insert(input).second) { + if (!inputs.insert(NodeName(input)).second && IsControlInput(input)) { VLOG(1) << "**** Removing duplicate control input: " << input << " from node " << node->DebugString(); node->mutable_input()->SwapElements(pos, node->input_size() - 1); diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py index 57e5a724c9..a4ca3f9a89 100644 --- a/tensorflow/python/framework/function_test.py +++ b/tensorflow/python/framework/function_test.py @@ -26,6 +26,7 @@ import numpy as np from tensorflow.core.framework import function_pb2 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 constant_op from tensorflow.python.framework import dtypes @@ -451,13 +452,17 @@ class FunctionTest(test.TestCase): lambda y: AssertFail(y), [x]) # pylint: enable=unnecessary-lambda + rewriter_config = rewriter_config_pb2.RewriterConfig( + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) # Enables inlining. - config = config_pb2.ConfigProto(graph_options=config_pb2.GraphOptions( - optimizer_options=config_pb2.OptimizerOptions( - opt_level=config_pb2.OptimizerOptions.L0, - do_common_subexpression_elimination=True, - do_function_inlining=True, - do_constant_folding=True))) + config = config_pb2.ConfigProto( + graph_options=config_pb2.GraphOptions( + optimizer_options=config_pb2.OptimizerOptions( + opt_level=config_pb2.OptimizerOptions.L0, + do_common_subexpression_elimination=True, + do_function_inlining=True, + do_constant_folding=True), + rewrite_options=rewriter_config)) with session.Session(config=config) as sess: # Since the 'False' branch is not taken, the assertion should not fire. -- GitLab From 95a568febcee480cd6d4e6a6bd687754b1ca1422 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 18 Jan 2018 14:20:14 -0800 Subject: [PATCH 016/423] Make bfloat16 work correctly with matmul PiperOrigin-RevId: 182437226 --- .../compiler/tf2xla/kernels/matmul_op.cc | 15 ++++++++++++++- .../kernel_tests/sparse_matmul_op_test.py | 19 +++++++++++++------ tensorflow/python/ops/gradient_checker.py | 14 ++++++++++++-- tensorflow/python/ops/math_ops.py | 8 +++++++- 4 files changed, 46 insertions(+), 10 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc index 644abd5905..886baf8115 100644 --- a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc @@ -29,10 +29,12 @@ constexpr std::array kMatmulTypes = { class MatMulOp : public XlaOpKernel { public: explicit MatMulOp(OpKernelConstruction* ctx, bool is_sparse = false) - : XlaOpKernel(ctx) { + : XlaOpKernel(ctx), is_sparse_(is_sparse) { OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_a", &transpose_a_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("transpose_b", &transpose_b_)); if (is_sparse) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("Ta", &a_type_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("Tb", &b_type_)); // SparseMatMul is actually dense matmul with a hint that one or // both of the inputs may contain a lot of zeroes. On CPU these // inputs are dynamically converted to sparse representation @@ -66,14 +68,25 @@ class MatMulOp : public XlaOpKernel { xla::ComputationDataHandle a = ctx->Input(0); xla::ComputationDataHandle b = ctx->Input(1); + if (is_sparse_) { + if (a_type_ == DT_BFLOAT16) { + a = ctx->builder()->ConvertElementType(a, xla::F32); + } + if (b_type_ == DT_BFLOAT16) { + b = ctx->builder()->ConvertElementType(b, xla::F32); + } + } auto lhs = (transpose_a_) ? ctx->builder()->Transpose(a, {1, 0}) : a; auto rhs = (transpose_b_) ? ctx->builder()->Transpose(b, {1, 0}) : b; ctx->SetOutput(0, ctx->builder()->Dot(lhs, rhs)); } private: + bool is_sparse_; bool transpose_a_; bool transpose_b_; + DataType a_type_; + DataType b_type_; }; REGISTER_XLA_OP(Name("MatMul").TypeConstraint("T", kMatmulTypes), MatMulOp); diff --git a/tensorflow/python/kernel_tests/sparse_matmul_op_test.py b/tensorflow/python/kernel_tests/sparse_matmul_op_test.py index 6ca4479671..4935ed6ca5 100644 --- a/tensorflow/python/kernel_tests/sparse_matmul_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_matmul_op_test.py @@ -69,7 +69,7 @@ class SparseMatMulTest(test.TestCase): np_ans = np.matrix(np_x) * np.matrix(np_y) self.assertShapeEqual(np_ans, tf_ans) - self.assertAllClose(np_ans, out, rtol=1e-4, atol=1e-4) + self.assertAllCloseAccordingToType(np_ans, out, rtol=1e-4, atol=1e-4) def testBasic(self): x = np.arange(0., 4.).reshape([4, 1]).astype(np.float32) @@ -128,7 +128,8 @@ class SparseMatMulTest(test.TestCase): class MatMulGradientTest(test.TestCase): - def _testGradients(self, tr_a, tr_b, sp_a, sp_b, a_dtype, b_dtype, name): + def _testGradients(self, tr_a, tr_b, sp_a, sp_b, a_dtype, b_dtype, delta, + name): with self.test_session(): a = constant_op.constant( RandMatrix( @@ -151,12 +152,12 @@ class MatMulGradientTest(test.TestCase): a, [2, 3] if tr_a else [3, 2], m, [3, 4], x_init_value=a.eval(), - delta=1 / 64.) + gradient_checker.compute_gradient_error( + delta=delta) + gradient_checker.compute_gradient_error( b, [4, 2] if tr_b else [2, 4], m, [3, 4], x_init_value=b.eval(), - delta=1 / 64.)) - self.assertLess(err, 1 / 128.) + delta=delta)) + self.assertLess(err, delta / 2.) def testGradientInput(self): for tr_a in [True, False]: @@ -165,9 +166,15 @@ class MatMulGradientTest(test.TestCase): for sp_b in [True, False]: for a_dtype in (dtypes.float32, dtypes.bfloat16): for b_dtype in (dtypes.float32, dtypes.bfloat16): + # Note: bfloat16 only has 7 mantissa bits, versus float32 with + # 10. Hence, we shift by 2 bits to pass the test. + if a_dtype == dtypes.bfloat16 and b_dtype == dtypes.bfloat16: + delta = 1 / 16. + else: + delta = 1 / 64. name = "sparse_matmul_%s_%s_%s_%s" % (tr_a, tr_b, sp_a, sp_b) self._testGradients(tr_a, tr_b, sp_a, sp_b, a_dtype, b_dtype, - name) + delta, name) if __name__ == "__main__": diff --git a/tensorflow/python/ops/gradient_checker.py b/tensorflow/python/ops/gradient_checker.py index 1ff1968055..b9f42f9eb2 100644 --- a/tensorflow/python/ops/gradient_checker.py +++ b/tensorflow/python/ops/gradient_checker.py @@ -29,6 +29,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients +from tensorflow.python.ops import math_ops from tensorflow.python.platform import tf_logging as logging @@ -151,6 +152,15 @@ def _compute_numeric_jacobian(x, x_shape, x_data, y, y_shape, delta, and "y_size" columns where "x_size" is the number of elements in x and "y_size" is the number of elements in y. """ + # bfloat16 doesn't have enough bits to represent high precision numbers such + # as delta. Convert to float32 here. Since numeric_jacobian is expected to + # be the groundtruth to compare against, it shouldn't lose any information. + if x.dtype == dtypes.bfloat16: + x = math_ops.cast(x, dtypes.float32) + if y.dtype == dtypes.bfloat16: + y = math_ops.cast(y, dtypes.float32) + if x_data.dtype == dtypes.bfloat16.as_numpy_dtype: + x_data = x_data.astype(np.float32) # To compute the jacobian, we treat x and y as one-dimensional vectors x_size = _product(x_shape) * (2 if x.dtype.is_complex else 1) @@ -206,8 +216,8 @@ def _compute_gradient(x, extra_feed_dict=None): """Computes the theoretical and numerical jacobian.""" t = dtypes.as_dtype(x.dtype) - allowed_types = [dtypes.float16, dtypes.float32, dtypes.float64, - dtypes.complex64, dtypes.complex128] + allowed_types = [dtypes.float16, dtypes.bfloat16, dtypes.float32, + dtypes.float64, dtypes.complex64, dtypes.complex128] assert t.base_dtype in allowed_types, "Don't support type %s for x" % t.name t2 = dtypes.as_dtype(y.dtype) assert t2.base_dtype in allowed_types, "Don't support type %s for y" % t2.name diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 6c5bdc661f..1c2720e973 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -2003,7 +2003,7 @@ def matmul(a, # matmul currently doesn't handle bfloat16 inputs. use_sparse_matmul = True if use_sparse_matmul: - return sparse_matmul( + ret = sparse_matmul( a, b, transpose_a=transpose_a, @@ -2011,6 +2011,12 @@ def matmul(a, a_is_sparse=a_is_sparse, b_is_sparse=b_is_sparse, name=name) + # sparse_matmul always returns float32, even with + # bfloat16 inputs. This prevents us from configuring bfloat16 training. + # casting to bfloat16 also matches non-sparse matmul behavior better. + if a.dtype == dtypes.bfloat16 and b.dtype == dtypes.bfloat16: + ret = cast(ret, dtypes.bfloat16) + return ret else: return gen_math_ops._mat_mul( a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name) -- GitLab From 65aa9ee2500b0108d89f5fd1368ec3b73b273082 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 17:16:58 -0800 Subject: [PATCH 017/423] Fix comparison bug in remove_trivial_binary PiperOrigin-RevId: 183167508 --- .../toco/graph_transformations/remove_trivial_binary.cc | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc index 8512e6bb5a..95a50c6179 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_binary.cc @@ -89,14 +89,14 @@ bool RemoveTrivialBinaryOperator::Run(Model* model, std::size_t op_index) { const auto& constant_input_float_data = constant_input_array.GetBuffer().data; bool is_trivial = false; - if (binary_op->type != OperatorType::kAdd) { + if (binary_op->type == OperatorType::kAdd) { is_trivial = AreAllBufferElementsEqualTo(constant_input_float_data, 0.f); - } else if (binary_op->type != OperatorType::kSub) { + } else if (binary_op->type == OperatorType::kSub) { is_trivial = index_of_constant_input == 1 && AreAllBufferElementsEqualTo(constant_input_float_data, 0.f); - } else if (binary_op->type != OperatorType::kMul) { + } else if (binary_op->type == OperatorType::kMul) { is_trivial = AreAllBufferElementsEqualTo(constant_input_float_data, 1.f); - } else if (binary_op->type != OperatorType::kDiv) { + } else if (binary_op->type == OperatorType::kDiv) { is_trivial = index_of_constant_input == 1 && AreAllBufferElementsEqualTo(constant_input_float_data, 1.f); } -- GitLab From 7c4f482a851d12a3b0187bbf31db65ff6b7a7ad3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 17:18:47 -0800 Subject: [PATCH 018/423] Add copy method to EagerVariableStore. EagerVariableStore.copy creates a new EagerVariableStore instance containing new variables so that they can be modified without affecting the variables in the old store. PiperOrigin-RevId: 183167776 --- .../kernel_tests/variable_scope_test.py | 24 ++++++++++++++ tensorflow/python/ops/variable_scope.py | 31 +++++++++++++++++++ 2 files changed, 55 insertions(+) diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py index 238d4b58d5..8527f116f9 100644 --- a/tensorflow/python/kernel_tests/variable_scope_test.py +++ b/tensorflow/python/kernel_tests/variable_scope_test.py @@ -131,6 +131,30 @@ class VariableScopeTest(test.TestCase): self.assertFalse(v in store.non_trainable_variables()) self.assertTrue(w in store.non_trainable_variables()) + # Test copying. + new_store = store.copy() + with new_store.as_default(): + new_v = variable_scope.get_variable("v") + new_w = variable_scope.get_variable("w") + self.assertEqual(new_v.numpy(), v.numpy()) + self.assertEqual(new_w.numpy(), w.numpy()) + self.assertTrue(new_v in new_store.variables()) + self.assertTrue(new_w in new_store.variables()) + self.assertTrue(new_v in new_store.trainable_variables()) + self.assertFalse(new_w in new_store.trainable_variables()) + self.assertFalse(new_v in new_store.non_trainable_variables()) + self.assertTrue(new_w in new_store.non_trainable_variables()) + + # Check that variables are separate instances. + for v in store.variables(): + v.assign(-1) + for v in new_store.variables(): + v.assign(1) + for v in store.variables(): + self.assertEqual(v.numpy(), -1) + for v in new_store.variables(): + self.assertEqual(v.numpy(), 1) + @test_util.run_in_graph_and_eager_modes() def testInitFromNonTensorValue(self): v = variable_scope.get_variable("v4", initializer=4, dtype=dtypes.int32) diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index c52d5fff5d..db594ac6a0 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -27,6 +27,7 @@ import sys import traceback import six +from six import iteritems from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.eager import context @@ -1242,6 +1243,36 @@ class EagerVariableStore(object): key=lambda x: x.name) # pylint: enable=protected-access + def copy(self): + """Copy this variable store and all of its contents. + + Variables contained in this store will be copied over to the new variable + store, meaning that they can be modified without affecting the variables in + this store. + + Returns: + A new EagerVariableStore instance containing copied variables. + """ + # pylint: disable=protected-access + new_store = EagerVariableStore() + for key, var in iteritems(self._store._vars): + # Strip device out of variable name. + try: + index = var.name.index(":") + except ValueError: + stripped_var_name = var.name + else: + stripped_var_name = var.name[:index] + + # Create new variable with same value, name, and "trainable" flag. + new_var = resource_variable_ops.ResourceVariable( + var.read_value(), + name=stripped_var_name, + trainable=var._trainable) + new_store._store._vars[key] = new_var + return new_store + # pylint: enable=protected-access + @tf_export("get_variable") def get_variable(name, -- GitLab From 0412e0946bdd2765d5c3dba0cc9b12b8650f564a Mon Sep 17 00:00:00 2001 From: Yunxing Dai Date: Wed, 24 Jan 2018 17:24:50 -0800 Subject: [PATCH 019/423] Add R3 and R5 tests to select and scatter. - Added evaluator support so that we can test S&S in arbitrary dimensions. RELNOTES: n/a PiperOrigin-RevId: 183168473 --- .../compiler/xla/service/hlo_evaluator.cc | 189 +++++++++++++++--- tensorflow/compiler/xla/tests/BUILD | 1 + .../xla/tests/select_and_scatter_test.cc | 189 +++++++++++------- 3 files changed, 276 insertions(+), 103 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index 2112cf57c7..e3f5c17e35 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -43,6 +43,7 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.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/logging.h" #include "tensorflow/core/platform/protobuf.h" #include "tensorflow/core/platform/types.h" @@ -166,6 +167,34 @@ StatusOr> ElementWiseUnaryOpImpl( return std::move(result); } +// For one particular placement of a window in a base shape (the placement is +// represented as `window_count_index`), iterates inside the window. Translates +// the window index into base index. If the base index is within bound, call `f` +// with the base index. +void IterateThroughWindow( + const Shape& window_shape, const Window& window, const Shape& base_shape, + const tensorflow::gtl::ArraySlice& window_count_index, + const std::function&)>& f) { + const int64 rank = ShapeUtil::Rank(base_shape); + DimensionVector window_index(rank); + std::fill(window_index.begin(), window_index.end(), 0); + do { + std::vector base_index(rank); + bool out_of_bound = false; + for (int64 i = 0; i < rank; ++i) { + base_index[i] = window_count_index[i] * window.dimensions(i).stride() + + window_index[i] - window.dimensions(i).padding_low(); + if (base_index[i] < 0 || base_index[i] >= base_shape.dimensions(i)) { + out_of_bound = true; + break; + } + } + if (!out_of_bound) { + f(base_index); + } + } while (IndexUtil::BumpIndices(window_shape, &window_index)); +} + } // namespace template @@ -1420,6 +1449,111 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { return Status::OK(); } + Status HandleSelectAndScatter(HloInstruction* select_and_scatter) override { + auto operand = select_and_scatter->operand(0); + auto source = select_and_scatter->operand(1); + const Window& window = select_and_scatter->window(); + + const Literal& init_literal = + parent_->GetEvaluatedLiteralFor(select_and_scatter->operand(2)); + TF_RET_CHECK(ShapeUtil::IsScalar(init_literal.shape())); + auto init_scalar = init_literal.Get({}); + + auto result = Literal::CreateFromShape(select_and_scatter->shape()); + + // Initialize result array with the init value. + TF_RETURN_IF_ERROR(result->Populate( + [&](tensorflow::gtl::ArraySlice output_index) { + return init_scalar; + })); + + std::vector window_dimension_sizes; + for (const auto& window_dimension : window.dimensions()) { + window_dimension_sizes.push_back(window_dimension.size()); + } + const Shape window_shape = ShapeUtil::MakeShape( + operand->shape().element_type(), window_dimension_sizes); + + HloComputation* select = select_and_scatter->select(); + HloComputation* scatter = select_and_scatter->scatter(); + + const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand); + const Literal& source_literal = parent_->GetEvaluatedLiteralFor(source); + + int64 rank = ShapeUtil::Rank(operand_literal.shape()); + + HloEvaluator embedded_evaluator; + DimensionVector source_index(rank); + + std::fill(source_index.begin(), source_index.end(), 0); + do { + // For each element in `source`, we place a window in `operand`. For each + // window placement, we iterate inside the window twice: + // + // 1. Find the selected index by applying `select` function to all + // elements. E.g., If the `select` function is GreaterEqual, the first + // iteration through the window finds the biggest value and returns its + // index. + // + // 2. Using the selected index, scatter value from `source` to result. We + // do this by iterating through the window, and compare each index with + // the selected index. + tensorflow::gtl::optional selected_val; + tensorflow::gtl::optional> selected_index; + + IterateThroughWindow( + window_shape, window, operand_literal.shape(), source_index, + [&](const std::vector& operand_index) { + auto curr_val = operand_literal.Get(operand_index); + if (!selected_val) { + selected_val = curr_val; + selected_index = operand_index; + } + const auto curr_val_literal = Literal::CreateR0(curr_val); + const auto selected_val_literal = + Literal::CreateR0(*selected_val); + + const std::vector args = { + curr_val_literal.get(), selected_val_literal.get()}; + std::unique_ptr computed_result = + embedded_evaluator.Evaluate(*select, args) + .ConsumeValueOrDie(); + bool selected = computed_result->Get({}); + if (selected) { + selected_val = curr_val; + selected_index = operand_index; + } + embedded_evaluator.ResetVisitStates(); + }); + + IterateThroughWindow( + window_shape, window, operand_literal.shape(), source_index, + [&](const std::vector& operand_index) { + if (std::equal(operand_index.begin(), operand_index.end(), + selected_index->begin())) { + auto source = source_literal.Get(source_index); + auto scattered = result->Get(operand_index); + const auto source_literal = Literal::CreateR0(source); + const auto scattered_literal = + Literal::CreateR0(scattered); + + const std::vector args = { + source_literal.get(), scattered_literal.get()}; + std::unique_ptr computed_result = + embedded_evaluator.Evaluate(*scatter, args) + .ConsumeValueOrDie(); + result->Set(operand_index, computed_result->Get({})); + // Clear visit states so that the we can use the evaluator again + // on the same computation. + embedded_evaluator.ResetVisitStates(); + } + }); + } while (IndexUtil::BumpIndices(source->shape(), &source_index)); + + parent_->evaluated_[select_and_scatter] = std::move(result); + return Status::OK(); + } + Status HandleReduceWindow(HloInstruction* reduce_window) override { auto operand = reduce_window->operand(0); const Window& window = reduce_window->window(); @@ -1468,39 +1602,28 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { std::fill(window_index.begin(), window_index.end(), 0); std::fill(operand_index.begin(), operand_index.end(), 0); - do { - bool out_of_bound = false; - for (int i = 0; i < operand_index.size(); ++i) { - operand_index[i] = - output_index[i] * window.dimensions(i).stride() + - window_index[i] - window.dimensions(i).padding_low(); - if (operand_index[i] < 0 || - operand_index[i] >= operand_literal.shape().dimensions(i)) { - out_of_bound = true; - break; - } - } - if (!out_of_bound) { - auto curr_val = operand_literal.Get(operand_index); - - // Evaluate computation with specified literal operands. - const auto curr_val_literal = - Literal::CreateR0(curr_val); - const auto result_val_literal = - Literal::CreateR0(result_val); - const std::vector args = { - curr_val_literal.get(), result_val_literal.get()}; - std::unique_ptr computed_result = - embedded_evaluator.Evaluate(*function, args) - .ConsumeValueOrDie(); - - // Clear visit states so that the we can use the evaluate again on - // the same computation. - embedded_evaluator.ResetVisitStates(); - - result_val = computed_result->Get({}); - } - } while (IndexUtil::BumpIndices(window_shape, &window_index)); + IterateThroughWindow( + window_shape, window, operand_literal.shape(), output_index, + [&](const std::vector& operand_index) { + auto curr_val = operand_literal.Get(operand_index); + + // Evaluate computation with specified literal operands. + const auto curr_val_literal = + Literal::CreateR0(curr_val); + const auto result_val_literal = + Literal::CreateR0(result_val); + const std::vector args = { + curr_val_literal.get(), result_val_literal.get()}; + std::unique_ptr computed_result = + embedded_evaluator.Evaluate(*function, args) + .ConsumeValueOrDie(); + + // Clear visit states so that the we can use the evaluate again + // on the same computation. + embedded_evaluator.ResetVisitStates(); + + result_val = computed_result->Get({}); + }); return result_val; })); diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index bc15bd9593..3afd52b6b2 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -1034,6 +1034,7 @@ xla_test( name = "select_and_scatter_test", timeout = "long", srcs = ["select_and_scatter_test.cc"], + tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:literal_util", diff --git a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc index 62ff349e9c..9ee94b8571 100644 --- a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc +++ b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc @@ -39,8 +39,8 @@ namespace xla { namespace { struct SelectAndScatterTestParam { - Array4D operand_shape; - Array4D source_shape; + std::vector operand_shape; + std::vector source_shape; Padding padding_type; tensorflow::gtl::ArraySlice window_dimensions; tensorflow::gtl::ArraySlice window_strides; @@ -69,83 +69,132 @@ class SelectAndScatterTest Computation min_f32_; }; -XLA_TEST_P(SelectAndScatterTest, R4Randomized) { - Array4D o(GetParam().operand_shape); +XLA_TEST_P(SelectAndScatterTest, ParamTest) { + auto operand_shape = GetParam().operand_shape; + Array o(operand_shape); o.FillRandom(1.5f); - auto operand = builder_.ConstantR4FromArray4D(o); + auto operand = builder_.ConstantFromArray(o); - Array4D s(GetParam().source_shape); + auto source_shape = GetParam().source_shape; + Array s(source_shape); s.FillRandom(12.0f); - auto source = builder_.ConstantR4FromArray4D(s); - - builder_.SelectAndScatter(operand, ge_f32_, GetParam().window_dimensions, - GetParam().window_strides, GetParam().padding_type, - source, builder_.ConstantR0(0.0f), add_f32_); + auto source = builder_.ConstantFromArray(s); - auto e = ReferenceUtil::SelectAndScatter4DGePlus( - o, s, 0.0f, GetParam().window_dimensions, GetParam().window_strides, - GetParam().padding_type == Padding::kSame); + auto select_and_scatter = builder_.SelectAndScatter( + operand, ge_f32_, GetParam().window_dimensions, GetParam().window_strides, + GetParam().padding_type, source, builder_.ConstantR0(0.0f), + add_f32_); - ComputeAndCompareR4(&builder_, *e, {}, ErrorSpec(1e-5)); + ComputeAndCompare(&builder_, select_and_scatter, {}, ErrorSpec(1e-5)); } INSTANTIATE_TEST_CASE_P( SelectAndScatterTest_Instantiation, SelectAndScatterTest, - ::testing::Values(SelectAndScatterTestParam{{6, 6, 256, 128}, - {3, 3, 256, 128}, - Padding::kSame, - {3, 3, 1, 1}, - {2, 2, 1, 1}}, - SelectAndScatterTestParam{{7, 7, 256, 128}, - {3, 3, 256, 128}, - Padding::kValid, - {3, 3, 1, 1}, - {2, 2, 1, 1}}, - SelectAndScatterTestParam{{6, 7, 256, 128}, - {3, 3, 256, 128}, - Padding::kValid, - {2, 3, 1, 1}, - {2, 2, 1, 1}}, - SelectAndScatterTestParam{{6, 7, 256, 128}, - {2, 3, 256, 128}, - Padding::kValid, - {2, 3, 1, 1}, - {3, 2, 1, 1}}, - SelectAndScatterTestParam{{9, 9, 16, 128}, - {3, 3, 16, 128}, - Padding::kValid, - {3, 3, 1, 1}, - {3, 3, 1, 1}}, - SelectAndScatterTestParam{{3, 3, 4, 4}, - {1, 1, 4, 4}, - Padding::kValid, - {3, 3, 1, 1}, - {3, 3, 1, 1}}, - SelectAndScatterTestParam{{3, 3, 4, 4}, - {1, 1, 4, 4}, - Padding::kValid, - {3, 3, 1, 1}, - {3, 3, 1, 1}}, - SelectAndScatterTestParam{{9, 3, 4, 4}, - {3, 1, 4, 4}, - Padding::kValid, - {3, 3, 1, 1}, - {3, 3, 1, 1}}, - SelectAndScatterTestParam{{7, 3, 4, 4}, - {3, 1, 4, 4}, - Padding::kValid, - {3, 3, 1, 1}, - {2, 3, 1, 1}}, - SelectAndScatterTestParam{{1, 1, 5, 5}, - {1, 1, 5, 5}, - Padding::kSame, - {3, 3, 1, 1}, - {3, 3, 1, 1}}, - SelectAndScatterTestParam{{7, 7, 8, 256}, - {4, 4, 8, 256}, - Padding::kSame, - {2, 2, 1, 1}, - {2, 2, 1, 1}})); + ::testing::Values( + SelectAndScatterTestParam{{6, 6, 6, 4, 4}, + {3, 3, 3, 4, 4}, + Padding::kSame, + {3, 3, 3, 1, 1}, + {2, 2, 2, 1, 1}}, + SelectAndScatterTestParam{{7, 7, 7, 4, 4}, + {3, 3, 3, 4, 4}, + Padding::kValid, + {3, 3, 3, 1, 1}, + {2, 2, 2, 1, 1}}, + + SelectAndScatterTestParam{{8, 8, 8, 4, 4}, + {1, 3, 3, 4, 4}, + Padding::kValid, + {8, 4, 4, 1, 1}, + {1, 2, 2, 1, 1}}, + SelectAndScatterTestParam{{6, 6, 256, 128}, + {3, 3, 256, 128}, + Padding::kSame, + {3, 3, 1, 1}, + {2, 2, 1, 1}}, + SelectAndScatterTestParam{{7, 7, 256, 128}, + {3, 3, 256, 128}, + Padding::kValid, + {3, 3, 1, 1}, + {2, 2, 1, 1}}, + SelectAndScatterTestParam{{6, 7, 256, 128}, + {3, 3, 256, 128}, + Padding::kValid, + {2, 3, 1, 1}, + {2, 2, 1, 1}}, + SelectAndScatterTestParam{{6, 7, 256, 128}, + {2, 3, 256, 128}, + Padding::kValid, + {2, 3, 1, 1}, + {3, 2, 1, 1}}, + SelectAndScatterTestParam{{9, 9, 16, 128}, + {3, 3, 16, 128}, + Padding::kValid, + {3, 3, 1, 1}, + {3, 3, 1, 1}}, + SelectAndScatterTestParam{{3, 3, 4, 4}, + {1, 1, 4, 4}, + Padding::kValid, + {3, 3, 1, 1}, + {3, 3, 1, 1}}, + SelectAndScatterTestParam{{3, 3, 4, 4}, + {1, 1, 4, 4}, + Padding::kValid, + {3, 3, 1, 1}, + {3, 3, 1, 1}}, + SelectAndScatterTestParam{{9, 3, 4, 4}, + {3, 1, 4, 4}, + Padding::kValid, + {3, 3, 1, 1}, + {3, 3, 1, 1}}, + SelectAndScatterTestParam{{7, 3, 4, 4}, + {3, 1, 4, 4}, + Padding::kValid, + {3, 3, 1, 1}, + {2, 3, 1, 1}}, + SelectAndScatterTestParam{{1, 1, 5, 5}, + {1, 1, 5, 5}, + Padding::kSame, + {3, 3, 1, 1}, + {3, 3, 1, 1}}, + SelectAndScatterTestParam{{7, 7, 8, 256}, + {4, 4, 8, 256}, + Padding::kSame, + {2, 2, 1, 1}, + {2, 2, 1, 1}}, + SelectAndScatterTestParam{ + {6, 4, 4}, {3, 4, 4}, Padding::kSame, {3, 1, 1}, {2, 1, 1}}, + SelectAndScatterTestParam{ + {6, 256, 128}, {3, 256, 128}, Padding::kSame, {3, 1, 1}, {2, 1, 1}}, + SelectAndScatterTestParam{{7, 256, 128}, + {3, 256, 128}, + Padding::kValid, + {3, 1, 1}, + {2, 1, 1}}, + SelectAndScatterTestParam{{6, 256, 128}, + {3, 256, 128}, + Padding::kValid, + {2, 1, 1}, + {2, 1, 1}}, + SelectAndScatterTestParam{{6, 256, 128}, + {2, 256, 128}, + Padding::kValid, + {2, 1, 1}, + {3, 1, 1}}, + SelectAndScatterTestParam{ + {9, 16, 128}, {3, 16, 128}, Padding::kValid, {3, 1, 1}, {3, 1, 1}}, + SelectAndScatterTestParam{ + {3, 4, 4}, {1, 4, 4}, Padding::kValid, {3, 1, 1}, {3, 1, 1}}, + SelectAndScatterTestParam{ + {3, 4, 4}, {1, 4, 4}, Padding::kValid, {3, 1, 1}, {3, 1, 1}}, + SelectAndScatterTestParam{ + {9, 4, 4}, {3, 4, 4}, Padding::kValid, {3, 1, 1}, {3, 1, 1}}, + SelectAndScatterTestParam{ + {7, 4, 4}, {3, 4, 4}, Padding::kValid, {3, 1, 1}, {2, 1, 1}}, + SelectAndScatterTestParam{ + {1, 5, 5}, {1, 5, 5}, Padding::kSame, {3, 1, 1}, {3, 1, 1}}, + SelectAndScatterTestParam{ + {7, 8, 256}, {4, 8, 256}, Padding::kSame, {2, 1, 1}, {2, 1, 1}})); // Test for F32 1D array, with a zero-element input. XLA_TEST_F(SelectAndScatterTest, R1S0F32) { -- GitLab From 8a5b0f457c89e3ae77f67628654c1b64c4e65000 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 24 Jan 2018 17:49:28 -0800 Subject: [PATCH 020/423] Make resource_variable_ops_test.py work with the C API enabled. PiperOrigin-RevId: 183171341 --- tensorflow/python/kernel_tests/resource_variable_ops_test.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 7b131a5b8c..b4b555591d 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -38,6 +38,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import test +@test_util.with_c_api class ResourceVariableOpsTest(test_util.TensorFlowTestCase): def tearDown(self): @@ -342,14 +343,14 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): v = resource_variable_ops.ResourceVariable( 2.0, caching_device="/job:localhost") self.assertEqual("/job:localhost", v.value().device) - with self.assertRaisesRegexp(ValueError, "No attr named '_class'"): + with self.assertRaises(ValueError): _ = v.value().op.get_attr("_class") with ops.colocate_with(v.op): w = resource_variable_ops.ResourceVariable( 2.0, caching_device="/job:localhost") self.assertEqual("/job:localhost", w.value().device) - with self.assertRaisesRegexp(ValueError, "No attr named '_class'"): + with self.assertRaises(ValueError): _ = w.value().op.get_attr("_class") def testSharedName(self): -- GitLab From 2c3205a96c50e2488815b6e8dd8d8c18dca5d431 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 14 Dec 2017 11:18:05 -0800 Subject: [PATCH 021/423] Enable bfloat16 tests and add a filter for currently failed tests. PiperOrigin-RevId: 179069257 --- tensorflow/compiler/tests/binary_ops_test.py | 2 +- .../compiler/tests/tensor_array_ops_test.py | 3 +- tensorflow/compiler/tests/unary_ops_test.py | 3 +- tensorflow/compiler/tests/xla_test.py | 105 ++++++++++++++---- tensorflow/compiler/tf2xla/lib/util.cc | 2 +- tensorflow/compiler/tf2xla/lib/util.h | 2 +- tensorflow/compiler/tf2xla/xla_op_registry.h | 8 +- tensorflow/core/kernels/split_op.cc | 2 + tensorflow/python/framework/test_util.py | 6 + 9 files changed, 100 insertions(+), 33 deletions(-) diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 654dc15e86..905dd9fc7b 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -547,7 +547,7 @@ class BinaryOpsTest(XLATestCase): self._testDivision(dtype) def testFloatDivision(self): - for dtype in self.float_types + self.complex_types: + for dtype in self.float_types | self.complex_types: self._testDivision(dtype) def _testRemainder(self, dtype): diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py index ac039e0162..a62925a181 100644 --- a/tensorflow/compiler/tests/tensor_array_ops_test.py +++ b/tensorflow/compiler/tests/tensor_array_ops_test.py @@ -330,8 +330,7 @@ class TensorArrayTest(xla_test.XLATestCase): # Find two different floating point types, create an array of # the first type, but try to read the other type. if len(self.float_types) > 1: - dtype1 = self.float_types[0] - dtype2 = self.float_types[1] + dtype1, dtype2 = list(self.float_types)[:2] with self.test_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtype1, tensor_array_name="foo", size=3) diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py index 0da7442a24..b0623c0fbc 100644 --- a/tensorflow/compiler/tests/unary_ops_test.py +++ b/tensorflow/compiler/tests/unary_ops_test.py @@ -573,7 +573,8 @@ class UnaryOpsTest(XLATestCase): def testCast(self): shapes = [[], [4], [2, 3], [2, 0, 4]] - types = [dtypes.bool, dtypes.int32, dtypes.float32] + self.complex_tf_types + types = (set([dtypes.bool, dtypes.int32, dtypes.float32]) | + self.complex_tf_types) for shape in shapes: for src_type in types: for dst_type in types: diff --git a/tensorflow/compiler/tests/xla_test.py b/tensorflow/compiler/tests/xla_test.py index 0be127997e..7e1f5c76ed 100644 --- a/tensorflow/compiler/tests/xla_test.py +++ b/tensorflow/compiler/tests/xla_test.py @@ -53,41 +53,100 @@ class XLATestCase(test.TestCase): super(XLATestCase, self).__init__(method_name) self.device = FLAGS.test_device self.has_custom_call = (self.device == 'XLA_CPU') - self.all_tf_types = [ + self._all_tf_types = set([ dtypes.as_dtype(types_pb2.DataType.Value(name)) for name in FLAGS.types.split(',') - ] - self.int_tf_types = [ - dtype for dtype in self.all_tf_types if dtype.is_integer - ] - self.float_tf_types = [ - dtype for dtype in self.all_tf_types if dtype.is_floating - ] - self.complex_tf_types = [ - dtype for dtype in self.all_tf_types if dtype.is_complex - ] - self.numeric_tf_types = ( - self.int_tf_types + self.float_tf_types + self.complex_tf_types) - - self.all_types = [dtype.as_numpy_dtype for dtype in self.all_tf_types] - self.int_types = [dtype.as_numpy_dtype for dtype in self.int_tf_types] - self.float_types = [dtype.as_numpy_dtype for dtype in self.float_tf_types] - self.complex_types = [ + ]) + self.int_tf_types = set([ + dtype for dtype in self._all_tf_types if dtype.is_integer + ]) + self._float_tf_types = set([ + dtype for dtype in self._all_tf_types if dtype.is_floating + ]) + self.complex_tf_types = set([ + dtype for dtype in self._all_tf_types if dtype.is_complex + ]) + self._numeric_tf_types = set( + self.int_tf_types | self._float_tf_types | self.complex_tf_types) + + self._all_types = set( + [dtype.as_numpy_dtype for dtype in self._all_tf_types]) + self.int_types = set([dtype.as_numpy_dtype for dtype in self.int_tf_types]) + self._float_types = set( + [dtype.as_numpy_dtype for dtype in self._float_tf_types]) + self.complex_types = set([ dtype.as_numpy_dtype for dtype in self.complex_tf_types - ] - self.numeric_types = self.int_types + self.float_types + self.complex_types + ]) + self._numeric_types = set( + self.int_types | self._float_types | self.complex_types) # Parse the manifest file, if any, into a regex identifying tests to # disable self.disabled_regex = None + self._method_types_filter = dict() + # TODO(xpan): Make it text proto if it doesn't scale. + # Each line of the manifest file specifies an entry. The entry can be + # 1) TestNameRegex // E.g. CumprodTest.* Or + # 2) TestName TypeName // E.g. AdamOptimizerTest.testSharing DT_BFLOAT16 + # The 1) disables the entire test. While 2) only filter some numeric types + # so that they are not used in those tests. + if FLAGS.disabled_manifest is not None: comments_re = re.compile('#.*$') manifest_file = open(FLAGS.disabled_manifest, 'r') - lines = manifest_file.read().splitlines() - lines = [comments_re.sub('', l).strip() for l in lines] - self.disabled_regex = re.compile('|'.join(lines)) + disabled_tests = [] + disabled_method_types = [] + for l in manifest_file.read().splitlines(): + entry = comments_re.sub('', l).strip().split(' ') + if len(entry) == 1: + disabled_tests.append(entry[0]) + elif len(entry) == 2: + disabled_method_types.append( + (entry[0], entry[1].strip().split(','))) + else: + raise ValueError('Bad entry in manifest file.') + + self.disabled_regex = re.compile('|'.join(disabled_tests)) + for method, types in disabled_method_types: + self._method_types_filter[method] = set([ + dtypes.as_dtype(types_pb2.DataType.Value(name)).as_numpy_dtype + for name in types]) manifest_file.close() + @property + def all_tf_types(self): + name = '{}.{}'.format(type(self).__name__, self._testMethodName) + tf_types = set([dtypes.as_dtype(t) + for t in self._method_types_filter.get(name, set())]) + return self._all_tf_types - tf_types + + @property + def float_types(self): + name = '{}.{}'.format(type(self).__name__, self._testMethodName) + return self._float_types - self._method_types_filter.get(name, set()) + + @property + def float_tf_types(self): + name = '{}.{}'.format(type(self).__name__, self._testMethodName) + return self._float_tf_types - self._method_types_filter.get(name, set()) + + @property + def numeric_tf_types(self): + name = '{}.{}'.format(type(self).__name__, self._testMethodName) + tf_types = set([dtypes.as_dtype(t) + for t in self._method_types_filter.get(name, set())]) + return self._numeric_tf_types - tf_types + + @property + def numeric_types(self): + name = '{}.{}'.format(type(self).__name__, self._testMethodName) + return self._numeric_types - self._method_types_filter.get(name, set()) + + @property + def all_types(self): + name = '{}.{}'.format(type(self).__name__, self._testMethodName) + return self._all_types - self._method_types_filter.get(name, set()) + def setUp(self): super(XLATestCase, self).setUp() name = '{}.{}'.format(type(self).__name__, self._testMethodName) diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc index 943248aedb..ce24b61b5d 100644 --- a/tensorflow/compiler/tf2xla/lib/util.cc +++ b/tensorflow/compiler/tf2xla/lib/util.cc @@ -28,7 +28,7 @@ limitations under the License. namespace tensorflow { xla::ComputationDataHandle Zeros(xla::ComputationBuilder* builder, - xla::Shape& shape) { + const xla::Shape& shape) { return builder->Broadcast( builder->ConstantLiteral(xla::Literal::Zero(shape.element_type())), xla::AsInt64Slice(shape.dimensions())); diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index 8fba6b5cf2..fb138b4f73 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -25,7 +25,7 @@ namespace tensorflow { // Returns a zero-filled tensor with shape `shape`. xla::ComputationDataHandle Zeros(xla::ComputationBuilder* builder, - xla::Shape& shape); + const xla::Shape& shape); // Returns a floating point scalar constant of 'type' with 'value'. // If 'type' is complex, returns a real value with zero imaginary component. diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.h b/tensorflow/compiler/tf2xla/xla_op_registry.h index 2959d2ab69..8bfd9758f7 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.h +++ b/tensorflow/compiler/tf2xla/xla_op_registry.h @@ -45,11 +45,11 @@ extern const char* const DEVICE_GPU_XLA_JIT; // "GPU_XLA_JIT" extern const char* const DEVICE_XLA_CPU; extern const char* const DEVICE_XLA_GPU; -constexpr std::array kFloatTypes = { - {DT_HALF, DT_FLOAT, DT_DOUBLE}}; -constexpr std::array kNumericTypes = { +constexpr std::array kFloatTypes = { + {DT_HALF, DT_FLOAT, DT_DOUBLE, DT_BFLOAT16}}; +constexpr std::array kNumericTypes = { {DT_UINT32, DT_UINT64, DT_INT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE, - DT_COMPLEX64}}; + DT_COMPLEX64, DT_BFLOAT16}}; constexpr std::array kCpuAllTypes = { {DT_UINT32, DT_UINT64, DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE, diff --git a/tensorflow/core/kernels/split_op.cc b/tensorflow/core/kernels/split_op.cc index 58e1a73be6..094ba8bb86 100644 --- a/tensorflow/core/kernels/split_op.cc +++ b/tensorflow/core/kernels/split_op.cc @@ -360,6 +360,8 @@ class SplitOpSYCL : public SplitOpBase { TF_CALL_ALL_TYPES(REGISTER_SPLIT); REGISTER_SPLIT(quint8); +// TODO(xpan): Merge bfloat16 into TF_CALL_ALL_TYPES +REGISTER_SPLIT(bfloat16); #undef REGISTER_SPLIT diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 26904aadab..644e3bb515 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -51,6 +51,7 @@ from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.eager import tape from tensorflow.python.framework import device as pydev +from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed @@ -1168,6 +1169,7 @@ class TensorFlowTestCase(googletest.TestCase): """ a = self._GetNdArray(a) b = self._GetNdArray(b) + # types with lower tol are put later to overwrite previous ones. if (a.dtype == np.float32 or b.dtype == np.float32 or a.dtype == np.complex64 or b.dtype == np.complex64): rtol = max(rtol, float_rtol) @@ -1175,6 +1177,10 @@ class TensorFlowTestCase(googletest.TestCase): if a.dtype == np.float16 or b.dtype == np.float16: rtol = max(rtol, half_rtol) atol = max(atol, half_atol) + if (a.dtype == dtypes.bfloat16.as_numpy_dtype or + b.dtype == dtypes.bfloat16.as_numpy_dtype): + rtol = max(rtol, half_rtol) + atol = max(atol, half_atol) self.assertAllClose(a, b, rtol=rtol, atol=atol) -- GitLab From f76387a10f5188b059bc13223c5e9040a3bb7143 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 24 Jan 2018 17:52:19 -0800 Subject: [PATCH 022/423] Make batch_sequences_with_states_test.py work with C API enabled. PiperOrigin-RevId: 183171572 --- .../batch_sequences_with_states_test.py | 26 ++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py b/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py index 2a0ef0e6b3..04538405e4 100644 --- a/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py +++ b/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py @@ -320,6 +320,18 @@ class BatchSequencesWithStatesTest(test.TestCase): def testNotAMultiple(self): num_unroll = 3 # Not a divisor of value_length - # so padding would have been necessary. + + # Use placeholder_with_default in sequences to make sure we get runtime + # error instead of shape inference error + sequences = { + "seq1": array_ops.placeholder_with_default(self.sequences["seq1"], + shape=(None, 5)), + "seq2": array_ops.placeholder_with_default(self.sequences["seq2"], + shape=(None, 4, 2)), + "seq3": self.sequences["seq3"], + "seq4": self.sequences["seq4"], + } + with self.test_session() as sess: with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, ".*should be a multiple of: 3, but saw " @@ -330,7 +342,7 @@ class BatchSequencesWithStatesTest(test.TestCase): with coord.stop_on_exception(): next_batch = sqss.batch_sequences_with_states( input_key=self.key, - input_sequences=self.sequences, + input_sequences=sequences, input_context=self.context, input_length=3, initial_states=self.initial_states, @@ -493,6 +505,18 @@ class BatchSequencesWithStatesTest(test.TestCase): expected_seq4_batch2=expected_seq4_batch2) +class BatchSequencesWithStatesTestWithCApi(BatchSequencesWithStatesTest): + + def setUp(self): + self._prev_value = ops._USE_C_API + ops._USE_C_API = True + super(BatchSequencesWithStatesTestWithCApi, self).setUp() + + def tearDown(self): + super(BatchSequencesWithStatesTestWithCApi, self).tearDown() + ops._USE_C_API = self._prev_value + + class PaddingTest(test.TestCase): def testPaddingInvalidLengths(self): -- GitLab From 4aacd356fe7354b044d7c5787fb2366219294658 Mon Sep 17 00:00:00 2001 From: Ian Langmore Date: Wed, 24 Jan 2018 17:56:55 -0800 Subject: [PATCH 023/423] VISIBILITY_FIX: Add 'auto_correlation' to _allowed_symbols. PiperOrigin-RevId: 183171955 --- tensorflow/contrib/distributions/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py index 59cc5eae06..60a187e541 100644 --- a/tensorflow/contrib/distributions/__init__.py +++ b/tensorflow/contrib/distributions/__init__.py @@ -85,6 +85,7 @@ from tensorflow.python.ops.distributions.uniform import * from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ + 'auto_correlation', 'bijectors', 'Cauchy', 'ConditionalDistribution', -- GitLab From 1323fc76958c9bf4907cf8bc53c9db0a85fb7d9b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 16 Jan 2018 18:20:02 -0800 Subject: [PATCH 024/423] Remove a duplicate bfloat16 registration. PiperOrigin-RevId: 182143007 --- tensorflow/core/kernels/identity_op.cc | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/core/kernels/identity_op.cc b/tensorflow/core/kernels/identity_op.cc index 7b8abf5494..5d288ff19c 100644 --- a/tensorflow/core/kernels/identity_op.cc +++ b/tensorflow/core/kernels/identity_op.cc @@ -102,7 +102,6 @@ REGISTER_SYCL_HOST_KERNEL(bool); IdentityOp) TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL); -REGISTER_GPU_KERNEL(bfloat16); REGISTER_GPU_KERNEL(Variant); #undef REGISTER_GPU_KERNEL -- GitLab From 764f90eca2ac7bdb858c30888ca7b4bb0c47dee1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 15 Dec 2017 10:09:07 -0800 Subject: [PATCH 025/423] Fix bfloat16 numerics issues in the tests. PiperOrigin-RevId: 179207115 --- tensorflow/compiler/tests/ftrl_test.py | 40 ++++++++++----------- tensorflow/compiler/tests/momentum_test.py | 23 ++++++------ tensorflow/compiler/tests/unary_ops_test.py | 2 +- tensorflow/python/framework/test_util.py | 10 ++++-- 4 files changed, 39 insertions(+), 36 deletions(-) diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py index 7e3871312c..f9db4cf201 100644 --- a/tensorflow/compiler/tests/ftrl_test.py +++ b/tensorflow/compiler/tests/ftrl_test.py @@ -161,9 +161,9 @@ class FtrlOptimizerTest(XLATestCase): ftrl_update.run() # Validate updated params - self.assertAllClose( + self.assertAllCloseAccordingToType( np.array([-2.55607247, -3.98729396]), var0.eval(), 1e-5, 1e-5) - self.assertAllClose( + self.assertAllCloseAccordingToType( np.array([-0.28232238, -0.56096673]), var1.eval(), 1e-5, 1e-5) def testFtrlWithL1(self): @@ -189,10 +189,10 @@ class FtrlOptimizerTest(XLATestCase): ftrl_update.run() # Validate updated params - self.assertAllClose(np.array([-7.66718769, -10.91273689]), var0.eval(), - rtol=1e-4) - self.assertAllClose(np.array([-0.93460727, -1.86147261]), var1.eval(), - rtol=1e-4) + self.assertAllCloseAccordingToType( + np.array([-7.66718769, -10.91273689]), var0.eval(), rtol=1e-4) + self.assertAllCloseAccordingToType( + np.array([-0.93460727, -1.86147261]), var1.eval(), rtol=1e-4) def testFtrlWithL1_L2(self): for dtype in self.float_types: @@ -217,10 +217,10 @@ class FtrlOptimizerTest(XLATestCase): ftrl_update.run() # Validate updated params - self.assertAllClose(np.array([-0.24059935, -0.46829352]), var0.eval(), - rtol=1e-5) - self.assertAllClose(np.array([-0.02406147, -0.04830509]), var1.eval(), - rtol=1e-5) + self.assertAllCloseAccordingToType( + np.array([-0.24059935, -0.46829352]), var0.eval(), rtol=1e-5) + self.assertAllCloseAccordingToType( + np.array([-0.02406147, -0.04830509]), var1.eval(), rtol=1e-5) def testFtrlWithL1_L2_L2Shrinkage(self): """Test the new FTRL op with support for l2 shrinkage. @@ -244,18 +244,18 @@ class FtrlOptimizerTest(XLATestCase): ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([4.0, 3.0], var1.eval()) + self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval()) + self.assertAllCloseAccordingToType([4.0, 3.0], var1.eval()) # Run 10 steps FTRL for _ in range(10): ftrl_update.run() # Validate updated params - self.assertAllClose(np.array([-0.21931979, -0.40642974]), var0.eval(), - rtol=1e-4) - self.assertAllClose(np.array([-0.0282721, -0.07188385]), var1.eval(), - rtol=1e-4) + self.assertAllCloseAccordingToType( + np.array([-0.21931979, -0.40642974]), var0.eval(), rtol=1e-4) + self.assertAllCloseAccordingToType( + np.array([-0.0282721, -0.07188385]), var1.eval(), rtol=1e-4) # When variables are initialized with Zero, FTRL-Proximal has two properties: # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical @@ -272,8 +272,8 @@ class FtrlOptimizerTest(XLATestCase): with self.test_session(), self.test_scope(): val2, val3 = self.equivAdagradTest_AdagradPart(steps, dtype) - self.assertAllClose(val0, val2, rtol=1e-4) - self.assertAllClose(val1, val3, rtol=1e-4) + self.assertAllCloseAccordingToType(val0, val2, rtol=1e-4) + self.assertAllCloseAccordingToType(val1, val3, rtol=1e-4) def testEquivGradientDescentwithoutRegularization(self): steps = 5 @@ -284,8 +284,8 @@ class FtrlOptimizerTest(XLATestCase): val2, val3 = self.equivGradientDescentTest_GradientDescentPart( steps, dtype) - self.assertAllClose(val0, val2, rtol=1e-5) - self.assertAllClose(val1, val3, rtol=1e-5) + self.assertAllCloseAccordingToType(val0, val2, rtol=1e-5) + self.assertAllCloseAccordingToType(val1, val3, rtol=1e-5) if __name__ == "__main__": diff --git a/tensorflow/compiler/tests/momentum_test.py b/tensorflow/compiler/tests/momentum_test.py index c00e3035a0..af9394e7d7 100644 --- a/tensorflow/compiler/tests/momentum_test.py +++ b/tensorflow/compiler/tests/momentum_test.py @@ -96,28 +96,27 @@ class MomentumOptimizerTest(XLATestCase): def testNesterovMomentum(self): for dtype in self.float_types: with self.test_session(), self.test_scope(): - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) - var0_np = np.array([1.0, 2.0], dtype=dtype) - var1_np = np.array([3.0, 4.0], dtype=dtype) + var0 = resource_variable_ops.ResourceVariable([0.1, 0.2], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([0.3, 0.4], dtype=dtype) + var0_np = np.array([0.1, 0.2], dtype=dtype) + var1_np = np.array([0.3, 0.4], dtype=dtype) accum0_np = np.array([0.0, 0.0], dtype=dtype) accum1_np = np.array([0.0, 0.0], dtype=dtype) - cost = 5 * var0 * var0 + 3 * var1 + cost = 0.4 * var0 * var0 + 0.9 * var1 global_step = resource_variable_ops.ResourceVariable( array_ops.zeros([], dtypes.int32), name="global_step") mom_op = momentum_lib.MomentumOptimizer( - learning_rate=2.0, momentum=0.9, use_nesterov=True) + learning_rate=0.1, momentum=0.9, use_nesterov=True) opt_op = mom_op.minimize(cost, global_step, [var0, var1]) variables.global_variables_initializer().run() for _ in range(1, 5): opt_op.run() var0_np, accum0_np = self._update_nesterov_momentum_numpy( - var0_np, accum0_np, var0_np * 10, 2.0, 0.9) - var1_np, accum1_np = self._update_nesterov_momentum_numpy(var1_np, - accum1_np, - 3, 2.0, 0.9) - self.assertAllClose(var0_np, var0.eval()) - self.assertAllClose(var1_np, var1.eval()) + var0_np, accum0_np, var0_np * 0.8, 0.1, 0.9) + var1_np, accum1_np = self._update_nesterov_momentum_numpy( + var1_np, accum1_np, 0.9, 0.1, 0.9) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) def testTensorLearningRateAndMomentum(self): for dtype in self.float_types: diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py index b0623c0fbc..ecba5a4fb0 100644 --- a/tensorflow/compiler/tests/unary_ops_test.py +++ b/tensorflow/compiler/tests/unary_ops_test.py @@ -67,7 +67,7 @@ class UnaryOpsTest(XLATestCase): output = op(pinp) result = session.run(output, {pinp: inp}) if equality_test is None: - equality_test = self.assertAllClose + equality_test = self.assertAllCloseAccordingToType equality_test(result, expected, rtol=rtol, atol=atol) def ListsAreClose(self, result, expected, rtol, atol): diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 644e3bb515..7627fb3e69 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -1151,7 +1151,9 @@ class TensorFlowTestCase(googletest.TestCase): float_rtol=1e-6, float_atol=1e-6, half_rtol=1e-3, - half_atol=1e-3): + half_atol=1e-3, + bfloat16_rtol=1e-2, + bfloat16_atol=1e-2): """Like assertAllClose, but also suitable for comparing fp16 arrays. In particular, the tolerance is reduced to 1e-3 if at least @@ -1166,6 +1168,8 @@ class TensorFlowTestCase(googletest.TestCase): float_atol: absolute tolerance for float32. half_rtol: relative tolerance for float16. half_atol: absolute tolerance for float16. + bfloat16_rtol: relative tolerance for bfloat16. + bfloat16_atol: absolute tolerance for bfloat16. """ a = self._GetNdArray(a) b = self._GetNdArray(b) @@ -1179,8 +1183,8 @@ class TensorFlowTestCase(googletest.TestCase): atol = max(atol, half_atol) if (a.dtype == dtypes.bfloat16.as_numpy_dtype or b.dtype == dtypes.bfloat16.as_numpy_dtype): - rtol = max(rtol, half_rtol) - atol = max(atol, half_atol) + rtol = max(rtol, bfloat16_rtol) + atol = max(atol, bfloat16_atol) self.assertAllClose(a, b, rtol=rtol, atol=atol) -- GitLab From cbd1ee59d28f94c369738cbba8b6a4faed1e5fad Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 24 Jan 2018 17:56:57 -0800 Subject: [PATCH 026/423] Don't close session in debug_gradients_test.py. PiperOrigin-RevId: 183171960 --- tensorflow/python/debug/lib/debug_gradients_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/debug/lib/debug_gradients_test.py b/tensorflow/python/debug/lib/debug_gradients_test.py index 6fd89e018a..b6c7280a41 100644 --- a/tensorflow/python/debug/lib/debug_gradients_test.py +++ b/tensorflow/python/debug/lib/debug_gradients_test.py @@ -39,7 +39,7 @@ class IdentifyGradientTest(test_util.TensorFlowTestCase): def setUp(self): self.sess = session.Session() - with self.sess: + with self.sess.as_default(): self.u = variables.Variable(2.0, name="u") self.v = variables.Variable(3.0, name="v") self.w = math_ops.multiply(self.u.value(), self.v.value(), name="w") -- GitLab From 4153e7afff4e17bbef866bd4811b0392ddb25b53 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 18:23:18 -0800 Subject: [PATCH 027/423] Refactoring pass. Add a container context class to shorten argument lists and expose context information in a more organized manner. Clean up names, docs and tests. Rename submodule to avoid clashing with the new @covert decorator. PiperOrigin-RevId: 183174971 --- tensorflow/BUILD | 2 +- tensorflow/contrib/py2tf/BUILD | 4 +- tensorflow/contrib/py2tf/api.py | 66 ++++---- tensorflow/contrib/py2tf/config.py | 1 + tensorflow/contrib/py2tf/conversion.py | 145 +++++++++--------- tensorflow/contrib/py2tf/conversion_test.py | 15 +- .../py2tf/{convert => converters}/BUILD | 43 +++--- .../py2tf/{convert => converters}/__init__.py | 0 .../break_canonicalization.py | 0 .../break_canonicalization_test.py | 20 +-- .../builtin_functions.py | 0 .../builtin_functions_test.py | 18 +-- .../{convert => converters}/call_trees.py | 0 .../call_trees_test.py | 20 +-- .../continue_canonicalization.py | 0 .../continue_canonicalization_test.py | 20 +-- .../{convert => converters}/control_flow.py | 0 .../control_flow_test.py | 24 +-- .../py2tf/converters/converter_test_base.py | 48 ++++++ .../{convert => converters}/decorators.py | 0 .../for_canonicalization.py | 0 .../for_canonicalization_test.py | 16 +- .../logical_expressions.py | 0 .../logical_expressions_test.py | 10 +- .../print_functions.py | 0 .../print_functions_test.py | 18 +-- .../side_effect_guards.py | 0 .../side_effect_guards_test.py | 18 +-- tensorflow/contrib/py2tf/pyct/BUILD | 1 + tensorflow/contrib/py2tf/pyct/context.py | 42 +++++ .../py2tf/pyct/static_analysis/type_info.py | 16 +- .../pyct/static_analysis/type_info_test.py | 61 +++----- tensorflow/contrib/py2tf/pyct/transformer.py | 18 ++- tensorflow/tools/pip_package/BUILD | 3 +- 34 files changed, 328 insertions(+), 301 deletions(-) rename tensorflow/contrib/py2tf/{convert => converters}/BUILD (79%) rename tensorflow/contrib/py2tf/{convert => converters}/__init__.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/break_canonicalization.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/break_canonicalization_test.py (84%) rename tensorflow/contrib/py2tf/{convert => converters}/builtin_functions.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/builtin_functions_test.py (68%) rename tensorflow/contrib/py2tf/{convert => converters}/call_trees.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/call_trees_test.py (78%) rename tensorflow/contrib/py2tf/{convert => converters}/continue_canonicalization.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/continue_canonicalization_test.py (83%) rename tensorflow/contrib/py2tf/{convert => converters}/control_flow.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/control_flow_test.py (79%) create mode 100644 tensorflow/contrib/py2tf/converters/converter_test_base.py rename tensorflow/contrib/py2tf/{convert => converters}/decorators.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/for_canonicalization.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/for_canonicalization_test.py (75%) rename tensorflow/contrib/py2tf/{convert => converters}/logical_expressions.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/logical_expressions_test.py (85%) rename tensorflow/contrib/py2tf/{convert => converters}/print_functions.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/print_functions_test.py (65%) rename tensorflow/contrib/py2tf/{convert => converters}/side_effect_guards.py (100%) rename tensorflow/contrib/py2tf/{convert => converters}/side_effect_guards_test.py (72%) create mode 100644 tensorflow/contrib/py2tf/pyct/context.py diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 2ea0e38c78..9099463c4f 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -527,7 +527,7 @@ filegroup( "//tensorflow/contrib/periodic_resample:all_files", "//tensorflow/contrib/predictor:all_files", "//tensorflow/contrib/py2tf:all_files", - "//tensorflow/contrib/py2tf/convert:all_files", + "//tensorflow/contrib/py2tf/converters:all_files", "//tensorflow/contrib/py2tf/pyct:all_files", "//tensorflow/contrib/py2tf/pyct/static_analysis:all_files", "//tensorflow/contrib/quantize:all_files", diff --git a/tensorflow/contrib/py2tf/BUILD b/tensorflow/contrib/py2tf/BUILD index 7358822ef5..d395de986d 100644 --- a/tensorflow/contrib/py2tf/BUILD +++ b/tensorflow/contrib/py2tf/BUILD @@ -26,7 +26,7 @@ py_library( srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ - "//tensorflow/contrib/py2tf/convert", + "//tensorflow/contrib/py2tf/converters", "//tensorflow/contrib/py2tf/pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis", "@gast_archive//:gast", @@ -46,7 +46,7 @@ py_library( srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], deps = [ - "//tensorflow/contrib/py2tf/convert", + "//tensorflow/contrib/py2tf/converters", "//tensorflow/contrib/py2tf/pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis", "@gast_archive//:gast", diff --git a/tensorflow/contrib/py2tf/api.py b/tensorflow/contrib/py2tf/api.py index 9a2b70c53c..1f250d5f57 100644 --- a/tensorflow/contrib/py2tf/api.py +++ b/tensorflow/contrib/py2tf/api.py @@ -83,7 +83,7 @@ def convert_inline(f, *args, **kwargs): return convert(arg_value_hints)(f)(*args, **kwargs) -def convert(recursive=False, arg_value_hints=None): +def convert(recursive=False, arg_types=None): """Decorator that compiles a function to graph mode. The decorator is dynamic - invoking compilation whenever the decorated fuction @@ -92,8 +92,7 @@ def convert(recursive=False, arg_value_hints=None): Args: recursive: Whether to recusrively convert any functions that the decorator function may call. - arg_value_hints: A dict mapping parameter names to objects that can hint - at the type of those parameters. + arg_types: See to_graph. Returns: A decorator that compiles the given function to graph mode. @@ -101,8 +100,8 @@ def convert(recursive=False, arg_value_hints=None): Raises: ValueError: If any of the arguments are illegal. """ - if arg_value_hints is None: - arg_value_hints = {} + if arg_types is None: + arg_types = {} def decorator(f): """Decorator implementation.""" @@ -111,22 +110,23 @@ def convert(recursive=False, arg_value_hints=None): def wrapper(*args, **kwargs): """Wrapper that calls the compiled version of the wrapped function.""" partial_types = () + arg_values = {} arg_names = tf_inspect.getargspec(f)[0] for name, arg in zip(arg_names, args): + arg_values[name] = arg arg_class = arg.__class__ - if tf_inspect.isclass(arg_class): - # If arg_value_hints specifies any name, use that instead. - # TODO(mdan): Shouldn't this just be in the func's globals? - if name not in arg_value_hints: - arg_value_hints[name] = (arg_class.__name__, arg_class) + # If arg_value_hints specifies any name, use that instead. + if name not in arg_types: + arg_types[name] = (arg_class.__name__, arg_class) + if name == 'self' and tf_inspect.isclass(arg_class): # Annotated methods need to specify that their owner type is partial, # otherwise other members they call will not be converted. - if name == 'self': - partial_types = (arg_class,) + partial_types = (arg_class,) wrapped = to_graph( f, recursive=recursive, - arg_value_hints=arg_value_hints, + arg_values=arg_values, + arg_types=arg_types, partial_types=partial_types) return wrapped(*args, **kwargs) @@ -138,7 +138,11 @@ def convert(recursive=False, arg_value_hints=None): return decorator -def to_graph(o, recursive=True, arg_value_hints=None, partial_types=None): +def to_graph(e, + recursive=True, + arg_values=None, + arg_types=None, + partial_types=None): """Compile a Python entity into equivalent TensorFlow code. Currently supported entities: @@ -148,11 +152,13 @@ def to_graph(o, recursive=True, arg_value_hints=None, partial_types=None): Classes are handled by converting all their methods into a new class. Args: - o: A Python function or class. + e: A Python entity. recursive: Whether to recusrively convert any functions that the decorator function may call. - arg_value_hints: A dict mapping parameter names to objects that can hint - at the type of those parameters. + arg_values: A dict containing value hints for symbols like function + parameters. + arg_types: A dict containing type hints for symbols like function + parameters. partial_types: A set of types (e.g. classes) that will not be converted entirely. Calls to member functions for these types will be renamed independently. @@ -165,7 +171,7 @@ def to_graph(o, recursive=True, arg_value_hints=None, partial_types=None): recursive=recursive, nocompile_decorators=(convert, graph_ready, convert_inline), partial_types=partial_types) - _, name = conversion.object_to_graph(o, conversion_map, arg_value_hints) + _, name = conversion.entity_to_graph(e, conversion_map, arg_values, arg_types) module = gast.Module([]) for import_line in config.COMPILED_IMPORT_STATEMENTS: @@ -176,16 +182,17 @@ def to_graph(o, recursive=True, arg_value_hints=None, partial_types=None): # The compiled code should see everything the entry function saw. # TODO(mdan): This might not work well if the call tree spans modules? - if tf_inspect.isfunction(o): - compiled_node.__dict__.update(six.get_function_globals(o)) + if tf_inspect.isfunction(e): + compiled_node.__dict__.update(six.get_function_globals(e)) compiled_fn = getattr(compiled_node, name) return compiled_fn -def to_code(o, +def to_code(e, recursive=True, - arg_value_hints=None, + arg_values=None, + arg_types=None, partial_types=None, indentation=' '): """Return the equivalent of an entity in TensorFlow code. @@ -193,14 +200,11 @@ def to_code(o, See `to_graph` for more details. Args: - o: A Python function or class. - recursive: Whether to recusrively convert any functions that the decorator - function may call. - arg_value_hints: A dict mapping parameter names to objects that can hint - at the type of those parameters. - partial_types: A set of types (e.g. classes) that will not be converted - entirely. Calls to member functions for these types will be renamed - independently. + e: A Python entity. + recursive: See to_graph. + arg_values: See to_graph. + arg_types: See to_graph. + partial_types: See to_graph. indentation: String, when to use for each level of indentation. Returns: @@ -210,7 +214,7 @@ def to_code(o, recursive=recursive, nocompile_decorators=(convert, graph_ready, convert_inline), partial_types=partial_types) - conversion.object_to_graph(o, conversion_map, arg_value_hints) + conversion.entity_to_graph(e, conversion_map, arg_values, arg_types) imports = '\n'.join(config.COMPILED_IMPORT_STATEMENTS) code = '\n'.join( diff --git a/tensorflow/contrib/py2tf/config.py b/tensorflow/contrib/py2tf/config.py index 0a9d52136e..8c502a7a9e 100644 --- a/tensorflow/contrib/py2tf/config.py +++ b/tensorflow/contrib/py2tf/config.py @@ -22,6 +22,7 @@ PYTHON_LITERALS = { 'None': None, 'False': False, 'True': True, + 'float': float, } DEFAULT_UNCOMPILED_MODULES = set(( diff --git a/tensorflow/contrib/py2tf/conversion.py b/tensorflow/contrib/py2tf/conversion.py index 38f1c0a14a..b484eebbd5 100644 --- a/tensorflow/contrib/py2tf/conversion.py +++ b/tensorflow/contrib/py2tf/conversion.py @@ -23,16 +23,17 @@ import six from tensorflow.contrib.py2tf import config from tensorflow.contrib.py2tf import naming -from tensorflow.contrib.py2tf.convert import break_canonicalization -from tensorflow.contrib.py2tf.convert import builtin_functions -from tensorflow.contrib.py2tf.convert import call_trees -from tensorflow.contrib.py2tf.convert import continue_canonicalization -from tensorflow.contrib.py2tf.convert import control_flow -from tensorflow.contrib.py2tf.convert import decorators -from tensorflow.contrib.py2tf.convert import for_canonicalization -from tensorflow.contrib.py2tf.convert import logical_expressions -from tensorflow.contrib.py2tf.convert import print_functions -from tensorflow.contrib.py2tf.convert import side_effect_guards +from tensorflow.contrib.py2tf.converters import break_canonicalization +from tensorflow.contrib.py2tf.converters import builtin_functions +from tensorflow.contrib.py2tf.converters import call_trees +from tensorflow.contrib.py2tf.converters import continue_canonicalization +from tensorflow.contrib.py2tf.converters import control_flow +from tensorflow.contrib.py2tf.converters import decorators +from tensorflow.contrib.py2tf.converters import for_canonicalization +from tensorflow.contrib.py2tf.converters import logical_expressions +from tensorflow.contrib.py2tf.converters import print_functions +from tensorflow.contrib.py2tf.converters import side_effect_guards +from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct.static_analysis import access from tensorflow.contrib.py2tf.pyct.static_analysis import live_values @@ -51,9 +52,9 @@ class ConversionMap(object): function may call. nocompile_decorators: tuple of decorator functions that toggle compilation off. - dependency_cache: dict[object]: ast; maps original objects to their + dependency_cache: dict[object]: ast; maps original entities to their converted AST - name_map: dict[string]: string; maps original objects to the name of + name_map: dict[string]: string; maps original entities to the name of their converted counterparts """ @@ -66,8 +67,8 @@ class ConversionMap(object): self.dependency_cache = {} self.name_map = {} - def new_namer(self, global_symbols): - return naming.Namer(global_symbols, self.recursive, self.name_map, + def new_namer(self, namespace): + return naming.Namer(namespace, self.recursive, self.name_map, self.partial_types) def update_name_map(self, namer): @@ -76,48 +77,47 @@ class ConversionMap(object): if self.name_map[o] != name: raise ValueError( 'Calls to %s were converted using multiple names (%s). This is ' - 'possible when an object with one of these names already ' + 'possible when an entity with one of these names already ' 'existed. To fix, avoid using any of these names.') else: self.name_map[o] = name - def add_to_cache(self, original_object, converted_ast): - self.dependency_cache[original_object] = converted_ast + def add_to_cache(self, original_entity, converted_ast): + self.dependency_cache[original_entity] = converted_ast -def object_to_graph(o, conversion_map, value_hints): - """Compile a Python object into equivalent TensorFlow. +def entity_to_graph(o, conversion_map, arg_values, arg_types): + """Compile a Python entity into equivalent TensorFlow. - The function will also recursively compile all the objects that `o` + The function will also recursively compile all the entities that `o` references, updating `dependency_cache`. This function is reentrant, and relies on dependency_cache to avoid generating duplicate code. Args: - o: A Python object. + o: A Python entity. conversion_map: A ConversionMap object. - value_hints: A dict containing value hints for symbols like function + arg_values: A dict containing value hints for symbols like function + parameters. + arg_types: A dict containing type hints for symbols like function parameters. Returns: A tuple (ast, new_name): - * ast: An AST representing an object with interface equivalent to `o`, + * ast: An AST representing an entity with interface equivalent to `o`, but which when executed it creates TF a graph. - * new_name: The symbol name under which the new object can be found. + * new_name: The symbol name under which the new entity can be found. Raises: - ValueError: if the object is not supported. + ValueError: if the entity type is not supported. """ - if value_hints is None: - value_hints = {} - if tf_inspect.isclass(o): - node, new_name = class_to_graph(o, conversion_map, value_hints) + node, new_name = class_to_graph(o, conversion_map) elif tf_inspect.isfunction(o): - node, new_name = function_to_graph(o, conversion_map, value_hints) + node, new_name = function_to_graph(o, conversion_map, arg_values, arg_types) elif tf_inspect.ismethod(o): - node, new_name = function_to_graph(o, conversion_map, value_hints) + node, new_name = function_to_graph(o, conversion_map, arg_values, arg_types) else: raise ValueError( 'Entity "%s" has unsupported type "%s". Only functions and classes are ' @@ -132,25 +132,26 @@ def object_to_graph(o, conversion_map, value_hints): # Class members are converted with their objects, unless they're # only converted partially. continue - object_to_graph(obj, conversion_map, None) + entity_to_graph(obj, conversion_map, {}, {}) return node, new_name -def class_to_graph(c, conversion_map, param_value_hints): - """Specialization of `object_to_graph` for classes.""" +def class_to_graph(c, conversion_map): + """Specialization of `entity_to_graph` for classes.""" converted_members = {} members = tf_inspect.getmembers(c, predicate=tf_inspect.ismethod) if not members: raise ValueError('Cannot convert %s: it has no member methods.') - if 'self' in param_value_hints: - raise ValueError('Hints may not be provided for reserved name "self".') - param_value_hints['self'] = (c.__name__, c) - class_globals = None for _, m in members: - node, _ = function_to_graph(m, conversion_map, param_value_hints, c) + node, _ = function_to_graph( + m, + conversion_map=conversion_map, + arg_values={}, + arg_types={'self': (c.__name__, c)}, + owner_type=c) # TODO(mdan): Do not assume all members have the same view of globals. if class_globals is None: class_globals = six.get_function_globals(m) @@ -167,10 +168,11 @@ def class_to_graph(c, conversion_map, param_value_hints): return node, class_name -def function_to_graph(f, conversion_map, param_value_hints, owner_type=None): - """Specialization of `object_to_graph` for callable functions.""" +def function_to_graph(f, conversion_map, arg_values, arg_types, + owner_type=None): + """Specialization of `entity_to_graph` for callable functions.""" node = parser.parse_object(f).body[0] - node_globals = six.get_function_globals(f) + namespace = six.get_function_globals(f) # This is needed for non-global functions. closure = six.get_function_closure(f) @@ -178,12 +180,17 @@ def function_to_graph(f, conversion_map, param_value_hints, owner_type=None): for e in closure: if callable(e.cell_contents): fn = e.cell_contents - node_globals[fn.__name__] = fn - - namer = conversion_map.new_namer(node_globals) - node = node_to_graph(node, tf_inspect.getsource(f), tf_inspect.getfile(f), - namer, node_globals, param_value_hints, - conversion_map.nocompile_decorators) + namespace[fn.__name__] = fn + + namer = conversion_map.new_namer(namespace) + ctx = context.EntityContext( + namer=namer, + source_code=tf_inspect.getsource(f), + source_file=tf_inspect.getfile(f), + namespace=namespace, + arg_values=arg_values, + arg_types=arg_types) + node = node_to_graph(node, ctx, conversion_map.nocompile_decorators) # Simulate a rename to ensure the top level is in the name map. This is needed # for top level functions, and it also helps the consistency verification made @@ -197,34 +204,26 @@ def function_to_graph(f, conversion_map, param_value_hints, owner_type=None): return node, conversion_map.name_map[f] -def _static_analysis_pass(node, source, f, namespace, value_hints): +def _static_analysis_pass(node, ctx): node = access.resolve(node) - node = live_values.resolve(node, namespace, config.PYTHON_LITERALS) - node = type_info.resolve(node, source, f, value_hints) + node = live_values.resolve(node, ctx.namespace, config.PYTHON_LITERALS) + node = type_info.resolve(node, ctx) return node -def node_to_graph(node, source, f, namer, namespace, value_hints, - nocompile_decorators): +def node_to_graph(node, ctx, nocompile_decorators): """Convert Python code to equivalent TF graph mode code. Args: node: A Python AST node representing the code to convert. - source: Optional string containing the source code of the node. Used in - error messages. - f: Optional string indicating the file where the node originated. None if - unknown. Used in error messages. - namer: A naming.Namer object. - namespace: Dict mapping symbol names to their corresponding live objects. - value_hints: A dict containing value hints for symbols like function - parameters. + ctx: An EntityContext object. nocompile_decorators: A tuple containing decorators to be stripped from functions during conversion. Returns: A tuple (node, deps): * node: A Python ast node, representing the converted code. - * deps: A set of strings, the fully qualified names of object + * deps: A set of strings, the fully qualified names of entity dependencies that this node has. """ # TODO(mdan): Verify arguments for correctness. @@ -241,30 +240,30 @@ def node_to_graph(node, source, f, namer, namespace, value_hints, # tree, which must be accounted. Although less efficient, it is most robust # to re-run the analysis. - node = _static_analysis_pass(node, source, f, namespace, value_hints) + node = _static_analysis_pass(node, ctx) node = decorators.transform(node, nocompile_decorators) - node = break_canonicalization.transform(node, namer) + node = break_canonicalization.transform(node, ctx.namer) # Note: sequencing continue canonicalization before for loop one avoids # dealing with the extra loop increment operation that the for # canonicalization creates. - node = continue_canonicalization.transform(node, namer) - namespace['len'] = len + node = continue_canonicalization.transform(node, ctx.namer) + ctx.namespace['len'] = len - node = _static_analysis_pass(node, None, None, namespace, value_hints) - node = for_canonicalization.transform(node, namer) + node = _static_analysis_pass(node, ctx) + node = for_canonicalization.transform(node, ctx.namer) # for_canonicalization may insert new global references. node = builtin_functions.transform(node) # builtin_functions may insert new global references. - namespace['print'] = print + ctx.namespace['print'] = print - node = _static_analysis_pass(node, None, None, namespace, value_hints) + node = _static_analysis_pass(node, ctx) node = print_functions.transform(node) - node = call_trees.transform(node, namer, namespace, + node = call_trees.transform(node, ctx.namer, ctx.namespace, config.DEFAULT_UNCOMPILED_MODULES, nocompile_decorators) - node = control_flow.transform(node, namer) + node = control_flow.transform(node, ctx.namer) node = logical_expressions.transform(node) - node = side_effect_guards.transform(node, namer) + node = side_effect_guards.transform(node, ctx.namer) return node diff --git a/tensorflow/contrib/py2tf/conversion_test.py b/tensorflow/contrib/py2tf/conversion_test.py index e48bfe4464..26f915f4f4 100644 --- a/tensorflow/contrib/py2tf/conversion_test.py +++ b/tensorflow/contrib/py2tf/conversion_test.py @@ -26,20 +26,23 @@ from tensorflow.python.platform import test class ConversionTest(test.TestCase): - def test_object_to_graph_unsupported_types(self): + def test_entity_to_graph_unsupported_types(self): with self.assertRaises(ValueError): - conversion.object_to_graph('dummy', None, {}) + conversion_map = conversion.ConversionMap(True, (), ()) + conversion.entity_to_graph('dummy', conversion_map, None, None) + + def test_entity_to_graph_callable(self): - def test_object_to_graph_callable(self): def f(a): return a conversion_map = conversion.ConversionMap(True, (), ()) - ast, new_name = conversion.object_to_graph(f, conversion_map, {}) + ast, new_name = conversion.entity_to_graph(f, conversion_map, None, None) self.assertTrue(isinstance(ast, gast.FunctionDef), ast) self.assertEqual('tf__f', new_name) - def test_object_to_graph_call_tree(self): + def test_entity_to_graph_call_tree(self): + def g(a): return a @@ -47,7 +50,7 @@ class ConversionTest(test.TestCase): return g(a) conversion_map = conversion.ConversionMap(True, (), ()) - conversion.object_to_graph(f, conversion_map, {}) + conversion.entity_to_graph(f, conversion_map, None, None) self.assertTrue(f in conversion_map.dependency_cache) self.assertTrue(g in conversion_map.dependency_cache) diff --git a/tensorflow/contrib/py2tf/convert/BUILD b/tensorflow/contrib/py2tf/converters/BUILD similarity index 79% rename from tensorflow/contrib/py2tf/convert/BUILD rename to tensorflow/contrib/py2tf/converters/BUILD index 050e2ef108..2b0a1234e6 100644 --- a/tensorflow/contrib/py2tf/convert/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -15,7 +15,7 @@ filegroup( ) py_library( - name = "convert", + name = "converters", srcs = [ "break_canonicalization.py", "builtin_functions.py", @@ -35,13 +35,26 @@ py_library( ], ) +py_library( + name = "test_lib", + srcs = [ + "converter_test_base.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + ":converters", + "//tensorflow/contrib/py2tf/pyct/static_analysis", + "@gast_archive//:gast", + ], +) + py_test( name = "break_canonicalization_test", srcs = ["break_canonicalization_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) @@ -50,9 +63,8 @@ py_test( name = "call_trees_test", srcs = ["call_trees_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) @@ -61,9 +73,8 @@ py_test( name = "continue_canonicalization_test", srcs = ["continue_canonicalization_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) @@ -72,9 +83,8 @@ py_test( name = "control_flow_test", srcs = ["control_flow_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) @@ -83,9 +93,8 @@ py_test( name = "builtin_functions_test", srcs = ["builtin_functions_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) @@ -94,9 +103,8 @@ py_test( name = "for_canonicalization_test", srcs = ["for_canonicalization_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) @@ -105,9 +113,8 @@ py_test( name = "logical_expressions_test", srcs = ["logical_expressions_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) @@ -116,9 +123,8 @@ py_test( name = "print_functions_test", srcs = ["print_functions_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", "@gast_archive//:gast", ], @@ -128,9 +134,8 @@ py_test( name = "side_effect_guards_test", srcs = ["side_effect_guards_test.py"], deps = [ - ":convert", + ":test_lib", "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", "//tensorflow/python:client_testlib", ], ) diff --git a/tensorflow/contrib/py2tf/convert/__init__.py b/tensorflow/contrib/py2tf/converters/__init__.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/__init__.py rename to tensorflow/contrib/py2tf/converters/__init__.py diff --git a/tensorflow/contrib/py2tf/convert/break_canonicalization.py b/tensorflow/contrib/py2tf/converters/break_canonicalization.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/break_canonicalization.py rename to tensorflow/contrib/py2tf/converters/break_canonicalization.py diff --git a/tensorflow/contrib/py2tf/convert/break_canonicalization_test.py b/tensorflow/contrib/py2tf/converters/break_canonicalization_test.py similarity index 84% rename from tensorflow/contrib/py2tf/convert/break_canonicalization_test.py rename to tensorflow/contrib/py2tf/converters/break_canonicalization_test.py index 23c4c4d3e2..b5ba2ad923 100644 --- a/tensorflow/contrib/py2tf/convert/break_canonicalization_test.py +++ b/tensorflow/contrib/py2tf/converters/break_canonicalization_test.py @@ -18,11 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import break_canonicalization -from tensorflow.contrib.py2tf.convert import control_flow +from tensorflow.contrib.py2tf.converters import break_canonicalization +from tensorflow.contrib.py2tf.converters import control_flow +from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access from tensorflow.python.platform import test @@ -32,12 +31,7 @@ class TestNamer(control_flow.SymbolNamer): return name_root -class BreakCanonicalizationTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - return node +class BreakCanonicalizationTest(converter_test_base.TestCase): def test_basic_break(self): @@ -50,7 +44,7 @@ class BreakCanonicalizationTest(test.TestCase): v.append(x) return v - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}, include_type_analysis=False) node = break_canonicalization.transform(node, TestNamer()) result = compiler.ast_to_object(node) @@ -82,7 +76,7 @@ class BreakCanonicalizationTest(test.TestCase): v.append(x) return v - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}, include_type_analysis=False) node = break_canonicalization.transform(node, TestNamer()) result = compiler.ast_to_object(node) @@ -110,7 +104,7 @@ class BreakCanonicalizationTest(test.TestCase): v.append(x) return v, u, w - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}, include_type_analysis=False) node = break_canonicalization.transform(node, TestNamer()) result = compiler.ast_to_object(node) diff --git a/tensorflow/contrib/py2tf/convert/builtin_functions.py b/tensorflow/contrib/py2tf/converters/builtin_functions.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/builtin_functions.py rename to tensorflow/contrib/py2tf/converters/builtin_functions.py diff --git a/tensorflow/contrib/py2tf/convert/builtin_functions_test.py b/tensorflow/contrib/py2tf/converters/builtin_functions_test.py similarity index 68% rename from tensorflow/contrib/py2tf/convert/builtin_functions_test.py rename to tensorflow/contrib/py2tf/converters/builtin_functions_test.py index ab02b362aa..b5358da6bc 100644 --- a/tensorflow/contrib/py2tf/convert/builtin_functions_test.py +++ b/tensorflow/contrib/py2tf/converters/builtin_functions_test.py @@ -18,32 +18,22 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import builtin_functions +from tensorflow.contrib.py2tf.converters import builtin_functions +from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access -from tensorflow.contrib.py2tf.pyct.static_analysis import live_values -from tensorflow.contrib.py2tf.pyct.static_analysis import type_info from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class BuiltinFunctionsTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, namespace, {}) - node = type_info.resolve(node, None, None, {}) - return node +class BuiltinFunctionsTest(converter_test_base.TestCase): def test_len(self): def test_fn(a): return len(a) - node = self._parse_and_analyze(test_fn, {'len': len}) + node = self.parse_and_analyze(test_fn, {'len': len}) node = builtin_functions.transform(node) result = compiler.ast_to_object(node) setattr(result, 'tf', array_ops) diff --git a/tensorflow/contrib/py2tf/convert/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/call_trees.py rename to tensorflow/contrib/py2tf/converters/call_trees.py diff --git a/tensorflow/contrib/py2tf/convert/call_trees_test.py b/tensorflow/contrib/py2tf/converters/call_trees_test.py similarity index 78% rename from tensorflow/contrib/py2tf/convert/call_trees_test.py rename to tensorflow/contrib/py2tf/converters/call_trees_test.py index 78a6b53910..8cb8d7be0f 100644 --- a/tensorflow/contrib/py2tf/convert/call_trees_test.py +++ b/tensorflow/contrib/py2tf/converters/call_trees_test.py @@ -18,12 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import call_trees +from tensorflow.contrib.py2tf.converters import call_trees +from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access -from tensorflow.contrib.py2tf.pyct.static_analysis import live_values -from tensorflow.contrib.py2tf.pyct.static_analysis import type_info from tensorflow.python.framework import constant_op from tensorflow.python.ops import math_ops from tensorflow.python.platform import test @@ -35,14 +32,7 @@ class TestNamer(call_trees.FunctionNamer): return 'renamed_%s' % original_name -class CallTreesTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, namespace, {}) - node = type_info.resolve(node, None, None, {}) - return node +class CallTreesTest(converter_test_base.TestCase): def test_basic(self): @@ -55,7 +45,7 @@ class CallTreesTest(test.TestCase): def test_fn_2(a): return test_fn_1(a) + 1 - node = self._parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1}) + node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1}) node = call_trees.transform(node, TestNamer(), {}, (), ()) result = compiler.ast_to_object(node) # Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 manually. @@ -70,7 +60,7 @@ class CallTreesTest(test.TestCase): a = math_ops.add(a, constant_op.constant(1)) return a - node = self._parse_and_analyze(test_fn, { + node = self.parse_and_analyze(test_fn, { 'math_ops': math_ops, 'constant_op': constant_op }) diff --git a/tensorflow/contrib/py2tf/convert/continue_canonicalization.py b/tensorflow/contrib/py2tf/converters/continue_canonicalization.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/continue_canonicalization.py rename to tensorflow/contrib/py2tf/converters/continue_canonicalization.py diff --git a/tensorflow/contrib/py2tf/convert/continue_canonicalization_test.py b/tensorflow/contrib/py2tf/converters/continue_canonicalization_test.py similarity index 83% rename from tensorflow/contrib/py2tf/convert/continue_canonicalization_test.py rename to tensorflow/contrib/py2tf/converters/continue_canonicalization_test.py index a041ff4641..c1fe903a2d 100644 --- a/tensorflow/contrib/py2tf/convert/continue_canonicalization_test.py +++ b/tensorflow/contrib/py2tf/converters/continue_canonicalization_test.py @@ -18,11 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import continue_canonicalization -from tensorflow.contrib.py2tf.convert import control_flow +from tensorflow.contrib.py2tf.converters import continue_canonicalization +from tensorflow.contrib.py2tf.converters import control_flow +from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access from tensorflow.python.platform import test @@ -32,12 +31,7 @@ class TestNamer(control_flow.SymbolNamer): return name_root -class ContinueCanonicalizationTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - return node +class ContinueCanonicalizationTest(converter_test_base.TestCase): def test_basic_continue(self): @@ -50,7 +44,7 @@ class ContinueCanonicalizationTest(test.TestCase): v.append(x) return v - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}, include_type_analysis=False) node = continue_canonicalization.transform(node, TestNamer()) result = compiler.ast_to_object(node) @@ -71,7 +65,7 @@ class ContinueCanonicalizationTest(test.TestCase): v.append(x) return v - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}, include_type_analysis=False) node = continue_canonicalization.transform(node, TestNamer()) result = compiler.ast_to_object(node) @@ -97,7 +91,7 @@ class ContinueCanonicalizationTest(test.TestCase): v.append(x) return v, u, w - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}, include_type_analysis=False) node = continue_canonicalization.transform(node, TestNamer()) result = compiler.ast_to_object(node) diff --git a/tensorflow/contrib/py2tf/convert/control_flow.py b/tensorflow/contrib/py2tf/converters/control_flow.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/control_flow.py rename to tensorflow/contrib/py2tf/converters/control_flow.py diff --git a/tensorflow/contrib/py2tf/convert/control_flow_test.py b/tensorflow/contrib/py2tf/converters/control_flow_test.py similarity index 79% rename from tensorflow/contrib/py2tf/convert/control_flow_test.py rename to tensorflow/contrib/py2tf/converters/control_flow_test.py index 64a317ee9c..054e33750d 100644 --- a/tensorflow/contrib/py2tf/convert/control_flow_test.py +++ b/tensorflow/contrib/py2tf/converters/control_flow_test.py @@ -18,12 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import control_flow +from tensorflow.contrib.py2tf.converters import control_flow +from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access -from tensorflow.contrib.py2tf.pyct.static_analysis import live_values -from tensorflow.contrib.py2tf.pyct.static_analysis import type_info from tensorflow.python.framework import constant_op from tensorflow.python.ops import control_flow_ops from tensorflow.python.platform import test @@ -40,14 +37,7 @@ class TestNamer(control_flow.SymbolNamer): i += 1 -class ControlFlowTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, namespace, {}) - node = type_info.resolve(node, None, None, {}) - return node +class ControlFlowTest(converter_test_base.TestCase): def test_simple_while(self): @@ -59,7 +49,7 @@ class ControlFlowTest(test.TestCase): i += 1 return s, i, n - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, TestNamer()) result = compiler.ast_to_object(node) setattr(result, 'tf', control_flow_ops) @@ -75,7 +65,7 @@ class ControlFlowTest(test.TestCase): n -= 1 return n - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, TestNamer()) result = compiler.ast_to_object(node) setattr(result, 'tf', control_flow_ops) @@ -94,7 +84,7 @@ class ControlFlowTest(test.TestCase): b = 2 * n return a, b - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, TestNamer()) result = compiler.ast_to_object(node) setattr(result, 'tf', control_flow_ops) @@ -112,7 +102,7 @@ class ControlFlowTest(test.TestCase): n = -n return n - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}) node = control_flow.transform(node, TestNamer()) result = compiler.ast_to_object(node) setattr(result, 'tf', control_flow_ops) diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py new file mode 100644 index 0000000000..ed006bad6d --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py @@ -0,0 +1,48 @@ +# 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. +# ============================================================================== +"""Base class for tests in this module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.pyct import context +from tensorflow.contrib.py2tf.pyct import parser +from tensorflow.contrib.py2tf.pyct.static_analysis import access +from tensorflow.contrib.py2tf.pyct.static_analysis import live_values +from tensorflow.contrib.py2tf.pyct.static_analysis import type_info +from tensorflow.python.platform import test + + +class TestCase(test.TestCase): + + def parse_and_analyze(self, + test_fn, + namespace, + arg_types=None, + include_type_analysis=True): + ctx = context.EntityContext( + namer=None, + source_code=None, + source_file=None, + namespace=namespace, + arg_values=None, + arg_types=arg_types) + node = parser.parse_object(test_fn) + node = access.resolve(node) + node = live_values.resolve(node, namespace, {}) + if include_type_analysis: + node = type_info.resolve(node, ctx) + return node diff --git a/tensorflow/contrib/py2tf/convert/decorators.py b/tensorflow/contrib/py2tf/converters/decorators.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/decorators.py rename to tensorflow/contrib/py2tf/converters/decorators.py diff --git a/tensorflow/contrib/py2tf/convert/for_canonicalization.py b/tensorflow/contrib/py2tf/converters/for_canonicalization.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/for_canonicalization.py rename to tensorflow/contrib/py2tf/converters/for_canonicalization.py diff --git a/tensorflow/contrib/py2tf/convert/for_canonicalization_test.py b/tensorflow/contrib/py2tf/converters/for_canonicalization_test.py similarity index 75% rename from tensorflow/contrib/py2tf/convert/for_canonicalization_test.py rename to tensorflow/contrib/py2tf/converters/for_canonicalization_test.py index 8de2d1a0f8..a6e6350fd4 100644 --- a/tensorflow/contrib/py2tf/convert/for_canonicalization_test.py +++ b/tensorflow/contrib/py2tf/converters/for_canonicalization_test.py @@ -18,11 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import control_flow -from tensorflow.contrib.py2tf.convert import for_canonicalization +from tensorflow.contrib.py2tf.converters import control_flow +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import for_canonicalization from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access from tensorflow.python.platform import test @@ -32,12 +31,7 @@ class TestNamer(control_flow.SymbolNamer): return name_root -class ControlFlowTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - return node +class ControlFlowTest(converter_test_base.TestCase): def test_basic_for(self): @@ -47,7 +41,7 @@ class ControlFlowTest(test.TestCase): s += e return s - node = self._parse_and_analyze(test_fn, {}) + node = self.parse_and_analyze(test_fn, {}) node = for_canonicalization.transform(node, TestNamer()) result = compiler.ast_to_object(node) diff --git a/tensorflow/contrib/py2tf/convert/logical_expressions.py b/tensorflow/contrib/py2tf/converters/logical_expressions.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/logical_expressions.py rename to tensorflow/contrib/py2tf/converters/logical_expressions.py diff --git a/tensorflow/contrib/py2tf/convert/logical_expressions_test.py b/tensorflow/contrib/py2tf/converters/logical_expressions_test.py similarity index 85% rename from tensorflow/contrib/py2tf/convert/logical_expressions_test.py rename to tensorflow/contrib/py2tf/converters/logical_expressions_test.py index f07fa017b9..d711065099 100644 --- a/tensorflow/contrib/py2tf/convert/logical_expressions_test.py +++ b/tensorflow/contrib/py2tf/converters/logical_expressions_test.py @@ -18,21 +18,21 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import logical_expressions +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import logical_expressions from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class GradientsFunctionTest(test.TestCase): +class GradientsFunctionTest(converter_test_base.TestCase): def test_equals(self): def test_fn(a, b): return a == b - node = parser.parse_object(test_fn) + node = self.parse_and_analyze(test_fn, {}) node = logical_expressions.transform(node) result = compiler.ast_to_object(node) setattr(result, 'tf', math_ops) @@ -46,7 +46,7 @@ class GradientsFunctionTest(test.TestCase): def test_fn(a, b, c): return (a or b) and (a or b or c) - node = parser.parse_object(test_fn) + node = self.parse_and_analyze(test_fn, {}) node = logical_expressions.transform(node) result = compiler.ast_to_object(node) setattr(result, 'tf', math_ops) diff --git a/tensorflow/contrib/py2tf/convert/print_functions.py b/tensorflow/contrib/py2tf/converters/print_functions.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/print_functions.py rename to tensorflow/contrib/py2tf/converters/print_functions.py diff --git a/tensorflow/contrib/py2tf/convert/print_functions_test.py b/tensorflow/contrib/py2tf/converters/print_functions_test.py similarity index 65% rename from tensorflow/contrib/py2tf/convert/print_functions_test.py rename to tensorflow/contrib/py2tf/converters/print_functions_test.py index 8b6c238aa4..475196ce10 100644 --- a/tensorflow/contrib/py2tf/convert/print_functions_test.py +++ b/tensorflow/contrib/py2tf/converters/print_functions_test.py @@ -20,30 +20,20 @@ from __future__ import print_function import gast -from tensorflow.contrib.py2tf.convert import print_functions +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import print_functions from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access -from tensorflow.contrib.py2tf.pyct.static_analysis import live_values -from tensorflow.contrib.py2tf.pyct.static_analysis import type_info from tensorflow.python.platform import test -class PrintFunctionsTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, namespace, {}) - node = type_info.resolve(node, None, None, {}) - return node +class PrintFunctionsTest(converter_test_base.TestCase): def test_transform(self): def test_fn(a): print(a) - node = self._parse_and_analyze(test_fn, {'print': print}) + node = self.parse_and_analyze(test_fn, {'print': print}) node = print_functions.transform(node) result = compiler.ast_to_object(node) diff --git a/tensorflow/contrib/py2tf/convert/side_effect_guards.py b/tensorflow/contrib/py2tf/converters/side_effect_guards.py similarity index 100% rename from tensorflow/contrib/py2tf/convert/side_effect_guards.py rename to tensorflow/contrib/py2tf/converters/side_effect_guards.py diff --git a/tensorflow/contrib/py2tf/convert/side_effect_guards_test.py b/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py similarity index 72% rename from tensorflow/contrib/py2tf/convert/side_effect_guards_test.py rename to tensorflow/contrib/py2tf/converters/side_effect_guards_test.py index 1715e9eb95..5c56973dc2 100644 --- a/tensorflow/contrib/py2tf/convert/side_effect_guards_test.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py @@ -18,12 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.convert import side_effect_guards +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import side_effect_guards from tensorflow.contrib.py2tf.pyct import compiler -from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct.static_analysis import access -from tensorflow.contrib.py2tf.pyct.static_analysis import live_values -from tensorflow.contrib.py2tf.pyct.static_analysis import type_info from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import state_ops @@ -37,14 +34,7 @@ class TestNamer(side_effect_guards.SymbolNamer): return name_root -class SideEffectGuardsTest(test.TestCase): - - def _parse_and_analyze(self, test_fn, namespace): - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, namespace, {}) - node = type_info.resolve(node, None, None, {}) - return node +class SideEffectGuardsTest(converter_test_base.TestCase): def test_transform(self): @@ -52,7 +42,7 @@ class SideEffectGuardsTest(test.TestCase): state_ops.assign(a, a + 1) return a - node = self._parse_and_analyze(test_fn, {'state_ops': state_ops}) + node = self.parse_and_analyze(test_fn, {'state_ops': state_ops}) node = side_effect_guards.transform(node, TestNamer()) result = compiler.ast_to_object(node) setattr(result, 'state_ops', state_ops) diff --git a/tensorflow/contrib/py2tf/pyct/BUILD b/tensorflow/contrib/py2tf/pyct/BUILD index 9dd564cb9f..e0331dbc97 100644 --- a/tensorflow/contrib/py2tf/pyct/BUILD +++ b/tensorflow/contrib/py2tf/pyct/BUILD @@ -20,6 +20,7 @@ py_library( "__init__.py", "anno.py", "compiler.py", + "context.py", "parser.py", "pretty_printer.py", "templates.py", diff --git a/tensorflow/contrib/py2tf/pyct/context.py b/tensorflow/contrib/py2tf/pyct/context.py new file mode 100644 index 0000000000..73f3613d09 --- /dev/null +++ b/tensorflow/contrib/py2tf/pyct/context.py @@ -0,0 +1,42 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Conversion context containers.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +class EntityContext(object): + """Contains information about an entity, like source code. + + Attributes: + namer: Namer that matches the contract of all converters. + source_code: The entity's source code. + source_file: The entity's source file. + namespace: Dict[str->*], containing symbols visible to the entity + (excluding parameters). + arg_values: Dict[str->*], containing parameter values, if known. + arg_types: Dict[str->*], containing parameter types, if known. + """ + + def __init__(self, namer, source_code, source_file, namespace, arg_values, + arg_types): + self.namer = namer + self.source_code = source_code + self.source_file = source_file + self.namespace = namespace + self.arg_values = {} if arg_values is None else arg_values + self.arg_types = {} if arg_types is None else arg_types diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py index b17af5d844..0042aa90ed 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py @@ -78,10 +78,9 @@ class TypeInfoResolver(transformer.Base): * Attribute (helps resolve object methods) """ - def __init__(self, value_hints, source, f): - super(TypeInfoResolver, self).__init__(source, f) + def __init__(self, context): + super(TypeInfoResolver, self).__init__(context) self.scope = Scope(None) - self.value_hints = value_hints self.function_level = 0 def visit_FunctionDef(self, node): @@ -122,13 +121,11 @@ class TypeInfoResolver(transformer.Base): self.generic_visit(node) if isinstance(node.ctx, gast.Param): self.scope.setval(node.id, gast.Name(node.id, gast.Load(), None)) - # TODO(mdan): Member functions should not need type hints. - # We could attemp to extract im_class from the live_val annotation. - if self.function_level == 1 and node.id in self.value_hints: + if self.function_level == 1 and node.id in self.context.arg_types: # Forge a node to hold the type information, so that method calls on # it can resolve the type. type_holder = gast.Name(node.id, gast.Load(), None) - type_string, type_obj = self.value_hints[node.id] + type_string, type_obj = self.context.arg_types[node.id] anno.setanno(type_holder, 'type', type_obj) anno.setanno(type_holder, 'type_fqn', tuple(type_string.split('.'))) self.scope.setval(node.id, type_holder) @@ -208,6 +205,5 @@ class TypeInfoResolver(transformer.Base): return node -def resolve(node, source, f, value_hints): - assert value_hints is not None - return TypeInfoResolver(value_hints, source, f).visit(node) +def resolve(node, context): + return TypeInfoResolver(context).visit(node) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py index 98dc7bf50f..a491f49ca3 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import transformer from tensorflow.contrib.py2tf.pyct.static_analysis import access @@ -55,17 +56,27 @@ class ScopeTest(test.TestCase): class TypeInfoResolverTest(test.TestCase): + def _parse_and_analyze(self, test_fn, namespace, arg_types=None): + ctx = context.EntityContext( + namer=None, + source_code=None, + source_file=None, + namespace=namespace, + arg_values=None, + arg_types=arg_types) + node = parser.parse_object(test_fn) + node = access.resolve(node) + node = live_values.resolve(node, namespace, {}) + node = type_info.resolve(node, ctx) + return node + def test_constructor_detection(self): def test_fn(): opt = training.GradientDescentOptimizer(0.1) return opt - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'training': training}, {}) - node = type_info.resolve(node, None, None, {}) - + node = self._parse_and_analyze(test_fn, {'training': training}) call_node = node.body[0].body[0].value self.assertEquals(training.GradientDescentOptimizer, anno.getanno(call_node, 'type')) @@ -78,11 +89,7 @@ class TypeInfoResolverTest(test.TestCase): opt = training.GradientDescentOptimizer(0.1) opt.minimize(0) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'training': training}, {}) - node = type_info.resolve(node, None, None, {}) - + node = self._parse_and_analyze(test_fn, {'training': training}) attr_call_node = node.body[0].body[1].value.func self.assertEquals((training.__name__, 'GradientDescentOptimizer'), anno.getanno(attr_call_node, 'type_fqn')) @@ -93,11 +100,7 @@ class TypeInfoResolverTest(test.TestCase): with session.Session() as sess: sess.run(x) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'session': session}, {}) - node = type_info.resolve(node, None, None, {}) - + node = self._parse_and_analyze(test_fn, {'session': session}) constructor_call = node.body[0].body[0].items[0].context_expr self.assertEquals(session.Session, anno.getanno(constructor_call, 'type')) self.assertEquals((session.__name__, 'Session'), @@ -116,33 +119,25 @@ class TypeInfoResolverTest(test.TestCase): opt = training.GradientDescentOptimizer(0.01) opt.minimize(0) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'training': training}, {}) with self.assertRaises(transformer.PyFlowParseError): - node = type_info.resolve(node, None, None, {}) + self._parse_and_analyze(test_fn, {'training': training}) def test_parameter_class_members(self): def test_fn(opt): opt.minimize(0) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'training': training}, {}) with self.assertRaises(transformer.PyFlowParseError): - node = type_info.resolve(node, None, None, {}) + self._parse_and_analyze(test_fn, {'training': training}) def test_parameter_class_members_with_value_hints(self): def test_fn(opt): opt.minimize(0) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'training': training}, {}) - node = type_info.resolve( - node, None, None, { + node = self._parse_and_analyze( + test_fn, {'training': training}, + arg_types={ 'opt': (('%s.GradientDescentOptimizer' % training.__name__), training.GradientDescentOptimizer(0.1)) }) @@ -161,11 +156,8 @@ class TypeInfoResolverTest(test.TestCase): foo = bar foo() - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'bar': bar}, {}) with self.assertRaises(transformer.PyFlowParseError): - node = type_info.resolve(node, None, None, {}) + self._parse_and_analyze(test_fn, {'bar': bar}) def test_nested_members(self): @@ -173,11 +165,8 @@ class TypeInfoResolverTest(test.TestCase): foo = training.GradientDescentOptimizer(0.1) foo.bar.baz() - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'training': training}, {}) with self.assertRaises(transformer.PyFlowParseError): - node = type_info.resolve(node, None, None, {}) + self._parse_and_analyze(test_fn, {'training': training}) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/pyct/transformer.py b/tensorflow/contrib/py2tf/pyct/transformer.py index 1658a1b694..d5aa23eaeb 100644 --- a/tensorflow/contrib/py2tf/pyct/transformer.py +++ b/tensorflow/contrib/py2tf/pyct/transformer.py @@ -30,23 +30,29 @@ class PyFlowParseError(SyntaxError): class Base(gast.NodeTransformer): """Base class for specialized transformers.""" - def __init__(self, source, f): + def __init__(self, context): + """Initialize the transformer. Subclasses should call this. + + Args: + context: An EntityContext. + """ self._lineno = 0 self._col_offset = 0 - self._source = source - self._file = f + self.context = context def visit(self, node): try: - if self._source and hasattr(node, 'lineno'): + source_code = self.context.source_code + source_file = self.context.source_file + if source_code and hasattr(node, 'lineno'): self._lineno = node.lineno self._col_offset = node.col_offset return super(Base, self).visit(node) except ValueError as e: msg = '%s\nOccurred at node:\n%s' % (str(e), pretty_printer.fmt(node)) - if self._source: + if source_code: line = self._source.splitlines()[self._lineno - 1] else: line = '' raise PyFlowParseError( - msg, (self._file, self._lineno, self._col_offset + 1, line)) + msg, (source_file, self._lineno, self._col_offset + 1, line)) diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index c789e2ba0c..598080ed27 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -152,7 +152,8 @@ sh_binary( "//tensorflow/contrib/nn:nn_py", "//tensorflow/contrib/predictor:predictor_pip", "//tensorflow/contrib/py2tf:py2tf_internal", - "//tensorflow/contrib/py2tf/convert:convert", + "//tensorflow/contrib/py2tf/converters:converters", + "//tensorflow/contrib/py2tf/converters:test_lib", "//tensorflow/contrib/py2tf/pyct:pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis:static_analysis", "//tensorflow/contrib/receptive_field:receptive_field_pip", -- GitLab From faf3547da8bde4aa05ac65562250901f1784e562 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Wed, 24 Jan 2018 19:01:16 -0800 Subject: [PATCH 028/423] [XLA] Allow buffers for CustomCalls to be reused. PiperOrigin-RevId: 183177944 --- tensorflow/compiler/xla/service/buffer_assignment.cc | 9 ++++----- tensorflow/compiler/xla/tests/local_client_aot_test.cc | 3 +-- .../compiler/xla/tests/local_client_aot_test_helper.cc | 3 +-- 3 files changed, 6 insertions(+), 9 deletions(-) diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 33fe11b81d..323620c131 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -846,14 +846,13 @@ Status BufferAssigner::AssignBuffersForComputation( continue; } - if (is_thread_local || instruction->opcode() == HloOpcode::kCustomCall) { - // Custom call operations never have reusable buffers. Also we do not - // reuse thread-local buffers for now, because they are dynamically - // allocated and their lifetimes are hard to compute. + 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); VLOG(3) << "New allocation #" << allocation->index() - << " for thread-local/CustomCall: " << *buffer; + << " for thread-local: " << *buffer; continue; } diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test.cc b/tensorflow/compiler/xla/tests/local_client_aot_test.cc index 569d5944ca..47cab79604 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test.cc @@ -44,8 +44,7 @@ TEST_F(LocalClientAotTest, Constant) { OpaqueData opaque_data{100, 20, 3}; void* parameters[] = {&opaque_data}; float out = 0; - char tmp[4] = {0}; - void* temporary_buffers[] = {nullptr, &out, &tmp}; + void* temporary_buffers[] = {nullptr, &out}; SumAndDouble(&out, &run_options, parameters, temporary_buffers); EXPECT_EQ(out, 246.0f); 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 4d3b513b09..3704ddd801 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc @@ -87,10 +87,9 @@ 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(), 3); + CHECK_EQ(result->buffer_sizes().size(), 2); 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], sizeof(float)); // temp buffer if (triple.isOSBinFormatELF()) { // Check the ELF magic. CHECK_EQ(result->object_file_data()[0], 0x7F); -- GitLab From b25e892311fbdb308f89605ede30fce1b138c7f6 Mon Sep 17 00:00:00 2001 From: Rui Zhao Date: Wed, 24 Jan 2018 19:32:10 -0800 Subject: [PATCH 029/423] Allow passing candidate sampling seed in to sampled_softmax_loss for testing. PiperOrigin-RevId: 183180196 --- tensorflow/python/ops/nn_impl.py | 16 ++++++++++++---- tensorflow/tools/api/golden/tensorflow.nn.pbtxt | 2 +- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index 3268fd0e0a..55fcd176d6 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -969,7 +969,8 @@ def _compute_sampled_logits(weights, subtract_log_q=True, remove_accidental_hits=False, partition_strategy="mod", - name=None): + name=None, + seed=None): """Helper function for nce_loss and sampled_softmax_loss functions. Computes sampled output training logits and labels suitable for implementing @@ -1007,6 +1008,8 @@ def _compute_sampled_logits(weights, if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported. Default is `"mod"`. See `tf.nn.embedding_lookup` for more details. name: A name for the operation (optional). + seed: random seed for candidate sampling. Default to None, which doesn't set + the op-level random seed for candidate sampling. Returns: out_logits: `Tensor` object with shape `[batch_size, num_true + num_sampled]`, for passing to either @@ -1036,7 +1039,8 @@ def _compute_sampled_logits(weights, num_true=num_true, num_sampled=num_sampled, unique=True, - range_max=num_classes) + range_max=num_classes, + seed=seed) # NOTE: pylint cannot tell that 'sampled_values' is a sequence # pylint: disable=unpacking-non-sequence sampled, true_expected_count, sampled_expected_count = ( @@ -1255,7 +1259,8 @@ def sampled_softmax_loss(weights, sampled_values=None, remove_accidental_hits=True, partition_strategy="mod", - name="sampled_softmax_loss"): + name="sampled_softmax_loss", + seed=None): """Computes and returns the sampled softmax training loss. This is a faster way to train a softmax classifier over a huge number of @@ -1316,6 +1321,8 @@ def sampled_softmax_loss(weights, if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported. Default is `"mod"`. See `tf.nn.embedding_lookup` for more details. name: A name for the operation (optional). + seed: random seed for candidate sampling. Default to None, which doesn't set + the op-level random seed for candidate sampling. Returns: A `batch_size` 1-D tensor of per-example sampled softmax losses. @@ -1333,7 +1340,8 @@ def sampled_softmax_loss(weights, subtract_log_q=True, remove_accidental_hits=remove_accidental_hits, partition_strategy=partition_strategy, - name=name) + name=name, + seed=seed) sampled_losses = nn_ops.softmax_cross_entropy_with_logits( labels=labels, logits=logits) # sampled_losses is a [batch_size] tensor. diff --git a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt index 8ce022e454..455590d866 100644 --- a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.nn.pbtxt @@ -262,7 +262,7 @@ tf_module { } member_method { name: "sampled_softmax_loss" - argspec: "args=[\'weights\', \'biases\', \'labels\', \'inputs\', \'num_sampled\', \'num_classes\', \'num_true\', \'sampled_values\', \'remove_accidental_hits\', \'partition_strategy\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'True\', \'mod\', \'sampled_softmax_loss\'], " + argspec: "args=[\'weights\', \'biases\', \'labels\', \'inputs\', \'num_sampled\', \'num_classes\', \'num_true\', \'sampled_values\', \'remove_accidental_hits\', \'partition_strategy\', \'name\', \'seed\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'True\', \'mod\', \'sampled_softmax_loss\', \'None\'], " } member_method { name: "selu" -- GitLab From 1a6216e61e804019cd64732d6f95d4d9bbedb594 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 19:47:58 -0800 Subject: [PATCH 030/423] [XLA] Fix HloModule clone. Currently the cloning of an instruction is usually "shallow": the called computations of the instruction are reused in the clone. This mechanism is useful when the hlo graph need to be modified in place (e.g. inliner, and some hlo passes). One exception is the fusion instruction: it's always "deep" copied, which means the fused computation is copied as well. So when we deep cloning an HLO module, don't re-copy the fused computation, and do let the instruction's clone function know where to put the copied fused computation. PiperOrigin-RevId: 183181206 --- tensorflow/compiler/xla/service/hlo_module.cc | 21 ++++++++-- .../compiler/xla/service/hlo_module_test.cc | 42 +++++++++++++++++++ 2 files changed, 60 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 58bb942211..99d8dd04e5 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -523,7 +523,15 @@ std::unique_ptr HloModule::Clone(const string& suffix) const { std::unordered_map clone_map; for (auto& computation : computations_) { - auto cloned_computation = computation->Clone(suffix); + if (computation->IsFusionComputation()) { + // Cloning of a fused computation is handled by its fusion instruction. + continue; + } + + // When cloning a computation, pass in the new module, so that for any + // fusion instruction in this computation, the fused computation will be + // deep cloned to the new module. + auto cloned_computation = computation->Clone(suffix, module.get()); InsertOrDie(&clone_map, computation.get(), cloned_computation.get()); if (entry_computation_ == computation.get()) { @@ -537,8 +545,15 @@ std::unique_ptr HloModule::Clone(const string& suffix) const { for (auto* instruction : cloned_computation->instructions()) { // Rewrite instruction's called_computation to point to the cloned // computations. - instruction->ReplaceCalledComputations( - [&](HloComputation* hlo) { return FindOrDie(clone_map, hlo); }); + instruction->ReplaceCalledComputations([&](HloComputation* hlo) { + if (hlo->IsFusionComputation()) { + // Cloning of a fused computation has already been handled when its + // fusion instruction is cloned. So this hlo computation is already + // the cloned one. + return hlo; + } + return FindOrDie(clone_map, hlo); + }); } } return module; diff --git a/tensorflow/compiler/xla/service/hlo_module_test.cc b/tensorflow/compiler/xla/service/hlo_module_test.cc index 0f5d3dccb7..cd51fa4e85 100644 --- a/tensorflow/compiler/xla/service/hlo_module_test.cc +++ b/tensorflow/compiler/xla/service/hlo_module_test.cc @@ -105,6 +105,48 @@ TEST_F(HloModuleTest, CloneTest) { } } +TEST_F(HloModuleTest, CloneHasFusion) { + auto module = CreateNewModule(); + + // Create the fused computation. + HloComputation* fused_computation; + { + auto b = HloComputation::Builder("Fused"); + auto x = b.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "x")); + b.AddInstruction( + HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, x, x)); + fused_computation = module->AddEmbeddedComputation(b.Build()); + } + + // Create the entry computation. + { + auto b = HloComputation::Builder("Entry"); + auto input = b.AddInstruction( + HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + b.AddInstruction( + HloInstruction::CreateFusion(r0f32_, HloInstruction::FusionKind::kInput, + /*operands=*/{input}, fused_computation)); + module->AddEntryComputation(b.Build()); + } + + auto post_order = module->MakeComputationPostOrder(); + auto cloned_module = module->Clone("copy"); + auto post_order_copied = cloned_module->MakeComputationPostOrder(); + + EXPECT_EQ(post_order.size(), post_order_copied.size()); + for (auto origin = post_order.begin(), copied = post_order_copied.begin(); + origin != post_order.end() && copied != post_order_copied.end(); + ++origin, ++copied) { + if ((*origin)->name() == "Fused") { + // Clone of the fused computation is handled when its fusion instruction + // is cloned, which always use suffix ".clone". + EXPECT_EQ((*origin)->name() + ".clone", (*copied)->name()); + } else { + EXPECT_EQ((*origin)->name() + ".copy", (*copied)->name()); + } + } +} + TEST_F(HloModuleTest, DiamondComputationsPostOrder) { // Create a module with a diamond call graph of computations. auto module = CreateNewModule(); -- GitLab From 0d11ed25e49e9dfbb45c15c1af9a5892e9146768 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 20:13:14 -0800 Subject: [PATCH 031/423] Make the graph generation for TFBT deterministic. PiperOrigin-RevId: 183183086 --- tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py index 23168bf493..b281a4c6d1 100644 --- a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py +++ b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py @@ -104,7 +104,7 @@ def run_handler_scheduled_ops(per_handler_ops, stamp, worker_device): batched_ops = collections.defaultdict(list) # Group the ops by their batching_key. Ops that share the same batching key # can be executed together. - for handler in per_handler_ops.keys(): + for handler in sorted(per_handler_ops.keys()): for op in per_handler_ops[handler]: batched_ops[(op.batching_key(), op.batch_runner_fn())].append(op) op_results = {} -- GitLab From b986f8944d22b922e9a5feb5b7234b9a0b1087ce Mon Sep 17 00:00:00 2001 From: Chris Leary Date: Wed, 24 Jan 2018 20:51:33 -0800 Subject: [PATCH 032/423] [XLA] Add more tests for ConvertElementType. PiperOrigin-RevId: 183185601 --- tensorflow/compiler/xla/tests/unary_op_test.cc | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/tensorflow/compiler/xla/tests/unary_op_test.cc b/tensorflow/compiler/xla/tests/unary_op_test.cc index fa4192e928..835e2d7e55 100644 --- a/tensorflow/compiler/xla/tests/unary_op_test.cc +++ b/tensorflow/compiler/xla/tests/unary_op_test.cc @@ -215,5 +215,23 @@ XLA_TEST_F(UnaryOpTest, SignAbsTestR2) { ComputeAndCompareR2(&builder, {{0, 0}, {0, 0}}, {}); } +XLA_TEST_F(UnaryOpTest, ConvertElementTypePredToS32) { + ComputationBuilder builder(client_, TestName()); + auto lhs = builder.ConstantR1({0, 1}); + auto rhs = builder.ConstantR1({1, 1}); + builder.ConvertElementType(builder.Eq(lhs, rhs), S32); + + ComputeAndCompareR1(&builder, {0, 1}, {}); +} + +XLA_TEST_F(UnaryOpTest, ConvertElementTypePredToF32) { + ComputationBuilder builder(client_, TestName()); + auto lhs = builder.ConstantR1({0, 1}); + auto rhs = builder.ConstantR1({1, 1}); + builder.ConvertElementType(builder.Eq(lhs, rhs), F32); + + ComputeAndCompareR1(&builder, {0.0, 1.0}, {}); +} + } // namespace } // namespace xla -- GitLab From 2fad3428e3b13c963375970cbfa9eea554a16486 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 19 Dec 2017 15:50:58 -0800 Subject: [PATCH 033/423] Add comparison ufuncs for numpy bfloat16 type. Fix 2 bfloat16 tests. PiperOrigin-RevId: 179614898 --- tensorflow/python/lib/core/bfloat16.cc | 106 ++++++++++++++++++++ tensorflow/python/lib/core/bfloat16_test.py | 18 ++++ tensorflow/python/lib/core/numpy.h | 1 + 3 files changed, 125 insertions(+) diff --git a/tensorflow/python/lib/core/bfloat16.cc b/tensorflow/python/lib/core/bfloat16.cc index 4902978e2d..7f07deebef 100644 --- a/tensorflow/python/lib/core/bfloat16.cc +++ b/tensorflow/python/lib/core/bfloat16.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include "tensorflow/python/lib/core/bfloat16.h" #include "tensorflow/core/framework/numeric_types.h" @@ -477,8 +479,61 @@ bool RegisterBfloat16Cast(int numpy_type, bool cast_is_safe) { return true; } +template +void BinaryUFunc(char** args, npy_intp* dimensions, npy_intp* steps, + void* data) { + const char* i0 = args[0]; + const char* i1 = args[1]; + char* o = args[2]; + for (npy_intp k = 0; k < *dimensions; k++) { + InType x = *reinterpret_cast(i0); + InType y = *reinterpret_cast(i1); + *reinterpret_cast(o) = Functor()(x, y); + i0 += steps[0]; + i1 += steps[1]; + o += steps[2]; + } +} + +template +void CompareUFunc(char** args, npy_intp* dimensions, npy_intp* steps, + void* data) { + BinaryUFunc(args, dimensions, steps, data); +} + +struct Bfloat16EqFunctor { + npy_bool operator()(bfloat16 a, bfloat16 b) { return a == b; } +}; +struct Bfloat16NeFunctor { + npy_bool operator()(bfloat16 a, bfloat16 b) { return a != b; } +}; +struct Bfloat16LtFunctor { + npy_bool operator()(bfloat16 a, bfloat16 b) { return a < b; } +}; +struct Bfloat16GtFunctor { + npy_bool operator()(bfloat16 a, bfloat16 b) { return a > b; } +}; +struct Bfloat16LeFunctor { + npy_bool operator()(bfloat16 a, bfloat16 b) { return a <= b; } +}; +struct Bfloat16GeFunctor { + npy_bool operator()(bfloat16 a, bfloat16 b) { return a >= b; } +}; + // Initializes the module. bool Initialize() { + // It's critical to import umath to avoid crash in open source build. + import_umath1(false); + + Safe_PyObjectPtr numpy_str = make_safe(MakePyString("numpy")); + if (!numpy_str) { + return false; + } + Safe_PyObjectPtr numpy = make_safe(PyImport_Import(numpy_str.get())); + if (!numpy) { + return false; + } + // We hit a mysterious crash if we haven't initialized numpy before this: PyBfloat16_Type.tp_base = &PyGenericArrType_Type; @@ -536,6 +591,57 @@ bool Initialize() { /*cast_is_safe=*/true)) { return false; } + + // Register ufuncs + auto register_ufunc = [&](const char* name, PyUFuncGenericFunction fn, + const std::array& types) { + Safe_PyObjectPtr ufunc_obj = + make_safe(PyObject_GetAttrString(numpy.get(), name)); + if (!ufunc_obj) { + return false; + } + PyUFuncObject* ufunc = reinterpret_cast(ufunc_obj.get()); + if (types.size() != ufunc->nargs) { + PyErr_Format(PyExc_AssertionError, + "ufunc %s takes %d arguments, loop takes %lu", name, + ufunc->nargs, types.size()); + return false; + } + if (PyUFunc_RegisterLoopForType(ufunc, npy_bfloat16_, fn, + const_cast(types.data()), + nullptr) < 0) { + return false; + } + return true; + }; + + // Comparisons + const std::array compare_types = {npy_bfloat16_, npy_bfloat16_, + NPY_BOOL}; + + if (!register_ufunc("equal", CompareUFunc, + compare_types)) { + return false; + } + if (!register_ufunc("not_equal", CompareUFunc, + compare_types)) { + return false; + } + if (!register_ufunc("less", CompareUFunc, compare_types)) { + return false; + } + if (!register_ufunc("greater", CompareUFunc, + compare_types)) { + return false; + } + if (!register_ufunc("less_equal", CompareUFunc, + compare_types)) { + return false; + } + if (!register_ufunc("greater_equal", CompareUFunc, + compare_types)) { + return false; + } return true; } diff --git a/tensorflow/python/lib/core/bfloat16_test.py b/tensorflow/python/lib/core/bfloat16_test.py index 0872348c51..985a11272c 100644 --- a/tensorflow/python/lib/core/bfloat16_test.py +++ b/tensorflow/python/lib/core/bfloat16_test.py @@ -172,6 +172,24 @@ class Bfloat16NumPyTest(test.TestCase): self.assertEqual("[[bfloat16(1) bfloat16(2) bfloat16(3)]]", str(x)) self.assertAllEqual(x, x) self.assertAllClose(x, x) + self.assertTrue((x == x).all()) + + def testComparisons(self): + x = np.array([401408, 7, -32], dtype=np.float32) + bx = x.astype(bfloat16) + y = np.array([82432, 7, 0], dtype=np.float32) + by = y.astype(bfloat16) + self.assertAllEqual(x == y, bx == by) + self.assertAllEqual(x != y, bx != by) + self.assertAllEqual(x < y, bx < by) + self.assertAllEqual(x > y, bx > by) + self.assertAllEqual(x <= y, bx <= by) + self.assertAllEqual(x >= y, bx >= by) + + def testEqual2(self): + a = np.array([401408], bfloat16) + b = np.array([82432], bfloat16) + self.assertFalse(a.__eq__(b)) def testCasts(self): for dtype in [ diff --git a/tensorflow/python/lib/core/numpy.h b/tensorflow/python/lib/core/numpy.h index 0eafe890db..25322b458b 100644 --- a/tensorflow/python/lib/core/numpy.h +++ b/tensorflow/python/lib/core/numpy.h @@ -32,6 +32,7 @@ limitations under the License. #include #include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" namespace tensorflow { -- GitLab From 72c420a32702f7a7638c0130a7d7dc1db4469840 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 24 Jan 2018 22:33:14 -0800 Subject: [PATCH 034/423] Automated g4 rollback of changelist 183171572 PiperOrigin-RevId: 183192221 --- .../batch_sequences_with_states_test.py | 26 +------------------ 1 file changed, 1 insertion(+), 25 deletions(-) diff --git a/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py b/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py index 04538405e4..2a0ef0e6b3 100644 --- a/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py +++ b/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py @@ -320,18 +320,6 @@ class BatchSequencesWithStatesTest(test.TestCase): def testNotAMultiple(self): num_unroll = 3 # Not a divisor of value_length - # so padding would have been necessary. - - # Use placeholder_with_default in sequences to make sure we get runtime - # error instead of shape inference error - sequences = { - "seq1": array_ops.placeholder_with_default(self.sequences["seq1"], - shape=(None, 5)), - "seq2": array_ops.placeholder_with_default(self.sequences["seq2"], - shape=(None, 4, 2)), - "seq3": self.sequences["seq3"], - "seq4": self.sequences["seq4"], - } - with self.test_session() as sess: with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, ".*should be a multiple of: 3, but saw " @@ -342,7 +330,7 @@ class BatchSequencesWithStatesTest(test.TestCase): with coord.stop_on_exception(): next_batch = sqss.batch_sequences_with_states( input_key=self.key, - input_sequences=sequences, + input_sequences=self.sequences, input_context=self.context, input_length=3, initial_states=self.initial_states, @@ -505,18 +493,6 @@ class BatchSequencesWithStatesTest(test.TestCase): expected_seq4_batch2=expected_seq4_batch2) -class BatchSequencesWithStatesTestWithCApi(BatchSequencesWithStatesTest): - - def setUp(self): - self._prev_value = ops._USE_C_API - ops._USE_C_API = True - super(BatchSequencesWithStatesTestWithCApi, self).setUp() - - def tearDown(self): - super(BatchSequencesWithStatesTestWithCApi, self).tearDown() - ops._USE_C_API = self._prev_value - - class PaddingTest(test.TestCase): def testPaddingInvalidLengths(self): -- GitLab From e52f17b1e273eaafbddd2581c65a535a198918e0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 16 Jan 2018 15:52:12 -0800 Subject: [PATCH 035/423] Enable bfloat16 for CPU kernels PiperOrigin-RevId: 182124532 --- tensorflow/contrib/batching/util/BUILD | 1 + tensorflow/contrib/ffmpeg/default/BUILD | 1 + .../contrib/tensor_forest/kernels/v4/BUILD | 2 + tensorflow/core/BUILD | 8 +- tensorflow/core/framework/numeric_types.h | 174 ++--------- tensorflow/core/framework/register_types.h | 18 +- tensorflow/core/kernels/concat_lib_cpu.cc | 1 - tensorflow/core/kernels/concat_op.cc | 1 - tensorflow/core/kernels/constant_op_gpu.cu.cc | 26 +- tensorflow/core/kernels/cross_op.cc | 1 + tensorflow/core/kernels/fill_functor.cc | 2 + .../core/kernels/save_restore_tensor.cc | 6 +- tensorflow/core/kernels/slice_op.cc | 3 - tensorflow/core/kernels/slice_op_cpu_impl.h | 1 - tensorflow/core/kernels/split_lib_cpu.cc | 1 - tensorflow/core/kernels/split_op.cc | 2 - tensorflow/core/kernels/split_v_op.cc | 1 - tensorflow/core/kernels/strided_slice_op.cc | 1 - .../core/kernels/strided_slice_op_impl.h | 1 - tensorflow/core/kernels/tensor_array_ops.cc | 2 - tensorflow/core/kernels/transpose_op.cc | 2 - tensorflow/core/lib/bfloat16/bfloat16.cc | 25 ++ tensorflow/core/lib/bfloat16/bfloat16.h | 276 ++++++++++++++++++ tensorflow/core/lib/hash/hash.h | 7 + tensorflow/core/lib/strings/strcat.h | 3 + .../core/platform/default/build_config.bzl | 1 + tensorflow/core/platform/types.h | 6 + tensorflow/python/BUILD | 1 + 28 files changed, 381 insertions(+), 193 deletions(-) create mode 100644 tensorflow/core/lib/bfloat16/bfloat16.cc create mode 100644 tensorflow/core/lib/bfloat16/bfloat16.h diff --git a/tensorflow/contrib/batching/util/BUILD b/tensorflow/contrib/batching/util/BUILD index f33a08cb81..3ccd3e08d9 100644 --- a/tensorflow/contrib/batching/util/BUILD +++ b/tensorflow/contrib/batching/util/BUILD @@ -28,6 +28,7 @@ cc_library( deps = [ "//tensorflow/core:framework_headers_lib", "//tensorflow/core:protos_all_cc", + "//third_party/eigen3", ], ) diff --git a/tensorflow/contrib/ffmpeg/default/BUILD b/tensorflow/contrib/ffmpeg/default/BUILD index 949ae9ad9e..6b455567d7 100644 --- a/tensorflow/contrib/ffmpeg/default/BUILD +++ b/tensorflow/contrib/ffmpeg/default/BUILD @@ -19,6 +19,7 @@ cc_library( ], deps = [ "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", ], ) diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD index b7876e1df6..794b76d858 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/BUILD +++ b/tensorflow/contrib/tensor_forest/kernels/v4/BUILD @@ -302,6 +302,7 @@ cc_library( "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc", ], [ + "//third_party/eigen3", "//tensorflow/contrib/decision_trees/proto:generic_tree_model_cc_headers_only", "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_cc_headers_only", ], @@ -322,6 +323,7 @@ cc_library( srcs = ["params.cc"], hdrs = ["params.h"], deps = [ + "//third_party/eigen3", "//tensorflow/core:framework_headers_lib", ] + if_static( [ diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 2956aae2e9..497281041f 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -274,7 +274,8 @@ cc_library( "platform/platform.h", "platform/protobuf.h", "platform/types.h", - ] + glob(tf_additional_proto_hdrs()) + glob(tf_env_time_hdrs()), + "lib/bfloat16/bfloat16.h", + ] + tf_additional_proto_hdrs() + glob(tf_env_time_hdrs()), copts = tf_copts(), deps = tf_lib_proto_parsing_deps(), ) @@ -286,6 +287,7 @@ cc_library( cc_library( name = "lib", hdrs = [ + "lib/bfloat16/bfloat16.h", "lib/core/arena.h", "lib/core/bitmap.h", "lib/core/bits.h", @@ -549,6 +551,7 @@ cc_library( "framework/numeric_types.h", "framework/tensor_types.h", "framework/type_traits.h", + "lib/bfloat16/bfloat16.h", "platform/default/dynamic_annotations.h", "platform/default/integral_types.h", "platform/default/logging.h", @@ -1562,6 +1565,7 @@ cc_library( "platform/jpeg.h", ]), hdrs = [ + "lib/bfloat16/bfloat16.h", "lib/core/stringpiece.h", "lib/jpeg/jpeg_handle.h", "lib/jpeg/jpeg_mem.h", @@ -1589,6 +1593,7 @@ cc_library( "platform/gif.h", ]), hdrs = [ + "lib/bfloat16/bfloat16.h", "lib/core/stringpiece.h", "lib/gif/gif_io.h", "lib/gtl/cleanup.h", @@ -1616,6 +1621,7 @@ cc_library( "platform/png.h", ]), hdrs = [ + "lib/bfloat16/bfloat16.h", "lib/core/casts.h", "lib/core/stringpiece.h", "lib/png/png_io.h", diff --git a/tensorflow/core/framework/numeric_types.h b/tensorflow/core/framework/numeric_types.h index e7268fd7a7..988a18da0e 100644 --- a/tensorflow/core/framework/numeric_types.h +++ b/tensorflow/core/framework/numeric_types.h @@ -41,165 +41,39 @@ typedef Eigen::QInt32 qint32; typedef Eigen::QInt16 qint16; typedef Eigen::QUInt16 quint16; -// see framework/bfloat16.h for description. -struct bfloat16 { - EIGEN_DEVICE_FUNC bfloat16() {} - - EIGEN_DEVICE_FUNC explicit bfloat16(const float v) { - if (Eigen::numext::isnan(v)) { - value = NAN_VALUE; - return; - } - const uint16_t* p = reinterpret_cast(&v); -#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - value = p[0]; -#else - value = p[1]; -#endif - } - - // Following the convention of numpy, converting between complex and - // float will lead to loss of imag value. - explicit EIGEN_DEVICE_FUNC bfloat16(const complex64& val) - : bfloat16(val.real()) {} - - explicit EIGEN_DEVICE_FUNC bfloat16(const complex128& val) - : bfloat16(static_cast(val.real())) {} - - template - explicit EIGEN_DEVICE_FUNC bfloat16(const T& val) - : bfloat16(static_cast(val)) {} - - EIGEN_DEVICE_FUNC explicit operator float() const { - float result; - - uint16_t* q = reinterpret_cast(&result); - -#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - q[0] = value; - q[1] = 0; -#else - q[0] = 0; - q[1] = value; -#endif - return result; - } - - EIGEN_DEVICE_FUNC explicit operator bool() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator Eigen::half() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator short() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator int() const { - return static_cast(float(*this)); - } +} // namespace tensorflow - EIGEN_DEVICE_FUNC explicit operator long() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator char() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator signed char() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator unsigned char() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator unsigned int() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator unsigned long() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator unsigned long long() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator long long() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator double() const { - return static_cast(float(*this)); - } - - EIGEN_DEVICE_FUNC explicit operator complex64() const { - return complex64(float(*this), float(0.0)); - } - - EIGEN_DEVICE_FUNC explicit operator complex128() const { - return complex128(double(*this), double(0.0)); - } - - static bfloat16 epsilon() { - bfloat16 x; - x.value = 0x3c00; // 0x1.0p-7 - return x; - } +namespace Eigen { +// TOOD(xpan): We probably need to overwrite more methods to have correct eigen +// behavior. E.g. loest(), is_integer, etc. See NumTraits.h in eigen. +template <> +struct NumTraits + : GenericNumTraits {}; - uint16_t value; +using ::tensorflow::operator==; +using ::tensorflow::operator!=; - // A value that represents "not a number". - static const uint16_t NAN_VALUE = 0x7FC0; -}; +namespace numext { -inline bfloat16 operator+(bfloat16 a, bfloat16 b) { - return bfloat16(static_cast(a) + static_cast(b)); -} -inline bfloat16 operator-(bfloat16 a, bfloat16 b) { - return bfloat16(static_cast(a) - static_cast(b)); -} -inline bfloat16 operator*(bfloat16 a, bfloat16 b) { - return bfloat16(static_cast(a) * static_cast(b)); -} -inline bfloat16 operator/(bfloat16 a, bfloat16 b) { - return bfloat16(static_cast(a) / static_cast(b)); -} -inline bfloat16 operator-(bfloat16 a) { - a.value ^= 0x8000; - return a; -} -inline bool operator<(bfloat16 a, bfloat16 b) { - return static_cast(a) < static_cast(b); -} -inline bool operator<=(bfloat16 a, bfloat16 b) { - return static_cast(a) <= static_cast(b); -} -inline bool operator==(bfloat16 a, bfloat16 b) { - return static_cast(a) == static_cast(b); -} -inline bool operator!=(bfloat16 a, bfloat16 b) { - return static_cast(a) != static_cast(b); -} -inline bool operator>(bfloat16 a, bfloat16 b) { - return static_cast(a) > static_cast(b); -} -inline bool operator>=(bfloat16 a, bfloat16 b) { - return static_cast(a) >= static_cast(b); +template <> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE tensorflow::bfloat16 log( + const tensorflow::bfloat16& x) { + return static_cast(::logf(static_cast(x))); } -} // end namespace tensorflow +template <> +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE tensorflow::bfloat16 exp( + const tensorflow::bfloat16& x) { + return static_cast(::expf(static_cast(x))); +} -namespace Eigen { template <> -struct NumTraits : GenericNumTraits {}; +EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE tensorflow::bfloat16 abs( + const tensorflow::bfloat16& x) { + return static_cast(::fabsf(static_cast(x))); +} -using ::tensorflow::operator==; -using ::tensorflow::operator!=; +} // namespace numext } // namespace Eigen #ifdef COMPILER_MSVC diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h index 0f186a7a06..320531f03a 100644 --- a/tensorflow/core/framework/register_types.h +++ b/tensorflow/core/framework/register_types.h @@ -155,11 +155,16 @@ limitations under the License. TF_CALL_uint8(m) TF_CALL_int8(m) #define TF_CALL_REAL_NUMBER_TYPES(m) \ + TF_CALL_INTEGRAL_TYPES(m) \ + TF_CALL_half(m) TF_CALL_bfloat16(m) TF_CALL_float(m) TF_CALL_double(m) + +#define TF_CALL_REAL_NUMBER_TYPES_NO_BFLOAT16(m) \ TF_CALL_INTEGRAL_TYPES(m) TF_CALL_half(m) TF_CALL_float(m) TF_CALL_double(m) -#define TF_CALL_REAL_NUMBER_TYPES_NO_INT32(m) \ - TF_CALL_half(m) TF_CALL_float(m) TF_CALL_double(m) TF_CALL_int64(m) \ - TF_CALL_uint16(m) TF_CALL_int16(m) TF_CALL_uint8(m) TF_CALL_int8(m) +#define TF_CALL_REAL_NUMBER_TYPES_NO_INT32(m) \ + TF_CALL_half(m) TF_CALL_bfloat16(m) TF_CALL_float(m) TF_CALL_double(m) \ + TF_CALL_int64(m) TF_CALL_uint16(m) TF_CALL_int16(m) TF_CALL_uint8(m) \ + TF_CALL_int8(m) // Call "m" for all number types, including complex64 and complex128. #define TF_CALL_NUMBER_TYPES(m) \ @@ -194,6 +199,13 @@ limitations under the License. #define TF_CALL_QUANTIZED_TYPES(m) \ TF_CALL_qint8(m) TF_CALL_quint8(m) TF_CALL_qint32(m) +// Types used for save and restore ops. +#define TF_CALL_SAVE_RESTORE_TYPES(m) \ + TF_CALL_INTEGRAL_TYPES(m) \ + TF_CALL_half(m) TF_CALL_float(m) TF_CALL_double(m) TF_CALL_complex64(m) \ + TF_CALL_complex128(m) TF_CALL_bool(m) TF_CALL_string(m) \ + TF_CALL_QUANTIZED_TYPES(m) + #ifdef TENSORFLOW_SYCL_NO_DOUBLE #define TF_CALL_SYCL_double(m) #else // TENSORFLOW_SYCL_NO_DOUBLE diff --git a/tensorflow/core/kernels/concat_lib_cpu.cc b/tensorflow/core/kernels/concat_lib_cpu.cc index 743e3acfd5..43731114c0 100644 --- a/tensorflow/core/kernels/concat_lib_cpu.cc +++ b/tensorflow/core/kernels/concat_lib_cpu.cc @@ -72,7 +72,6 @@ REGISTER(qint8) REGISTER(quint16) REGISTER(qint16) REGISTER(qint32) -REGISTER(bfloat16) TF_CALL_variant(REGISTER) #if defined(IS_MOBILE_PLATFORM) && !defined(SUPPORT_SELECTIVE_REGISTRATION) && \ diff --git a/tensorflow/core/kernels/concat_op.cc b/tensorflow/core/kernels/concat_op.cc index 8e480aa995..ae1b5da32e 100644 --- a/tensorflow/core/kernels/concat_op.cc +++ b/tensorflow/core/kernels/concat_op.cc @@ -172,7 +172,6 @@ REGISTER_CONCAT(qint8); REGISTER_CONCAT(quint16); REGISTER_CONCAT(qint16); REGISTER_CONCAT(qint32); -REGISTER_CONCAT(bfloat16); #undef REGISTER_CONCAT diff --git a/tensorflow/core/kernels/constant_op_gpu.cu.cc b/tensorflow/core/kernels/constant_op_gpu.cu.cc index d1a1e34ec3..3487606778 100644 --- a/tensorflow/core/kernels/constant_op_gpu.cu.cc +++ b/tensorflow/core/kernels/constant_op_gpu.cu.cc @@ -77,7 +77,7 @@ struct FillFunctor { #define DEFINE_FILL_GPU(T) template struct FillFunctor; TF_CALL_REAL_NUMBER_TYPES(DEFINE_FILL_GPU); -DEFINE_FILL_GPU(bool); +TF_CALL_bool(DEFINE_FILL_GPU); #undef DEFINE_FILL_GPU // Partial specialization of FillFunctor. @@ -88,15 +88,9 @@ struct SetZeroFunctor { } }; -#define DEFINE_SETZERO_GPU(T) template struct SetZeroFunctor -DEFINE_SETZERO_GPU(bool); -DEFINE_SETZERO_GPU(Eigen::half); -DEFINE_SETZERO_GPU(float); -DEFINE_SETZERO_GPU(double); -DEFINE_SETZERO_GPU(complex64); -DEFINE_SETZERO_GPU(complex128); -DEFINE_SETZERO_GPU(int32); -DEFINE_SETZERO_GPU(int64); +#define DEFINE_SETZERO_GPU(T) template struct SetZeroFunctor; +TF_CALL_NUMBER_TYPES(DEFINE_SETZERO_GPU); +TF_CALL_bool(DEFINE_SETZERO_GPU); #undef DEFINE_SETZERO_GPU // Partial specialization of FillFunctor. @@ -107,15 +101,9 @@ struct SetOneFunctor { } }; -#define DEFINE_SETONE_GPU(T) template struct SetOneFunctor -DEFINE_SETONE_GPU(bool); -DEFINE_SETONE_GPU(Eigen::half); -DEFINE_SETONE_GPU(float); -DEFINE_SETONE_GPU(double); -DEFINE_SETONE_GPU(complex64); -DEFINE_SETONE_GPU(complex128); -DEFINE_SETONE_GPU(int32); -DEFINE_SETONE_GPU(int64); +#define DEFINE_SETONE_GPU(T) template struct SetOneFunctor; +TF_CALL_NUMBER_TYPES(DEFINE_SETONE_GPU); +TF_CALL_bool(DEFINE_SETONE_GPU); #undef DEFINE_SETONE_GPU } // end namespace functor diff --git a/tensorflow/core/kernels/cross_op.cc b/tensorflow/core/kernels/cross_op.cc index 05a33a97b4..b29524f1f9 100644 --- a/tensorflow/core/kernels/cross_op.cc +++ b/tensorflow/core/kernels/cross_op.cc @@ -105,6 +105,7 @@ TF_CALL_REAL_NUMBER_TYPES(DECLARE_GPU_KERNEL); REGISTER_KERNEL_BUILDER( \ Name("Cross").Device(DEVICE_GPU).TypeConstraint("T"), \ CrossOp); + TF_CALL_REAL_NUMBER_TYPES(REGISTER_GPU_KERNEL); #undef REGISTER_GPU_KERNEL #endif diff --git a/tensorflow/core/kernels/fill_functor.cc b/tensorflow/core/kernels/fill_functor.cc index ea0cc139f3..6bc004c236 100644 --- a/tensorflow/core/kernels/fill_functor.cc +++ b/tensorflow/core/kernels/fill_functor.cc @@ -41,6 +41,7 @@ void SetZeroFunctor::operator()( template struct SetZeroFunctor; DEFINE_SETZERO_CPU(bool); DEFINE_SETZERO_CPU(Eigen::half); +DEFINE_SETZERO_CPU(bfloat16); DEFINE_SETZERO_CPU(float); DEFINE_SETZERO_CPU(double); DEFINE_SETZERO_CPU(uint8); @@ -85,6 +86,7 @@ void SetOneFunctor::operator()( template struct SetOneFunctor; DEFINE_SETONE_CPU(bool); DEFINE_SETONE_CPU(Eigen::half); +DEFINE_SETONE_CPU(bfloat16); DEFINE_SETONE_CPU(float); DEFINE_SETONE_CPU(double); DEFINE_SETONE_CPU(uint8); diff --git a/tensorflow/core/kernels/save_restore_tensor.cc b/tensorflow/core/kernels/save_restore_tensor.cc index 6b06cf650a..2a0d94c8bc 100644 --- a/tensorflow/core/kernels/save_restore_tensor.cc +++ b/tensorflow/core/kernels/save_restore_tensor.cc @@ -109,8 +109,7 @@ void SaveTensors( break; switch (input.dtype()) { - TF_CALL_POD_STRING_TYPES(WRITER_ADD) - TF_CALL_QUANTIZED_TYPES(WRITER_ADD) + TF_CALL_SAVE_RESTORE_TYPES(WRITER_ADD) default: context->SetStatus(errors::Unimplemented("Saving data type ", DataTypeString(input.dtype()), @@ -225,8 +224,7 @@ void RestoreTensor(OpKernelContext* context, break; switch (type) { - TF_CALL_POD_STRING_TYPES(READER_COPY) - TF_CALL_QUANTIZED_TYPES(READER_COPY) + TF_CALL_SAVE_RESTORE_TYPES(READER_COPY) default: context->SetStatus(errors::Unimplemented( "Restoring data type ", DataTypeString(type), " not yet supported")); diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc index d46701749b..12c6901067 100644 --- a/tensorflow/core/kernels/slice_op.cc +++ b/tensorflow/core/kernels/slice_op.cc @@ -439,7 +439,6 @@ namespace functor { DECLARE_CPU_SPEC(T, 7); TF_CALL_ALL_TYPES(DECLARE_FOR_N); -DECLARE_FOR_N(bfloat16); #undef DECLARE_FOR_N #undef DECLARE_CPU_SPEC @@ -456,7 +455,6 @@ DECLARE_FOR_N(bfloat16); TF_CALL_POD_STRING_TYPES(REGISTER_SLICE); TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE); -REGISTER_SLICE(bfloat16); #undef REGISTER_SLICE #else #define REGISTER_SLICE(type) \ @@ -469,7 +467,6 @@ REGISTER_SLICE(bfloat16); TF_CALL_POD_STRING_TYPES(REGISTER_SLICE); TF_CALL_QUANTIZED_TYPES(REGISTER_SLICE); -REGISTER_SLICE(bfloat16); #undef REGISTER_SLICE #endif // INTEL_MKL diff --git a/tensorflow/core/kernels/slice_op_cpu_impl.h b/tensorflow/core/kernels/slice_op_cpu_impl.h index a70805658e..58dc7df3e0 100644 --- a/tensorflow/core/kernels/slice_op_cpu_impl.h +++ b/tensorflow/core/kernels/slice_op_cpu_impl.h @@ -30,7 +30,6 @@ using CpuDevice = Eigen::ThreadPoolDevice; template struct functor::Slice; TF_CALL_ALL_TYPES(DEFINE_CPU_KERNELS); -DEFINE_CPU_KERNELS(bfloat16); #undef DEFINE_CPU_KERNELS diff --git a/tensorflow/core/kernels/split_lib_cpu.cc b/tensorflow/core/kernels/split_lib_cpu.cc index 6583f96a91..25026208d1 100644 --- a/tensorflow/core/kernels/split_lib_cpu.cc +++ b/tensorflow/core/kernels/split_lib_cpu.cc @@ -41,7 +41,6 @@ void Split::operator()( TF_CALL_ALL_TYPES(DEFINE_CPU_KERNELS) DEFINE_CPU_KERNELS(quint8) -DEFINE_CPU_KERNELS(bfloat16) #ifdef TENSORFLOW_USE_SYCL template diff --git a/tensorflow/core/kernels/split_op.cc b/tensorflow/core/kernels/split_op.cc index 094ba8bb86..58e1a73be6 100644 --- a/tensorflow/core/kernels/split_op.cc +++ b/tensorflow/core/kernels/split_op.cc @@ -360,8 +360,6 @@ class SplitOpSYCL : public SplitOpBase { TF_CALL_ALL_TYPES(REGISTER_SPLIT); REGISTER_SPLIT(quint8); -// TODO(xpan): Merge bfloat16 into TF_CALL_ALL_TYPES -REGISTER_SPLIT(bfloat16); #undef REGISTER_SPLIT diff --git a/tensorflow/core/kernels/split_v_op.cc b/tensorflow/core/kernels/split_v_op.cc index 3316e5fcc9..f1078ac349 100644 --- a/tensorflow/core/kernels/split_v_op.cc +++ b/tensorflow/core/kernels/split_v_op.cc @@ -406,7 +406,6 @@ class SplitVOpGPU : public SplitVOpBase { REGISTER_SPLIT(type, int64); TF_CALL_ALL_TYPES(REGISTER_SPLIT_LEN); -REGISTER_SPLIT_LEN(bfloat16); #undef REGISTER_SPLIT_LEN #undef REGISTER_SPLIT diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc index 73b6d4cf6a..7c213e14d2 100644 --- a/tensorflow/core/kernels/strided_slice_op.cc +++ b/tensorflow/core/kernels/strided_slice_op.cc @@ -386,7 +386,6 @@ class StridedSliceAssignOp : public OpKernel { StridedSliceAssignOp) TF_CALL_ALL_TYPES(REGISTER_STRIDED_SLICE); -REGISTER_STRIDED_SLICE(bfloat16); #undef REGISTER_STRIDED_SLICE diff --git a/tensorflow/core/kernels/strided_slice_op_impl.h b/tensorflow/core/kernels/strided_slice_op_impl.h index afe3a051e6..a84ba38ef4 100644 --- a/tensorflow/core/kernels/strided_slice_op_impl.h +++ b/tensorflow/core/kernels/strided_slice_op_impl.h @@ -288,7 +288,6 @@ DECLARE_FOR_N_GPU(int64); #endif // END GOOGLE_CUDA TF_CALL_ALL_TYPES(DECLARE_FOR_N_CPU); -DECLARE_FOR_N_CPU(bfloat16); #ifdef TENSORFLOW_USE_SYCL #define PREVENT_FOR_N_SYCL(T) \ diff --git a/tensorflow/core/kernels/tensor_array_ops.cc b/tensorflow/core/kernels/tensor_array_ops.cc index cca6d0e35f..9a3d2e124e 100644 --- a/tensorflow/core/kernels/tensor_array_ops.cc +++ b/tensorflow/core/kernels/tensor_array_ops.cc @@ -709,7 +709,6 @@ TF_CALL_POD_STRING_TYPES(REGISTER_GATHER_AND_PACK); REGISTER_GATHER_AND_PACK(quint8); REGISTER_GATHER_AND_PACK(qint8); REGISTER_GATHER_AND_PACK(qint32); -REGISTER_GATHER_AND_PACK(bfloat16); #undef REGISTER_GATHER_AND_PACK @@ -940,7 +939,6 @@ TF_CALL_POD_STRING_TYPES(REGISTER_CONCAT); REGISTER_CONCAT(quint8); REGISTER_CONCAT(qint8); REGISTER_CONCAT(qint32); -REGISTER_CONCAT(bfloat16); #undef REGISTER_CONCAT diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc index 96c051c636..2e0d18b634 100644 --- a/tensorflow/core/kernels/transpose_op.cc +++ b/tensorflow/core/kernels/transpose_op.cc @@ -230,7 +230,6 @@ Status ConjugateTransposeCpuOp::DoTranspose(OpKernelContext* ctx, .HostMemory("perm"), \ MklConjugateTransposeCpuOp); TF_CALL_ALL_TYPES(REGISTER); -REGISTER(bfloat16); #undef REGISTER #else // INTEL_MKL @@ -247,7 +246,6 @@ REGISTER(bfloat16); .HostMemory("perm"), \ ConjugateTransposeCpuOp); TF_CALL_ALL_TYPES(REGISTER) -REGISTER(bfloat16); #undef REGISTER #endif // INTEL_MKL diff --git a/tensorflow/core/lib/bfloat16/bfloat16.cc b/tensorflow/core/lib/bfloat16/bfloat16.cc new file mode 100644 index 0000000000..a591717fd1 --- /dev/null +++ b/tensorflow/core/lib/bfloat16/bfloat16.cc @@ -0,0 +1,25 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/lib/bfloat16/bfloat16.h" + +#include "third_party/eigen3/Eigen/Core" + +namespace tensorflow { + +B16_DEVICE_FUNC bfloat16::operator Eigen::half() const { + return static_cast(float(*this)); +} +} // end namespace tensorflow diff --git a/tensorflow/core/lib/bfloat16/bfloat16.h b/tensorflow/core/lib/bfloat16/bfloat16.h new file mode 100644 index 0000000000..f9cca0ef2a --- /dev/null +++ b/tensorflow/core/lib/bfloat16/bfloat16.h @@ -0,0 +1,276 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CORE_LIB_BFLOAT16_BFLOAT16_H_ +#define TENSORFLOW_CORE_LIB_BFLOAT16_BFLOAT16_H_ + +#include + +#ifdef __CUDACC__ +// All functions callable from CUDA code must be qualified with __device__ +#define B16_DEVICE_FUNC __host__ __device__ + +#else +#define B16_DEVICE_FUNC + +#endif + +namespace Eigen { +struct half; +} + +namespace tensorflow { + +// Single precision complex. +typedef std::complex complex64; +// Double precision complex. +typedef std::complex complex128; + +// see framework/bfloat16.h for description. +struct bfloat16 { + B16_DEVICE_FUNC bfloat16() {} + + B16_DEVICE_FUNC explicit bfloat16(const float v) { + if (float_isnan(v)) { + value = NAN_VALUE; + return; + } + const uint16_t* p = reinterpret_cast(&v); +#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ + value = p[0]; +#else + value = p[1]; +#endif + } + + B16_DEVICE_FUNC explicit bfloat16(const double val) + : bfloat16(static_cast(val)) {} + // Following the convention of numpy, converting between complex and + // float will lead to loss of imag value. + B16_DEVICE_FUNC explicit bfloat16(const complex64& val) + : bfloat16(val.real()) {} + + B16_DEVICE_FUNC explicit bfloat16(const complex128& val) + : bfloat16(static_cast(val.real())) {} + + B16_DEVICE_FUNC explicit bfloat16(const unsigned short val) + : bfloat16(static_cast(val)) {} + + B16_DEVICE_FUNC explicit bfloat16(const unsigned int val) + : bfloat16(static_cast(val)) {} + + B16_DEVICE_FUNC explicit bfloat16(const int val) + : bfloat16(static_cast(val)) {} + + B16_DEVICE_FUNC explicit bfloat16(const long val) + : bfloat16(static_cast(val)) {} + + B16_DEVICE_FUNC explicit bfloat16(const long long val) + : bfloat16(static_cast(val)) {} + + template + B16_DEVICE_FUNC explicit bfloat16(const T& val) + : bfloat16(static_cast(val)) {} + + B16_DEVICE_FUNC explicit operator float() const { + float result; + + uint16_t* q = reinterpret_cast(&result); + +#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ + q[0] = value; + q[1] = 0; +#else + q[0] = 0; + q[1] = value; +#endif + return result; + } + + B16_DEVICE_FUNC explicit operator bool() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator Eigen::half() const; + + B16_DEVICE_FUNC explicit operator short() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator int() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator long() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator char() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator signed char() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator unsigned char() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator unsigned short() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator unsigned int() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator unsigned long() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator unsigned long long() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator long long() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator double() const { + return static_cast(float(*this)); + } + + B16_DEVICE_FUNC explicit operator complex64() const { + return complex64(float(*this), float(0.0)); + } + + B16_DEVICE_FUNC explicit operator complex128() const { + return complex128(double(*this), double(0.0)); + } + + static bfloat16 epsilon() { + bfloat16 x; + x.value = 0x3c00; // 0x1.0p-7 + return x; + } + + uint16_t value; + + // A value that represents "not a number". + static const uint16_t NAN_VALUE = 0x7FC0; + + private: + B16_DEVICE_FUNC bool float_isnan(const float& x) { +#ifdef __CUDA_ARCH__ + return ::isnan(x); +#else + return std::isnan(x); +#endif + } +}; + +B16_DEVICE_FUNC inline std::ostream& operator<<(std::ostream& os, + const bfloat16& dt) { + os << static_cast(dt); + return os; +} + +B16_DEVICE_FUNC inline bfloat16 operator+(bfloat16 a, bfloat16 b) { + return bfloat16(static_cast(a) + static_cast(b)); +} +B16_DEVICE_FUNC inline bfloat16 operator+(bfloat16 a, int b) { + return bfloat16(static_cast(a) + static_cast(b)); +} +B16_DEVICE_FUNC inline bfloat16 operator+(int a, bfloat16 b) { + return bfloat16(static_cast(a) + static_cast(b)); +} +B16_DEVICE_FUNC inline bfloat16 operator-(bfloat16 a, bfloat16 b) { + return bfloat16(static_cast(a) - static_cast(b)); +} +B16_DEVICE_FUNC inline bfloat16 operator*(bfloat16 a, bfloat16 b) { + return bfloat16(static_cast(a) * static_cast(b)); +} +B16_DEVICE_FUNC inline bfloat16 operator/(bfloat16 a, bfloat16 b) { + return bfloat16(static_cast(a) / static_cast(b)); +} +B16_DEVICE_FUNC inline bfloat16 operator-(bfloat16 a) { + a.value ^= 0x8000; + return a; +} +B16_DEVICE_FUNC inline bool operator<(bfloat16 a, bfloat16 b) { + return static_cast(a) < static_cast(b); +} +B16_DEVICE_FUNC inline bool operator<=(bfloat16 a, bfloat16 b) { + return static_cast(a) <= static_cast(b); +} +B16_DEVICE_FUNC inline bool operator==(bfloat16 a, bfloat16 b) { + return static_cast(a) == static_cast(b); +} +B16_DEVICE_FUNC inline bool operator!=(bfloat16 a, bfloat16 b) { + return static_cast(a) != static_cast(b); +} +B16_DEVICE_FUNC inline bool operator>(bfloat16 a, bfloat16 b) { + return static_cast(a) > static_cast(b); +} +B16_DEVICE_FUNC inline bool operator>=(bfloat16 a, bfloat16 b) { + return static_cast(a) >= static_cast(b); +} +B16_DEVICE_FUNC inline bfloat16& operator+=(bfloat16& a, bfloat16 b) { + a = a + b; + return a; +} +B16_DEVICE_FUNC inline bfloat16& operator-=(bfloat16& a, bfloat16 b) { + a = a - b; + return a; +} +B16_DEVICE_FUNC inline bfloat16 operator++(bfloat16& a) { + a += bfloat16(1); + return a; +} +B16_DEVICE_FUNC inline bfloat16 operator--(bfloat16& a) { + a -= bfloat16(1); + return a; +} +B16_DEVICE_FUNC inline bfloat16 operator++(bfloat16& a, int) { + bfloat16 original_value = a; + ++a; + return original_value; +} +B16_DEVICE_FUNC inline bfloat16 operator--(bfloat16& a, int) { + bfloat16 original_value = a; + --a; + return original_value; +} +B16_DEVICE_FUNC inline bfloat16& operator*=(bfloat16& a, bfloat16 b) { + a = a * b; + return a; +} +B16_DEVICE_FUNC inline bfloat16& operator/=(bfloat16& a, bfloat16 b) { + a = a / b; + return a; +} +} // end namespace tensorflow + +namespace std { +template <> +struct hash { + size_t operator()(const tensorflow::bfloat16& v) const { + return hash()(static_cast(v)); + } +}; +} // namespace std + +#endif // TENSORFLOW_CORE_LIB_BFLOAT16_BFLOAT16_H_ diff --git a/tensorflow/core/lib/hash/hash.h b/tensorflow/core/lib/hash/hash.h index 0fb12966af..4d312ab7e8 100644 --- a/tensorflow/core/lib/hash/hash.h +++ b/tensorflow/core/lib/hash/hash.h @@ -64,6 +64,13 @@ struct hash { } }; +template <> +struct hash { + size_t operator()(const bfloat16& t) const { + return std::hash()(static_cast(t)); + } +}; + template <> struct hash { size_t operator()(const string& s) const { diff --git a/tensorflow/core/lib/strings/strcat.h b/tensorflow/core/lib/strings/strcat.h index 8e35549ed4..5835b0101d 100644 --- a/tensorflow/core/lib/strings/strcat.h +++ b/tensorflow/core/lib/strings/strcat.h @@ -119,6 +119,9 @@ class AlphaNum { AlphaNum(float f) // NOLINT(runtime/explicit) : piece_(digits_, strlen(FloatToBuffer(f, digits_))) {} + AlphaNum(bfloat16 f) // NOLINT(runtime/explicit) + : piece_(digits_, strlen(FloatToBuffer(static_cast(f), digits_))) { + } AlphaNum(double f) // NOLINT(runtime/explicit) : piece_(digits_, strlen(DoubleToBuffer(f, digits_))) {} diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index 948334d27b..4ff750909e 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -509,6 +509,7 @@ def tf_additional_cloud_kernel_deps(): def tf_lib_proto_parsing_deps(): return [ ":protos_all_cc", + "//third_party/eigen3", "//tensorflow/core/platform/default/build_config:proto_parsing", ] diff --git a/tensorflow/core/platform/types.h b/tensorflow/core/platform/types.h index 93b82ecb7a..41428bb2c8 100644 --- a/tensorflow/core/platform/types.h +++ b/tensorflow/core/platform/types.h @@ -29,6 +29,12 @@ limitations under the License. #error Define the appropriate PLATFORM_ macro for this platform #endif +#if defined(PLATFORM_WINDOWS) +#include "tensorflow/core/platform/windows/cpu_info.h" +#endif + +#include "tensorflow/core/lib/bfloat16/bfloat16.h" + namespace tensorflow { // Define tensorflow::string to refer to appropriate platform specific type. diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 375a5a0720..46e81646a7 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -384,6 +384,7 @@ tf_cc_shared_object( }), deps = [ "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", ], ) -- GitLab From 86967885684433c86d4764d82e5d975e3ef4ab8e Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Wed, 24 Jan 2018 23:36:20 -0800 Subject: [PATCH 036/423] Fix eager Pooling1D unit test for data_format='channels_first' PiperOrigin-RevId: 183196050 --- tensorflow/python/layers/pooling_test.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/layers/pooling_test.py b/tensorflow/python/layers/pooling_test.py index e4d4ed4a2a..7533674e5a 100644 --- a/tensorflow/python/layers/pooling_test.py +++ b/tensorflow/python/layers/pooling_test.py @@ -96,33 +96,41 @@ class PoolingTest(test.TestCase): def testCreateMaxPooling1D(self): width = 7 - images = random_ops.random_uniform((5, width, 4)) + channels = 3 + images = random_ops.random_uniform((5, width, channels)) layer = pooling_layers.MaxPooling1D(2, strides=2) output = layer.apply(images) - self.assertListEqual(output.get_shape().as_list(), [5, 3, 4]) + self.assertListEqual(output.get_shape().as_list(), + [5, width // 2, channels]) def testCreateAveragePooling1D(self): width = 7 - images = random_ops.random_uniform((5, width, 4)) + channels = 3 + images = random_ops.random_uniform((5, width, channels)) layer = pooling_layers.AveragePooling1D(2, strides=2) output = layer.apply(images) - self.assertListEqual(output.get_shape().as_list(), [5, 3, 4]) + self.assertListEqual(output.get_shape().as_list(), + [5, width // 2, channels]) def testCreateMaxPooling1DChannelsFirst(self): width = 7 - images = random_ops.random_uniform((5, 4, width)) + channels = 3 + images = random_ops.random_uniform((5, channels, width)) layer = pooling_layers.MaxPooling1D( 2, strides=2, data_format='channels_first') output = layer.apply(images) - self.assertListEqual(output.get_shape().as_list(), [5, 4, 3]) + self.assertListEqual(output.get_shape().as_list(), + [5, channels, width // 2]) def testCreateAveragePooling1DChannelsFirst(self): width = 7 - images = random_ops.random_uniform((5, 4, width)) + channels = 3 + images = random_ops.random_uniform((5, channels, width)) layer = pooling_layers.AveragePooling1D( 2, strides=2, data_format='channels_first') output = layer.apply(images) - self.assertListEqual(output.get_shape().as_list(), [5, 4, 3]) + self.assertListEqual(output.get_shape().as_list(), + [5, channels, width // 2]) def testCreateMaxPooling3D(self): depth, height, width = 6, 7, 9 -- GitLab From 9814329d58a392af905266a38f68e7212db6eecf Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 00:05:16 -0800 Subject: [PATCH 037/423] Add WarmStartSettings configuration for all Estimators. PiperOrigin-RevId: 183197945 --- tensorflow/python/estimator/canned/dnn.py | 31 +++++------------ .../estimator/canned/dnn_linear_combined.py | 31 +++++------------ tensorflow/python/estimator/canned/linear.py | 31 +++++------------ tensorflow/python/estimator/estimator.py | 34 ++++++++++++++++++- tensorflow/python/estimator/estimator_test.py | 28 +++++++++++++++ .../python/estimator/warm_starting_util.py | 6 ++-- .../tensorflow.estimator.-estimator.pbtxt | 2 +- 7 files changed, 89 insertions(+), 74 deletions(-) diff --git a/tensorflow/python/estimator/canned/dnn.py b/tensorflow/python/estimator/canned/dnn.py index ba96d738ae..0f274a23c0 100644 --- a/tensorflow/python/estimator/canned/dnn.py +++ b/tensorflow/python/estimator/canned/dnn.py @@ -22,7 +22,6 @@ import six from tensorflow.python.estimator import estimator from tensorflow.python.estimator import model_fn -from tensorflow.python.estimator import warm_starting_util from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.canned import optimizers from tensorflow.python.feature_column import feature_column as feature_column_lib @@ -340,8 +339,8 @@ class DNNClassifier(estimator.Estimator): loss_reduction=loss_reduction) def _model_fn(features, labels, mode, config): - """Call the defined shared _dnn_model_fn and possibly warm-start.""" - estimator_spec = _dnn_model_fn( + """Call the defined shared _dnn_model_fn.""" + return _dnn_model_fn( features=features, labels=labels, mode=mode, @@ -353,17 +352,10 @@ class DNNClassifier(estimator.Estimator): dropout=dropout, input_layer_partitioner=input_layer_partitioner, config=config) - # pylint: disable=protected-access - warm_start_settings = warm_starting_util._get_default_warm_start_settings( - warm_start_from) - if warm_start_settings: - warm_starting_util._warm_start(warm_start_settings) - # pylint: enable=protected-access - - return estimator_spec super(DNNClassifier, self).__init__( - model_fn=_model_fn, model_dir=model_dir, config=config) + model_fn=_model_fn, model_dir=model_dir, config=config, + warm_start_from=warm_start_from) class DNNRegressor(estimator.Estimator): @@ -490,8 +482,8 @@ class DNNRegressor(estimator.Estimator): """ def _model_fn(features, labels, mode, config): - """Call the defined shared _dnn_model_fn and possibly warm-start.""" - estimator_spec = _dnn_model_fn( + """Call the defined shared _dnn_model_fn.""" + return _dnn_model_fn( features=features, labels=labels, mode=mode, @@ -506,14 +498,7 @@ class DNNRegressor(estimator.Estimator): dropout=dropout, input_layer_partitioner=input_layer_partitioner, config=config) - # pylint: disable=protected-access - warm_start_settings = warm_starting_util._get_default_warm_start_settings( - warm_start_from) - if warm_start_settings: - warm_starting_util._warm_start(warm_start_settings) - # pylint: enable=protected-access - - return estimator_spec super(DNNRegressor, self).__init__( - model_fn=_model_fn, model_dir=model_dir, config=config) + model_fn=_model_fn, model_dir=model_dir, config=config, + warm_start_from=warm_start_from) diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py index d29c892662..1a0f4c5c39 100644 --- a/tensorflow/python/estimator/canned/dnn_linear_combined.py +++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py @@ -23,7 +23,6 @@ import math import six from tensorflow.python.estimator import estimator -from tensorflow.python.estimator import warm_starting_util from tensorflow.python.estimator.canned import dnn from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.canned import linear @@ -385,8 +384,8 @@ class DNNLinearCombinedClassifier(estimator.Estimator): loss_reduction=loss_reduction) def _model_fn(features, labels, mode, config): - """Call the _dnn_linear_combined_model_fn and possibly warm-start.""" - estimator_spec = _dnn_linear_combined_model_fn( + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn( features=features, labels=labels, mode=mode, @@ -400,17 +399,10 @@ class DNNLinearCombinedClassifier(estimator.Estimator): dnn_dropout=dnn_dropout, input_layer_partitioner=input_layer_partitioner, config=config) - # pylint: disable=protected-access - warm_start_settings = warm_starting_util._get_default_warm_start_settings( - warm_start_from) - if warm_start_settings: - warm_starting_util._warm_start(warm_start_settings) - # pylint: enable=protected-access - - return estimator_spec super(DNNLinearCombinedClassifier, self).__init__( - model_fn=_model_fn, model_dir=model_dir, config=config) + model_fn=_model_fn, model_dir=model_dir, config=config, + warm_start_from=warm_start_from) class DNNLinearCombinedRegressor(estimator.Estimator): @@ -554,8 +546,8 @@ class DNNLinearCombinedRegressor(estimator.Estimator): 'must be defined.') def _model_fn(features, labels, mode, config): - """Call the _dnn_linear_combined_model_fn and possibly warm-start.""" - estimator_spec = _dnn_linear_combined_model_fn( + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn( features=features, labels=labels, mode=mode, @@ -572,14 +564,7 @@ class DNNLinearCombinedRegressor(estimator.Estimator): dnn_dropout=dnn_dropout, input_layer_partitioner=input_layer_partitioner, config=config) - # pylint: disable=protected-access - warm_start_settings = warm_starting_util._get_default_warm_start_settings( - warm_start_from) - if warm_start_settings: - warm_starting_util._warm_start(warm_start_settings) - # pylint: enable=protected-access - - return estimator_spec super(DNNLinearCombinedRegressor, self).__init__( - model_fn=_model_fn, model_dir=model_dir, config=config) + model_fn=_model_fn, model_dir=model_dir, config=config, + warm_start_from=warm_start_from) diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py index 7a80dfacc2..a5b1172e72 100644 --- a/tensorflow/python/estimator/canned/linear.py +++ b/tensorflow/python/estimator/canned/linear.py @@ -23,7 +23,6 @@ import math import six from tensorflow.python.estimator import estimator -from tensorflow.python.estimator import warm_starting_util from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.canned import optimizers from tensorflow.python.feature_column import feature_column as feature_column_lib @@ -305,8 +304,8 @@ class LinearClassifier(estimator.Estimator): loss_reduction=loss_reduction) def _model_fn(features, labels, mode, config): - """Call the defined shared _linear_model_fn and possibly warm-start.""" - estimator_spec = _linear_model_fn( + """Call the defined shared _linear_model_fn.""" + return _linear_model_fn( features=features, labels=labels, mode=mode, @@ -315,19 +314,12 @@ class LinearClassifier(estimator.Estimator): optimizer=optimizer, partitioner=partitioner, config=config) - # pylint: disable=protected-access - warm_start_settings = warm_starting_util._get_default_warm_start_settings( - warm_start_from) - if warm_start_settings: - warm_starting_util._warm_start(warm_start_settings) - # pylint: enable=protected-access - - return estimator_spec super(LinearClassifier, self).__init__( model_fn=_model_fn, model_dir=model_dir, - config=config) + config=config, + warm_start_from=warm_start_from) class LinearRegressor(estimator.Estimator): @@ -432,8 +424,8 @@ class LinearRegressor(estimator.Estimator): loss_reduction=loss_reduction) def _model_fn(features, labels, mode, config): - """Call the defined shared _linear_model_fn and possibly warm-start.""" - estimator_spec = _linear_model_fn( + """Call the defined shared _linear_model_fn.""" + return _linear_model_fn( features=features, labels=labels, mode=mode, @@ -442,16 +434,9 @@ class LinearRegressor(estimator.Estimator): optimizer=optimizer, partitioner=partitioner, config=config) - # pylint: disable=protected-access - warm_start_settings = warm_starting_util._get_default_warm_start_settings( - warm_start_from) - if warm_start_settings: - warm_starting_util._warm_start(warm_start_settings) - # pylint: enable=protected-access - - return estimator_spec super(LinearRegressor, self).__init__( model_fn=_model_fn, model_dir=model_dir, - config=config) + config=config, + warm_start_from=warm_start_from) diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 2e914fa7e0..face20d530 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -35,6 +35,7 @@ from tensorflow.python.eager import context from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator import run_config from tensorflow.python.estimator import util +from tensorflow.python.estimator import warm_starting_util from tensorflow.python.estimator.export.export import build_all_signature_defs from tensorflow.python.estimator.export.export import get_temp_export_dir from tensorflow.python.estimator.export.export import get_timestamped_export_dir @@ -96,9 +97,22 @@ class Estimator(object): @end_compatibility """ - def __init__(self, model_fn, model_dir=None, config=None, params=None): + def __init__(self, model_fn, model_dir=None, config=None, params=None, + warm_start_from=None): """Constructs an `Estimator` instance. + See @{$estimators} for more information. To warm-start an `Estimator`: + + ```python + estimator = tf.estimator.DNNClassifier( + feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb], + hidden_units=[1024, 512, 256], + warm_start_from="/path/to/checkpoint/dir") + ``` + + For more details on warm-start configuration, see + @{tf.estimator.WarmStartSettings$WarmStartSettings}. + Args: model_fn: Model function. Follows the signature: @@ -135,6 +149,12 @@ class Estimator(object): config: Configuration object. params: `dict` of hyper parameters that will be passed into `model_fn`. Keys are names of parameters, values are basic python types. + warm_start_from: Optional string filepath to a checkpoint to warm-start + from, or a `tf.estimator.WarmStartSettings` object to + fully configure warm-starting. If the string filepath is + provided instead of a `WarmStartSettings`, then all + variables are warm-started, and it is assumed that + vocabularies and Tensor names are unchanged. Raises: RuntimeError: If eager execution is enabled. @@ -192,6 +212,11 @@ class Estimator(object): self._model_fn = model_fn self._params = copy.deepcopy(params or {}) + # pylint: disable=protected-access + self._warm_start_settings = ( + warm_starting_util._get_default_warm_start_settings(warm_start_from)) + # pylint: enable=protected-access + @property def model_dir(self): return self._model_dir @@ -781,6 +806,13 @@ class Estimator(object): worker_hooks.extend(input_hooks) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.TRAIN, self.config) + + if self._warm_start_settings: + logging.info('Warm-starting with WarmStartSettings: %s' % + (self._warm_start_settings,)) + # pylint: disable=protected-access + warm_starting_util._warm_start(self._warm_start_settings) + # pylint: enable=protected-access # 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 diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index ed1676a92d..833f3dcac3 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -52,6 +52,7 @@ 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 variable_scope from tensorflow.python.ops import variables from tensorflow.python.ops.losses import losses from tensorflow.python.platform import gfile @@ -629,6 +630,33 @@ class EstimatorTrainTest(test.TestCase): self.assertEqual( 10, estimator._load_global_step_from_checkpoint_dir(est.model_dir)) + def test_warm_starts(self): + def _make_model_fn(x): + def _variable_creating_model_fn(features, labels, mode): + _, _ = features, labels + variable_scope.get_variable('x', initializer=x) + 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)) + return _variable_creating_model_fn + + est = estimator.Estimator(model_fn=_make_model_fn(42.)) + est.train(dummy_input_fn, steps=10) + + warm_started_est = estimator.Estimator( + model_fn=_make_model_fn(36.), + warm_start_from=est.model_dir) + warm_started_est.train(dummy_input_fn, steps=5) + # warm_start is called after the model_fn, so x should have the value + # from the checkpoint. + self.assertEqual(42., warm_started_est.get_variable_value('x')) + # global_step should not be warm-started. + self.assertEqual( + 5, estimator._load_global_step_from_checkpoint_dir( + warm_started_est.model_dir)) + def test_max_step(self): est = estimator.Estimator(model_fn=model_fn_global_step_incrementer) est.train(dummy_input_fn, max_steps=5) diff --git a/tensorflow/python/estimator/warm_starting_util.py b/tensorflow/python/estimator/warm_starting_util.py index c748b318b7..ad95c71234 100644 --- a/tensorflow/python/estimator/warm_starting_util.py +++ b/tensorflow/python/estimator/warm_starting_util.py @@ -402,10 +402,10 @@ def _warm_start_var_with_vocab(var, def _warm_start(warm_start_settings): - """Warmstarts a model using the given settings. + """Warm-starts a model using the given settings. - Currently, this is intended for use only in canned Estimators. Once made - public, it can be used in any model_fn. + If you are using a tf.estimator.Estimator, this will automatically be called + during training. Args: warm_start_settings: An object of `WarmStartSettings`. diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt index d0bf043754..76f527f796 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt @@ -20,7 +20,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'model_fn\', \'model_dir\', \'config\', \'params\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'model_fn\', \'model_dir\', \'config\', \'params\', \'warm_start_from\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " } member_method { name: "evaluate" -- GitLab From 15b368d31f38547aa3eb69f2c05dacff8ce8858a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 04:48:59 -0800 Subject: [PATCH 038/423] Fixes FunctionLibraryRuntime to be destroyed before ExecutorImpl when NewLocalExecutor returns an error status. PiperOrigin-RevId: 183220585 --- tensorflow/core/common_runtime/direct_session.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc index e9bdd922ba..20c59ad42b 100644 --- a/tensorflow/core/common_runtime/direct_session.cc +++ b/tensorflow/core/common_runtime/direct_session.cc @@ -1143,8 +1143,8 @@ Status DirectSession::GetOrCreateExecutors( options.debug_options = run_state_args->debug_options; } - std::shared_ptr ek(new ExecutorsAndKeys); std::unique_ptr func_info(new FunctionInfo); + std::shared_ptr ek(new ExecutorsAndKeys); // The executor_lock_ is intentionally released while executor is // being created. -- GitLab From 028ef1e67201700e8d9d77af64655f1dd20ae665 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 08:05:30 -0800 Subject: [PATCH 039/423] Drop the manually_create field from RnnState. Initially, I thought that the shape of RNN state arrays could always be determined by shape propagation. Then I came across some graphs where this wasn't so easy to infer, so I introduced manually_create thinking of it as a hack. Today I took another look at dropping that hack, and had a "D'oh" moment when I realized that the cyclic nature of RNN graphs makes it impossible to infer the shapes of all arrays by usual propagation. For example, in a LSTM cell, the input array is concatenated with a state array, so if we don't already know the shape of that state array, shape propagation stops there. Thus, this change removes manually_create by making toco always behave as if manually_create=true, i.e. early-creating all RNN state arrays with the shape explicitly specified by the user. The next TODO item here (see model_flags.proto) is to introduce a generic 'shape' field, so far the current 'size' field only allows specifying 1-D shapes. PiperOrigin-RevId: 183239252 --- .../lite/toco/allocate_transient_arrays.cc | 4 +--- .../contrib/lite/toco/model_cmdline_flags.cc | 3 --- tensorflow/contrib/lite/toco/model_flags.proto | 15 +++------------ tensorflow/contrib/lite/toco/tooling_util.cc | 13 ++++++------- 4 files changed, 10 insertions(+), 25 deletions(-) diff --git a/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc b/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc index 5961d30bf5..49cc1fc2aa 100644 --- a/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc +++ b/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc @@ -158,9 +158,7 @@ std::size_t TransientArraySize(const Model& model, const string& array_name, LOG(FATAL) << "A RNN state array, " << array_name << ", still does not " << "have a known data type after all graph transformations have " - << "run. That's mostly a toco bug --- sorry. For now, you can " - << "work around this issue by adding manually_create:true in the " - << "--rnn_state description of this RNN state."; + << "run."; } } LOG(FATAL) << "An array, " << array_name << ", still does not " diff --git a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc index 790b3443ce..36520d9c55 100644 --- a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc @@ -327,9 +327,6 @@ void ReadModelFlagsFromCommandLineFlags( CHECK(absl::SimpleAtoi(value, &size)); CHECK_GT(size, 0); rnn_state_proto->set_size(size); - } else if (key == "manually_create") { - CHECK_EQ(absl::AsciiStrToLower(value), "true"); - rnn_state_proto->set_manually_create(true); } else { LOG(FATAL) << "Unknown key '" << key << "' in --rnn_states"; } diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto index 13fea29a07..9070ddc883 100644 --- a/tensorflow/contrib/lite/toco/model_flags.proto +++ b/tensorflow/contrib/lite/toco/model_flags.proto @@ -81,19 +81,10 @@ message RnnState { optional string state_array = 1; optional string back_edge_source_array = 2; optional bool discardable = 5; - // TODO(benoitjacob): drop the 'size' field. Should be redundant with - // --input_shapes and shapes propagation. + // size allows to specify a 1-D shape for the RNN state array. + // Will be expanded with 1's to fit the model. + // TODO(benoitjacob): should allow a generic, explicit shape. optional int32 size = 3; - // TODO(benoitjacob): manually_create is a temporary hack: - // due to discrepancies between the current toco dims tracking and - // TensorFlow shapes, for some models we need to manually create RNN state - // arrays with a specified shape. - // Maybe we should actually implement back-edges as operators of their own, - // which would remove the need for much special-casing, including here, - // we could probably consistently let PropagateFixedSizes handle state - // arrays. - // TODO(benoitjacob): should really drop manually_create now. - optional bool manually_create = 4; } // ModelFlags encodes properties of a model that, depending on the file diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 99a54a300b..df785a5102 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -958,7 +958,9 @@ void CheckModelCounts(const Model& model) { void MakeArrayDims(int num_dims, int batch, int height, int width, int depth, std::vector* out_dims) { CHECK(out_dims->empty()); - if (num_dims == 1) { + if (num_dims == 0) { + return; + } else if (num_dims == 1) { CHECK_EQ(batch, 1); *out_dims = {depth}; } else if (num_dims == 2) { @@ -990,13 +992,13 @@ void CreateOrCheckRnnStateArray(const string& name, int size, Model* model) { if (array.has_shape()) { num_dims = array.shape().dimensions_count(); } - std::vector dims; - MakeArrayDims(num_dims, batch, 1, 1, size, &dims); CHECK(array.data_type == ArrayDataType::kFloat || array.data_type == ArrayDataType::kNone); array.data_type = ArrayDataType::kFloat; - if (!array.has_shape()) { + if (!array.has_shape() && num_dims >= 0) { Shape* shape = array.mutable_shape(); + std::vector dims; + MakeArrayDims(num_dims, batch, 1, 1, size, &dims); *shape->mutable_dims() = dims; } } @@ -1185,9 +1187,6 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { } // Creation of the RNN state arrays for (const auto& rnn_state : model->flags.rnn_states()) { - if (!rnn_state.manually_create()) { - continue; - } CreateOrCheckRnnStateArray(rnn_state.state_array(), rnn_state.size(), model); } -- GitLab From 10d7ddfa9bb95d65f7245dae4230a00b0badde06 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 08:20:51 -0800 Subject: [PATCH 040/423] Automated g4 rollback of changelist 183239252 PiperOrigin-RevId: 183241034 --- .../lite/toco/allocate_transient_arrays.cc | 4 +++- .../contrib/lite/toco/model_cmdline_flags.cc | 3 +++ tensorflow/contrib/lite/toco/model_flags.proto | 15 ++++++++++++--- tensorflow/contrib/lite/toco/tooling_util.cc | 13 +++++++------ 4 files changed, 25 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc b/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc index 49cc1fc2aa..5961d30bf5 100644 --- a/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc +++ b/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc @@ -158,7 +158,9 @@ std::size_t TransientArraySize(const Model& model, const string& array_name, LOG(FATAL) << "A RNN state array, " << array_name << ", still does not " << "have a known data type after all graph transformations have " - << "run."; + << "run. That's mostly a toco bug --- sorry. For now, you can " + << "work around this issue by adding manually_create:true in the " + << "--rnn_state description of this RNN state."; } } LOG(FATAL) << "An array, " << array_name << ", still does not " diff --git a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc index 36520d9c55..790b3443ce 100644 --- a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc @@ -327,6 +327,9 @@ void ReadModelFlagsFromCommandLineFlags( CHECK(absl::SimpleAtoi(value, &size)); CHECK_GT(size, 0); rnn_state_proto->set_size(size); + } else if (key == "manually_create") { + CHECK_EQ(absl::AsciiStrToLower(value), "true"); + rnn_state_proto->set_manually_create(true); } else { LOG(FATAL) << "Unknown key '" << key << "' in --rnn_states"; } diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto index 9070ddc883..13fea29a07 100644 --- a/tensorflow/contrib/lite/toco/model_flags.proto +++ b/tensorflow/contrib/lite/toco/model_flags.proto @@ -81,10 +81,19 @@ message RnnState { optional string state_array = 1; optional string back_edge_source_array = 2; optional bool discardable = 5; - // size allows to specify a 1-D shape for the RNN state array. - // Will be expanded with 1's to fit the model. - // TODO(benoitjacob): should allow a generic, explicit shape. + // TODO(benoitjacob): drop the 'size' field. Should be redundant with + // --input_shapes and shapes propagation. optional int32 size = 3; + // TODO(benoitjacob): manually_create is a temporary hack: + // due to discrepancies between the current toco dims tracking and + // TensorFlow shapes, for some models we need to manually create RNN state + // arrays with a specified shape. + // Maybe we should actually implement back-edges as operators of their own, + // which would remove the need for much special-casing, including here, + // we could probably consistently let PropagateFixedSizes handle state + // arrays. + // TODO(benoitjacob): should really drop manually_create now. + optional bool manually_create = 4; } // ModelFlags encodes properties of a model that, depending on the file diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index df785a5102..99a54a300b 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -958,9 +958,7 @@ void CheckModelCounts(const Model& model) { void MakeArrayDims(int num_dims, int batch, int height, int width, int depth, std::vector* out_dims) { CHECK(out_dims->empty()); - if (num_dims == 0) { - return; - } else if (num_dims == 1) { + if (num_dims == 1) { CHECK_EQ(batch, 1); *out_dims = {depth}; } else if (num_dims == 2) { @@ -992,13 +990,13 @@ void CreateOrCheckRnnStateArray(const string& name, int size, Model* model) { if (array.has_shape()) { num_dims = array.shape().dimensions_count(); } + std::vector dims; + MakeArrayDims(num_dims, batch, 1, 1, size, &dims); CHECK(array.data_type == ArrayDataType::kFloat || array.data_type == ArrayDataType::kNone); array.data_type = ArrayDataType::kFloat; - if (!array.has_shape() && num_dims >= 0) { + if (!array.has_shape()) { Shape* shape = array.mutable_shape(); - std::vector dims; - MakeArrayDims(num_dims, batch, 1, 1, size, &dims); *shape->mutable_dims() = dims; } } @@ -1187,6 +1185,9 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { } // Creation of the RNN state arrays for (const auto& rnn_state : model->flags.rnn_states()) { + if (!rnn_state.manually_create()) { + continue; + } CreateOrCheckRnnStateArray(rnn_state.state_array(), rnn_state.size(), model); } -- GitLab From 9acbe517b5fa5d3a88770c7a02450d755691beda Mon Sep 17 00:00:00 2001 From: mktozk Date: Fri, 26 Jan 2018 02:01:37 +0900 Subject: [PATCH 041/423] Fix Conv3DTranspose in tf.keras (#16307) * Update convolutional.py Fix Conv3DTranspose * Update tensorflow.keras.layers.-conv3-d-transpose.pbtxt * Update tensorflow.keras.layers.-convolution3-d-transpose.pbtxt * Update convolutional.py * Fix compute_output_shape() in Conv3DTranspose * Fix compute_output_shape() in Conv3DTranspose * Update convolutional.py --- .../python/keras/_impl/keras/layers/convolutional.py | 2 +- tensorflow/python/layers/convolutional.py | 9 ++++++--- .../tensorflow.keras.layers.-conv3-d-transpose.pbtxt | 1 + ...nsorflow.keras.layers.-convolution3-d-transpose.pbtxt | 1 + 4 files changed, 9 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional.py b/tensorflow/python/keras/_impl/keras/layers/convolutional.py index 22496e8a76..f0f5e1fb46 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional.py @@ -563,7 +563,7 @@ class Conv2DTranspose(tf_convolutional_layers.Conv2DTranspose, Layer): return dict(list(base_config.items()) + list(config.items())) -class Conv3DTranspose(tf_convolutional_layers.Conv3D, Layer): +class Conv3DTranspose(tf_convolutional_layers.Conv3DTranspose, Layer): """Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py index d5147b237b..e8dba3cea3 100644 --- a/tensorflow/python/layers/convolutional.py +++ b/tensorflow/python/layers/convolutional.py @@ -1904,6 +1904,7 @@ class Conv3DTranspose(Conv3D): dtype=self.dtype) else: self.bias = None + self.built = True def call(self, inputs): inputs_shape = array_ops.shape(inputs) @@ -1974,6 +1975,8 @@ class Conv3DTranspose(Conv3D): if self.use_bias: outputs_shape = outputs.shape.as_list() + if outputs_shape[0] is None: + outputs_shape[0] = -1 if self.data_format == 'channels_first': outputs_4d = array_ops.reshape(outputs, [ outputs_shape[0], outputs_shape[1], @@ -2007,11 +2010,11 @@ class Conv3DTranspose(Conv3D): output_shape[c_axis] = self.filters output_shape[d_axis] = utils.deconv_output_length( - output_shape[d_axis], stride_d, kernel_d, self.padding) + output_shape[d_axis], kernel_d, self.padding, stride_d) output_shape[h_axis] = utils.deconv_output_length( - output_shape[h_axis], stride_h, kernel_h, self.padding) + output_shape[h_axis], kernel_h, self.padding, stride_h) output_shape[w_axis] = utils.deconv_output_length( - output_shape[w_axis], stride_w, kernel_w, self.padding) + output_shape[w_axis], kernel_w, self.padding, stride_w) return tensor_shape.TensorShape(output_shape) diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt index d898c54627..11e05f884d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.keras.layers.Conv3DTranspose" tf_class { is_instance: "" + is_instance: "" is_instance: "" is_instance: "" is_instance: "" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt index a7001bbe34..58724a1e16 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt @@ -1,6 +1,7 @@ path: "tensorflow.keras.layers.Convolution3DTranspose" tf_class { is_instance: "" + is_instance: "" is_instance: "" is_instance: "" is_instance: "" -- GitLab From 76d621efecc63a08821c294390115ff5f96d47e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Kapica?= Date: Thu, 25 Jan 2018 18:02:17 +0100 Subject: [PATCH 042/423] RGB<->YIQ colorspace conversion (#15555) * image colorspace conversion using tensordot op * fixed bug with assuming input tensor is 4D * fix * fix - push with all changes commited * fix tuple object in np.random.rand * updated goldens --- tensorflow/python/ops/image_ops.py | 4 + tensorflow/python/ops/image_ops_impl.py | 103 ++++++++++++++++++ tensorflow/python/ops/image_ops_test.py | 58 ++++++++++ .../tools/api/golden/tensorflow.image.pbtxt | 16 +++ 4 files changed, 181 insertions(+) diff --git a/tensorflow/python/ops/image_ops.py b/tensorflow/python/ops/image_ops.py index 3b0b5a978c..de12c5f63f 100644 --- a/tensorflow/python/ops/image_ops.py +++ b/tensorflow/python/ops/image_ops.py @@ -49,6 +49,10 @@ See the @{$python/image} guide. @@grayscale_to_rgb @@hsv_to_rgb @@rgb_to_hsv +@@rgb_to_yiq +@@yiq_to_rgb +@@rgb_to_yuv +@@yuv_to_rgb @@convert_image_dtype @@adjust_brightness @@random_brightness diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 9bd452155c..721efcf786 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1668,3 +1668,106 @@ def non_max_suppression(boxes, return gen_image_ops._non_max_suppression_v2(boxes, scores, max_output_size, iou_threshold) # pylint: enable=protected-access + + +_rgb_to_yiq_kernel = [[0.299, 0.59590059, 0.2115], + [0.587, -0.27455667, -0.52273617], + [0.114, -0.32134392, 0.31119955]] + + +def rgb_to_yiq(images): + """Converts one or more images from RGB to YIQ. + + Outputs a tensor of the same shape as the `images` tensor, containing the YIQ + value of the pixels. + The output is only well defined if the value in images are in [0,1]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor(_rgb_to_yiq_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) + + +_yiq_to_rgb_kernel = [[1, 1, 1], + [0.95598634, -0.27201283, -1.10674021], + [0.6208248, -0.64720424, 1.70423049]] + + +def yiq_to_rgb(images): + """Converts one or more images from YIQ to RGB. + + Outputs a tensor of the same shape as the `images` tensor, containing the RGB + value of the pixels. + The output is only well defined if the Y value in images are in [0,1], + I value are in [-0.5957,0.5957] and Q value are in [-0.5226,0.5226]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor(_yiq_to_rgb_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) + + +_rgb_to_yuv_kernel = [[0.299, -0.14714119, 0.61497538], + [0.587, -0.28886916, -0.51496512], + [0.114, 0.43601035, -0.10001026]] + + +def rgb_to_yuv(images): + """Converts one or more images from RGB to YUV. + + Outputs a tensor of the same shape as the `images` tensor, containing the YUV + value of the pixels. + The output is only well defined if the value in images are in [0,1]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor(_rgb_to_yuv_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) + + +_yuv_to_rgb_kernel = [[1, 1, 1], + [0, -0.394642334, 2.03206185], + [1.13988303, -0.58062185, 0]] + + +def yuv_to_rgb(images): + """Converts one or more images from YUV to RGB. + + Outputs a tensor of the same shape as the `images` tensor, containing the RGB + value of the pixels. + The output is only well defined if the Y value in images are in [0,1], + U and V value are in [-0.5,0.5]. + + Args: + images: 2-D or higher rank. Image data to convert. Last dimension must be + size 3. + + Returns: + images: tensor with the same shape as `images`. + """ + images = ops.convert_to_tensor(images, name='images') + kernel = ops.convert_to_tensor(_yuv_to_rgb_kernel, dtype=images.dtype, name='kernel') + ndims = images.get_shape().ndims + return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) + diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 0c5ed2150d..9834384634 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -85,6 +85,64 @@ class RGBToHSVTest(test_util.TensorFlowTestCase): self.assertAllClose(rgb_tf, rgb_np) +class RGBToYIQTest(test_util.TensorFlowTestCase): + + def testBatch(self): + # Build an arbitrary RGB image + np.random.seed(7) + batch_size = 5 + shape = (batch_size, 2, 7, 3) + + for nptype in [np.float32, np.float64]: + inp = np.random.rand(*shape).astype(nptype) + + # Convert to YIQ and back, as a batch and individually + with self.test_session(use_gpu=True) as sess: + batch0 = constant_op.constant(inp) + batch1 = image_ops.rgb_to_yiq(batch0) + batch2 = image_ops.yiq_to_rgb(batch1) + split0 = array_ops.unstack(batch0) + split1 = list(map(image_ops.rgb_to_yiq, split0)) + split2 = list(map(image_ops.yiq_to_rgb, split1)) + join1 = array_ops.stack(split1) + join2 = array_ops.stack(split2) + batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2]) + + # Verify that processing batch elements together is the same as separate + self.assertAllClose(batch1, join1, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, join2, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, inp, rtol=1e-4, atol=1e-4) + + +class RGBToYUVTest(test_util.TensorFlowTestCase): + + def testBatch(self): + # Build an arbitrary RGB image + np.random.seed(7) + batch_size = 5 + shape = (batch_size, 2, 7, 3) + + for nptype in [np.float32, np.float64]: + inp = np.random.rand(*shape).astype(nptype) + + # Convert to YUV and back, as a batch and individually + with self.test_session(use_gpu=True) as sess: + batch0 = constant_op.constant(inp) + batch1 = image_ops.rgb_to_yuv(batch0) + batch2 = image_ops.yuv_to_rgb(batch1) + split0 = array_ops.unstack(batch0) + split1 = list(map(image_ops.rgb_to_yuv, split0)) + split2 = list(map(image_ops.yuv_to_rgb, split1)) + join1 = array_ops.stack(split1) + join2 = array_ops.stack(split2) + batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2]) + + # Verify that processing batch elements together is the same as separate + self.assertAllClose(batch1, join1, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, join2, rtol=1e-4, atol=1e-4) + self.assertAllClose(batch2, inp, rtol=1e-4, atol=1e-4) + + class GrayscaleToRGBTest(test_util.TensorFlowTestCase): def _RGBToGrayscale(self, images): diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 441621a2a0..baedf596e8 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -168,6 +168,14 @@ tf_module { name: "rgb_to_hsv" argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "rgb_to_yiq" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "rgb_to_yuv" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "rot90" argspec: "args=[\'image\', \'k\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\'], " @@ -184,4 +192,12 @@ tf_module { name: "transpose_image" argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "yiq_to_rgb" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "yuv_to_rgb" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } } -- GitLab From bab793233e42b7654771769d4d4a7974b432883c Mon Sep 17 00:00:00 2001 From: Yunxing Dai Date: Thu, 25 Jan 2018 09:32:22 -0800 Subject: [PATCH 043/423] Add shard count to select and scatter tests RELNOTES: n/a PiperOrigin-RevId: 183250076 --- tensorflow/compiler/xla/tests/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 3afd52b6b2..02ad9d982f 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -1034,6 +1034,7 @@ xla_test( name = "select_and_scatter_test", timeout = "long", srcs = ["select_and_scatter_test.cc"], + shard_count = 40, tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:array2d", -- GitLab From 03bb1c4a6014fdf3f10f301f093ec02d84f717c7 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Thu, 25 Jan 2018 09:34:11 -0800 Subject: [PATCH 044/423] Add functions to encapsulate the logic for checking and setting tensor type. PiperOrigin-RevId: 183250334 --- tensorflow/contrib/lite/kernels/kernel_util.h | 16 ++++++++++++++++ tensorflow/contrib/lite/kernels/pad.cc | 17 ++++++++--------- 2 files changed, 24 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h index 1cf30ecff9..bfdfba00f5 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.h +++ b/tensorflow/contrib/lite/kernels/kernel_util.h @@ -44,6 +44,22 @@ inline TfLiteTensor* GetOptionalInputTensor(TfLiteContext* context, return nullptr; } +// Determines whether tensor is constant. +inline bool IsConstantTensor(TfLiteTensor* tensor) { + return tensor->allocation_type == kTfLiteMmapRo; +} + +// Determines whether tensor is dynamic. Note that a tensor can be non-const and +// not dynamic. This function specificially checks for a dynamic tensor. +inline bool IsDynamicTensor(TfLiteTensor* tensor) { + return tensor->allocation_type == kTfLiteDynamic; +} + +// Sets tensor to dynamic. +inline void SetTensorToDynamic(TfLiteTensor* tensor) { + tensor->allocation_type = kTfLiteDynamic; +} + // Calculates the multiplication factor for a quantized convolution (or // quantized depthwise convolution) involving the given tensors. Returns an // error if the scales of the tensors are not compatible. diff --git a/tensorflow/contrib/lite/kernels/pad.cc b/tensorflow/contrib/lite/kernels/pad.cc index 569bf0fe8f..4003ed10df 100644 --- a/tensorflow/contrib/lite/kernels/pad.cc +++ b/tensorflow/contrib/lite/kernels/pad.cc @@ -51,17 +51,14 @@ struct PadContext { // paddings data is present. TfLiteStatus ResizeOutputTensor(TfLiteContext* context, PadContext* op_context) { - // TODO(nupurgarg): Our current implementations rely on the inputs being 4D. - TF_LITE_ENSURE_EQ(context, op_context->dims, 4); - // Ensures the paddings array is dims x 2. TF_LITE_ENSURE_EQ(context, SizeOfDimension(op_context->paddings, 0), op_context->dims); TF_LITE_ENSURE_EQ(context, SizeOfDimension(op_context->paddings, 1), 2); // Determines the size of the output tensor. - const TfLiteIntArray* input_size = op_context->input->dims; - TfLiteIntArray* output_size = TfLiteIntArrayCreate(op_context->dims); + TfLiteIntArray* input_size = op_context->input->dims; + TfLiteIntArray* output_size = TfLiteIntArrayCopy(input_size); const int32* paddings_data = GetTensorData(op_context->paddings); for (int idx = 0; idx < op_context->dims; ++idx) { @@ -85,11 +82,13 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { PadContext op_context(context, node); TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); - // TODO(nupurgarg): Create wrapper functions for dynamic tensor logic. + // TODO(nupurgarg): Our current implementations rely on the inputs being 4D. + TF_LITE_ENSURE_EQ(context, op_context.dims, 4); + // Exit early if paddings is a non-const tensor. Set output tensor to // dynamic so output size can be determined in Eval. - if (op_context.paddings->allocation_type != kTfLiteMmapRo) { - op_context.output->allocation_type = kTfLiteDynamic; + if (!IsConstantTensor(op_context.paddings)) { + SetTensorToDynamic(op_context.output); return kTfLiteOk; } return ResizeOutputTensor(context, &op_context); @@ -100,7 +99,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { PadContext op_context(context, node); // Resize the output tensor if the output tensor is dynamic. - if (op_context.output->allocation_type == kTfLiteDynamic) { + if (IsDynamicTensor(op_context.output)) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); TfLiteTensorRealloc(op_context.output->bytes, op_context.output); } -- GitLab From ee8b13791a921964cad1fc699e72ff1f738b704f Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 25 Jan 2018 09:42:55 -0800 Subject: [PATCH 045/423] Improve SkipNBytes to RandomInputStream (#14557) * Convert SkipNBytes in InputStreamInterface to pure virtual This fix tries to address the issue raised in 14512 where SkipNBytes will takes a long time as it needs to read through the file even though it is possible to random access. This fix convert SkipNBytes in InputStreamInterface to pure virtual so that it is possible to have different implementations based on the underlying file type (seekable vs. no seekable) This fix fixes 14512. Signed-off-by: Yong Tang * Add missing implementation in inputstream_interface_test.cc Signed-off-by: Yong Tang * Update implementation of RandomInputStream So that if random access read of 1 byte to the expected pos is able to complete then return immediately. Otherwise, read through until the pos is reached. Signed-off-by: Yong Tang * Add SkipNBytes to SnappyInputBuffer Signed-off-by: Yong Tang * Remove inputstream_interface.cc from makefile Signed-off-by: Yong Tang * Remove pure virtual and use override for RandomInputStream Signed-off-by: Yong Tang --- tensorflow/core/lib/io/random_inputstream.cc | 37 ++++++++++++++++++++ tensorflow/core/lib/io/random_inputstream.h | 2 ++ 2 files changed, 39 insertions(+) diff --git a/tensorflow/core/lib/io/random_inputstream.cc b/tensorflow/core/lib/io/random_inputstream.cc index 8b8c1392a1..09336e79cd 100644 --- a/tensorflow/core/lib/io/random_inputstream.cc +++ b/tensorflow/core/lib/io/random_inputstream.cc @@ -57,6 +57,43 @@ Status RandomAccessInputStream::ReadNBytes(int64 bytes_to_read, return Status::OK(); } +// To limit memory usage, the default implementation of SkipNBytes() only reads +// 8MB at a time. +static constexpr int64 kMaxSkipSize = 8 * 1024 * 1024; + +Status RandomAccessInputStream::SkipNBytes(int64 bytes_to_skip) { + if (bytes_to_skip < 0) { + return errors::InvalidArgument("Can't skip a negative number of bytes"); + } + std::unique_ptr scratch(new char[kMaxSkipSize]); + // Try to read 1 bytes first, if we could complete the read then EOF is + // not reached yet and we could return. + if (bytes_to_skip > 0) { + StringPiece data; + Status s = file_->Read(pos_ + bytes_to_skip - 1, 1, &data, scratch.get()); + if ((s.ok() || errors::IsOutOfRange(s)) && data.size() == 1) { + pos_ += bytes_to_skip; + return Status::OK(); + } + } + // Read kDefaultSkipSize at a time till bytes_to_skip. + while (bytes_to_skip > 0) { + int64 bytes_to_read = std::min(kMaxSkipSize, bytes_to_skip); + StringPiece data; + Status s = file_->Read(pos_, bytes_to_read, &data, scratch.get()); + if (s.ok() || errors::IsOutOfRange(s)) { + pos_ += data.size(); + } else { + return s; + } + if (data.size() < bytes_to_read) { + return errors::OutOfRange("reached end of file"); + } + bytes_to_skip -= bytes_to_read; + } + return Status::OK(); +} + int64 RandomAccessInputStream::Tell() const { return pos_; } } // namespace io diff --git a/tensorflow/core/lib/io/random_inputstream.h b/tensorflow/core/lib/io/random_inputstream.h index 09ebe9ba49..bdbdbd71ff 100644 --- a/tensorflow/core/lib/io/random_inputstream.h +++ b/tensorflow/core/lib/io/random_inputstream.h @@ -34,6 +34,8 @@ class RandomAccessInputStream : public InputStreamInterface { Status ReadNBytes(int64 bytes_to_read, string* result) override; + Status SkipNBytes(int64 bytes_to_skip) override; + int64 Tell() const override; Status Seek(int64 position) { -- GitLab From e487c1d00559cb0af036e0ab981f6eca065217bd Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Thu, 25 Jan 2018 09:44:04 -0800 Subject: [PATCH 046/423] [TF RNN] Ensure dynamic_rnn runs at least one step to propagate dynamic shape info. PiperOrigin-RevId: 183251689 --- tensorflow/python/ops/rnn.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index a1008f1c83..a10e1963d1 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -812,7 +812,10 @@ def _dynamic_rnn_loop(cell, return (time + 1, output_ta_t, new_state) if in_graph_mode: - loop_bound = max_sequence_length + # Make sure that we run at least 1 step, if necessary, to ensure + # the TensorArrays pick up the dynamic shape. + loop_bound = math_ops.minimum( + time_steps, math_ops.maximum(1, max_sequence_length)) else: # Using max_sequence_length isn't currently supported in the Eager branch. loop_bound = time_steps -- GitLab From f8adc061e8c1134082e8716725c7f1899917d340 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Thu, 25 Jan 2018 09:53:10 -0800 Subject: [PATCH 047/423] Surface error if input is not flat list of eager tensors PiperOrigin-RevId: 183253112 --- tensorflow/python/eager/pywrap_tfe_src.cc | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 6162644036..647f03351d 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -766,6 +766,9 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors, return; } std::vector input_ids = MakeTensorIDList(input_tensors); + if (PyErr_Occurred()) { + return; + } std::vector output_info; PyObject* seq = PySequence_Fast(output_tensors, "expected a sequence of integer tensor ids"); -- GitLab From f492fac8e31f15b9529d20f9787cafc888669fbc Mon Sep 17 00:00:00 2001 From: Sam Matzek Date: Thu, 25 Jan 2018 12:39:31 -0600 Subject: [PATCH 048/423] Build libjpeg-turbo ALTIVEC SIMD (#16409) The libjpeg-turbo package has ALTIVEC SIMD and this updates the third_party build to build the ALTIVEC SIMD on the appropriate platform. --- third_party/jpeg/jpeg.BUILD | 51 +++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 37924125cf..ca2d38d687 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -34,6 +34,10 @@ libjpegturbo_copts = select({ "-mfloat-abi=softfp", "-fprefetch-loop-arrays", ], + ":linux_ppc64le": [ + "-mcpu=power8", + "-mtune=power8", + ], "//conditions:default": [], }) @@ -123,10 +127,50 @@ cc_library( ":k8": [":simd_x86_64"], ":armeabi-v7a": [":simd_armv7a"], ":arm64-v8a": [":simd_armv8a"], + ":linux_ppc64le": [":simd_altivec"], "//conditions:default": [":simd_none"], }), ) +cc_library( + name = "simd_altivec", + srcs = [ + "jchuff.h", + "jconfig.h", + "jdct.h", + "jerror.h", + "jinclude.h", + "jmorecfg.h", + "jpegint.h", + "jpeglib.h", + "jsimd.h", + "jsimddct.h", + "simd/jsimd.h", + "simd/jccolor-altivec.c", + "simd/jcgray-altivec.c", + "simd/jcsample-altivec.c", + "simd/jdcolor-altivec.c", + "simd/jdmerge-altivec.c", + "simd/jdsample-altivec.c", + "simd/jfdctfst-altivec.c", + "simd/jfdctint-altivec.c", + "simd/jidctfst-altivec.c", + "simd/jidctint-altivec.c", + "simd/jquanti-altivec.c", + "simd/jsimd_powerpc.c", + "simd/jsimd_altivec.h", + "simd/jcsample.h", + ], + hdrs = [ + "simd/jdmrgext-altivec.c", # should have been named .inc + "simd/jccolext-altivec.c", # should have been named .inc + "simd/jcgryext-altivec.c", # should have been named .inc + "simd/jdcolext-altivec.c", # should have been named .inc + ], + copts = libjpegturbo_copts, + nocopts = libjpegturbo_nocopts, +) + cc_library( name = "simd_x86_64", srcs = [ @@ -381,6 +425,7 @@ genrule( ":k8": "cp $(location jconfig_nowin_simd.h) $@", ":armeabi-v7a": "cp $(location jconfig_nowin_simd.h) $@", ":arm64-v8a": "cp $(location jconfig_nowin_simd.h) $@", + ":linux_ppc64le": "cp $(location jconfig_nowin_simd.h) $@", "//conditions:default": "cp $(location jconfig_nowin_nosimd.h) $@", }), ) @@ -498,3 +543,9 @@ config_setting( name = "windows_msvc", values = {"cpu": "x64_windows_msvc"}, ) + +config_setting( + name = "linux_ppc64le", + values = {"cpu": "ppc"}, + +) -- GitLab From 949dd29d3a8bdc21328c9e94721b344310686eab Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 10:36:25 -0800 Subject: [PATCH 049/423] Simplify the template mechanism by specifying templates using multi-line strings instead of functions. This loses the syntax verification on templates, but it avoids the clutter of lint overrides and the duplication of parameter names, so things are more readable. Addresses #16318 PiperOrigin-RevId: 183260854 --- .../converters/break_canonicalization.py | 26 ++--- .../py2tf/converters/builtin_functions.py | 7 +- .../contrib/py2tf/converters/call_trees.py | 19 +--- .../converters/continue_canonicalization.py | 22 ++--- .../contrib/py2tf/converters/control_flow.py | 96 +++++++------------ .../py2tf/converters/for_canonicalization.py | 28 +++--- .../py2tf/converters/side_effect_guards.py | 26 ++--- tensorflow/contrib/py2tf/pyct/templates.py | 55 ++++++----- .../contrib/py2tf/pyct/templates_test.py | 36 ++++--- 9 files changed, 132 insertions(+), 183 deletions(-) diff --git a/tensorflow/contrib/py2tf/converters/break_canonicalization.py b/tensorflow/contrib/py2tf/converters/break_canonicalization.py index ef58573445..2ae65e3007 100644 --- a/tensorflow/contrib/py2tf/converters/break_canonicalization.py +++ b/tensorflow/contrib/py2tf/converters/break_canonicalization.py @@ -33,31 +33,25 @@ class BreakCanonicalizationTransformer(gast.NodeTransformer): self.break_uses = [] def _create_break_check(self): - - def template(var_name): - (not var_name) # pylint:disable=pointless-statement - - expr, = templates.replace( - template, var_name=gast.Name(self.break_uses[-1][1], None, None)) + template = """ + (not var_name) + """ + expr, = templates.replace(template, var_name=self.break_uses[-1][1]) return expr.value def _create_break_trigger(self): - - def template(var_name): # pylint:disable=unused-argument + template = """ var_name = True - - block = templates.replace( - template, var_name=gast.Name(self.break_uses[-1][1], None, None)) + """ + block = templates.replace(template, var_name=self.break_uses[-1][1]) block.append(gast.Continue()) return block def _create_break_init(self): - - def template(var_name): # pylint:disable=unused-argument + template = """ var_name = False - - assign, = templates.replace( - template, var_name=gast.Name(self.break_uses[-1][1], None, None)) + """ + assign, = templates.replace(template, var_name=self.break_uses[-1][1]) return assign # TODO(mdan): Surely the transformer supports this better? diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions.py b/tensorflow/contrib/py2tf/converters/builtin_functions.py index b80c96c97a..7f6b64a34c 100644 --- a/tensorflow/contrib/py2tf/converters/builtin_functions.py +++ b/tensorflow/contrib/py2tf/converters/builtin_functions.py @@ -29,10 +29,9 @@ class BuiltinFunctionTransformer(gast.NodeTransformer): # TODO(mdan): Bring print_functions in here. def _convert_len(self, node): - - def template(args): - tf.shape(args)[0] # pylint:disable=undefined-variable,expression-not-assigned - + template = """ + tf.shape(args)[0] + """ new_call = templates.replace(template, args=node.args)[0].value return new_call diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py index df071f596f..0aae030450 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees.py +++ b/tensorflow/contrib/py2tf/converters/call_trees.py @@ -151,7 +151,7 @@ class CallTreeTransformer(gast.NodeTransformer): else: new_name = self.namer.compiled_function_name( '__'.join(target_fqn), live_object=target_obj) - node.func = gast.Name(id=new_name, ctx=gast.Load(), annotation=None) + node.func = gast.Name(new_name, gast.Load(), None) return node def _rename_member_function_of_known_type(self, node): @@ -184,26 +184,17 @@ class CallTreeTransformer(gast.NodeTransformer): def _wrap_to_py_func_no_return(self, node): args_scope = anno.getanno(node, 'args_scope') # TODO(mdan): Properly handle varargs, kwargs, etc. - args = tuple(gast.Name(n, gast.Load(), None) for n in args_scope.used) - - # pylint:disable=undefined-variable,unused-argument,function-redefined - - def template(call, wrapper, args): - + template = """ def wrapper(args): call(args) return 1 - tf.py_func(wrapper, [args], [tf.int64]) - - # pylint:enable=undefined-variable,unused-argument,function-redefined - - wrapper_name = self.namer.compiled_function_name(node.func.id) + """ wrapper_def, call_expr = templates.replace( template, call=node.func, - wrapper=gast.Name(wrapper_name, gast.Load(), None), - args=args) + wrapper=self.namer.compiled_function_name(node.func.id), + args=tuple(gast.Name(n, gast.Load(), None) for n in args_scope.used)) anno.setanno(call_expr.value, 'args_scope', args_scope) # TODO(mdan): Rename this annotation to 'graph_ready' anno.setanno(wrapper_def, 'skip_processing', True) diff --git a/tensorflow/contrib/py2tf/converters/continue_canonicalization.py b/tensorflow/contrib/py2tf/converters/continue_canonicalization.py index 7f8ace77a8..486f0f6509 100644 --- a/tensorflow/contrib/py2tf/converters/continue_canonicalization.py +++ b/tensorflow/contrib/py2tf/converters/continue_canonicalization.py @@ -33,32 +33,28 @@ class ContinueCanonicalizationTransformer(gast.NodeTransformer): self.continuation_uses = [] def _create_continuation_check(self): - - def template(var_name): + template = """ if not var_name: pass - - cond, = templates.replace( - template, var_name=gast.Name(self.continuation_uses[-1][1], None, None)) + """ + cond, = templates.replace(template, var_name=self.continuation_uses[-1][1]) cond.body = [] return cond def _create_continuation_trigger(self): - - def template(var_name): # pylint:disable=unused-argument + template = """ var_name = True - + """ assign, = templates.replace( - template, var_name=gast.Name(self.continuation_uses[-1][1], None, None)) + template, var_name=self.continuation_uses[-1][1]) return assign def _create_continuation_init(self): - - def template(var_name): # pylint:disable=unused-argument + template = """ var_name = False - + """ assign, = templates.replace( - template, var_name=gast.Name(self.continuation_uses[-1][1], None, None)) + template, var_name=self.continuation_uses[-1][1]) return assign def _visit_and_reindent_if_necessary(self, nodes): diff --git a/tensorflow/contrib/py2tf/converters/control_flow.py b/tensorflow/contrib/py2tf/converters/control_flow.py index 8ebd9ad93d..a40c7b28f7 100644 --- a/tensorflow/contrib/py2tf/converters/control_flow.py +++ b/tensorflow/contrib/py2tf/converters/control_flow.py @@ -75,29 +75,6 @@ class ControlFlowTransformer(gast.NodeTransformer): raise ValueError( 'The else branch creates new symbols that the if branch does not.') - def template( # pylint:disable=missing-docstring - test, - body_name, - body, - orelse_name, - orelse, - aliased, - aliases, # pylint:disable=unused-argument - aliased_results, - results): # pylint:disable=unused-argument - - def body_name(): # pylint:disable=function-redefined - aliases, = aliased, # pylint:disable=unused-variable - body # pylint:disable=pointless-statement - return (aliased_results,) - - def orelse_name(): # pylint:disable=function-redefined - aliases, = aliased, # pylint:disable=unused-variable - orelse # pylint:disable=pointless-statement - return (aliased_results,) - - results = tf.cond(test, body_name, orelse_name) # pylint:disable=undefined-variable - all_modified = tuple(body_scope.modified | orelse_scope.modified) all_referenced = body_scope.referenced | orelse_scope.referenced @@ -107,10 +84,10 @@ class ControlFlowTransformer(gast.NodeTransformer): need_alias = ( (body_scope.modified | orelse_scope.modified) - (body_scope.created | orelse_scope.created)) - aliased = tuple(need_alias) - aliases = tuple( - self.namer.new_symbol(s, all_referenced) for s in aliased) - alias_map = dict(zip(aliased, aliases)) + aliased_orig_names = tuple(need_alias) + aliased_new_names = tuple( + self.namer.new_symbol(s, all_referenced) for s in aliased_orig_names) + alias_map = dict(zip(aliased_orig_names, aliased_new_names)) node_body = node.body node_body = [SymbolRenamer(alias_map).visit(n) for n in node_body] node_orelse = node.orelse @@ -122,20 +99,29 @@ class ControlFlowTransformer(gast.NodeTransformer): results = gast.Tuple( tuple(gast.Name(s, None, None) for s in all_modified), None) + template = """ + def body_name(): + aliased_new_names, = aliased_orig_names, + body + return (all_results,) + def orelse_name(): + aliased_new_names, = aliased_orig_names, + orelse + return (all_results,) + results = tf.cond(test, body_name, orelse_name) + """ + body_name = self.namer.new_symbol('if_true', all_referenced) return templates.replace( template, test=node.test, - body_name=gast.Name( - self.namer.new_symbol('if_true', all_referenced), None, None), + body_name=body_name, body=node_body, - orelse_name=gast.Name( - self.namer.new_symbol('if_false', all_referenced), None, None), + orelse_name=self.namer.new_symbol('if_false', all_referenced), orelse=node_orelse, - aliased=tuple(gast.Name(s, None, None) for s in aliased), - aliases=tuple(gast.Name(s, None, None) for s in aliases), - aliased_results=tuple( - gast.Name(alias_map[s] if s in aliased else s, None, None) - for s in all_modified), + aliased_orig_names=tuple(aliased_orig_names), + aliased_new_names=tuple(aliased_new_names), + all_results=tuple(alias_map[s] if s in aliased_orig_names else s + for s in all_modified), results=results) def visit_While(self, node): @@ -144,38 +130,28 @@ class ControlFlowTransformer(gast.NodeTransformer): body_scope = anno.getanno(node, 'body_scope') body_closure = tuple(body_scope.modified - body_scope.created) - def template( - state, # pylint:disable=unused-argument - state_ast_tuple, # pylint:disable=unused-argument - test_name, - test, # pylint:disable=unused-argument - body_name, - body): - - def test_name(state): # pylint:disable=function-redefined,unused-argument - return test - - def body_name(state): # pylint:disable=function-redefined,unused-argument - body # pylint:disable=pointless-statement - return state, - - state_ast_tuple = tf.while_loop(test_name, body_name, [state]) # pylint:disable=undefined-variable - - test_name = self.namer.new_symbol('loop_test', body_scope.referenced) - body_name = self.namer.new_symbol('loop_body', body_scope.referenced) if len(body_closure) == 1: - state = gast.Name(body_closure[0], None, None) + state = body_closure[0] state_ast_tuple = state else: - state = tuple(gast.Name(n, None, None) for n in body_closure) - state_ast_tuple = gast.Tuple(state, None) + state = tuple(body_closure) + state_ast_tuple = gast.Tuple( + tuple(gast.Name(n, None, None) for n in state), None) + template = """ + def test_name(state): + return test + def body_name(state): + body + return state, + state_ast_tuple = tf.while_loop(test_name, body_name, [state]) + """ node = templates.replace( template, state=state, state_ast_tuple=state_ast_tuple, - test_name=gast.Name(test_name, gast.Load(), None), + test_name=self.namer.new_symbol('loop_test', body_scope.referenced), test=node.test, - body_name=gast.Name(body_name, gast.Load(), None), + body_name=self.namer.new_symbol('loop_body', body_scope.referenced), body=node.body) return node diff --git a/tensorflow/contrib/py2tf/converters/for_canonicalization.py b/tensorflow/contrib/py2tf/converters/for_canonicalization.py index 52360789cd..c284689b90 100644 --- a/tensorflow/contrib/py2tf/converters/for_canonicalization.py +++ b/tensorflow/contrib/py2tf/converters/for_canonicalization.py @@ -42,46 +42,40 @@ class ForLoopCanonicalizationTransformer(gast.NodeTransformer): # Or maybe we should replace range with tf.range? if anno.hasanno(node, 'extra_cond'): - - def template(loop_iter, target, body, i, n, extra_cond): # pylint:disable=unused-argument + template = """ i = 0 - n = len(loop_iter) # pylint:disable=undefined-variable + n = len(loop_iter) while i < n and extra_cond: # TODO(mdan): Use TensorListFromTensor(loop_iter) here. target = loop_iter[i] - body # pylint:disable=pointless-statement + body i += 1 - + """ return templates.replace( template, loop_iter=node.iter, target=node.target, body=node.body, - i=gast.Name( - self.namer.new_symbol('i', body_scope.referenced), None, None), - n=gast.Name( - self.namer.new_symbol('n', body_scope.referenced), None, None), + i=self.namer.new_symbol('i', body_scope.referenced), + n=self.namer.new_symbol('n', body_scope.referenced), extra_cond=anno.getanno(node, 'extra_cond')) else: - - def template(loop_iter, target, body, i, n): # pylint:disable=unused-argument + template = """ i = 0 - n = len(loop_iter) # pylint:disable=undefined-variable + n = len(loop_iter) while i < n: # TODO(mdan): Use TensorListFromTensor(loop_iter) here. target = loop_iter[i] body # pylint:disable=pointless-statement i += 1 - + """ return templates.replace( template, loop_iter=node.iter, target=node.target, body=node.body, - i=gast.Name( - self.namer.new_symbol('i', body_scope.referenced), None, None), - n=gast.Name( - self.namer.new_symbol('n', body_scope.referenced), None, None)) + i=self.namer.new_symbol('i', body_scope.referenced), + n=self.namer.new_symbol('n', body_scope.referenced)) def visit_Continue(self, node): assert False, 'continue statement should be desugared at this point' diff --git a/tensorflow/contrib/py2tf/converters/side_effect_guards.py b/tensorflow/contrib/py2tf/converters/side_effect_guards.py index 1f25303fba..a88828ff80 100644 --- a/tensorflow/contrib/py2tf/converters/side_effect_guards.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards.py @@ -94,12 +94,10 @@ class SideEffectGuardTransformer(gast.NodeTransformer): return node def _gate_symbols(self, guard_statement, guarded_args): - - def template(args): # pylint:disable=unused-argument - (args,) = (tf.identity(a) for a in (args,)) # pylint:disable=undefined-variable - - guards = templates.replace( - template, args=tuple(gast.Name(a, None, None) for a in guarded_args)) + template = """ + (args,) = (tf.identity(a) for a in (args,)) + """ + guards = templates.replace(template, args=tuple(guarded_args)) guard_statement.body.extend(guards) return guard_statement @@ -110,29 +108,25 @@ class SideEffectGuardTransformer(gast.NodeTransformer): # opt.minimize(loss) # or: # tf.py_func(...) - args_scope = anno.getanno(node.value, 'args_scope') temp_name = self.namer.new_symbol('temp', args_scope.parent.referenced) # TODO(mdan): Unsafe reference modification! args_scope.mark_write(temp_name) - - def template(call, temp_result): + template = """ temp_result = call if temp_result is not None: if not isinstance(temp_result, (list, tuple)): temp_result = (temp_result,) - ctx = tf.control_dependencies(temp_result) # pylint:disable=undefined-variable + ctx = tf.control_dependencies(temp_result) else: - ctx = contextmanager(lambda: (yield))() # pylint:disable=undefined-variable + ctx = contextmanager(lambda: (yield))() with ctx: # TODO(mdan): Also insert ops to re-fetch if variables are involved. pass # Will be removed below. - - # TODO(mdan): This is brittle. Reorganize this mechanism. + """ + # TODO(mdan): This is brittle. Reorganize the mechanism. statements = templates.replace( - template, - call=node.value, - temp_result=gast.Name(temp_name, None, None)) + template, call=node.value, temp_result=temp_name) control_deps_guard = statements[-1] control_deps_guard.body = [] diff --git a/tensorflow/contrib/py2tf/pyct/templates.py b/tensorflow/contrib/py2tf/pyct/templates.py index 4fadc793e6..77c5fbe02a 100644 --- a/tensorflow/contrib/py2tf/pyct/templates.py +++ b/tensorflow/contrib/py2tf/pyct/templates.py @@ -80,37 +80,46 @@ class ReplaceTransformer(gast.NodeTransformer): return node +def _strings_to_names(n): + if isinstance(n, str): + # Note: the node will receive the ctx value from the template, see + # ReplaceTransformer.visit_Name. + return gast.Name(id=n, ctx=None, annotation=None) + if isinstance(n, list): + return [_strings_to_names(e) for e in n] + if isinstance(n, tuple): + return tuple(_strings_to_names(e) for e in n) + return n + + def replace(template, **replacements): """Replace placeholders in a Python template. + AST Name and Tuple nodes always receive the context that inferred from + the template. However, when replacing more complex nodes (that can potentially + contain Name children), then the caller is responsible for setting the + appropriate context. + Args: - template: A function to be used as a template. Any placeholder is expected - to also be a function argument. + template: A string representing Python code. Any symbol name can be used + that appears in the template code can be used as placeholder. **replacements: A mapping from placeholder names to (lists of) AST nodes - that these placeholders will be replaced by. + that these placeholders will be replaced by. String values are also + supported as a shorthand for AST Name nodes with the respective ID. Returns: - body: An AST node or list of AST nodes with the replacements made. If the - template was a function, a list will be returned. If the template was a - node, the same node will be returned. If the template was a string, an - AST node will be returned (a `Module` node in the case of a multi-line - string, an `Expr` node otherwise). + An AST node or list of AST nodes with the replacements made. If the + template was a function, a list will be returned. If the template was a + node, the same node will be returned. If the template was a string, an + AST node will be returned (a `Module` node in the case of a multi-line + string, an `Expr` node otherwise). Raises: - ValueError: If a function is used as a template and an incorrect set of - replacements was passed. + ValueError: if the arguments are incorrect. """ - tree = parser.parse_object(template).body[0] - placeholders = set(arg.id for arg in tree.args.args) - tree.args.args = [] - if tree.args.vararg: - placeholders.add(tree.args.vararg) - tree.args.vararg = None - if set(replacements.keys()) != placeholders: - raise ValueError( - 'too many or few replacements. replacements: %s; placeholders: %s' % - (replacements.keys(), placeholders)) - - # Perform the replacement, stripping the function into which the template was - # wrapped. + if not isinstance(template, str): + raise ValueError('Expected string template, got %s' % type(template)) + tree = parser.parse_str(template) + for k in replacements: + replacements[k] = _strings_to_names(replacements[k]) return ReplaceTransformer(replacements).visit(tree).body diff --git a/tensorflow/contrib/py2tf/pyct/templates_test.py b/tensorflow/contrib/py2tf/pyct/templates_test.py index 2ad8b9317b..1143131283 100644 --- a/tensorflow/contrib/py2tf/pyct/templates_test.py +++ b/tensorflow/contrib/py2tf/pyct/templates_test.py @@ -28,46 +28,42 @@ from tensorflow.python.platform import test class TemplatesTest(test.TestCase): def test_replace_variable(self): - def template(a): # pylint:disable=unused-argument - def test_fn(a): # pylint:disable=unused-variable + template = """ + def test_fn(a): a += 1 a = 2 * a + 1 - return b # pylint:disable=undefined-variable + return b + """ - node = templates.replace( - template, a=gast.Name('b', gast.Load(), None))[0] + node = templates.replace(template, a='b')[0] result = compiler.ast_to_object(node) self.assertEquals(7, result.test_fn(2)) def test_replace_function_name(self): - def template(fname): # pylint:disable=unused-argument - def fname(a): # pylint:disable=function-redefined + template = """ + def fname(a): a += 1 a = 2 * a + 1 return a + """ - node = templates.replace( - template, fname=gast.Name('test_fn', gast.Load(), None))[0] + node = templates.replace(template, fname='test_fn')[0] result = compiler.ast_to_object(node) self.assertEquals(7, result.test_fn(2)) def test_code_block(self): - def template(block): # pylint:disable=unused-argument - def test_fn(a): # pylint:disable=unused-variable - block # pylint:disable=pointless-statement + template = """ + def test_fn(a): + block return a + """ node = templates.replace( template, block=[ - gast.Assign( - [ - gast.Name('a', gast.Store(), None) - ], - gast.BinOp( - gast.Name('a', gast.Load(), None), - gast.Add(), - gast.Num(1))), + gast.Assign([ + gast.Name('a', None, None) + ], gast.BinOp(gast.Name('a', None, None), gast.Add(), gast.Num(1))), ] * 2)[0] result = compiler.ast_to_object(node) self.assertEquals(3, result.test_fn(1)) -- GitLab From dcb918a8f64790c615d1ec018b7b6e141a6a8653 Mon Sep 17 00:00:00 2001 From: Jerome Date: Fri, 26 Jan 2018 02:41:30 +0800 Subject: [PATCH 050/423] Modified Implementation of ndlstm_base_dynamic. (#16402) * Added ctc_loss_dense_labels. This does the conversion of dense labels into sparse ones to be passed into the core ctc_loss function. * Removed constant_op from the import. * Matched ctc_loss_dense_labels with the other layers ops. * Added ctc_loss_dense_labels to contrib.layers __init__.py file * Added missing comma to list of ops. * Reordred arguments for ctc_loss_dense_labels Labels should be first then inputs for ctc_loss. * Removed ctc_loss_dense_labels. Replaced it with dense_to_sparse instead so that there'll be only one ctc_loss function. * Replaced ctc_loss_dense_labels with dense_to_sparse * Fixed dense_to_sparse. Some of the names of the variables did not match with that of the parameters. * Updated documentation for dense_to_sparse since it can accept a tensor of any shape. * Added test case for dense_to_sparse. * Updated documentation. Dense to sparse accepts int tensors. * Fixed testDenseFromConstantToSparse. The sparse_to_dense order of arguments in the test are wrong and the expected constant should be of int64. * Modified implementation of ndlstm_base_dynamic. It now uses a BasicLSTMCell that has state_is_tuple=True to address deprecation. Right now it is still unknown why it was set to false in the first place. --- tensorflow/contrib/ndlstm/python/lstm1d.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/ndlstm/python/lstm1d.py b/tensorflow/contrib/ndlstm/python/lstm1d.py index d3c3531f40..b24e332e4a 100644 --- a/tensorflow/contrib/ndlstm/python/lstm1d.py +++ b/tensorflow/contrib/ndlstm/python/lstm1d.py @@ -22,7 +22,6 @@ from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.framework.python.ops import variables from tensorflow.python.framework import constant_op from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import rnn @@ -85,18 +84,11 @@ def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False): Output sequence (length, batch_size, noutput) """ with variable_scope.variable_scope(scope, "SeqLstm", [inputs]): - # TODO(tmb) make batch size, sequence_length dynamic - # example: sequence_length = tf.shape(inputs)[0] - _, batch_size, _ = _shape(inputs) - lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False) - state = array_ops.zeros([batch_size, lstm_cell.state_size]) - sequence_length = int(inputs.get_shape()[0]) - sequence_lengths = math_ops.to_int64( - array_ops.fill([batch_size], sequence_length)) + lstm_cell = rnn_cell.BasicLSTMCell(noutput) if reverse: inputs = array_ops.reverse_v2(inputs, [0]) outputs, _ = rnn.dynamic_rnn( - lstm_cell, inputs, sequence_lengths, state, time_major=True) + lstm_cell, inputs, time_major=True, dtype=inputs.dtype) if reverse: outputs = array_ops.reverse_v2(outputs, [0]) return outputs -- GitLab From ab9ff0047b10dbca2bf819d4656a59213f98184a Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Fri, 26 Jan 2018 03:52:06 +0900 Subject: [PATCH 051/423] fix typos (#16384) --- tensorflow/compiler/xla/client/computation_builder.h | 2 +- tensorflow/tools/docs/pretty_docs.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h index d82ba63e8a..ea4cdb7667 100644 --- a/tensorflow/compiler/xla/client/computation_builder.h +++ b/tensorflow/compiler/xla/client/computation_builder.h @@ -67,7 +67,7 @@ class ComputationBuilder { // 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 ClearOpMetdata. + // OpMetadata attached until a call to ClearOpMetadata. void SetOpMetadata(const OpMetadata& metadata) { metadata_ = metadata; } // Clears the HloMetadata state. diff --git a/tensorflow/tools/docs/pretty_docs.py b/tensorflow/tools/docs/pretty_docs.py index c033c16ae9..b5df633800 100644 --- a/tensorflow/tools/docs/pretty_docs.py +++ b/tensorflow/tools/docs/pretty_docs.py @@ -323,7 +323,7 @@ class _Metadata(object): """ def __init__(self, name): - """Creata a Metadata builder. + """Create a Metadata builder. Args: name: The name of the page being described by the Metadata block. -- GitLab From 5b8e8ce30c4f424b5a9c2906e2d95bc177181133 Mon Sep 17 00:00:00 2001 From: ted chang Date: Thu, 25 Jan 2018 11:05:08 -0800 Subject: [PATCH 052/423] Add additional argument to freeze_graph (#15906) --- tensorflow/python/tools/freeze_graph.py | 22 ++++++++++++++------ tensorflow/python/tools/freeze_graph_test.py | 3 ++- 2 files changed, 18 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py index 0ddf09260b..a2e86a1c43 100644 --- a/tensorflow/python/tools/freeze_graph.py +++ b/tensorflow/python/tools/freeze_graph.py @@ -72,7 +72,8 @@ def freeze_graph_with_def_protos(input_graph_def, variable_names_blacklist="", input_meta_graph_def=None, input_saved_model_dir=None, - saved_model_tags=None): + saved_model_tags=None, + checkpoint_version=saver_pb2.SaverDef.V2): """Converts all variables in a graph and checkpoint into constants.""" del restore_op_name, filename_tensor_name # Unused by updated loading code. @@ -100,7 +101,8 @@ def freeze_graph_with_def_protos(input_graph_def, _ = importer.import_graph_def(input_graph_def, name="") with session.Session() as sess: if input_saver_def: - saver = saver_lib.Saver(saver_def=input_saver_def) + saver = saver_lib.Saver(saver_def=input_saver_def, + write_version=checkpoint_version) saver.restore(sess, input_checkpoint) elif input_meta_graph_def: restorer = saver_lib.import_meta_graph( @@ -124,7 +126,8 @@ def freeze_graph_with_def_protos(input_graph_def, # 'global_step' or a similar housekeeping element) so skip it. continue var_list[key] = tensor - saver = saver_lib.Saver(var_list=var_list) + saver = saver_lib.Saver(var_list=var_list, + write_version=checkpoint_version) saver.restore(sess, input_checkpoint) if initializer_nodes: sess.run(initializer_nodes.split(",")) @@ -217,7 +220,8 @@ def freeze_graph(input_graph, variable_names_blacklist="", input_meta_graph=None, input_saved_model_dir=None, - saved_model_tags=tag_constants.SERVING): + saved_model_tags=tag_constants.SERVING, + checkpoint_version=saver_pb2.SaverDef.V2): """Converts all variables in a graph and checkpoint into constants.""" input_graph_def = None if input_saved_model_dir: @@ -236,7 +240,8 @@ def freeze_graph(input_graph, input_graph_def, input_saver_def, input_checkpoint, output_node_names, restore_op_name, filename_tensor_name, output_graph, clear_devices, initializer_nodes, variable_names_whitelist, variable_names_blacklist, - input_meta_graph_def, input_saved_model_dir, saved_model_tags.split(",")) + input_meta_graph_def, input_saved_model_dir, + saved_model_tags.split(","), checkpoint_version=checkpoint_version) def main(unused_args): @@ -246,7 +251,7 @@ def main(unused_args): FLAGS.output_graph, FLAGS.clear_devices, FLAGS.initializer_nodes, FLAGS.variable_names_whitelist, FLAGS.variable_names_blacklist, FLAGS.input_meta_graph, FLAGS.input_saved_model_dir, - FLAGS.saved_model_tags) + FLAGS.saved_model_tags, checkpoint_version=checkpoint_version) if __name__ == "__main__": @@ -267,6 +272,11 @@ if __name__ == "__main__": type=str, default="", help="TensorFlow variables file to load.") + parser.add_argument( + "--checkpoint_version", + type=int, + default=saver_pb2.SaverDef.V2, + help="Tensorflow variable file format") parser.add_argument( "--output_graph", type=str, diff --git a/tensorflow/python/tools/freeze_graph_test.py b/tensorflow/python/tools/freeze_graph_test.py index feeed7102c..342732465d 100644 --- a/tensorflow/python/tools/freeze_graph_test.py +++ b/tensorflow/python/tools/freeze_graph_test.py @@ -86,7 +86,8 @@ class FreezeGraphTest(test_util.TensorFlowTestCase): freeze_graph.freeze_graph( input_graph_path, input_saver_def_path, input_binary, checkpoint_path, output_node_names, restore_op_name, filename_tensor_name, - output_graph_path, clear_devices, "", "", input_meta_graph) + output_graph_path, clear_devices, "", "", input_meta_graph, + checkpoint_version=saver_write_version) # Now we make sure the variable is now a constant, and that the graph still # produces the expected result. -- GitLab From 2a16133061ba3f8fa60c0338cd629f2211f9b17d Mon Sep 17 00:00:00 2001 From: ted chang Date: Thu, 25 Jan 2018 11:16:27 -0800 Subject: [PATCH 053/423] Add checkpoint file prefix check (#14341) Additionally fix Two failed tests caused by the PR Fixes #9465 --- .../contrib/slim/python/slim/evaluation_test.py | 2 +- tensorflow/python/training/saver.py | 11 ++++++++--- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 870f504d10..f5a9299d26 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -236,7 +236,7 @@ class SingleEvaluationTest(test.TestCase): def _prepareCheckpoint(self, checkpoint_path): init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) - saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1) + saver = saver_lib.Saver() with self.test_session() as sess: sess.run(init_op) saver.save(sess, checkpoint_path) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 2c59b82ebe..4f3773c0fc 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1592,9 +1592,9 @@ class Saver(object): [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes). Returns: - A string: path prefix used for the checkpoint files. If the saver is - sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn' - is the number of shards created. + A string: path prefix used for the checkpoint files. If checkpoint + format is V1 and the saver is sharded, this string ends with: + '-?????-of-nnnnn' where 'nnnnn' is the number of shards created. If the saver is empty, returns None. Raises: @@ -1744,6 +1744,11 @@ class Saver(object): return if save_path is None: raise ValueError("Can't load save_path when it is None.") + if (os.path.isfile(save_path) and + self._write_version != saver_pb2.SaverDef.V1): + raise ValueError("The specified path: %s is a file." + " Please specify only the path prefix" + " to the checkpoint files." % save_path) logging.info("Restoring parameters from %s", save_path) if context.in_graph_mode(): sess.run(self.saver_def.restore_op_name, -- GitLab From a8c4e8d96de7c0978851a5f9718bbd6b8056d862 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 25 Jan 2018 11:17:08 -0800 Subject: [PATCH 054/423] [XLA] Make xla_hlo_profile_test less flaky Instead of relying on some oeprations always taking longer than others (and this appearing in a specific order in the rendered HLO profile), pick them out by opcode. PiperOrigin-RevId: 183268593 --- tensorflow/compiler/xla/BUILD | 1 + tensorflow/compiler/xla/map_util.h | 21 +++ tensorflow/compiler/xla/tests/BUILD | 1 + .../xla/tests/xla_hlo_profile_test.cc | 125 +++++++++++++----- 4 files changed, 112 insertions(+), 36 deletions(-) diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 438f1443f1..c22fd37129 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -182,6 +182,7 @@ cc_library( deps = [ ":status", ":status_macros", + ":statusor", ":types", ":xla_data_proto", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/map_util.h b/tensorflow/compiler/xla/map_util.h index 50659c1240..0ad0b91330 100644 --- a/tensorflow/compiler/xla/map_util.h +++ b/tensorflow/compiler/xla/map_util.h @@ -16,6 +16,11 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_MAP_UTIL_H_ #define TENSORFLOW_COMPILER_XLA_MAP_UTIL_H_ +#include +#include + +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -44,6 +49,22 @@ typename Collection::value_type::second_type& FindOrDie( return it->second; } +// Like FindOrDie but returns an error instead of dying if `key` is not in +// `container`. +template +StatusOr< + std::reference_wrapper> +MaybeFind(const Collection& collection, + const typename Collection::value_type::first_type& key) { + typename Collection::const_iterator it = collection.find(key); + if (it == collection.end()) { + std::ostringstream os; + os << key; + return NotFound("key not found: %s", os.str().c_str()); + } + return {it->second}; +} + // Inserts the key-value pair into the collection. Dies if key was already // present. template diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 02ad9d982f..ac11081699 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -351,6 +351,7 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/service:platform_util", diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 1d2f436194..9ad2a19853 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -19,12 +19,14 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/computation_builder.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/platform_util.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" #include "tensorflow/compiler/xla/tests/test_utils.h" #include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/platform/regexp.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/platform/types.h" @@ -32,6 +34,7 @@ limitations under the License. namespace xla { namespace { namespace se = ::perftools::gputools; +namespace gtl = ::tensorflow::gtl; class HloProfileTest : public ClientLibraryTestBase {}; @@ -43,39 +46,74 @@ struct ParsedProfileOutputLine { string trops; string bytes_per_sec; string bytes_per_cycle; - string name; + string opcode; }; -StatusOr ParseProfileOutputLine(const string& line, - bool expect_flops, - bool expect_trops) { +::testing::AssertionResult HasFlops( + const ParsedProfileOutputLine& parsed_line) { + if (RE2::FullMatch(parsed_line.flops, "[0-9.TGMk]+FLOP/s")) { + return ::testing::AssertionSuccess() + << "'flops' field present in " << parsed_line.opcode << ": '" + << parsed_line.flops << "'"; + } + + return ::testing::AssertionFailure() + << "'flops' field absent in " << parsed_line.opcode << ": '" + << parsed_line.flops << "'"; +} + +::testing::AssertionResult HasTrops( + const ParsedProfileOutputLine& parsed_line) { + if (RE2::FullMatch(parsed_line.trops, "[0-9.TGMk]+TROP/s")) { + return ::testing::AssertionSuccess() + << "'trops' field present in " << parsed_line.opcode << ": '" + << parsed_line.trops << "'"; + } + + return ::testing::AssertionFailure() + << "'trops' field absent in " << parsed_line.opcode << ": '" + << parsed_line.trops << "'"; +} + +Status ParseOneProfileOutputLine( + const string& line, bool expect_hlo, + gtl::FlatMap* parsed_results) { string separator = "[^:]*:: +"; string match_percentage = "\\d+\\.\\d\\d%"; string match_cycles = "(\\d+) cycles +\\( *(" + match_percentage + ")\\)"; string match_usecs = "([0-9.]+) usec"; - string match_flops = expect_flops ? "([0-9.TGMk]+)FLOP/s" : "()"; - string match_trops = expect_trops ? "([0-9.TGMk]+)TROP/s" : "()"; + string match_flops = "([^ ]+)"; + string match_trops = "([^ ]+)"; string match_bytes_per_sec = "([0-9.TGMKi]+)B/s"; string match_bytes_per_cycle = "([0-9.TGMKi]+)B/cycle"; + + // The underlined part is what we're trying to match with match_opcode: + // + // %dot33 = f32[256,256]{1,0} dot(...) + // ^^^ + + string match_opcode = + expect_hlo ? "%[^=]+= [^ ]+ ([^(]+)\\(.*" : "(\\[total\\])"; string regexp_pattern = tensorflow::strings::StrCat( " +", match_cycles, separator, match_usecs, separator, match_flops, separator, match_trops, separator, match_bytes_per_sec, separator, - match_bytes_per_cycle, separator, "(.*)"); + match_bytes_per_cycle, separator, match_opcode); - RE2 pattern(regexp_pattern); ParsedProfileOutputLine parsed_line; bool matched = RE2::FullMatch( - line, pattern, &parsed_line.cycles, &parsed_line.cycles_percentage, + line, regexp_pattern, &parsed_line.cycles, &parsed_line.cycles_percentage, &parsed_line.usec, &parsed_line.flops, &parsed_line.trops, &parsed_line.bytes_per_sec, &parsed_line.bytes_per_cycle, - &parsed_line.name); + &parsed_line.opcode); if (!matched) { return tensorflow::errors::InvalidArgument( "Input did not match regexp. Input: ", line, ", Regexp: ", regexp_pattern); } - return parsed_line; + InsertOrDie(parsed_results, parsed_line.opcode, parsed_line); + + return Status::OK(); } // Returns void so that we can ASSERT. @@ -148,7 +186,7 @@ XLA_TEST_F(HloProfileTest, DISABLED_ON_CPU_PARALLEL(ProfileSingleComputation)) { ClientLibrary::GetOrCreateLocalClient(platform)); ComputationBuilder builder(client, TestName()); - auto result = builder.Tanh(builder.Dot( + auto result = builder.Tanh(builder.Add( builder.Parameter(0, ShapeUtil::MakeShape(F32, {m, k}), "dot_lhs"), builder.Parameter(1, ShapeUtil::MakeShape(F32, {k, n}), "dot_rhs"))); @@ -161,31 +199,43 @@ XLA_TEST_F(HloProfileTest, DISABLED_ON_CPU_PARALLEL(ProfileSingleComputation)) { std::vector profile_output_lines = tensorflow::str_util::Split(profile_output, '\n'); - TF_ASSERT_OK_AND_ASSIGN( - ParsedProfileOutputLine total_profile, - ParseProfileOutputLine(profile_output_lines[1], /*expect_flops=*/true, - /*expect_trops=*/true)); + gtl::FlatMap parsed_profile_lines; - TF_ASSERT_OK_AND_ASSIGN( - ParsedProfileOutputLine dot_profile, - ParseProfileOutputLine(profile_output_lines[2], /*expect_flops=*/true, - /*expect_trops=*/false)); + TF_ASSERT_OK(ParseOneProfileOutputLine( + profile_output_lines[1], /*expect_hlo=*/false, &parsed_profile_lines)); - TF_ASSERT_OK_AND_ASSIGN( - ParsedProfileOutputLine tanh_profile, - ParseProfileOutputLine(profile_output_lines[3], /*expect_flops=*/false, - /*expect_trops=*/true)); + TF_ASSERT_OK(ParseOneProfileOutputLine( + profile_output_lines[2], /*expect_hlo=*/true, &parsed_profile_lines)); + + TF_ASSERT_OK(ParseOneProfileOutputLine( + profile_output_lines[3], /*expect_hlo=*/true, &parsed_profile_lines)); + + TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine total_profile, + MaybeFind(parsed_profile_lines, "[total]")); + TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine dot_profile, + MaybeFind(parsed_profile_lines, "add")); + TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine tanh_profile, + MaybeFind(parsed_profile_lines, "tanh")); EXPECT_GT(total_profile.cycles, 0); EXPECT_EQ(total_profile.cycles_percentage, "100.00%"); + EXPECT_TRUE(HasFlops(total_profile)); + EXPECT_TRUE(HasTrops(total_profile)); + EXPECT_GT(total_profile.cycles, dot_profile.cycles); EXPECT_NE(dot_profile.cycles_percentage, "0.00%"); EXPECT_NE(dot_profile.cycles_percentage, "100.00%"); + EXPECT_TRUE(HasFlops(dot_profile)); + EXPECT_FALSE(HasTrops(dot_profile)); + EXPECT_GT(total_profile.cycles, tanh_profile.cycles); EXPECT_NE(tanh_profile.cycles_percentage, "0.00%"); EXPECT_NE(tanh_profile.cycles_percentage, "100.00%"); + + EXPECT_FALSE(HasFlops(tanh_profile)); + EXPECT_TRUE(HasTrops(tanh_profile)); } // TODO(b/71364943): This test exposes a bug in the parallel CPU backend. @@ -220,7 +270,7 @@ XLA_TEST_F(HloProfileTest, auto matrix = builder.GetTupleElement(state, 1); auto next_iteration = builder.Add(builder.GetTupleElement(state, 0), builder.ConstantR0(1)); - builder.Tuple({next_iteration, builder.Dot(matrix, matrix)}); + builder.Tuple({next_iteration, builder.Add(matrix, matrix)}); TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } @@ -249,20 +299,23 @@ XLA_TEST_F(HloProfileTest, ASSERT_NE(while_body_profile_start, profile_output_lines.end()); - TF_ASSERT_OK_AND_ASSIGN( - ParsedProfileOutputLine total_while_body_profile, - ParseProfileOutputLine(*std::next(while_body_profile_start, 1), - /*expect_flops=*/false, - /*expect_trops=*/false)); + gtl::FlatMap parsed_profile_lines; - TF_ASSERT_OK_AND_ASSIGN( - ParsedProfileOutputLine dot_profile, - ParseProfileOutputLine(*std::next(while_body_profile_start, 2), - /*expect_flops=*/false, - /*expect_trops=*/false)); + TF_ASSERT_OK( + ParseOneProfileOutputLine(*std::next(while_body_profile_start, 1), + /*expect_hlo=*/false, &parsed_profile_lines)); + + TF_ASSERT_OK( + ParseOneProfileOutputLine(*std::next(while_body_profile_start, 2), + /*expect_hlo=*/true, &parsed_profile_lines)); + + TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine total_while_body_profile, + MaybeFind(parsed_profile_lines, "[total]")); + TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine dot_profile, + MaybeFind(parsed_profile_lines, "add")); EXPECT_GT(total_while_body_profile.cycles, 0); - EXPECT_EQ(total_while_body_profile.name, "[total]"); + EXPECT_EQ(total_while_body_profile.opcode, "[total]"); EXPECT_EQ(total_while_body_profile.cycles_percentage, "100.00%"); EXPECT_GT(total_while_body_profile.cycles, dot_profile.cycles); -- GitLab From 45d47f30243dd4a26705f24b2a82188b0ec9b7d2 Mon Sep 17 00:00:00 2001 From: Andrew Harp Date: Thu, 25 Jan 2018 14:56:22 -0500 Subject: [PATCH 055/423] Don't load libcupti.so from regular path on Android (#16303) * Don't load libcupti.so from regular path on Android * replace NVIDIA_TEGRA with ANDROID_TEGRA to be less redundant and more specific --- tensorflow/contrib/makefile/Makefile | 6 +++--- tensorflow/core/common_runtime/gpu/gpu_device.cc | 2 +- tensorflow/core/framework/register_types.h | 2 +- tensorflow/stream_executor/cuda/cuda_diagnostics.cc | 2 +- tensorflow/stream_executor/dso_loader.cc | 8 ++++++++ 5 files changed, 14 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile index dd5770dc99..c50f8ceec0 100644 --- a/tensorflow/contrib/makefile/Makefile +++ b/tensorflow/contrib/makefile/Makefile @@ -377,10 +377,10 @@ $(MARCH_OPTION) \ ifeq ($(BUILD_FOR_TEGRA),1) NVCC := $(JETPACK)/cuda/bin/nvcc - NVCCFLAGS := -x=cu -D__CUDACC__ -DNVCC -DNVIDIA_TEGRA -ccbin $(NDK_ROOT)/toolchains/$(TOOLCHAIN)/prebuilt/$(ANDROID_HOST_OS_ARCH)/bin/$(BIN_PREFIX)-g++ --std c++11 --expt-relaxed-constexpr -m64 -gencode arch=compute_53,\"code=sm_53\" -gencode arch=compute_62,\"code=sm_62\" -DEIGEN_AVOID_STL_ARRAY -DTENSORFLOW_USE_EIGEN_THREADPOOL -DLANG_CXX11 -DEIGEN_HAS_C99_MATH -DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=5.3 + NVCCFLAGS := -x=cu -D__CUDACC__ -DNVCC -DANDROID_TEGRA -ccbin $(NDK_ROOT)/toolchains/$(TOOLCHAIN)/prebuilt/$(ANDROID_HOST_OS_ARCH)/bin/$(BIN_PREFIX)-g++ --std c++11 --expt-relaxed-constexpr -m64 -gencode arch=compute_53,\"code=sm_53\" -gencode arch=compute_62,\"code=sm_62\" -DEIGEN_AVOID_STL_ARRAY -DTENSORFLOW_USE_EIGEN_THREADPOOL -DLANG_CXX11 -DEIGEN_HAS_C99_MATH -DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=5.3 CXXFLAGS4NVCC =\ -DIS_SLIM_BUILD \ --DNVIDIA_TEGRA \ +-DANDROID_TEGRA \ -fno-exceptions \ -DNDEBUG $(OPTFLAGS) \ -march=armv8-a \ @@ -391,7 +391,7 @@ $(MARCH_OPTION) \ CXXFLAGS +=\ -DGOOGLE_CUDA=1 \ -D__ANDROID_TYPES_FULL__ \ --DNVIDIA_TEGRA \ +-DANDROID_TEGRA \ -DEIGEN_AVOID_STL_ARRAY \ -DEIGEN_HAS_C99_MATH \ -DLANG_CXX11 -DTENSORFLOW_USE_EIGEN_THREADPOOL -DTF_EXTRA_CUDA_CAPABILITIES=5.3 diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc index 0e5b6b7ef8..933d700f60 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc @@ -762,7 +762,7 @@ int64 MinSystemMemory(int64 available_memory) { // is necessary. min_system_memory *= 2; #endif -#if defined(NVIDIA_TEGRA) +#if defined(ANDROID_TEGRA) // 1GB system mem for NVIDIA Tegra devices since they use the same mem for RAM and Video RAM min_system_memory = 1<<30; #endif diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h index edc93aec7f..e062adffe8 100644 --- a/tensorflow/core/framework/register_types.h +++ b/tensorflow/core/framework/register_types.h @@ -53,7 +53,7 @@ limitations under the License. */ #if !defined(IS_MOBILE_PLATFORM) || defined(SUPPORT_SELECTIVE_REGISTRATION) || \ - defined(NVIDIA_TEGRA) + defined(ANDROID_TEGRA) // All types are supported, so all macros are invoked. // diff --git a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc index f35542e18f..933c103f52 100644 --- a/tensorflow/stream_executor/cuda/cuda_diagnostics.cc +++ b/tensorflow/stream_executor/cuda/cuda_diagnostics.cc @@ -232,7 +232,7 @@ port::StatusOr Diagnostician::FindDsoVersion() { result = StringToDriverVersion(version); } #else -#if !defined(PLATFORM_WINDOWS) && !defined(NVIDIA_TEGRA) +#if !defined(PLATFORM_WINDOWS) && !defined(ANDROID_TEGRA) // Callback used when iterating through DSOs. Looks for the driver-interfacing // DSO and yields its version number into the callback data, when found. auto iterate_phdr = diff --git a/tensorflow/stream_executor/dso_loader.cc b/tensorflow/stream_executor/dso_loader.cc index 5210a81092..d71938634d 100644 --- a/tensorflow/stream_executor/dso_loader.cc +++ b/tensorflow/stream_executor/dso_loader.cc @@ -96,10 +96,18 @@ string GetCudnnVersion() { return TF_CUDNN_VERSION; } } /* static */ port::Status DsoLoader::GetLibcuptiDsoHandle(void** dso_handle) { +#if defined(ANDROID_TEGRA) + // On Android devices the CUDA version number is not added to the library name. + return GetDsoHandle(FindDsoPath(port::Env::Default()->FormatLibraryFileName( + "cupti", ""), + GetCudaCuptiLibraryPath()), + dso_handle); +#else return GetDsoHandle(FindDsoPath(port::Env::Default()->FormatLibraryFileName( "cupti", GetCudaVersion()), GetCudaCuptiLibraryPath()), dso_handle); +#endif } static mutex& GetRpathMutex() { -- GitLab From 351c0a533a111636333b4ebeede16485cf679ca9 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 25 Jan 2018 12:02:36 -0800 Subject: [PATCH 056/423] Add C0330 bad-continuation check to pylint. PiperOrigin-RevId: 183270896 --- .../copy_graph/python/util/copy_elements.py | 75 +- .../python/layers/feature_column_test.py | 150 ++- .../learn/python/learn/datasets/__init__.py | 23 +- .../learn/python/learn/datasets/synthetic.py | 66 +- .../python/learn/estimators/estimator.py | 262 ++--- .../python/learn/estimators/estimator_test.py | 223 +++-- .../learn/estimators/estimators_test.py | 32 +- .../contrib/learn/python/learn/monitors.py | 102 +- .../learn/python/learn/utils/export_test.py | 34 +- .../learn/python/learn/utils/gc_test.py | 49 +- .../contrib/metrics/python/ops/metric_ops.py | 77 +- .../metrics/python/ops/metric_ops_test.py | 933 ++++++++---------- .../examples/cifar10/cifar10_pruning.py | 86 +- tensorflow/contrib/mpi_collectives/mpi_ops.py | 22 +- .../training/elastic_average_optimizer.py | 115 ++- .../elastic_average_optimizer_test.py | 99 +- .../predictor/predictor_factories_test.py | 4 +- .../kernel_tests/sparsemax_loss_test.py | 34 +- .../python/kernel_tests/sparsemax_test.py | 48 +- .../reading_data/fully_connected_reader.py | 49 +- .../examples/label_image/label_image.py | 41 +- tensorflow/python/client/session.py | 185 ++-- tensorflow/python/client/session_test.py | 310 +++--- .../inputs/queues/feeding_functions.py | 101 +- .../python/kernel_tests/array_ops_test.py | 97 +- .../python/kernel_tests/diag_op_test.py | 225 ++--- .../python/kernel_tests/map_stage_op_test.py | 105 +- .../python/kernel_tests/pooling_ops_test.py | 72 +- .../python/kernel_tests/reader_ops_test.py | 23 +- .../python/kernel_tests/relu_op_test.py | 20 +- .../kernel_tests/sparse_slice_op_test.py | 102 +- .../python/kernel_tests/stage_op_test.py | 34 +- tensorflow/python/layers/maxout.py | 34 +- tensorflow/python/ops/array_grad.py | 116 ++- tensorflow/python/ops/control_flow_ops.py | 481 +++++---- tensorflow/python/ops/data_flow_ops.py | 390 +++++--- tensorflow/python/ops/gradients_impl.py | 91 +- tensorflow/python/ops/nn_grad.py | 298 +++--- tensorflow/python/ops/nn_grad_test.py | 13 +- tensorflow/python/ops/special_math_ops.py | 116 +-- .../python/ops/special_math_ops_test.py | 74 +- tensorflow/python/tools/inspect_checkpoint.py | 14 +- .../python/training/coordinator_test.py | 70 +- tensorflow/tools/ci_build/ci_sanity.sh | 8 +- tensorflow/tools/compatibility/ast_edits.py | 71 +- tensorflow/tools/compatibility/tf_upgrade.py | 4 +- 46 files changed, 2937 insertions(+), 2641 deletions(-) diff --git a/tensorflow/contrib/copy_graph/python/util/copy_elements.py b/tensorflow/contrib/copy_graph/python/util/copy_elements.py index bae66ffd42..b806799202 100644 --- a/tensorflow/contrib/copy_graph/python/util/copy_elements.py +++ b/tensorflow/contrib/copy_graph/python/util/copy_elements.py @@ -35,10 +35,10 @@ from tensorflow.python.ops.variables import Variable from tensorflow.python.client.session import Session from tensorflow.python.framework import ops -__all__ = ["copy_op_to_graph", "copy_variable_to_graph", "get_copied_op"] +__all__ = ['copy_op_to_graph', 'copy_variable_to_graph', 'get_copied_op'] -def copy_variable_to_graph(org_instance, to_graph, scope=""): +def copy_variable_to_graph(org_instance, to_graph, scope=''): """Given a `Variable` instance from one `Graph`, initializes and returns a copy of it from another `Graph`, under the specified scope (default `""`). @@ -56,12 +56,11 @@ def copy_variable_to_graph(org_instance, to_graph, scope=""): """ if not isinstance(org_instance, Variable): - raise TypeError(str(org_instance) + " is not a Variable") + raise TypeError(str(org_instance) + ' is not a Variable') #The name of the new variable - if scope != "": - new_name = (scope + '/' + - org_instance.name[:org_instance.name.index(':')]) + if scope != '': + new_name = (scope + '/' + org_instance.name[:org_instance.name.index(':')]) else: new_name = org_instance.name[:org_instance.name.index(':')] @@ -73,15 +72,15 @@ def copy_variable_to_graph(org_instance, to_graph, scope=""): for name, collection in org_instance.graph._collections.items(): if org_instance in collection: if (name == ops.GraphKeys.GLOBAL_VARIABLES or - name == ops.GraphKeys.TRAINABLE_VARIABLES or - scope == ''): + name == ops.GraphKeys.TRAINABLE_VARIABLES or scope == ''): collections.append(name) else: collections.append(scope + '/' + name) #See if its trainable. - trainable = (org_instance in org_instance.graph.get_collection( - ops.GraphKeys.TRAINABLE_VARIABLES)) + trainable = ( + org_instance in org_instance.graph.get_collection( + ops.GraphKeys.TRAINABLE_VARIABLES)) #Get the initial value with org_instance.graph.as_default(): temp_session = Session() @@ -89,17 +88,17 @@ def copy_variable_to_graph(org_instance, to_graph, scope=""): #Initialize the new variable with to_graph.as_default(): - new_var = Variable(init_value, - trainable, - name=new_name, - collections=collections, - validate_shape=False) + new_var = Variable( + init_value, + trainable, + name=new_name, + collections=collections, + validate_shape=False) return new_var -def copy_op_to_graph(org_instance, to_graph, variables, - scope=""): +def copy_op_to_graph(org_instance, to_graph, variables, scope=''): """Returns a copy of an operation from another Graph under a specified scope. Given an `Operation` `org_instance` from one `Graph`, @@ -139,14 +138,12 @@ def copy_op_to_graph(org_instance, to_graph, variables, #If a variable by the new name already exists, return the #correspondng tensor that will act as an input if new_name in copied_variables: - return to_graph.get_tensor_by_name( - copied_variables[new_name].name) + return to_graph.get_tensor_by_name(copied_variables[new_name].name) #If an instance of the same name exists, return appropriately try: - already_present = to_graph.as_graph_element(new_name, - allow_tensor=True, - allow_operation=True) + already_present = to_graph.as_graph_element( + new_name, allow_tensor=True, allow_operation=True) return already_present except: pass @@ -184,20 +181,21 @@ def copy_op_to_graph(org_instance, to_graph, variables, #If it has an original_op parameter, copy it if op._original_op is not None: - new_original_op = copy_op_to_graph(op._original_op, to_graph, - variables, scope) + new_original_op = copy_op_to_graph(op._original_op, to_graph, variables, + scope) else: new_original_op = None #If it has control inputs, call this function recursively on each. - new_control_inputs = [copy_op_to_graph(x, to_graph, variables, - scope) - for x in op.control_inputs] + new_control_inputs = [ + copy_op_to_graph(x, to_graph, variables, scope) + for x in op.control_inputs + ] #If it has inputs, call this function recursively on each. - new_inputs = [copy_op_to_graph(x, to_graph, variables, - scope) - for x in op.inputs] + new_inputs = [ + copy_op_to_graph(x, to_graph, variables, scope) for x in op.inputs + ] #Make a new node_def based on that of the original. #An instance of tensorflow.core.framework.node_def_pb2.NodeDef, it @@ -216,13 +214,8 @@ def copy_op_to_graph(org_instance, to_graph, variables, op_def = deepcopy(op._op_def) #Initialize a new Operation instance - new_op = ops.Operation(new_node_def, - to_graph, - new_inputs, - output_types, - new_control_inputs, - input_types, - new_original_op, + new_op = ops.Operation(new_node_def, to_graph, new_inputs, output_types, + new_control_inputs, input_types, new_original_op, op_def) #Use Graph's hidden methods to add the op to_graph._add_op(new_op) # pylint: disable=protected-access @@ -233,10 +226,10 @@ def copy_op_to_graph(org_instance, to_graph, variables, return new_op else: - raise TypeError("Could not copy instance: " + str(org_instance)) + raise TypeError('Could not copy instance: ' + str(org_instance)) -def get_copied_op(org_instance, graph, scope=""): +def get_copied_op(org_instance, graph, scope=''): """Given an `Operation` instance from some `Graph`, returns its namesake from `graph`, under the specified scope (default `""`). @@ -259,5 +252,5 @@ def get_copied_op(org_instance, graph, scope=""): else: new_name = org_instance.name - return graph.as_graph_element(new_name, allow_tensor=True, - allow_operation=True) + return graph.as_graph_element( + new_name, allow_tensor=True, allow_operation=True) diff --git a/tensorflow/contrib/layers/python/layers/feature_column_test.py b/tensorflow/contrib/layers/python/layers/feature_column_test.py index 2eaea23177..fc8f153fe3 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column_test.py +++ b/tensorflow/contrib/layers/python/layers/feature_column_test.py @@ -221,8 +221,8 @@ class FeatureColumnTest(test.TestCase): weighted_sparse_col = fc.weighted_sparse_column(ids, "weights") self.assertEqual(weighted_sparse_col.name, "ids_weighted_by_weights") - b = fc.shared_embedding_columns([sparse_col, weighted_sparse_col], - dimension=4, combiner="mean") + b = fc.shared_embedding_columns( + [sparse_col, weighted_sparse_col], dimension=4, combiner="mean") self.assertEqual(len(b), 2) self.assertEqual(b[0].shared_embedding_name, "a1_ids_weighted_by_weights_shared_embedding") @@ -230,8 +230,8 @@ class FeatureColumnTest(test.TestCase): "a1_ids_weighted_by_weights_shared_embedding") # Tries reversing order to check compatibility condition. - b = fc.shared_embedding_columns([weighted_sparse_col, sparse_col], - dimension=4, combiner="mean") + b = fc.shared_embedding_columns( + [weighted_sparse_col, sparse_col], dimension=4, combiner="mean") self.assertEqual(len(b), 2) self.assertEqual(b[0].shared_embedding_name, "a1_ids_weighted_by_weights_shared_embedding") @@ -240,18 +240,17 @@ class FeatureColumnTest(test.TestCase): # Tries adding two weighted columns to check compatibility between them. weighted_sparse_col_2 = fc.weighted_sparse_column(ids, "weights_2") - b = fc.shared_embedding_columns([weighted_sparse_col, - weighted_sparse_col_2], - dimension=4, combiner="mean") + b = fc.shared_embedding_columns( + [weighted_sparse_col, weighted_sparse_col_2], + dimension=4, + combiner="mean") self.assertEqual(len(b), 2) self.assertEqual( b[0].shared_embedding_name, - "ids_weighted_by_weights_ids_weighted_by_weights_2_shared_embedding" - ) + "ids_weighted_by_weights_ids_weighted_by_weights_2_shared_embedding") self.assertEqual( b[1].shared_embedding_name, - "ids_weighted_by_weights_ids_weighted_by_weights_2_shared_embedding" - ) + "ids_weighted_by_weights_ids_weighted_by_weights_2_shared_embedding") def testSharedEmbeddingColumnDeterminism(self): # Tests determinism in auto-generated shared_embedding_name. @@ -286,10 +285,10 @@ class FeatureColumnTest(test.TestCase): columns = fc.shared_embedding_columns( [a1, a2], dimension=4, combiner="mean") columns_copy = copy.deepcopy(columns) - self.assertEqual( - columns_copy[0].shared_embedding_name, "a1_a2_shared_embedding") - self.assertEqual( - columns_copy[1].shared_embedding_name, "a1_a2_shared_embedding") + self.assertEqual(columns_copy[0].shared_embedding_name, + "a1_a2_shared_embedding") + self.assertEqual(columns_copy[1].shared_embedding_name, + "a1_a2_shared_embedding") def testOneHotColumn(self): a = fc.sparse_column_with_keys("a", ["a", "b", "c", "d"]) @@ -336,11 +335,11 @@ class FeatureColumnTest(test.TestCase): weighted_ids = fc.weighted_sparse_column(ids, "weights") one_hot = fc.one_hot_column(weighted_ids) features = { - 'ids': constant_op.constant([['marlo', 'unknown', 'omar']]), - 'weights': constant_op.constant([[2., 4., 6.]]) + "ids": constant_op.constant([["marlo", "unknown", "omar"]]), + "weights": constant_op.constant([[2., 4., 6.]]) } one_hot_tensor = feature_column_ops.input_from_feature_columns( - features, [one_hot]) + features, [one_hot]) with self.test_session() as sess: sess.run(variables.global_variables_initializer()) sess.run(lookup_ops.tables_initializer()) @@ -349,11 +348,9 @@ class FeatureColumnTest(test.TestCase): def testMissingValueInOneHotColumnForSparseColumnWithKeys(self): ids = fc.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"]) one_hot = fc.one_hot_column(ids) - features = { - 'ids': constant_op.constant([['marlo', 'unknown', 'omar']]) - } + features = {"ids": constant_op.constant([["marlo", "unknown", "omar"]])} one_hot_tensor = feature_column_ops.input_from_feature_columns( - features, [one_hot]) + features, [one_hot]) with self.test_session() as sess: sess.run(variables.global_variables_initializer()) sess.run(lookup_ops.tables_initializer()) @@ -379,8 +376,7 @@ class FeatureColumnTest(test.TestCase): self.assertEqual(d4.default_value, None) self.assertEqual(d4.is_sparse, True) # Default value is a list but dimension is None. - with self.assertRaisesRegexp(ValueError, - "Only scalar default value.*"): + with self.assertRaisesRegexp(ValueError, "Only scalar default value.*"): fc._real_valued_var_len_column("g5", default_value=[2., 3.]) def testRealValuedVarLenColumnDtypes(self): @@ -390,18 +386,19 @@ class FeatureColumnTest(test.TestCase): "rvc": parsing_ops.VarLenFeature(dtype=dtypes.float32) }, rvc.config) - rvc = fc._real_valued_var_len_column("rvc", default_value=0, - is_sparse=False) - self.assertDictEqual( - { - "rvc": parsing_ops.FixedLenSequenceFeature(shape=[], - dtype=dtypes.float32, - allow_missing=True, - default_value=0.0) - }, rvc.config) - - rvc = fc._real_valued_var_len_column("rvc", dtype=dtypes.int32, - default_value=0, is_sparse=True) + rvc = fc._real_valued_var_len_column( + "rvc", default_value=0, is_sparse=False) + self.assertDictEqual({ + "rvc": + parsing_ops.FixedLenSequenceFeature( + shape=[], + dtype=dtypes.float32, + allow_missing=True, + default_value=0.0) + }, rvc.config) + + rvc = fc._real_valued_var_len_column( + "rvc", dtype=dtypes.int32, default_value=0, is_sparse=True) self.assertDictEqual( { "rvc": parsing_ops.VarLenFeature(dtype=dtypes.int32) @@ -409,8 +406,8 @@ class FeatureColumnTest(test.TestCase): with self.assertRaisesRegexp(TypeError, "dtype must be convertible to float"): - fc._real_valued_var_len_column("rvc", dtype=dtypes.string, - default_value="", is_sparse=True) + fc._real_valued_var_len_column( + "rvc", dtype=dtypes.string, default_value="", is_sparse=True) def testRealValuedColumn(self): a = fc.real_valued_column("aaa") @@ -504,13 +501,13 @@ class FeatureColumnTest(test.TestCase): for output_rank in range(1, 3 + len(dimensions)): with variable_scope.variable_scope("output_rank_{}".format(output_rank)): real_valued_output = real_valued_column._to_dnn_input_layer( - constant_op.constant( - real_valued_input, dtype=dtypes.float32), + constant_op.constant(real_valued_input, dtype=dtypes.float32), output_rank=output_rank) with self.test_session() as sess: real_valued_eval = sess.run(real_valued_output) - expected_shape = (input_shape[:output_rank - 1] + - [np.prod(input_shape[output_rank - 1:])]) + expected_shape = ( + input_shape[:output_rank - 1] + + [np.prod(input_shape[output_rank - 1:])]) self.assertEquals(expected_shape, list(real_valued_eval.shape)) def testRealValuedColumnDensification(self): @@ -520,8 +517,7 @@ class FeatureColumnTest(test.TestCase): "sparse_real_valued1", is_sparse=True) sparse_tensor = sparse_tensor_lib.SparseTensor( values=[2.0, 5.0], indices=[[0, 0], [2, 0]], dense_shape=[3, 1]) - with self.assertRaisesRegexp( - ValueError, "Set is_sparse to False"): + with self.assertRaisesRegexp(ValueError, "Set is_sparse to False"): real_valued_column._to_dnn_input_layer(sparse_tensor) def testRealValuedColumnDeepCopy(self): @@ -549,9 +545,8 @@ class FeatureColumnTest(test.TestCase): def testBucketizedColumnRequiresRealValuedColumnDimension(self): with self.assertRaisesRegexp( TypeError, "source_column must be an instance of _RealValuedColumn.*"): - fc.bucketized_column(fc._real_valued_var_len_column("bbb", - is_sparse=True), - [0]) + fc.bucketized_column( + fc._real_valued_var_len_column("bbb", is_sparse=True), [0]) def testBucketizedColumnRequiresSortedBuckets(self): with self.assertRaisesRegexp(ValueError, @@ -654,20 +649,14 @@ class FeatureColumnTest(test.TestCase): def testRealValuedColumnDtypes(self): rvc = fc.real_valued_column("rvc") - self.assertDictEqual( - { - "rvc": parsing_ops.FixedLenFeature( - [1], dtype=dtypes.float32) - }, - rvc.config) + self.assertDictEqual({ + "rvc": parsing_ops.FixedLenFeature([1], dtype=dtypes.float32) + }, rvc.config) rvc = fc.real_valued_column("rvc", dtype=dtypes.int32) - self.assertDictEqual( - { - "rvc": parsing_ops.FixedLenFeature( - [1], dtype=dtypes.int32) - }, - rvc.config) + self.assertDictEqual({ + "rvc": parsing_ops.FixedLenFeature([1], dtype=dtypes.int32) + }, rvc.config) with self.assertRaisesRegexp(ValueError, "dtype must be convertible to float"): @@ -702,8 +691,9 @@ class FeatureColumnTest(test.TestCase): batch_size = 4 dense_scalar_input = [1, 2, 3, 4] sparse_column = fc.sparse_column_with_integerized_feature("values", 10) - features = {"values": - constant_op.constant(dense_scalar_input, dtype=dtypes.int64)} + features = { + "values": constant_op.constant(dense_scalar_input, dtype=dtypes.int64) + } sparse_column.insert_transformed_feature(features) sparse_output = features[sparse_column] expected_shape = [batch_size, 1] @@ -731,8 +721,7 @@ class FeatureColumnTest(test.TestCase): def testSparseColumnKeysDeepCopy(self): """Tests deepcopy of sparse_column_with_keys.""" - column = fc.sparse_column_with_keys( - "a", keys=["key0", "key1", "key2"]) + column = fc.sparse_column_with_keys("a", keys=["key0", "key1", "key2"]) self.assertEqual("a", column.name) column_copy = copy.deepcopy(column) self.assertEqual("a", column_copy.name) @@ -785,8 +774,9 @@ class FeatureColumnTest(test.TestCase): a = fc.sparse_column_with_hash_bucket("cross_aaa", hash_bucket_size=100) b = fc.sparse_column_with_hash_bucket("cross_bbb", hash_bucket_size=100) cross_col = fc.crossed_column(set([a, b]), hash_bucket_size=10000) - one_hot_col = fc.one_hot_column(fc.sparse_column_with_hash_bucket( - "sparse_column_for_one_hot", hash_bucket_size=100)) + one_hot_col = fc.one_hot_column( + fc.sparse_column_with_hash_bucket( + "sparse_column_for_one_hot", hash_bucket_size=100)) scattered_embedding_col = fc.scattered_embedding_column( "scattered_embedding_column", size=100, dimension=10, hash_key=1) feature_columns = set([ @@ -809,17 +799,13 @@ class FeatureColumnTest(test.TestCase): "str_id_weights_column": parsing_ops.VarLenFeature(dtypes.float32), "real_valued_column1": - parsing_ops.FixedLenFeature( - [1], dtype=dtypes.float32), + parsing_ops.FixedLenFeature([1], dtype=dtypes.float32), "real_valued_column2": - parsing_ops.FixedLenFeature( - [5], dtype=dtypes.float32), + parsing_ops.FixedLenFeature([5], dtype=dtypes.float32), "real_valued_column_for_bucketization1": - parsing_ops.FixedLenFeature( - [1], dtype=dtypes.float32), + parsing_ops.FixedLenFeature([1], dtype=dtypes.float32), "real_valued_column_for_bucketization2": - parsing_ops.FixedLenFeature( - [4], dtype=dtypes.float32), + parsing_ops.FixedLenFeature([4], dtype=dtypes.float32), "cross_aaa": parsing_ops.VarLenFeature(dtypes.string), "cross_bbb": @@ -849,11 +835,14 @@ class FeatureColumnTest(test.TestCase): real_valued_col0 = fc._real_valued_var_len_column( "real_valued_column0", is_sparse=True) real_valued_col1 = fc._real_valued_var_len_column( - "real_valued_column1", dtype=dtypes.int64, default_value=0, + "real_valued_column1", + dtype=dtypes.int64, + default_value=0, is_sparse=False) feature_columns = set([real_valued_col0, real_valued_col1]) expected_config = { - "real_valued_column0": parsing_ops.VarLenFeature(dtype=dtypes.float32), + "real_valued_column0": + parsing_ops.VarLenFeature(dtype=dtypes.float32), "real_valued_column1": parsing_ops.FixedLenSequenceFeature( [], dtype=dtypes.int64, allow_missing=True, default_value=0), @@ -874,7 +863,9 @@ class FeatureColumnTest(test.TestCase): real_valued_col5 = fc._real_valued_var_len_column( "real_valued_column5", default_value=2, is_sparse=True) real_valued_col6 = fc._real_valued_var_len_column( - "real_valued_column6", dtype=dtypes.int64, default_value=1, + "real_valued_column6", + dtype=dtypes.int64, + default_value=1, is_sparse=False) feature_columns = [ real_valued_col1, real_valued_col2, real_valued_col3, real_valued_col4, @@ -902,8 +893,7 @@ class FeatureColumnTest(test.TestCase): parsing_ops.VarLenFeature(dtype=dtypes.float32), "real_valued_column6": parsing_ops.FixedLenSequenceFeature( - [], dtype=dtypes.int64, allow_missing=True, - default_value=1) + [], dtype=dtypes.int64, allow_missing=True, default_value=1) }, config) @@ -1104,8 +1094,8 @@ class FeatureColumnTest(test.TestCase): # This will initialize the crossed column weights from provided checkpoint # and return a [4, 1] tensor which is same as weights variable. Since we # won't modify weights, this should be same as 'saved_col_weights'. - _, col_weights, _ = (feature_column_ops.weighted_sum_from_feature_columns( - { + _, col_weights, _ = ( + feature_column_ops.weighted_sum_from_feature_columns({ sparse_col_1.name: input_tensor, sparse_col_2.name: input_tensor }, [crossed_col_initialized], 1)) diff --git a/tensorflow/contrib/learn/python/learn/datasets/__init__.py b/tensorflow/contrib/learn/python/learn/datasets/__init__.py index a3521b4109..7240b0de14 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/__init__.py +++ b/tensorflow/contrib/learn/python/learn/datasets/__init__.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Dataset utilities and synthetic/reference datasets.""" from __future__ import absolute_import @@ -46,11 +45,12 @@ DATASETS = { # List of all synthetic datasets SYNTHETIC = { - # All of these will return ['data', 'target'] -> base.Dataset - 'circles': synthetic.circles, - 'spirals': synthetic.spirals + # All of these will return ['data', 'target'] -> base.Dataset + 'circles': synthetic.circles, + 'spirals': synthetic.spirals } + def load_dataset(name, size='small', test_with_fake_data=False): """Loads dataset by name. @@ -83,23 +83,28 @@ def make_dataset(name, n_samples=100, noise=None, seed=42, *args, **kwargs): seed: int or None, seed for noise Returns: - Shuffled features and labels for given synthetic dataset of type `base.Dataset` + Shuffled features and labels for given synthetic dataset of type + `base.Dataset` Raises: ValueError: Raised if `name` not found Note: - - This is a generic synthetic data generator - individual generators might have more parameters! + - This is a generic synthetic data generator - individual generators might + have more parameters! See documentation for individual parameters - - Note that the `noise` parameter uses `numpy.random.normal` and depends on `numpy`'s seed + - Note that the `noise` parameter uses `numpy.random.normal` and depends on + `numpy`'s seed TODO: - Support multiclass datasets - - Need shuffling routine. Currently synthetic datasets are reshuffled to avoid train/test correlation, + - Need shuffling routine. Currently synthetic datasets are reshuffled to + avoid train/test correlation, but that hurts reprodusability """ # seed = kwargs.pop('seed', None) if name not in SYNTHETIC: raise ValueError('Synthetic dataset not found or not implemeted: %s' % name) else: - return SYNTHETIC[name](n_samples=n_samples, noise=noise, seed=seed, *args, **kwargs) + return SYNTHETIC[name]( + n_samples=n_samples, noise=noise, seed=seed, *args, **kwargs) diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py index 907dc0f3df..649996c49c 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Synthetic dataset generators.""" from __future__ import absolute_import @@ -23,18 +22,27 @@ import numpy as np from tensorflow.contrib.learn.python.learn.datasets.base import Dataset -def circles(n_samples=100, noise=None, seed=None, factor=0.8, n_classes=2, *args, **kwargs): + +def circles(n_samples=100, + noise=None, + seed=None, + factor=0.8, + n_classes=2, + *args, + **kwargs): """Create circles separated by some value Args: n_samples: int, number of datapoints to generate noise: float or None, standard deviation of the Gaussian noise added seed: int or None, seed for the noise - factor: float, size factor of the inner circles with respect to the outer ones + factor: float, size factor of the inner circles with respect to the outer + ones n_classes: int, number of classes to generate Returns: - Shuffled features and labels for 'circles' synthetic dataset of type `base.Dataset` + Shuffled features and labels for 'circles' synthetic dataset of type + `base.Dataset` Note: The multi-class support might not work as expected if `noise` is enabled @@ -54,7 +62,7 @@ def circles(n_samples=100, noise=None, seed=None, factor=0.8, n_classes=2, *args if seed is not None: np.random.seed(seed) # Algo: 1) Generate initial circle, 2) For ever class generate a smaller radius circle - linspace = np.linspace(0, 2*np.pi, n_samples // n_classes) + linspace = np.linspace(0, 2 * np.pi, n_samples // n_classes) circ_x = np.empty(0, dtype=np.int32) circ_y = np.empty(0, dtype=np.int32) base_cos = np.cos(linspace) @@ -66,12 +74,12 @@ def circles(n_samples=100, noise=None, seed=None, factor=0.8, n_classes=2, *args circ_y = np.append(circ_y, base_sin) base_cos *= factor base_sin *= factor - y = np.append(y, label*np.ones(n_samples // n_classes, dtype=np.int32)) + y = np.append(y, label * np.ones(n_samples // n_classes, dtype=np.int32)) # Add more points if n_samples is not divisible by n_classes (unbalanced!) extras = n_samples % n_classes - circ_x = np.append(circ_x, np.cos(np.random.rand(extras)*2*np.pi)) - circ_y = np.append(circ_y, np.sin(np.random.rand(extras)*2*np.pi)) + circ_x = np.append(circ_x, np.cos(np.random.rand(extras) * 2 * np.pi)) + circ_y = np.append(circ_y, np.sin(np.random.rand(extras) * 2 * np.pi)) y = np.append(y, np.zeros(extras, dtype=np.int32)) # Reshape the features/labels @@ -85,10 +93,13 @@ def circles(n_samples=100, noise=None, seed=None, factor=0.8, n_classes=2, *args return Dataset(data=X[indices], target=y[indices]) -def spirals(n_samples=100, noise=None, seed=None, - mode = 'archimedes', - n_loops = 2, - *args, **kwargs): +def spirals(n_samples=100, + noise=None, + seed=None, + mode='archimedes', + n_loops=2, + *args, + **kwargs): """Create spirals Currently only binary classification is supported for spiral generation @@ -104,7 +115,8 @@ def spirals(n_samples=100, noise=None, seed=None, 'fermat': a spiral with branch distances decreasing (sqrt) Returns: - Shuffled features and labels for 'spirals' synthetic dataset of type `base.Dataset` + Shuffled features and labels for 'spirals' synthetic dataset of type + `base.Dataset` Raises: ValueError: If the generation `mode` is not valid @@ -112,34 +124,35 @@ def spirals(n_samples=100, noise=None, seed=None, TODO: - Generation of unbalanced data """ - n_classes = 2 # I am not sure how to make it multiclass + n_classes = 2 # I am not sure how to make it multiclass _modes = { - 'archimedes': _archimedes_spiral, - 'bernoulli': _bernoulli_spiral, - 'fermat': _fermat_spiral + 'archimedes': _archimedes_spiral, + 'bernoulli': _bernoulli_spiral, + 'fermat': _fermat_spiral } if mode is None or mode not in _modes: - raise ValueError("Cannot generate spiral with mode %s"%mode) + raise ValueError('Cannot generate spiral with mode %s' % mode) if seed is not None: np.random.seed(seed) - linspace = np.linspace(0, 2*n_loops*np.pi, n_samples // n_classes) + linspace = np.linspace(0, 2 * n_loops * np.pi, n_samples // n_classes) spir_x = np.empty(0, dtype=np.int32) spir_y = np.empty(0, dtype=np.int32) y = np.empty(0, dtype=np.int32) for label in range(n_classes): - base_cos, base_sin = _modes[mode](linspace, label*np.pi, *args, **kwargs) + base_cos, base_sin = _modes[mode](linspace, label * np.pi, *args, **kwargs) spir_x = np.append(spir_x, base_cos) spir_y = np.append(spir_y, base_sin) - y = np.append(y, label*np.ones(n_samples // n_classes, dtype=np.int32)) + y = np.append(y, label * np.ones(n_samples // n_classes, dtype=np.int32)) # Add more points if n_samples is not divisible by n_classes (unbalanced!) extras = n_samples % n_classes if extras > 0: - x_exrta, y_extra = _modes[mode](np.random.rand(extras)*2*np.pi, *args, **kwargs) + x_exrta, y_extra = _modes[mode](np.random.rand(extras) * 2 * np.pi, *args, + **kwargs) spir_x = np.append(spir_x, x_extra) spir_y = np.append(spir_y, y_extra) y = np.append(y, np.zeros(extras, dtype=np.int32)) @@ -162,7 +175,8 @@ def _archimedes_spiral(theta, theta_offset=0., *args, **kwargs): theta: array-like, angles from polar coordinates to be converted theta_offset: float, angle offset in radians (2*pi = 0) """ - x, y = theta*np.cos(theta + theta_offset), theta*np.sin(theta + theta_offset) + x, y = theta * np.cos(theta + theta_offset), theta * np.sin( + theta + theta_offset) x_norm = np.max(np.abs(x)) y_norm = np.max(np.abs(y)) x, y = x / x_norm, y / y_norm @@ -181,7 +195,8 @@ def _bernoulli_spiral(theta, theta_offset=0., *args, **kwargs): """ exp_scale = kwargs.pop('exp_scale', 0.1) - x, y = np.exp(exp_scale*theta)*np.cos(theta + theta_offset), np.exp(exp_scale*theta)*np.sin(theta + theta_offset) + x, y = np.exp(exp_scale * theta) * np.cos(theta + theta_offset), np.exp( + exp_scale * theta) * np.sin(theta + theta_offset) x_norm = np.max(np.abs(x)) y_norm = np.max(np.abs(y)) x, y = x / x_norm, y / y_norm @@ -195,7 +210,8 @@ def _fermat_spiral(theta, theta_offset=0., *args, **kwargs): theta: array-like, angles from polar coordinates to be converted theta_offset: float, angle offset in radians (2*pi = 0) """ - x, y = np.sqrt(theta)*np.cos(theta + theta_offset), np.sqrt(theta)*np.sin(theta + theta_offset) + x, y = np.sqrt(theta) * np.cos(theta + theta_offset), np.sqrt(theta) * np.sin( + theta + theta_offset) x_norm = np.max(np.abs(x)) y_norm = np.max(np.abs(y)) x, y = x / x_norm, y / y_norm diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py index 50c74add86..8d59fe66d9 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Base Estimator class.""" from __future__ import absolute_import @@ -76,7 +75,6 @@ from tensorflow.python.util import compat from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect - AS_ITERABLE_DATE = '2016-09-15' AS_ITERABLE_INSTRUCTIONS = ( 'The default behavior of predict() is changing. The default value for\n' @@ -223,8 +221,11 @@ def _get_replica_device_setter(config): if config.num_ps_replicas > 0: return device_setter.replica_device_setter( - ps_tasks=config.num_ps_replicas, worker_device=worker_device, - merge_devices=True, ps_ops=ps_ops, cluster=config.cluster_spec) + ps_tasks=config.num_ps_replicas, + worker_device=worker_device, + merge_devices=True, + ps_ops=ps_ops, + cluster=config.cluster_spec) else: return None @@ -284,10 +285,10 @@ def _make_metrics_ops(metrics, features, labels, predictions): raise ValueError('Invalid metric for {}. It returned a tuple with ' 'len {}, expected 2.'.format(name, len(name))) if not isinstance(predictions, dict): - raise ValueError( - 'Metrics passed provide (name, prediction), ' - 'but predictions are not dict. ' - 'Metrics: %s, Predictions: %s.' % (metrics, predictions)) + raise ValueError('Metrics passed provide (name, prediction), ' + 'but predictions are not dict. ' + 'Metrics: %s, Predictions: %s.' % (metrics, + predictions)) # Here are two options: labels are single Tensor or a dict. if isinstance(labels, dict) and name[1] in labels: # If labels are dict and the prediction name is in it, apply metric. @@ -298,10 +299,10 @@ def _make_metrics_ops(metrics, features, labels, predictions): else: # Single head metrics. if isinstance(predictions, dict): - raise ValueError( - 'Metrics passed provide only name, no prediction, ' - 'but predictions are dict. ' - 'Metrics: %s, Labels: %s.' % (metrics, labels_tensor_or_dict)) + raise ValueError('Metrics passed provide only name, no prediction, ' + 'but predictions are dict. ' + 'Metrics: %s, Labels: %s.' % (metrics, + labels_tensor_or_dict)) result[name] = metric(predictions, labels_tensor_or_dict) return result @@ -369,9 +370,8 @@ def _write_dict_to_summary(output_dir, dictionary, current_global_step): logging.info( 'Summary for np.ndarray is not visible in Tensorboard by default. ' 'Consider using a Tensorboard plugin for visualization (see ' - 'https://github.com/tensorflow/tensorboard-plugin-example/blob/master/README.md ' # pylint:disable=line-too-long - 'for more information).' - ) + 'https://github.com/tensorflow/tensorboard-plugin-example/blob/master/README.md' + ' for more information).') else: logging.warn( 'Skipping summary for %s, must be a float, np.float32, np.int64, ' @@ -385,8 +385,8 @@ GraphRewriteSpec = collections.namedtuple('GraphRewriteSpec', ['tags', 'transforms']) -class BaseEstimator( - sklearn.BaseEstimator, evaluable.Evaluable, trainable.Trainable): +class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, + trainable.Trainable): """Abstract BaseEstimator class to train and evaluate TensorFlow models. Users should not instantiate or subclass this class. Instead, use an @@ -428,7 +428,7 @@ class BaseEstimator( # necessary. # pylint: disable=g-doc-exception raise ValueError( - "model_dir are set both in constructor and RunConfig, but with " + 'model_dir are set both in constructor and RunConfig, but with ' "different values. In constructor: '{}', in RunConfig: " "'{}' ".format(model_dir, self._config.model_dir)) # pylint: enable=g-doc-exception @@ -457,12 +457,16 @@ class BaseEstimator( # TODO(wicke): make RunConfig immutable, and then return it without a copy. return copy.deepcopy(self._config) - @deprecated_args( - SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, ('x', None), - ('y', None), ('batch_size', None) - ) - def fit(self, x=None, y=None, input_fn=None, steps=None, batch_size=None, - monitors=None, max_steps=None): + @deprecated_args(SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, + ('x', None), ('y', None), ('batch_size', None)) + def fit(self, + x=None, + y=None, + input_fn=None, + steps=None, + batch_size=None, + monitors=None, + max_steps=None): # pylint: disable=g-doc-args,g-doc-return-or-yield """See `Trainable`. @@ -494,13 +498,15 @@ class BaseEstimator( logging.info('Loss for final step: %s.', loss) return self - @deprecated_args( - SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, ('x', None), - ('y', None), ('batch_size', None) - ) - def partial_fit( - self, x=None, y=None, input_fn=None, steps=1, batch_size=None, - monitors=None): + @deprecated_args(SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, + ('x', None), ('y', None), ('batch_size', None)) + def partial_fit(self, + x=None, + y=None, + input_fn=None, + steps=1, + batch_size=None, + monitors=None): """Incremental fit on a batch of samples. This method is expected to be called several times consecutively @@ -536,13 +542,16 @@ class BaseEstimator( """ logging.warning('The current implementation of partial_fit is not optimized' ' for use in a loop. Consider using fit() instead.') - return self.fit(x=x, y=y, input_fn=input_fn, steps=steps, - batch_size=batch_size, monitors=monitors) + return self.fit( + x=x, + y=y, + input_fn=input_fn, + steps=steps, + batch_size=batch_size, + monitors=monitors) - @deprecated_args( - SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, ('x', None), - ('y', None), ('batch_size', None) - ) + @deprecated_args(SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, + ('x', None), ('y', None), ('batch_size', None)) def evaluate(self, x=None, y=None, @@ -584,13 +593,14 @@ class BaseEstimator( eval_results.update({'global_step': global_step}) return eval_results - @deprecated_args( - SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, ('x', None), - ('batch_size', None), ('as_iterable', True) - ) - def predict( - self, x=None, input_fn=None, batch_size=None, outputs=None, - as_iterable=True): + @deprecated_args(SCIKIT_DECOUPLE_DATE, SCIKIT_DECOUPLE_INSTRUCTIONS, + ('x', None), ('batch_size', None), ('as_iterable', True)) + def predict(self, + x=None, + input_fn=None, + batch_size=None, + outputs=None, + as_iterable=True): """Returns predictions for given features. Args: @@ -651,16 +661,17 @@ class BaseEstimator( return self._model_dir @deprecated('2017-03-25', 'Please use Estimator.export_savedmodel() instead.') - def export(self, - export_dir, - input_fn=export._default_input_fn, # pylint: disable=protected-access - input_feature_key=None, - use_deprecated_input_fn=True, - signature_fn=None, - prediction_key=None, - default_batch_size=1, - exports_to_keep=None, - checkpoint_path=None): + def export( + self, + export_dir, + input_fn=export._default_input_fn, # pylint: disable=protected-access + input_feature_key=None, + use_deprecated_input_fn=True, + signature_fn=None, + prediction_key=None, + default_batch_size=1, + exports_to_keep=None, + checkpoint_path=None): """Exports inference graph into given dir. Args: @@ -798,8 +809,8 @@ class BaseEstimator( logging.debug('Setting feature info to %s.', str(self._features_info)) if labels is not None: if self._labels_info is not None: - logging.debug('Given labels: %s, required signatures: %s.', - str(labels), str(self._labels_info)) + logging.debug('Given labels: %s, required signatures: %s.', str(labels), + str(self._labels_info)) if not tensor_signature.tensors_compatible(labels, self._labels_info): raise ValueError('Labels are incompatible with given information. ' 'Given labels: %s, required signatures: %s.' % @@ -850,13 +861,13 @@ class BaseEstimator( if not checkpoint_path: latest_path = saver.latest_checkpoint(self._model_dir) if not latest_path: - raise NotFittedError("Couldn't find trained model at %s." - % self._model_dir) + raise NotFittedError( + "Couldn't find trained model at %s." % self._model_dir) checkpoint_path = latest_path # Setup output directory. - eval_dir = os.path.join(self._model_dir, 'eval' if not name else - 'eval_' + name) + eval_dir = os.path.join(self._model_dir, 'eval' + if not name else 'eval_' + name) with ops.Graph().as_default() as g: random_seed.set_random_seed(self._config.tf_random_seed) @@ -879,8 +890,7 @@ class BaseEstimator( 'Use steps=None if intended.') if steps: hooks.append( - evaluation.StopAfterNEvalsHook( - steps, log_progress=log_progress)) + evaluation.StopAfterNEvalsHook(steps, log_progress=log_progress)) global_step_key = 'global_step' while global_step_key in eval_dict: @@ -916,8 +926,8 @@ class BaseEstimator( # Check that model has been trained. checkpoint_path = saver.latest_checkpoint(self._model_dir) if not checkpoint_path: - raise NotFittedError("Couldn't find trained model at %s." - % self._model_dir) + raise NotFittedError( + "Couldn't find trained model at %s." % self._model_dir) with ops.Graph().as_default() as g: random_seed.set_random_seed(self._config.tf_random_seed) @@ -979,7 +989,8 @@ class BaseEstimator( existing_keys = predictions.keys() predictions = { key: value - for key, value in six.iteritems(predictions) if key in outputs + for key, value in six.iteritems(predictions) + if key in outputs } if not predictions: raise ValueError('Expected to run at least one output from %s, ' @@ -1045,8 +1056,7 @@ class BaseEstimator( chief_only_hooks=chief_hooks + model_fn_ops.training_chief_hooks, save_checkpoint_secs=0, # Saving is handled by a hook. save_summaries_steps=self._config.save_summary_steps, - config=self._session_config - ) as mon_sess: + config=self._session_config) as mon_sess: loss = None while not mon_sess.should_stop(): _, loss = mon_sess.run([model_fn_ops.train_op, model_fn_ops.loss]) @@ -1137,8 +1147,7 @@ class Estimator(BaseEstimator): if params is not None and 'params' not in model_fn_args: raise ValueError('Estimator\'s model_fn (%s) does not have a params ' 'argument, but params (%s) were passed to the ' - 'Estimator\'s constructor.' % - (model_fn, params)) + 'Estimator\'s constructor.' % (model_fn, params)) if params is None and 'params' in model_fn_args: logging.warning('Estimator\'s model_fn (%s) includes params ' 'argument, but params are not passed to Estimator.', @@ -1192,8 +1201,9 @@ class Estimator(BaseEstimator): # Custom metrics should overwrite defaults. if metrics: - model_fn_ops.eval_metric_ops.update(_make_metrics_ops( - metrics, features, labels, model_fn_ops.predictions)) + model_fn_ops.eval_metric_ops.update( + _make_metrics_ops(metrics, features, labels, + model_fn_ops.predictions)) return model_fn_ops @@ -1238,8 +1248,8 @@ class Estimator(BaseEstimator): Raises: ValueError: if `metrics` don't match `labels`. """ - model_fn_ops = self._call_model_fn( - features, labels, model_fn_lib.ModeKeys.EVAL, metrics) + model_fn_ops = self._call_model_fn(features, labels, + model_fn_lib.ModeKeys.EVAL, metrics) if metric_key.MetricKey.LOSS not in model_fn_ops.eval_metric_ops: model_fn_ops.eval_metric_ops[metric_key.MetricKey.LOSS] = ( @@ -1263,14 +1273,16 @@ class Estimator(BaseEstimator): self._labels_info) return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.INFER) - def export_savedmodel( - self, export_dir_base, serving_input_fn, - default_output_alternative_key=None, - assets_extra=None, - as_text=False, - checkpoint_path=None, - graph_rewrite_specs=(GraphRewriteSpec((tag_constants.SERVING,), ()),), - strip_default_attrs=False): + def export_savedmodel(self, + export_dir_base, + serving_input_fn, + default_output_alternative_key=None, + assets_extra=None, + as_text=False, + checkpoint_path=None, + graph_rewrite_specs=(GraphRewriteSpec( + (tag_constants.SERVING,), ()),), + strip_default_attrs=False): # pylint: disable=line-too-long """Exports inference graph as a SavedModel into given dir. @@ -1297,7 +1309,8 @@ class Estimator(BaseEstimator): default serving tag ("serve") and no rewriting. strip_default_attrs: Boolean. If `True`, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see - [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes). + [Stripping Default-Valued + Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes). Returns: The string path to the exported directory. @@ -1313,8 +1326,8 @@ class Estimator(BaseEstimator): # Locate the latest checkpoint checkpoint_path = saver.latest_checkpoint(self._model_dir) if not checkpoint_path: - raise NotFittedError("Couldn't find trained model at %s." - % self._model_dir) + raise NotFittedError( + "Couldn't find trained model at %s." % self._model_dir) export_dir = saved_model_export_utils.get_timestamped_export_dir( export_dir_base) @@ -1348,10 +1361,10 @@ class Estimator(BaseEstimator): saved_model_export_utils.get_output_alternatives( model_fn_ops, default_output_alternative_key)) - init_op = control_flow_ops.group( - variables.local_variables_initializer(), - resources.initialize_resources(resources.shared_resources()), - lookup_ops.tables_initializer()) + init_op = control_flow_ops.group(variables.local_variables_initializer(), + resources.initialize_resources( + resources.shared_resources()), + lookup_ops.tables_initializer()) # Build the SignatureDefs from all pairs of input and output alternatives signature_def_map = saved_model_export_utils.build_all_signature_defs( @@ -1381,10 +1394,10 @@ class Estimator(BaseEstimator): # TODO(soergel): switch to main_op or otherwise update when dust settles builder.add_meta_graph_and_variables( - session, untransformed_tags, + session, + untransformed_tags, signature_def_map=signature_def_map, - assets_collection=ops.get_collection( - ops.GraphKeys.ASSET_FILEPATHS), + assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS), legacy_init_op=init_op, strip_default_attrs=strip_default_attrs) @@ -1395,12 +1408,16 @@ class Estimator(BaseEstimator): if graph_rewrite_specs[1:]: # Prepare the input_names and output_names needed for the # meta_graph_transform call below. - input_names = [tensor.name - for input_dict in input_alternatives.values() - for tensor in input_dict.values()] - output_names = [tensor.name - for output_alternative in output_alternatives.values() - for tensor in output_alternative[1].values()] + input_names = [ + tensor.name + for input_dict in input_alternatives.values() + for tensor in input_dict.values() + ] + output_names = [ + tensor.name + for output_alternative in output_alternatives.values() + for tensor in output_alternative[1].values() + ] # Write the additional MetaGraphDefs for graph_rewrite_spec in graph_rewrite_specs[1:]: @@ -1419,11 +1436,11 @@ class Estimator(BaseEstimator): # Add the extra assets if assets_extra: - assets_extra_path = os.path.join(compat.as_bytes(temp_export_dir), - compat.as_bytes('assets.extra')) + assets_extra_path = os.path.join( + compat.as_bytes(temp_export_dir), compat.as_bytes('assets.extra')) for dest_relative, source in assets_extra.items(): - dest_absolute = os.path.join(compat.as_bytes(assets_extra_path), - compat.as_bytes(dest_relative)) + dest_absolute = os.path.join( + compat.as_bytes(assets_extra_path), compat.as_bytes(dest_relative)) dest_path = os.path.dirname(dest_absolute) gfile.MakeDirs(dest_path) gfile.Copy(source, dest_absolute) @@ -1443,25 +1460,36 @@ class SKCompat(sklearn.BaseEstimator): def fit(self, x, y, batch_size=128, steps=None, max_steps=None, monitors=None): - input_fn, feed_fn = _get_input_fn(x, y, input_fn=None, feed_fn=None, - batch_size=batch_size, shuffle=True, - epochs=None) + input_fn, feed_fn = _get_input_fn( + x, + y, + input_fn=None, + feed_fn=None, + batch_size=batch_size, + shuffle=True, + epochs=None) all_monitors = [] if feed_fn: all_monitors = [basic_session_run_hooks.FeedFnHook(feed_fn)] if monitors: all_monitors.extend(monitors) - self._estimator.fit(input_fn=input_fn, - steps=steps, - max_steps=max_steps, - monitors=all_monitors) + self._estimator.fit( + input_fn=input_fn, + steps=steps, + max_steps=max_steps, + monitors=all_monitors) return self def score(self, x, y, batch_size=128, steps=None, metrics=None, name=None): - input_fn, feed_fn = _get_input_fn(x, y, input_fn=None, - feed_fn=None, batch_size=batch_size, - shuffle=False, epochs=1) + input_fn, feed_fn = _get_input_fn( + x, + y, + input_fn=None, + feed_fn=None, + batch_size=batch_size, + shuffle=False, + epochs=1) if metrics is not None and not isinstance(metrics, dict): raise ValueError('Metrics argument should be None or dict. ' 'Got %s.' % metrics) @@ -1477,8 +1505,13 @@ class SKCompat(sklearn.BaseEstimator): def predict(self, x, batch_size=128, outputs=None): input_fn, feed_fn = _get_input_fn( - x, None, input_fn=None, feed_fn=None, batch_size=batch_size, - shuffle=False, epochs=1) + x, + None, + input_fn=None, + feed_fn=None, + batch_size=batch_size, + shuffle=False, + epochs=1) results = list( self._estimator._infer_model( input_fn=input_fn, @@ -1489,7 +1522,6 @@ class SKCompat(sklearn.BaseEstimator): if not isinstance(results[0], dict): return np.concatenate([output for output in results], axis=0) return { - key: np.concatenate( - [output[key] for output in results], axis=0) + key: np.concatenate([output[key] for output in results], axis=0) for key in results[0] } diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py b/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py index 5f682838b7..d81a534b79 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py @@ -111,8 +111,8 @@ def boston_eval_fn(): constant_op.constant(boston.data), [n_examples, _BOSTON_INPUT_DIM]) labels = array_ops.reshape( constant_op.constant(boston.target), [n_examples, 1]) - return array_ops.concat([features, features], 0), array_ops.concat( - [labels, labels], 0) + return array_ops.concat([features, features], + 0), array_ops.concat([labels, labels], 0) def extract(data, key): @@ -147,7 +147,10 @@ def linear_model_fn(features, labels, mode): (_, features), = features.items() prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( - loss, training_util.get_global_step(), optimizer='Adagrad', learning_rate=0.1) + loss, + training_util.get_global_step(), + optimizer='Adagrad', + learning_rate=0.1) return prediction, loss, train_op @@ -157,7 +160,10 @@ def linear_model_fn_with_model_fn_ops(features, labels, mode): model_fn.ModeKeys.INFER) prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( - loss, training_util.get_global_step(), optimizer='Adagrad', learning_rate=0.1) + loss, + training_util.get_global_step(), + optimizer='Adagrad', + learning_rate=0.1) return model_fn.ModelFnOps( mode=mode, predictions=prediction, loss=loss, train_op=train_op) @@ -168,7 +174,10 @@ def logistic_model_no_mode_fn(features, labels): labels = array_ops.one_hot(labels, 3, 1, 0) prediction, loss = (models.logistic_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( - loss, training_util.get_global_step(), optimizer='Adagrad', learning_rate=0.1) + loss, + training_util.get_global_step(), + optimizer='Adagrad', + learning_rate=0.1) return { 'class': math_ops.argmax(prediction, 1), 'prob': prediction @@ -184,14 +193,12 @@ def _build_estimator_for_export_tests(tmpdir): def _input_fn(): iris = base.load_iris() return { - 'feature': constant_op.constant( - iris.data, dtype=dtypes.float32) + 'feature': constant_op.constant(iris.data, dtype=dtypes.float32) }, constant_op.constant( iris.target, shape=[150], dtype=dtypes.int32) feature_columns = [ - feature_column_lib.real_valued_column( - 'feature', dimension=4) + feature_column_lib.real_valued_column('feature', dimension=4) ] est = linear.LinearRegressor(feature_columns) @@ -291,8 +298,8 @@ class CheckCallsMonitor(monitors_lib.BaseMonitor): self.begin_calls == self.expect_calls) -def _model_fn_ops( - expected_features, expected_labels, actual_features, actual_labels, mode): +def _model_fn_ops(expected_features, expected_labels, actual_features, + actual_labels, mode): assert_ops = tuple([ check_ops.assert_equal( expected_features[k], actual_features[k], name='assert_%s' % k) @@ -310,11 +317,11 @@ def _model_fn_ops( def _make_input_fn(features, labels): + def _input_fn(): - return { - k: constant_op.constant(v) - for k, v in six.iteritems(features) - }, constant_op.constant(labels) + return {k: constant_op.constant(v) + for k, v in six.iteritems(features)}, constant_op.constant(labels) + return _input_fn @@ -369,11 +376,13 @@ class EstimatorModelFnTest(test.TestCase): self.assertEqual(expected_params, params) self.assertTrue(config.i_am_test) return _model_fn_ops(features, labels, arg0, arg1, mode) + partial_model_fn = functools.partial( _model_fn, foo=expected_foo, bar=expected_bar) est = estimator.Estimator( - model_fn=partial_model_fn, params=expected_params, + model_fn=partial_model_fn, + params=expected_params, config=expected_config) self.assertEqual(0, model_fn_call_count[0]) est.fit(input_fn=_make_input_fn(features, labels), steps=1) @@ -382,7 +391,12 @@ class EstimatorModelFnTest(test.TestCase): def testModelFnWithModelDir(self): expected_param = {'some_param': 'some_value'} expected_model_dir = tempfile.mkdtemp() - def _argument_checker(features, labels, mode, params, config=None, + + def _argument_checker(features, + labels, + mode, + params, + config=None, model_dir=None): _, _, _ = features, labels, config self.assertEqual(model_fn.ModeKeys.TRAIN, mode) @@ -390,9 +404,11 @@ class EstimatorModelFnTest(test.TestCase): self.assertEqual(model_dir, expected_model_dir) return (constant_op.constant(0.), constant_op.constant(0.), training_util.get_global_step().assign_add(1)) - est = estimator.Estimator(model_fn=_argument_checker, - params=expected_param, - model_dir=expected_model_dir) + + est = estimator.Estimator( + model_fn=_argument_checker, + params=expected_param, + model_dir=expected_model_dir) est.fit(input_fn=boston_input_fn, steps=1) def testInvalidModelFn_no_train_op(self): @@ -447,8 +463,7 @@ class EstimatorModelFnTest(test.TestCase): est.predict(input_fn=boston_input_fn) with self.assertRaisesRegexp(ValueError, 'Missing prediction'): est.predict( - input_fn=functools.partial( - boston_input_fn, num_epochs=1), + input_fn=functools.partial(boston_input_fn, num_epochs=1), as_iterable=True) def testModelFnScaffoldInTraining(self): @@ -498,15 +513,17 @@ class EstimatorModelFnTest(test.TestCase): self.assertTrue(self.mock_saver.restore.called) est.predict(input_fn=input_fn) self.assertTrue(self.mock_saver.restore.called) + def serving_input_fn(): - serialized_tf_example = array_ops.placeholder(dtype=dtypes.string, - shape=[None], - name='input_example_tensor') + serialized_tf_example = array_ops.placeholder( + dtype=dtypes.string, shape=[None], name='input_example_tensor') features, labels = input_fn() - return input_fn_utils.InputFnOps( - features, labels, {'examples': serialized_tf_example}) + return input_fn_utils.InputFnOps(features, labels, { + 'examples': serialized_tf_example + }) - est.export_savedmodel(os.path.join(est.model_dir, 'export'), serving_input_fn) + est.export_savedmodel( + os.path.join(est.model_dir, 'export'), serving_input_fn) self.assertTrue(self.mock_saver.restore.called) @@ -550,33 +567,28 @@ class EstimatorTest(test.TestCase): def testRunConfigModelDir(self): config = run_config.RunConfig(model_dir='test_dir') - est = estimator.Estimator(model_fn=linear_model_fn, - config=config) + est = estimator.Estimator(model_fn=linear_model_fn, config=config) self.assertEqual('test_dir', est.config.model_dir) self.assertEqual('test_dir', est.model_dir) def testModelDirAndRunConfigModelDir(self): config = run_config.RunConfig(model_dir='test_dir') - est = estimator.Estimator(model_fn=linear_model_fn, - config=config, - model_dir='test_dir') + est = estimator.Estimator( + model_fn=linear_model_fn, config=config, model_dir='test_dir') self.assertEqual('test_dir', est.config.model_dir) with self.assertRaisesRegexp( - ValueError, - 'model_dir are set both in constructor and RunConfig, ' + ValueError, 'model_dir are set both in constructor and RunConfig, ' 'but with different'): - estimator.Estimator(model_fn=linear_model_fn, - config=config, - model_dir='different_dir') + estimator.Estimator( + model_fn=linear_model_fn, config=config, model_dir='different_dir') def testModelDirIsCopiedToRunConfig(self): config = run_config.RunConfig() self.assertIsNone(config.model_dir) - est = estimator.Estimator(model_fn=linear_model_fn, - model_dir='test_dir', - config=config) + est = estimator.Estimator( + model_fn=linear_model_fn, model_dir='test_dir', config=config) self.assertEqual('test_dir', est.config.model_dir) self.assertEqual('test_dir', est.model_dir) @@ -656,25 +668,27 @@ class EstimatorTest(test.TestCase): boston = base.load_boston() output_dir = tempfile.mkdtemp() est = estimator.SKCompat( - estimator.Estimator( - model_fn=linear_model_fn, model_dir=output_dir)) + estimator.Estimator(model_fn=linear_model_fn, model_dir=output_dir)) float64_labels = boston.target.astype(np.float64) est.fit(x=boston.data, y=float64_labels, steps=50) scores = est.score( x=boston.data, y=float64_labels, - metrics={'MSE': metric_ops.streaming_mean_squared_error}) + metrics={ + 'MSE': metric_ops.streaming_mean_squared_error + }) del est # Create another estimator object with the same output dir. est2 = estimator.SKCompat( - estimator.Estimator( - model_fn=linear_model_fn, model_dir=output_dir)) + estimator.Estimator(model_fn=linear_model_fn, model_dir=output_dir)) # Check we can evaluate and predict. scores2 = est2.score( x=boston.data, y=float64_labels, - metrics={'MSE': metric_ops.streaming_mean_squared_error}) + metrics={ + 'MSE': metric_ops.streaming_mean_squared_error + }) self.assertAllClose(scores['MSE'], scores2['MSE']) predictions = np.array(list(est2.predict(x=boston.data))) other_score = _sklearn.mean_squared_error(predictions, float64_labels) @@ -685,14 +699,15 @@ class EstimatorTest(test.TestCase): scores3 = est2.score( x=boston.data, y=float64_labels, - metrics={'MSE': metric_ops.streaming_mean_squared_error}) + metrics={ + 'MSE': metric_ops.streaming_mean_squared_error + }) self.assertLess(scores3['MSE'], scores['MSE']) def test_checkpoint_contains_relative_paths(self): tmpdir = tempfile.mkdtemp() est = estimator.Estimator( - model_dir=tmpdir, - model_fn=linear_model_fn_with_model_fn_ops) + model_dir=tmpdir, model_fn=linear_model_fn_with_model_fn_ops) est.fit(input_fn=boston_input_fn, steps=5) checkpoint_file_content = file_io.read_file_to_string( @@ -700,22 +715,20 @@ class EstimatorTest(test.TestCase): ckpt = checkpoint_state_pb2.CheckpointState() text_format.Merge(checkpoint_file_content, ckpt) self.assertEqual(ckpt.model_checkpoint_path, 'model.ckpt-5') - self.assertAllEqual( - ['model.ckpt-1', 'model.ckpt-5'], ckpt.all_model_checkpoint_paths) + self.assertAllEqual(['model.ckpt-1', 'model.ckpt-5'], + ckpt.all_model_checkpoint_paths) def test_train_save_copy_reload(self): tmpdir = tempfile.mkdtemp() model_dir1 = os.path.join(tmpdir, 'model_dir1') est1 = estimator.Estimator( - model_dir=model_dir1, - model_fn=linear_model_fn_with_model_fn_ops) + model_dir=model_dir1, model_fn=linear_model_fn_with_model_fn_ops) est1.fit(input_fn=boston_input_fn, steps=5) model_dir2 = os.path.join(tmpdir, 'model_dir2') os.renames(model_dir1, model_dir2) est2 = estimator.Estimator( - model_dir=model_dir2, - model_fn=linear_model_fn_with_model_fn_ops) + model_dir=model_dir2, model_fn=linear_model_fn_with_model_fn_ops) self.assertEqual(5, est2.get_variable_value('global_step')) est2.fit(input_fn=boston_input_fn, steps=5) self.assertEqual(10, est2.get_variable_value('global_step')) @@ -724,7 +737,9 @@ class EstimatorTest(test.TestCase): boston = base.load_boston() est = estimator.SKCompat( estimator.Estimator( - model_fn=linear_model_params_fn, params={'learning_rate': 0.01})) + model_fn=linear_model_params_fn, params={ + 'learning_rate': 0.01 + })) est.fit(x=boston.data, y=boston.target, steps=100) def testHooksNotChanged(self): @@ -824,11 +839,13 @@ class EstimatorTest(test.TestCase): def testMonitorsForFit(self): est = estimator.Estimator(model_fn=linear_model_fn) - est.fit(input_fn=boston_input_fn, - steps=21, - monitors=[CheckCallsMonitor(expect_calls=21)]) + est.fit( + input_fn=boston_input_fn, + steps=21, + monitors=[CheckCallsMonitor(expect_calls=21)]) def testHooksForEvaluate(self): + class CheckCallHook(session_run_hook.SessionRunHook): def __init__(self): @@ -874,7 +891,9 @@ class EstimatorTest(test.TestCase): est.evaluate( input_fn=boston_input_fn, steps=200, - metrics={'MSE': _streaming_mean_squared_error_histogram}) + metrics={ + 'MSE': _streaming_mean_squared_error_histogram + }) events = util_test.latest_events(est.model_dir + '/eval') output_values = {} for e in events: @@ -903,7 +922,9 @@ class EstimatorTest(test.TestCase): est.evaluate( input_fn=boston_input_fn, steps=200, - metrics={'PMT': _streaming_precition_mean_tensor}) + metrics={ + 'PMT': _streaming_precition_mean_tensor + }) events = util_test.latest_events(est.model_dir + '/eval') output_values = {} for e in events: @@ -956,8 +977,8 @@ class EstimatorTest(test.TestCase): self.assertTrue( gfile.Exists( os.path.join( - compat.as_bytes(export_dir), compat.as_bytes( - 'saved_model.pb')))) + compat.as_bytes(export_dir), + compat.as_bytes('saved_model.pb')))) self.assertTrue( gfile.Exists( os.path.join( @@ -1017,11 +1038,11 @@ class EstimatorTest(test.TestCase): self.assertTrue('input_example_tensor' in graph_ops) self.assertTrue('ParseExample/ParseExample' in graph_ops) self.assertTrue('linear/linear/feature/matmul' in graph_ops) - self.assertItemsEqual( - ['bogus_lookup', 'feature'], - [compat.as_str_any(x) for x in graph.get_collection( - constants.COLLECTION_DEF_KEY_FOR_INPUT_FEATURE_KEYS)]) - + self.assertItemsEqual(['bogus_lookup', 'feature'], [ + compat.as_str_any(x) + for x in graph.get_collection( + constants.COLLECTION_DEF_KEY_FOR_INPUT_FEATURE_KEYS) + ]) # cleanup gfile.DeleteRecursively(tmpdir) @@ -1039,8 +1060,8 @@ class EstimatorTest(test.TestCase): self.assertTrue( gfile.Exists( os.path.join( - compat.as_bytes(export_dir), compat.as_bytes( - 'saved_model.pb')))) + compat.as_bytes(export_dir), + compat.as_bytes('saved_model.pb')))) self.assertTrue( gfile.Exists( os.path.join( @@ -1083,19 +1104,22 @@ class EstimatorTest(test.TestCase): export_dir_base = os.path.join( compat.as_bytes(tmpdir), compat.as_bytes('export')) export_dir = est.export_savedmodel( - export_dir_base, serving_input_fn, assets_extra=assets_extra, + export_dir_base, + serving_input_fn, + assets_extra=assets_extra, graph_rewrite_specs=[ estimator.GraphRewriteSpec(['tag_1'], []), estimator.GraphRewriteSpec(['tag_2', 'tag_3'], - ['strip_unused_nodes'])]) + ['strip_unused_nodes']) + ]) self.assertTrue(gfile.Exists(export_dir_base)) self.assertTrue(gfile.Exists(export_dir)) self.assertTrue( gfile.Exists( os.path.join( - compat.as_bytes(export_dir), compat.as_bytes( - 'saved_model.pb')))) + compat.as_bytes(export_dir), + compat.as_bytes('saved_model.pb')))) self.assertTrue( gfile.Exists( os.path.join( @@ -1208,18 +1232,15 @@ class InferRealValuedColumnsTest(test.TestCase): self.assertEqual(1, len(feature_columns)) feature_column = feature_columns[0] self.assertEqual('', feature_column.name) - self.assertEqual( - { - '': - parsing_ops.FixedLenFeature( - shape=expected_shape, dtype=expected_dtype) - }, - feature_column.config) + self.assertEqual({ + '': + parsing_ops.FixedLenFeature( + shape=expected_shape, dtype=expected_dtype) + }, feature_column.config) def testInt32Input(self): feature_columns = estimator.infer_real_valued_columns_from_input( - np.ones( - shape=[7, 8], dtype=np.int32)) + np.ones(shape=[7, 8], dtype=np.int32)) self._assert_single_feature_column([8], dtypes.int32, feature_columns) def testInt32InputFn(self): @@ -1229,8 +1250,7 @@ class InferRealValuedColumnsTest(test.TestCase): def testInt64Input(self): feature_columns = estimator.infer_real_valued_columns_from_input( - np.ones( - shape=[7, 8], dtype=np.int64)) + np.ones(shape=[7, 8], dtype=np.int64)) self._assert_single_feature_column([8], dtypes.int64, feature_columns) def testInt64InputFn(self): @@ -1240,8 +1260,7 @@ class InferRealValuedColumnsTest(test.TestCase): def testFloat32Input(self): feature_columns = estimator.infer_real_valued_columns_from_input( - np.ones( - shape=[7, 8], dtype=np.float32)) + np.ones(shape=[7, 8], dtype=np.float32)) self._assert_single_feature_column([8], dtypes.float32, feature_columns) def testFloat32InputFn(self): @@ -1251,8 +1270,7 @@ class InferRealValuedColumnsTest(test.TestCase): def testFloat64Input(self): feature_columns = estimator.infer_real_valued_columns_from_input( - np.ones( - shape=[7, 8], dtype=np.float64)) + np.ones(shape=[7, 8], dtype=np.float64)) self._assert_single_feature_column([8], dtypes.float64, feature_columns) def testFloat64InputFn(self): @@ -1271,8 +1289,8 @@ class InferRealValuedColumnsTest(test.TestCase): ValueError, 'on integer or non floating types are not supported'): # pylint: disable=g-long-lambda estimator.infer_real_valued_columns_from_input_fn( - lambda: (constant_op.constant(False, shape=[7, 8], dtype=dtypes.bool), - None)) + lambda: (constant_op.constant(False, shape=[7, 8], dtype=dtypes.bool), None) + ) def testStringInput(self): with self.assertRaisesRegexp( @@ -1309,8 +1327,9 @@ class ReplicaDeviceSetterTest(test.TestCase): def testVariablesAreOnPs(self): tf_config = {'cluster': {run_config.TaskType.PS: ['fake_ps_0']}} - with test.mock.patch.dict('os.environ', - {'TF_CONFIG': json.dumps(tf_config)}): + with test.mock.patch.dict('os.environ', { + 'TF_CONFIG': json.dumps(tf_config) + }): config = run_config.RunConfig() with ops.device(estimator._get_replica_device_setter(config)): @@ -1337,14 +1356,14 @@ class ReplicaDeviceSetterTest(test.TestCase): def testMutableHashTableIsOnPs(self): tf_config = {'cluster': {run_config.TaskType.PS: ['fake_ps_0']}} - with test.mock.patch.dict('os.environ', - {'TF_CONFIG': json.dumps(tf_config)}): + with test.mock.patch.dict('os.environ', { + 'TF_CONFIG': json.dumps(tf_config) + }): config = run_config.RunConfig() with ops.device(estimator._get_replica_device_setter(config)): default_val = constant_op.constant([-1, -1], dtypes.int64) - table = lookup.MutableHashTable(dtypes.string, dtypes.int64, - default_val) + table = lookup.MutableHashTable(dtypes.string, dtypes.int64, default_val) input_string = constant_op.constant(['brain', 'salad', 'tank']) output = table.lookup(input_string) self.assertDeviceEqual('/job:ps/task:0', table._table_ref.device) @@ -1354,8 +1373,7 @@ class ReplicaDeviceSetterTest(test.TestCase): with ops.device( estimator._get_replica_device_setter(run_config.RunConfig())): default_val = constant_op.constant([-1, -1], dtypes.int64) - table = lookup.MutableHashTable(dtypes.string, dtypes.int64, - default_val) + table = lookup.MutableHashTable(dtypes.string, dtypes.int64, default_val) input_string = constant_op.constant(['brain', 'salad', 'tank']) output = table.lookup(input_string) self.assertDeviceEqual('', table._table_ref.device) @@ -1371,8 +1389,9 @@ class ReplicaDeviceSetterTest(test.TestCase): 'index': 3 } } - with test.mock.patch.dict('os.environ', - {'TF_CONFIG': json.dumps(tf_config)}): + with test.mock.patch.dict('os.environ', { + 'TF_CONFIG': json.dumps(tf_config) + }): config = run_config.RunConfig() with ops.device(estimator._get_replica_device_setter(config)): diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimators_test.py b/tensorflow/contrib/learn/python/learn/estimators/estimators_test.py index 8131e0fde6..2113fae394 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimators_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimators_test.py @@ -72,9 +72,11 @@ class FeatureEngineeringFunctionTest(test.TestCase): # predictions = transformed_x (9) self.assertEqual(9., prediction) metrics = estimator.evaluate( - input_fn=input_fn, steps=1, - metrics={"label": - metric_spec.MetricSpec(lambda predictions, labels: labels)}) + input_fn=input_fn, + steps=1, + metrics={ + "label": metric_spec.MetricSpec(lambda predictions, labels: labels) + }) # labels = transformed_y (99) self.assertEqual(99., metrics["label"]) @@ -82,10 +84,10 @@ class FeatureEngineeringFunctionTest(test.TestCase): def input_fn(): return { - "x": constant_op.constant(["9."]) - }, { - "y": constant_op.constant(["99."]) - } + "x": constant_op.constant(["9."]) + }, { + "y": constant_op.constant(["99."]) + } def feature_engineering_fn(features, labels): # Github #12205: raise a TypeError if called twice. @@ -104,15 +106,17 @@ class FeatureEngineeringFunctionTest(test.TestCase): return predictions, loss, update_global_step estimator = estimator_lib.Estimator( - model_fn=model_fn, feature_engineering_fn=feature_engineering_fn) + model_fn=model_fn, feature_engineering_fn=feature_engineering_fn) estimator.fit(input_fn=input_fn, steps=1) prediction = next(estimator.predict(input_fn=input_fn, as_iterable=True)) # predictions = transformed_x (9) self.assertEqual(9., prediction) metrics = estimator.evaluate( - input_fn=input_fn, steps=1, - metrics={"label": - metric_spec.MetricSpec(lambda predictions, labels: labels)}) + input_fn=input_fn, + steps=1, + metrics={ + "label": metric_spec.MetricSpec(lambda predictions, labels: labels) + }) # labels = transformed_y (99) self.assertEqual(99., metrics["label"]) @@ -150,12 +154,10 @@ class FeatureEngineeringFunctionTest(test.TestCase): # predictions = x prediction_with_fe_fn = next( - estimator_with_fe_fn.predict( - input_fn=input_fn, as_iterable=True)) + estimator_with_fe_fn.predict(input_fn=input_fn, as_iterable=True)) self.assertEqual(9., prediction_with_fe_fn) prediction_without_fe_fn = next( - estimator_without_fe_fn.predict( - input_fn=input_fn, as_iterable=True)) + estimator_without_fe_fn.predict(input_fn=input_fn, as_iterable=True)) self.assertEqual(1., prediction_without_fe_fn) diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py index 3e0b1ad21a..0948dee7e2 100644 --- a/tensorflow/contrib/learn/python/learn/monitors.py +++ b/tensorflow/contrib/learn/python/learn/monitors.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Monitors instrument the training process. @@get_default_monitors @@ -151,8 +150,8 @@ class BaseMonitor(object): ValueError: if we've not begun an epoch, or `epoch` number does not match. """ if self._current_epoch != epoch: - raise ValueError( - "epoch_end expected %s but got %s.", self._current_epoch, epoch) + raise ValueError("epoch_end expected %s but got %s.", self._current_epoch, + epoch) self._current_epoch = None def step_begin(self, step): @@ -171,8 +170,8 @@ class BaseMonitor(object): ValueError: if we've already begun a step, or `step` < 0, or `step` > `max_steps`. """ - if (step < 0) or ( - (self._max_steps is not None) and (step > self._max_steps)): + if (step < 0) or ((self._max_steps is not None) and + (step > self._max_steps)): raise ValueError("Invalid step %s." % step) self._current_step = step return [] @@ -203,8 +202,8 @@ class BaseMonitor(object): ValueError: if we've not begun a step, or `step` number does not match. """ if self._current_step != step: - raise ValueError( - "step_end expected %s but got %s.", self._current_step, step) + raise ValueError("step_end expected %s but got %s.", self._current_step, + step) self._current_step = None return False @@ -253,6 +252,7 @@ class EveryN(BaseMonitor): treatment. """ + # TODO(ipolosukhin): Add also every n seconds. def __init__(self, every_n_steps=100, first_n_steps=1): @@ -475,8 +475,8 @@ class LoggingTrainable(EveryN): super(LoggingTrainable, self).every_n_step_begin(step) # Get a list of trainable variables at the beginning of every N steps. # We cannot get this in __init__ because train_op has not been generated. - trainables = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES, - scope=self._scope) + trainables = ops.get_collection( + ops.GraphKeys.TRAINABLE_VARIABLES, scope=self._scope) self._names = {} for var in trainables: self._names[var.name] = var.value().name @@ -561,12 +561,19 @@ class ValidationMonitor(EveryN): provided. """ - def __init__(self, x=None, y=None, input_fn=None, batch_size=None, + def __init__(self, + x=None, + y=None, + input_fn=None, + batch_size=None, eval_steps=None, - every_n_steps=100, metrics=None, hooks=None, + every_n_steps=100, + metrics=None, + hooks=None, early_stopping_rounds=None, early_stopping_metric="loss", - early_stopping_metric_minimize=True, name=None): + early_stopping_metric_minimize=True, + name=None): """Initializes a ValidationMonitor. Args: @@ -597,8 +604,8 @@ class ValidationMonitor(EveryN): Raises: ValueError: If both x and input_fn are provided. """ - super(ValidationMonitor, self).__init__(every_n_steps=every_n_steps, - first_n_steps=-1) + super(ValidationMonitor, self).__init__( + every_n_steps=every_n_steps, first_n_steps=-1) # TODO(mdan): Checks like this are already done by evaluate. if x is None and input_fn is None: raise ValueError("Either x or input_fn should be provided.") @@ -654,20 +661,27 @@ class ValidationMonitor(EveryN): def _evaluate_estimator(self): if isinstance(self._estimator, core_estimator.Estimator): - if any((x is not None for x in - [self.x, self.y, self.batch_size, self.metrics])): + if any((x is not None + for x in [self.x, self.y, self.batch_size, self.metrics])): raise ValueError( "tf.estimator.Estimator does not support following " "arguments: x, y, batch_size, metrics. Should set as `None` " "in ValidationMonitor") return self._estimator.evaluate( - input_fn=self.input_fn, steps=self.eval_steps, hooks=self.hooks, + input_fn=self.input_fn, + steps=self.eval_steps, + hooks=self.hooks, name=self.name) else: return self._estimator.evaluate( - x=self.x, y=self.y, input_fn=self.input_fn, - batch_size=self.batch_size, steps=self.eval_steps, - metrics=self.metrics, hooks=self.hooks, name=self.name) + x=self.x, + y=self.y, + input_fn=self.input_fn, + batch_size=self.batch_size, + steps=self.eval_steps, + metrics=self.metrics, + hooks=self.hooks, + name=self.name) def every_n_step_end(self, step, outputs): super(ValidationMonitor, self).every_n_step_end(step, outputs) @@ -700,8 +714,9 @@ class ValidationMonitor(EveryN): # Early stopping logic. if self.early_stopping_rounds is not None: if self.early_stopping_metric not in validation_outputs: - raise ValueError("Metric %s missing from outputs %s." % ( - self.early_stopping_metric, set(validation_outputs.keys()))) + raise ValueError("Metric %s missing from outputs %s." % + (self.early_stopping_metric, + set(validation_outputs.keys()))) current_value = validation_outputs[self.early_stopping_metric] if (self._best_value is None or (self.early_stopping_metric_minimize and (current_value < self._best_value)) or @@ -712,9 +727,9 @@ class ValidationMonitor(EveryN): self._best_value_step = step stop_now = (step - self._best_value_step >= self.early_stopping_rounds) if stop_now: - logging.info("Stopping. Best step: {} with {} = {}." - .format(self._best_value_step, - self.early_stopping_metric, self._best_value)) + logging.info("Stopping. Best step: {} with {} = {}.".format( + self._best_value_step, self.early_stopping_metric, + self._best_value)) self._early_stopped = True return True return False @@ -763,8 +778,11 @@ class CaptureVariable(EveryN): self._var_values[step] = _extract_output(outputs, self._var_name) -def get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, - output_dir=None, summary_writer=None): +def get_default_monitors(loss_op=None, + summary_op=None, + save_summary_steps=100, + output_dir=None, + summary_writer=None): """Returns a default set of typically-used monitors. Args: @@ -782,9 +800,12 @@ def get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, if loss_op is not None: monitors.append(PrintTensor(tensor_names={"loss": loss_op.name})) if summary_op is not None: - monitors.append(SummarySaver(summary_op, save_steps=save_summary_steps, - output_dir=output_dir, - summary_writer=summary_writer)) + monitors.append( + SummarySaver( + summary_op, + save_steps=save_summary_steps, + output_dir=output_dir, + summary_writer=summary_writer)) return monitors @@ -794,8 +815,10 @@ class GraphDump(BaseMonitor): Note, this is very expensive, prefer `PrintTensor` in production. """ - IGNORE_OPS = ["Const", "Assign", "Identity", "Placeholder", - "RandomUniform", "Cast", "RestoreSlice"] + IGNORE_OPS = [ + "Const", "Assign", "Identity", "Placeholder", "RandomUniform", "Cast", + "RestoreSlice" + ] def __init__(self, ignore_ops=None): """Initializes GraphDump monitor. @@ -881,8 +904,8 @@ class ExportMonitor(EveryN): """Monitor that exports Estimator every N steps.""" @deprecation.deprecated("2017-03-25", - "ExportMonitor is deprecated. Please pass an " - "ExportStrategy to Experiment instead.") + "ExportMonitor is deprecated. Please pass an " + "ExportStrategy to Experiment instead.") def __init__(self, every_n_steps, export_dir, @@ -1088,8 +1111,7 @@ class CheckpointSaver(BaseMonitor): class StepCounter(EveryN): """Steps per second monitor.""" - def __init__(self, every_n_steps=100, output_dir=None, - summary_writer=None): + def __init__(self, every_n_steps=100, output_dir=None, summary_writer=None): super(StepCounter, self).__init__(every_n_steps=every_n_steps) self._summary_tag = "global_step/sec" self._last_reported_step = None @@ -1101,7 +1123,8 @@ class StepCounter(EveryN): def set_estimator(self, estimator): super(StepCounter, self).set_estimator(estimator) if self._summary_writer is None: - self._summary_writer = core_summary.FileWriterCache.get(estimator.model_dir) + self._summary_writer = core_summary.FileWriterCache.get( + estimator.model_dir) def every_n_step_end(self, current_step, outputs): current_time = time.time() @@ -1109,8 +1132,9 @@ class StepCounter(EveryN): added_steps = current_step - self._last_reported_step elapsed_time = current_time - self._last_reported_time steps_per_sec = added_steps / elapsed_time - summary = Summary(value=[Summary.Value(tag=self._summary_tag, - simple_value=steps_per_sec)]) + summary = Summary(value=[ + Summary.Value(tag=self._summary_tag, simple_value=steps_per_sec) + ]) self._summary_writer.add_summary(summary, current_step) self._last_reported_step = current_step self._last_reported_time = current_time diff --git a/tensorflow/contrib/learn/python/learn/utils/export_test.py b/tensorflow/contrib/learn/python/learn/utils/export_test.py index 95070ada3b..9bfb1fc952 100644 --- a/tensorflow/contrib/learn/python/learn/utils/export_test.py +++ b/tensorflow/contrib/learn/python/learn/utils/export_test.py @@ -50,6 +50,7 @@ def _training_input_fn(): class ExportTest(test.TestCase): + def _get_default_signature(self, export_meta_filename): """ Gets the default signature from the export.meta file. """ with session.Session(): @@ -69,18 +70,18 @@ class ExportTest(test.TestCase): # Only the written checkpoints are exported. self.assertTrue( saver.checkpoint_exists(os.path.join(export_dir, '00000001', 'export')), - 'Exported checkpoint expected but not found: %s' % - os.path.join(export_dir, '00000001', 'export')) + 'Exported checkpoint expected but not found: %s' % os.path.join( + export_dir, '00000001', 'export')) self.assertTrue( saver.checkpoint_exists(os.path.join(export_dir, '00000010', 'export')), - 'Exported checkpoint expected but not found: %s' % - os.path.join(export_dir, '00000010', 'export')) + 'Exported checkpoint expected but not found: %s' % os.path.join( + export_dir, '00000010', 'export')) self.assertEquals( six.b(os.path.join(export_dir, '00000010')), export_monitor.last_export_dir) # Validate the signature signature = self._get_default_signature( - os.path.join(export_dir, '00000010', 'export.meta')) + os.path.join(export_dir, '00000010', 'export.meta')) self.assertTrue(signature.HasField(expected_signature)) def testExportMonitor_EstimatorProvidesSignature(self): @@ -116,8 +117,7 @@ class ExportTest(test.TestCase): def _serving_input_fn(): return { _X_KEY: - random_ops.random_uniform( - shape=(1,), minval=0.0, maxval=1000.0) + random_ops.random_uniform(shape=(1,), minval=0.0, maxval=1000.0) }, None input_feature_key = 'my_example_key' @@ -160,8 +160,7 @@ class ExportTest(test.TestCase): input_feature_key: None, _X_KEY: - random_ops.random_uniform( - shape=(1,), minval=0.0, maxval=1000.0) + random_ops.random_uniform(shape=(1,), minval=0.0, maxval=1000.0) }, None monitor = learn.monitors.ExportMonitor( @@ -182,8 +181,7 @@ class ExportTest(test.TestCase): def _serving_input_fn(): return { input_feature_key: - array_ops.placeholder( - dtype=dtypes.string, shape=(1,)) + array_ops.placeholder(dtype=dtypes.string, shape=(1,)) }, None monitor = learn.monitors.ExportMonitor( @@ -204,11 +202,9 @@ class ExportTest(test.TestCase): def _serving_input_fn(): return { input_feature_key: - array_ops.placeholder( - dtype=dtypes.string, shape=(1,)), + array_ops.placeholder(dtype=dtypes.string, shape=(1,)), _X_KEY: - random_ops.random_uniform( - shape=(1,), minval=0.0, maxval=1000.0) + random_ops.random_uniform(shape=(1,), minval=0.0, maxval=1000.0) }, None export_dir = os.path.join(tempfile.mkdtemp(), 'export') @@ -227,8 +223,8 @@ class ExportTest(test.TestCase): def _regression_signature(examples, unused_features, predictions): signatures = {} - signatures['regression'] = (exporter.regression_signature(examples, - predictions)) + signatures['regression'] = ( + exporter.regression_signature(examples, predictions)) return signatures['regression'], signatures random.seed(42) @@ -248,10 +244,10 @@ class ExportTest(test.TestCase): with self.assertRaises(errors.NotFoundError): saver.checkpoint_exists(os.path.join(export_dir, '00000000', 'export')) self.assertTrue( - saver.checkpoint_exists(os.path.join(export_dir, '00000010', 'export'))) + saver.checkpoint_exists(os.path.join(export_dir, '00000010', 'export'))) # Validate the signature signature = self._get_default_signature( - os.path.join(export_dir, '00000010', 'export.meta')) + os.path.join(export_dir, '00000010', 'export.meta')) self.assertTrue(signature.HasField('regression_signature')) diff --git a/tensorflow/contrib/learn/python/learn/utils/gc_test.py b/tensorflow/contrib/learn/python/learn/utils/gc_test.py index 76cfd88e1d..e7d091e18a 100644 --- a/tensorflow/contrib/learn/python/learn/utils/gc_test.py +++ b/tensorflow/contrib/learn/python/learn/utils/gc_test.py @@ -34,12 +34,13 @@ def _create_parser(base_dir): # create a simple parser that pulls the export_version from the directory. def parser(path): # Modify the path object for RegEx match for Windows Paths - if os.name == 'nt': - match = re.match("^" + compat.as_str_any(base_dir).replace('\\','/') + "/(\\d+)$", - compat.as_str_any(path.path).replace('\\','/')) + if os.name == "nt": + match = re.match( + "^" + compat.as_str_any(base_dir).replace("\\", "/") + "/(\\d+)$", + compat.as_str_any(path.path).replace("\\", "/")) else: match = re.match("^" + compat.as_str_any(base_dir) + "/(\\d+)$", - compat.as_str_any(path.path)) + compat.as_str_any(path.path)) if not match: return None return path._replace(export_version=int(match.group(1))) @@ -63,7 +64,9 @@ class GcTest(test_util.TensorFlowTestCase): def testModExportVersion(self): paths = [ - gc.Path("/foo", 4), gc.Path("/foo", 5), gc.Path("/foo", 6), + gc.Path("/foo", 4), + gc.Path("/foo", 5), + gc.Path("/foo", 6), gc.Path("/foo", 9) ] mod = gc.mod_export_version(2) @@ -73,14 +76,21 @@ class GcTest(test_util.TensorFlowTestCase): def testOneOfEveryNExportVersions(self): paths = [ - gc.Path("/foo", 0), gc.Path("/foo", 1), gc.Path("/foo", 3), - gc.Path("/foo", 5), gc.Path("/foo", 6), gc.Path("/foo", 7), - gc.Path("/foo", 8), gc.Path("/foo", 33) + gc.Path("/foo", 0), + gc.Path("/foo", 1), + gc.Path("/foo", 3), + gc.Path("/foo", 5), + gc.Path("/foo", 6), + gc.Path("/foo", 7), + gc.Path("/foo", 8), + gc.Path("/foo", 33) ] one_of = gc.one_of_every_n_export_versions(3) self.assertEqual( one_of(paths), [ - gc.Path("/foo", 3), gc.Path("/foo", 6), gc.Path("/foo", 8), + gc.Path("/foo", 3), + gc.Path("/foo", 6), + gc.Path("/foo", 8), gc.Path("/foo", 33) ]) @@ -98,13 +108,19 @@ class GcTest(test_util.TensorFlowTestCase): f = gc.union(gc.largest_export_versions(3), gc.mod_export_version(3)) self.assertEqual( f(paths), [ - gc.Path("/foo", 0), gc.Path("/foo", 3), gc.Path("/foo", 6), - gc.Path("/foo", 7), gc.Path("/foo", 8), gc.Path("/foo", 9) + gc.Path("/foo", 0), + gc.Path("/foo", 3), + gc.Path("/foo", 6), + gc.Path("/foo", 7), + gc.Path("/foo", 8), + gc.Path("/foo", 9) ]) def testNegation(self): paths = [ - gc.Path("/foo", 4), gc.Path("/foo", 5), gc.Path("/foo", 6), + gc.Path("/foo", 4), + gc.Path("/foo", 5), + gc.Path("/foo", 6), gc.Path("/foo", 9) ] mod = gc.negation(gc.mod_export_version(2)) @@ -121,8 +137,7 @@ class GcTest(test_util.TensorFlowTestCase): gfile.MakeDirs(os.path.join(base_dir, "ignore")) self.assertEqual( - gc.get_paths(base_dir, _create_parser(base_dir)), - [ + gc.get_paths(base_dir, _create_parser(base_dir)), [ gc.Path(os.path.join(base_dir, "0"), 0), gc.Path(os.path.join(base_dir, "1"), 1), gc.Path(os.path.join(base_dir, "2"), 2) @@ -131,10 +146,10 @@ class GcTest(test_util.TensorFlowTestCase): def testMixedStrTypes(self): temp_dir = compat.as_bytes(test.get_temp_dir()) - for sub_dir in ['str', b'bytes', u'unicode']: + for sub_dir in ["str", b"bytes", u"unicode"]: base_dir = os.path.join( - (temp_dir if isinstance(sub_dir, bytes) else temp_dir.decode()), - sub_dir) + (temp_dir + if isinstance(sub_dir, bytes) else temp_dir.decode()), sub_dir) self.assertFalse(gfile.Exists(base_dir)) gfile.MakeDirs(os.path.join(compat.as_str_any(base_dir), "42")) gc.get_paths(base_dir, _create_parser(base_dir)) diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index c3de1c4c62..55946c128b 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -339,9 +339,9 @@ def streaming_mean_tensor(values, name=name) -@deprecated( - None, 'Please switch to tf.metrics.accuracy. Note that the order of the ' - 'labels and predictions arguments has been switched.') +@deprecated(None, + 'Please switch to tf.metrics.accuracy. Note that the order of the ' + 'labels and predictions arguments has been switched.') def streaming_accuracy(predictions, labels, weights=None, @@ -936,8 +936,9 @@ def streaming_curve_points(labels=None, if curve != 'ROC' and curve != 'PR': raise ValueError('curve must be either ROC or PR, %s unknown' % (curve)) kepsilon = _EPSILON # to account for floating point imprecisions - thresholds = [(i + 1) * 1.0 / (num_thresholds - 1) - for i in range(num_thresholds - 2)] + thresholds = [ + (i + 1) * 1.0 / (num_thresholds - 1) for i in range(num_thresholds - 2) + ] thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon] values, update_ops = _streaming_confusion_matrix_at_thresholds( @@ -973,9 +974,8 @@ def streaming_curve_points(labels=None, return points, update_op -@deprecated( - None, 'Please switch to tf.metrics.auc. Note that the order of the ' - 'labels and predictions arguments has been switched.') +@deprecated(None, 'Please switch to tf.metrics.auc. Note that the order of the ' + 'labels and predictions arguments has been switched.') def streaming_auc(predictions, labels, weights=None, @@ -1105,8 +1105,7 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): # For conformance, set precision to 1 when the number of positive # classifications is 0. y_axis_values = array_ops.where( - math_ops.greater(splits, 0), - math_ops.truediv(true_positives, splits), + math_ops.greater(splits, 0), math_ops.truediv(true_positives, splits), array_ops.ones_like(true_positives, dtype=dtypes.float64)) # Calculate trapezoid areas. @@ -1119,9 +1118,8 @@ def _compute_dynamic_auc(labels, predictions, curve='ROC'): # exception seems excessive) so we return 0, otherwise we finish computing. return control_flow_ops.cond( math_ops.logical_or( - math_ops.equal(total_positive, 0), - math_ops.equal(total_positive, size) - ), + math_ops.equal(total_positive, 0), math_ops.equal( + total_positive, size)), true_fn=lambda: array_ops.constant(0, dtypes.float64), false_fn=continue_computing_dynamic_auc) @@ -1185,10 +1183,10 @@ def streaming_dynamic_auc(labels, array_ops.ones_like(labels, dtypes.int64), message='labels must be 0 or 1, at least one is >1') ]): - preds_accum, update_preds = streaming_concat(predictions, - name='concat_preds') - labels_accum, update_labels = streaming_concat(labels, - name='concat_labels') + preds_accum, update_preds = streaming_concat( + predictions, name='concat_preds') + labels_accum, update_labels = streaming_concat( + labels, name='concat_labels') update_op = control_flow_ops.group(update_labels, update_preds) auc = _compute_dynamic_auc(labels_accum, preds_accum, curve=curve) if updates_collections: @@ -1571,9 +1569,9 @@ def streaming_precision_at_thresholds(predictions, name=name) -@deprecated( - None, 'Please switch to tf.metrics.recall_at_thresholds. Note that the ' - 'order of the labels and predictions arguments has been switched.') +@deprecated(None, + 'Please switch to tf.metrics.recall_at_thresholds. Note that the ' + 'order of the labels and predictions arguments has been switched.') def streaming_recall_at_thresholds(predictions, labels, thresholds, @@ -3299,8 +3297,13 @@ def count(values, return count_, update_op -def cohen_kappa(labels, predictions_idx, num_classes, weights=None, - metrics_collections=None, updates_collections=None, name=None): +def cohen_kappa(labels, + predictions_idx, + num_classes, + weights=None, + metrics_collections=None, + updates_collections=None, + name=None): """Calculates Cohen's kappa. [Cohen's kappa](https://en.wikipedia.org/wiki/Cohen's_kappa) is a statistic @@ -3367,14 +3370,15 @@ def cohen_kappa(labels, predictions_idx, num_classes, weights=None, labels = array_ops.squeeze(labels, axis=[-1]) predictions_idx, labels, weights = ( metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access - predictions=predictions_idx, labels=labels, weights=weights)) + predictions=predictions_idx, + labels=labels, + weights=weights)) predictions_idx.get_shape().assert_is_compatible_with(labels.get_shape()) - stat_dtype = (dtypes.int64 - if weights is None or weights.dtype.is_integer - else dtypes.float32) - po = metrics_impl.metric_variable( - (num_classes,), stat_dtype, name='po') + stat_dtype = ( + dtypes.int64 + if weights is None or weights.dtype.is_integer else dtypes.float32) + po = metrics_impl.metric_variable((num_classes,), stat_dtype, name='po') pe_row = metrics_impl.metric_variable( (num_classes,), stat_dtype, name='pe_row') pe_col = metrics_impl.metric_variable( @@ -3382,9 +3386,12 @@ def cohen_kappa(labels, predictions_idx, num_classes, weights=None, # Table of the counts of agreement: counts_in_table = confusion_matrix.confusion_matrix( - labels, predictions_idx, - num_classes=num_classes, weights=weights, - dtype=stat_dtype, name="counts_in_table") + labels, + predictions_idx, + num_classes=num_classes, + weights=weights, + dtype=stat_dtype, + name='counts_in_table') po_t = array_ops.diag_part(counts_in_table) pe_row_t = math_ops.reduce_sum(counts_in_table, axis=0) @@ -3404,12 +3411,14 @@ def cohen_kappa(labels, predictions_idx, num_classes, weights=None, math_ops.to_double(total)) # kappa = (po - pe) / (N - pe) k = metrics_impl._safe_scalar_div( # pylint: disable=protected-access - po_sum - pe_sum, total - pe_sum, name=name) + po_sum - pe_sum, + total - pe_sum, + name=name) return k kappa = _calculate_k(po, pe_row, pe_col, name='value') - update_op = _calculate_k(update_po, update_pe_row, update_pe_col, - name='update_op') + update_op = _calculate_k( + update_po, update_pe_row, update_pe_col, name='update_op') if metrics_collections: ops.add_to_collections(metrics_collections, kappa) diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index 89aa29f711..e067f08bab 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -46,8 +46,7 @@ def _enqueue_vector(sess, queue, values, shape=None): shape = (1, len(values)) dtype = queue.dtypes[0] sess.run( - queue.enqueue(constant_op.constant( - values, dtype=dtype, shape=shape))) + queue.enqueue(constant_op.constant(values, dtype=dtype, shape=shape))) def _binary_2d_label_to_sparse_value(labels): @@ -79,8 +78,8 @@ def _binary_2d_label_to_sparse_value(labels): batch += 1 shape = [len(labels), len(labels[0])] return sparse_tensor.SparseTensorValue( - np.array(indices, np.int64), - np.array(values, np.int64), np.array(shape, np.int64)) + np.array(indices, np.int64), np.array(values, np.int64), + np.array(shape, np.int64)) def _binary_2d_label_to_sparse(labels): @@ -125,8 +124,8 @@ def _binary_3d_label_to_sparse_value(labels): assert label == 0 shape = [len(labels), len(labels[0]), len(labels[0][0])] return sparse_tensor.SparseTensorValue( - np.array(indices, np.int64), - np.array(values, np.int64), np.array(shape, np.int64)) + np.array(indices, np.int64), np.array(values, np.int64), + np.array(shape, np.int64)) def _binary_3d_label_to_sparse(labels): @@ -669,20 +668,18 @@ class StreamingTruePositivesTest(test.TestCase): for expand_predictions in [True, False]: for expand_labels in [True, False]: for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) if expand_predictions: predictions = array_ops.expand_dims(predictions, 2) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) if expand_labels: labels = array_ops.expand_dims(labels, 2) - tp, tp_update_op = metrics.streaming_true_positives(predictions, - labels) + tp, tp_update_op = metrics.streaming_true_positives( + predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -692,14 +689,12 @@ class StreamingTruePositivesTest(test.TestCase): def testWeighted(self): for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) tp, tp_update_op = metrics.streaming_true_positives( predictions, labels, weights=37.0) @@ -717,28 +712,25 @@ class StreamingFalseNegativesTest(test.TestCase): ops.reset_default_graph() def testVars(self): - metrics.streaming_false_negatives((0, 1, 0), - (0, 1, 1)) + metrics.streaming_false_negatives((0, 1, 0), (0, 1, 1)) _assert_metric_variables(self, ('false_negatives/count:0',)) def testUnweighted(self): for expand_predictions in [True, False]: for expand_labels in [True, False]: for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) if expand_predictions: predictions = array_ops.expand_dims(predictions, 2) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) if expand_labels: labels = array_ops.expand_dims(labels, 2) - fn, fn_update_op = metrics.streaming_false_negatives(predictions, - labels) + fn, fn_update_op = metrics.streaming_false_negatives( + predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -748,14 +740,12 @@ class StreamingFalseNegativesTest(test.TestCase): def testWeighted(self): for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) fn, fn_update_op = metrics.streaming_false_negatives( predictions, labels, weights=((3.0,), (5.0,), (7.0,))) @@ -773,28 +763,25 @@ class StreamingFalsePositivesTest(test.TestCase): ops.reset_default_graph() def testVars(self): - metrics.streaming_false_positives((0, 1, 0), - (0, 1, 1)) + metrics.streaming_false_positives((0, 1, 0), (0, 1, 1)) _assert_metric_variables(self, ('false_positives/count:0',)) def testUnweighted(self): for expand_predictions in [True, False]: for expand_labels in [True, False]: for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) if expand_predictions: predictions = array_ops.expand_dims(predictions, 2) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) if expand_labels: labels = array_ops.expand_dims(labels, 2) - fp, fp_update_op = metrics.streaming_false_positives(predictions, - labels) + fp, fp_update_op = metrics.streaming_false_positives( + predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -804,20 +791,17 @@ class StreamingFalsePositivesTest(test.TestCase): def testWeighted(self): for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) fp, fp_update_op = metrics.streaming_false_positives( predictions, labels, - weights=((1.0, 2.0, 3.0, 5.0), - (7.0, 11.0, 13.0, 17.0), - (19.0, 23.0, 29.0, 31.0))) + weights=((1.0, 2.0, 3.0, 5.0), (7.0, 11.0, 13.0, 17.0), (19.0, 23.0, + 29.0, 31.0))) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -833,28 +817,25 @@ class StreamingTrueNegativesTest(test.TestCase): ops.reset_default_graph() def testVars(self): - metrics.streaming_true_negatives((0, 1, 0), - (0, 1, 1)) + metrics.streaming_true_negatives((0, 1, 0), (0, 1, 1)) _assert_metric_variables(self, ('true_negatives/count:0',)) def testUnweighted(self): for expand_predictions in [True, False]: for expand_labels in [True, False]: for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) if expand_predictions: predictions = array_ops.expand_dims(predictions, 2) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) if expand_labels: labels = array_ops.expand_dims(labels, 2) - tn, tn_update_op = metrics.streaming_true_negatives(predictions, - labels) + tn, tn_update_op = metrics.streaming_true_negatives( + predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -864,14 +845,12 @@ class StreamingTrueNegativesTest(test.TestCase): def testWeighted(self): for dtype in (dtypes_lib.bool, dtypes_lib.int32, dtypes_lib.float32): - predictions = math_ops.cast(constant_op.constant( - ((1, 0, 1, 0), - (0, 1, 1, 1), - (0, 0, 0, 0))), dtype=dtype) - labels = math_ops.cast(constant_op.constant( - ((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))), dtype=dtype) + predictions = math_ops.cast( + constant_op.constant(((1, 0, 1, 0), (0, 1, 1, 1), (0, 0, 0, 0))), + dtype=dtype) + labels = math_ops.cast( + constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))), + dtype=dtype) tn, tn_update_op = metrics.streaming_true_negatives( predictions, labels, weights=((0.0, 2.0, 3.0, 5.0),)) @@ -894,12 +873,9 @@ class StreamingTruePositivesAtThresholdsTest(test.TestCase): _assert_metric_variables(self, ('true_positives:0',)) def testUnweighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) tp, tp_update_op = metrics.streaming_true_positives_at_thresholds( predictions, labels, thresholds=(0.15, 0.5, 0.85)) @@ -910,12 +886,9 @@ class StreamingTruePositivesAtThresholdsTest(test.TestCase): self.assertAllEqual((3, 1, 0), tp.eval()) def testWeighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) tp, tp_update_op = metrics.streaming_true_positives_at_thresholds( predictions, labels, weights=37.0, thresholds=(0.15, 0.5, 0.85)) @@ -937,16 +910,14 @@ class StreamingFalseNegativesAtThresholdsTest(test.TestCase): (0.0, 1.0, 0.0), (0, 1, 1), thresholds=( 0.15, 0.5, - 0.85,)) + 0.85, + )) _assert_metric_variables(self, ('false_negatives:0',)) def testUnweighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) fn, fn_update_op = metrics.streaming_false_negatives_at_thresholds( predictions, labels, thresholds=(0.15, 0.5, 0.85)) @@ -957,12 +928,9 @@ class StreamingFalseNegativesAtThresholdsTest(test.TestCase): self.assertAllEqual((0, 2, 3), fn.eval()) def testWeighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) fn, fn_update_op = metrics.streaming_false_negatives_at_thresholds( predictions, labels, @@ -988,12 +956,9 @@ class StreamingFalsePositivesAtThresholdsTest(test.TestCase): _assert_metric_variables(self, ('false_positives:0',)) def testUnweighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) fp, fp_update_op = metrics.streaming_false_positives_at_thresholds( predictions, labels, thresholds=(0.15, 0.5, 0.85)) @@ -1004,18 +969,14 @@ class StreamingFalsePositivesAtThresholdsTest(test.TestCase): self.assertAllEqual((7, 4, 2), fp.eval()) def testWeighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) fp, fp_update_op = metrics.streaming_false_positives_at_thresholds( predictions, labels, - weights=((1.0, 2.0, 3.0, 5.0), - (7.0, 11.0, 13.0, 17.0), - (19.0, 23.0, 29.0, 31.0)), + weights=((1.0, 2.0, 3.0, 5.0), (7.0, 11.0, 13.0, 17.0), (19.0, 23.0, + 29.0, 31.0)), thresholds=(0.15, 0.5, 0.85)) with self.test_session() as sess: @@ -1037,12 +998,9 @@ class StreamingTrueNegativesAtThresholdsTest(test.TestCase): _assert_metric_variables(self, ('true_negatives:0',)) def testUnweighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) tn, tn_update_op = metrics.streaming_true_negatives_at_thresholds( predictions, labels, thresholds=(0.15, 0.5, 0.85)) @@ -1053,12 +1011,9 @@ class StreamingTrueNegativesAtThresholdsTest(test.TestCase): self.assertAllEqual((2, 5, 7), tn.eval()) def testWeighted(self): - predictions = constant_op.constant(((0.9, 0.2, 0.8, 0.1), - (0.2, 0.9, 0.7, 0.6), - (0.1, 0.2, 0.4, 0.3))) - labels = constant_op.constant(((0, 1, 1, 0), - (1, 0, 0, 0), - (0, 0, 0, 0))) + predictions = constant_op.constant( + ((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6), (0.1, 0.2, 0.4, 0.3))) + labels = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0))) tn, tn_update_op = metrics.streaming_true_negatives_at_thresholds( predictions, labels, @@ -1393,8 +1348,7 @@ class StreamingFPRTest(test.TestCase): (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1) labels = random_ops.random_uniform( (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2) - fpr, update_op = metrics.streaming_false_positive_rate( - predictions, labels) + fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1413,8 +1367,7 @@ class StreamingFPRTest(test.TestCase): predictions = constant_op.constant(np_inputs) labels = constant_op.constant(np_inputs) - fpr, update_op = metrics.streaming_false_positive_rate( - predictions, labels) + fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1424,8 +1377,7 @@ class StreamingFPRTest(test.TestCase): def testSomeCorrect(self): predictions = constant_op.constant([1, 0, 1, 0], shape=(1, 4)) labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4)) - fpr, update_op = metrics.streaming_false_positive_rate( - predictions, labels) + fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1467,8 +1419,7 @@ class StreamingFPRTest(test.TestCase): predictions = constant_op.constant(np_inputs) labels = constant_op.constant(1 - np_inputs) - fpr, update_op = metrics.streaming_false_positive_rate( - predictions, labels) + fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1478,8 +1429,7 @@ class StreamingFPRTest(test.TestCase): def testZeroFalsePositivesAndTrueNegativesGivesZeroFPR(self): predictions = array_ops.ones((1, 4)) labels = array_ops.ones((1, 4)) - fpr, update_op = metrics.streaming_false_positive_rate( - predictions, labels) + fpr, update_op = metrics.streaming_false_positive_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1521,8 +1471,7 @@ class StreamingFNRTest(test.TestCase): (10, 3), maxval=1, dtype=dtypes_lib.int64, seed=1) labels = random_ops.random_uniform( (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2) - fnr, update_op = metrics.streaming_false_negative_rate( - predictions, labels) + fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1541,8 +1490,7 @@ class StreamingFNRTest(test.TestCase): predictions = constant_op.constant(np_inputs) labels = constant_op.constant(np_inputs) - fnr, update_op = metrics.streaming_false_negative_rate( - predictions, labels) + fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1552,8 +1500,7 @@ class StreamingFNRTest(test.TestCase): def testSomeCorrect(self): predictions = constant_op.constant([1, 0, 1, 0], shape=(1, 4)) labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4)) - fnr, update_op = metrics.streaming_false_negative_rate( - predictions, labels) + fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1595,8 +1542,7 @@ class StreamingFNRTest(test.TestCase): predictions = constant_op.constant(np_inputs) labels = constant_op.constant(1 - np_inputs) - fnr, update_op = metrics.streaming_false_negative_rate( - predictions, labels) + fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1606,8 +1552,7 @@ class StreamingFNRTest(test.TestCase): def testZeroFalseNegativesAndTruePositivesGivesZeroFNR(self): predictions = array_ops.zeros((1, 4)) labels = array_ops.zeros((1, 4)) - fnr, update_op = metrics.streaming_false_negative_rate( - predictions, labels) + fnr, update_op = metrics.streaming_false_negative_rate(predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -1944,16 +1889,17 @@ class StreamingAUCTest(test.TestCase): enqueue_ops[i].append(x_queue.enqueue(x_batches[i, :])) return x_queue.dequeue() - for weights in (None, np.ones(num_samples), np.random.exponential( - scale=1.0, size=num_samples)): + for weights in (None, np.ones(num_samples), + np.random.exponential(scale=1.0, size=num_samples)): expected_auc = _np_auc(predictions, labels, weights) with self.test_session() as sess: enqueue_ops = [[] for i in range(num_batches)] tf_predictions = _enqueue_as_batches(predictions, enqueue_ops) tf_labels = _enqueue_as_batches(labels, enqueue_ops) - tf_weights = (_enqueue_as_batches(weights, enqueue_ops) if - weights is not None else None) + tf_weights = ( + _enqueue_as_batches(weights, enqueue_ops) + if weights is not None else None) for i in range(num_batches): sess.run(enqueue_ops[i]) @@ -1985,17 +1931,18 @@ class StreamingDynamicAUCTest(test.TestCase): def testUnknownCurve(self): with self.assertRaisesRegexp( ValueError, 'curve must be either ROC or PR, TEST_CURVE unknown'): - metrics.streaming_dynamic_auc(labels=array_ops.ones((10, 1)), - predictions=array_ops.ones((10, 1)), - curve='TEST_CURVE') + metrics.streaming_dynamic_auc( + labels=array_ops.ones((10, 1)), + predictions=array_ops.ones((10, 1)), + curve='TEST_CURVE') def testVars(self): metrics.streaming_dynamic_auc( labels=array_ops.ones((10, 1)), predictions=array_ops.ones((10, 1))) - _assert_metric_variables(self, ['dynamic_auc/concat_labels/array:0', - 'dynamic_auc/concat_labels/size:0', - 'dynamic_auc/concat_preds/array:0', - 'dynamic_auc/concat_preds/size:0']) + _assert_metric_variables(self, [ + 'dynamic_auc/concat_labels/array:0', 'dynamic_auc/concat_labels/size:0', + 'dynamic_auc/concat_preds/array:0', 'dynamic_auc/concat_preds/size:0' + ]) def testMetricsCollection(self): my_collection_name = '__metrics__' @@ -2049,8 +1996,8 @@ class StreamingDynamicAUCTest(test.TestCase): def testNonZeroOnePredictions(self): with self.test_session() as sess: - predictions = constant_op.constant([2.5, -2.5, 2.5, -2.5], - dtype=dtypes_lib.float32) + predictions = constant_op.constant( + [2.5, -2.5, 2.5, -2.5], dtype=dtypes_lib.float32) labels = constant_op.constant([1, 0, 1, 0]) auc, update_op = metrics.streaming_dynamic_auc(labels, predictions) sess.run(variables.local_variables_initializer()) @@ -2122,9 +2069,10 @@ class StreamingDynamicAUCTest(test.TestCase): num_batches = 100 labels = np.array([]) predictions = np.array([]) - tf_labels = variables.Variable(array_ops.ones(batch_size, dtypes_lib.int32), - collections=[ops.GraphKeys.LOCAL_VARIABLES], - dtype=dtypes_lib.int32) + tf_labels = variables.Variable( + array_ops.ones(batch_size, dtypes_lib.int32), + collections=[ops.GraphKeys.LOCAL_VARIABLES], + dtype=dtypes_lib.int32) tf_predictions = variables.Variable( array_ops.ones(batch_size), collections=[ops.GraphKeys.LOCAL_VARIABLES], @@ -2195,8 +2143,7 @@ class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase): gotten_result: A PrecisionRecallData object. """ gotten_dict = {k: t.eval() for k, t in gotten_result._asdict().items()} - self.assertItemsEqual( - list(expected_dict.keys()), list(gotten_dict.keys())) + self.assertItemsEqual(list(expected_dict.keys()), list(gotten_dict.keys())) for key, expected_values in expected_dict.items(): self.assertAllClose(expected_values, gotten_dict[key]) @@ -2261,60 +2208,65 @@ class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase): sess.run(update_op) # Then verify idempotency. - initial_result = {k: value.eval().tolist() for k, value in - result._asdict().items()} + initial_result = { + k: value.eval().tolist() + for k, value in result._asdict().items() + } for _ in range(3): self._testResultsEqual(initial_result, result) def testAllTruePositives(self): - self._testCase([[1]], [[True]], { - 'tp': [1, 1, 1], - 'fp': [0, 0, 0], - 'tn': [0, 0, 0], - 'fn': [0, 0, 0], - 'precision': [1.0, 1.0, 1.0], - 'recall': [1.0, 1.0, 1.0], - 'thresholds': [0.0, 0.5, 1.0], - }) + self._testCase( + [[1]], [[True]], { + 'tp': [1, 1, 1], + 'fp': [0, 0, 0], + 'tn': [0, 0, 0], + 'fn': [0, 0, 0], + 'precision': [1.0, 1.0, 1.0], + 'recall': [1.0, 1.0, 1.0], + 'thresholds': [0.0, 0.5, 1.0], + }) def testAllTrueNegatives(self): - self._testCase([[0]], [[False]], { - 'tp': [0, 0, 0], - 'fp': [1, 0, 0], - 'tn': [0, 1, 1], - 'fn': [0, 0, 0], - 'precision': [0.0, 0.0, 0.0], - 'recall': [0.0, 0.0, 0.0], - 'thresholds': [0.0, 0.5, 1.0], - }) + self._testCase( + [[0]], [[False]], { + 'tp': [0, 0, 0], + 'fp': [1, 0, 0], + 'tn': [0, 1, 1], + 'fn': [0, 0, 0], + 'precision': [0.0, 0.0, 0.0], + 'recall': [0.0, 0.0, 0.0], + 'thresholds': [0.0, 0.5, 1.0], + }) def testAllFalsePositives(self): - self._testCase([[1]], [[False]], { - 'tp': [0, 0, 0], - 'fp': [1, 1, 1], - 'tn': [0, 0, 0], - 'fn': [0, 0, 0], - 'precision': [0.0, 0.0, 0.0], - 'recall': [0.0, 0.0, 0.0], - 'thresholds': [0.0, 0.5, 1.0], - }) + self._testCase( + [[1]], [[False]], { + 'tp': [0, 0, 0], + 'fp': [1, 1, 1], + 'tn': [0, 0, 0], + 'fn': [0, 0, 0], + 'precision': [0.0, 0.0, 0.0], + 'recall': [0.0, 0.0, 0.0], + 'thresholds': [0.0, 0.5, 1.0], + }) def testAllFalseNegatives(self): - self._testCase([[0]], [[True]], { - 'tp': [1, 0, 0], - 'fp': [0, 0, 0], - 'tn': [0, 0, 0], - 'fn': [0, 1, 1], - 'precision': [1.0, 0.0, 0.0], - 'recall': [1.0, 0.0, 0.0], - 'thresholds': [0.0, 0.5, 1.0], - }) + self._testCase( + [[0]], [[True]], { + 'tp': [1, 0, 0], + 'fp': [0, 0, 0], + 'tn': [0, 0, 0], + 'fn': [0, 1, 1], + 'precision': [1.0, 0.0, 0.0], + 'recall': [1.0, 0.0, 0.0], + 'thresholds': [0.0, 0.5, 1.0], + }) def testManyValues(self): self._testCase( [[0.2, 0.3, 0.4, 0.6, 0.7, 0.8]], - [[True, False, False, True, True, True]], - { + [[True, False, False, True, True, True]], { 'tp': [4, 3, 0], 'fp': [2, 0, 0], 'tn': [0, 2, 2], @@ -2327,8 +2279,7 @@ class StreamingPrecisionRecallAtEqualThresholdsTest(test.TestCase): def testManyValuesWithWeights(self): self._testCase( [[0.2, 0.3, 0.4, 0.6, 0.7, 0.8]], - [[True, False, False, True, True, True]], - { + [[True, False, False, True, True, True]], { 'tp': [1.5, 1.5, 0.0], 'fp': [2.5, 0.0, 0.0], 'tn': [0.0, 2.5, 2.5], @@ -2644,11 +2595,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase): labels = random_ops.random_uniform( (10, 3), maxval=2, dtype=dtypes_lib.int64, seed=2) thresholds = [0, 0.5, 1.0] - prec, prec_op = metrics.streaming_precision_at_thresholds(predictions, - labels, - thresholds) - rec, rec_op = metrics.streaming_recall_at_thresholds(predictions, labels, - thresholds) + prec, prec_op = metrics.streaming_precision_at_thresholds( + predictions, labels, thresholds) + rec, rec_op = metrics.streaming_recall_at_thresholds( + predictions, labels, thresholds) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -2672,11 +2622,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase): predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32) labels = constant_op.constant(inputs) thresholds = [0.5] - prec, prec_op = metrics.streaming_precision_at_thresholds(predictions, - labels, - thresholds) - rec, rec_op = metrics.streaming_recall_at_thresholds(predictions, labels, - thresholds) + prec, prec_op = metrics.streaming_precision_at_thresholds( + predictions, labels, thresholds) + rec, rec_op = metrics.streaming_recall_at_thresholds( + predictions, labels, thresholds) sess.run(variables.local_variables_initializer()) sess.run([prec_op, rec_op]) @@ -2690,11 +2639,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase): [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32) labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4)) thresholds = [0.5] - prec, prec_op = metrics.streaming_precision_at_thresholds(predictions, - labels, - thresholds) - rec, rec_op = metrics.streaming_recall_at_thresholds(predictions, labels, - thresholds) + prec, prec_op = metrics.streaming_precision_at_thresholds( + predictions, labels, thresholds) + rec, rec_op = metrics.streaming_recall_at_thresholds( + predictions, labels, thresholds) sess.run(variables.local_variables_initializer()) sess.run([prec_op, rec_op]) @@ -2709,11 +2657,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase): predictions = constant_op.constant(inputs, dtype=dtypes_lib.float32) labels = constant_op.constant(1 - inputs, dtype=dtypes_lib.float32) thresholds = [0.5] - prec, prec_op = metrics.streaming_precision_at_thresholds(predictions, - labels, - thresholds) - rec, rec_op = metrics.streaming_recall_at_thresholds(predictions, labels, - thresholds) + prec, prec_op = metrics.streaming_precision_at_thresholds( + predictions, labels, thresholds) + rec, rec_op = metrics.streaming_recall_at_thresholds( + predictions, labels, thresholds) sess.run(variables.local_variables_initializer()) sess.run([prec_op, rec_op]) @@ -2779,11 +2726,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase): [1, 0, 1, 0], shape=(1, 4), dtype=dtypes_lib.float32) labels = constant_op.constant([0, 1, 1, 1], shape=(1, 4)) thresholds = [-1.0, 2.0] # lower/higher than any values - prec, prec_op = metrics.streaming_precision_at_thresholds(predictions, - labels, - thresholds) - rec, rec_op = metrics.streaming_recall_at_thresholds(predictions, labels, - thresholds) + prec, prec_op = metrics.streaming_precision_at_thresholds( + predictions, labels, thresholds) + rec, rec_op = metrics.streaming_recall_at_thresholds( + predictions, labels, thresholds) prec_low = prec[0] prec_high = prec[1] @@ -2803,11 +2749,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase): predictions = array_ops.zeros([4], dtype=dtypes_lib.float32) labels = array_ops.zeros([4]) thresholds = [0.5] - prec, prec_op = metrics.streaming_precision_at_thresholds(predictions, - labels, - thresholds) - rec, rec_op = metrics.streaming_recall_at_thresholds(predictions, labels, - thresholds) + prec, prec_op = metrics.streaming_precision_at_thresholds( + predictions, labels, thresholds) + rec, rec_op = metrics.streaming_recall_at_thresholds( + predictions, labels, thresholds) sess.run(variables.local_variables_initializer()) sess.run([prec_op, rec_op]) @@ -2872,12 +2817,10 @@ class StreamingPrecisionRecallThresholdsTest(test.TestCase): tf_predictions = predictions_queue.dequeue() tf_labels = labels_queue.dequeue() - prec, prec_op = metrics.streaming_precision_at_thresholds(tf_predictions, - tf_labels, - thresholds) - rec, rec_op = metrics.streaming_recall_at_thresholds(tf_predictions, - tf_labels, - thresholds) + prec, prec_op = metrics.streaming_precision_at_thresholds( + tf_predictions, tf_labels, thresholds) + rec, rec_op = metrics.streaming_recall_at_thresholds( + tf_predictions, tf_labels, thresholds) sess.run(variables.local_variables_initializer()) for _ in range(int(num_samples / batch_size)): @@ -2921,8 +2864,7 @@ class StreamingFPRThresholdsTest(test.TestCase): labels=array_ops.ones((10, 1)), thresholds=[0, 0.5, 1.0], updates_collections=[my_collection_name]) - self.assertListEqual( - ops.get_collection(my_collection_name), [update_op]) + self.assertListEqual(ops.get_collection(my_collection_name), [update_op]) def testValueTensorIsIdempotent(self): predictions = random_ops.random_uniform( @@ -3271,8 +3213,7 @@ class StreamingFNRThresholdsTest(test.TestCase): labels=array_ops.ones((10, 1)), thresholds=[0, 0.5, 1.0], updates_collections=[my_collection_name]) - self.assertListEqual( - ops.get_collection(my_collection_name), [update_op]) + self.assertListEqual(ops.get_collection(my_collection_name), [update_op]) def testValueTensorIsIdempotent(self): predictions = random_ops.random_uniform( @@ -3492,8 +3433,7 @@ class StreamingRecallAtKTest(test.TestCase): def testVars(self): metrics.streaming_recall_at_k( predictions=array_ops.ones((self._batch_size, self._num_classes)), - labels=array_ops.ones( - (self._batch_size,), dtype=dtypes_lib.int32), + labels=array_ops.ones((self._batch_size,), dtype=dtypes_lib.int32), k=1) _assert_metric_variables(self, ('recall_at_1/count:0', 'recall_at_1/total:0')) @@ -3502,8 +3442,7 @@ class StreamingRecallAtKTest(test.TestCase): my_collection_name = '__metrics__' mean, _ = metrics.streaming_recall_at_k( predictions=array_ops.ones((self._batch_size, self._num_classes)), - labels=array_ops.ones( - (self._batch_size,), dtype=dtypes_lib.int32), + labels=array_ops.ones((self._batch_size,), dtype=dtypes_lib.int32), k=1, metrics_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [mean]) @@ -3512,8 +3451,7 @@ class StreamingRecallAtKTest(test.TestCase): my_collection_name = '__updates__' _, update_op = metrics.streaming_recall_at_k( predictions=array_ops.ones((self._batch_size, self._num_classes)), - labels=array_ops.ones( - (self._batch_size,), dtype=dtypes_lib.int32), + labels=array_ops.ones((self._batch_size,), dtype=dtypes_lib.int32), k=1, updates_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [update_op]) @@ -3715,9 +3653,17 @@ class StreamingSparsePrecisionTest(test.TestCase): # top_k_predictions has rank < 2. top_k_predictions = [9, 4, 6, 2, 0] sp_labels = sparse_tensor.SparseTensorValue( - indices=np.array([[0,], [1,], [2,]], np.int64), + indices=np.array([[ + 0, + ], [ + 1, + ], [ + 2, + ]], np.int64), values=np.array([2, 7, 8], np.int64), - dense_shape=np.array([10,], np.int64)) + dense_shape=np.array([ + 10, + ], np.int64)) with self.assertRaises(ValueError): precision, _ = metrics.streaming_sparse_precision_at_top_k( @@ -3774,8 +3720,9 @@ class StreamingSparsePrecisionTest(test.TestCase): # average of the 2 examples. labels = np.array([labels_ex1, labels_ex2], dtype=np.int64) predictions = (predictions_ex1, predictions_ex2) - streaming_precision = [(ex1 + ex2) / 2 - for ex1, ex2 in zip(precision_ex1, precision_ex2)] + streaming_precision = [ + (ex1 + ex2) / 2 for ex1, ex2 in zip(precision_ex1, precision_ex2) + ] streaming_average_precision = [ (ex1 + ex2) / 2 for ex1, ex2 in zip(avg_precision_ex1, avg_precision_ex2) @@ -3835,29 +3782,29 @@ class StreamingSparsePrecisionTest(test.TestCase): (predictions_top_k_ex1[:k],), labels, expected=avg_precision_ex1[i]) def test_average_precision_at_top_k_static_shape_check(self): - predictions_top_k = array_ops.placeholder(shape=(2, None), - dtype=dtypes_lib.int64) + predictions_top_k = array_ops.placeholder( + shape=(2, None), dtype=dtypes_lib.int64) labels = np.array(((1,), (2,)), dtype=np.int64) # Fails due to non-static predictions_idx shape. with self.assertRaises(ValueError): - metric_ops.streaming_sparse_average_precision_at_top_k(predictions_top_k, - labels) + metric_ops.streaming_sparse_average_precision_at_top_k( + predictions_top_k, labels) predictions_top_k = (2, 1) # Fails since rank of predictions_idx is less than one. with self.assertRaises(ValueError): - metric_ops.streaming_sparse_average_precision_at_top_k(predictions_top_k, - labels) + metric_ops.streaming_sparse_average_precision_at_top_k( + predictions_top_k, labels) predictions_top_k = ((2,), (1,)) # Valid static shape. - metric_ops.streaming_sparse_average_precision_at_top_k(predictions_top_k, - labels) + metric_ops.streaming_sparse_average_precision_at_top_k( + predictions_top_k, labels) def test_one_label_at_k1_nan(self): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value( - [[0, 0, 0, 1], [0, 0, 1, 0]]) + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) dense_labels = np.array([[3], [2]], dtype=np.int64) for labels in (sparse_labels, dense_labels): @@ -3871,8 +3818,8 @@ class StreamingSparsePrecisionTest(test.TestCase): def test_one_label_at_k1(self): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value( - [[0, 0, 0, 1], [0, 0, 1, 0]]) + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) dense_labels = np.array([[3], [2]], dtype=np.int64) for labels in (sparse_labels, dense_labels): @@ -3971,8 +3918,8 @@ class StreamingSparsePrecisionTest(test.TestCase): [5, 7, 2, 9, 6], ] sp_labels = sparse_tensor.SparseTensorValue( - indices=[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], - [1, 3]], + indices=[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], [1, + 3]], # values -1 and 10 are outside the [0, n_classes) range and are ignored. values=np.array([2, 7, -1, 8, 1, 2, 5, 10], np.int64), dense_shape=[2, 4]) @@ -4324,8 +4271,8 @@ class StreamingSparseRecallTest(test.TestCase): def test_one_label_at_k1_nan(self): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value( - [[0, 0, 0, 1], [0, 0, 1, 0]]) + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) dense_labels = np.array([[3], [2]], dtype=np.int64) # Classes 0,1 have 0 labels, 0 predictions, classes -1 and 4 are out of @@ -4340,8 +4287,8 @@ class StreamingSparseRecallTest(test.TestCase): def test_one_label_at_k1_no_predictions(self): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value( - [[0, 0, 0, 1], [0, 0, 1, 0]]) + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) dense_labels = np.array([[3], [2]], dtype=np.int64) for labels in (sparse_labels, dense_labels): @@ -4354,8 +4301,8 @@ class StreamingSparseRecallTest(test.TestCase): def test_one_label_at_k1(self): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value( - [[0, 0, 0, 1], [0, 0, 1, 0]]) + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) dense_labels = np.array([[3], [2]], dtype=np.int64) for labels in (sparse_labels, dense_labels): @@ -4374,8 +4321,8 @@ class StreamingSparseRecallTest(test.TestCase): def test_one_label_at_k1_weighted(self): predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] top_k_predictions = [[3], [3]] - sparse_labels = _binary_2d_label_to_sparse_value( - [[0, 0, 0, 1], [0, 0, 1, 0]]) + sparse_labels = _binary_2d_label_to_sparse_value([[0, 0, 0, 1], + [0, 0, 1, 0]]) dense_labels = np.array([[3], [2]], dtype=np.int64) for labels in (sparse_labels, dense_labels): @@ -4647,8 +4594,8 @@ class StreamingSparseRecallTest(test.TestCase): [5, 7, 2, 9, 6], ] sp_labels = sparse_tensor.SparseTensorValue( - indices=[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], - [1, 3]], + indices=[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], [1, + 3]], # values -1 and 10 are outside the [0, n_classes) range. values=np.array([2, 7, -1, 8, 1, 2, 5, 10], np.int64), dense_shape=[2, 4]) @@ -4661,10 +4608,7 @@ class StreamingSparseRecallTest(test.TestCase): expected=2.0 / 2, class_id=2) self._test_sparse_recall_at_top_k( - sp_labels, - top_k_predictions, - expected=2.0 / 2, - class_id=2) + sp_labels, top_k_predictions, expected=2.0 / 2, class_id=2) # Class 5: 1 label, incorrect. self._test_streaming_sparse_recall_at_k( @@ -4674,10 +4618,7 @@ class StreamingSparseRecallTest(test.TestCase): expected=1.0 / 1, class_id=5) self._test_sparse_recall_at_top_k( - sp_labels, - top_k_predictions, - expected=1.0 / 1, - class_id=5) + sp_labels, top_k_predictions, expected=1.0 / 1, class_id=5) # Class 7: 1 label, incorrect. self._test_streaming_sparse_recall_at_k( @@ -4687,10 +4628,7 @@ class StreamingSparseRecallTest(test.TestCase): expected=0.0 / 1, class_id=7) self._test_sparse_recall_at_top_k( - sp_labels, - top_k_predictions, - expected=0.0 / 1, - class_id=7) + sp_labels, top_k_predictions, expected=0.0 / 1, class_id=7) # All classes: 8 labels, 3 correct. self._test_streaming_sparse_recall_at_k( @@ -4740,10 +4678,8 @@ class StreamingSparseRecallTest(test.TestCase): [9, 4, 6, 2, 0], ]] sparse_labels = _binary_3d_label_to_sparse_value( - [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], - [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], - [[0, 1, 1, 0, 0, 1, 0, 0, 0, 0], - [0, 0, 1, 0, 0, 0, 0, 1, 1, 0]]]) + [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], + [[0, 1, 1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0]]]) dense_labels = np.array( [[[2, 7, 8], [1, 2, 5]], [ [1, 2, 5], @@ -4771,10 +4707,8 @@ class StreamingSparseRecallTest(test.TestCase): [9, 4, 6, 2, 0], ]] labels = _binary_3d_label_to_sparse_value( - [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], - [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], - [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], - [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]]) + [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], + [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]]) # Class 2: 4 labels, all correct. self._test_streaming_sparse_recall_at_k( @@ -4813,10 +4747,8 @@ class StreamingSparseRecallTest(test.TestCase): [9, 4, 6, 2, 0], ]] labels = _binary_3d_label_to_sparse_value( - [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], - [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], - [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], - [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]]) + [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], + [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]]) for class_id in xrange(10): self._test_streaming_sparse_recall_at_k( @@ -4867,10 +4799,8 @@ class StreamingSparseRecallTest(test.TestCase): [9, 4, 6, 2, 0], ]] labels = _binary_3d_label_to_sparse_value( - [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], - [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], - [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], - [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]]) + [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]], + [[0, 1, 1, 0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]]) # Class 2: 2 labels, both correct. self._test_streaming_sparse_recall_at_k( @@ -4963,10 +4893,8 @@ class StreamingSparseRecallTest(test.TestCase): weights=[[0, 1], [0, 1]]) def test_sparse_tensor_value(self): - predictions = [[0.1, 0.3, 0.2, 0.4], - [0.1, 0.2, 0.3, 0.4]] - labels = [[0, 0, 1, 0], - [0, 0, 0, 1]] + predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]] + labels = [[0, 0, 1, 0], [0, 0, 0, 1]] expected_recall = 0.5 with self.test_session(): _, recall = metrics.streaming_sparse_recall_at_k( @@ -5009,8 +4937,8 @@ class StreamingMeanAbsoluteErrorTest(test.TestCase): def testValueTensorIsIdempotent(self): predictions = random_ops.random_normal((10, 3), seed=1) labels = random_ops.random_normal((10, 3), seed=2) - error, update_op = metrics.streaming_mean_absolute_error(predictions, - labels) + error, update_op = metrics.streaming_mean_absolute_error( + predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -5031,8 +4959,8 @@ class StreamingMeanAbsoluteErrorTest(test.TestCase): [1, 3, 2, 3], shape=(1, 4), dtype=dtypes_lib.float32) weights = constant_op.constant([0, 1, 0, 1], shape=(1, 4)) - error, update_op = metrics.streaming_mean_absolute_error(predictions, - labels, weights) + error, update_op = metrics.streaming_mean_absolute_error( + predictions, labels, weights) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -5075,8 +5003,8 @@ class StreamingMeanRelativeErrorTest(test.TestCase): predictions = random_ops.random_normal((10, 3), seed=1) labels = random_ops.random_normal((10, 3), seed=2) normalizer = random_ops.random_normal((10, 3), seed=3) - error, update_op = metrics.streaming_mean_relative_error(predictions, - labels, normalizer) + error, update_op = metrics.streaming_mean_relative_error( + predictions, labels, normalizer) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -5200,8 +5128,8 @@ class StreamingMeanSquaredErrorTest(test.TestCase): [1, 3, 2, 3], shape=(1, 4), dtype=dtypes_lib.float32) weights = constant_op.constant([0, 1, 0, 1], shape=(1, 4)) - error, update_op = metrics.streaming_mean_squared_error(predictions, labels, - weights) + error, update_op = metrics.streaming_mean_squared_error( + predictions, labels, weights) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -5224,8 +5152,8 @@ class StreamingMeanSquaredErrorTest(test.TestCase): _enqueue_vector(sess, labels_queue, [2, 4, 6]) labels = labels_queue.dequeue() - error, update_op = metrics.streaming_mean_squared_error(predictions, - labels) + error, update_op = metrics.streaming_mean_squared_error( + predictions, labels) sess.run(variables.local_variables_initializer()) sess.run(update_op) @@ -5292,10 +5220,10 @@ class StreamingMeanSquaredErrorTest(test.TestCase): _enqueue_vector(sess, labels_queue, [2, 4, 6]) labels = labels_queue.dequeue() - mae, ma_update_op = metrics.streaming_mean_absolute_error(predictions, - labels) - mse, ms_update_op = metrics.streaming_mean_squared_error(predictions, - labels) + mae, ma_update_op = metrics.streaming_mean_absolute_error( + predictions, labels) + mse, ms_update_op = metrics.streaming_mean_squared_error( + predictions, labels) sess.run(variables.local_variables_initializer()) sess.run([ma_update_op, ms_update_op]) @@ -5336,8 +5264,8 @@ class StreamingRootMeanSquaredErrorTest(test.TestCase): def testValueTensorIsIdempotent(self): predictions = random_ops.random_normal((10, 3), seed=1) labels = random_ops.random_normal((10, 3), seed=2) - error, update_op = metrics.streaming_root_mean_squared_error(predictions, - labels) + error, update_op = metrics.streaming_root_mean_squared_error( + predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -5357,8 +5285,8 @@ class StreamingRootMeanSquaredErrorTest(test.TestCase): 0.0, shape=(1, 3), dtype=dtypes_lib.float32) labels = constant_op.constant(0.0, shape=(1, 3), dtype=dtypes_lib.float32) - rmse, update_op = metrics.streaming_root_mean_squared_error(predictions, - labels) + rmse, update_op = metrics.streaming_root_mean_squared_error( + predictions, labels) sess.run(variables.local_variables_initializer()) self.assertEqual(0, sess.run(update_op)) @@ -5372,8 +5300,8 @@ class StreamingRootMeanSquaredErrorTest(test.TestCase): labels = constant_op.constant( [1, 3, 2], shape=(1, 3), dtype=dtypes_lib.float32) - rmse, update_op = metrics.streaming_root_mean_squared_error(predictions, - labels) + rmse, update_op = metrics.streaming_root_mean_squared_error( + predictions, labels) sess.run(variables.local_variables_initializer()) self.assertAlmostEqual(math.sqrt(6), update_op.eval(), 5) @@ -5387,9 +5315,8 @@ class StreamingRootMeanSquaredErrorTest(test.TestCase): [1, 3, 2, 3], shape=(1, 4), dtype=dtypes_lib.float32) weights = constant_op.constant([0, 1, 0, 1], shape=(1, 4)) - rmse, update_op = metrics.streaming_root_mean_squared_error(predictions, - labels, - weights) + rmse, update_op = metrics.streaming_root_mean_squared_error( + predictions, labels, weights) sess.run(variables.local_variables_initializer()) self.assertAlmostEqual(math.sqrt(13), sess.run(update_op)) @@ -5404,8 +5331,8 @@ class StreamingCovarianceTest(test.TestCase): def testVars(self): metrics.streaming_covariance( - predictions=math_ops.to_float(math_ops.range(10)) + array_ops.ones( - [10, 10]), + predictions=math_ops.to_float(math_ops.range(10)) + + array_ops.ones([10, 10]), labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10])) _assert_metric_variables(self, ( 'covariance/comoment:0', @@ -5417,8 +5344,8 @@ class StreamingCovarianceTest(test.TestCase): def testMetricsCollection(self): my_collection_name = '__metrics__' cov, _ = metrics.streaming_covariance( - predictions=math_ops.to_float(math_ops.range(10)) + array_ops.ones( - [10, 10]), + predictions=math_ops.to_float(math_ops.range(10)) + + array_ops.ones([10, 10]), labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]), metrics_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [cov]) @@ -5426,8 +5353,8 @@ class StreamingCovarianceTest(test.TestCase): def testUpdatesCollection(self): my_collection_name = '__updates__' _, update_op = metrics.streaming_covariance( - predictions=math_ops.to_float(math_ops.range(10)) + array_ops.ones( - [10, 10]), + predictions=math_ops.to_float(math_ops.range(10)) + + array_ops.ones([10, 10]), labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]), updates_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [update_op]) @@ -5487,9 +5414,8 @@ class StreamingCovarianceTest(test.TestCase): cov, update_op = metrics.streaming_covariance( predictions, labels, weights=weights) - expected_cov = np.cov([2, 4, 6, 8], - [1, 3, 2, 7], - fweights=[0, 1, 3, 1])[0, 1] + expected_cov = np.cov( + [2, 4, 6, 8], [1, 3, 2, 7], fweights=[0, 1, 3, 1])[0, 1] sess.run(variables.local_variables_initializer()) self.assertAlmostEqual(expected_cov, sess.run(update_op)) self.assertAlmostEqual(expected_cov, cov.eval()) @@ -5514,17 +5440,18 @@ class StreamingCovarianceTest(test.TestCase): predictions_t: predictions[stride * i:stride * (i + 1)], labels_t: labels[stride * i:stride * (i + 1)] } - self.assertEqual(np.isnan(prev_expected_cov), - np.isnan(sess.run(cov, feed_dict=feed_dict))) + self.assertEqual( + np.isnan(prev_expected_cov), + np.isnan(sess.run(cov, feed_dict=feed_dict))) if not np.isnan(prev_expected_cov): - self.assertAlmostEqual( - prev_expected_cov, sess.run(cov, feed_dict=feed_dict), 5) + self.assertAlmostEqual(prev_expected_cov, + sess.run(cov, feed_dict=feed_dict), 5) expected_cov = np.cov(predictions[:stride * (i + 1)], labels[:stride * (i + 1)])[0, 1] - self.assertAlmostEqual( - expected_cov, sess.run(update_op, feed_dict=feed_dict), 5) - self.assertAlmostEqual( - expected_cov, sess.run(cov, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_cov, + sess.run(update_op, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_cov, sess.run(cov, feed_dict=feed_dict), + 5) prev_expected_cov = expected_cov def testMultiUpdateWithErrorAndWeights(self): @@ -5552,18 +5479,20 @@ class StreamingCovarianceTest(test.TestCase): labels_t: labels[stride * i:stride * (i + 1)], weights_t: weights[stride * i:stride * (i + 1)] } - self.assertEqual(np.isnan(prev_expected_cov), - np.isnan(sess.run(cov, feed_dict=feed_dict))) + self.assertEqual( + np.isnan(prev_expected_cov), + np.isnan(sess.run(cov, feed_dict=feed_dict))) if not np.isnan(prev_expected_cov): - self.assertAlmostEqual( - prev_expected_cov, sess.run(cov, feed_dict=feed_dict), 5) - expected_cov = np.cov(predictions[:stride * (i + 1)], - labels[:stride * (i + 1)], - fweights=weights[:stride * (i + 1)])[0, 1] - self.assertAlmostEqual( - expected_cov, sess.run(update_op, feed_dict=feed_dict), 5) - self.assertAlmostEqual( - expected_cov, sess.run(cov, feed_dict=feed_dict), 5) + self.assertAlmostEqual(prev_expected_cov, + sess.run(cov, feed_dict=feed_dict), 5) + expected_cov = np.cov( + predictions[:stride * (i + 1)], + labels[:stride * (i + 1)], + fweights=weights[:stride * (i + 1)])[0, 1] + self.assertAlmostEqual(expected_cov, + sess.run(update_op, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_cov, sess.run(cov, feed_dict=feed_dict), + 5) prev_expected_cov = expected_cov @@ -5574,8 +5503,8 @@ class StreamingPearsonRTest(test.TestCase): def testVars(self): metrics.streaming_pearson_correlation( - predictions=math_ops.to_float(math_ops.range(10)) + array_ops.ones( - [10, 10]), + predictions=math_ops.to_float(math_ops.range(10)) + + array_ops.ones([10, 10]), labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10])) _assert_metric_variables(self, ( 'pearson_r/covariance/comoment:0', @@ -5595,8 +5524,8 @@ class StreamingPearsonRTest(test.TestCase): def testMetricsCollection(self): my_collection_name = '__metrics__' pearson_r, _ = metrics.streaming_pearson_correlation( - predictions=math_ops.to_float(math_ops.range(10)) + array_ops.ones( - [10, 10]), + predictions=math_ops.to_float(math_ops.range(10)) + + array_ops.ones([10, 10]), labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]), metrics_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [pearson_r]) @@ -5604,8 +5533,8 @@ class StreamingPearsonRTest(test.TestCase): def testUpdatesCollection(self): my_collection_name = '__updates__' _, update_op = metrics.streaming_pearson_correlation( - predictions=math_ops.to_float(math_ops.range(10)) + array_ops.ones( - [10, 10]), + predictions=math_ops.to_float(math_ops.range(10)) + + array_ops.ones([10, 10]), labels=math_ops.to_float(math_ops.range(10)) + array_ops.ones([10, 10]), updates_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [update_op]) @@ -5613,8 +5542,8 @@ class StreamingPearsonRTest(test.TestCase): def testValueTensorIsIdempotent(self): labels = random_ops.random_normal((10, 3), seed=2) predictions = labels * 0.5 + random_ops.random_normal((10, 3), seed=1) * 0.5 - pearson_r, update_op = metrics.streaming_pearson_correlation(predictions, - labels) + pearson_r, update_op = metrics.streaming_pearson_correlation( + predictions, labels) with self.test_session() as sess: sess.run(variables.local_variables_initializer()) @@ -5633,8 +5562,8 @@ class StreamingPearsonRTest(test.TestCase): predictions = math_ops.to_float(math_ops.range(10)) labels = math_ops.to_float(math_ops.range(10)) - pearson_r, update_op = metrics.streaming_pearson_correlation(predictions, - labels) + pearson_r, update_op = metrics.streaming_pearson_correlation( + predictions, labels) expected_r = np.corrcoef(np.arange(10), np.arange(10))[0, 1] sess.run(variables.local_variables_initializer()) @@ -5648,8 +5577,8 @@ class StreamingPearsonRTest(test.TestCase): labels = constant_op.constant( [1, 3, 2], shape=(1, 3), dtype=dtypes_lib.float32) - pearson_r, update_op = metrics.streaming_pearson_correlation(predictions, - labels) + pearson_r, update_op = metrics.streaming_pearson_correlation( + predictions, labels) expected_r = np.corrcoef([2, 4, 6], [1, 3, 2])[0, 1] sess.run(variables.local_variables_initializer()) @@ -5698,17 +5627,18 @@ class StreamingPearsonRTest(test.TestCase): predictions_t: predictions[stride * i:stride * (i + 1)], labels_t: labels[stride * i:stride * (i + 1)] } - self.assertEqual(np.isnan(prev_expected_r), - np.isnan(sess.run(pearson_r, feed_dict=feed_dict))) + self.assertEqual( + np.isnan(prev_expected_r), + np.isnan(sess.run(pearson_r, feed_dict=feed_dict))) if not np.isnan(prev_expected_r): - self.assertAlmostEqual( - prev_expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5) + self.assertAlmostEqual(prev_expected_r, + sess.run(pearson_r, feed_dict=feed_dict), 5) expected_r = np.corrcoef(predictions[:stride * (i + 1)], labels[:stride * (i + 1)])[0, 1] - self.assertAlmostEqual( - expected_r, sess.run(update_op, feed_dict=feed_dict), 5) - self.assertAlmostEqual( - expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_r, + sess.run(update_op, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_r, + sess.run(pearson_r, feed_dict=feed_dict), 5) prev_expected_r = expected_r def testMultiUpdateWithErrorAndWeights(self): @@ -5736,19 +5666,21 @@ class StreamingPearsonRTest(test.TestCase): labels_t: labels[stride * i:stride * (i + 1)], weights_t: weights[stride * i:stride * (i + 1)] } - self.assertEqual(np.isnan(prev_expected_r), - np.isnan(sess.run(pearson_r, feed_dict=feed_dict))) + self.assertEqual( + np.isnan(prev_expected_r), + np.isnan(sess.run(pearson_r, feed_dict=feed_dict))) if not np.isnan(prev_expected_r): - self.assertAlmostEqual( - prev_expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5) - cmat = np.cov(predictions[:stride * (i + 1)], - labels[:stride * (i + 1)], - fweights=weights[:stride * (i + 1)]) + self.assertAlmostEqual(prev_expected_r, + sess.run(pearson_r, feed_dict=feed_dict), 5) + cmat = np.cov( + predictions[:stride * (i + 1)], + labels[:stride * (i + 1)], + fweights=weights[:stride * (i + 1)]) expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1]) - self.assertAlmostEqual( - expected_r, sess.run(update_op, feed_dict=feed_dict), 5) - self.assertAlmostEqual( - expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_r, + sess.run(update_op, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_r, + sess.run(pearson_r, feed_dict=feed_dict), 5) prev_expected_r = expected_r def testMultiUpdateWithErrorAndSingletonBatches(self): @@ -5758,7 +5690,7 @@ class StreamingPearsonRTest(test.TestCase): predictions = np.random.randn(n) labels = 0.5 * predictions + np.random.randn(n) stride = 10 - weights = (np.arange(n).reshape(n//stride, stride) % stride == 0) + weights = (np.arange(n).reshape(n // stride, stride) % stride == 0) for row in weights: np.random.shuffle(row) # Now, weights is one-hot by row - one item per batch has non-zero weight. @@ -5778,19 +5710,20 @@ class StreamingPearsonRTest(test.TestCase): labels_t: labels[stride * i:stride * (i + 1)], weights_t: weights[stride * i:stride * (i + 1)] } - cmat = np.cov(predictions[:stride * (i + 1)], - labels[:stride * (i + 1)], - fweights=weights[:stride * (i + 1)]) + cmat = np.cov( + predictions[:stride * (i + 1)], + labels[:stride * (i + 1)], + fweights=weights[:stride * (i + 1)]) expected_r = cmat[0, 1] / np.sqrt(cmat[0, 0] * cmat[1, 1]) actual_r = sess.run(update_op, feed_dict=feed_dict) self.assertEqual(np.isnan(expected_r), np.isnan(actual_r)) - self.assertEqual(np.isnan(expected_r), - np.isnan(sess.run(pearson_r, feed_dict=feed_dict))) + self.assertEqual( + np.isnan(expected_r), + np.isnan(sess.run(pearson_r, feed_dict=feed_dict))) if not np.isnan(expected_r): - self.assertAlmostEqual( - expected_r, actual_r, 5) - self.assertAlmostEqual( - expected_r, sess.run(pearson_r, feed_dict=feed_dict), 5) + self.assertAlmostEqual(expected_r, actual_r, 5) + self.assertAlmostEqual(expected_r, + sess.run(pearson_r, feed_dict=feed_dict), 5) class StreamingMeanCosineDistanceTest(test.TestCase): @@ -6191,20 +6124,14 @@ class StreamingMeanIOUTest(test.TestCase): self.assertAlmostEqual(desired_output, miou.eval()) def testUpdateOpEvalIsAccumulatedConfusionMatrix(self): - predictions = array_ops.concat( - [ - constant_op.constant( - 0, shape=[5]), constant_op.constant( - 1, shape=[5]) - ], - 0) - labels = array_ops.concat( - [ - constant_op.constant( - 0, shape=[3]), constant_op.constant( - 1, shape=[7]) - ], - 0) + predictions = array_ops.concat([ + constant_op.constant(0, shape=[5]), + constant_op.constant(1, shape=[5]) + ], 0) + labels = array_ops.concat([ + constant_op.constant(0, shape=[3]), + constant_op.constant(1, shape=[7]) + ], 0) num_classes = 2 with self.test_session() as sess: miou, update_op = metrics.streaming_mean_iou(predictions, labels, @@ -6238,29 +6165,20 @@ class StreamingMeanIOUTest(test.TestCase): self.assertEqual(0., miou.eval()) def testResultsWithSomeMissing(self): - predictions = array_ops.concat( - [ - constant_op.constant( - 0, shape=[5]), constant_op.constant( - 1, shape=[5]) - ], - 0) - labels = array_ops.concat( - [ - constant_op.constant( - 0, shape=[3]), constant_op.constant( - 1, shape=[7]) - ], - 0) + predictions = array_ops.concat([ + constant_op.constant(0, shape=[5]), + constant_op.constant(1, shape=[5]) + ], 0) + labels = array_ops.concat([ + constant_op.constant(0, shape=[3]), + constant_op.constant(1, shape=[7]) + ], 0) num_classes = 2 - weights = array_ops.concat( - [ - constant_op.constant( - 0, shape=[1]), constant_op.constant( - 1, shape=[8]), constant_op.constant( - 0, shape=[1]) - ], - 0) + weights = array_ops.concat([ + constant_op.constant(0, shape=[1]), + constant_op.constant(1, shape=[8]), + constant_op.constant(0, shape=[1]) + ], 0) with self.test_session() as sess: miou, update_op = metrics.streaming_mean_iou( predictions, labels, num_classes, weights=weights) @@ -6270,56 +6188,45 @@ class StreamingMeanIOUTest(test.TestCase): self.assertAlmostEqual(desired_miou, miou.eval()) def testMissingClassInLabels(self): - labels = constant_op.constant([ - [[0, 0, 1, 1, 0, 0], - [1, 0, 0, 0, 0, 1]], - [[1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 0]]]) - predictions = constant_op.constant([ - [[0, 0, 2, 1, 1, 0], - [0, 1, 2, 2, 0, 1]], - [[0, 0, 2, 1, 1, 1], - [1, 1, 2, 0, 0, 0]]]) + labels = constant_op.constant([[[0, 0, 1, 1, 0, 0], [1, 0, 0, 0, 0, 1]], + [[1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]]]) + predictions = constant_op.constant( + [[[0, 0, 2, 1, 1, 0], [0, 1, 2, 2, 0, 1]], [[0, 0, 2, 1, 1, 1], + [1, 1, 2, 0, 0, 0]]]) num_classes = 3 with self.test_session() as sess: - miou, update_op = metrics.streaming_mean_iou( - predictions, labels, num_classes) + miou, update_op = metrics.streaming_mean_iou(predictions, labels, + num_classes) sess.run(variables.local_variables_initializer()) self.assertAllEqual([[7, 4, 3], [3, 5, 2], [0, 0, 0]], update_op.eval()) - self.assertAlmostEqual( - 1 / 3 * (7 / (7 + 3 + 7) + 5 / (5 + 4 + 5) + 0 / (0 + 5 + 0)), - miou.eval()) + self.assertAlmostEqual(1 / 3 * (7 / (7 + 3 + 7) + 5 / (5 + 4 + 5) + 0 / + (0 + 5 + 0)), miou.eval()) def testMissingClassOverallSmall(self): labels = constant_op.constant([0]) predictions = constant_op.constant([0]) num_classes = 2 with self.test_session() as sess: - miou, update_op = metrics.streaming_mean_iou( - predictions, labels, num_classes) + miou, update_op = metrics.streaming_mean_iou(predictions, labels, + num_classes) sess.run(variables.local_variables_initializer()) self.assertAllEqual([[1, 0], [0, 0]], update_op.eval()) self.assertAlmostEqual(1, miou.eval()) def testMissingClassOverallLarge(self): - labels = constant_op.constant([ - [[0, 0, 1, 1, 0, 0], - [1, 0, 0, 0, 0, 1]], - [[1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 0]]]) - predictions = constant_op.constant([ - [[0, 0, 1, 1, 0, 0], - [1, 1, 0, 0, 1, 1]], - [[0, 0, 0, 1, 1, 1], - [1, 1, 1, 0, 0, 0]]]) + labels = constant_op.constant([[[0, 0, 1, 1, 0, 0], [1, 0, 0, 0, 0, 1]], + [[1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]]]) + predictions = constant_op.constant( + [[[0, 0, 1, 1, 0, 0], [1, 1, 0, 0, 1, 1]], [[0, 0, 0, 1, 1, 1], + [1, 1, 1, 0, 0, 0]]]) num_classes = 3 with self.test_session() as sess: - miou, update_op = metrics.streaming_mean_iou( - predictions, labels, num_classes) + miou, update_op = metrics.streaming_mean_iou(predictions, labels, + num_classes) sess.run(variables.local_variables_initializer()) self.assertAllEqual([[9, 5, 0], [3, 7, 0], [0, 0, 0]], update_op.eval()) - self.assertAlmostEqual( - 1 / 2 * (9 / (9 + 3 + 5) + 7 / (7 + 5 + 3)), miou.eval()) + self.assertAlmostEqual(1 / 2 * (9 / (9 + 3 + 5) + 7 / (7 + 5 + 3)), + miou.eval()) class StreamingConcatTest(test.TestCase): @@ -6683,7 +6590,8 @@ class CohenKappaTest(test.TestCase): _assert_metric_variables(self, ( 'cohen_kappa/po:0', 'cohen_kappa/pe_row:0', - 'cohen_kappa/pe_col:0',)) + 'cohen_kappa/pe_col:0', + )) def testMetricsCollection(self): my_collection_name = '__metrics__' @@ -6705,9 +6613,9 @@ class CohenKappaTest(test.TestCase): def testValueTensorIsIdempotent(self): predictions = random_ops.random_uniform( - (10, 1), maxval=3, dtype=dtypes_lib.int64, seed=1) + (10, 1), maxval=3, dtype=dtypes_lib.int64, seed=1) labels = random_ops.random_uniform( - (10, 1), maxval=3, dtype=dtypes_lib.int64, seed=2) + (10, 1), maxval=3, dtype=dtypes_lib.int64, seed=2) kappa, update_op = metrics.cohen_kappa(labels, predictions, 3) with self.test_session() as sess: @@ -6723,10 +6631,7 @@ class CohenKappaTest(test.TestCase): self.assertAlmostEqual(initial_kappa, kappa.eval(), 5) def testBasic(self): - confusion_matrix = np.array([ - [9, 3, 1], - [4, 8, 2], - [2, 1, 6]]) + confusion_matrix = np.array([[9, 3, 1], [4, 8, 2], [2, 1, 6]]) # overall total = 36 # po = [9, 8, 6], sum(po) = 23 # pe_row = [15, 12, 9], pe_col = [13, 14, 9], so pe = [5.42, 4.67, 2.25] @@ -6738,8 +6643,10 @@ class CohenKappaTest(test.TestCase): labels, predictions = self._confusion_matrix_to_samples(confusion_matrix) dtypes = [dtypes_lib.int16, dtypes_lib.int32, dtypes_lib.int64] - shapes = [(len(labels,)), # 1-dim - (len(labels), 1)] # 2-dim + shapes = [ + (len(labels,)), # 1-dim + (len(labels), 1) + ] # 2-dim weights = [None, np.ones_like(labels)] for dtype in dtypes: @@ -6795,10 +6702,7 @@ class CohenKappaTest(test.TestCase): self.assertAlmostEqual(expect, kappa.eval(), 5) def testWeighted(self): - confusion_matrix = np.array([ - [9, 3, 1], - [4, 8, 2], - [2, 1, 6]]) + confusion_matrix = np.array([[9, 3, 1], [4, 8, 2], [2, 1, 6]]) labels, predictions = self._confusion_matrix_to_samples(confusion_matrix) num_samples = np.sum(confusion_matrix, dtype=np.int32) weights = (np.arange(0, num_samples) % 5) / 5.0 @@ -6809,31 +6713,26 @@ class CohenKappaTest(test.TestCase): with self.test_session() as sess: predictions = constant_op.constant(predictions, dtype=dtypes_lib.float32) labels = constant_op.constant(labels) - kappa, update_op = metrics.cohen_kappa(labels, predictions, 4, - weights=weights) + kappa, update_op = metrics.cohen_kappa( + labels, predictions, 4, weights=weights) sess.run(variables.local_variables_initializer()) self.assertAlmostEqual(expect, sess.run(update_op), 5) self.assertAlmostEqual(expect, kappa.eval(), 5) def testWithMultipleUpdates(self): - confusion_matrix = np.array([ - [90, 30, 10, 20], - [40, 80, 20, 30], - [20, 10, 60, 35], - [15, 25, 30, 25]]) + confusion_matrix = np.array([[90, 30, 10, 20], [40, 80, 20, 30], + [20, 10, 60, 35], [15, 25, 30, 25]]) labels, predictions = self._confusion_matrix_to_samples(confusion_matrix) num_samples = np.sum(confusion_matrix, dtype=np.int32) weights = (np.arange(0, num_samples) % 5) / 5.0 num_classes = confusion_matrix.shape[0] batch_size = num_samples // 10 - predictions_t = array_ops.placeholder(dtypes_lib.float32, - shape=(batch_size,)) - labels_t = array_ops.placeholder(dtypes_lib.int32, - shape=(batch_size,)) - weights_t = array_ops.placeholder(dtypes_lib.float32, - shape=(batch_size,)) + predictions_t = array_ops.placeholder( + dtypes_lib.float32, shape=(batch_size,)) + labels_t = array_ops.placeholder(dtypes_lib.int32, shape=(batch_size,)) + weights_t = array_ops.placeholder(dtypes_lib.float32, shape=(batch_size,)) kappa, update_op = metrics.cohen_kappa( labels_t, predictions_t, num_classes, weights=weights_t) with self.test_session() as sess: @@ -6841,10 +6740,13 @@ class CohenKappaTest(test.TestCase): for idx in range(0, num_samples, batch_size): batch_start, batch_end = idx, idx + batch_size - sess.run(update_op, - feed_dict={labels_t: labels[batch_start:batch_end], - predictions_t: predictions[batch_start:batch_end], - weights_t: weights[batch_start:batch_end]}) + sess.run( + update_op, + feed_dict={ + labels_t: labels[batch_start:batch_end], + predictions_t: predictions[batch_start:batch_end], + weights_t: weights[batch_start:batch_end] + }) # Calculated by v0.19: sklearn.metrics.cohen_kappa_score( # labels_np, predictions_np, sample_weight=weights_np) expect = 0.289965397924 @@ -6862,7 +6764,8 @@ class CohenKappaTest(test.TestCase): with self.assertRaises(ValueError): metrics.cohen_kappa(invalid_labels, predictions, 3) - invalid_predictions = array_ops.placeholder(dtypes_lib.float32, shape=(4, 2)) + invalid_predictions = array_ops.placeholder( + dtypes_lib.float32, shape=(4, 2)) labels = array_ops.placeholder(dtypes_lib.int32, shape=(4, 1)) with self.assertRaises(ValueError): metrics.cohen_kappa(labels, invalid_predictions, 3) diff --git a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py index 0d1de869f6..73dd56398c 100644 --- a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py +++ b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py @@ -54,10 +54,10 @@ BATCH_SIZE = 128 DATA_DIR = '/tmp/cifar10_data' # Constants describing the training process. -MOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average. -NUM_EPOCHS_PER_DECAY = 350.0 # Epochs after which learning rate decays. +MOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average. +NUM_EPOCHS_PER_DECAY = 350.0 # Epochs after which learning rate decays. LEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor. -INITIAL_LEARNING_RATE = 0.1 # Initial learning rate. +INITIAL_LEARNING_RATE = 0.1 # Initial learning rate. # If a model is trained with multiple GPUs, prefix all Op names with tower_name # to differentiate the operations. Note that this prefix is removed from the @@ -82,8 +82,7 @@ def _activation_summary(x): # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.summary.histogram(tensor_name + '/activations', x) - tf.summary.scalar(tensor_name + '/sparsity', - tf.nn.zero_fraction(x)) + tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) def _variable_on_cpu(name, shape, initializer): @@ -120,10 +119,9 @@ def _variable_with_weight_decay(name, shape, stddev, wd): Variable Tensor """ dtype = tf.float32 - var = _variable_on_cpu( - name, - shape, - tf.truncated_normal_initializer(stddev=stddev, dtype=dtype)) + var = _variable_on_cpu(name, shape, + tf.truncated_normal_initializer( + stddev=stddev, dtype=dtype)) if wd is not None: weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) @@ -188,10 +186,8 @@ def inference(images): # Note that the masks are applied only to the weight tensors # conv1 with tf.variable_scope('conv1') as scope: - kernel = _variable_with_weight_decay('weights', - shape=[5, 5, 3, 64], - stddev=5e-2, - wd=0.0) + kernel = _variable_with_weight_decay( + 'weights', shape=[5, 5, 3, 64], stddev=5e-2, wd=0.0) conv = tf.nn.conv2d( images, pruning.apply_mask(kernel, scope), [1, 1, 1, 1], padding='SAME') @@ -201,18 +197,20 @@ def inference(images): _activation_summary(conv1) # pool1 - pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], - padding='SAME', name='pool1') + pool1 = tf.nn.max_pool( + conv1, + ksize=[1, 3, 3, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='pool1') # norm1 - norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, - name='norm1') + norm1 = tf.nn.lrn( + pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') # conv2 with tf.variable_scope('conv2') as scope: - kernel = _variable_with_weight_decay('weights', - shape=[5, 5, 64, 64], - stddev=5e-2, - wd=0.0) + kernel = _variable_with_weight_decay( + 'weights', shape=[5, 5, 64, 64], stddev=5e-2, wd=0.0) conv = tf.nn.conv2d( norm1, pruning.apply_mask(kernel, scope), [1, 1, 1, 1], padding='SAME') biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1)) @@ -221,19 +219,23 @@ def inference(images): _activation_summary(conv2) # norm2 - norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, - name='norm2') + norm2 = tf.nn.lrn( + conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') # pool2 - pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], - strides=[1, 2, 2, 1], padding='SAME', name='pool2') + pool2 = tf.nn.max_pool( + norm2, + ksize=[1, 3, 3, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='pool2') # local3 with tf.variable_scope('local3') as scope: # Move everything into depth so we can perform a single matrix multiply. reshape = tf.reshape(pool2, [BATCH_SIZE, -1]) dim = reshape.get_shape()[1].value - weights = _variable_with_weight_decay('weights', shape=[dim, 384], - stddev=0.04, wd=0.004) + weights = _variable_with_weight_decay( + 'weights', shape=[dim, 384], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1)) local3 = tf.nn.relu( tf.matmul(reshape, pruning.apply_mask(weights, scope)) + biases, @@ -242,8 +244,8 @@ def inference(images): # local4 with tf.variable_scope('local4') as scope: - weights = _variable_with_weight_decay('weights', shape=[384, 192], - stddev=0.04, wd=0.004) + weights = _variable_with_weight_decay( + 'weights', shape=[384, 192], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1)) local4 = tf.nn.relu( tf.matmul(local3, pruning.apply_mask(weights, scope)) + biases, @@ -255,8 +257,8 @@ def inference(images): # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits # and performs the softmax internally for efficiency. with tf.variable_scope('softmax_linear') as scope: - weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES], - stddev=1/192.0, wd=0.0) + weights = _variable_with_weight_decay( + 'weights', [192, NUM_CLASSES], stddev=1 / 192.0, wd=0.0) biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0)) softmax_linear = tf.add( @@ -337,11 +339,12 @@ def train(total_loss, global_step): decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) # Decay the learning rate exponentially based on the number of steps. - lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, - global_step, - decay_steps, - LEARNING_RATE_DECAY_FACTOR, - staircase=True) + lr = tf.train.exponential_decay( + INITIAL_LEARNING_RATE, + global_step, + decay_steps, + LEARNING_RATE_DECAY_FACTOR, + staircase=True) tf.summary.scalar('learning_rate', lr) # Generate moving averages of all losses and associated summaries. @@ -365,8 +368,8 @@ def train(total_loss, global_step): tf.summary.histogram(var.op.name + '/gradients', grad) # Track the moving averages of all trainable variables. - variable_averages = tf.train.ExponentialMovingAverage( - MOVING_AVERAGE_DECAY, global_step) + variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, + global_step) variables_averages_op = variable_averages.apply(tf.trainable_variables()) with tf.control_dependencies([apply_gradient_op, variables_averages_op]): @@ -383,10 +386,13 @@ def maybe_download_and_extract(): filename = DATA_URL.split('/')[-1] filepath = os.path.join(dest_directory, filename) if not os.path.exists(filepath): + def _progress(count, block_size, total_size): - sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename, - float(count * block_size) / float(total_size) * 100.0)) + sys.stdout.write('\r>> Downloading %s %.1f%%' % + (filename, + float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() + filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) print() statinfo = os.stat(filepath) diff --git a/tensorflow/contrib/mpi_collectives/mpi_ops.py b/tensorflow/contrib/mpi_collectives/mpi_ops.py index 81567cc688..bd7096d9ce 100644 --- a/tensorflow/contrib/mpi_collectives/mpi_ops.py +++ b/tensorflow/contrib/mpi_collectives/mpi_ops.py @@ -46,17 +46,16 @@ def _load_library(name, op_list=None): if lib_op.name == expected_op: break else: - raise NameError( - 'Could not find operator %s in dynamic library %s' % - (expected_op, name)) + raise NameError('Could not find operator %s in dynamic library %s' % + (expected_op, name)) return library except errors.NotFoundError: logging.warning('%s file could not be loaded.', name) -MPI_LIB = _load_library('mpi_collectives.so', ['MPISize', 'MPIRank', - 'MPILocalRank', 'MPIAllgather', - 'MPIAllreduce']) +MPI_LIB = _load_library( + 'mpi_collectives.so', + ['MPISize', 'MPIRank', 'MPILocalRank', 'MPIAllgather', 'MPIAllreduce']) def size(name=None): @@ -151,15 +150,14 @@ def allgather(tensor, name=None): """ # Specify that first allgather is to collect the tensor gather sizes, # indicated by passing in a scalar (0-D tensor) of value 0 - sizes_flag = tf.constant(0, dtype=tf.int64, name="size_flag_const") - my_size = tf.slice(tf.shape(tensor, out_type=tf.int64), [0], [1], name="size_slice") + sizes_flag = tf.constant(0, dtype=tf.int64, name='size_flag_const') + my_size = tf.slice( + tf.shape(tensor, out_type=tf.int64), [0], [1], name='size_slice') if name is None: - name = "allgather" - sizing_name = "{}_sizing".format(name) + name = 'allgather' + sizing_name = '{}_sizing'.format(name) sizes = MPI_LIB.mpi_allgather(my_size, sizes_flag, name=sizing_name) return MPI_LIB.mpi_allgather(tensor, sizes, name=name) ops.NotDifferentiable('MPIAllgather') - - diff --git a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py index 6132cba1f5..716ee9cdf7 100644 --- a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Wrapper optimizer for Elastic Average SGD """ from __future__ import absolute_import from __future__ import division @@ -78,23 +77,24 @@ class ElasticAverageCustomGetter(object): def __call__(self, getter, name, trainable, collections, *args, **kwargs): if trainable: with ops.device(self._worker_device): - local_var = getter(name, trainable=True, - collections=[ops.GraphKeys.LOCAL_VARIABLES], - *args, **kwargs) + local_var = getter( + name, + trainable=True, + collections=[ops.GraphKeys.LOCAL_VARIABLES], + *args, + **kwargs) global_center_variable = variable_scope.variable( - name='%s/%s' % - (GLOBAL_VARIABLE_NAME, - name), - initial_value=local_var.initialized_value(), - trainable=False, - collections=[ops.GraphKeys.GLOBAL_VARIABLES]) + name='%s/%s' % (GLOBAL_VARIABLE_NAME, name), + initial_value=local_var.initialized_value(), + trainable=False, + collections=[ops.GraphKeys.GLOBAL_VARIABLES]) with ops.device(self._worker_device): local_center_variable = variable_scope.variable( - name='%s/%s' % (LOCAL_VARIABLE_NAME, name), - initial_value=local_var.initialized_value(), - trainable=False, - collections=[ops.GraphKeys.LOCAL_VARIABLES]) + name='%s/%s' % (LOCAL_VARIABLE_NAME, name), + initial_value=local_var.initialized_value(), + trainable=False, + collections=[ops.GraphKeys.LOCAL_VARIABLES]) self._local_map[local_var] = local_center_variable self._global_map[local_var] = global_center_variable @@ -117,16 +117,15 @@ class ElasticAverageOptimizer(optimizer.Optimizer): # Default value as paper described BETA = 0.9 - def __init__( - self, - opt, - num_worker, - ea_custom_getter, - communication_period=10, - moving_rate=None, - rho=None, - use_locking=True, - name="ElasticAverageOptimizer"): + def __init__(self, + opt, + num_worker, + ea_custom_getter, + communication_period=10, + moving_rate=None, + rho=None, + use_locking=True, + name='ElasticAverageOptimizer'): """Construct a new gradient descent optimizer. Args: @@ -160,13 +159,15 @@ class ElasticAverageOptimizer(optimizer.Optimizer): self._rho = rho self._local_step = variable_scope.get_variable( - initializer=0, - trainable=False, - collections=[ops.GraphKeys.LOCAL_VARIABLES], - name="local_step") + initializer=0, + trainable=False, + collections=[ops.GraphKeys.LOCAL_VARIABLES], + name='local_step') self._opt._prepare() - def compute_gradients(self, loss, var_list=None, + def compute_gradients(self, + loss, + var_list=None, gate_gradients=optimizer.Optimizer.GATE_OP, aggregation_method=None, colocate_gradients_with_ops=False, @@ -204,16 +205,18 @@ class ElasticAverageOptimizer(optimizer.Optimizer): if not var_list: var_list = variables.trainable_variables() - elastic_difference = [math_ops.subtract(v, lv) for v, lv in zip( - variables.trainable_variables(), - [self._local_map[var] for var in var_list])] + elastic_difference = [ + math_ops.subtract(v, lv) + for v, lv in zip(variables.trainable_variables(), + [self._local_map[var] for var in var_list]) + ] distance_loss = self._rho * math_ops.add_n( - [gen_nn_ops.l2_loss(ed) for ed in elastic_difference]) + [gen_nn_ops.l2_loss(ed) for ed in elastic_difference]) total_loss = loss + distance_loss - return self._opt.compute_gradients(total_loss, var_list, - gate_gradients, aggregation_method, + return self._opt.compute_gradients(total_loss, var_list, gate_gradients, + aggregation_method, colocate_gradients_with_ops, grad_loss) def apply_gradients(self, grads_and_vars, global_step=None, name=None): @@ -241,7 +244,7 @@ class ElasticAverageOptimizer(optimizer.Optimizer): apply_updates = self._opt.apply_gradients(grads_and_vars) with ops.control_dependencies([apply_updates]): local_update = state_ops.assign_add( - self._local_step, 1, name='local_step_update').op + self._local_step, 1, name='local_step_update').op # update global variables. def _Update_global_variables(): @@ -259,12 +262,16 @@ class ElasticAverageOptimizer(optimizer.Optimizer): differences.append(math_ops.subtract(v, lv)) for lvar, diff in zip(local_vars, differences): with ops.device(lvar.device): - update_ops.append(state_ops.assign_sub(lvar, math_ops.multiply( - self._moving_rate, diff))) + update_ops.append( + state_ops.assign_sub(lvar, + math_ops.multiply(self._moving_rate, + diff))) for var, diff in zip(global_center_vars, differences): with ops.device(var.device): - update_ops.append(state_ops.assign_add(var, math_ops.multiply( - self._moving_rate, diff))) + update_ops.append( + state_ops.assign_add(var, + math_ops.multiply(self._moving_rate, + diff))) if global_step: with ops.colocate_with(global_step): update_ops.append(state_ops.assign_add(global_step, 1)) @@ -272,10 +279,10 @@ class ElasticAverageOptimizer(optimizer.Optimizer): return variable_update with ops.control_dependencies([local_update]): - condition = math_ops.equal(math_ops.mod( - self._local_step, self._period), 0) + condition = math_ops.equal( + math_ops.mod(self._local_step, self._period), 0) conditional_update = control_flow_ops.cond( - condition, _Update_global_variables, control_flow_ops.no_op) + condition, _Update_global_variables, control_flow_ops.no_op) return conditional_update def get_init_op(self, task_index): @@ -285,10 +292,12 @@ class ElasticAverageOptimizer(optimizer.Optimizer): def _Add_sync_queues_and_barrier(enqueue_after_list): """Adds ops to enqueu on all worker queues""" sync_queues = [ - data_flow_ops.FIFOQueue(self._num_worker, [dtypes.bool], shapes=[[]], - shared_name='%s%s' % ( - 'variable_init_sync_queue', i)) for i in - range(self._num_worker)] + data_flow_ops.FIFOQueue( + self._num_worker, [dtypes.bool], + shapes=[[]], + shared_name='%s%s' % ('variable_init_sync_queue', i)) + for i in range(self._num_worker) + ] queue_ops = [] # For each other worker, add an entry in a queue token = constant_op.constant(False) @@ -299,7 +308,7 @@ class ElasticAverageOptimizer(optimizer.Optimizer): else: queue_ops.append(q.enqueue(token)) queue_ops.append( - sync_queues[task_index].dequeue_many(len(sync_queues) - 1)) + sync_queues[task_index].dequeue_many(len(sync_queues) - 1)) return control_flow_ops.group(*queue_ops) init_ops = [] @@ -307,11 +316,10 @@ class ElasticAverageOptimizer(optimizer.Optimizer): global_center_vars = [self._global_map[var] for var in local_vars] local_center_vars = [self._local_map[var] for var in local_vars] if not (local_vars and global_center_vars and local_center_vars): - raise ValueError( - 'The lists of local_variables, global_center_variables, ' - 'local_center_variables should not be empty ') - for lvar, gc_var, lc_var in zip( - local_vars, global_center_vars, local_center_vars): + raise ValueError('The lists of local_variables, global_center_variables, ' + 'local_center_variables should not be empty ') + for lvar, gc_var, lc_var in zip(local_vars, global_center_vars, + local_center_vars): init_ops.append(state_ops.assign(lvar, gc_var)) init_ops.append(state_ops.assign(lc_var, gc_var)) @@ -325,6 +333,7 @@ class ElasticAverageOptimizer(optimizer.Optimizer): class _ElasticAverageOptimizerHook(session_run_hook.SessionRunHook): + def __init__(self, ea_optimizer, is_chief, task_index): """Creates hook to handle ElasticAverageOptimizer initialization ops. diff --git a/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py index 446e91018d..37539b9599 100644 --- a/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py +++ b/tensorflow/contrib/opt/python/training/elastic_average_optimizer_test.py @@ -38,20 +38,20 @@ def create_local_cluster(num_workers, num_ps, protocol="grpc"): worker_ports = [portpicker.pick_unused_port() for _ in range(num_workers)] ps_ports = [portpicker.pick_unused_port() for _ in range(num_ps)] cluster_dict = { - "worker": ["localhost:%s" % port for port in worker_ports], - "ps": ["localhost:%s" % port for port in ps_ports] + "worker": ["localhost:%s" % port for port in worker_ports], + "ps": ["localhost:%s" % port for port in ps_ports] } cs = server_lib.ClusterSpec(cluster_dict) workers = [ - server_lib.Server( - cs, job_name="worker", protocol=protocol, task_index=ix, start=True) - for ix in range(num_workers) + server_lib.Server( + cs, job_name="worker", protocol=protocol, task_index=ix, start=True) + for ix in range(num_workers) ] ps_servers = [ - server_lib.Server( - cs, job_name="ps", protocol=protocol, task_index=ix, start=True) - for ix in range(num_ps) + server_lib.Server( + cs, job_name="ps", protocol=protocol, task_index=ix, start=True) + for ix in range(num_ps) ] return cluster_dict, workers, ps_servers @@ -68,15 +68,14 @@ def _get_workers(num_workers, period, workers, moving_rate): is_chief = (worker_id == 0) with graph.as_default(): worker_device = "/job:worker/task:%d/cpu:0" % (worker_id) - ea_coustom = ElasticAverageCustomGetter( - worker_device=worker_device) - with variable_scope.variable_scope('', - custom_getter=ea_coustom), ops.device( - device_setter.replica_device_setter(worker_device=worker_device, - ps_device="/job:ps/task:0/cpu:0", - ps_tasks=1)): - global_step = variables.Variable(0, name='global_step', - trainable=False) + ea_coustom = ElasticAverageCustomGetter(worker_device=worker_device) + with variable_scope.variable_scope( + "", custom_getter=ea_coustom), ops.device( + device_setter.replica_device_setter( + worker_device=worker_device, + ps_device="/job:ps/task:0/cpu:0", + ps_tasks=1)): + global_step = variables.Variable(0, name="global_step", trainable=False) var_0 = variable_scope.get_variable(initializer=0.0, name="v0") var_1 = variable_scope.get_variable(initializer=1.0, name="v1") @@ -86,21 +85,19 @@ def _get_workers(num_workers, period, workers, moving_rate): sgd_opt = gradient_descent.GradientDescentOptimizer(1.0) opt = ElasticAverageOptimizer( - opt=sgd_opt, - num_worker=num_workers, - moving_rate=moving_rate, - communication_period=period, - ea_custom_getter=ea_coustom - ) + opt=sgd_opt, + num_worker=num_workers, + moving_rate=moving_rate, + communication_period=period, + ea_custom_getter=ea_coustom) train_op = [ - opt.apply_gradients( - ([grads_0, var_0], - [grads_1, var_1]), global_step) + opt.apply_gradients(([grads_0, var_0], [grads_1, var_1]), + global_step) ] easgd_hook = opt.make_session_run_hook(is_chief, worker_id) # Creates MonitoredSession - sess = training.MonitoredTrainingSession(workers[worker_id].target, - hooks=[easgd_hook]) + sess = training.MonitoredTrainingSession( + workers[worker_id].target, hooks=[easgd_hook]) sessions.append(sess) graphs.append(graph) @@ -110,6 +107,7 @@ def _get_workers(num_workers, period, workers, moving_rate): class ElasticAverageOptimizerTest(test.TestCase): + def _run(self, train_op, sess): sess.run(train_op) @@ -117,15 +115,14 @@ class ElasticAverageOptimizerTest(test.TestCase): num_workers = 1 communication_period = 2 num_ps = 1 - cluster, workers, _ = create_local_cluster(num_workers=num_workers, - num_ps=num_ps) + cluster, workers, _ = create_local_cluster( + num_workers=num_workers, num_ps=num_ps) - sessions, graphs, train_ops = _get_workers(num_workers, - communication_period, - workers, 1.0) + sessions, graphs, train_ops = _get_workers( + num_workers, communication_period, workers, 1.0) - var_0 = graphs[0].get_tensor_by_name('v0:0') - var_1 = graphs[0].get_tensor_by_name('v1:0') + var_0 = graphs[0].get_tensor_by_name("v0:0") + var_1 = graphs[0].get_tensor_by_name("v1:0") global_step = training_util.get_global_step(graphs[0]) var_0_g = graphs[0].get_tensor_by_name(GLOBAL_VARIABLE_NAME + "/v0:0") var_1_g = graphs[0].get_tensor_by_name(GLOBAL_VARIABLE_NAME + "/v1:0") @@ -166,18 +163,17 @@ class ElasticAverageOptimizerTest(test.TestCase): num_workers = 2 communication_period = 1 num_ps = 2 - cluster, workers, _ = create_local_cluster(num_workers=num_workers, - num_ps=num_ps) + cluster, workers, _ = create_local_cluster( + num_workers=num_workers, num_ps=num_ps) - sessions, graphs, train_ops = _get_workers(num_workers, - communication_period, - workers, 0.5) + sessions, graphs, train_ops = _get_workers( + num_workers, communication_period, workers, 0.5) - var_0 = graphs[0].get_tensor_by_name('v0:0') - var_1 = graphs[0].get_tensor_by_name('v1:0') + var_0 = graphs[0].get_tensor_by_name("v0:0") + var_1 = graphs[0].get_tensor_by_name("v1:0") - var_0_1 = graphs[1].get_tensor_by_name('v0:0') - var_1_1 = graphs[1].get_tensor_by_name('v1:0') + var_0_1 = graphs[1].get_tensor_by_name("v0:0") + var_1_1 = graphs[1].get_tensor_by_name("v1:0") var_0_g = graphs[0].get_tensor_by_name(GLOBAL_VARIABLE_NAME + "/v0:0") var_1_g = graphs[0].get_tensor_by_name(GLOBAL_VARIABLE_NAME + "/v1:0") @@ -201,25 +197,24 @@ class ElasticAverageOptimizerTest(test.TestCase): def testPS2TasksWithClusterSpecClass(self): cluster_spec = server_lib.ClusterSpec({ - "ps": ["ps0:2222", "ps1:2222"], - "worker": ["worker0:2222", "worker1:2222", "worker2:2222"] + "ps": ["ps0:2222", "ps1:2222"], + "worker": ["worker0:2222", "worker1:2222", "worker2:2222"] }) - ea_coustom = ElasticAverageCustomGetter( - worker_device="/job:worker/task:0") + ea_coustom = ElasticAverageCustomGetter(worker_device="/job:worker/task:0") from tensorflow.python.training import device_setter with ops.device( device_setter.replica_device_setter(cluster=cluster_spec, worker_device="/job:worker/task:0", ps_device="/job:ps")), \ - variable_scope.variable_scope('', custom_getter=ea_coustom): + variable_scope.variable_scope("", custom_getter=ea_coustom): v = variable_scope.get_variable(initializer=[1, 2], name="v") - w = variable_scope.get_variable(initializer=[2, 1], name='w') - v_g, w_g = ea_coustom._global_map[v],ea_coustom._global_map[w] + w = variable_scope.get_variable(initializer=[2, 1], name="w") + v_g, w_g = ea_coustom._global_map[v], ea_coustom._global_map[w] self.assertDeviceEqual("/job:worker/task:0", v.device) self.assertDeviceEqual("job:ps/task:0", v_g.device) self.assertDeviceEqual("/job:worker/task:0", w.device) self.assertDeviceEqual("job:ps/task:1", w_g.device) -if __name__ == '__main__': +if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/predictor/predictor_factories_test.py b/tensorflow/contrib/predictor/predictor_factories_test.py index e8443e718d..578d9424b2 100644 --- a/tensorflow/contrib/predictor/predictor_factories_test.py +++ b/tensorflow/contrib/predictor/predictor_factories_test.py @@ -50,8 +50,8 @@ class PredictorFactoriesTest(test.TestCase): def testFromContribEstimator(self): estimator = testing_common.get_arithmetic_estimator(core=False) input_fn = testing_common.get_arithmetic_input_fn(core=False) - predictor_factories.from_contrib_estimator(estimator, input_fn, - output_alternative_key='sum') + predictor_factories.from_contrib_estimator( + estimator, input_fn, output_alternative_key='sum') def testFromContribEstimatorWithCoreEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=True) diff --git a/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_loss_test.py b/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_loss_test.py index c8b4e472c9..360e7dbe75 100644 --- a/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_loss_test.py +++ b/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_loss_test.py @@ -105,8 +105,8 @@ class SparsemaxLossTest(test.TestCase): tf_loss_op, tf_loss_out = self._tf_sparsemax_loss(z, q, dtype, use_gpu) np_loss = self._np_sparsemax_loss(z, q).astype(dtype) - self.assertAllCloseAccordingToType(np_loss, tf_loss_out, - half_atol=1e-2, half_rtol=5e-3) + self.assertAllCloseAccordingToType( + np_loss, tf_loss_out, half_atol=1e-2, half_rtol=5e-3) self.assertShapeEqual(np_loss, tf_loss_op) def _test_constant_add(self, dtype, random, use_gpu): @@ -116,17 +116,17 @@ class SparsemaxLossTest(test.TestCase): q = np.zeros((test_obs, 10)) q[np.arange(0, test_obs), np.random.randint(0, 10, size=test_obs)] = 1 - _, tf_loss_zpc = self._tf_sparsemax_loss( - z + c, q, dtype, use_gpu - ) + _, tf_loss_zpc = self._tf_sparsemax_loss(z + c, q, dtype, use_gpu) - _, tf_loss_z = self._tf_sparsemax_loss( - z, q, dtype, use_gpu - ) + _, tf_loss_z = self._tf_sparsemax_loss(z, q, dtype, use_gpu) - self.assertAllCloseAccordingToType(tf_loss_zpc, tf_loss_z, - float_atol=5e-6, float_rtol=5e-6, - half_atol=1e-2, half_rtol=1e-2) + self.assertAllCloseAccordingToType( + tf_loss_zpc, + tf_loss_z, + float_atol=5e-6, + float_rtol=5e-6, + half_atol=1e-2, + half_rtol=1e-2) def _test_sparsemax_loss_positive(self, dtype, random, use_gpu): """check sparsemax-loss proposition 4""" @@ -170,10 +170,7 @@ class SparsemaxLossTest(test.TestCase): with self.test_session(use_gpu=use_gpu): err = gradient_checker.compute_gradient_error( - logits, z.shape, - loss_op, (test_obs, ), - x_init_value=z, delta=1e-9 - ) + logits, z.shape, loss_op, (test_obs,), x_init_value=z, delta=1e-9) self.assertLess(err, 1e-4) @@ -192,8 +189,8 @@ class SparsemaxLossTest(test.TestCase): tf_grad = loss_grad_op.eval() np_grad = self._np_sparsemax_loss_grad(z, q).astype(dtype) - self.assertAllCloseAccordingToType(np_grad, tf_grad, - half_atol=1e-2, half_rtol=5e-3) + self.assertAllCloseAccordingToType( + np_grad, tf_grad, half_atol=1e-2, half_rtol=5e-3) self.assertShapeEqual(np_grad, loss_grad_op) def _test_dtype(self, dtype): @@ -220,5 +217,6 @@ class SparsemaxLossTest(test.TestCase): def testDouble(self): self._test_dtype('float64') -if __name__ == "__main__": + +if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_test.py b/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_test.py index 82d36ee9cb..259e62bd86 100644 --- a/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_test.py +++ b/tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_test.py @@ -83,8 +83,8 @@ class SparsemaxTest(test.TestCase): tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(z, dtype, use_gpu) p_sparemax = self._np_sparsemax(z).astype(dtype) - self.assertAllCloseAccordingToType(p_sparemax, tf_sparsemax_out, - half_atol=5e-3) + self.assertAllCloseAccordingToType( + p_sparemax, tf_sparsemax_out, half_atol=5e-3) self.assertShapeEqual(p_sparemax, tf_sparsemax_op) def _test_sparsemax_of_zero(self, dtype, random, use_gpu): @@ -111,9 +111,8 @@ class SparsemaxTest(test.TestCase): p_expected = np.zeros((test_obs, 10), dtype=dtype) p_expected[np.arange(0, test_obs), z_sort_arg[:, 0]] = 1 - tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax( - (1 / epsilon) * z, dtype, use_gpu - ) + tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax((1 / epsilon) * z, + dtype, use_gpu) self.assertAllCloseAccordingToType(p_expected, tf_sparsemax_out) self.assertShapeEqual(p_expected, tf_sparsemax_op) @@ -123,16 +122,12 @@ class SparsemaxTest(test.TestCase): z = random.uniform(low=-3, high=3, size=(test_obs, 10)).astype(dtype) c = random.uniform(low=-3, high=3, size=(test_obs, 1)).astype(dtype) - _, tf_sparsemax_zpc = self._tf_sparsemax( - z + c, dtype, use_gpu - ) + _, tf_sparsemax_zpc = self._tf_sparsemax(z + c, dtype, use_gpu) - _, tf_sparsemax_z = self._tf_sparsemax( - z, dtype, use_gpu - ) + _, tf_sparsemax_z = self._tf_sparsemax(z, dtype, use_gpu) - self.assertAllCloseAccordingToType(tf_sparsemax_zpc, tf_sparsemax_z, - half_atol=5e-3) + self.assertAllCloseAccordingToType( + tf_sparsemax_zpc, tf_sparsemax_z, half_atol=5e-3) def _test_permutation(self, dtype, random, use_gpu): """check sparsemax proposition 3""" @@ -143,12 +138,11 @@ class SparsemaxTest(test.TestCase): per = random.permutation(10) tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax( - z[i, per].reshape(1, -1), dtype, use_gpu - ) + z[i, per].reshape(1, -1), dtype, use_gpu) p_expected = p[i, per].reshape(1, -1) - self.assertAllCloseAccordingToType(p_expected, tf_sparsemax_out, - half_atol=5e-3) + self.assertAllCloseAccordingToType( + p_expected, tf_sparsemax_out, half_atol=5e-3) self.assertShapeEqual(p_expected, tf_sparsemax_op) def _test_diffrence(self, dtype, random, use_gpu): @@ -166,18 +160,14 @@ class SparsemaxTest(test.TestCase): continue self.assertTrue( - 0 <= p[val, j] - p[val, i] <= z[val, j] - z[val, i] + etol, - "0 <= %.10f <= %.10f" % ( - p[val, j] - p[val, i], z[val, j] - z[val, i] + etol - ) - ) + 0 <= p[val, j] - p[val, i] <= z[val, j] - z[val, i] + etol, + '0 <= %.10f <= %.10f' % (p[val, j] - p[val, i], + z[val, j] - z[val, i] + etol)) def _test_two_dimentional(self, dtype, random, use_gpu): """check two dimentation sparsemax case""" t = np.linspace(-2, 2, test_obs, dtype=dtype) - z = np.vstack([ - t, np.zeros(test_obs, dtype=dtype) - ]).T + z = np.vstack([t, np.zeros(test_obs, dtype=dtype)]).T tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(z, dtype, use_gpu) @@ -196,10 +186,7 @@ class SparsemaxTest(test.TestCase): with self.test_session(use_gpu=use_gpu): err = gradient_checker.compute_gradient_error( - logits, z.shape, - sparsemax_op, z.shape, - x_init_value=z, delta=1e-9 - ) + logits, z.shape, sparsemax_op, z.shape, x_init_value=z, delta=1e-9) self.assertLess(err, 1e-4) @@ -248,5 +235,6 @@ class SparsemaxTest(test.TestCase): def testDouble(self): self._test_dtype('float64') -if __name__ == "__main__": + +if __name__ == '__main__': test.main() 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 fa4c1c0da5..461fb1c517 100644 --- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py +++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Train and Eval the MNIST network. This version is like fully_connected_feed.py but uses data converted @@ -65,6 +64,7 @@ def decode(serialized_example): return image, label + def augment(image, label): # OPTIONAL: Could reshape into a 28x28 image and apply distortions # here. Since we are not applying any distortions in this @@ -72,12 +72,14 @@ def augment(image, label): # into a vector, we don't bother. return image, label + def normalize(image, label): # Convert from [0, 255] -> [-0.5, 0.5] floats. image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 return image, label + def inputs(train, batch_size, num_epochs): """Reads input data num_epochs times. @@ -98,9 +100,10 @@ def inputs(train, batch_size, num_epochs): over the dataset once. On the other hand there is no special initialization required. """ - if not num_epochs: num_epochs = None - filename = os.path.join(FLAGS.train_dir, - TRAIN_FILE if train else VALIDATION_FILE) + if not num_epochs: + num_epochs = None + filename = os.path.join(FLAGS.train_dir, TRAIN_FILE + if train else VALIDATION_FILE) with tf.name_scope('input'): # TFRecordDataset opens a protobuf and reads entries line by line @@ -127,13 +130,11 @@ def run_training(): # Tell TensorFlow that the model will be built into the default Graph. with tf.Graph().as_default(): # Input images and labels. - image_batch, label_batch = inputs(train=True, batch_size=FLAGS.batch_size, - num_epochs=FLAGS.num_epochs) + image_batch, label_batch = inputs( + train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs) # Build a Graph that computes predictions from the inference model. - logits = mnist.inference(image_batch, - FLAGS.hidden1, - FLAGS.hidden2) + logits = mnist.inference(image_batch, FLAGS.hidden1, FLAGS.hidden2) # Add to the Graph the loss calculation. loss = mnist.loss(logits, label_batch) @@ -152,7 +153,7 @@ def run_training(): sess.run(init_op) try: step = 0 - while True: #train until OutOfRangeError + while True: #train until OutOfRangeError start_time = time.time() # Run one step of the model. The return values are @@ -168,10 +169,12 @@ def run_training(): # Print an overview fairly often. if step % 100 == 0: print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, - duration)) + duration)) step += 1 except tf.errors.OutOfRangeError: - print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step)) + print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, + step)) + def main(_): run_training() @@ -183,37 +186,27 @@ if __name__ == '__main__': '--learning_rate', type=float, default=0.01, - help='Initial learning rate.' - ) + help='Initial learning rate.') parser.add_argument( '--num_epochs', type=int, default=2, - help='Number of epochs to run trainer.' - ) + help='Number of epochs to run trainer.') parser.add_argument( '--hidden1', type=int, default=128, - help='Number of units in hidden layer 1.' - ) + help='Number of units in hidden layer 1.') parser.add_argument( '--hidden2', type=int, default=32, - help='Number of units in hidden layer 2.' - ) - parser.add_argument( - '--batch_size', - type=int, - default=100, - help='Batch size.' - ) + help='Number of units in hidden layer 2.') + parser.add_argument('--batch_size', type=int, default=100, help='Batch size.') parser.add_argument( '--train_dir', type=str, default='/tmp/data', - help='Directory with the training data.' - ) + help='Directory with the training data.') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/label_image/label_image.py b/tensorflow/examples/label_image/label_image.py index d62b73384c..1c1bd57d71 100644 --- a/tensorflow/examples/label_image/label_image.py +++ b/tensorflow/examples/label_image/label_image.py @@ -23,6 +23,7 @@ import sys import numpy as np import tensorflow as tf + def load_graph(model_file): graph = tf.Graph() graph_def = tf.GraphDef() @@ -34,22 +35,26 @@ def load_graph(model_file): return graph -def read_tensor_from_image_file(file_name, input_height=299, input_width=299, - input_mean=0, input_std=255): + +def read_tensor_from_image_file(file_name, + input_height=299, + input_width=299, + input_mean=0, + input_std=255): input_name = "file_reader" output_name = "normalized" file_reader = tf.read_file(file_name, input_name) if file_name.endswith(".png"): - image_reader = tf.image.decode_png(file_reader, channels = 3, - name='png_reader') + image_reader = tf.image.decode_png( + file_reader, channels=3, name="png_reader") elif file_name.endswith(".gif"): - image_reader = tf.squeeze(tf.image.decode_gif(file_reader, - name='gif_reader')) + image_reader = tf.squeeze( + tf.image.decode_gif(file_reader, name="gif_reader")) elif file_name.endswith(".bmp"): - image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader') + image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader") else: - image_reader = tf.image.decode_jpeg(file_reader, channels = 3, - name='jpeg_reader') + image_reader = tf.image.decode_jpeg( + file_reader, channels=3, name="jpeg_reader") float_caster = tf.cast(image_reader, tf.float32) dims_expander = tf.expand_dims(float_caster, 0) resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width]) @@ -59,6 +64,7 @@ def read_tensor_from_image_file(file_name, input_height=299, input_width=299, return result + def load_labels(label_file): label = [] proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines() @@ -66,6 +72,7 @@ def load_labels(label_file): label.append(l.rstrip()) return label + if __name__ == "__main__": file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg" model_file = \ @@ -110,11 +117,12 @@ if __name__ == "__main__": output_layer = args.output_layer graph = load_graph(model_file) - t = read_tensor_from_image_file(file_name, - input_height=input_height, - input_width=input_width, - input_mean=input_mean, - input_std=input_std) + t = read_tensor_from_image_file( + file_name, + input_height=input_height, + input_width=input_width, + input_mean=input_mean, + input_std=input_std) input_name = "import/" + input_layer output_name = "import/" + output_layer @@ -122,8 +130,9 @@ if __name__ == "__main__": output_operation = graph.get_operation_by_name(output_name) with tf.Session(graph=graph) as sess: - results = sess.run(output_operation.outputs[0], - {input_operation.outputs[0]: t}) + results = sess.run(output_operation.outputs[0], { + input_operation.outputs[0]: t + }) results = np.squeeze(results) top_k = results.argsort()[-5:][::-1] diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index 1481a4d035..e6f94396b8 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """A client interface for TensorFlow.""" from __future__ import absolute_import @@ -71,8 +70,9 @@ def _get_indexed_slices_value_from_fetches(fetched_vals): def _get_feeds_for_indexed_slices(feed, feed_val): - return list(zip([feed.values, feed.indices] if feed.dense_shape is None else - [feed.values, feed.indices, feed.dense_shape], feed_val)) + return list( + zip([feed.values, feed.indices] if feed.dense_shape is None else + [feed.values, feed.indices, feed.dense_shape], feed_val)) # List of extensions supported to convert run arguments into actual fetches and @@ -124,6 +124,7 @@ _REGISTERED_EXPANSIONS = [ lambda fetch: ([fetch], lambda fetched_vals: fetched_vals[0]), lambda feed, feed_val: [(feed, feed_val)], lambda feed: [feed])] + # pylint: enable=g-long-lambda @@ -132,8 +133,11 @@ def _convert_to_numpy_obj(numpy_dtype, obj): return numpy_dtype(obj) if numpy_dtype is not object else str(obj) -def register_session_run_conversion_functions(tensor_type, fetch_function, - feed_function=None, feed_function_for_partial_run=None): +def register_session_run_conversion_functions( + tensor_type, + fetch_function, + feed_function=None, + feed_function_for_partial_run=None): """Register fetch and feed conversion functions for `tf.Session.run()`. This function registers a triple of conversion functions for fetching and/or @@ -174,11 +178,11 @@ def register_session_run_conversion_functions(tensor_type, fetch_function, """ for conversion_function in _REGISTERED_EXPANSIONS: if issubclass(conversion_function[0], tensor_type): - raise ValueError( - '%s has already been registered so ignore it.', tensor_type) + raise ValueError('%s has already been registered so ignore it.', + tensor_type) return - _REGISTERED_EXPANSIONS.insert(0, - (tensor_type, fetch_function, feed_function, feed_function_for_partial_run)) + _REGISTERED_EXPANSIONS.insert(0, (tensor_type, fetch_function, feed_function, + feed_function_for_partial_run)) class _FetchMapper(object): @@ -233,8 +237,8 @@ class _FetchMapper(object): An instance of a subclass of `_FetchMapper` that handles the shape. """ if fetch is None: - raise TypeError('Fetch argument %r has invalid type %r' % - (fetch, type(fetch))) + raise TypeError('Fetch argument %r has invalid type %r' % (fetch, + type(fetch))) elif isinstance(fetch, (list, tuple)): # NOTE(touts): This is also the code path for namedtuples. return _ListFetchMapper(fetch) @@ -247,8 +251,8 @@ class _FetchMapper(object): fetches, contraction_fn = fetch_fn(fetch) return _ElementFetchMapper(fetches, contraction_fn) # Did not find anything. - raise TypeError('Fetch argument %r has invalid type %r' % - (fetch, type(fetch))) + raise TypeError('Fetch argument %r has invalid type %r' % (fetch, + type(fetch))) class _ElementFetchMapper(_FetchMapper): @@ -277,8 +281,8 @@ class _ElementFetchMapper(_FetchMapper): fetch, allow_tensor=True, allow_operation=True)) except TypeError as e: raise TypeError('Fetch argument %r has invalid type %r, ' - 'must be a string or Tensor. (%s)' - % (fetch, type(fetch), str(e))) + 'must be a string or Tensor. (%s)' % + (fetch, type(fetch), str(e))) except ValueError as e: raise ValueError('Fetch argument %r cannot be interpreted as a ' 'Tensor. (%s)' % (fetch, str(e))) @@ -376,8 +380,9 @@ class _DictFetchMapper(_FetchMapper): """ self._fetch_type = type(fetches) self._keys = fetches.keys() - self._mappers = [_FetchMapper.for_fetch(fetch) - for fetch in fetches.values()] + self._mappers = [ + _FetchMapper.for_fetch(fetch) for fetch in fetches.values() + ] self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers) def unique_fetches(self): @@ -401,6 +406,7 @@ class _FetchHandler(object): result structure matching the user-provided structure for fetches, but containing the corresponding results. """ + # TODO(touts): Make this class also take care of destructuring the feed # dict instead of doing it in the callers. @@ -551,8 +557,11 @@ class _DeviceAttributes(object): return self._memory_limit_bytes def __repr__(self): - return '_DeviceAttributes(%s, %s, %d)' % (self.name, self.device_type, - self.memory_limit_bytes,) + return '_DeviceAttributes(%s, %s, %d)' % ( + self.name, + self.device_type, + self.memory_limit_bytes, + ) class BaseSession(SessionInterface): @@ -601,8 +610,8 @@ class BaseSession(SessionInterface): if config is not None: if not isinstance(config, config_pb2.ConfigProto): - raise TypeError('config must be a tf.ConfigProto, but got %s' - % type(config)) + raise TypeError( + 'config must be a tf.ConfigProto, but got %s' % type(config)) self._config = config self._add_shapes = config.graph_options.infer_shapes else: @@ -976,8 +985,8 @@ class BaseSession(SessionInterface): for tensor_type, _, _, feed_fn in _REGISTERED_EXPANSIONS: if isinstance(feed, tensor_type): return feed_fn(feed) - raise TypeError('Feed argument %r has invalid type %r' - % (feed, type(feed))) + raise TypeError('Feed argument %r has invalid type %r' % (feed, + type(feed))) # Check session. if self._closed: @@ -998,8 +1007,8 @@ class BaseSession(SessionInterface): for feed in feeds: for subfeed in _feed_fn(feed): try: - subfeed_t = self.graph.as_graph_element(subfeed, allow_tensor=True, - allow_operation=False) + subfeed_t = self.graph.as_graph_element( + subfeed, allow_tensor=True, allow_operation=False) if self._created_with_new_api: # pylint: disable=protected-access feed_list.append(subfeed_t._as_tf_output()) @@ -1007,8 +1016,7 @@ class BaseSession(SessionInterface): else: feed_list.append(compat.as_bytes(subfeed_t.name)) except Exception as e: - e.message = ('Cannot interpret feed_list key as Tensor: ' - + e.message) + e.message = ('Cannot interpret feed_list key as Tensor: ' + e.message) e.args = (e.message,) raise e @@ -1041,12 +1049,13 @@ class BaseSession(SessionInterface): def _run(self, handle, fetches, feed_dict, options, run_metadata): """Perform either run or partial_run, depending the presence of `handle`.""" + def _feed_fn(feed, feed_val): for tensor_type, _, feed_fn, _ in _REGISTERED_EXPANSIONS: if isinstance(feed, tensor_type): return feed_fn(feed, feed_val) - raise TypeError('Feed argument %r has invalid type %r' - % (feed, type(feed))) + raise TypeError('Feed argument %r has invalid type %r' % (feed, + type(feed))) # Check session. if self._closed: @@ -1066,11 +1075,11 @@ class BaseSession(SessionInterface): for feed, feed_val in feed_dict.items(): for subfeed, subfeed_val in _feed_fn(feed, feed_val): try: - subfeed_t = self.graph.as_graph_element(subfeed, allow_tensor=True, - allow_operation=False) + subfeed_t = self.graph.as_graph_element( + subfeed, allow_tensor=True, allow_operation=False) except Exception as e: - raise TypeError('Cannot interpret feed_dict key as Tensor: ' - + e.args[0]) + raise TypeError( + 'Cannot interpret feed_dict key as Tensor: ' + e.args[0]) if isinstance(subfeed_val, ops.Tensor): raise TypeError('The value of a feed cannot be a tf.Tensor object. ' @@ -1081,10 +1090,9 @@ class BaseSession(SessionInterface): if isinstance(subfeed_val, int) and _convert_to_numpy_obj( subfeed_dtype, subfeed_val) != subfeed_val: raise TypeError( - 'Type of feed value ' + str(subfeed_val) + ' with type ' + - str(type(subfeed_val)) + - ' is not compatible with Tensor type ' + - str(subfeed_dtype) + + 'Type of feed value ' + str(subfeed_val) + ' with type ' + str( + type(subfeed_val)) + + ' is not compatible with Tensor type ' + str(subfeed_dtype) + '. Try explicitly setting the type of the feed tensor' ' to a larger type (e.g. int64).') @@ -1098,10 +1106,10 @@ class BaseSession(SessionInterface): if (not is_tensor_handle_feed and not subfeed_t.get_shape().is_compatible_with(np_val.shape)): - raise ValueError( - 'Cannot feed value of shape %r for Tensor %r, ' - 'which has shape %r' - % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape()))) + raise ValueError('Cannot feed value of shape %r for Tensor %r, ' + 'which has shape %r' % + (np_val.shape, subfeed_t.name, + str(subfeed_t.get_shape()))) if not self.graph.is_feedable(subfeed_t): raise ValueError('Tensor %s may not be fed.' % subfeed_t) @@ -1130,10 +1138,7 @@ class BaseSession(SessionInterface): results = [] return fetch_handler.build_results(self, results) - def make_callable(self, - fetches, - feed_list=None, - accept_options=False): + def make_callable(self, fetches, feed_list=None, accept_options=False): """Returns a Python callable that runs a particular step. The returned callable will take `len(feed_list)` arguments whose types @@ -1176,9 +1181,12 @@ class BaseSession(SessionInterface): # `Session._run()` so that we can convert the feeds to a list of # strings here. def _generic_run(*feed_args, **kwargs): - feed_dict = {feed: feed_val - for feed, feed_val in zip(feed_list, feed_args)} + feed_dict = { + feed: feed_val + for feed, feed_val in zip(feed_list, feed_args) + } return self.run(fetches, feed_dict=feed_dict, **kwargs) + return _generic_run # Ensure any changes to the graph are reflected in the runtime. @@ -1198,12 +1206,11 @@ class BaseSession(SessionInterface): fetch_list = _name_list(fetch_handler.fetches()) target_list = _name_list(fetch_handler.targets()) - def _callable_template_with_options_and_metadata( - fetch_list, - target_list, - fetch_handler, - options=None, - run_metadata=None): + def _callable_template_with_options_and_metadata(fetch_list, + target_list, + fetch_handler, + options=None, + run_metadata=None): """Template callable that accepts RunOptions and RunMetadata.""" options_ptr = tf_session.TF_NewBufferFromString( compat.as_bytes(options.SerializeToString())) if options else None @@ -1215,9 +1222,9 @@ class BaseSession(SessionInterface): self._session, options_ptr, {}, fetch_list, target_list, run_metadata_ptr, status) else: - results = tf_session.TF_Run( - self._session, options_ptr, {}, fetch_list, target_list, status, - run_metadata_ptr) + results = tf_session.TF_Run(self._session, options_ptr, {}, + fetch_list, target_list, status, + run_metadata_ptr) if fetch_handler: results = fetch_handler.build_results(self, results) else: @@ -1233,37 +1240,40 @@ class BaseSession(SessionInterface): return results if accept_options: - return functools.partial( - _callable_template_with_options_and_metadata, fetch_list, - target_list, fetch_handler) + return functools.partial(_callable_template_with_options_and_metadata, + fetch_list, target_list, fetch_handler) elif isinstance(fetches, ops.Operation): # Special case for fetching a single operation, because the # function will have no return value. assert not fetch_list assert len(target_list) == 1 + def _single_operation_run(): with errors.raise_exception_on_not_ok_status() as status: if self._created_with_new_api: - tf_session.TF_SessionRun_wrapper( - self._session, None, {}, [], target_list, None, status) + tf_session.TF_SessionRun_wrapper(self._session, None, {}, [], + target_list, None, status) else: - tf_session.TF_Run( - self._session, None, {}, [], target_list, status, None) + tf_session.TF_Run(self._session, None, {}, [], target_list, status, + None) + return _single_operation_run elif isinstance(fetches, ops.Tensor): # Special case for fetching a single tensor, because the # function can return the result of `TF_Run()` directly. assert len(fetch_list) == 1 assert not target_list + def _single_tensor_run(): with errors.raise_exception_on_not_ok_status() as status: if self._created_with_new_api: results = tf_session.TF_SessionRun_wrapper( self._session, None, {}, fetch_list, [], None, status) else: - results = tf_session.TF_Run( - self._session, None, {}, fetch_list, [], status, None) + results = tf_session.TF_Run(self._session, None, {}, fetch_list, [], + status, None) return results[0] + return _single_tensor_run else: # In all other cases, we must use `fetch_handler` to build the @@ -1274,16 +1284,17 @@ class BaseSession(SessionInterface): results = tf_session.TF_SessionRun_wrapper( self._session, None, {}, fetch_list, target_list, None, status) else: - results = tf_session.TF_Run( - self._session, None, {}, fetch_list, target_list, status, None) + results = tf_session.TF_Run(self._session, None, {}, fetch_list, + target_list, status, None) return fetch_handler.build_results(self, results) + return _fetch_handler_run # Captures the name of a node in an error status. _NODEDEF_NAME_RE = re.compile(r'\[\[Node: ([^ ]*?) =') - def _do_run(self, handle, target_list, fetch_list, feed_dict, - options, run_metadata): + def _do_run(self, handle, target_list, fetch_list, feed_dict, options, + run_metadata): """Runs a step based on the given fetches and feeds. Args: @@ -1320,13 +1331,12 @@ class BaseSession(SessionInterface): self._extend_graph() with errors.raise_exception_on_not_ok_status() as status: if self._created_with_new_api: - return tf_session.TF_SessionRun_wrapper( - session, options, feed_dict, fetch_list, target_list, - run_metadata, status) + return tf_session.TF_SessionRun_wrapper(session, options, feed_dict, + fetch_list, target_list, + run_metadata, status) else: - return tf_session.TF_Run(session, options, - feed_dict, fetch_list, target_list, - status, run_metadata) + return tf_session.TF_Run(session, options, feed_dict, fetch_list, + target_list, status, run_metadata) def _prun_fn(session, handle, feed_dict, fetch_list): if target_list: @@ -1365,20 +1375,20 @@ class BaseSession(SessionInterface): def _extend_graph(self): # Nothing to do if we're using the new session interface # TODO(skyewm): remove this function altogether eventually - if self._created_with_new_api: return + if self._created_with_new_api: + return # Ensure any changes to the graph are reflected in the runtime. with self._extend_lock: if self._graph.version > self._current_version: # pylint: disable=protected-access graph_def, self._current_version = self._graph._as_graph_def( - from_version=self._current_version, - add_shapes=self._add_shapes) + from_version=self._current_version, add_shapes=self._add_shapes) # pylint: enable=protected-access with errors.raise_exception_on_not_ok_status() as status: - tf_session.TF_ExtendGraph( - self._session, graph_def.SerializeToString(), status) + tf_session.TF_ExtendGraph(self._session, + graph_def.SerializeToString(), status) self._opened = True # The threshold to run garbage collection to delete dead tensors. @@ -1398,9 +1408,8 @@ class BaseSession(SessionInterface): feeds = {} fetches = [] for deleter_key, tensor_handle in enumerate(tensors_to_delete): - holder, deleter = session_ops._get_handle_deleter(self.graph, - deleter_key, - tensor_handle) + holder, deleter = session_ops._get_handle_deleter( + self.graph, deleter_key, tensor_handle) feeds[holder] = tensor_handle fetches.append(deleter) self.run(fetches, feed_dict=feeds) @@ -1471,7 +1480,8 @@ class Session(BaseSession): sess.run(...) ``` - The [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto) + The + [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto) protocol buffer exposes various configuration options for a session. For example, to create a session that uses soft constraints for device placement, and log the resulting placement decisions, @@ -1502,7 +1512,8 @@ class Session(BaseSession): @{$distributed$Distributed TensorFlow} for more examples. graph: (Optional.) The `Graph` to be launched (described above). - config: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto) + config: (Optional.) A + [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto) protocol buffer with configuration options for the session. """ @@ -1526,8 +1537,8 @@ class Session(BaseSession): def __exit__(self, exec_type, exec_value, exec_tb): if exec_type is errors.OpError: logging.error('Session closing due to OpError: %s', (exec_value,)) - self._default_session_context_manager.__exit__( - exec_type, exec_value, exec_tb) + self._default_session_context_manager.__exit__(exec_type, exec_value, + exec_tb) self._default_graph_context_manager.__exit__(exec_type, exec_value, exec_tb) self._default_session_context_manager = None diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index c579fba339..768a5db88a 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Tests for tensorflow.python.client.session.Session.""" from __future__ import absolute_import from __future__ import division @@ -57,7 +56,6 @@ from tensorflow.python.platform import googletest from tensorflow.python.training import server_lib from tensorflow.python.util import compat - # NOTE(mrry): Dummy shape registration for ops used in the tests, since they # don't have C++ op registrations on which to attach C++ shape fns. ops.RegisterShape('ConstructionFails')(common_shapes.unknown_shape) @@ -95,14 +93,18 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertAllEqual(arr, copy_val) # Test without feed. copy_val = copy.eval() - self.assertAllEqual(np.asarray([[10.0, 10.0, 10.0], [10.0, 10.0, 10.0]], - dtype=np.float32), copy_val) + self.assertAllEqual( + np.asarray( + [[10.0, 10.0, 10.0], [10.0, 10.0, 10.0]], dtype=np.float32), + 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})): + config=config_pb2.ConfigProto(device_count={ + 'CPU': 2 + })): inp = constant_op.constant(10.0, name='W1') self.assertAllEqual(inp.eval(), 10.0) @@ -161,20 +163,23 @@ class SessionTest(test_util.TensorFlowTestCase): def exc_predicate(e): return (e.op is None and e.node_def is None and e.error_code == error_codes_pb2.INVALID_ARGUMENT) + with self.assertRaisesOpError(exc_predicate): # Run with a bogus handle. s.partial_run('foo', r1, feed_dict={a: 1, b: 2}) def testOpConstructionErrorPayload(self): - if ops._USE_C_API: return # No shape registration for 'ConstructionFails' + if ops._USE_C_API: + return # No shape registration for 'ConstructionFails' with session.Session(): failing_op = ops.get_default_graph().create_op( 'ConstructionFails', [], [], name='f') def exc_predicate(e): - return (e.op == failing_op - and e.error_code == error_codes_pb2.INVALID_ARGUMENT) + return (e.op == failing_op and + e.error_code == error_codes_pb2.INVALID_ARGUMENT) + with self.assertRaisesOpError(exc_predicate): failing_op.run() @@ -191,9 +196,9 @@ class SessionTest(test_util.TensorFlowTestCase): # pylint: enable=protected-access def exc_predicate(e): - return (e.op == c.op - and e.op._original_op == b.op - and e.op._original_op._original_op == a.op) + return (e.op == c.op and e.op._original_op == b.op and + e.op._original_op._original_op == a.op) + with self.assertRaisesOpError(exc_predicate): c.eval() @@ -341,8 +346,12 @@ class SessionTest(test_util.TensorFlowTestCase): b = control_flow_ops.no_op() # An op, not a tensor. c = constant_op.constant(c_val) # List of lists, tuples, namedtuple, and dict - res = sess.run([[a, b, c], (a, b, c), ABC(a=a, b=b, c=c), - {'a': a.name, 'c': c, 'b': b}]) + res = sess.run([[a, b, c], (a, b, c), + ABC(a=a, b=b, c=c), { + 'a': a.name, + 'c': c, + 'b': b + }]) self.assertTrue(isinstance(res, list)) self.assertEqual(4, len(res)) self.assertTrue(isinstance(res[0], list)) @@ -365,8 +374,11 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertEqual(b_val, res[3]['b']) self.assertEqual(c_val, res[3]['c']) # Tuple of lists, tuples, namedtuple, and dict - res = sess.run(([a, b, c], (a.name, b, c), ABC(a=a, b=b, c=c), - {'a': a, 'c': c, 'b': b})) + res = sess.run(([a, b, c], (a.name, b, c), ABC(a=a, b=b, c=c), { + 'a': a, + 'c': c, + 'b': b + })) self.assertTrue(isinstance(res, tuple)) self.assertEqual(4, len(res)) self.assertTrue(isinstance(res[0], list)) @@ -389,10 +401,16 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertEqual(b_val, res[3]['b']) self.assertEqual(c_val, res[3]['c']) # Namedtuple of lists, tuples, namedtuples, and dict - res = sess.run(DEFG(d=[a, b, c], - e=(a, b, c), - f=ABC(a=a.name, b=b, c=c), - g={'a': a, 'c': c, 'b': b})) + res = sess.run( + DEFG( + d=[a, b, c], + e=(a, b, c), + f=ABC(a=a.name, b=b, c=c), + g={ + 'a': a, + 'c': c, + 'b': b + })) self.assertTrue(isinstance(res, DEFG)) self.assertTrue(isinstance(res.d, list)) self.assertEqual(3, len(res.d)) @@ -414,10 +432,16 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertEqual(b_val, res.g['b']) self.assertEqual(c_val, res.g['c']) # Dict of lists, tuples, namedtuples, and dict - res = sess.run({'d': [a, b, c], - 'e': (a, b, c), - 'f': ABC(a=a, b=b, c=c), - 'g': {'a': a.name, 'c': c, 'b': b}}) + res = sess.run({ + 'd': [a, b, c], + 'e': (a, b, c), + 'f': ABC(a=a, b=b, c=c), + 'g': { + 'a': a.name, + 'c': c, + 'b': b + } + }) self.assertTrue(isinstance(res, dict)) self.assertEqual(4, len(res)) self.assertTrue(isinstance(res['d'], list)) @@ -516,8 +540,7 @@ class SessionTest(test_util.TensorFlowTestCase): values = np.array([1.0, 2.0]).astype(np.float32) shape = np.array([7, 9, 2]).astype(np.int64) sp = sparse_tensor.SparseTensor( - constant_op.constant(indices), - constant_op.constant(values), + constant_op.constant(indices), constant_op.constant(values), constant_op.constant(shape)) # Single fetch, use as tuple sp_out = s.run(sp) @@ -587,14 +610,17 @@ class SessionTest(test_util.TensorFlowTestCase): sp = sparse_tensor.SparseTensor( array_ops.placeholder(dtype=np.int64, shape=(2, 3)), array_ops.placeholder(dtype=np.float32, shape=(2,)), - array_ops.placeholder(dtype=np.int64, shape=(3,)),) + array_ops.placeholder(dtype=np.int64, shape=(3,)), + ) sp_indices = array_ops.identity(sp.indices) sp_values = array_ops.identity(sp.values) sp_shape = array_ops.identity(sp.dense_shape) sp2 = sparse_tensor.SparseTensor(sp_indices, sp_values, sp_shape) # Feed with tuple indices_out, values_out, shape_out = s.run( - [sp_indices, sp_values, sp_shape], {sp: (indices, values, shape)}) + [sp_indices, sp_values, sp_shape], { + sp: (indices, values, shape) + }) self.assertAllEqual(indices_out, indices) self.assertAllEqual(values_out, values) self.assertAllEqual(shape_out, shape) @@ -605,20 +631,23 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertAllEqual(sp_out.dense_shape, shape) # Feed with SparseTensorValue indices_out, values_out, shape_out = s.run( - [sp_indices, sp_values, sp_shape], - {sp: sparse_tensor.SparseTensorValue(indices, values, shape)}) + [sp_indices, sp_values, sp_shape], { + sp: sparse_tensor.SparseTensorValue(indices, values, shape) + }) self.assertAllEqual(indices_out, indices) self.assertAllEqual(values_out, values) self.assertAllEqual(shape_out, shape) # Feed with SparseTensorValue, fetch SparseTensorValue - sp2_out = s.run( - sp2, {sp: sparse_tensor.SparseTensorValue(indices, values, shape)}) + sp2_out = s.run(sp2, { + sp: sparse_tensor.SparseTensorValue(indices, values, shape) + }) self.assertAllEqual(sp2_out.indices, indices) self.assertAllEqual(sp2_out.values, values) self.assertAllEqual(sp2_out.dense_shape, shape) # Feed SparseTensorValue and fetch sp directly. - sp_out = s.run( - sp, {sp: sparse_tensor.SparseTensorValue(indices, values, shape)}) + sp_out = s.run(sp, { + sp: sparse_tensor.SparseTensorValue(indices, values, shape) + }) self.assertAllEqual(sp_out.indices, indices) self.assertAllEqual(sp_out.values, values) self.assertAllEqual(sp_out.dense_shape, shape) @@ -635,20 +664,24 @@ class SessionTest(test_util.TensorFlowTestCase): sp2 = sparse_tensor.SparseTensor(sp_indices, sp_values, sp_shape) # Feed with tuple indices_out, values_out, shape_out = s.run( - [sp_indices, sp_values, sp_shape], {sp: (indices, values, shape)}) + [sp_indices, sp_values, sp_shape], { + sp: (indices, values, shape) + }) self.assertAllEqual(indices_out, indices) self.assertAllEqual(values_out, values) self.assertAllEqual(shape_out, shape) # Feed with SparseTensorValue indices_out, values_out, shape_out = s.run( - [sp_indices, sp_values, sp_shape], - {sp: sparse_tensor.SparseTensorValue(indices, values, shape)}) + [sp_indices, sp_values, sp_shape], { + sp: sparse_tensor.SparseTensorValue(indices, values, shape) + }) self.assertAllEqual(indices_out, indices) self.assertAllEqual(values_out, values) self.assertAllEqual(shape_out, shape) # Feed with SparseTensorValue, fetch SparseTensorValue - sp2_out = s.run( - sp2, {sp: sparse_tensor.SparseTensorValue(indices, values, shape)}) + sp2_out = s.run(sp2, { + sp: sparse_tensor.SparseTensorValue(indices, values, shape) + }) self.assertAllEqual(sp2_out.indices, indices) self.assertAllEqual(sp2_out.values, values) self.assertAllEqual(sp2_out.dense_shape, shape) @@ -666,20 +699,24 @@ class SessionTest(test_util.TensorFlowTestCase): sp2 = sparse_tensor.SparseTensor(sp_indices, sp_values, sp_shape) # Feed with tuple indices_out, values_out, shape_out = s.run( - [sp_indices, sp_values, sp_shape], {sp: (indices, values, shape)}) + [sp_indices, sp_values, sp_shape], { + sp: (indices, values, shape) + }) self.assertAllEqual(indices_out, indices) self.assertAllEqual(values_out, values) self.assertAllEqual(shape_out, shape) # Feed with SparseTensorValue indices_out, values_out, shape_out = s.run( - [sp_indices, sp_values, sp_shape], - {sp: sparse_tensor.SparseTensorValue(indices, values, shape)}) + [sp_indices, sp_values, sp_shape], { + sp: sparse_tensor.SparseTensorValue(indices, values, shape) + }) self.assertAllEqual(indices_out, indices) self.assertAllEqual(values_out, values) self.assertAllEqual(shape_out, shape) # Feed with SparseTensorValue, fetch SparseTensorValue - sp2_out = s.run( - sp2, {sp: sparse_tensor.SparseTensorValue(indices, values, shape)}) + sp2_out = s.run(sp2, { + sp: sparse_tensor.SparseTensorValue(indices, values, shape) + }) self.assertAllEqual(sp2_out.indices, indices) self.assertAllEqual(sp2_out.values, values) self.assertAllEqual(sp2_out.dense_shape, shape) @@ -689,9 +726,8 @@ class SessionTest(test_util.TensorFlowTestCase): indices = np.array([[3, 2, 0], [4, 5, 1]]).astype(np.int64) values = np.array([1.0, 2.0]).astype(np.float32) shape = np.array([7, 9, 2]).astype(np.int64) - sp = array_ops.sparse_placeholder(dtype=np.float32, - shape=shape, - name='placeholder1') + sp = array_ops.sparse_placeholder( + dtype=np.float32, shape=shape, name='placeholder1') self.assertAllEqual(sp.dense_shape.eval(session=s), shape) self.assertAllEqual(tensor_util.constant_value(sp.dense_shape), shape) sp_indices = array_ops.identity(sp.indices) @@ -699,7 +735,9 @@ class SessionTest(test_util.TensorFlowTestCase): sp_shape = array_ops.identity(sp.dense_shape) # Feed with tuple indices_out, values_out, shape_out = s.run( - [sp_indices, sp_values, sp_shape], {sp: (indices, values)}) + [sp_indices, sp_values, sp_shape], { + sp: (indices, values) + }) self.assertAllEqual(indices_out, indices) self.assertAllEqual(values_out, values) self.assertAllEqual(shape_out, shape) @@ -745,33 +783,34 @@ class SessionTest(test_util.TensorFlowTestCase): indices = np.array([[3, 2, 0], [4, 5, 1]]).astype(np.int64) dense_shape = np.array([7, 9, 2]).astype(np.int64) ind = ops.IndexedSlices( - array_ops.placeholder(dtype=np.float32, - shape=(2,)), - array_ops.placeholder(dtype=np.int64, - shape=(2, 3)), - array_ops.placeholder(dtype=np.int64, - shape=(3,)),) + array_ops.placeholder(dtype=np.float32, shape=(2,)), + array_ops.placeholder(dtype=np.int64, shape=(2, 3)), + array_ops.placeholder(dtype=np.int64, shape=(3,)), + ) ind_values = array_ops.identity(ind.values) ind_indices = array_ops.identity(ind.indices) ind_dense_shape = array_ops.identity(ind.dense_shape) ind2 = ops.IndexedSlices(ind_values, ind_indices, ind_dense_shape) # Feed with tuple values_out, indices_out, dense_shape_out = s.run( - [ind_values, ind_indices, ind_dense_shape], - {ind: (values, indices, dense_shape)}) + [ind_values, ind_indices, ind_dense_shape], { + ind: (values, indices, dense_shape) + }) self.assertAllEqual(values_out, values) self.assertAllEqual(indices_out, indices) self.assertAllEqual(dense_shape_out, dense_shape) # Feed with IndexedSlicesValue values_out, indices_out, dense_shape_out = s.run( - [ind_values, ind_indices, ind_dense_shape], - {ind: ops.IndexedSlicesValue(values, indices, dense_shape)}) + [ind_values, ind_indices, ind_dense_shape], { + ind: ops.IndexedSlicesValue(values, indices, dense_shape) + }) self.assertAllEqual(values_out, values) self.assertAllEqual(indices_out, indices) self.assertAllEqual(dense_shape_out, dense_shape) # Feed with IndexedSlicesValue, fetch IndexedSlicesValue - ind2_out = s.run(ind2, {ind: ops.IndexedSlicesValue(values, indices, - dense_shape)}) + ind2_out = s.run(ind2, { + ind: ops.IndexedSlicesValue(values, indices, dense_shape) + }) self.assertAllEqual(ind2_out.values, values) self.assertAllEqual(ind2_out.indices, indices) self.assertAllEqual(ind2_out.dense_shape, dense_shape) @@ -816,28 +855,27 @@ class SessionTest(test_util.TensorFlowTestCase): indices = np.array([[3, 2, 0], [4, 5, 1]]).astype(np.int64) dense_shape = None ind = ops.IndexedSlices( - array_ops.placeholder(dtype=np.float32, - shape=(2,)), - array_ops.placeholder(dtype=np.int64, - shape=(2, 3)), - None) + array_ops.placeholder(dtype=np.float32, shape=(2,)), + array_ops.placeholder(dtype=np.int64, shape=(2, 3)), None) ind_values = array_ops.identity(ind.values) ind_indices = array_ops.identity(ind.indices) ind2 = ops.IndexedSlices(ind_values, ind_indices) # Feed with tuple - values_out, indices_out = s.run( - [ind_values, ind_indices], {ind: (values, indices)}) + values_out, indices_out = s.run([ind_values, ind_indices], { + ind: (values, indices) + }) self.assertAllEqual(values_out, values) self.assertAllEqual(indices_out, indices) # Feed with IndexedSlicesValue - values_out, indices_out = s.run( - [ind_values, ind_indices], - {ind: ops.IndexedSlicesValue(values, indices, dense_shape)}) + values_out, indices_out = s.run([ind_values, ind_indices], { + ind: ops.IndexedSlicesValue(values, indices, dense_shape) + }) self.assertAllEqual(values_out, values) self.assertAllEqual(indices_out, indices) # Feed with IndexedSlicesValue, fetch IndexedSlicesValue - ind2_out = s.run(ind2, {ind: ops.IndexedSlicesValue(values, indices, - dense_shape)}) + ind2_out = s.run(ind2, { + ind: ops.IndexedSlicesValue(values, indices, dense_shape) + }) self.assertAllEqual(ind2_out.values, values) self.assertAllEqual(ind2_out.indices, indices) self.assertAllEqual(ind2_out.dense_shape, dense_shape) @@ -986,8 +1024,9 @@ class SessionTest(test_util.TensorFlowTestCase): constructed_events = [threading.Event() for _ in range(10)] continue_event = threading.Event() for i, constructed_event in enumerate(constructed_events): - t = self.checkedThread(target=self._testDefaultGraphInThread, - args=(constructed_event, continue_event, i)) + t = self.checkedThread( + target=self._testDefaultGraphInThread, + args=(constructed_event, continue_event, i)) threads.append(t) for t in threads: t.start() @@ -1006,6 +1045,7 @@ class SessionTest(test_util.TensorFlowTestCase): ev.wait() val = c.eval(session=sess) self.assertEqual(val, 5.0) + threads = [self.checkedThread(target=run_step) for _ in range(100)] for t in threads: t.start() @@ -1038,11 +1078,10 @@ class SessionTest(test_util.TensorFlowTestCase): def testGraphDef(self): with session.Session() as sess: - self.assertProtoEquals( - 'versions { producer: %d min_consumer: %d }' % ( - versions.GRAPH_DEF_VERSION, - versions.GRAPH_DEF_VERSION_MIN_CONSUMER), - sess.graph_def) + self.assertProtoEquals('versions { producer: %d min_consumer: %d }' % + (versions.GRAPH_DEF_VERSION, + versions.GRAPH_DEF_VERSION_MIN_CONSUMER), + sess.graph_def) c = constant_op.constant(5.0, name='c') self.assertEquals(len(sess.graph_def.node), 1) d = constant_op.constant(6.0, name='d') @@ -1072,6 +1111,7 @@ class SessionTest(test_util.TensorFlowTestCase): lambda e: 'Attempted to use a closed Session.' in str(e)): while True: sess.run(c) + t = threading.Thread(target=update_thread) t.start() time.sleep(0.1) @@ -1177,17 +1217,11 @@ class SessionTest(test_util.TensorFlowTestCase): def testFeedAndFetch(self): with session.Session() as sess: - for dtype in [dtypes.float16, - dtypes.float32, - dtypes.float64, - dtypes.int32, - dtypes.uint8, - dtypes.int16, - dtypes.int8, - dtypes.int64, - dtypes.bool, - dtypes.complex64, - dtypes.complex128]: + for dtype in [ + dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, + dtypes.uint8, dtypes.int16, dtypes.int8, dtypes.int64, dtypes.bool, + dtypes.complex64, dtypes.complex128 + ]: for shape in [(32, 4, 128), (37,), (2, 0, 6), (0, 0, 0)]: np_dtype = dtype.as_numpy_dtype @@ -1206,13 +1240,19 @@ class SessionTest(test_util.TensorFlowTestCase): np_array = np_array.astype(np_dtype) self.assertAllEqual(np_array, - sess.run(out_t, feed_dict={feed_t: np_array})) + sess.run(out_t, feed_dict={ + feed_t: np_array + })) # Check that we can also get the feed back. self.assertAllEqual(np_array, - sess.run(feed_t, feed_dict={feed_t: np_array})) + sess.run(feed_t, feed_dict={ + feed_t: np_array + })) # Also check that we can get both back. - out_v, feed_v = sess.run([out_t, feed_t], - feed_dict={feed_t: np_array}) + out_v, feed_v = sess.run( + [out_t, feed_t], feed_dict={ + feed_t: np_array + }) self.assertAllEqual(np_array, out_v) self.assertAllEqual(np_array, feed_v) @@ -1257,9 +1297,11 @@ class SessionTest(test_util.TensorFlowTestCase): trace_level=config_pb2.RunOptions.FULL_TRACE) run_metadata = config_pb2.RunMetadata() self.assertEqual(0, len(run_metadata.step_stats.dev_stats)) - self.assertAllClose( - 42.0, - tensor_runner(41.0, options=run_options, run_metadata=run_metadata)) + self.assertAllClose(42.0, + tensor_runner( + 41.0, + options=run_options, + run_metadata=run_metadata)) self.assertGreater(len(run_metadata.step_stats.dev_stats), 0) def testFeedError(self): @@ -1296,8 +1338,9 @@ class SessionTest(test_util.TensorFlowTestCase): size = 1 for s in shape: size *= s - c_list = np.array([compat.as_bytes(str(i)) for i in xrange(size)], - dtype=np.object).reshape(shape) if size > 0 else [] + c_list = np.array( + [compat.as_bytes(str(i)) for i in xrange(size)], + dtype=np.object).reshape(shape) if size > 0 else [] c = constant_op.constant(c_list) self.assertAllEqual(c.eval(), c_list) @@ -1307,13 +1350,16 @@ class SessionTest(test_util.TensorFlowTestCase): size = 1 for s in shape: size *= s - c_list = np.array([compat.as_bytes(str(i)) for i in xrange(size)], - dtype=np.object).reshape(shape) + c_list = np.array( + [compat.as_bytes(str(i)) for i in xrange(size)], + dtype=np.object).reshape(shape) feed_t = array_ops.placeholder(dtype=dtypes.string, shape=shape) c = array_ops.identity(feed_t) self.assertAllEqual(sess.run(c, feed_dict={feed_t: c_list}), c_list) - self.assertAllEqual(sess.run(feed_t, feed_dict={feed_t: c_list}), - c_list) + self.assertAllEqual( + sess.run(feed_t, feed_dict={ + feed_t: c_list + }), c_list) c_v, feed_v = sess.run([c, feed_t], feed_dict={feed_t: c_list}) self.assertAllEqual(c_v, c_list) self.assertAllEqual(feed_v, c_list) @@ -1329,8 +1375,10 @@ class SessionTest(test_util.TensorFlowTestCase): def testStringFeedWithUnicode(self): with session.Session(): - c_list = [u'\n\x01\x00', u'\n\x00\x01', - u'\u26a3 unicode', u'\U0001f60e deal with it'] + c_list = [ + u'\n\x01\x00', u'\n\x00\x01', u'\u26a3 unicode', + u'\U0001f60e deal with it' + ] feed_t = array_ops.placeholder(dtype=dtypes.string, shape=[len(c_list)]) c = array_ops.identity(feed_t) @@ -1423,9 +1471,10 @@ class SessionTest(test_util.TensorFlowTestCase): sess.run(constant_op.constant(1.0), run_metadata=run_metadata) self.assertTrue(not run_metadata.HasField('step_stats')) - sess.run(constant_op.constant(1.0), - options=run_options, - run_metadata=run_metadata) + sess.run( + constant_op.constant(1.0), + options=run_options, + run_metadata=run_metadata) self.assertTrue(run_metadata.HasField('step_stats')) self.assertEquals(len(run_metadata.step_stats.dev_stats), 1) @@ -1439,23 +1488,26 @@ class SessionTest(test_util.TensorFlowTestCase): with session.Session() as sess: # all combinations are valid sess.run(constant_op.constant(1.0), options=None, run_metadata=None) - sess.run(constant_op.constant(1.0), options=None, - run_metadata=run_metadata) + sess.run( + constant_op.constant(1.0), options=None, run_metadata=run_metadata) self.assertTrue(not run_metadata.HasField('step_stats')) - sess.run(constant_op.constant(1.0), options=run_options, - run_metadata=None) + sess.run( + constant_op.constant(1.0), options=run_options, run_metadata=None) self.assertTrue(not run_metadata.HasField('step_stats')) - sess.run(constant_op.constant(1.0), options=run_options, - run_metadata=run_metadata) + sess.run( + constant_op.constant(1.0), + options=run_options, + run_metadata=run_metadata) self.assertTrue(run_metadata.HasField('step_stats')) self.assertEquals(len(run_metadata.step_stats.dev_stats), 1) def testFeedShapeCompatibility(self): # TODO(nolivia): C API doesn't yet handle marking nodes as not feedable. - if ops._USE_C_API: return + if ops._USE_C_API: + return with session.Session() as sess: some_tensor = constant_op.constant([2.0, 2.0, 2.0, 2.0]) @@ -1499,8 +1551,11 @@ class SessionTest(test_util.TensorFlowTestCase): d = math_ops.multiply(c, c) for step in xrange(120): run_metadata = config_pb2.RunMetadata() - sess.run(d, feed_dict={a: 1.0}, - options=run_options, run_metadata=run_metadata) + sess.run( + d, + feed_dict={a: 1.0}, + options=run_options, + run_metadata=run_metadata) if step == 99: self.assertTrue(run_metadata.HasField('cost_graph')) else: @@ -1569,8 +1624,7 @@ class SessionTest(test_util.TensorFlowTestCase): def testTimeoutWithShortOperations(self): num_epochs = 5 - q = data_flow_ops.FIFOQueue( - capacity=50, dtypes=[dtypes.int32], shapes=[()]) + q = data_flow_ops.FIFOQueue(capacity=50, dtypes=[dtypes.int32], shapes=[()]) enqueue_op = q.enqueue_many(constant_op.constant([1, 2])) # Use a 10-second timeout, which should be longer than any @@ -1582,7 +1636,9 @@ class SessionTest(test_util.TensorFlowTestCase): self.assertEqual(sess.run(q.size()), num_epochs * 2) def testRegisterFetchAndFeedConversionFunctions(self): + class SquaredTensor(object): + def __init__(self, tensor): self.sq = math_ops.square(tensor) @@ -1591,24 +1647,27 @@ class SessionTest(test_util.TensorFlowTestCase): feed_fn2 = lambda feed: [feed.sq] session.register_session_run_conversion_functions(SquaredTensor, fetch_fn, - feed_fn1, feed_fn2) + feed_fn1, feed_fn2) with self.assertRaises(ValueError): - session.register_session_run_conversion_functions(SquaredTensor, - fetch_fn, feed_fn1, feed_fn2) + session.register_session_run_conversion_functions(SquaredTensor, fetch_fn, + feed_fn1, feed_fn2) with self.test_session() as sess: np1 = np.array([1.0, 1.5, 2.0, 2.5]) np2 = np.array([3.0, 3.5, 4.0, 4.5]) squared_tensor = SquaredTensor(np2) squared_eval = sess.run(squared_tensor) self.assertAllClose(np2 * np2, squared_eval) - squared_eval = sess.run(squared_tensor, feed_dict={ - squared_tensor : np1 * np1}) + squared_eval = sess.run( + squared_tensor, feed_dict={ + squared_tensor: np1 * np1 + }) self.assertAllClose(np1 * np1, squared_eval) partial_run = sess.partial_run_setup([squared_tensor], []) squared_eval = sess.partial_run(partial_run, squared_tensor) self.assertAllClose(np2 * np2, squared_eval) def testDefaultLogDevicePlacement(self): + class CaptureStderr(str): """Class to capture stderr from C++ shared library.""" @@ -1719,6 +1778,7 @@ class SessionTest(test_util.TensorFlowTestCase): def runTestAddFunctionToSession(self, target=''): """Add a function to a session after the graph has already been run.""" + @function.Defun(dtypes.float32) def foo(x): return x + 1 @@ -1753,6 +1813,7 @@ class SessionTest(test_util.TensorFlowTestCase): TypeError, 'Type of feed value 1 with type <(\w+) \'int\'> is not'): sess.run(a, feed_dict={a: 1}) + class GraphMutationTest(test_util.TensorFlowTestCase): def setUp(self): @@ -1803,8 +1864,7 @@ class GraphMutationTest(test_util.TensorFlowTestCase): with session.Session(graph=g) as sess: self.assertAllEqual(1.0, sess.run(b)) - b.op._set_attr('DstT', - attr_value_pb2.AttrValue(type=types_pb2.DT_FLOAT)) + b.op._set_attr('DstT', attr_value_pb2.AttrValue(type=types_pb2.DT_FLOAT)) with self.assertRaisesRegexp( errors.FailedPreconditionError, 'Cast.*was changed by setting attribute after it was run'): diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py index 75c0e61d47..8e5d8141a1 100644 --- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py +++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py @@ -47,10 +47,9 @@ except ImportError: def _fill_array(arr, seq, fillvalue=0): - """ - Recursively fills padded arr with elements from seq. - If length of seq is less than arr padded length, fillvalue used. + """Recursively fills padded arr with elements from seq. + If length of seq is less than arr padded length, fillvalue used. Args: arr: Padded tensor of shape [batch_size, ..., max_padded_dim_len]. seq: Non-padded list of data sampels of shape @@ -84,28 +83,30 @@ def _pad_if_needed(batch_key_item, fillvalue=0): Raises: ValueError if data samples have different shapes (except last padded dim). """ - shapes = [seq.shape[:-1] if len(seq.shape) > 0 else -1 - for seq in batch_key_item] + shapes = [ + seq.shape[:-1] if len(seq.shape) > 0 else -1 for seq in batch_key_item + ] if not all(shapes[0] == x for x in shapes): raise ValueError("Array shapes must match.") - last_length = [seq.shape[-1] if len(seq.shape) > 0 else 0 - for seq in batch_key_item] + last_length = [ + seq.shape[-1] if len(seq.shape) > 0 else 0 for seq in batch_key_item + ] if all([x == last_length[0] for x in last_length]): return batch_key_item batch_size = len(batch_key_item) max_sequence_length = max(last_length) result_batch = np.zeros( - shape=[batch_size] + list(shapes[0]) + [max_sequence_length], - dtype=batch_key_item[0].dtype) + shape=[batch_size] + list(shapes[0]) + [max_sequence_length], + dtype=batch_key_item[0].dtype) _fill_array(result_batch, batch_key_item, fillvalue) return result_batch -def _get_integer_indices_for_next_batch( - batch_indices_start, batch_size, epoch_end, array_length, - current_epoch, total_epochs): +def _get_integer_indices_for_next_batch(batch_indices_start, batch_size, + epoch_end, array_length, current_epoch, + total_epochs): """Returns the integer indices for next batch. If total epochs is not None and current epoch is the final epoch, the end @@ -135,8 +136,9 @@ def _get_integer_indices_for_next_batch( "Already emitted %s epochs." % current_epoch) batch_indices_end = batch_indices_start + batch_size - batch_indices = [j % array_length for j in - range(batch_indices_start, batch_indices_end)] + batch_indices = [ + j % array_length for j in range(batch_indices_start, batch_indices_end) + ] epoch_end_indices = [i for i, x in enumerate(batch_indices) if x == epoch_end] current_epoch += len(epoch_end_indices) @@ -320,16 +322,20 @@ class _GeneratorFeedFn(object): raise KeyError("key mismatch between dicts emitted by GenFun " "Expected {} keys; got {}".format( self._keys, data_row.keys())) - list_dict.setdefault(self._col_placeholders[index], - list()).append(data_row[key]) + list_dict.setdefault(self._col_placeholders[index], list()).append( + data_row[key]) list_dict_size += 1 if self._pad_value is not None: - feed_dict = {key: np.asarray(_pad_if_needed(item, self._pad_value)) - for key, item in list(list_dict.items())} + feed_dict = { + key: np.asarray(_pad_if_needed(item, self._pad_value)) + for key, item in list(list_dict.items()) + } else: - feed_dict = {key: np.asarray(item) - for key, item in list(list_dict.items())} + feed_dict = { + key: np.asarray(item) + for key, item in list(list_dict.items()) + } return feed_dict @@ -382,9 +388,8 @@ def _enqueue_data(data, queue_shapes = [(), data.shape[1:]] get_feed_fn = _ArrayFeedFn elif isinstance(data, collections.OrderedDict): - types = [dtypes.int64] + [ - dtypes.as_dtype(col.dtype) for col in data.values() - ] + types = [dtypes.int64 + ] + [dtypes.as_dtype(col.dtype) for col in data.values()] queue_shapes = [()] + [col.shape[1:] for col in data.values()] get_feed_fn = _OrderedDictNumpyFeedFn elif isinstance(data, tp.FunctionType): @@ -447,11 +452,11 @@ def _enqueue_data(data, seed=seed) elif pad_data: min_after_dequeue = 0 # just for the summary text - queue_shapes = list(map( - lambda x: tuple(list(x[:-1]) + [None]) if len(x) > 0 else x, - queue_shapes)) + queue_shapes = list( + map(lambda x: tuple(list(x[:-1]) + [None]) if len(x) > 0 else x, + queue_shapes)) queue = data_flow_ops.PaddingFIFOQueue( - capacity, dtypes=types, shapes=queue_shapes) + capacity, dtypes=types, shapes=queue_shapes) else: min_after_dequeue = 0 # just for the summary text queue = data_flow_ops.FIFOQueue( @@ -470,31 +475,35 @@ def _enqueue_data(data, if not pad_data: feed_fns.append( - get_feed_fn( - placeholders, - data, - enqueue_size, - random_start=shuffle, - seed=seed_i, - num_epochs=num_epochs)) + get_feed_fn( + placeholders, + data, + enqueue_size, + random_start=shuffle, + seed=seed_i, + num_epochs=num_epochs)) else: feed_fns.append( - get_feed_fn( - placeholders, - data, - enqueue_size, - random_start=shuffle, - seed=seed_i, - num_epochs=num_epochs, - pad_value=pad_value)) + get_feed_fn( + placeholders, + data, + enqueue_size, + random_start=shuffle, + seed=seed_i, + num_epochs=num_epochs, + pad_value=pad_value)) runner = fqr._FeedingQueueRunner( # pylint: disable=protected-access - queue=queue, enqueue_ops=enqueue_ops, feed_fns=feed_fns) + queue=queue, + enqueue_ops=enqueue_ops, + feed_fns=feed_fns) queue_runner.add_queue_runner(runner) - full = (math_ops.cast( - math_ops.maximum(0, queue.size() - min_after_dequeue), - dtypes.float32) * (1. / (capacity - min_after_dequeue))) + full = ( + math_ops.cast( + math_ops.maximum(0, + queue.size() - min_after_dequeue), dtypes.float32) + * (1. / (capacity - min_after_dequeue))) # Note that name contains a '/' at the end so we intentionally do not place # a '/' after %s below. summary_name = ("queue/%sfraction_over_%d_of_%d_full" % diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index ec6184aacd..a96b88d96f 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -82,7 +82,9 @@ class BatchMatrixTransposeTest(test_util.TensorFlowTestCase): matrix_ph = array_ops.placeholder(dtypes.int32) transposed = array_ops.matrix_transpose(matrix_ph) self.assertAllEqual( - expected_transposed, transposed.eval(feed_dict={matrix_ph: matrix})) + expected_transposed, transposed.eval(feed_dict={ + matrix_ph: matrix + })) def testBatchMatrixDynamicallyDefined(self): matrix_0 = [[1, 2, 3], [4, 5, 6]] @@ -96,7 +98,9 @@ class BatchMatrixTransposeTest(test_util.TensorFlowTestCase): transposed = array_ops.matrix_transpose(batch_matrix_ph) self.assertAllEqual( expected_transposed, - transposed.eval(feed_dict={batch_matrix_ph: batch_matrix})) + transposed.eval(feed_dict={ + batch_matrix_ph: batch_matrix + })) def testTensorWithStaticRankLessThanTwoRaisesBecauseNotAMatrix(self): vector = [1, 2, 3] @@ -203,8 +207,10 @@ class BooleanMaskTest(test_util.TensorFlowTestCase): masked_tensor = sess.run( array_ops.boolean_mask(ph_tensor, ph_mask), - feed_dict={ph_tensor: arr, - ph_mask: mask}) + feed_dict={ + ph_tensor: arr, + ph_mask: mask + }) np.testing.assert_allclose(masked_tensor, arr[mask]) def testMaskDimensionsSetToNoneRaises(self): @@ -280,7 +286,8 @@ class ReverseV2Test(test_util.TensorFlowTestCase): for axis_dtype in [dtypes.int32, dtypes.int64]: with self.test_session(use_gpu=use_gpu): x_tf = array_ops.reverse_v2(x_np, - constant_op.constant([0], dtype=axis_dtype)).eval() + constant_op.constant( + [0], dtype=axis_dtype)).eval() self.assertAllEqual(x_tf, np.asarray(x_np)[::-1]) def _reverse2DimAuto(self, np_dtype): @@ -290,16 +297,17 @@ class ReverseV2Test(test_util.TensorFlowTestCase): for use_gpu in [False, True]: for axis_dtype in [dtypes.int32, dtypes.int64]: with self.test_session(use_gpu=use_gpu): - x_tf_1 = reverse_f(x_np, - constant_op.constant([0], dtype=axis_dtype)).eval() - x_tf_2 = reverse_f(x_np, - constant_op.constant([-2], dtype=axis_dtype)).eval() - x_tf_3 = reverse_f(x_np, - constant_op.constant([1], dtype=axis_dtype)).eval() - x_tf_4 = reverse_f(x_np, - constant_op.constant([-1], dtype=axis_dtype)).eval() + x_tf_1 = reverse_f(x_np, constant_op.constant( + [0], dtype=axis_dtype)).eval() + x_tf_2 = reverse_f(x_np, constant_op.constant( + [-2], dtype=axis_dtype)).eval() + x_tf_3 = reverse_f(x_np, constant_op.constant( + [1], dtype=axis_dtype)).eval() + x_tf_4 = reverse_f(x_np, constant_op.constant( + [-1], dtype=axis_dtype)).eval() x_tf_5 = reverse_f(x_np, - constant_op.constant([1, 0], dtype=axis_dtype)).eval() + constant_op.constant([1, 0], + dtype=axis_dtype)).eval() self.assertAllEqual(x_tf_1, np.asarray(x_np)[::-1, :]) self.assertAllEqual(x_tf_2, np.asarray(x_np)[::-1, :]) self.assertAllEqual(x_tf_3, np.asarray(x_np)[:, ::-1]) @@ -324,18 +332,16 @@ class ReverseV2Test(test_util.TensorFlowTestCase): def testReverse1DimAuto(self): for dtype in [ - np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, - np.bool, np.float16, np.float32, - np.float64, np.complex64, np.complex128, + np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, np.bool, + np.float16, np.float32, np.float64, np.complex64, np.complex128, np.array(b"").dtype.type ]: self._reverse1DimAuto(dtype) def testReverse2DimAuto(self): for dtype in [ - np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, - np.bool, np.float16, np.float32, - np.float64, np.complex64, np.complex128, + np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, np.bool, + np.float16, np.float32, np.float64, np.complex64, np.complex128, np.array(b"").dtype.type ]: self._reverse2DimAuto(dtype) @@ -711,8 +717,8 @@ class GradSliceChecker(object): slice_val_grad2, = gradients_impl.gradients( slice_val_grad, dy, grad_ys=self.var) self.sess.run(assign) - slice_val_grad_evaled, slice_val_grad2_evaled = (self.sess.run( - [slice_val_grad, slice_val_grad2])) + slice_val_grad_evaled, slice_val_grad2_evaled = ( + self.sess.run([slice_val_grad, slice_val_grad2])) analytic_grad2_evaled = analytic_grad2.eval() self.test.assertAllEqual(slice_val_grad2_evaled, analytic_grad2_evaled) @@ -987,9 +993,10 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): with self.test_session(): res = array_ops.sequence_mask(constant_op.constant([1, 3, 2]), 5) self.assertAllEqual(res.get_shape(), [3, 5]) - self.assertAllEqual(res.eval(), [[True, False, False, False, False], - [True, True, True, False, False], - [True, True, False, False, False]]) + self.assertAllEqual( + res.eval(), + [[True, False, False, False, False], [True, True, True, False, False], + [True, True, False, False, False]]) # test dtype and default maxlen: res = array_ops.sequence_mask( @@ -998,17 +1005,17 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): self.assertAllEqual(res.get_shape().as_list(), [3, 4]) else: self.assertAllEqual(res.get_shape().as_list(), [3, None]) - self.assertAllEqual(res.eval(), [[0.0, 0.0, 0.0, - 0.0], [1.0, 0.0, 0.0, 0.0], - [1.0, 1.0, 1.0, 1.0]]) + self.assertAllEqual( + res.eval(), + [[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0]]) def testTwoDimensional(self): with self.test_session(): res = array_ops.sequence_mask(constant_op.constant([[1, 3, 2]]), 5) self.assertAllEqual(res.get_shape(), [1, 3, 5]) - self.assertAllEqual(res.eval(), [[[True, False, False, False, False], - [True, True, True, False, False], - [True, True, False, False, False]]]) + self.assertAllEqual(res.eval(), [[[True, False, False, False, False], [ + True, True, True, False, False + ], [True, True, False, False, False]]]) # test dtype and default maxlen: res = array_ops.sequence_mask( @@ -1017,12 +1024,10 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): self.assertAllEqual(res.get_shape().as_list(), [2, 3, 4]) else: self.assertAllEqual(res.get_shape().as_list(), [2, 3, None]) - self.assertAllEqual(res.eval(), [[[0.0, 0.0, 0.0, 0.0], - [1.0, 0.0, 0.0, 0.0], - [1.0, 1.0, 1.0, 1.0]], - [[1.0, 0.0, 0.0, 0.0], - [1.0, 1.0, 0.0, 0.0], - [1.0, 1.0, 1.0, 0.0]]]) + self.assertAllEqual( + res.eval(), + [[[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0]], + [[1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 1.0, 0.0]]]) def testDtypes(self): @@ -1031,9 +1036,10 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): constant_op.constant([1, 3, 2], dtype=lengths_dtype), constant_op.constant(5, dtype=maxlen_dtype)) self.assertAllEqual(res.get_shape(), [3, 5]) - self.assertAllEqual(res.eval(), [[True, False, False, False, False], - [True, True, True, False, False], - [True, True, False, False, False]]) + self.assertAllEqual( + res.eval(), + [[True, False, False, False, False], [True, True, True, False, False], + [True, True, False, False, False]]) with self.test_session(): check_dtypes(dtypes.int32, dtypes.int32) @@ -1088,13 +1094,14 @@ class PadTest(test_util.TensorFlowTestCase): def testEager(self): with context.eager_mode(): t = constant_op.constant([[1, 2, 3], [4, 5, 6]]) - paddings = constant_op.constant([[1, 1,], [2, 2]]) + paddings = constant_op.constant([[ + 1, + 1, + ], [2, 2]]) padded = array_ops.pad(t, paddings, "CONSTANT") self.assertAllEqual(padded.numpy(), - [[0, 0, 0, 0, 0, 0, 0], - [0, 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, 0, 1, 2, 3, 0, 0], + [0, 0, 4, 5, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) class InvertPermutationTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/kernel_tests/diag_op_test.py b/tensorflow/python/kernel_tests/diag_op_test.py index 6cfa9b37fe..0825d8fc6b 100644 --- a/tensorflow/python/kernel_tests/diag_op_test.py +++ b/tensorflow/python/kernel_tests/diag_op_test.py @@ -84,11 +84,8 @@ class MatrixSetDiagTest(test.TestCase): def testSquare(self): with self.test_session(use_gpu=True): v = np.array([1.0, 2.0, 3.0]) - mat = np.array([[0.0, 1.0, 0.0], - [1.0, 0.0, 1.0], - [1.0, 1.0, 1.0]]) - mat_set_diag = np.array([[1.0, 1.0, 0.0], - [1.0, 2.0, 1.0], + mat = np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 1.0]]) + mat_set_diag = np.array([[1.0, 1.0, 0.0], [1.0, 2.0, 1.0], [1.0, 1.0, 3.0]]) output = array_ops.matrix_set_diag(mat, v) self.assertEqual((3, 3), output.get_shape()) @@ -135,19 +132,12 @@ class MatrixSetDiagTest(test.TestCase): def testRectangularBatch(self): with self.test_session(use_gpu=True): - v_batch = np.array([[-1.0, -2.0], - [-4.0, -5.0]]) - mat_batch = np.array( - [[[1.0, 0.0, 3.0], - [0.0, 2.0, 0.0]], - [[4.0, 0.0, 4.0], - [0.0, 5.0, 0.0]]]) - - mat_set_diag_batch = np.array( - [[[-1.0, 0.0, 3.0], - [0.0, -2.0, 0.0]], - [[-4.0, 0.0, 4.0], - [0.0, -5.0, 0.0]]]) + v_batch = np.array([[-1.0, -2.0], [-4.0, -5.0]]) + mat_batch = np.array([[[1.0, 0.0, 3.0], [0.0, 2.0, 0.0]], + [[4.0, 0.0, 4.0], [0.0, 5.0, 0.0]]]) + + mat_set_diag_batch = np.array([[[-1.0, 0.0, 3.0], [0.0, -2.0, 0.0]], + [[-4.0, 0.0, 4.0], [0.0, -5.0, 0.0]]]) output = array_ops.matrix_set_diag(mat_batch, v_batch) self.assertEqual((2, 2, 3), output.get_shape()) self.assertAllEqual(mat_set_diag_batch, output.eval()) @@ -178,10 +168,14 @@ class MatrixSetDiagTest(test.TestCase): np.random.rand(*diag_shape), dtype=dtypes_lib.float32) y = array_ops.matrix_set_diag(x, x_diag) error_x = gradient_checker.compute_gradient_error( - x, x.get_shape().as_list(), y, y.get_shape().as_list()) + x, + x.get_shape().as_list(), y, + y.get_shape().as_list()) self.assertLess(error_x, 1e-4) error_x_diag = gradient_checker.compute_gradient_error( - x_diag, x_diag.get_shape().as_list(), y, y.get_shape().as_list()) + x_diag, + x_diag.get_shape().as_list(), y, + y.get_shape().as_list()) self.assertLess(error_x_diag, 1e-4) def testGradWithNoShapeInformation(self): @@ -192,12 +186,13 @@ class MatrixSetDiagTest(test.TestCase): output = array_ops.matrix_set_diag(mat, v) grads = gradients_impl.gradients(output, [mat, v], grad_ys=grad_input) grad_input_val = np.random.rand(3, 3).astype(np.float32) - grad_vals = sess.run(grads, - feed_dict={ - v: 2 * np.ones(3), - mat: np.ones((3, 3)), - grad_input: grad_input_val - }) + grad_vals = sess.run( + grads, + feed_dict={ + v: 2 * np.ones(3), + mat: np.ones((3, 3)), + grad_input: grad_input_val + }) self.assertAllEqual(np.diag(grad_input_val), grad_vals[1]) self.assertAllEqual(grad_input_val - np.diag(np.diag(grad_input_val)), grad_vals[0]) @@ -242,13 +237,9 @@ class MatrixDiagPartTest(test.TestCase): def testRectangularBatch(self): with self.test_session(use_gpu=True): - v_batch = np.array([[1.0, 2.0], - [4.0, 5.0]]) - mat_batch = np.array( - [[[1.0, 0.0, 0.0], - [0.0, 2.0, 0.0]], - [[4.0, 0.0, 0.0], - [0.0, 5.0, 0.0]]]) + v_batch = np.array([[1.0, 2.0], [4.0, 5.0]]) + mat_batch = np.array([[[1.0, 0.0, 0.0], [0.0, 2.0, 0.0]], + [[4.0, 0.0, 0.0], [0.0, 5.0, 0.0]]]) self.assertEqual(mat_batch.shape, (2, 2, 3)) mat_batch_diag = array_ops.matrix_diag_part(mat_batch) self.assertEqual((2, 2), mat_batch_diag.get_shape()) @@ -301,19 +292,13 @@ class DiagTest(test.TestCase): def testRankOneIntTensor(self): x = np.array([1, 2, 3]) - expected_ans = np.array( - [[1, 0, 0], - [0, 2, 0], - [0, 0, 3]]) + expected_ans = np.array([[1, 0, 0], [0, 2, 0], [0, 0, 3]]) self.diagOp(x, np.int32, expected_ans) self.diagOp(x, np.int64, expected_ans) def testRankOneFloatTensor(self): x = np.array([1.1, 2.2, 3.3]) - expected_ans = np.array( - [[1.1, 0, 0], - [0, 2.2, 0], - [0, 0, 3.3]]) + expected_ans = np.array([[1.1, 0, 0], [0, 2.2, 0], [0, 0, 3.3]]) self.diagOp(x, np.float32, expected_ans) self.diagOp(x, np.float64, expected_ans) @@ -321,123 +306,105 @@ class DiagTest(test.TestCase): for dtype in [np.complex64, np.complex128]: x = np.array([1.1 + 1.1j, 2.2 + 2.2j, 3.3 + 3.3j], dtype=dtype) expected_ans = np.array( - [[1.1 + 1.1j, 0 + 0j, 0 + 0j], - [0 + 0j, 2.2 + 2.2j, 0 + 0j], - [0 + 0j, 0 + 0j, 3.3 + 3.3j]], dtype=dtype) + [[1.1 + 1.1j, 0 + 0j, 0 + 0j], [0 + 0j, 2.2 + 2.2j, 0 + 0j], + [0 + 0j, 0 + 0j, 3.3 + 3.3j]], + dtype=dtype) self.diagOp(x, dtype, expected_ans) def testRankTwoIntTensor(self): x = np.array([[1, 2, 3], [4, 5, 6]]) - expected_ans = np.array( - [[[[1, 0, 0], [0, 0, 0]], - [[0, 2, 0], [0, 0, 0]], - [[0, 0, 3], [0, 0, 0]]], - [[[0, 0, 0], [4, 0, 0]], - [[0, 0, 0], [0, 5, 0]], - [[0, 0, 0], [0, 0, 6]]]]) + expected_ans = np.array([[[[1, 0, 0], [0, 0, 0]], [[0, 2, 0], [0, 0, 0]], + [[0, 0, 3], [0, 0, 0]]], + [[[0, 0, 0], [4, 0, 0]], [[0, 0, 0], [0, 5, 0]], + [[0, 0, 0], [0, 0, 6]]]]) self.diagOp(x, np.int32, expected_ans) self.diagOp(x, np.int64, expected_ans) def testRankTwoFloatTensor(self): x = np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]]) expected_ans = np.array( - [[[[1.1, 0, 0], [0, 0, 0]], - [[0, 2.2, 0], [0, 0, 0]], - [[0, 0, 3.3], [0, 0, 0]]], - [[[0, 0, 0], [4.4, 0, 0]], - [[0, 0, 0], [0, 5.5, 0]], - [[0, 0, 0], [0, 0, 6.6]]]]) + [[[[1.1, 0, 0], [0, 0, 0]], [[0, 2.2, 0], [0, 0, 0]], + [[0, 0, 3.3], [0, 0, 0]]], [[[0, 0, 0], [4.4, 0, 0]], + [[0, 0, 0], [0, 5.5, 0]], [[0, 0, 0], + [0, 0, 6.6]]]]) self.diagOp(x, np.float32, expected_ans) self.diagOp(x, np.float64, expected_ans) def testRankTwoComplexTensor(self): for dtype in [np.complex64, np.complex128]: - x = np.array([[1.1 + 1.1j, 2.2 + 2.2j, 3.3 + 3.3j], - [4.4 + 4.4j, 5.5 + 5.5j, 6.6 + 6.6j]], dtype=dtype) + x = np.array( + [[1.1 + 1.1j, 2.2 + 2.2j, 3.3 + 3.3j], + [4.4 + 4.4j, 5.5 + 5.5j, 6.6 + 6.6j]], + dtype=dtype) expected_ans = np.array( - [[[[1.1 + 1.1j, 0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j, 0 + 0j]], - [[0 + 0j, 2.2 + 2.2j, 0 + 0j], [0 + 0j, 0 + 0j, 0 + 0j]], - [[0 + 0j, 0 + 0j, 3.3 + 3.3j], [0 + 0j, 0 + 0j, 0 + 0j]]], - [[[0 + 0j, 0 + 0j, 0 + 0j], [4.4 + 4.4j, 0 + 0j, 0 + 0j]], - [[0 + 0j, 0 + 0j, 0 + 0j], [0 + 0j, 5.5 + 5.5j, 0 + 0j]], - [[0 + 0j, 0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j, 6.6 + 6.6j]]]], - dtype=dtype) + [[[[1.1 + 1.1j, 0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j, 0 + 0j]], [ + [0 + 0j, 2.2 + 2.2j, 0 + 0j], [0 + 0j, 0 + 0j, 0 + 0j] + ], [[0 + 0j, 0 + 0j, 3.3 + 3.3j], [0 + 0j, 0 + 0j, 0 + 0j]]], [[ + [0 + 0j, 0 + 0j, 0 + 0j], [4.4 + 4.4j, 0 + 0j, 0 + 0j] + ], [[0 + 0j, 0 + 0j, 0 + 0j], [0 + 0j, 5.5 + 5.5j, 0 + 0j] + ], [[0 + 0j, 0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j, 6.6 + 6.6j]]]], + dtype=dtype) self.diagOp(x, dtype, expected_ans) def testRankThreeFloatTensor(self): - x = np.array([[[1.1, 2.2], [3.3, 4.4]], - [[5.5, 6.6], [7.7, 8.8]]]) - expected_ans = np.array( - [[[[[[1.1, 0], [0, 0]], [[0, 0], [0, 0]]], - [[[0, 2.2], [0, 0]], [[0, 0], [0, 0]]]], - [[[[0, 0], [3.3, 0]], [[0, 0], [0, 0]]], - [[[0, 0], [0, 4.4]], [[0, 0], [0, 0]]]]], - [[[[[0, 0], [0, 0]], [[5.5, 0], [0, 0]]], - [[[0, 0], [0, 0]], [[0, 6.6], [0, 0]]]], - [[[[0, 0], [0, 0]], [[0, 0], [7.7, 0]]], - [[[0, 0], [0, 0]], [[0, 0], [0, 8.8]]]]]]) + x = np.array([[[1.1, 2.2], [3.3, 4.4]], [[5.5, 6.6], [7.7, 8.8]]]) + expected_ans = np.array([[[[[[1.1, 0], [0, 0]], [[0, 0], [0, 0]]], + [[[0, 2.2], [0, 0]], [[0, 0], [0, 0]]]], + [[[[0, 0], [3.3, 0]], [[0, 0], [0, 0]]], + [[[0, 0], [0, 4.4]], [[0, 0], [0, 0]]]]], + [[[[[0, 0], [0, 0]], [[5.5, 0], [0, 0]]], + [[[0, 0], [0, 0]], [[0, 6.6], [0, 0]]]], + [[[[0, 0], [0, 0]], [[0, 0], [7.7, 0]]], + [[[0, 0], [0, 0]], [[0, 0], [0, 8.8]]]]]]) self.diagOp(x, np.float32, expected_ans) self.diagOp(x, np.float64, expected_ans) def testRankThreeComplexTensor(self): for dtype in [np.complex64, np.complex128]: - x = np.array([[[1.1 + 1.1j, 2.2 + 2.2j], [3.3 + 3.3j, 4.4 + 4.4j]], - [[5.5 + 5.5j, 6.6 + 6.6j], [7.7 + 7.7j, 8.8 + 8.8j]]], - dtype=dtype) + x = np.array( + [[[1.1 + 1.1j, 2.2 + 2.2j], [3.3 + 3.3j, 4.4 + 4.4j]], + [[5.5 + 5.5j, 6.6 + 6.6j], [7.7 + 7.7j, 8.8 + 8.8j]]], + dtype=dtype) expected_ans = np.array( - [[[[[[1.1 + 1.1j, 0 + 0j], [0 + 0j, 0 + 0j]], - [[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]]], - [[[0 + 0j, 2.2 + 2.2j], [0 + 0j, 0 + 0j]], - [[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]]]], - [[[[0 + 0j, 0 + 0j], [3.3 + 3.3j, 0 + 0j]], - [[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]]], - [[[0 + 0j, 0 + 0j], [0 + 0j, 4.4 + 4.4j]], - [[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]]]]], - [[[[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], - [[5.5 + 5.5j, 0 + 0j], [0 + 0j, 0 + 0j]]], - [[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], - [[0 + 0j, 6.6 + 6.6j], [0 + 0j, 0 + 0j]]]], - [[[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], - [[0 + 0j, 0 + 0j], [7.7 + 7.7j, 0 + 0j]]], - [[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], - [[0 + 0j, 0 + 0j], [0 + 0j, 8.8 + 8.8j]]]]]], + [[[[[[1.1 + 1.1j, 0 + 0j], [0 + 0j, 0 + 0j]], [[0 + 0j, 0 + 0j], [ + 0 + 0j, 0 + 0j + ]]], [[[0 + 0j, 2.2 + 2.2j], [0 + 0j, 0 + 0j]], [[0 + 0j, 0 + 0j], [ + 0 + 0j, 0 + 0j + ]]]], [[[[0 + 0j, 0 + 0j], [3.3 + 3.3j, 0 + 0j]], [[0 + 0j, 0 + 0j], [ + 0 + 0j, 0 + 0j + ]]], [[[0 + 0j, 0 + 0j], [0 + 0j, 4.4 + 4.4j]], [[0 + 0j, 0 + 0j], [ + 0 + 0j, 0 + 0j + ]]]]], [[[[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], [ + [5.5 + 5.5j, 0 + 0j], [0 + 0j, 0 + 0j] + ]], [[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], [[0 + 0j, 6.6 + 6.6j], [ + 0 + 0j, 0 + 0j + ]]]], [[[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], [[0 + 0j, 0 + 0j], [ + 7.7 + 7.7j, 0 + 0j + ]]], [[[0 + 0j, 0 + 0j], [0 + 0j, 0 + 0j]], + [[0 + 0j, 0 + 0j], [0 + 0j, 8.8 + 8.8j]]]]]], dtype=dtype) self.diagOp(x, dtype, expected_ans) def testRankFourNumberTensor(self): for dtype in [np.float32, np.float64, np.int64, np.int32]: # Input with shape [2, 1, 2, 3] - x = np.array([[[[ 1, 2, 3], - [ 4, 5, 6]]], - [[[ 7, 8, 9], - [10, 11, 12]]]], dtype=dtype) + x = np.array( + [[[[1, 2, 3], [4, 5, 6]]], [[[7, 8, 9], [10, 11, 12]]]], dtype=dtype) # Output with shape [2, 1, 2, 3, 2, 1, 2, 3] expected_ans = np.array( - [[[[[[[[1, 0, 0], [0, 0, 0]]], - [[[0, 0, 0], [0, 0, 0]]]], - [[[[0, 2, 0], [0, 0, 0]]], - [[[0, 0, 0], [0, 0, 0]]]], - [[[[0, 0, 3], [0, 0, 0]]], - [[[0, 0, 0], [0, 0, 0]]]]], - [[[[[0, 0, 0], [4, 0, 0]]], - [[[0, 0, 0], [0, 0, 0]]]], - [[[[0, 0, 0], [0, 5, 0]]], - [[[0, 0, 0], [0, 0, 0]]]], - [[[[0, 0, 0], [0, 0, 6]]], - [[[0, 0, 0], [0, 0, 0]]]]]]], - - [[[[[[[0, 0, 0], [0, 0, 0]]], - [[[7, 0, 0], [0, 0, 0]]]], - [[[[0, 0, 0], [0, 0, 0]]], - [[[0, 8, 0], [0, 0, 0]]]], - [[[[0, 0, 0], [0, 0, 0]]], - [[[0, 0, 9], [0, 0, 0]]]]], - [[[[[0, 0, 0], [0, 0, 0]]], - [[[0, 0, 0], [10, 0, 0]]]], - [[[[0, 0, 0], [0, 0, 0]]], - [[[0, 0, 0], [0, 11, 0]]]], - [[[[0, 0, 0], [0, 0, 0]]], - [[[0, 0, 0], [0, 0, 12]]]]]]]], dtype=dtype) + [[[[[[[[1, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]]], [ + [[[0, 2, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]] + ], [[[[0, 0, 3], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]]]], [[ + [[[0, 0, 0], [4, 0, 0]]], [[[0, 0, 0], [0, 0, 0]]] + ], [[[[0, 0, 0], [0, 5, 0]]], [[[0, 0, 0], [0, 0, 0]]]], [ + [[[0, 0, 0], [0, 0, 6]]], [[[0, 0, 0], [0, 0, 0]]] + ]]]], [[[[[[[0, 0, 0], [0, 0, 0]]], [[[7, 0, 0], [0, 0, 0]]]], [ + [[[0, 0, 0], [0, 0, 0]]], [[[0, 8, 0], [0, 0, 0]]] + ], [[[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 9], [0, 0, 0]]]]], [[ + [[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [10, 0, 0]]] + ], [[[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 11, 0]]] + ], [[[[0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 12]]]]]]]], + dtype=dtype) self.diagOp(x, dtype, expected_ans) def testInvalidRank(self): @@ -537,7 +504,9 @@ class DiagGradOpTest(test.TestCase): x1 = constant_op.constant(np.random.rand(*shape), dtype=dtype) y = array_ops.diag(x1) error = gradient_checker.compute_gradient_error( - x1, x1.get_shape().as_list(), y, y.get_shape().as_list()) + x1, + x1.get_shape().as_list(), y, + y.get_shape().as_list()) tf_logging.info("error = %f", error) self.assertLess(error, 1e-4) @@ -555,7 +524,9 @@ class DiagGradPartOpTest(test.TestCase): x1 = constant_op.constant(np.random.rand(*shape), dtype=dtype) y = array_ops.diag_part(x1) error = gradient_checker.compute_gradient_error( - x1, x1.get_shape().as_list(), y, y.get_shape().as_list()) + x1, + x1.get_shape().as_list(), y, + y.get_shape().as_list()) tf_logging.info("error = %f", error) self.assertLess(error, 1e-4) diff --git a/tensorflow/python/kernel_tests/map_stage_op_test.py b/tensorflow/python/kernel_tests/map_stage_op_test.py index 8b66945059..acfafde9e0 100644 --- a/tensorflow/python/kernel_tests/map_stage_op_test.py +++ b/tensorflow/python/kernel_tests/map_stage_op_test.py @@ -26,6 +26,7 @@ from tensorflow.python.platform import test TIMEOUT = 1 + class MapStageTest(test.TestCase): def testSimple(self): @@ -83,7 +84,7 @@ class MapStageTest(test.TestCase): [dtypes.float32, dtypes.float32], shapes=[[], [128, 128]], names=['x', 'v']) - stage = stager.put(pi,{'x': x, 'v': v}) + stage = stager.put(pi, {'x': x, 'v': v}) key, ret = stager.get(gi) z = ret['x'] y = ret['v'] @@ -128,8 +129,11 @@ class MapStageTest(test.TestCase): gi = array_ops.placeholder(dtypes.int64) p = array_ops.placeholder(dtypes.int32, name='p') with ops.device(test.gpu_device_name()): - stager = data_flow_ops.MapStagingArea([dtypes.int32, ], shapes=[[]]) - stage = stager.put(pi,[x], [0]) + stager = data_flow_ops.MapStagingArea( + [ + dtypes.int32, + ], shapes=[[]]) + stage = stager.put(pi, [x], [0]) peek = stager.peek(gi) size = stager.size() @@ -158,7 +162,7 @@ class MapStageTest(test.TestCase): [dtypes.float32, dtypes.float32], shapes=[[], [128, 128]], names=['x', 'v']) - stage = stager.put(pi,{'x': x, 'v': v}) + stage = stager.put(pi, {'x': x, 'v': v}) size = stager.size() clear = stager.clear() @@ -172,7 +176,6 @@ class MapStageTest(test.TestCase): sess.run(clear) self.assertEqual(sess.run(size), 0) - def testCapacity(self): capacity = 3 @@ -182,8 +185,10 @@ class MapStageTest(test.TestCase): pi = array_ops.placeholder(dtypes.int64, name='pi') gi = array_ops.placeholder(dtypes.int64, name='gi') with ops.device(test.gpu_device_name()): - stager = data_flow_ops.MapStagingArea([dtypes.int32, ], - capacity=capacity, shapes=[[]]) + stager = data_flow_ops.MapStagingArea( + [ + dtypes.int32, + ], capacity=capacity, shapes=[[]]) stage = stager.put(pi, [x], [0]) get = stager.get() @@ -222,9 +227,8 @@ class MapStageTest(test.TestCase): self.fail("Expected to timeout on iteration '{}' " "but instead timed out on iteration '{}' " "Staging Area size is '{}' and configured " - "capacity is '{}'.".format(capacity, i, - sess.run(size), - capacity)) + "capacity is '{}'.".format(capacity, i, sess.run(size), + capacity)) # Should have capacity elements in the staging area self.assertTrue(sess.run(size) == capacity) @@ -236,8 +240,8 @@ class MapStageTest(test.TestCase): self.assertTrue(sess.run(size) == 0) def testMemoryLimit(self): - memory_limit = 512*1024 # 512K - chunk = 200*1024 # 256K + memory_limit = 512 * 1024 # 512K + chunk = 200 * 1024 # 256K capacity = memory_limit // chunk with ops.Graph().as_default() as G: @@ -246,8 +250,8 @@ class MapStageTest(test.TestCase): pi = array_ops.placeholder(dtypes.int64, name='pi') gi = array_ops.placeholder(dtypes.int64, name='gi') with ops.device(test.gpu_device_name()): - stager = data_flow_ops.MapStagingArea([dtypes.uint8], - memory_limit=memory_limit, shapes=[[]]) + stager = data_flow_ops.MapStagingArea( + [dtypes.uint8], memory_limit=memory_limit, shapes=[[]]) stage = stager.put(pi, [x], [0]) get = stager.get() size = stager.size() @@ -287,9 +291,8 @@ class MapStageTest(test.TestCase): self.fail("Expected to timeout on iteration '{}' " "but instead timed out on iteration '{}' " "Staging Area size is '{}' and configured " - "capacity is '{}'.".format(capacity, i, - sess.run(size), - capacity)) + "capacity is '{}'.".format(capacity, i, sess.run(size), + capacity)) # Should have capacity elements in the staging area self.assertTrue(sess.run(size) == capacity) @@ -310,8 +313,10 @@ class MapStageTest(test.TestCase): pi = array_ops.placeholder(dtypes.int64, name='pi') gi = array_ops.placeholder(dtypes.int64, name='gi') with ops.device(test.gpu_device_name()): - stager = data_flow_ops.MapStagingArea([dtypes.int32, ], - shapes=[[]], ordered=True) + stager = data_flow_ops.MapStagingArea( + [ + dtypes.int32, + ], shapes=[[]], ordered=True) stage = stager.put(pi, [x], [0]) get = stager.get() size = stager.size() @@ -349,7 +354,7 @@ class MapStageTest(test.TestCase): stager = data_flow_ops.MapStagingArea( [dtypes.float32, dtypes.float32, dtypes.float32], names=['x', 'v', 'f']) - stage_xf = stager.put(pi,{'x': x, 'f': f}) + stage_xf = stager.put(pi, {'x': x, 'f': f}) stage_v = stager.put(pi, {'v': v}) key, ret = stager.get(gi) size = stager.size() @@ -373,12 +378,13 @@ class MapStageTest(test.TestCase): self.assertTrue(sess.run([size, isize]) == [1, 1]) # We can now obtain tuple associated with key 0 self.assertTrue( - sess.run([key, ret], - feed_dict={gi: 0}) == [0, { - 'x': 1, - 'f': 2, - 'v': 1 - }]) + sess.run([key, ret], feed_dict={ + gi: 0 + }) == [0, { + 'x': 1, + 'f': 2, + 'v': 1 + }]) # 0 complete and 1 incomplete entry self.assertTrue(sess.run([size, isize]) == [0, 1]) @@ -386,12 +392,13 @@ class MapStageTest(test.TestCase): sess.run(stage_v, feed_dict={pi: 1, v: 3}) # We can now obtain tuple associated with key 1 self.assertTrue( - sess.run([key, ret], - feed_dict={gi: 1}) == [1, { - 'x': 1, - 'f': 2, - 'v': 3 - }]) + sess.run([key, ret], feed_dict={ + gi: 1 + }) == [1, { + 'x': 1, + 'f': 2, + 'v': 3 + }]) def testPartialIndexInsert(self): with ops.Graph().as_default() as G: @@ -450,7 +457,7 @@ class MapStageTest(test.TestCase): stager = data_flow_ops.MapStagingArea( [dtypes.float32, dtypes.float32, dtypes.float32], names=['x', 'v', 'f']) - stage_xf = stager.put(pi,{'x': x, 'f': f}) + stage_xf = stager.put(pi, {'x': x, 'f': f}) stage_v = stager.put(pi, {'v': v}) peek_xf = stager.peek(pei, ['x', 'f']) peek_v = stager.peek(pei, ['v']) @@ -487,11 +494,12 @@ class MapStageTest(test.TestCase): # We can now obtain 'x' and 'f' values associated with key 0 self.assertTrue( - sess.run([key_xf, get_xf], - feed_dict={gi: 0}) == [0, { - 'x': 1, - 'f': 2 - }]) + sess.run([key_xf, get_xf], feed_dict={ + gi: 0 + }) == [0, { + 'x': 1, + 'f': 2 + }]) # Still have 1 complete and 1 incomplete entry self.assertTrue(sess.run([size, isize]) == [1, 1]) @@ -499,14 +507,15 @@ class MapStageTest(test.TestCase): with self.assertRaises(errors.InvalidArgumentError) as cm: sess.run([key_xf, get_xf], feed_dict={gi: 0}) - exc_str = ("Tensor at index '0' for key '0' " - "has already been removed.") + exc_str = ("Tensor at index '0' for key '0' " 'has already been removed.') self.assertTrue(exc_str in cm.exception.message) # Obtain 'v' value associated with key 0 self.assertTrue( - sess.run([key_v, get_v], feed_dict={gi: 0}) == [0, { + sess.run([key_v, get_v], feed_dict={ + gi: 0 + }) == [0, { 'v': 1 }]) # 0 complete and 1 incomplete entry @@ -523,7 +532,9 @@ class MapStageTest(test.TestCase): self.assertTrue(sess.run([size, isize]) == [1, 0]) # We can now obtain 'x' and 'f' values associated with key 1 self.assertTrue( - sess.run([pop_key_v, pop_v], feed_dict={pi: 1}) == [1, { + sess.run([pop_key_v, pop_v], feed_dict={ + pi: 1 + }) == [1, { 'v': 1 }]) # Nothing is left @@ -557,18 +568,20 @@ class MapStageTest(test.TestCase): self.assertTrue(sess.run([size, isize]) == [1, 0]) # Partial get using indices - self.assertTrue(sess.run([key_xf, get_xf], - feed_dict={gi: 0}) == [0, [1, 2]]) + self.assertTrue( + sess.run([key_xf, get_xf], feed_dict={ + gi: 0 + }) == [0, [1, 2]]) # Still some of key 0 left self.assertTrue(sess.run([size, isize]) == [1, 0]) # Partial get of remaining index - self.assertTrue(sess.run([key_v, get_v], - feed_dict={gi: 0}) == [0, [3]]) + self.assertTrue(sess.run([key_v, get_v], feed_dict={gi: 0}) == [0, [3]]) # All gone self.assertTrue(sess.run([size, isize]) == [0, 0]) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index 5c0ea8ec8e..3263ed1a60 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -159,8 +159,10 @@ class PoolingTest(test.TestCase): elif data_format == "NCHW": t = test_util.NCHWToNHWC(t) if v2: - actual = t.eval(feed_dict={ksize_placeholder: ksize, - strides_placeholder: strides}) + actual = t.eval(feed_dict={ + ksize_placeholder: ksize, + strides_placeholder: strides + }) else: actual = t.eval() self.assertShapeEqual(actual, t) @@ -195,8 +197,15 @@ class PoolingTest(test.TestCase): self._VerifyOneType(pool_func, input_sizes, ksize, strides, padding, data_format, dtypes.float16, expected, use_gpu, v2) - def _VerifyValues(self, pool_func, input_sizes, ksize, strides, padding, - expected, use_gpu, v2=False): + def _VerifyValues(self, + pool_func, + input_sizes, + ksize, + strides, + padding, + expected, + use_gpu, + v2=False): """Verifies the output values of the pooling function. Args: @@ -1148,16 +1157,16 @@ class PoolingTest(test.TestCase): def _testMaxPoolGradSamePadding3_1(self, data_format, use_gpu): for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]: self._ConstructAndTestGradient( - pool_func, - input_sizes=[1, 7, 7, 1], - output_sizes=[1, 7, 7, 1], - window_rows=3, - window_cols=3, - row_stride=1, - col_stride=1, - padding="SAME", - data_format=data_format, - use_gpu=use_gpu) + pool_func, + input_sizes=[1, 7, 7, 1], + output_sizes=[1, 7, 7, 1], + window_rows=3, + window_cols=3, + row_stride=1, + col_stride=1, + padding="SAME", + data_format=data_format, + use_gpu=use_gpu) def testMaxPoolGrad(self): for (data_format, use_gpu) in GetTestConfigs(): @@ -1202,17 +1211,14 @@ class PoolingTest(test.TestCase): pool_func = gen_nn_ops._max_pool_v2 if v2 else nn_ops.max_pool with self.test_session(use_gpu=use_gpu): input_tensor = constant_op.constant(input_data, shape=input_sizes) - output_tensor = pool_func(input_tensor, - [1, window_rows, window_cols, 1], + output_tensor = pool_func(input_tensor, [1, window_rows, window_cols, 1], [1, row_stride, col_stride, 1], padding) output_backprop_tensor = constant_op.constant( output_backprop, shape=output_sizes) - input_backprop_tensor = self._MaxPoolGrad(input_tensor, output_tensor, - output_backprop_tensor, - window_rows, window_cols, - row_stride, col_stride, - padding, v2) + input_backprop_tensor = self._MaxPoolGrad( + input_tensor, output_tensor, output_backprop_tensor, window_rows, + window_cols, row_stride, col_stride, padding, v2) actual_input_backprop = input_backprop_tensor.eval() self.assertShapeEqual(actual_input_backprop, input_backprop_tensor) @@ -1414,13 +1420,15 @@ class PoolingTest(test.TestCase): def _testMaxPoolGradDirectWithNans2_2(self): input_data = [float("nan")] * 16 output_backprop = [ - float("nan"), 12.0, 13.0, 15.0, float("nan"), 17.0, 19.0, 20.0, + float("nan"), 12.0, 13.0, 15.0, + float("nan"), 17.0, 19.0, 20.0, float("nan") ] # Test the CPU implementation, which propagates diffs in case of NaN expected_input_backprop_tf_cpu = [ - float("nan"), 12.0, 13.0, 0.0, 15.0, float("nan"), 17.0, 0.0, 19.0, - 20.0, float("nan"), 0.0, 0.0, 0.0, 0.0, 0.0 + float("nan"), 12.0, 13.0, 0.0, 15.0, + float("nan"), 17.0, 0.0, 19.0, 20.0, + float("nan"), 0.0, 0.0, 0.0, 0.0, 0.0 ] for v2 in [True, False]: self._testMaxPoolGradDirect( @@ -1636,10 +1644,9 @@ class PoolingTest(test.TestCase): Returns: A Tensor. """ - return gen_nn_ops._max_pool_grad_grad(orig_input, orig_output, grad, - [1, window_rows, window_cols, - 1], [1, row_stride, col_stride, - 1], padding) + return gen_nn_ops._max_pool_grad_grad( + orig_input, orig_output, grad, [1, window_rows, window_cols, 1], + [1, row_stride, col_stride, 1], padding) def testAvgPoolGrad(self): for (data_format, use_gpu) in GetTestConfigs(): @@ -1793,8 +1800,7 @@ class PoolingTest(test.TestCase): ]: with self.assertRaises(ValueError): pool_func( - array_ops.placeholder( - dtypes.float32, shape=[1, 3]), + array_ops.placeholder(dtypes.float32, shape=[1, 3]), ksize=[1, 1, 1, 1], strides=[1, 1, 1, 1], padding="SAME") @@ -1820,15 +1826,13 @@ class PoolingTest(test.TestCase): with self.assertRaisesRegexp(ValueError, "Negative dimension size"): sess.run( pool_func( - array_ops.placeholder( - dtypes.float32, shape=[32, 20, 20, 3]), + array_ops.placeholder(dtypes.float32, shape=[32, 20, 20, 3]), ksize=[1, 20, 21, 1], strides=[1, 1, 1, 1], padding="VALID")) with self.assertRaisesRegexp(ValueError, "Negative dimension size"): pool_func( - array_ops.placeholder( - dtypes.float32, shape=[32, 20, 20, 3]), + array_ops.placeholder(dtypes.float32, shape=[32, 20, 20, 3]), ksize=[1, 21, 20, 1], strides=[1, 1, 1, 1], padding="VALID") diff --git a/tensorflow/python/kernel_tests/reader_ops_test.py b/tensorflow/python/kernel_tests/reader_ops_test.py index 223a4b2c87..82a27eebee 100644 --- a/tensorflow/python/kernel_tests/reader_ops_test.py +++ b/tensorflow/python/kernel_tests/reader_ops_test.py @@ -428,7 +428,7 @@ class FixedLengthRecordReaderTest(test.TestCase): for i in range(self._num_files): fn = os.path.join(self.get_temp_dir(), "fixed_length_record.%d.txt" % i) filenames.append(fn) - with open(fn+".tmp", "wb") as f: + with open(fn + ".tmp", "wb") as f: f.write(b"H" * self._header_bytes) if num_records > 0: f.write(self._Record(i, 0)) @@ -437,7 +437,7 @@ class FixedLengthRecordReaderTest(test.TestCase): f.write(b"G" * gap_bytes) f.write(self._Record(i, j)) f.write(b"F" * self._footer_bytes) - with open(fn+".tmp", "rb") as f: + with open(fn + ".tmp", "rb") as f: cdata = zlib.compress(f.read()) with open(fn, "wb") as zf: zf.write(cdata) @@ -455,7 +455,7 @@ class FixedLengthRecordReaderTest(test.TestCase): all_records_str = "".join([ str(i)[0] for i in range(self._record_bytes + self._hop_bytes * - (num_overlapped_records - 1)) + (num_overlapped_records - 1)) ]) f.write(compat.as_bytes(all_records_str)) f.write(b"F" * self._footer_bytes) @@ -467,7 +467,7 @@ class FixedLengthRecordReaderTest(test.TestCase): fn = os.path.join(self.get_temp_dir(), "fixed_length_overlapped_record.%d.txt" % i) filenames.append(fn) - with open(fn+".tmp", "wb") as f: + with open(fn + ".tmp", "wb") as f: f.write(b"H" * self._header_bytes) if num_overlapped_records > 0: all_records_str = "".join([ @@ -477,7 +477,7 @@ class FixedLengthRecordReaderTest(test.TestCase): ]) f.write(compat.as_bytes(all_records_str)) f.write(b"F" * self._footer_bytes) - with open(fn+".tmp", "rb") as f: + with open(fn + ".tmp", "rb") as f: cdata = zlib.compress(f.read()) with open(fn, "wb") as zf: zf.write(cdata) @@ -509,7 +509,10 @@ class FixedLengthRecordReaderTest(test.TestCase): "\\(requested 1, current size 0\\)"): k, v = sess.run([key, value]) - def _TestOneEpochWithHopBytes(self, files, num_overlapped_records, encoding=None): + def _TestOneEpochWithHopBytes(self, + files, + num_overlapped_records, + encoding=None): with self.test_session() as sess: reader = io_ops.FixedLengthRecordReader( header_bytes=self._header_bytes, @@ -565,13 +568,15 @@ class FixedLengthRecordReaderTest(test.TestCase): def testGzipOneEpochWithHopBytes(self): for num_overlapped_records in [0, 2]: - files = self._CreateGzipOverlappedRecordFiles(num_overlapped_records, ) - self._TestOneEpochWithHopBytes(files, num_overlapped_records, encoding="GZIP") + files = self._CreateGzipOverlappedRecordFiles(num_overlapped_records,) + self._TestOneEpochWithHopBytes( + files, num_overlapped_records, encoding="GZIP") def testZlibOneEpochWithHopBytes(self): for num_overlapped_records in [0, 2]: files = self._CreateZlibOverlappedRecordFiles(num_overlapped_records) - self._TestOneEpochWithHopBytes(files, num_overlapped_records, encoding="ZLIB") + self._TestOneEpochWithHopBytes( + files, num_overlapped_records, encoding="ZLIB") class TFRecordReaderTest(test.TestCase): diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py index dd11ba700d..6b4091ae5d 100644 --- a/tensorflow/python/kernel_tests/relu_op_test.py +++ b/tensorflow/python/kernel_tests/relu_op_test.py @@ -48,8 +48,8 @@ class ReluTest(test.TestCase): self.assertAllClose( np.array([[0.0, 0.7, 0.0, 0.3, 0.0], [0.1, 0.0, 0.5, 0.0, 0.9]]), self._npRelu( - np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, 0.9] - ]))) + np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, + 0.9]]))) def _testRelu(self, np_features, use_gpu=False): np_relu = self._npRelu(np_features) @@ -163,8 +163,8 @@ class Relu6Test(test.TestCase): self.assertAllClose( np.array([[0.0, 0.7, 0.0, 0.3, 6.0], [0.1, 0.0, 6.0, 0.0, 0.9]]), self._npRelu6( - np.array([[-0.9, 0.7, -0.5, 0.3, 6.0], [0.1, -0.3, 6.5, -0.7, 0.9] - ]))) + np.array([[-0.9, 0.7, -0.5, 0.3, 6.0], [0.1, -0.3, 6.5, -0.7, + 0.9]]))) def _testRelu6(self, np_features, use_gpu=False): np_relu6 = self._npRelu6(np_features) @@ -231,8 +231,8 @@ class EluTest(test.TestCase): np.array([[-0.59343034025, 0.7, -0.39346934028, 0.3, -0.09516258196], [0.1, -0.25918177931, 0.5, -0.5034146962, 0.9]]), self._npElu( - np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, 0.9] - ]))) + np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, + 0.9]]))) def _testElu(self, np_features, use_gpu=False): np_elu = self._npElu(np_features) @@ -330,11 +330,11 @@ class SeluTest(test.TestCase): def testNpSelu(self): self.assertAllClose( - np.array([[-1.0433095, 0.73549069, -0.6917582, 0.3152103 , -0.16730527], - [0.1050701 , -0.45566732, 0.5253505, -0.88505305, 0.9456309]]), + np.array([[-1.0433095, 0.73549069, -0.6917582, 0.3152103, -0.16730527], + [0.1050701, -0.45566732, 0.5253505, -0.88505305, 0.9456309]]), self._npSelu( - np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, 0.9] - ]))) + np.array([[-0.9, 0.7, -0.5, 0.3, -0.1], [0.1, -0.3, 0.5, -0.7, + 0.9]]))) def _testSelu(self, np_features, use_gpu=False): np_selu = self._npSelu(np_features) diff --git a/tensorflow/python/kernel_tests/sparse_slice_op_test.py b/tensorflow/python/kernel_tests/sparse_slice_op_test.py index 762e400447..da116601f8 100644 --- a/tensorflow/python/kernel_tests/sparse_slice_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_slice_op_test.py @@ -32,11 +32,12 @@ class SparseSliceOpTest(test.TestCase): # [ |11| |13|14| ] # [20| | |23| |25] # [30| |32|33| |35] - ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4], - [2, 0], [2, 3], [2, 5], [3, 0], [3, 2], [3, 3], - [3, 5]]).astype(np.int64) - val = np.array( - [0, 2, 4, 5, 11, 13, 14, 20, 23, 25, 30, 32, 33, 35]).astype(np.int64) + ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, + 4], [2, 0], + [2, 3], [2, 5], [3, 0], [3, 2], [3, 3], [3, 5]]).astype( + np.int64) + val = np.array([0, 2, 4, 5, 11, 13, 14, 20, 23, 25, 30, 32, 33, 35]).astype( + np.int64) shape = np.array([4, 6]).astype(np.int64) return sparse_tensor.SparseTensor(ind, val, shape) @@ -65,50 +66,49 @@ class SparseSliceOpTest(test.TestCase): # [ |'c1'| |'d1'] # [ | |'e1'| ] ind = np.array([[0, 0, 0], [0, 0, 1], [0, 2, 0], [0, 2, 1], [1, 1, 0], - [1, 1, 1], [1, 3, 0], [1, 3, 1], [2, 2, 0], - [2, 2, 1]]).astype(np.int64) + [1, 1, 1], [1, 3, 0], [1, 3, 1], [2, 2, 0], [2, 2, + 1]]).astype( + np.int64) val = np.array(['a0', 'a1', 'b0', 'b1', 'c0', 'c1', 'd0', 'd1', 'e0', 'e1']) shape = np.array([3, 4, 2]).astype(np.int64) return sparse_tensor.SparseTensorValue(ind, val, shape) def _SparseTensor_3x4x2(self): - return sparse_tensor.SparseTensor.from_value(self._SparseTensorValue_3x4x2( - )) + return sparse_tensor.SparseTensor.from_value( + self._SparseTensorValue_3x4x2()) def testSliceMatrixRows(self): with self.test_session(use_gpu=False): - sp_input=self._SparseTensor_4x6() + sp_input = self._SparseTensor_4x6() sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [2, 6]) sp_tensor1 = sparse_ops.sparse_slice(sp_input, [2, 0], [3, 7]) - self.assertAllEqual(sp_tensor0.indices.eval(), [[0, 0], [0, 2], [0, 4], - [0, 5], [1, 1], [1, 3], - [1, 4]]) + self.assertAllEqual( + sp_tensor0.indices.eval(), + [[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4]]) self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5, 11, 13, 14]) self.assertAllEqual(sp_tensor0.dense_shape.eval(), [2, 6]) - self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 0], [0, 3], [0, 5], - [1, 0], [1, 2], [1, 3], - [1, 5]]) + self.assertAllEqual( + sp_tensor1.indices.eval(), + [[0, 0], [0, 3], [0, 5], [1, 0], [1, 2], [1, 3], [1, 5]]) self.assertAllEqual(sp_tensor1.values.eval(), [20, 23, 25, 30, 32, 33, 35]) self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 6]) def testSliceMatrixUnevenCols(self): with self.test_session(use_gpu=False): - sp_input=self._SparseTensor_5x7() + sp_input = self._SparseTensor_5x7() sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [5, 3]) sp_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 3], [5, 2]) sp_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 5], [5, 2]) - self.assertAllEqual(sp_tensor0.indices.eval(), - [[0, 0], [0, 2], [1, 1], [2, 0], [3, 0], [3, 2], - [4, 1]]) - self.assertAllEqual(sp_tensor0.values.eval(), - [0, 2, 11, 20, 30, 32, 41]) + self.assertAllEqual( + sp_tensor0.indices.eval(), + [[0, 0], [0, 2], [1, 1], [2, 0], [3, 0], [3, 2], [4, 1]]) + self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 11, 20, 30, 32, 41]) self.assertAllEqual(sp_tensor0.dense_shape.eval(), [5, 3]) self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 1], [1, 0], [1, 1], [2, 0], [3, 0], [4, 1]]) - self.assertAllEqual(sp_tensor1.values.eval(), - [4, 13, 14, 23, 33, 44]) + self.assertAllEqual(sp_tensor1.values.eval(), [4, 13, 14, 23, 33, 44]) self.assertAllEqual(sp_tensor1.dense_shape.eval(), [5, 2]) self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]]) @@ -137,7 +137,7 @@ class SparseSliceOpTest(test.TestCase): def testSliceMatrixUnevenRows(self): with self.test_session(use_gpu=False): - sp_input=self._SparseTensor_5x7() + sp_input = self._SparseTensor_5x7() sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [3, 7]) sp_tensor1 = sparse_ops.sparse_slice(sp_input, [3, 0], [3, 7]) self.assertAllEqual(sp_tensor0.indices.eval(), @@ -146,9 +146,9 @@ class SparseSliceOpTest(test.TestCase): self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25]) self.assertAllEqual(sp_tensor0.dense_shape.eval(), [3, 7]) - self.assertAllEqual(sp_tensor1.indices.eval(), - [[0, 0], [0, 2], [0, 3], [0, 5], [1, 1], [1, 4], - [1, 6]]) + self.assertAllEqual( + sp_tensor1.indices.eval(), + [[0, 0], [0, 2], [0, 3], [0, 5], [1, 1], [1, 4], [1, 6]]) self.assertAllEqual(sp_tensor1.values.eval(), [30, 32, 33, 35, 41, 44, 46]) self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 7]) @@ -156,9 +156,9 @@ class SparseSliceOpTest(test.TestCase): sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [2, 7]) sp_tensor1 = sparse_ops.sparse_slice(sp_input, [2, 0], [2, 7]) sp_tensor2 = sparse_ops.sparse_slice(sp_input, [4, 0], [2, 7]) - self.assertAllEqual(sp_tensor0.indices.eval(), - [[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], - [1, 4], [1, 6]]) + self.assertAllEqual( + sp_tensor0.indices.eval(), + [[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4], [1, 6]]) self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5, 11, 13, 14, 16]) self.assertAllEqual(sp_tensor0.dense_shape.eval(), [2, 7]) @@ -166,45 +166,42 @@ class SparseSliceOpTest(test.TestCase): self.assertAllEqual(sp_tensor1.values.eval(), [20, 23, 25, 30, 32, 33, 35]) self.assertAllEqual(sp_tensor1.dense_shape.eval(), [2, 7]) - self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 1], [0, 4], - [0, 6]]) + self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 1], [0, 4], [0, 6]]) self.assertAllEqual(sp_tensor2.values.eval(), [41, 44, 46]) self.assertAllEqual(sp_tensor2.dense_shape.eval(), [1, 7]) return def testSliceAllRows(self): with self.test_session(use_gpu=False): - sp_input=self._SparseTensor_4x6() + sp_input = self._SparseTensor_4x6() sp_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [1, 6]) sp_tensor1 = sparse_ops.sparse_slice(sp_input, [1, 0], [1, 6]) sp_tensor2 = sparse_ops.sparse_slice(sp_input, [2, 0], [1, 7]) sp_tensor3 = sparse_ops.sparse_slice(sp_input, [3, 0], [2, 7]) - self.assertAllEqual(sp_tensor0.indices.eval(), [[0, 0], [0, 2], [0, 4], - [0, 5]]) + self.assertAllEqual(sp_tensor0.indices.eval(), + [[0, 0], [0, 2], [0, 4], [0, 5]]) self.assertAllEqual(sp_tensor0.values.eval(), [0, 2, 4, 5]) self.assertAllEqual(sp_tensor0.dense_shape.eval(), [1, 6]) - self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 1], [0, 3], [0, - 4]]) + self.assertAllEqual(sp_tensor1.indices.eval(), [[0, 1], [0, 3], [0, 4]]) self.assertAllEqual(sp_tensor1.values.eval(), [11, 13, 14]) self.assertAllEqual(sp_tensor1.dense_shape.eval(), [1, 6]) - self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 0], [0, 3], [0, - 5]]) + self.assertAllEqual(sp_tensor2.indices.eval(), [[0, 0], [0, 3], [0, 5]]) self.assertAllEqual(sp_tensor2.values.eval(), [20, 23, 25]) self.assertAllEqual(sp_tensor2.dense_shape.eval(), [1, 6]) - self.assertAllEqual(sp_tensor3.indices.eval(), [[0, 0], [0, 2], [0, 3], - [0, 5]]) + self.assertAllEqual(sp_tensor3.indices.eval(), + [[0, 0], [0, 2], [0, 3], [0, 5]]) self.assertAllEqual(sp_tensor3.values.eval(), [30, 32, 33, 35]) self.assertAllEqual(sp_tensor3.dense_shape.eval(), [1, 6]) def testSliceColumns(self): with self.test_session(use_gpu=False): - sp_input=self._SparseTensor_4x6() + sp_input = self._SparseTensor_4x6() sparse_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [4, 2]) sparse_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 2], [5, 2]) sparse_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 3]) - self.assertAllEqual(sparse_tensor0.indices.eval(), [[0, 0], [1, 1], - [2, 0], [3, 0]]) + self.assertAllEqual(sparse_tensor0.indices.eval(), + [[0, 0], [1, 1], [2, 0], [3, 0]]) self.assertAllEqual(sparse_tensor0.values.eval(), [0, 11, 20, 30]) self.assertAllEqual(sparse_tensor0.dense_shape.eval(), [4, 2]) self.assertAllEqual(sparse_tensor1.indices.eval(), @@ -218,15 +215,15 @@ class SparseSliceOpTest(test.TestCase): def testSliceAllColumns(self): with self.test_session(use_gpu=False): - sp_input=self._SparseTensor_4x6() + sp_input = self._SparseTensor_4x6() sparse_tensor0 = sparse_ops.sparse_slice(sp_input, [0, 0], [4, 1]) sparse_tensor1 = sparse_ops.sparse_slice(sp_input, [0, 1], [4, 1]) sparse_tensor2 = sparse_ops.sparse_slice(sp_input, [0, 2], [4, 1]) sparse_tensor3 = sparse_ops.sparse_slice(sp_input, [0, 3], [4, 1]) sparse_tensor4 = sparse_ops.sparse_slice(sp_input, [0, 4], [5, 1]) sparse_tensor5 = sparse_ops.sparse_slice(sp_input, [0, 5], [6, 3]) - self.assertAllEqual(sparse_tensor0.indices.eval(), [[0, 0], [2, 0], - [3, 0]]) + self.assertAllEqual(sparse_tensor0.indices.eval(), + [[0, 0], [2, 0], [3, 0]]) self.assertAllEqual(sparse_tensor0.values.eval(), [0, 20, 30]) self.assertAllEqual(sparse_tensor0.dense_shape.eval(), [4, 1]) self.assertAllEqual(sparse_tensor1.indices.eval(), [[1, 0]]) @@ -235,17 +232,18 @@ class SparseSliceOpTest(test.TestCase): self.assertAllEqual(sparse_tensor2.indices.eval(), [[0, 0], [3, 0]]) self.assertAllEqual(sparse_tensor2.values.eval(), [2, 32]) self.assertAllEqual(sparse_tensor2.dense_shape.eval(), [4, 1]) - self.assertAllEqual(sparse_tensor3.indices.eval(), [[1, 0], [2, 0], - [3, 0]]) + self.assertAllEqual(sparse_tensor3.indices.eval(), + [[1, 0], [2, 0], [3, 0]]) self.assertAllEqual(sparse_tensor3.dense_shape.eval(), [4, 1]) self.assertAllEqual(sparse_tensor3.values.eval(), [13, 23, 33]) self.assertAllEqual(sparse_tensor4.indices.eval(), [[0, 0], [1, 0]]) self.assertAllEqual(sparse_tensor4.values.eval(), [4, 14]) self.assertAllEqual(sparse_tensor4.dense_shape.eval(), [4, 1]) - self.assertAllEqual(sparse_tensor5.indices.eval(), [[0, 0], [2, 0], - [3, 0]]) + self.assertAllEqual(sparse_tensor5.indices.eval(), + [[0, 0], [2, 0], [3, 0]]) self.assertAllEqual(sparse_tensor5.values.eval(), [5, 25, 35]) self.assertAllEqual(sparse_tensor5.dense_shape.eval(), [4, 1]) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/stage_op_test.py b/tensorflow/python/kernel_tests/stage_op_test.py index 64b3388c5c..dd06d30391 100644 --- a/tensorflow/python/kernel_tests/stage_op_test.py +++ b/tensorflow/python/kernel_tests/stage_op_test.py @@ -25,8 +25,8 @@ from tensorflow.python.platform import test TIMEOUT = 1 -class StageTest(test.TestCase): +class StageTest(test.TestCase): def testSimple(self): with ops.Graph().as_default() as G: @@ -116,7 +116,10 @@ class StageTest(test.TestCase): x = array_ops.placeholder(dtypes.int32, name='x') p = array_ops.placeholder(dtypes.int32, name='p') with ops.device(test.gpu_device_name()): - stager = data_flow_ops.StagingArea([dtypes.int32, ], shapes=[[]]) + stager = data_flow_ops.StagingArea( + [ + dtypes.int32, + ], shapes=[[]]) stage = stager.put([x]) peek = stager.peek(p) ret = stager.get() @@ -162,8 +165,10 @@ class StageTest(test.TestCase): with ops.device('/cpu:0'): x = array_ops.placeholder(dtypes.int32, name='x') with ops.device(test.gpu_device_name()): - stager = data_flow_ops.StagingArea([dtypes.int32, ], - capacity=capacity, shapes=[[]]) + stager = data_flow_ops.StagingArea( + [ + dtypes.int32, + ], capacity=capacity, shapes=[[]]) stage = stager.put([x]) ret = stager.get() size = stager.size() @@ -201,9 +206,8 @@ class StageTest(test.TestCase): self.fail("Expected to timeout on iteration '{}' " "but instead timed out on iteration '{}' " "Staging Area size is '{}' and configured " - "capacity is '{}'.".format(capacity, i, - sess.run(size), - capacity)) + "capacity is '{}'.".format(capacity, i, sess.run(size), + capacity)) # Should have capacity elements in the staging area self.assertTrue(sess.run(size) == capacity) @@ -216,16 +220,18 @@ class StageTest(test.TestCase): self.assertTrue(sess.run(size) == 0) def testMemoryLimit(self): - memory_limit = 512*1024 # 512K - chunk = 200*1024 # 256K + memory_limit = 512 * 1024 # 512K + chunk = 200 * 1024 # 256K capacity = memory_limit // chunk with ops.Graph().as_default() as G: with ops.device('/cpu:0'): x = array_ops.placeholder(dtypes.uint8, name='x') with ops.device(test.gpu_device_name()): - stager = data_flow_ops.StagingArea([dtypes.uint8, ], - memory_limit=memory_limit, shapes=[[]]) + stager = data_flow_ops.StagingArea( + [ + dtypes.uint8, + ], memory_limit=memory_limit, shapes=[[]]) stage = stager.put([x]) ret = stager.get() size = stager.size() @@ -264,9 +270,8 @@ class StageTest(test.TestCase): self.fail("Expected to timeout on iteration '{}' " "but instead timed out on iteration '{}' " "Staging Area size is '{}' and configured " - "capacity is '{}'.".format(capacity, i, - sess.run(size), - capacity)) + "capacity is '{}'.".format(capacity, i, sess.run(size), + capacity)) # Should have capacity elements in the staging area self.assertTrue(sess.run(size) == capacity) @@ -277,5 +282,6 @@ class StageTest(test.TestCase): self.assertTrue(sess.run(size) == 0) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/layers/maxout.py b/tensorflow/python/layers/maxout.py index ed048845a0..20ce6c9770 100644 --- a/tensorflow/python/layers/maxout.py +++ b/tensorflow/python/layers/maxout.py @@ -31,15 +31,18 @@ from tensorflow.python.layers import base def maxout(inputs, num_units, axis=-1, name=None): """Adds a maxout op from https://arxiv.org/abs/1302.4389 - "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, + "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron + Courville, Yoshua Bengio - Usually the operation is performed in the filter/channel dimension. This can also be + Usually the operation is performed in the filter/channel dimension. This can + also be used after fully-connected layers to reduce number of features. Arguments: inputs: Tensor input - num_units: Specifies how many features will remain after maxout in the `axis` dimension + num_units: Specifies how many features will remain after maxout in the `axis` + dimension (usually channel). This must be multiple of number of `axis`. axis: The dimension where max pooling will be performed. Default is the last dimension. @@ -57,15 +60,18 @@ def maxout(inputs, num_units, axis=-1, name=None): class MaxOut(base.Layer): """Adds a maxout op from https://arxiv.org/abs/1302.4389 - "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua + "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron + Courville, Yoshua Bengio - Usually the operation is performed in the filter/channel dimension. This can also be + Usually the operation is performed in the filter/channel dimension. This can + also be used after fully-connected layers to reduce number of features. Arguments: inputs: Tensor input - num_units: Specifies how many features will remain after maxout in the `axis` dimension + num_units: Specifies how many features will remain after maxout in the + `axis` dimension (usually channel). This must be multiple of number of `axis`. axis: The dimension where max pooling will be performed. Default is the @@ -79,13 +85,8 @@ class MaxOut(base.Layer): ValueError: if num_units is not multiple of number of features. """ - def __init__(self, - num_units, - axis=-1, - name=None, - **kwargs): - super(MaxOut, self).__init__( - name=name, trainable=False, **kwargs) + def __init__(self, num_units, axis=-1, name=None, **kwargs): + super(MaxOut, self).__init__(name=name, trainable=False, **kwargs) self.axis = axis self.num_units = num_units @@ -95,8 +96,8 @@ class MaxOut(base.Layer): num_channels = shape[self.axis] if num_channels % self.num_units: raise ValueError('number of features({}) is not ' - 'a multiple of num_units({})' - .format(num_channels, self.num_units)) + 'a multiple of num_units({})'.format( + num_channels, self.num_units)) shape[self.axis] = -1 shape += [num_channels // self.num_units] @@ -104,6 +105,7 @@ class MaxOut(base.Layer): for i in range(len(shape)): if shape[i] is None: shape[i] = gen_array_ops.shape(inputs)[i] - outputs = math_ops.reduce_max(gen_array_ops.reshape(inputs, shape), -1, keep_dims=False) + outputs = math_ops.reduce_max( + gen_array_ops.reshape(inputs, shape), -1, keep_dims=False) return outputs diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py index 55cae0bcbf..c9292184e6 100644 --- a/tensorflow/python/ops/array_grad.py +++ b/tensorflow/python/ops/array_grad.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Gradients for operators defined in array_ops.py.""" from __future__ import absolute_import @@ -131,8 +130,8 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): # extract the size of each input along the concat dimension sizes = array_ops.squeeze( array_ops.slice( - array_ops.stack( - sizes, axis=1), [non_neg_concat_dim, 0], [1, -1])) + array_ops.stack(sizes, axis=1), [non_neg_concat_dim, 0], + [1, -1])) out_grads = array_ops.split(grad, sizes, non_neg_concat_dim) else: offset = gen_array_ops._concat_offset(non_neg_concat_dim, sizes) @@ -167,8 +166,7 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): new_values = array_ops.slice( grad.values, begin, array_ops.concat([[-1], array_ops.slice(size, [1], [-1])], 0)) - out_grads.append( - ops.IndexedSlices(new_values, grad.indices, size)) + out_grads.append(ops.IndexedSlices(new_values, grad.indices, size)) # Lint complains begin = begin + ... begin = math_ops.add(begin, size * mask) else: @@ -178,30 +176,33 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index): for size in sizes: size_concat_dim = array_ops.gather(size, non_neg_concat_dim) if size_concat_dim.dtype != grad.indices.dtype: - size_concat_dim = math_ops.cast(size_concat_dim, - dtype=grad.indices.dtype) + size_concat_dim = math_ops.cast( + size_concat_dim, dtype=grad.indices.dtype) end = start + size_concat_dim # Compute the 1-D Tensor of indices relevant for this input. indices_to_select = array_ops.squeeze( - array_ops.where(math_ops.logical_and(grad.indices >= start, - grad.indices < end)), + array_ops.where( + math_ops.logical_and(grad.indices >= start, + grad.indices < end)), squeeze_dims=[1]) new_indices = array_ops.gather(grad.indices, indices_to_select) - start new_values = array_ops.gather(grad.values, indices_to_select) - out_grads.append( - ops.IndexedSlices(new_values, new_indices, size)) + out_grads.append(ops.IndexedSlices(new_values, new_indices, size)) start = end else: raise TypeError("Expected Tensor or IndexedSlices, got %s" % type(grad)) - return (out_grads + [None] if end_value_index <= dim_index - else [None] + out_grads) + return (out_grads + [None] + if end_value_index <= dim_index else [None] + out_grads) @ops.RegisterGradient("Concat") def _ConcatGrad(op, grad): return _ConcatGradHelper( - op, grad, start_value_index=1, end_value_index=len(op.inputs), + op, + grad, + start_value_index=1, + end_value_index=len(op.inputs), dim_index=0) @@ -287,9 +288,13 @@ def _SplitGrad(op, *grads): @ops.RegisterGradient("SplitV") def _SplitVGrad(op, *grads): returnval = array_ops.concat(list(grads), op.inputs[2]) - returnval = [returnval] + [None,] * (len(op.inputs) - 1) + returnval = [returnval] + [ + None, + ] * ( + len(op.inputs) - 1) return returnval + ops.NotDifferentiable("Const") @@ -334,9 +339,9 @@ def _MatrixSetDiagGrad(op, grad): matrix_shape = array_ops.slice(grad_shape, [grad_rank - 2], [2]) min_dim = math_ops.reduce_min(matrix_shape) diag_shape = array_ops.concat([batch_shape, [min_dim]], 0) - grad_input = array_ops.matrix_set_diag( - grad, array_ops.zeros( - diag_shape, dtype=grad.dtype)) + grad_input = array_ops.matrix_set_diag(grad, + array_ops.zeros( + diag_shape, dtype=grad.dtype)) grad_diag = array_ops.matrix_diag_part(grad) return (grad_input, grad_diag) @@ -444,8 +449,8 @@ def _GatherV2Grad(op, grad): values_transpose = array_ops.transpose(values, transpose_dims) num_segments = params_shape[axis] - params_grad = math_ops.unsorted_segment_sum( - values_transpose, indices, num_segments) + params_grad = math_ops.unsorted_segment_sum(values_transpose, indices, + num_segments) # Inverts the above transpose by moving dimension 0 back to its original # position. @@ -536,13 +541,10 @@ def _ConjugateTransposeGrad(op, grad): ops.NotDifferentiable("Shape") - ops.NotDifferentiable("ShapeN") - ops.NotDifferentiable("Rank") - ops.NotDifferentiable("Size") @@ -590,6 +592,7 @@ def _PadGrad(op, grad): else: return x_grad, None + ops.RegisterGradient("Pad")(_PadGrad) ops.RegisterGradient("PadV2")(_PadGrad) @@ -625,30 +628,34 @@ def _ReverseV2Grad(op, grad): def _SpaceToBatchGrad(op, grad): # Its gradient is the opposite op: BatchToSpace. block_size = op.get_attr("block_size") - return [array_ops.batch_to_space(grad, op.inputs[1], block_size=block_size), - None] + return [ + array_ops.batch_to_space(grad, op.inputs[1], block_size=block_size), None + ] @ops.RegisterGradient("SpaceToBatchND") def _SpaceToBatchNDGrad(op, grad): # Its gradient is the opposite op: BatchToSpaceND. - return [array_ops.batch_to_space_nd(grad, op.inputs[1], op.inputs[2]), - None, None] + return [ + array_ops.batch_to_space_nd(grad, op.inputs[1], op.inputs[2]), None, None + ] @ops.RegisterGradient("BatchToSpace") def _BatchToSpaceGrad(op, grad): # Its gradient is the opposite op: SpaceToBatch. block_size = op.get_attr("block_size") - return [array_ops.space_to_batch(grad, op.inputs[1], block_size=block_size), - None] + return [ + array_ops.space_to_batch(grad, op.inputs[1], block_size=block_size), None + ] @ops.RegisterGradient("BatchToSpaceND") def _BatchToSpaceNDGrad(op, grad): # Its gradient is the opposite op: SpaceToBatchND. - return [array_ops.space_to_batch_nd(grad, op.inputs[1], op.inputs[2]), - None, None] + return [ + array_ops.space_to_batch_nd(grad, op.inputs[1], op.inputs[2]), None, None + ] @ops.RegisterGradient("SpaceToDepth") @@ -712,30 +719,28 @@ def _QuantizeAndDequantizeV3Grad(_, grad): def _ExtractImagePatchesGrad(op, grad): batch_size, rows_in, cols_in, channels = [ - dim.value for dim in op.inputs[0].get_shape() + dim.value for dim in op.inputs[0].get_shape() ] input_bhwc = array_ops.shape(op.inputs[0]) batch_size = input_bhwc[0] channels = input_bhwc[3] - _, rows_out, cols_out, _ = [ - dim.value for dim in op.outputs[0].get_shape() - ] - _, ksize_r, ksize_c, _ = op.get_attr('ksizes') - _, stride_r, stride_h, _ = op.get_attr('strides') - _, rate_r, rate_c, _ = op.get_attr('rates') - padding = op.get_attr('padding') + _, rows_out, cols_out, _ = [dim.value for dim in op.outputs[0].get_shape()] + _, ksize_r, ksize_c, _ = op.get_attr("ksizes") + _, stride_r, stride_h, _ = op.get_attr("strides") + _, rate_r, rate_c, _ = op.get_attr("rates") + padding = op.get_attr("padding") ksize_r_eff = ksize_r + (ksize_r - 1) * (rate_r - 1) ksize_c_eff = ksize_c + (ksize_c - 1) * (rate_c - 1) - if padding == b'SAME': + if padding == b"SAME": rows_out = int(ceil(rows_in / stride_r)) cols_out = int(ceil(cols_in / stride_h)) pad_rows = ((rows_out - 1) * stride_r + ksize_r_eff - rows_in) // 2 pad_cols = ((cols_out - 1) * stride_h + ksize_c_eff - cols_in) // 2 - elif padding == b'VALID': + elif padding == b"VALID": rows_out = int(ceil((rows_in - ksize_r_eff + 1) / stride_r)) cols_out = int(ceil((cols_in - ksize_c_eff + 1) / stride_h)) pad_rows = (rows_out - 1) * stride_r + ksize_r_eff - rows_in @@ -744,10 +749,9 @@ def _ExtractImagePatchesGrad(op, grad): pad_rows, pad_cols = max(0, pad_rows), max(0, pad_cols) grad_expanded = array_ops.transpose( - array_ops.reshape(grad, (batch_size, rows_out, - cols_out, ksize_r, ksize_c, channels)), - (1, 2, 3, 4, 0, 5) - ) + array_ops.reshape( + grad, (batch_size, rows_out, cols_out, ksize_r, ksize_c, channels)), + (1, 2, 3, 4, 0, 5)) grad_flat = array_ops.reshape(grad_expanded, (-1, batch_size * channels)) row_steps = range(0, rows_out * stride_r, stride_r) @@ -759,29 +763,21 @@ def _ExtractImagePatchesGrad(op, grad): r_low, c_low = row_steps[i] - pad_rows, col_steps[j] - pad_cols r_high, c_high = r_low + ksize_r_eff, c_low + ksize_c_eff - idx.extend([(r * (cols_in) + c, - i * (cols_out * ksize_r * ksize_c) + - j * (ksize_r * ksize_c) + - ri * (ksize_c) + ci) + idx.extend([(r * (cols_in) + c, i * (cols_out * ksize_r * ksize_c) + j * + (ksize_r * ksize_c) + ri * (ksize_c) + ci) for (ri, r) in enumerate(range(r_low, r_high, rate_r)) for (ci, c) in enumerate(range(c_low, c_high, rate_c)) - if 0 <= r and r < rows_in and 0 <= c and c < cols_in - ]) + if 0 <= r and r < rows_in and 0 <= c and c < cols_in]) - sp_shape = (rows_in * cols_in, - rows_out * cols_out * ksize_r * ksize_c) + sp_shape = (rows_in * cols_in, rows_out * cols_out * ksize_r * ksize_c) 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.constant(idx, dtype=ops.dtypes.int64), + array_ops.ones((len(idx),), dtype=ops.dtypes.float32), sp_shape) jac = sparse_ops.sparse_tensor_dense_matmul(sp_mat, grad_flat) - grad_out = array_ops.reshape( - jac, (rows_in, cols_in, batch_size, channels) - ) + grad_out = array_ops.reshape(jac, (rows_in, cols_in, batch_size, channels)) grad_out = array_ops.transpose(grad_out, (2, 0, 1, 3)) return [grad_out] diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index d379eccc20..49191c647d 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Control Flow Operations. See the @{$python/control_flow_ops} guide. @@ -84,7 +83,6 @@ from tensorflow.python.util import nest from tensorflow.python.util import tf_should_use from tensorflow.python.util.tf_export import tf_export - # We override the 'tuple' for a control flow op, so we keep python's # existing 'tuple' for later use in this module. _basetuple = tuple @@ -156,9 +154,10 @@ def Assert(condition, data, summarize=None, name=None): xs = ops.convert_n_to_tensor(data) data_str = [_summarize_eager(x, summarize) for x in xs] raise errors.InvalidArgumentError( - node_def=None, op=None, - message="Expected '%s' to be true. Summarized data: %s" % ( - condition, "\n".join(data_str))) + node_def=None, + op=None, + message="Expected '%s' to be true. Summarized data: %s" % + (condition, "\n".join(data_str))) return with ops.name_scope(name, "Assert", [condition, data]) as name: @@ -167,15 +166,15 @@ def Assert(condition, data, summarize=None, name=None): # As a simple heuristic, we assume that string and int32 are # on host to avoid the need to use cond. If it is not case, # we will pay the price copying the tensor to host memory. - return gen_logging_ops._assert( - condition, data, summarize, name="Assert") + return gen_logging_ops._assert(condition, data, summarize, name="Assert") else: condition = ops.convert_to_tensor(condition, name="Condition") + def true_assert(): return gen_logging_ops._assert( condition, data, summarize, name="Assert") - guarded_assert = cond( - condition, no_op, true_assert, name="AssertGuard") + + guarded_assert = cond(condition, no_op, true_assert, name="AssertGuard") if context.in_eager_mode(): return return guarded_assert.op @@ -215,7 +214,7 @@ def _Identity(data, name=None): def _NextIteration(data, name=None): data = ops.internal_convert_to_tensor_or_indexed_slices(data, as_ref=True) if isinstance(data, ops.Tensor): - if data.dtype._is_ref_dtype: # pylint: disable=protected-access + if data.dtype._is_ref_dtype: # pylint: disable=protected-access return ref_next_iteration(data, name=name) else: return next_iteration(data, name=name) @@ -234,8 +233,13 @@ def _NextIteration(data, name=None): return sparse_tensor.SparseTensor(indices, values, dense_shape) -def _Enter(data, frame_name, is_constant=False, parallel_iterations=10, - use_ref=True, use_input_shape=True, name=None): +def _Enter(data, + frame_name, + is_constant=False, + parallel_iterations=10, + use_ref=True, + use_input_shape=True, + name=None): """Creates or finds a child frame, and makes `data` available to it. The unique `frame_name` is used by the `Executor` to identify frames. If @@ -257,35 +261,51 @@ def _Enter(data, frame_name, is_constant=False, parallel_iterations=10, data = ops.internal_convert_to_tensor_or_indexed_slices(data, as_ref=True) if isinstance(data, ops.Tensor): if data.dtype._is_ref_dtype and use_ref: # pylint: disable=protected-access - result = ref_enter(data, frame_name, is_constant, parallel_iterations, - name=name) + result = ref_enter( + data, frame_name, is_constant, parallel_iterations, name=name) else: - result = enter(data, frame_name, is_constant, parallel_iterations, - name=name) + result = enter( + data, frame_name, is_constant, parallel_iterations, name=name) if use_input_shape: result.set_shape(data.get_shape()) return result else: if not isinstance(data, (ops.IndexedSlices, sparse_tensor.SparseTensor)): raise TypeError("Type %s not supported" % type(data)) - values = _Enter(data.values, frame_name, is_constant, - parallel_iterations=parallel_iterations, - use_input_shape=use_input_shape, name=name) - indices = enter(data.indices, frame_name, is_constant, - parallel_iterations, name="indices") + values = _Enter( + data.values, + frame_name, + is_constant, + parallel_iterations=parallel_iterations, + use_input_shape=use_input_shape, + name=name) + indices = enter( + data.indices, + frame_name, + is_constant, + parallel_iterations, + name="indices") if use_input_shape: indices.set_shape(data.indices.get_shape()) if isinstance(data, ops.IndexedSlices): dense_shape = data.dense_shape if dense_shape is not None: - dense_shape = enter(dense_shape, frame_name, is_constant, - parallel_iterations, name="dense_shape") + dense_shape = enter( + dense_shape, + frame_name, + is_constant, + parallel_iterations, + name="dense_shape") if use_input_shape: dense_shape.set_shape(data.dense_shape.get_shape()) return ops.IndexedSlices(values, indices, dense_shape) else: - dense_shape = enter(data.dense_shape, frame_name, is_constant, - parallel_iterations, name="dense_shape") + dense_shape = enter( + data.dense_shape, + frame_name, + is_constant, + parallel_iterations, + name="dense_shape") if use_input_shape: dense_shape.set_shape(data.dense_shape.get_shape()) return sparse_tensor.SparseTensor(indices, values, dense_shape) @@ -444,8 +464,10 @@ def merge(inputs, name=None): if any([inp is None for inp in inputs]): raise ValueError("At least one of the merge inputs is None: %s" % inputs) with ops.name_scope(name, "Merge", inputs) as name: - inputs = [ops.internal_convert_to_tensor_or_indexed_slices(inp, as_ref=True) - for inp in inputs] + inputs = [ + ops.internal_convert_to_tensor_or_indexed_slices(inp, as_ref=True) + for inp in inputs + ] if all([isinstance(v, ops.Tensor) for v in inputs]): if all([v.dtype._is_ref_dtype for v in inputs]): # pylint: disable=protected-access return gen_control_flow_ops._ref_merge(inputs, name) @@ -475,6 +497,8 @@ def merge(inputs, name=None): else: dense_shape = None return ops.IndexedSlices(values, indices, dense_shape), chosen_index + + # pylint: enable=protected-access @@ -488,7 +512,9 @@ def _convert_tensorarray_to_flow(tensor_or_tensor_array): def _make_tensor_array(ta, t_or_flow): # pylint: disable=protected-access new_ta = tensor_array_ops.TensorArray( - dtype=ta.dtype, handle=ta.handle, flow=t_or_flow, + dtype=ta.dtype, + handle=ta.handle, + flow=t_or_flow, infer_shape=ta._infer_shape, colocate_with_first_write_call=ta._colocate_with_first_write_call) new_ta._colocate_with = ta._colocate_with @@ -500,13 +526,13 @@ def _make_tensor_array(ta, t_or_flow): def _convert_flows_to_tensorarrays(tensors_or_tensorarrays, tensors_or_flows): if len(tensors_or_tensorarrays) != len(tensors_or_flows): raise ValueError( - "Lengths of original Tensor list and new list do not match: %d vs. %d" - % (len(tensors_or_tensorarrays), len(tensors_or_flows))) + "Lengths of original Tensor list and new list do not match: %d vs. %d" % + (len(tensors_or_tensorarrays), len(tensors_or_flows))) return [ _make_tensor_array(ta, t_or_flow) - if isinstance(ta, tensor_array_ops.TensorArray) - else t_or_flow - for (ta, t_or_flow) in zip(tensors_or_tensorarrays, tensors_or_flows)] + if isinstance(ta, tensor_array_ops.TensorArray) else t_or_flow + for (ta, t_or_flow) in zip(tensors_or_tensorarrays, tensors_or_flows) + ] def _ShapeLessThanOrEqual(shape1, shape2): @@ -545,8 +571,8 @@ def _SetShapeInvariants(input_vars, enter_vars, shapes): raise ValueError( "The shape invariant specified for %s is not compatible with " "the initial shape of the loop variable. It enters the loop " - "with shape %s, but the specified shape invariant is %s." - % (inp.name, inp.get_shape(), shape)) + "with shape %s, but the specified shape invariant is %s." % + (inp.name, inp.get_shape(), shape)) var.set_shape(shape) else: if not isinstance(var, (ops.IndexedSlices, sparse_tensor.SparseTensor)): @@ -557,8 +583,8 @@ def _SetShapeInvariants(input_vars, enter_vars, shapes): "The shape invariant specified for %s is not compatible with " "the initial shape of the values tensor of this IndexedSlices. " "It enters the loop with shape %s, but the specified shape " - "invariant is %s." - % (inp.values.name, inp.values.get_shape(), shape)) + "invariant is %s." % (inp.values.name, inp.values.get_shape(), + shape)) var.values.set_shape(shape) var.indices.set_shape(tensor_shape.TensorShape([shape[0]])) if var.dense_shape is not None: @@ -569,8 +595,8 @@ def _SetShapeInvariants(input_vars, enter_vars, shapes): "The shape invariant specified for %s is not compatible with " "the initial shape of the shape tensor of this SparseTensor. " "It enters the loop with shape %s, but the specified shape " - "invariant is %s." - % (inp.dense_shape.name, inp.dense_shape.get_shape(), shape)) + "invariant is %s." % (inp.dense_shape.name, + inp.dense_shape.get_shape(), shape)) var.values.set_shape(tensor_shape.TensorShape([None])) var.indices.set_shape(tensor_shape.TensorShape([None, shape.ndims])) var.dense_shape.set_shape(shape) @@ -599,8 +625,8 @@ def _EnforceShapeInvariant(merge_var, next_var): "The shape for %s is not an invariant for the loop. It enters " "the loop with shape %s, but has shape %s after one iteration. " "Provide shape invariants using either the `shape_invariants` " - "argument of tf.while_loop or set_shape() on the loop variables." - % (merge_var.name, m_shape, n_shape)) + "argument of tf.while_loop or set_shape() on the loop variables." % + (merge_var.name, m_shape, n_shape)) else: if not isinstance(var, (ops.IndexedSlices, sparse_tensor.SparseTensor)): raise TypeError("Type %s not supported" % type(var)) @@ -623,9 +649,9 @@ def _EnforceShapeInvariant(merge_var, next_var): "the loop with shape (%s, %s, %s), but has shape (%s, %s, %s) " "after one iteration. Provide shape invariants using either the " "`shape_invariants` argument of tf.while_loop or set_shape() " - "on the loop variables." - % (merge_var.name, m_values_shape, m_indices_shape, m_shape_shape, - n_values_shape, n_indices_shape, n_shape_shape)) + "on the loop variables." % + (merge_var.name, m_values_shape, m_indices_shape, m_shape_shape, + n_values_shape, n_indices_shape, n_shape_shape)) else: m_values_shape = merge_var.values.get_shape() m_indices_shape = merge_var.indices.get_shape() @@ -637,12 +663,12 @@ def _EnforceShapeInvariant(merge_var, next_var): not _ShapeLessThanOrEqual(n_indices_shape, m_indices_shape) or not _ShapeLessThanOrEqual(n_shape_shape, m_shape_shape)): raise ValueError( - "The shape for %s is not an invariant for the loop. It enters " - "the loop with shape (%s, %s, %s), but has shape (%s, %s, %s) " - "after one iteration. Provide shape invariants using either " - "the `shape_invariants` argument of tf.while_loop or set_shape() " - "on the loop variables." - % (merge_var.name, m_values_shape, m_indices_shape, m_shape_shape, + "The shape for %s is not an invariant for the loop. It enters " + "the loop with shape (%s, %s, %s), but has shape (%s, %s, %s) " + "after one iteration. Provide shape invariants using either " + "the `shape_invariants` argument of tf.while_loop or set_shape() " + "on the loop variables." % + (merge_var.name, m_values_shape, m_indices_shape, m_shape_shape, n_values_shape, n_indices_shape, n_shape_shape)) @@ -657,7 +683,7 @@ def _AddNextAndBackEdge(m, v, enforce_shape_invariant=True): # the types don't match. # TODO(skyewm): call this for other cases below (needs testing) _EnforceShapeInvariant(m, v) - m.op._update_input(1, v) # pylint: disable=protected-access + m.op._update_input(1, v) # pylint: disable=protected-access elif isinstance(m, ops.IndexedSlices): # pylint: disable=protected-access v = math_ops._as_indexed_slices(v, optimize=False) @@ -720,8 +746,7 @@ def GetMaxSizeFromNestedMaximumIterations(value, while_ctxt): raise ValueError( "Cannot create a gradient accumulator for tensor '%s' inside " "XLA while_loop because maximum_iterations was not passed to " - "the tf.while_loop call ('%s')." - % (value_name, while_ctxt.name)) + "the tf.while_loop call ('%s')." % (value_name, while_ctxt.name)) # pylint: disable=protected-access max_iter_ctxt = max_iter.op._get_control_flow_context() @@ -742,9 +767,9 @@ def GetMaxSizeFromNestedMaximumIterations(value, while_ctxt): "while_loop. maximum_iterations tensor '%s' for while_loop context " "'%s' must be statically known (e.g. a constant value or known " "shape dimension), or be defined at or outside the while loop " - "context '%s' (currently defined in '%s')." % ( - value_name, max_iter.name, while_ctxt.name, - curr_ctxt_name, max_iter_ctxt.name)) + "context '%s' (currently defined in '%s')." % + (value_name, max_iter.name, while_ctxt.name, curr_ctxt_name, + max_iter_ctxt.name)) max_size *= const_max_iter # Find the next outer WhileContext (or stop if we reach the @@ -808,9 +833,11 @@ class GradLoopState(object): outer_forward_ctxt = forward_ctxt.outer_context # Add the forward loop counter. - if outer_forward_ctxt: outer_forward_ctxt.Enter() + if outer_forward_ctxt: + outer_forward_ctxt.Enter() cnt, forward_index = forward_ctxt.AddForwardLoopCounter(outer_grad_state) - if outer_forward_ctxt: outer_forward_ctxt.Exit() + if outer_forward_ctxt: + outer_forward_ctxt.Exit() self._forward_context = forward_ctxt self._forward_index = forward_index @@ -835,7 +862,8 @@ class GradLoopState(object): real_cnt, outer_grad_state) outer_grad_ctxt.Exit() else: - if outer_forward_ctxt: outer_forward_ctxt.Enter() + if outer_forward_ctxt: + outer_forward_ctxt.Enter() self._grad_context = WhileContext( maximum_iterations=forward_ctxt.maximum_iterations, parallel_iterations=forward_ctxt.parallel_iterations, @@ -845,7 +873,8 @@ class GradLoopState(object): grad_state=self) self._grad_index = self._grad_context.AddBackpropLoopCounter( cnt, outer_grad_state) - if outer_forward_ctxt: outer_forward_ctxt.Exit() + if outer_forward_ctxt: + outer_forward_ctxt.Exit() @property def outer_grad_state(self): @@ -973,7 +1002,8 @@ class GradLoopState(object): # curr_ctxt is the context that tf.gradients was called in. curr_ctxt = ops.get_default_graph()._get_control_flow_context() # pylint: disable=protected-access with ops.control_dependencies(None): - if curr_ctxt: curr_ctxt.Enter() + if curr_ctxt: + curr_ctxt.Enter() with ops.colocate_with(value): # We only need to pass maximum_iterations to the stack if # we're inside an XLA context. @@ -984,11 +1014,10 @@ class GradLoopState(object): value, self.forward_context) # pylint: disable=protected-access acc = gen_data_flow_ops._stack_v2( - max_size=max_size, - elem_type=value.dtype.base_dtype, - name="f_acc") + max_size=max_size, elem_type=value.dtype.base_dtype, name="f_acc") # pylint: enable=protected-access - if curr_ctxt: curr_ctxt.Exit() + if curr_ctxt: + curr_ctxt.Exit() # Make acc available in the forward context. enter_acc = self.forward_context.AddValue(acc) @@ -1009,8 +1038,7 @@ class GradLoopState(object): else: # value is in a cond context within the forward context. if not isinstance(value_ctxt, CondContext): - raise TypeError( - "value_ctxt is not a CondContext: %s" % value_ctxt) + raise TypeError("value_ctxt is not a CondContext: %s" % value_ctxt) if dead_branch: # The special case for creating a zero tensor for a dead # branch of a switch. See ControlFlowState.ZerosLike(). @@ -1134,8 +1162,8 @@ class GradLoopState(object): if real_value is None: # Add the stack pop op in the grad context. - real_value = cur_grad_state.AddBackpropAccumulatedValue(history_value, - cur_value) + real_value = cur_grad_state.AddBackpropAccumulatedValue( + history_value, cur_value) if cur_grad_state != self: real_value = self._grad_context.AddValue(real_value) self._history_map[value.name] = real_value @@ -1154,7 +1182,7 @@ class ControlFlowState(object): """Maintain the mapping from the loops to their grad states.""" def __init__(self): - self._map = {} # maps forward loop context to GradLoopState + self._map = {} # maps forward loop context to GradLoopState def GetGradState(self, op, before): """Return the grad state for this op if it's in a forward loop context.""" @@ -1318,7 +1346,8 @@ class ControlFlowState(object): Returns: A zero tensor of the same shape of op.outputs[index]. """ - if util.IsLoopSwitch(op): return None + if util.IsLoopSwitch(op): + return None dead_branch = util.IsSwitch(op) forward_ctxt = _GetWhileContext(op) grad_state = self._map.get(forward_ctxt) @@ -1361,8 +1390,8 @@ class ControlFlowState(object): grad_state.grad_context.Enter() # Create a zero tensor with the right shape. - shape = grad_state.AddBackpropAccumulatedValue( - history_zeros_shape, zeros_shape, dead_branch) + shape = grad_state.AddBackpropAccumulatedValue(history_zeros_shape, + zeros_shape, dead_branch) result = array_ops.zeros(shape, val.dtype) return result @@ -1393,12 +1422,14 @@ class ControlFlowState(object): else: # Create a zeros in the outer grad context. outer_grad_ctxt = grad_state.grad_context.outer_context - if outer_grad_ctxt: outer_grad_ctxt.Enter() + if outer_grad_ctxt: + outer_grad_ctxt.Enter() enter_grad_op = b_merge.op.inputs[0].op enter_grad = enter_grad_op.inputs[0] grad_shape = array_ops.shape_internal(enter_grad, optimize=False) grad_val = array_ops.zeros(grad_shape) - if outer_grad_ctxt: outer_grad_ctxt.Exit() + if outer_grad_ctxt: + outer_grad_ctxt.Exit() # Use the zeros for iterations > 0. grad_state.grad_context.Enter() next_grad_val = _NextIteration(grad_val) @@ -1470,8 +1501,7 @@ class ControlFlowContext(object): self._outer_context = ops.get_default_graph()._get_control_flow_context() self._context_stack = [] if values_def: - self._init_values_from_proto(values_def, - import_scope=import_scope) + self._init_values_from_proto(values_def, import_scope=import_scope) else: # Values that have been already seen in this context. self._values = set() @@ -1532,19 +1562,16 @@ class ControlFlowContext(object): """ values_def = control_flow_pb2.ValuesDef() values_def.values.extend( - [ops.strip_name_scope(v, export_scope) - for v in sorted(self._values)]) + [ops.strip_name_scope(v, export_scope) for v in sorted(self._values)]) for k, v in self._external_values.items(): k = ops.strip_name_scope(k, export_scope) - values_def.external_values[k] = ops.strip_name_scope( - v.name, export_scope) + values_def.external_values[k] = ops.strip_name_scope(v.name, export_scope) return values_def @staticmethod def _from_proto(values_def, import_scope=None): """Returns a `ControlFlowContext` created from `values_def`.""" - return ControlFlowContext(values_def=values_def, - import_scope=import_scope) + return ControlFlowContext(values_def=values_def, import_scope=import_scope) def AddName(self, name): self._values.add(name) @@ -1599,6 +1626,7 @@ class ControlFlowContext(object): op._remove_all_control_inputs() op._add_control_inputs(internal_control_inputs) return internal_control_inputs + # pylint: enable=protected-access def AddInnerOp(self, op): @@ -1626,8 +1654,13 @@ class ControlFlowContext(object): class CondContext(ControlFlowContext): """The context for the conditional construct.""" - def __init__(self, pred=None, pivot=None, branch=None, - name="cond_text", context_def=None, import_scope=None): + def __init__(self, + pred=None, + pivot=None, + branch=None, + name="cond_text", + context_def=None, + import_scope=None): """Creates a `CondContext`. Args: @@ -1647,9 +1680,9 @@ class CondContext(ControlFlowContext): else: # Initializes the default fields. ControlFlowContext.__init__(self) - self._pred = pred # The boolean tensor for the cond predicate - self._pivot = pivot # The predicate tensor in this branch - self._branch = branch # 0 or 1 representing this branch + self._pred = pred # The boolean tensor for the cond predicate + self._pivot = pivot # The predicate tensor in this branch + self._branch = branch # 0 or 1 representing this branch # Values considered to have been already seen in this context. self._values.add(pred.name) @@ -1665,15 +1698,14 @@ class CondContext(ControlFlowContext): assert isinstance(context_def, control_flow_pb2.CondContextDef) # Create from context_def. g = ops.get_default_graph() - self._name = ops.prepend_name_scope( - context_def.context_name, import_scope) - self._pred = g.as_graph_element(ops.prepend_name_scope( - context_def.pred_name, import_scope)) - self._pivot = g.as_graph_element(ops.prepend_name_scope( - context_def.pivot_name, import_scope)) + self._name = ops.prepend_name_scope(context_def.context_name, import_scope) + self._pred = g.as_graph_element( + ops.prepend_name_scope(context_def.pred_name, import_scope)) + self._pivot = g.as_graph_element( + ops.prepend_name_scope(context_def.pivot_name, import_scope)) self._branch = context_def.branch - super(CondContext, self).__init__(values_def=context_def.values_def, - import_scope=import_scope) + super(CondContext, self).__init__( + values_def=context_def.values_def, import_scope=import_scope) @property def pred(self): @@ -1711,18 +1743,16 @@ class CondContext(ControlFlowContext): Returns: A `CondContextDef` protocol buffer. """ - if (export_scope is None or - self.name.startswith(export_scope)): + if (export_scope is None or self.name.startswith(export_scope)): context_def = control_flow_pb2.CondContextDef() - context_def.context_name = ops.strip_name_scope( - self.name, export_scope) - context_def.pred_name = ops.strip_name_scope( - self._pred.name, export_scope) - context_def.pivot_name = ops.strip_name_scope( - self._pivot.name, export_scope) + context_def.context_name = ops.strip_name_scope(self.name, export_scope) + context_def.pred_name = ops.strip_name_scope(self._pred.name, + export_scope) + context_def.pivot_name = ops.strip_name_scope(self._pivot.name, + export_scope) context_def.branch = self._branch - context_def.values_def.MergeFrom(super(CondContext, self)._to_proto( - export_scope)) + context_def.values_def.MergeFrom( + super(CondContext, self)._to_proto(export_scope)) return context_def else: @@ -1731,8 +1761,7 @@ class CondContext(ControlFlowContext): @staticmethod def from_proto(context_def, import_scope=None): """Returns a `CondContext` object created from `context_def`.""" - return CondContext(context_def=context_def, - import_scope=import_scope) + return CondContext(context_def=context_def, import_scope=import_scope) def AddValue(self, val): """Add `val` to the current context and its outer context recursively.""" @@ -1846,8 +1875,8 @@ class CondContext(ControlFlowContext): if original_result is None: return no_op(), None else: - original_result = nest.map_structure( - array_ops.identity, original_result) + original_result = nest.map_structure(array_ops.identity, + original_result) if original_result is None: return None, None @@ -1871,11 +1900,15 @@ def _UnpackIfSingleton(res): # pylint: disable=g-doc-args @tf_export("cond") @deprecation.deprecated_args( - None, - "fn1/fn2 are deprecated in favor of the true_fn/false_fn arguments.", + None, "fn1/fn2 are deprecated in favor of the true_fn/false_fn arguments.", "fn1", "fn2") -def cond(pred, true_fn=None, false_fn=None, strict=False, name=None, - fn1=None, fn2=None): +def cond(pred, + true_fn=None, + false_fn=None, + strict=False, + name=None, + fn1=None, + fn2=None): """Return `true_fn()` if the predicate `pred` is true else `false_fn()`. `true_fn` and `false_fn` both return lists of output tensors. `true_fn` and @@ -2044,6 +2077,8 @@ def cond(pred, true_fn=None, false_fn=None, strict=False, name=None, if not strict: merges = _UnpackIfSingleton(merges) return merges + + # pylint: enable=g-doc-args # pylint: enable=redefined-outer-name @@ -2139,8 +2174,7 @@ class WhileContext(ControlFlowContext): assert isinstance(context_def, control_flow_pb2.WhileContextDef) # Create from context_def. g = ops.get_default_graph() - self._name = ops.prepend_name_scope( - context_def.context_name, import_scope) + self._name = ops.prepend_name_scope(context_def.context_name, import_scope) if context_def.maximum_iterations_name: self._maximum_iterations = g.as_graph_element( ops.prepend_name_scope(context_def.maximum_iterations_name, @@ -2150,25 +2184,27 @@ class WhileContext(ControlFlowContext): self._parallel_iterations = context_def.parallel_iterations self._back_prop = context_def.back_prop self._swap_memory = context_def.swap_memory - self._pivot_for_pred = g.as_graph_element(ops.prepend_name_scope( - context_def.pivot_for_pred_name, import_scope)) + self._pivot_for_pred = g.as_graph_element( + ops.prepend_name_scope(context_def.pivot_for_pred_name, import_scope)) # We use this node to control constants created by the body lambda. - self._pivot_for_body = g.as_graph_element(ops.prepend_name_scope( - context_def.pivot_for_body_name, import_scope)) + self._pivot_for_body = g.as_graph_element( + ops.prepend_name_scope(context_def.pivot_for_body_name, import_scope)) # The boolean tensor for loop termination condition. Used in code # generation for gradient computation. self._pivot = g.as_graph_element( ops.prepend_name_scope(context_def.pivot_name, import_scope)) # The list of exit tensors for loop variables. - self._loop_exits = [g.as_graph_element( - ops.prepend_name_scope(exit_name, import_scope)) - for exit_name in context_def.loop_exit_names] + self._loop_exits = [ + g.as_graph_element(ops.prepend_name_scope(exit_name, import_scope)) + for exit_name in context_def.loop_exit_names + ] # The list of enter tensors for loop variables. - self._loop_enters = [g.as_graph_element( - ops.prepend_name_scope(enter_name, import_scope)) - for enter_name in context_def.loop_enter_names] - super(WhileContext, self).__init__(values_def=context_def.values_def, - import_scope=import_scope) + self._loop_enters = [ + g.as_graph_element(ops.prepend_name_scope(enter_name, import_scope)) + for enter_name in context_def.loop_enter_names + ] + super(WhileContext, self).__init__( + values_def=context_def.values_def, import_scope=import_scope) @property def maximum_iterations(self): @@ -2219,11 +2255,9 @@ class WhileContext(ControlFlowContext): Returns: A `WhileContextDef` protocol buffer. """ - if (export_scope is None or - self.name.startswith(export_scope)): + if (export_scope is None or self.name.startswith(export_scope)): context_def = control_flow_pb2.WhileContextDef() - context_def.context_name = ops.strip_name_scope( - self.name, export_scope) + context_def.context_name = ops.strip_name_scope(self.name, export_scope) context_def.parallel_iterations = self._parallel_iterations if self._maximum_iterations is not None: context_def.maximum_iterations_name = ops.strip_name_scope( @@ -2234,17 +2268,16 @@ class WhileContext(ControlFlowContext): self._pivot_for_pred.name, export_scope) context_def.pivot_for_body_name = ops.strip_name_scope( self._pivot_for_body.name, export_scope) - context_def.pivot_name = ops.strip_name_scope( - self._pivot.name, export_scope) - context_def.loop_exit_names.extend( - [ops.strip_name_scope(l.name, export_scope) - for l in self._loop_exits]) - context_def.loop_enter_names.extend( - [ops.strip_name_scope(l.name, export_scope) - for l in self._loop_enters]) + context_def.pivot_name = ops.strip_name_scope(self._pivot.name, + export_scope) + context_def.loop_exit_names.extend([ + ops.strip_name_scope(l.name, export_scope) for l in self._loop_exits + ]) + context_def.loop_enter_names.extend([ + ops.strip_name_scope(l.name, export_scope) for l in self._loop_enters + ]) context_def.values_def.MergeFrom( - super(WhileContext, self)._to_proto( - export_scope=export_scope)) + super(WhileContext, self)._to_proto(export_scope=export_scope)) return context_def else: @@ -2261,8 +2294,7 @@ class WhileContext(ControlFlowContext): Returns: A `WhileContext` Python object. """ - return WhileContext(context_def=context_def, - import_scope=import_scope) + return WhileContext(context_def=context_def, import_scope=import_scope) def GetWhileContext(self): return self @@ -2299,8 +2331,11 @@ class WhileContext(ControlFlowContext): result = self._outer_context.AddValue(val) # Create an Enter to make `result` known to this loop context. with ops.control_dependencies(None): - enter = _Enter(result, self._name, is_constant=True, - parallel_iterations=self._parallel_iterations) + enter = _Enter( + result, + self._name, + is_constant=True, + parallel_iterations=self._parallel_iterations) enter.graph.prevent_feeding(enter) if self._outer_context: self._outer_context.AddInnerOp(enter.op) @@ -2378,6 +2413,7 @@ class WhileContext(ControlFlowContext): def _MaybeAddControlDependency(self, op): """Add a control input to the op if it only depends on loop invariants.""" + def _IsOpFree(op): """Determines if `op` needs a control dependency.""" if op.control_inputs: @@ -2390,6 +2426,7 @@ class WhileContext(ControlFlowContext): if not util.IsLoopConstantEnter(x.op): return False return True + if _IsOpFree(op): # pylint: disable=protected-access op._add_control_input(self.GetControlPivot().op) @@ -2423,9 +2460,12 @@ class WhileContext(ControlFlowContext): self.Enter() self.AddName(n.name) - enter_n = _Enter(n, self._name, is_constant=False, - parallel_iterations=self._parallel_iterations, - name="f_count") + enter_n = _Enter( + n, + self._name, + is_constant=False, + parallel_iterations=self._parallel_iterations, + name="f_count") self.loop_enters.append(enter_n) merge_n = merge([enter_n, enter_n])[0] @@ -2465,9 +2505,12 @@ class WhileContext(ControlFlowContext): self.Enter() self.AddName(count.name) - enter_count = _Enter(count, self._name, is_constant=False, - parallel_iterations=self._parallel_iterations, - name="b_count") + enter_count = _Enter( + count, + self._name, + is_constant=False, + parallel_iterations=self._parallel_iterations, + name="b_count") self.loop_enters.append(enter_count) merge_count = merge([enter_count, enter_count])[0] @@ -2525,9 +2568,11 @@ class WhileContext(ControlFlowContext): # without running any iterations. shape = grad.get_shape() if shape.is_fully_defined(): - if self.outer_context: self.outer_context.Enter() + if self.outer_context: + self.outer_context.Enter() acc = constant_op.constant(0, grad.dtype, shape=shape, name="b_acc") - if self.outer_context: self.outer_context.Exit() + if self.outer_context: + self.outer_context.Exit() else: value = op.inputs[0] if (isinstance(self.outer_context, WhileContext) and @@ -2546,16 +2591,21 @@ class WhileContext(ControlFlowContext): acc = array_ops.zeros(real_shape, grad.dtype) self.outer_context.Exit() else: - if self.outer_context: self.outer_context.Enter() + if self.outer_context: + self.outer_context.Enter() zeros_shape = array_ops.shape_internal(value, optimize=False) acc = array_ops.zeros(zeros_shape, grad.dtype) - if self.outer_context: self.outer_context.Exit() + if self.outer_context: + self.outer_context.Exit() self.Enter() self.AddName(acc.name) - enter_acc = _Enter(acc, self._name, is_constant=False, - parallel_iterations=self._parallel_iterations, - name="b_acc") + enter_acc = _Enter( + acc, + self._name, + is_constant=False, + parallel_iterations=self._parallel_iterations, + name="b_acc") self.loop_enters.append(enter_acc) merge_acc = merge([enter_acc, enter_acc], name="b_acc")[0] @@ -2588,14 +2638,17 @@ class WhileContext(ControlFlowContext): dense_shape = grad.dense_shape self.Exit() - if self.outer_context: self.outer_context.Enter() + if self.outer_context: + self.outer_context.Enter() if values.get_shape().is_fully_defined(): values_shape = tensor_shape.TensorShape( [tensor_shape.Dimension(1)] + values.get_shape().dims[1:]) - if self.outer_context: self.outer_context.Enter() - values_acc = constant_op.constant(0, values.dtype, shape=values_shape, - name="b_acc") - if self.outer_context: self.outer_context.Exit() + if self.outer_context: + self.outer_context.Enter() + values_acc = constant_op.constant( + 0, values.dtype, shape=values_shape, name="b_acc") + if self.outer_context: + self.outer_context.Exit() else: values_shape = _resource_safe_shape(op.inputs[0])[1:] values_shape = array_ops.concat([[1], values_shape], 0) @@ -2604,16 +2657,19 @@ class WhileContext(ControlFlowContext): shape_acc = None if dense_shape is not None: if dense_shape.get_shape().is_fully_defined(): - if self.outer_context: self.outer_context.Enter() - shape_acc = constant_op.constant(0, dense_shape.dtype, - shape=dense_shape.get_shape()) - if self.outer_context: self.outer_context.Exit() + if self.outer_context: + self.outer_context.Enter() + shape_acc = constant_op.constant( + 0, dense_shape.dtype, shape=dense_shape.get_shape()) + if self.outer_context: + self.outer_context.Exit() else: shape_acc = array_ops.zeros_like( array_ops.shape_internal(op.inputs[0], optimize=False), optimize=False) - if self.outer_context: self.outer_context.Exit() + if self.outer_context: + self.outer_context.Exit() self.Enter() self.AddName(values_acc.name) @@ -2626,9 +2682,15 @@ class WhileContext(ControlFlowContext): # Set use_input_shape=False since the accumulator tensors will grow in # size. If use_input_shape=True, the _update_input call below will result in # incompatible shapes. - enter_acc = [_Enter(x, self._name, is_constant=False, - parallel_iterations=self._parallel_iterations, - use_input_shape=False, name="b_acc") for x in init_acc] + enter_acc = [ + _Enter( + x, + self._name, + is_constant=False, + parallel_iterations=self._parallel_iterations, + use_input_shape=False, + name="b_acc") for x in init_acc + ] # Manually set appropriate partial shapes. enter_acc[0].set_shape([None]) if values_acc.shape.dims is not None: @@ -2645,8 +2707,7 @@ class WhileContext(ControlFlowContext): ] if shape_acc is not None: # For the shape we just keep the maximum - acc_indexed_slices.append( - math_ops.maximum(dense_shape, switch_acc[2][1])) + acc_indexed_slices.append(math_ops.maximum(dense_shape, switch_acc[2][1])) next_acc = [_NextIteration(x) for x in acc_indexed_slices] for xm, xn in zip(merge_acc, next_acc): @@ -2657,7 +2718,8 @@ class WhileContext(ControlFlowContext): self.ExitResult(exit_acc) return ops.IndexedSlices( - indices=exit_acc[0], values=exit_acc[1], + indices=exit_acc[0], + values=exit_acc[1], dense_shape=exit_acc[2] if shape_acc is not None else None) def _InitializeValues(self, values): @@ -2690,10 +2752,14 @@ class WhileContext(ControlFlowContext): if self._outer_context: real_vars = [self._outer_context.AddValue(x) for x in loop_vars] with ops.control_dependencies(None): - enter_vars = [_Enter(x, self._name, is_constant=False, - parallel_iterations=self._parallel_iterations, - use_input_shape=(shape_invariants is None)) - for x in real_vars] + enter_vars = [ + _Enter( + x, + self._name, + is_constant=False, + parallel_iterations=self._parallel_iterations, + use_input_shape=(shape_invariants is None)) for x in real_vars + ] for x in enter_vars: x.graph.prevent_feeding(x) if self._outer_context: @@ -2754,11 +2820,13 @@ class WhileContext(ControlFlowContext): summary_ref = ops.get_collection_ref(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access summary_ref[:] = pre_summaries with ops.control_dependencies(new_summaries): + def map_fn(x): # TODO(apassos) figure out how to trigger with tensor arrays as well if isinstance(x, tensor_array_ops.TensorArray): return x return array_ops.identity(x) + body_result = nest.map_structure(map_fn, body_result) # Compare the structure types of input and output of body. @@ -2815,8 +2883,7 @@ class WhileContext(ControlFlowContext): packed_exit_vars = nest.pack_sequence_as( structure=original_body_result, flat_sequence=exit_vars_with_tensor_arrays) - return (packed_exit_vars[0] if len(exit_vars) == 1 - else packed_exit_vars) + return (packed_exit_vars[0] if len(exit_vars) == 1 else packed_exit_vars) def _FixControlInputsAndContext(self, enters): graph = ops.get_default_graph() @@ -2834,8 +2901,9 @@ class WhileContext(ControlFlowContext): for x in xs: inp_op = x.op.inputs[0].op control_inputs = graph._control_dependencies_for_inputs([inp_op]) - outer_control_inputs = [op for op in control_inputs - if self._IsInOuterContext(op)] + outer_control_inputs = [ + op for op in control_inputs if self._IsInOuterContext(op) + ] x.op._set_control_flow_context(self) x.op._add_control_inputs(outer_control_inputs) graph._record_op_seen_by_control_dependencies(x.op) @@ -2847,9 +2915,15 @@ class WhileContext(ControlFlowContext): # pylint: disable=redefined-outer-name @tf_export("while_loop") -def while_loop(cond, body, loop_vars, shape_invariants=None, - parallel_iterations=10, back_prop=True, swap_memory=False, - name=None, maximum_iterations=None): +def while_loop(cond, + body, + loop_vars, + shape_invariants=None, + parallel_iterations=10, + back_prop=True, + swap_memory=False, + name=None, + maximum_iterations=None): """Repeat `body` while the condition `cond` is true. `cond` is a callable returning a boolean scalar tensor. `body` is a callable @@ -3024,6 +3098,8 @@ def while_loop(cond, body, loop_vars, shape_invariants=None, return result[1] else: return result + + # pylint: enable=redefined-outer-name @@ -3051,8 +3127,9 @@ def _AsTensorList(x, p): if isinstance(v, ops.Tensor): l.append(array_ops.identity(v)) else: - l.append(ops.IndexedSlices(array_ops.identity(v.values), - array_ops.identity(v.indices))) + l.append( + ops.IndexedSlices( + array_ops.identity(v.values), array_ops.identity(v.indices))) return l @@ -3062,8 +3139,7 @@ def _CheckResults(a, b): for x, y in zip(a, b): assert x.dtype == y.dtype, ( "Values returned by a() [%s] and b() [%s] must have " - "the same type: %s, %s." % - (x.name, y.name, x.dtype.name, y.dtype.name)) + "the same type: %s, %s." % (x.name, y.name, x.dtype.name, y.dtype.name)) def with_dependencies(dependencies, output_tensor, name=None): @@ -3099,9 +3175,9 @@ def with_dependencies(dependencies, output_tensor, name=None): if isinstance(output_tensor, ops.Tensor): return _Identity(output_tensor, name=name) else: - return ops.IndexedSlices(_Identity(output_tensor.values, name=name), - output_tensor.indices, - output_tensor.dense_shape) + return ops.IndexedSlices( + _Identity(output_tensor.values, name=name), output_tensor.indices, + output_tensor.dense_shape) def _GroupControlDeps(dev, deps, name=None): @@ -3173,6 +3249,7 @@ def group(*inputs, **kwargs): def device_key(dev): """A sort key that allows None to be compared to strings.""" return "" if dev is None else dev + for dev in sorted(six.iterkeys(ops_on_device), key=device_key): deps.append(_GroupControlDeps(dev, ops_on_device[dev])) @@ -3463,12 +3540,14 @@ class XLAControlFlowContext(ControlFlowContext): return x -ops.register_proto_function(ops.GraphKeys.COND_CONTEXT, - proto_type=control_flow_pb2.CondContextDef, - to_proto=CondContext.to_proto, - from_proto=CondContext.from_proto) +ops.register_proto_function( + ops.GraphKeys.COND_CONTEXT, + proto_type=control_flow_pb2.CondContextDef, + to_proto=CondContext.to_proto, + from_proto=CondContext.from_proto) -ops.register_proto_function(ops.GraphKeys.WHILE_CONTEXT, - proto_type=control_flow_pb2.WhileContextDef, - to_proto=WhileContext.to_proto, - from_proto=WhileContext.from_proto) +ops.register_proto_function( + ops.GraphKeys.WHILE_CONTEXT, + proto_type=control_flow_pb2.WhileContextDef, + to_proto=WhileContext.to_proto, + from_proto=WhileContext.from_proto) diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py index 34f0bf7b78..95e45bff06 100644 --- a/tensorflow/python/ops/data_flow_ops.py +++ b/tensorflow/python/ops/data_flow_ops.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. #============================================================================== - """Data Flow Operations.""" # pylint: disable=g-bad-name from __future__ import absolute_import @@ -40,6 +39,7 @@ from tensorflow.python.ops import math_ops # pylint: disable=wildcard-import from tensorflow.python.ops.gen_data_flow_ops import * from tensorflow.python.util.tf_export import tf_export + # pylint: enable=wildcard-import @@ -54,17 +54,19 @@ def _as_type_list(dtypes): return list(dtypes) -def _as_shape_list(shapes, dtypes, unknown_dim_allowed=False, +def _as_shape_list(shapes, + dtypes, + unknown_dim_allowed=False, unknown_rank_allowed=False): """Convert shapes to a list of tuples of int (or None).""" del dtypes if unknown_dim_allowed: - if (not isinstance(shapes, collections.Sequence) - or not shapes - or any(shape is None or isinstance(shape, int) for shape in shapes)): + if (not isinstance(shapes, collections.Sequence) or not shapes or + any(shape is None or isinstance(shape, int) for shape in shapes)): raise ValueError( "When providing partial shapes, a list of shapes must be provided.") - if shapes is None: return None + if shapes is None: + return None if isinstance(shapes, tensor_shape.TensorShape): shapes = [shapes] if not isinstance(shapes, (tuple, list)): @@ -103,7 +105,8 @@ def _shape_common(s1, s2): return tensor_shape.unknown_shape() d = [ d1 if d1 is not None and d1 == d2 else None - for (d1, d2) in zip(s1.as_list(), s2.as_list())] + for (d1, d2) in zip(s1.as_list(), s2.as_list()) + ] return tensor_shape.TensorShape(d) @@ -195,8 +198,7 @@ class QueueBase(object): TypeError: When `queues` is not a list of `QueueBase` objects, or when the data types of `queues` are not all the same. """ - if ((not queues) or - (not isinstance(queues, list)) or + if ((not queues) or (not isinstance(queues, list)) or (not all(isinstance(x, QueueBase) for x in queues))): raise TypeError("A list of queues expected") @@ -210,12 +212,16 @@ class QueueBase(object): queue_shapes = [q.shapes for q in queues] reduced_shapes = [ - six.moves.reduce(_shape_common, s) for s in zip(*queue_shapes)] + six.moves.reduce(_shape_common, s) for s in zip(*queue_shapes) + ] queue_refs = array_ops.stack([x.queue_ref for x in queues]) selected_queue = array_ops.gather(queue_refs, index) - return QueueBase(dtypes=dtypes, shapes=reduced_shapes, names=names, - queue_ref=selected_queue) + return QueueBase( + dtypes=dtypes, + shapes=reduced_shapes, + names=names, + queue_ref=selected_queue) @property def queue_ref(self): @@ -282,8 +288,8 @@ class QueueBase(object): tensors = [] for i, (val, dtype) in enumerate(zip(vals, self._dtypes)): - tensors.append(ops.convert_to_tensor(val, dtype=dtype, - name="component_%d" % i)) + tensors.append( + ops.convert_to_tensor(val, dtype=dtype, name="component_%d" % i)) return tensors @@ -555,11 +561,13 @@ class QueueBase(object): name = "%s_Close" % self._name if self._queue_ref.dtype == _dtypes.resource: return gen_data_flow_ops._queue_close_v2( - self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues, + self._queue_ref, + cancel_pending_enqueues=cancel_pending_enqueues, name=name) else: return gen_data_flow_ops._queue_close( - self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues, + self._queue_ref, + cancel_pending_enqueues=cancel_pending_enqueues, name=name) def is_closed(self, name=None): @@ -577,9 +585,9 @@ class QueueBase(object): if name is None: name = "%s_Is_Closed" % self._name if self._queue_ref.dtype == _dtypes.resource: - return gen_data_flow_ops.queue_is_closed_v2(self._queue_ref,name=name) + return gen_data_flow_ops.queue_is_closed_v2(self._queue_ref, name=name) else: - return gen_data_flow_ops.queue_is_closed_(self._queue_ref,name=name) + return gen_data_flow_ops.queue_is_closed_(self._queue_ref, name=name) def size(self, name=None): """Compute the number of elements in this queue. @@ -611,8 +619,14 @@ class RandomShuffleQueue(QueueBase): @end_compatibility """ - def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None, - names=None, seed=None, shared_name=None, + def __init__(self, + capacity, + min_after_dequeue, + dtypes, + shapes=None, + names=None, + seed=None, + shared_name=None, name="random_shuffle_queue"): """Create a queue that dequeues elements in a random order. @@ -670,9 +684,14 @@ class RandomShuffleQueue(QueueBase): string = (str(seed1) + shared_name).encode("utf-8") seed2 = int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF queue_ref = gen_data_flow_ops._random_shuffle_queue_v2( - component_types=dtypes, shapes=shapes, capacity=capacity, - min_after_dequeue=min_after_dequeue, seed=seed1, seed2=seed2, - shared_name=shared_name, name=name) + component_types=dtypes, + shapes=shapes, + capacity=capacity, + min_after_dequeue=min_after_dequeue, + seed=seed1, + seed2=seed2, + shared_name=shared_name, + name=name) super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref) @@ -690,8 +709,13 @@ class FIFOQueue(QueueBase): @end_compatibility """ - def __init__(self, capacity, dtypes, shapes=None, names=None, - shared_name=None, name="fifo_queue"): + def __init__(self, + capacity, + dtypes, + shapes=None, + names=None, + shared_name=None, + name="fifo_queue"): """Creates a queue that dequeues elements in a first-in first-out order. A `FIFOQueue` has bounded capacity; supports multiple concurrent @@ -725,8 +749,11 @@ class FIFOQueue(QueueBase): shapes = _as_shape_list(shapes, dtypes) names = _as_name_list(names, dtypes) queue_ref = gen_data_flow_ops._fifo_queue_v2( - component_types=dtypes, shapes=shapes, capacity=capacity, - shared_name=shared_name, name=name) + component_types=dtypes, + shapes=shapes, + capacity=capacity, + shared_name=shared_name, + name=name) super(FIFOQueue, self).__init__(dtypes, shapes, names, queue_ref) @@ -747,7 +774,12 @@ class PaddingFIFOQueue(QueueBase): @end_compatibility """ - def __init__(self, capacity, dtypes, shapes, names=None, shared_name=None, + def __init__(self, + capacity, + dtypes, + shapes, + names=None, + shared_name=None, name="padding_fifo_queue"): """Creates a queue that dequeues elements in a first-in first-out order. @@ -792,12 +824,15 @@ class PaddingFIFOQueue(QueueBase): names = _as_name_list(names, dtypes) if len(dtypes) != len(shapes): raise ValueError("Shapes must be provided for all components, " - "but received %d dtypes and %d shapes." - % (len(dtypes), len(shapes))) + "but received %d dtypes and %d shapes." % (len(dtypes), + len(shapes))) queue_ref = gen_data_flow_ops._padding_fifo_queue_v2( - component_types=dtypes, shapes=shapes, capacity=capacity, - shared_name=shared_name, name=name) + component_types=dtypes, + shapes=shapes, + capacity=capacity, + shared_name=shared_name, + name=name) super(PaddingFIFOQueue, self).__init__(dtypes, shapes, names, queue_ref) @@ -815,7 +850,12 @@ class PriorityQueue(QueueBase): @end_compatibility """ - def __init__(self, capacity, types, shapes=None, names=None, shared_name=None, + def __init__(self, + capacity, + types, + shapes=None, + names=None, + shared_name=None, name="priority_queue"): """Creates a queue that dequeues elements in a first-in first-out order. @@ -856,14 +896,17 @@ class PriorityQueue(QueueBase): shapes = _as_shape_list(shapes, types) queue_ref = gen_data_flow_ops._priority_queue_v2( - component_types=types, shapes=shapes, capacity=capacity, - shared_name=shared_name, name=name) + component_types=types, + shapes=shapes, + capacity=capacity, + shared_name=shared_name, + name=name) priority_dtypes = [_dtypes.int64] + types priority_shapes = [()] + shapes if shapes else shapes - super(PriorityQueue, self).__init__( - priority_dtypes, priority_shapes, names, queue_ref) + super(PriorityQueue, self).__init__(priority_dtypes, priority_shapes, names, + queue_ref) # TODO(josh11b): class BatchQueue(QueueBase): @@ -943,8 +986,10 @@ class Barrier(object): self._shapes = [tensor_shape.unknown_shape() for _ in self._types] self._barrier_ref = gen_data_flow_ops._barrier( - component_types=self._types, shapes=self._shapes, - shared_name=shared_name, name=name) + component_types=self._types, + shapes=self._shapes, + shared_name=shared_name, + name=name) if context.in_graph_mode(): self._name = self._barrier_ref.op.name.split("/")[-1] else: @@ -1028,12 +1073,13 @@ class Barrier(object): """ if name is None: name = "%s_BarrierTakeMany" % self._name - ret = gen_data_flow_ops._barrier_take_many(self._barrier_ref, - num_elements, - self._types, - allow_small_batch, - timeout, - name=name) + ret = gen_data_flow_ops._barrier_take_many( + self._barrier_ref, + num_elements, + self._types, + allow_small_batch, + timeout, + name=name) # NOTE(mrry): Not using a shape function because we need access to # the Barrier object. @@ -1048,8 +1094,7 @@ class Barrier(object): op.outputs[1].set_shape(tensor_shape.vector(batch_dim)) # keys for output, shape in zip(op.outputs[2:], self._shapes): # value_list output.set_shape( - tensor_shape.TensorShape([batch_dim]).concatenate( - shape)) + tensor_shape.TensorShape([batch_dim]).concatenate(shape)) return ret @@ -1298,8 +1343,8 @@ class SparseConditionalAccumulator(ConditionalAccumulatorBase): name="sparse_conditional_accumulator"): accumulator_ref = gen_data_flow_ops.sparse_conditional_accumulator( dtype=dtype, shape=shape, shared_name=shared_name, name=name) - super(SparseConditionalAccumulator, - self).__init__(dtype, shape, accumulator_ref) + super(SparseConditionalAccumulator, self).__init__(dtype, shape, + accumulator_ref) def apply_indexed_slices_grad(self, grad, local_step=0, name=None): """Attempts to apply a gradient to the accumulator. @@ -1368,8 +1413,8 @@ class SparseConditionalAccumulator(ConditionalAccumulatorBase): local_step=local_step, gradient_indices=math_ops.to_int64(grad_indices), gradient_values=grad_values, - gradient_shape=math_ops.to_int64([] if grad_shape is None else - grad_shape), + gradient_shape=math_ops.to_int64([] + if grad_shape is None else grad_shape), has_known_shape=(grad_shape is not None), name=name) @@ -1431,11 +1476,16 @@ class BaseStagingArea(object): _identifier = 0 _lock = threading.Lock() - def __init__(self, dtypes, shapes=None, names=None, shared_name=None, - capacity=0, memory_limit=0): + def __init__(self, + dtypes, + shapes=None, + names=None, + shared_name=None, + capacity=0, + memory_limit=0): if shared_name is None: - self._name = (ops.get_default_graph() - .unique_name(self.__class__.__name__)) + self._name = ( + ops.get_default_graph().unique_name(self.__class__.__name__)) elif isinstance(shared_name, six.string_types): self._name = shared_name else: @@ -1532,8 +1582,9 @@ class BaseStagingArea(object): (sorted(vals.keys()), sorted(self._names))) # The order of values in `self._names` indicates the order in which the # tensors in the dictionary `vals` must be listed. - vals, indices, n = zip(*[(vals[k], i, k) for i, k in enumerate(self._names) - if k in vals]) + vals, indices, n = zip(*[(vals[k], i, k) + for i, k in enumerate(self._names) + if k in vals]) else: if self._names: raise ValueError("You must enqueue a dictionary in a staging area " @@ -1541,7 +1592,7 @@ class BaseStagingArea(object): if indices is None: raise ValueError("Indices must be supplied when inserting a list " - "of tensors") + "of tensors") if len(indices) != len(vals): raise ValueError("Number of indices '%s' doesn't match " @@ -1553,8 +1604,8 @@ class BaseStagingArea(object): # Sanity check number of values if not len(vals) <= len(self._dtypes): - raise ValueError("Unexpected number of inputs '%s' vs '%s'" % ( - len(vals), len(self._dtypes))) + raise ValueError("Unexpected number of inputs '%s' vs '%s'" % + (len(vals), len(self._dtypes))) tensors = [] @@ -1562,14 +1613,14 @@ class BaseStagingArea(object): dtype, shape = self._dtypes[i], self._shapes[i] # Check dtype if not val.dtype == dtype: - raise ValueError("Datatypes do not match. '%s' != '%s'" %( - str(val.dtype), str(dtype))) + raise ValueError("Datatypes do not match. '%s' != '%s'" % + (str(val.dtype), str(dtype))) # Check shape val.get_shape().assert_is_compatible_with(shape) - tensors.append(ops.convert_to_tensor(val, dtype=dtype, - name="component_%d" % i)) + tensors.append( + ops.convert_to_tensor(val, dtype=dtype, name="component_%d" % i)) return tensors, indices @@ -1632,6 +1683,7 @@ class BaseStagingArea(object): else: return [vals] + class StagingArea(BaseStagingArea): """Class for staging inputs. No ordering guarantees. @@ -1666,8 +1718,13 @@ class StagingArea(BaseStagingArea): """ - def __init__(self, dtypes, shapes=None, names=None, shared_name=None, - capacity=0, memory_limit=0): + def __init__(self, + dtypes, + shapes=None, + names=None, + shared_name=None, + capacity=0, + memory_limit=0): """Constructs a staging area object. The two optional lists, `shapes` and `names`, must be of the same length @@ -1702,9 +1759,8 @@ class StagingArea(BaseStagingArea): ValueError: If one of the arguments is invalid. """ - super(StagingArea, self).__init__(dtypes, shapes, - names, shared_name, - capacity, memory_limit) + super(StagingArea, self).__init__(dtypes, shapes, names, shared_name, + capacity, memory_limit) def put(self, values, name=None): """Create an op that places a value into the staging area. @@ -1726,14 +1782,18 @@ class StagingArea(BaseStagingArea): self._scope_vals(values)) as scope: # Hard-code indices for this staging area - indices = (list(six.moves.range(len(values))) - if isinstance(values, (list, tuple)) else None) + indices = ( + list(six.moves.range(len(values))) + if isinstance(values, (list, tuple)) else None) vals, _ = self._check_put_dtypes(values, indices) with ops.colocate_with(self._coloc_op): - op = gen_data_flow_ops.stage(values=vals, shared_name=self._name, - name=scope, capacity=self._capacity, - memory_limit=self._memory_limit) + op = gen_data_flow_ops.stage( + values=vals, + shared_name=self._name, + name=scope, + capacity=self._capacity, + memory_limit=self._memory_limit) return op @@ -1741,7 +1801,7 @@ class StagingArea(BaseStagingArea): with ops.colocate_with(self._coloc_op): ret = get_fn() - indices = list(six.moves.range(len(self._dtypes))) # Hard coded + indices = list(six.moves.range(len(self._dtypes))) # Hard coded return self._get_return_value(ret, indices) def get(self, name=None): @@ -1769,10 +1829,12 @@ class StagingArea(BaseStagingArea): if name is None: name = "%s_get" % self._name + # pylint: disable=bad-continuation fn = lambda: gen_data_flow_ops.unstage(dtypes=self._dtypes, shared_name=self._name, name=name, capacity=self._capacity, memory_limit=self._memory_limit) + # pylint: enable=bad-continuation return self.__internal_get(fn, name) @@ -1797,10 +1859,12 @@ class StagingArea(BaseStagingArea): if name is None: name = "%s_peek" % self._name + # pylint: disable=bad-continuation fn = lambda: gen_data_flow_ops.stage_peek(index, dtypes=self._dtypes, shared_name=self._name, name=name, capacity=self._capacity, memory_limit=self._memory_limit) + # pylint: enable=bad-continuation return self.__internal_get(fn, name) @@ -1816,9 +1880,12 @@ class StagingArea(BaseStagingArea): if name is None: name = "%s_size" % self._name - return gen_data_flow_ops.stage_size(name=name, shared_name=self._name, - dtypes=self._dtypes, capacity=self._capacity, - memory_limit=self._memory_limit) + return gen_data_flow_ops.stage_size( + name=name, + shared_name=self._name, + dtypes=self._dtypes, + capacity=self._capacity, + memory_limit=self._memory_limit) def clear(self, name=None): """Clears the staging area. @@ -1832,14 +1899,16 @@ class StagingArea(BaseStagingArea): if name is None: name = "%s_clear" % self._name - return gen_data_flow_ops.stage_clear(name=name, shared_name=self._name, - dtypes=self._dtypes, capacity=self._capacity, - memory_limit=self._memory_limit) + return gen_data_flow_ops.stage_clear( + name=name, + shared_name=self._name, + dtypes=self._dtypes, + capacity=self._capacity, + memory_limit=self._memory_limit) + class MapStagingArea(BaseStagingArea): - """ - A `MapStagingArea` is a TensorFlow data structure that stores tensors across - multiple steps, and exposes operations that can put and get tensors. + """A `MapStagingArea` is a TensorFlow data structure that stores tensors across multiple steps, and exposes operations that can put and get tensors. Each `MapStagingArea` element is a (key, value) pair. Only int64 keys are supported, other types should be @@ -1852,7 +1921,8 @@ class MapStagingArea(BaseStagingArea): It supports multiple concurrent producers and consumers; and provides exactly-once delivery. - Each value tuple of a `MapStagingArea` is a fixed-length tuple of tensors whose + Each value tuple of a `MapStagingArea` is a fixed-length tuple of tensors + whose dtypes are described by `dtypes`, and whose shapes are optionally described by the `shapes` argument. @@ -1896,10 +1966,16 @@ class MapStagingArea(BaseStagingArea): associated with it are removed. """ - def __init__(self, dtypes, shapes=None, names=None, shared_name=None, - ordered=False, capacity=0, memory_limit=0): - """ - Args: + def __init__(self, + dtypes, + shapes=None, + names=None, + shared_name=None, + ordered=False, + capacity=0, + memory_limit=0): + """Args: + dtypes: A list of types. The length of dtypes must equal the number of tensors in each element. capacity: (Optional.) Maximum number of elements. @@ -1925,9 +2001,8 @@ class MapStagingArea(BaseStagingArea): """ - super(MapStagingArea, self).__init__(dtypes, shapes, - names, shared_name, - capacity, memory_limit) + super(MapStagingArea, self).__init__(dtypes, shapes, names, shared_name, + capacity, memory_limit) # Defer to different methods depending if the map is ordered self._ordered = ordered @@ -1950,8 +2025,7 @@ class MapStagingArea(BaseStagingArea): self._clear_fn = gen_data_flow_ops.map_clear def put(self, key, vals, indices=None, name=None): - """ - Create an op that stores the (key, vals) pair in the staging area. + """Create an op that stores the (key, vals) pair in the staging area. Incomplete puts are possible, preferably using a dictionary for vals as the appropriate dtypes and shapes can be inferred from the value names @@ -1973,7 +2047,8 @@ class MapStagingArea(BaseStagingArea): The created op Raises: - ValueError: If the number or type of inputs don't match the staging area. + ValueError: If the number or type of inputs don't match the staging + area. """ with ops.name_scope(name, "%s_put" % self._name, @@ -1982,10 +2057,15 @@ class MapStagingArea(BaseStagingArea): vals, indices = self._check_put_dtypes(vals, indices) with ops.colocate_with(self._coloc_op): - op = self._put_fn(key, indices, vals, dtypes=self._dtypes, - shared_name=self._name, name=scope, - capacity=self._capacity, - memory_limit=self._memory_limit) + op = self._put_fn( + key, + indices, + vals, + dtypes=self._dtypes, + shared_name=self._name, + name=scope, + capacity=self._capacity, + memory_limit=self._memory_limit) return op def _get_indices_and_dtypes(self, indices=None): @@ -2001,13 +2081,13 @@ class MapStagingArea(BaseStagingArea): if all(isinstance(i, str) for i in indices): if self._names is None: raise ValueError("String indices provided '%s', but this Staging Area " - "was not created with names." % indices) + "was not created with names." % indices) try: indices = [self._names.index(n) for n in indices] except ValueError: raise ValueError("Named index '%s' not in " - "Staging Area names '%s'" % (n, self._names)) + "Staging Area names '%s'" % (n, self._names)) elif all(isinstance(i, int) for i in indices): pass else: @@ -2018,10 +2098,8 @@ class MapStagingArea(BaseStagingArea): return indices, dtypes - def peek(self, key, indices=None, name=None): - """ - Peeks at staging area data associated with the key. + """Peeks at staging area data associated with the key. If the key is not in the staging area, it will block until the associated (key, value) is inserted. @@ -2044,22 +2122,22 @@ class MapStagingArea(BaseStagingArea): indices, dtypes = self._get_indices_and_dtypes(indices) with ops.colocate_with(self._coloc_op): - result = self._peek_fn(key, shared_name=self._name, - indices=indices, - dtypes=dtypes, - name=name, - capacity=self._capacity, - memory_limit=self._memory_limit) + result = self._peek_fn( + key, + shared_name=self._name, + indices=indices, + dtypes=dtypes, + name=name, + capacity=self._capacity, + memory_limit=self._memory_limit) return self._get_return_value(result, indices) def get(self, key=None, indices=None, name=None): - """ - If the key is provided, the associated (key, value) - is returned from the staging area. If the key is not - in the staging area, this method will block until - the associated (key, value) is inserted. + """If the key is provided, the associated (key, value) is returned from the staging area. + If the key is not in the staging area, this method will block until + the associated (key, value) is inserted. If no key is provided and the staging area is ordered, the (key, value) with the smallest key will be returned. Otherwise, a random (key, value) will be returned. @@ -2084,12 +2162,10 @@ class MapStagingArea(BaseStagingArea): return self._pop(key, indices=indices, name=name) def _pop(self, key, indices=None, name=None): - """ - Remove and return the associated (key, value) - is returned from the staging area. If the key is not - in the staging area, this method will block until - the associated (key, value) is inserted. + """Remove and return the associated (key, value) is returned from the staging area. + If the key is not in the staging area, this method will block until + the associated (key, value) is inserted. Args: key: Key associated with the required data indices: Partial list of tensors to retrieve (optional). @@ -2107,21 +2183,21 @@ class MapStagingArea(BaseStagingArea): indices, dtypes = self._get_indices_and_dtypes(indices) with ops.colocate_with(self._coloc_op): - result = self._pop_fn(key, shared_name=self._name, - indices=indices, - dtypes=dtypes, - name=name, - capacity=self._capacity, - memory_limit=self._memory_limit) + result = self._pop_fn( + key, + shared_name=self._name, + indices=indices, + dtypes=dtypes, + name=name, + capacity=self._capacity, + memory_limit=self._memory_limit) return key, self._get_return_value(result, indices) def _popitem(self, indices=None, name=None): - """ - If the staging area is ordered, - the (key, value) with the smallest key will be returned. - Otherwise, a random (key, value) will be returned. + """If the staging area is ordered, the (key, value) with the smallest key will be returned. + Otherwise, a random (key, value) will be returned. If the staging area is empty when this operation executes, it will block until there is an element to dequeue. @@ -2142,12 +2218,13 @@ class MapStagingArea(BaseStagingArea): indices, dtypes = self._get_indices_and_dtypes(indices) with ops.colocate_with(self._coloc_op): - key, result = self._popitem_fn(shared_name=self._name, - indices=indices, - dtypes=dtypes, - name=name, - capacity=self._capacity, - memory_limit=self._memory_limit) + key, result = self._popitem_fn( + shared_name=self._name, + indices=indices, + dtypes=dtypes, + name=name, + capacity=self._capacity, + memory_limit=self._memory_limit) # Separate keys and results out from # underlying namedtuple @@ -2157,8 +2234,7 @@ class MapStagingArea(BaseStagingArea): return key, result def size(self, name=None): - """ - Returns the number of elements in the staging area. + """Returns the number of elements in the staging area. Args: name: A name for the operation (optional) @@ -2169,14 +2245,15 @@ class MapStagingArea(BaseStagingArea): if name is None: name = "%s_size" % self._name - return self._size_fn(shared_name=self._name, - name=name, dtypes=self._dtypes, - capacity=self._capacity, - memory_limit=self._memory_limit) + return self._size_fn( + shared_name=self._name, + name=name, + dtypes=self._dtypes, + capacity=self._capacity, + memory_limit=self._memory_limit) def incomplete_size(self, name=None): - """ - Returns the number of incomplete elements in the staging area. + """Returns the number of incomplete elements in the staging area. Args: name: A name for the operation (optional) @@ -2187,16 +2264,15 @@ class MapStagingArea(BaseStagingArea): if name is None: name = "%s_incomplete_size" % self._name - return self._incomplete_size_fn(shared_name=self._name, - name=name, dtypes=self._dtypes, - capacity=self._capacity, - memory_limit=self._memory_limit) - - + return self._incomplete_size_fn( + shared_name=self._name, + name=name, + dtypes=self._dtypes, + capacity=self._capacity, + memory_limit=self._memory_limit) def clear(self, name=None): - """ - Clears the staging area. + """Clears the staging area. Args: name: A name for the operation (optional) @@ -2207,10 +2283,12 @@ class MapStagingArea(BaseStagingArea): if name is None: name = "%s_clear" % self._name - return self._clear_fn(shared_name=self._name, - name=name, dtypes=self._dtypes, - capacity=self._capacity, - memory_limit=self._memory_limit) + return self._clear_fn( + shared_name=self._name, + name=name, + dtypes=self._dtypes, + capacity=self._capacity, + memory_limit=self._memory_limit) class RecordInput(object): diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 5d4b9ecd8b..314726ede6 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -52,7 +52,6 @@ from tensorflow.python.ops import tensor_array_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export - # Warn the user if we convert a sparse representation to dense with at # least this number of elements. _LARGE_SPARSE_NUM_ELEMENTS = 100000000 @@ -235,9 +234,10 @@ def _DefaultGradYs(grad_ys, ys, colocate_gradients_with_ops): raise TypeError( "Gradients of complex tensors must set grad_ys (y.dtype = %r)" % y.dtype) - new_grad_ys.append(array_ops.fill( - array_ops.shape(y), constant_op.constant( - 1, dtype=y.dtype, name="grad_ys_%d" % i))) + new_grad_ys.append( + array_ops.fill( + array_ops.shape(y), + constant_op.constant(1, dtype=y.dtype, name="grad_ys_%d" % i))) continue if y.dtype.is_floating or y.dtype.is_integer: if not grad_y.dtype.is_floating and not grad_y.dtype.is_integer: @@ -492,11 +492,12 @@ def gradients(ys, name, "gradients", list(ys) + list(xs) + list(stop_gradients) + list(grad_ys)) as grad_scope: ys = ops.convert_n_to_tensor_or_indexed_slices(ys, name="y") - xs = [x.handle if isinstance(x, resource_variable_ops.ResourceVariable) - else x - for x in xs] - xs = ops.internal_convert_n_to_tensor_or_indexed_slices(xs, name="x", - as_ref=True) + xs = [ + x.handle if isinstance(x, resource_variable_ops.ResourceVariable) else x + for x in xs + ] + xs = ops.internal_convert_n_to_tensor_or_indexed_slices( + xs, name="x", as_ref=True) grad_ys = _DefaultGradYs(grad_ys, ys, colocate_gradients_with_ops) # The approach we take here is as follows: Create a list of all ops in the @@ -513,9 +514,8 @@ def gradients(ys, to_ops = [t.op for t in ys] from_ops = [t.op for t in xs] stop_gradient_ops = [t.op for t in stop_gradients] - pending_count, loop_state = _PendingCount(ops.get_default_graph(), to_ops, - from_ops, - colocate_gradients_with_ops) + pending_count, loop_state = _PendingCount( + ops.get_default_graph(), to_ops, from_ops, colocate_gradients_with_ops) # Iterate over the collected ops. # @@ -588,9 +588,8 @@ def gradients(ys, # output, it means that the cost does not depend on output[i], # therefore dC/doutput[i] is 0. for i, out_grad in enumerate(out_grads): - if (not isinstance(out_grad, ops.Tensor) and - not out_grad) and ((not grad_fn and is_func_call) or - _IsTrainable(op.outputs[i])): + if (not isinstance(out_grad, ops.Tensor) and not out_grad) and ( + (not grad_fn and is_func_call) or _IsTrainable(op.outputs[i])): # Only trainable outputs or outputs for a function call that # will use SymbolicGradient get a zero gradient. Gradient # functions should ignore the gradient for other outputs. @@ -607,17 +606,17 @@ def gradients(ys, if grad_fn: # If grad_fn was found, do not use SymbolicGradient even for # functions. - in_grads = _MaybeCompile( - grad_scope, op, func_call, lambda: grad_fn(op, *out_grads)) + in_grads = _MaybeCompile(grad_scope, op, func_call, + lambda: grad_fn(op, *out_grads)) else: # For function call ops, we add a 'SymbolicGradient' # node to the graph to compute gradients. - in_grads = _MaybeCompile( - grad_scope, op, func_call, lambda: _SymGrad(op, out_grads)) + in_grads = _MaybeCompile(grad_scope, op, func_call, + lambda: _SymGrad(op, out_grads)) in_grads = _AsList(in_grads) _VerifyGeneratedGradients(in_grads, op) - if gate_gradients and len( - [x for x in in_grads if x is not None]) > 1: + if gate_gradients and len([x for x in in_grads + if x is not None]) > 1: with ops.device(None): with ops.colocate_with(None, ignore_existing=True): in_grads = control_flow_ops.tuple(in_grads) @@ -637,8 +636,8 @@ def gradients(ys, "Incompatible shapes between op input and calculated " "input gradient. Forward operation: %s. Input index: %d. " "Original input shape: %s. " - "Calculated input gradient shape: %s" - % (op.name, i, t_in.shape, in_grad.shape)) + "Calculated input gradient shape: %s" % + (op.name, i, t_in.shape, in_grad.shape)) _SetGrad(grads, t_in, in_grad) if loop_state: loop_state.ExitGradWhileContext(op, before=False) @@ -670,8 +669,8 @@ def _UpdatePendingAndEnqueueReady(grads, op, queue, pending_count, loop_state): pending_count[x.op._id] -= 1 ready = (pending_count[x.op._id] == 0) if loop_state and not ready: - ready = (pending_count[x.op._id] > 0 and - control_flow_util.IsLoopSwitch(x.op)) + ready = ( + pending_count[x.op._id] > 0 and control_flow_util.IsLoopSwitch(x.op)) # pylint: enable=protected-access if ready: if control_flow_util.IsLoopExit(x.op): @@ -725,8 +724,8 @@ def _GetGrad(grads, t): if not op_grads: return None t_grad = op_grads[t.value_index] - assert not isinstance(t_grad, list), ( - "gradients list should have been aggregated by now.") + assert not isinstance( + t_grad, list), ("gradients list should have been aggregated by now.") return t_grad @@ -745,9 +744,8 @@ def _HandleNestedIndexedSlices(grad): else: assert isinstance(grad.values, ops.IndexedSlices) g = _HandleNestedIndexedSlices(grad.values) - return ops.IndexedSlices(g.values, - array_ops.gather(grad.indices, g.indices), - g.dense_shape) + return ops.IndexedSlices(g.values, array_ops.gather( + grad.indices, g.indices), g.dense_shape) def _AccumulatorShape(inputs): @@ -849,8 +847,8 @@ def _AggregatedGrads(grads, op, loop_state, aggregation_method=None): AggregationMethod.ADD_N, AggregationMethod.EXPERIMENTAL_TREE, AggregationMethod.EXPERIMENTAL_ACCUMULATE_N ]: - raise ValueError("Invalid aggregation_method specified %s." % - aggregation_method) + raise ValueError( + "Invalid aggregation_method specified %s." % aggregation_method) out_grads = _GetGrads(grads, op) for i, out_grad in enumerate(out_grads): if loop_state: @@ -859,7 +857,8 @@ def _AggregatedGrads(grads, op, loop_state, aggregation_method=None): continue # Grads have to be Tensors or IndexedSlices if (isinstance(out_grad, collections.Sequence) and not all([ - isinstance(g, (ops.Tensor, ops.IndexedSlices)) for g in out_grad + isinstance(g, (ops.Tensor, ops.IndexedSlices)) + for g in out_grad if g is not None ])): raise TypeError("gradients have to be either all Tensors " @@ -903,8 +902,8 @@ def _AggregatedGrads(grads, op, loop_state, aggregation_method=None): else: used = "add_n" out_grads[i] = _MultiDeviceAddN(out_grad) - logging.vlog(2, " _AggregatedGrads %d x %s using %s", - len(out_grad), tensor_shape, used) + logging.vlog(2, " _AggregatedGrads %d x %s using %s", len(out_grad), + tensor_shape, used) else: out_grad = math_ops._as_indexed_slices_list( [g for g in out_grad if g is not None]) @@ -967,7 +966,8 @@ def _hessian_vector_product(ys, xs, v): assert len(grads) == length elemwise_products = [ math_ops.multiply(grad_elem, array_ops.stop_gradient(v_elem)) - for grad_elem, v_elem in zip(grads, v) if grad_elem is not None + for grad_elem, v_elem in zip(grads, v) + if grad_elem is not None ] # Second backprop @@ -975,8 +975,12 @@ def _hessian_vector_product(ys, xs, v): @tf_export("hessians") -def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False, - gate_gradients=False, aggregation_method=None): +def hessians(ys, + xs, + name="hessians", + colocate_gradients_with_ops=False, + gate_gradients=False, + aggregation_method=None): """Constructs the Hessian of sum of `ys` with respect to `x` in `xs`. `hessians()` adds ops to the graph to output the Hessian matrix of `ys` @@ -1004,9 +1008,9 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False, """ xs = _AsList(xs) kwargs = { - 'colocate_gradients_with_ops': colocate_gradients_with_ops, - 'gate_gradients': gate_gradients, - 'aggregation_method': aggregation_method + "colocate_gradients_with_ops": colocate_gradients_with_ops, + "gate_gradients": gate_gradients, + "aggregation_method": aggregation_method } # Compute first-order derivatives and iterate for each x in xs. hessians = [] @@ -1031,8 +1035,7 @@ def hessians(ys, xs, name="hessians", colocate_gradients_with_ops=False, ) _shape = array_ops.shape(x) - _reshaped_hessian = array_ops.reshape( - hessian.stack(), array_ops.concat((_shape, _shape), 0) - ) + _reshaped_hessian = array_ops.reshape(hessian.stack(), + array_ops.concat((_shape, _shape), 0)) hessians.append(_reshaped_hessian) return hessians diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py index cfff73774b..5e6cafd6aa 100644 --- a/tensorflow/python/ops/nn_grad.py +++ b/tensorflow/python/ops/nn_grad.py @@ -89,52 +89,63 @@ def _Conv2DBackpropFilterGrad(op, grad): @ops.RegisterGradient("Conv3D") def _Conv3DGrad(op, grad): data_format = op.get_attr("data_format") - return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]), - op.inputs[1], - grad, - strides=op.get_attr("strides"), - padding=op.get_attr("padding"), - data_format=data_format), - nn_ops.conv3d_backprop_filter_v2(op.inputs[0], - array_ops.shape(op.inputs[1]), - grad, - strides=op.get_attr("strides"), - padding=op.get_attr("padding"), - data_format=data_format)] + return [ + nn_ops.conv3d_backprop_input_v2( + array_ops.shape(op.inputs[0]), + op.inputs[1], + grad, + strides=op.get_attr("strides"), + padding=op.get_attr("padding"), + data_format=data_format), + nn_ops.conv3d_backprop_filter_v2( + op.inputs[0], + array_ops.shape(op.inputs[1]), + grad, + strides=op.get_attr("strides"), + padding=op.get_attr("padding"), + data_format=data_format) + ] @ops.RegisterGradient("Conv3DBackpropInputV2") def _Conv3DBackpropInputGrad(op, grad): data_format = op.get_attr("data_format") - return [None, - nn_ops.conv3d_backprop_filter_v2(grad, - array_ops.shape(op.inputs[1]), - op.inputs[2], - strides=op.get_attr("strides"), - padding=op.get_attr("padding"), - data_format=data_format), - nn_ops.conv3d(grad, - op.inputs[1], - strides=op.get_attr("strides"), - padding=op.get_attr("padding"), - data_format=data_format)] + return [ + None, + nn_ops.conv3d_backprop_filter_v2( + grad, + array_ops.shape(op.inputs[1]), + op.inputs[2], + strides=op.get_attr("strides"), + padding=op.get_attr("padding"), + data_format=data_format), + nn_ops.conv3d( + grad, + op.inputs[1], + strides=op.get_attr("strides"), + padding=op.get_attr("padding"), + data_format=data_format) + ] @ops.RegisterGradient("Conv3DBackpropFilterV2") def _Conv3DBackpropFilterGrad(op, grad): data_format = op.get_attr("data_format") - return [nn_ops.conv3d_backprop_input_v2(array_ops.shape(op.inputs[0]), - grad, - op.inputs[2], - strides=op.get_attr("strides"), - padding=op.get_attr("padding"), - data_format=data_format), - None, - nn_ops.conv3d(op.inputs[0], - grad, - strides=op.get_attr("strides"), - padding=op.get_attr("padding"), - data_format=data_format)] + return [ + nn_ops.conv3d_backprop_input_v2( + array_ops.shape(op.inputs[0]), + grad, + op.inputs[2], + strides=op.get_attr("strides"), + padding=op.get_attr("padding"), + data_format=data_format), None, + nn_ops.conv3d( + op.inputs[0], + grad, + strides=op.get_attr("strides"), + padding=op.get_attr("padding"), + data_format=data_format) + ] @ops.RegisterGradient("AvgPool3D") @@ -150,12 +161,13 @@ def _AvgPool3DGrad(op, grad): @ops.RegisterGradient("AvgPool3DGrad") def _AvgPool3DGradGrad(op, grad): - return (array_ops.stop_gradient(op.inputs[0]), gen_nn_ops.avg_pool3d( - grad, - op.get_attr("ksize"), - op.get_attr("strides"), - op.get_attr("padding"), - data_format=op.get_attr("data_format"))) + return (array_ops.stop_gradient(op.inputs[0]), + gen_nn_ops.avg_pool3d( + grad, + op.get_attr("ksize"), + op.get_attr("strides"), + op.get_attr("padding"), + data_format=op.get_attr("data_format"))) @ops.RegisterGradient("MaxPool3D") @@ -173,9 +185,9 @@ def _MaxPool3DGrad(op, grad): @ops.RegisterGradient("MaxPool3DGrad") def _MaxPool3DGradGrad(op, grad): return (array_ops.zeros( - shape=array_ops.shape(op.inputs[0]), - dtype=op.inputs[0].dtype), array_ops.zeros( - shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), + shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), + array_ops.zeros( + shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), gen_nn_ops._max_pool3d_grad_grad( op.inputs[0], op.inputs[1], @@ -189,9 +201,9 @@ def _MaxPool3DGradGrad(op, grad): @ops.RegisterGradient("MaxPool3DGradGrad") def _MaxPool3DGradGradGrad(op, grad): return (array_ops.zeros( - shape=array_ops.shape(op.inputs[0]), - dtype=op.inputs[0].dtype), array_ops.zeros( - shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), + shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), + array_ops.zeros( + shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), gen_nn_ops._max_pool3d_grad( op.inputs[0], op.inputs[1], @@ -272,8 +284,9 @@ def _BiasAddGrad(op, received_grad): data_format = op.get_attr("data_format") except ValueError: data_format = None - return (received_grad, gen_nn_ops.bias_add_grad(out_backprop=received_grad, - data_format=data_format)) + return (received_grad, + gen_nn_ops.bias_add_grad( + out_backprop=received_grad, data_format=data_format)) @ops.RegisterGradient("BiasAddGrad") @@ -346,10 +359,9 @@ def _ReluGrad(op, grad): def _EluGradGrad(op, grad): elu_x = op.inputs[1] return (gen_nn_ops._elu_grad(grad, op.outputs[0]), - array_ops.where(elu_x < 0, - grad * op.inputs[0], - array_ops.zeros(shape=array_ops.shape(elu_x), - dtype=elu_x.dtype))) + array_ops.where(elu_x < 0, grad * op.inputs[0], + array_ops.zeros( + shape=array_ops.shape(elu_x), dtype=elu_x.dtype))) @ops.RegisterGradient("SeluGrad") @@ -357,9 +369,11 @@ def _SeluGradGrad(op, grad): x = op.inputs[1] scale_alpha = 1.7580993408473768599402175208123 return (gen_nn_ops._elu_grad(grad, op.outputs[0]), - array_ops.where( - x < 0., gen_nn_ops._elu_grad(grad, op.outputs[0] + scale_alpha), - array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype))) + array_ops.where(x < 0., + gen_nn_ops._elu_grad(grad, + op.outputs[0] + scale_alpha), + array_ops.zeros( + shape=array_ops.shape(x), dtype=x.dtype))) @ops.RegisterGradient("Relu6") @@ -370,8 +384,8 @@ def _Relu6Grad(op, grad): @ops.RegisterGradient("Relu6Grad") def _Relu6GradGrad(op, grad): x = op.inputs[1] - return (gen_nn_ops._relu6_grad(grad, x), array_ops.zeros( - shape=array_ops.shape(x), dtype=x.dtype)) + return (gen_nn_ops._relu6_grad(grad, x), + array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)) @ops.RegisterGradient("Elu") @@ -410,8 +424,8 @@ def _SoftsignGrad(op, grad): @ops.RegisterGradient("ReluGrad") def _ReluGradGrad(op, grad): x = op.inputs[1] - return (gen_nn_ops._relu_grad(grad, x), array_ops.zeros( - shape=array_ops.shape(x), dtype=x.dtype)) + return (gen_nn_ops._relu_grad(grad, x), + array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype)) def _BroadcastMul(vec, mat): @@ -455,8 +469,8 @@ def _SoftmaxCrossEntropyWithLogitsGrad(op, grad_loss, grad_grad): softmax = nn_ops.softmax(logits) grad += ((grad_grad - array_ops.squeeze( - math_ops.matmul(grad_grad[:, None, :], - softmax[:, :, None]), axis=1)) * softmax) + math_ops.matmul(grad_grad[:, None, :], softmax[:, :, None]), axis=1)) * + softmax) return grad, _BroadcastMul(grad_loss, -nn_ops.log_softmax(logits)) @@ -473,7 +487,8 @@ def _SparseSoftmaxCrossEntropyWithLogitsGrad(op, grad_0, _): # so we make sure we prevent silently incorrect results by raising # an error if the second derivative is requested via prevent_gradient. sparse_softmax_grad_without_gradient = array_ops.prevent_gradient( - op.outputs[1], message="Currently there is no way to take the second " + op.outputs[1], + message="Currently there is no way to take the second " "derivative of sparse_softmax_cross_entropy_with_logits due to the fused " "implementation's interaction with tf.gradients()") return _BroadcastMul(grad_0, sparse_softmax_grad_without_gradient), None @@ -531,14 +546,16 @@ def _DepthwiseConv2dNativeGrad(op, grad): @ops.RegisterGradient("Dilation2D") def _Dilation2DGrad(op, grad): - return [nn_ops.dilation2d_backprop_input(op.inputs[0], op.inputs[1], grad, - op.get_attr("strides"), - op.get_attr("rates"), - op.get_attr("padding")), - nn_ops.dilation2d_backprop_filter(op.inputs[0], op.inputs[1], grad, - op.get_attr("strides"), - op.get_attr("rates"), - op.get_attr("padding"))] + return [ + nn_ops.dilation2d_backprop_input(op.inputs[0], op.inputs[1], grad, + op.get_attr("strides"), + op.get_attr("rates"), + op.get_attr("padding")), + nn_ops.dilation2d_backprop_filter(op.inputs[0], op.inputs[1], grad, + op.get_attr("strides"), + op.get_attr("rates"), + op.get_attr("padding")) + ] @ops.RegisterGradient("LRN") @@ -547,8 +564,10 @@ def _LRNGrad(op, grad): bias = op.get_attr("bias") alpha = op.get_attr("alpha") beta = op.get_attr("beta") - return [gen_nn_ops._lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius, - bias, alpha, beta)] + return [ + gen_nn_ops._lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius, + bias, alpha, beta) + ] @ops.RegisterGradient("AvgPool") @@ -564,54 +583,58 @@ def _AvgPoolGrad(op, grad): @ops.RegisterGradient("AvgPoolGrad") def _AvgPoolGradGrad(op, grad): - return (array_ops.stop_gradient(op.inputs[0]), gen_nn_ops._avg_pool( - grad, - op.get_attr("ksize"), - op.get_attr("strides"), - op.get_attr("padding"), - data_format=op.get_attr("data_format"))) + return (array_ops.stop_gradient(op.inputs[0]), + gen_nn_ops._avg_pool( + grad, + op.get_attr("ksize"), + op.get_attr("strides"), + op.get_attr("padding"), + data_format=op.get_attr("data_format"))) @ops.RegisterGradient("MaxPool") def _MaxPoolGrad(op, grad): - return gen_nn_ops._max_pool_grad(op.inputs[0], - op.outputs[0], - grad, - op.get_attr("ksize"), - op.get_attr("strides"), - padding=op.get_attr("padding"), - data_format=op.get_attr("data_format")) + return gen_nn_ops._max_pool_grad( + op.inputs[0], + op.outputs[0], + grad, + op.get_attr("ksize"), + op.get_attr("strides"), + padding=op.get_attr("padding"), + data_format=op.get_attr("data_format")) @ops.RegisterGradient("MaxPoolV2") def _MaxPoolGradV2(op, grad): ksize = op.inputs[1] strides = op.inputs[2] - return gen_nn_ops.max_pool_grad_v2(op.inputs[0], - op.outputs[0], - grad, - ksize, - strides, - padding=op.get_attr("padding"), - data_format=op.get_attr("data_format")), None, None + return gen_nn_ops.max_pool_grad_v2( + op.inputs[0], + op.outputs[0], + grad, + ksize, + strides, + padding=op.get_attr("padding"), + data_format=op.get_attr("data_format")), None, None @ops.RegisterGradient("MaxPoolWithArgmax") def _MaxPoolGradWithArgmax(op, grad, unused_argmax_grad): - return gen_nn_ops._max_pool_grad_with_argmax(op.inputs[0], - grad, - op.outputs[1], - op.get_attr("ksize"), - op.get_attr("strides"), - padding=op.get_attr("padding")) + return gen_nn_ops._max_pool_grad_with_argmax( + op.inputs[0], + grad, + op.outputs[1], + op.get_attr("ksize"), + op.get_attr("strides"), + padding=op.get_attr("padding")) @ops.RegisterGradient("MaxPoolGrad") def _MaxPoolGradGrad(op, grad): return (array_ops.zeros( - shape=array_ops.shape(op.inputs[0]), - dtype=op.inputs[0].dtype), array_ops.zeros( - shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), + shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), + array_ops.zeros( + shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), gen_nn_ops._max_pool_grad_grad( op.inputs[0], op.inputs[1], @@ -627,9 +650,9 @@ def _MaxPoolGradGradV2(op, grad): ksize = op.inputs[3] strides = op.inputs[4] return (array_ops.zeros( - shape=array_ops.shape(op.inputs[0]), - dtype=op.inputs[0].dtype), array_ops.zeros( - shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), + shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), + array_ops.zeros( + shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), gen_nn_ops.max_pool_grad_grad_v2( op.inputs[0], op.inputs[1], @@ -643,9 +666,9 @@ def _MaxPoolGradGradV2(op, grad): @ops.RegisterGradient("MaxPoolGradGrad") def _MaxPoolGradGradGrad(op, grad): return (array_ops.zeros( - shape=array_ops.shape(op.inputs[0]), - dtype=op.inputs[0].dtype), array_ops.zeros( - shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), + shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype), + array_ops.zeros( + shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype), gen_nn_ops._max_pool_grad( op.inputs[0], op.inputs[1], @@ -674,10 +697,9 @@ def _FractionalMaxPoolGrad(op, grad_0, unused_grad_1, unused_grad_2): Input backprop for FractionalMaxPool op. """ # pylint: disable=protected-access - return gen_nn_ops._fractional_max_pool_grad(op.inputs[0], op.outputs[0], - grad_0, op.outputs[1], - op.outputs[2], - op.get_attr("overlapping")) + return gen_nn_ops._fractional_max_pool_grad( + op.inputs[0], op.outputs[0], grad_0, op.outputs[1], op.outputs[2], + op.get_attr("overlapping")) @ops.RegisterGradient("FractionalAvgPool") @@ -761,8 +783,9 @@ def _BaseFusedBatchNormGrad(op, use_v2, *grad): epsilon = op.get_attr("epsilon") data_format = op.get_attr("data_format") is_training = op.get_attr("is_training") - grad_fun = (gen_nn_ops.fused_batch_norm_grad_v2 if use_v2 - else gen_nn_ops.fused_batch_norm_grad) + grad_fun = ( + gen_nn_ops.fused_batch_norm_grad_v2 + if use_v2 else gen_nn_ops.fused_batch_norm_grad) if is_training: return grad_fun( grad_y, @@ -786,7 +809,7 @@ def _BaseFusedBatchNormGrad(op, use_v2, *grad): pop_mean, pop_var, epsilon=epsilon, - data_format='NHWC', + data_format="NHWC", is_training=is_training) if data_format == b"NCHW": dx = array_ops.transpose(dx, [0, 3, 1, 2]) @@ -803,18 +826,28 @@ def _FusedBatchNormV2Grad(op, *grad): return _BaseFusedBatchNormGrad(op, True, *grad) -def _BatchNormGrad(grad_y, x, scale, pop_mean, pop_var, epsilon, data_format, is_training=True): +def _BatchNormGrad(grad_y, + x, + scale, + pop_mean, + pop_var, + epsilon, + data_format, + is_training=True): """Returns the gradients for the 3 inputs of BatchNorm. Args: grad_y: A `Tensor` of 4 dimensions for gradient for y. x: A `Tensor` of 4 dimensions for x. scale: A `Tensor` of 1 dimension for scaling. - pop_mean: A `Tensor` of 1 dimension for the population mean. Only used when is_training=False. - pop_var: A `Tensor` of 1 dimension for the population variance. Only used when is_training=False. + pop_mean: A `Tensor` of 1 dimension for the population mean. Only used when + is_training=False. + pop_var: A `Tensor` of 1 dimension for the population variance. Only used + when is_training=False. epsilon: A small float number added to the variance of x. data_format: The data format for input. Either b"NHWC" or b"NCHW". - is_training: A bool value to indicate the operation is for training (default) + is_training: A bool value to indicate the operation is for training + (default) or inference. Returns: @@ -900,7 +933,7 @@ def _FusedBatchNormGradGrad(op, *grad): grad_grad_scale = grad[1] grad_grad_offset = grad[2] grad_x, grad_scale, grad_offset = _BatchNormGrad( - grad_y, x, scale, pop_mean, pop_var, epsilon, data_format, is_training) + grad_y, x, scale, pop_mean, pop_var, epsilon, data_format, is_training) grad_initial = [grad_grad_x, grad_grad_scale, grad_grad_offset] grad_grad_y, grad_x, grad_scale = gradients_impl.gradients( [grad_x, grad_scale, grad_offset], [grad_y, x, scale], grad_initial) @@ -954,14 +987,15 @@ def _TopKGrad(op, grad, _): # Substitute grad to appropriate locations and fill the rest with zeros, # finally reshaping it to the original input shape. - return [array_ops.reshape( - sparse_ops.sparse_to_dense(ind, - array_ops.reshape( - math_ops.reduce_prod(in_shape), [1]), - array_ops.reshape(grad, [-1]), - validate_indices=False), - in_shape), array_ops.zeros( - [], dtype=dtypes.int32)] + return [ + array_ops.reshape( + sparse_ops.sparse_to_dense( + ind, + array_ops.reshape(math_ops.reduce_prod(in_shape), [1]), + array_ops.reshape(grad, [-1]), + validate_indices=False), in_shape), + array_ops.zeros([], dtype=dtypes.int32) + ] @ops.RegisterGradient("NthElement") @@ -983,11 +1017,9 @@ def _NthElementGrad(op, grad): # dimension. If there are multiple elements then the gradient will be # divided between them. indicators = math_ops.cast( - math_ops.equal(array_ops.expand_dims(output, -1), input), - grad.dtype) + math_ops.equal(array_ops.expand_dims(output, -1), input), grad.dtype) grad = array_ops.expand_dims(grad, -1) - num_selected = array_ops.expand_dims( - math_ops.reduce_sum(indicators, -1), -1) + num_selected = array_ops.expand_dims(math_ops.reduce_sum(indicators, -1), -1) return [math_ops.div(indicators, num_selected) * grad, None] diff --git a/tensorflow/python/ops/nn_grad_test.py b/tensorflow/python/ops/nn_grad_test.py index f7541c0e89..aa7539ae9f 100644 --- a/tensorflow/python/ops/nn_grad_test.py +++ b/tensorflow/python/ops/nn_grad_test.py @@ -30,17 +30,20 @@ from tensorflow.python.platform import test class Relu6OpTest(test.TestCase): + def testRelu6GradGrad(self): - inputs = constant_op.constant([[-2, -1, 1, 3], [5, 7, 8, 9]], - dtype=dtypes.float32) + inputs = constant_op.constant( + [[-2, -1, 1, 3], [5, 7, 8, 9]], dtype=dtypes.float32) x_init_value = np.array([[-3.5, -1.5, 2, 4], [4.5, 7.5, 8.5, 11]]) r = nn_ops.relu6(inputs) r_g = gradients_impl.gradients(r, inputs)[0] with self.test_session(): error = gradient_checker.compute_gradient_error( - inputs, inputs.get_shape().as_list(), - r_g, r_g.get_shape().as_list(), - x_init_value=x_init_value) + inputs, + inputs.get_shape().as_list(), + r_g, + r_g.get_shape().as_list(), + x_init_value=x_init_value) self.assertLess(error, 1e-4) diff --git a/tensorflow/python/ops/special_math_ops.py b/tensorflow/python/ops/special_math_ops.py index 1990087072..15127862a4 100644 --- a/tensorflow/python/ops/special_math_ops.py +++ b/tensorflow/python/ops/special_math_ops.py @@ -155,27 +155,24 @@ def einsum(equation, *inputs, **kwargs): indices in its subscript, or - the input shapes are inconsistent along a particular axis. """ - name = kwargs.pop("name", None) + name = kwargs.pop('name', None) if kwargs: - raise TypeError("invalid keyword arguments for this function: " + - ", ".join([format(key) - for key in sorted(list(kwargs.keys()))])) - with ops.name_scope(name, "einsum", [equation, inputs]) as name: + raise TypeError('invalid keyword arguments for this function: ' + ', '.join( + [format(key) for key in sorted(list(kwargs.keys()))])) + with ops.name_scope(name, 'einsum', [equation, inputs]) as name: if '...' in equation: raise ValueError('Subscripts with ellipses are not yet supported.') match = re.match('([a-z,]+)(->[a-z]*)?', equation) if not match: - raise ValueError( - 'Indices have incorrect format: %s' % equation - ) + raise ValueError('Indices have incorrect format: %s' % equation) inputs = list(inputs) input_axis_labels = match.group(1).split(',') if len(inputs) != len(input_axis_labels): - raise ValueError('Got %d arguments for equation "%s", expecting %d' % ( - len(inputs), equation, len(input_axis_labels))) + raise ValueError('Got %d arguments for equation "%s", expecting %d' % + (len(inputs), equation, len(input_axis_labels))) axis_labels = set(''.join(input_axis_labels)) if match.group(2): @@ -188,10 +185,8 @@ def einsum(equation, *inputs, **kwargs): for ax in axes_: counts[ax] += 1 - output_axis_labels = ''.join(sorted( - ax for ax in indices - if counts[ax] == 1 - )) + output_axis_labels = ''.join( + sorted(ax for ax in indices if counts[ax] == 1)) for a in axis_labels: input_count = sum(1 for s in input_axis_labels if a in s) @@ -203,22 +198,23 @@ def einsum(equation, *inputs, **kwargs): temp = inputs[0] temp_axis_labels = input_axis_labels[0] - for i in xrange(len(inputs)-1): - axes_to_sum = (set(temp_axis_labels) & set(input_axis_labels[i+1]) - - set(output_axis_labels)) - temp, temp_axis_labels = _einsum_reduction(temp, - temp_axis_labels, - inputs[i+1], - input_axis_labels[i+1], - axes_to_sum) + for i in xrange(len(inputs) - 1): + axes_to_sum = ( + set(temp_axis_labels) & + set(input_axis_labels[i + 1]) - set(output_axis_labels)) + temp, temp_axis_labels = _einsum_reduction( + temp, temp_axis_labels, inputs[i + 1], input_axis_labels[i + 1], + axes_to_sum) missing_indices = set(temp_axis_labels) - set(output_axis_labels) if missing_indices: - reduction_indices = [i for i, a in enumerate(temp_axis_labels) - if a not in output_axis_labels] + reduction_indices = [ + i for i, a in enumerate(temp_axis_labels) + if a not in output_axis_labels + ] temp = math_ops.reduce_sum(temp, reduction_indices=reduction_indices) - temp_axis_labels = ''.join(a for a in temp_axis_labels - if a in output_axis_labels) + temp_axis_labels = ''.join( + a for a in temp_axis_labels if a in output_axis_labels) if sorted(temp_axis_labels) != sorted(output_axis_labels): raise ValueError('Invalid equation: %s' % equation) @@ -296,8 +292,10 @@ def _einsum_reduction(t0, t0_axis_labels, t1, t1_axis_labels, axes_to_sum): return (1, a) axis_labels = [t0_axis_labels, t1_axis_labels] - sorted_axes = [sorted(sym_list, key=lambda a: sort_key(i, a)) - for i, sym_list in enumerate(axis_labels)] + sorted_axes = [ + sorted(sym_list, key=lambda a: sort_key(i, a)) + for i, sym_list in enumerate(axis_labels) + ] inputs = [t0, t1] for i, axes_str in enumerate(axis_labels): perm = [axes_str.find(a) for a in sorted_axes[i]] @@ -325,30 +323,30 @@ def _einsum_reduction(t0, t0_axis_labels, t1, t1_axis_labels, axes_to_sum): num_broadcast_elements_t0 = _total_size( t0_shape[len(preserved_axes):-len(axes_to_sum)]) num_summed_elements = _total_size(t0_shape[-len(axes_to_sum):]) - new_shape = (t0_shape[:len(preserved_axes)] - + [num_broadcast_elements_t0, num_summed_elements]) + new_shape = ( + t0_shape[:len(preserved_axes)] + + [num_broadcast_elements_t0, num_summed_elements]) t0 = _reshape_if_necessary(t0, new_shape) t1_shape = _get_shape(t1) num_broadcast_elements_t1 = _total_size( - t1_shape[len(preserved_axes)+len(axes_to_sum):]) - new_shape = (t1_shape[:len(preserved_axes)] - + [num_summed_elements, num_broadcast_elements_t1]) + t1_shape[len(preserved_axes) + len(axes_to_sum):]) + new_shape = ( + t1_shape[:len(preserved_axes)] + + [num_summed_elements, num_broadcast_elements_t1]) t1 = _reshape_if_necessary(t1, new_shape) product = math_ops.matmul(t0, t1) # Undo compaction of broadcast axes uncompacted_shape = ( - t0_shape[:len(preserved_axes)+len(broadcast_axes[0])] - + t1_shape[len(t1_shape)-len(broadcast_axes[1]):] - ) + t0_shape[:len(preserved_axes) + len(broadcast_axes[0])] + + t1_shape[len(t1_shape) - len(broadcast_axes[1]):]) product = _reshape_if_necessary(product, uncompacted_shape) product_axes = ( - sorted_axes[0][:len(preserved_axes)+len(broadcast_axes[0])] + - sorted_axes[1][len(sorted_axes[1])-len(broadcast_axes[1]):] - ) + sorted_axes[0][:len(preserved_axes) + len(broadcast_axes[0])] + + sorted_axes[1][len(sorted_axes[1]) - len(broadcast_axes[1]):]) return product, ''.join(product_axes) @@ -402,13 +400,11 @@ def _total_size(shape_values): def _exponential_space_einsum(equation, *inputs): """Fallback implementation that supports summing an index over > 2 inputs.""" if '...' in equation: - raise ValueError("Subscripts with ellipses are not yet supported.") + raise ValueError('Subscripts with ellipses are not yet supported.') match = re.match('([a-z,]+)(->[a-z]*)?', equation) if not match: - raise ValueError( - 'Indices have incorrect format: %s' % equation - ) + raise ValueError('Indices have incorrect format: %s' % equation) inputs = list(inputs) idx_in = match.group(1).split(',') @@ -425,21 +421,15 @@ def _exponential_space_einsum(equation, *inputs): for ax in axes_: counts[ax] += 1 - idx_out = ''.join(sorted( - ax for ax in indices - if counts[ax] == 1 - )) + idx_out = ''.join(sorted(ax for ax in indices if counts[ax] == 1)) if len(idx_in) != len(inputs): - raise ValueError( - 'Expected %d inputs but got %d' % (len(idx_in), len(inputs)) - ) + raise ValueError('Expected %d inputs but got %d' % (len(idx_in), + len(inputs))) missing_idx = set(idx_out).difference(idx_all) if missing_idx: - raise ValueError( - 'Unknown output axes: %s' % missing_idx - ) + raise ValueError('Unknown output axes: %s' % missing_idx) axis_order = {} for ax in indices: @@ -452,18 +442,17 @@ def _exponential_space_einsum(equation, *inputs): for i, (input_, axes_) in enumerate(zip(inputs, idx_in)): if input_.get_shape().ndims != len(axes_): raise ValueError( - 'Input %d with axes %s has incorrect' \ - ' number of dimensions (expected %d, got %d)' % ( - i, axes_, len(axes_), input_.get_shape().ndims - ) + 'Input %d with axes %s has incorrect' \ + ' number of dimensions (expected %d, got %d)' % ( + i, axes_, len(axes_), input_.get_shape().ndims + ) ) sorted_idx = sorted(axes_, key=axis_order.get) if len(set(axes_)) != len(axes_): raise ValueError( - 'Subscript not supported: an axis appears more than once: %s' % axes_ - ) + 'Subscript not supported: an axis appears more than once: %s' % axes_) if list(axes_) != sorted_idx: permuted = [axes_.find(ax) for ax in sorted_idx] @@ -487,16 +476,15 @@ def _exponential_space_einsum(equation, *inputs): dims.append(dim) if len(set(dims)) > 1: - raise ValueError( - 'Dimension mismatch on axis: %s' % ax - ) + raise ValueError('Dimension mismatch on axis: %s' % ax) if ax not in idx_out: reduction_idx.append(j) # reshape, multiply - expanded_inputs = [array_ops.reshape(input_, shape) - for input_, shape in zip(inputs, shapes)] + expanded_inputs = [ + array_ops.reshape(input_, shape) for input_, shape in zip(inputs, shapes) + ] expanded_output = 1 for input_ in expanded_inputs: expanded_output *= input_ diff --git a/tensorflow/python/ops/special_math_ops_test.py b/tensorflow/python/ops/special_math_ops_test.py index c1a66717d8..2c212f4548 100644 --- a/tensorflow/python/ops/special_math_ops_test.py +++ b/tensorflow/python/ops/special_math_ops_test.py @@ -39,8 +39,9 @@ class LBetaTest(test.TestCase): x_one_half = [2, 1.] with self.test_session(use_gpu=True): self.assertAllClose(1, math_ops.exp(special_math_ops.lbeta(x_one)).eval()) - self.assertAllClose( - 0.5, math_ops.exp(special_math_ops.lbeta(x_one_half)).eval()) + self.assertAllClose(0.5, + math_ops.exp( + special_math_ops.lbeta(x_one_half)).eval()) self.assertEqual([], special_math_ops.lbeta(x_one).get_shape()) def test_one_dimensional_arg_dynamic(self): @@ -70,8 +71,9 @@ class LBetaTest(test.TestCase): # Should evaluate to 1/2. x_one_half = [[2, 1.], [2, 1.]] with self.test_session(use_gpu=True): - self.assertAllClose( - [0.5, 0.5], math_ops.exp(special_math_ops.lbeta(x_one_half)).eval()) + self.assertAllClose([0.5, 0.5], + math_ops.exp( + special_math_ops.lbeta(x_one_half)).eval()) self.assertEqual((2,), special_math_ops.lbeta(x_one_half).get_shape()) def test_two_dimensional_arg_dynamic(self): @@ -86,10 +88,12 @@ class LBetaTest(test.TestCase): # Should evaluate to 1/2. x_one_half = [[2, 1.], [2, 1.]] with self.test_session(use_gpu=True): - self.assertAllClose( - [0.5, 0.5], math_ops.exp(special_math_ops.lbeta(x_one_half)).eval()) + self.assertAllClose([0.5, 0.5], + math_ops.exp( + special_math_ops.lbeta(x_one_half)).eval()) self.assertEqual( - (2,), array_ops.shape(special_math_ops.lbeta(x_one_half)).eval()) + (2,), + array_ops.shape(special_math_ops.lbeta(x_one_half)).eval()) self.assertEqual( tensor_shape.TensorShape([2]), special_math_ops.lbeta(x_one_half).get_shape()) @@ -97,8 +101,8 @@ class LBetaTest(test.TestCase): def test_complicated_shape(self): with self.test_session(use_gpu=True): x = ops.convert_to_tensor(np.random.rand(3, 2, 2)) - self.assertAllEqual( - (3, 2), array_ops.shape(special_math_ops.lbeta(x)).eval()) + self.assertAllEqual((3, 2), + array_ops.shape(special_math_ops.lbeta(x)).eval()) self.assertEqual( tensor_shape.TensorShape([3, 2]), special_math_ops.lbeta(x).get_shape()) @@ -155,7 +159,6 @@ class EinsumTest(test.TestCase): 'ijk->i', 'ijk->kji', 'ji,kj->ik', - 'ikl,kji->kl', 'klj,lki->ij', 'ijk,ilj->kli', @@ -164,7 +167,6 @@ class EinsumTest(test.TestCase): 'i,ijk,j->k', 'ij,ij,jk,kl->il', 'ij,kj,il,jm->ml', - 'a,ab,abc->abc', 'a,b,ab->ab', 'ab,ab,c->', @@ -173,25 +175,21 @@ class EinsumTest(test.TestCase): 'ab,ab,cd,cd->ac', 'ab,ab,cd,cd->cd', 'ab,ab,cd,cd,ef,ef->', - 'ab,cd,ef->abcdef', 'ab,cd,ef->acdf', 'ab,cd,de->abcde', 'ab,cd,de->be', 'ab,bcd,cd->abcd', 'ab,bcd,cd->abd', - 'eb,cb,fb->cef', 'abcd,ad', 'bd,db,eac->ace', 'ba,ac,da->bcd', - 'ab,ab', 'ab,ba', 'abc,abc', 'abc,bac', 'abc,cba', - 'dba,ead,cad->bce', 'aef,fbc,dca->bde', ] @@ -234,10 +232,8 @@ class EinsumTest(test.TestCase): def test_invalid(self): for axes in self.invalid_cases: inputs = [ - array_ops.placeholder( - dtypes.float32, shape=(3, 4)), - array_ops.placeholder( - dtypes.float32, shape=(3, 4)), + array_ops.placeholder(dtypes.float32, shape=(3, 4)), + array_ops.placeholder(dtypes.float32, shape=(3, 4)), ] with self.assertRaises(ValueError): _ = special_math_ops.einsum(axes, *inputs) @@ -245,16 +241,22 @@ class EinsumTest(test.TestCase): def test_invalid_keyword_arguments(self): m0 = array_ops.placeholder(dtypes.int32, shape=(1, None)) m1 = array_ops.placeholder(dtypes.int32, shape=(None, 1)) - with self.assertRaisesRegexp(TypeError, + with self.assertRaisesRegexp( + TypeError, 'invalid keyword arguments for this function: invalid1, invalid2'): - _ = special_math_ops.einsum('ij,jk->ik', m0, m1, name="name", - invalid1="value1", invalid2="value2") + _ = special_math_ops.einsum( + 'ij,jk->ik', + m0, + m1, + name='name', + invalid1='value1', + invalid2='value2') def test_dim_mismatch(self): for axes, input_shapes in self.dim_mismatch_cases: inputs = [ - array_ops.placeholder( - dtypes.float32, shape=shape) for shape in input_shapes + array_ops.placeholder(dtypes.float32, shape=shape) + for shape in input_shapes ] with self.assertRaises(ValueError): _ = special_math_ops.einsum(axes, *inputs) @@ -291,8 +293,8 @@ class EinsumTest(test.TestCase): m0: [[1, 2, 3]], m1: [[2], [1], [1]], } - np.testing.assert_almost_equal( - [[7]], sess.run(out, feed_dict=feed_dict)) + np.testing.assert_almost_equal([[7]], sess.run( + out, feed_dict=feed_dict)) with ops.Graph().as_default(): m0 = array_ops.placeholder(dtypes.int32, shape=(None, 3)) @@ -312,11 +314,11 @@ class EinsumTest(test.TestCase): out = special_math_ops.einsum('ijk,kl->ijl', m0, m1) with session.Session() as sess: feed_dict = { - m0: [[[1,2]]], + m0: [[[1, 2]]], m1: [[3], [2]], } - np.testing.assert_almost_equal( - [[[7]]], sess.run(out, feed_dict=feed_dict)) + np.testing.assert_almost_equal([[[7]]], + sess.run(out, feed_dict=feed_dict)) with ops.Graph().as_default(): m0 = array_ops.placeholder(dtypes.int32, shape=(2, 1)) @@ -325,10 +327,10 @@ class EinsumTest(test.TestCase): with session.Session() as sess: feed_dict = { m0: [[3], [2]], - m1: [[[1,2]]], + m1: [[[1, 2]]], } - np.testing.assert_almost_equal( - [[[7]]], sess.run(out, feed_dict=feed_dict)) + np.testing.assert_almost_equal([[[7]]], + sess.run(out, feed_dict=feed_dict)) with ops.Graph().as_default(): m0 = array_ops.placeholder(dtypes.int32, shape=(None, None, 2)) @@ -339,8 +341,8 @@ class EinsumTest(test.TestCase): m0: [[[1, 2]]], m1: [3, 2], } - np.testing.assert_almost_equal( - [[7]], sess.run(out, feed_dict=feed_dict)) + np.testing.assert_almost_equal([[7]], sess.run( + out, feed_dict=feed_dict)) with ops.Graph().as_default(): m0 = array_ops.placeholder(dtypes.int32, shape=(None, 2, None, 2)) @@ -351,8 +353,8 @@ class EinsumTest(test.TestCase): m0: [[[[1, 2]], [[2, 1]]]], m1: [[3, 2]], } - np.testing.assert_almost_equal( - [[[7, 8]]], sess.run(out, feed_dict=feed_dict)) + np.testing.assert_almost_equal([[[7, 8]]], + sess.run(out, feed_dict=feed_dict)) if __name__ == '__main__': diff --git a/tensorflow/python/tools/inspect_checkpoint.py b/tensorflow/python/tools/inspect_checkpoint.py index 8716058e61..dd876cbe7f 100644 --- a/tensorflow/python/tools/inspect_checkpoint.py +++ b/tensorflow/python/tools/inspect_checkpoint.py @@ -97,8 +97,9 @@ def parse_numpy_printoption(kv_str): raise argparse.ArgumentTypeError( "Setting '%s' from the command line is not supported." % k) try: - v = (v_type(v_str) if v_type is not bool - else flags.BooleanParser().parse(v_str)) + v = ( + v_type(v_str) + if v_type is not bool else flags.BooleanParser().parse(v_str)) except ValueError as e: raise argparse.ArgumentTypeError(e.message) np.set_printoptions(**{k: v}) @@ -121,9 +122,12 @@ if __name__ == "__main__": parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( - "--file_name", type=str, default="", help="Checkpoint filename. " - "Note, if using Checkpoint V2 format, file_name is the " - "shared prefix between all files in the checkpoint.") + "--file_name", + type=str, + default="", + help="Checkpoint filename. " + "Note, if using Checkpoint V2 format, file_name is the " + "shared prefix between all files in the checkpoint.") parser.add_argument( "--tensor_name", type=str, diff --git a/tensorflow/python/training/coordinator_test.py b/tensorflow/python/training/coordinator_test.py index 149d3eed41..3e4ac1dfff 100644 --- a/tensorflow/python/training/coordinator_test.py +++ b/tensorflow/python/training/coordinator_test.py @@ -85,8 +85,8 @@ class CoordinatorTest(test.TestCase): self.assertFalse(coord.wait_for_stop(0.1)) wait_for_stop_ev = threading.Event() has_stopped_ev = threading.Event() - t = threading.Thread(target=StopOnEvent, - args=(coord, wait_for_stop_ev, has_stopped_ev)) + t = threading.Thread( + target=StopOnEvent, args=(coord, wait_for_stop_ev, has_stopped_ev)) t.start() self.assertFalse(coord.should_stop()) self.assertFalse(coord.wait_for_stop(0.01)) @@ -100,7 +100,8 @@ class CoordinatorTest(test.TestCase): threads = [ threading.Thread(target=SleepABit, args=(0.01,)), threading.Thread(target=SleepABit, args=(0.02,)), - threading.Thread(target=SleepABit, args=(0.01,))] + threading.Thread(target=SleepABit, args=(0.01,)) + ] for t in threads: t.start() coord.join(threads) @@ -112,7 +113,8 @@ class CoordinatorTest(test.TestCase): threads = [ threading.Thread(target=SleepABit, args=(0.01, coord)), threading.Thread(target=SleepABit, args=(0.02, coord)), - threading.Thread(target=SleepABit, args=(0.01, coord))] + threading.Thread(target=SleepABit, args=(0.01, coord)) + ] for t in threads: t.start() WaitForThreadsToRegister(coord, 3) @@ -125,7 +127,8 @@ class CoordinatorTest(test.TestCase): threads = [ threading.Thread(target=SleepABit, args=(0.01, coord)), threading.Thread(target=SleepABit, args=(0.02,)), - threading.Thread(target=SleepABit, args=(0.01, coord))] + threading.Thread(target=SleepABit, args=(0.01, coord)) + ] for t in threads: t.start() WaitForThreadsToRegister(coord, 2) @@ -135,14 +138,17 @@ class CoordinatorTest(test.TestCase): self.assertFalse(t.is_alive()) def testJoinGraceExpires(self): + def TestWithGracePeriod(stop_grace_period): coord = coordinator.Coordinator() wait_for_stop_ev = threading.Event() has_stopped_ev = threading.Event() threads = [ - threading.Thread(target=StopOnEvent, - args=(coord, wait_for_stop_ev, has_stopped_ev)), - threading.Thread(target=SleepABit, args=(10.0,))] + threading.Thread( + target=StopOnEvent, + args=(coord, wait_for_stop_ev, has_stopped_ev)), + threading.Thread(target=SleepABit, args=(10.0,)) + ] for t in threads: t.daemon = True t.start() @@ -150,6 +156,7 @@ class CoordinatorTest(test.TestCase): has_stopped_ev.wait() with self.assertRaisesRegexp(RuntimeError, "threads still running"): coord.join(threads, stop_grace_period_secs=stop_grace_period) + TestWithGracePeriod(1e-10) TestWithGracePeriod(0.002) TestWithGracePeriod(1.0) @@ -159,16 +166,16 @@ class CoordinatorTest(test.TestCase): wait_for_stop_ev = threading.Event() has_stopped_ev = threading.Event() threads = [ - threading.Thread(target=StopOnEvent, - args=(coord, wait_for_stop_ev, has_stopped_ev)), - threading.Thread(target=SleepABit, args=(10.0,))] + threading.Thread( + target=StopOnEvent, args=(coord, wait_for_stop_ev, has_stopped_ev)), + threading.Thread(target=SleepABit, args=(10.0,)) + ] for t in threads: t.daemon = True t.start() wait_for_stop_ev.set() has_stopped_ev.wait() - coord.join( - threads, stop_grace_period_secs=1., ignore_live_threads=True) + coord.join(threads, stop_grace_period_secs=1., ignore_live_threads=True) def testJoinRaiseReportExcInfo(self): coord = coordinator.Coordinator() @@ -180,7 +187,8 @@ class CoordinatorTest(test.TestCase): args=(coord, ev_1, ev_2, RuntimeError("First"), False)), threading.Thread( target=RaiseOnEvent, - args=(coord, ev_2, None, RuntimeError("Too late"), False))] + args=(coord, ev_2, None, RuntimeError("Too late"), False)) + ] for t in threads: t.start() @@ -199,7 +207,8 @@ class CoordinatorTest(test.TestCase): args=(coord, ev_1, ev_2, RuntimeError("First"), True)), threading.Thread( target=RaiseOnEvent, - args=(coord, ev_2, None, RuntimeError("Too late"), True))] + args=(coord, ev_2, None, RuntimeError("Too late"), True)) + ] for t in threads: t.start() @@ -214,9 +223,8 @@ class CoordinatorTest(test.TestCase): threading.Thread( target=RaiseOnEvent, args=(coord, ev_1, None, - errors_impl.OutOfRangeError(None, None, "First"), - True)) - ] + errors_impl.OutOfRangeError(None, None, "First"), True)) + ] for t in threads: t.start() @@ -230,7 +238,7 @@ class CoordinatorTest(test.TestCase): threading.Thread( target=RaiseOnEvent, args=(coord, ev_1, None, ValueError("Clean stop"), True)) - ] + ] for t in threads: t.start() @@ -247,7 +255,8 @@ class CoordinatorTest(test.TestCase): args=(coord, ev_1, ev_2, RuntimeError("First"))), threading.Thread( target=RaiseOnEventUsingContextHandler, - args=(coord, ev_2, None, RuntimeError("Too late")))] + args=(coord, ev_2, None, RuntimeError("Too late"))) + ] for t in threads: t.start() @@ -262,7 +271,7 @@ class CoordinatorTest(test.TestCase): threading.Thread( target=RaiseOnEvent, args=(coord, ev_1, None, RuntimeError("First"), True)), - ] + ] for t in threads: t.start() @@ -274,7 +283,7 @@ class CoordinatorTest(test.TestCase): threading.Thread( target=RaiseOnEvent, args=(coord, ev_1, None, RuntimeError("Second"), True)), - ] + ] for t in threads: t.start() with self.assertRaisesRegexp(RuntimeError, "Second"): @@ -337,24 +346,29 @@ class LooperTest(test.TestCase): def testTargetArgs(self): n = [3] coord = coordinator.Coordinator() - thread = coordinator.LooperThread.loop(coord, 0, target=_StopAt0, - args=(coord, n)) + thread = coordinator.LooperThread.loop( + coord, 0, target=_StopAt0, args=(coord, n)) coord.join([thread]) self.assertEqual(0, n[0]) def testTargetKwargs(self): n = [3] coord = coordinator.Coordinator() - thread = coordinator.LooperThread.loop(coord, 0, target=_StopAt0, - kwargs={"coord": coord, "n": n}) + thread = coordinator.LooperThread.loop( + coord, 0, target=_StopAt0, kwargs={ + "coord": coord, + "n": n + }) coord.join([thread]) self.assertEqual(0, n[0]) def testTargetMixedArgs(self): n = [3] coord = coordinator.Coordinator() - thread = coordinator.LooperThread.loop(coord, 0, target=_StopAt0, - args=(coord,), kwargs={"n": n}) + thread = coordinator.LooperThread.loop( + coord, 0, target=_StopAt0, args=(coord,), kwargs={ + "n": n + }) coord.join([thread]) self.assertEqual(0, n[0]) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index aa341b144c..27fa1b89ce 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -177,7 +177,13 @@ do_pylint() { echo "pylint took $((PYLINT_END_TIME - PYLINT_START_TIME)) s" echo "" - grep -E '(\[E|\[W0311|\[W0312)' ${OUTPUT_FILE} > ${ERRORS_FILE} + # Report only what we care about + # Ref https://pylint.readthedocs.io/en/latest/technical_reference/features.html + # E: all errors + # W0311 bad-indentation + # W0312 mixed-indentation + # C0330 bad-continuation + grep -E '(\[E|\[W0311|\[W0312|\[C0330)' ${OUTPUT_FILE} > ${ERRORS_FILE} N_ERRORS=0 while read -r LINE; do diff --git a/tensorflow/tools/compatibility/ast_edits.py b/tensorflow/tools/compatibility/ast_edits.py index e7e4c91692..23cc4a21a9 100644 --- a/tensorflow/tools/compatibility/ast_edits.py +++ b/tensorflow/tools/compatibility/ast_edits.py @@ -45,8 +45,9 @@ class APIChangeSpec(object): """ -class _FileEditTuple(collections.namedtuple( - "_FileEditTuple", ["comment", "line", "start", "old", "new"])): +class _FileEditTuple( + collections.namedtuple("_FileEditTuple", + ["comment", "line", "start", "old", "new"])): """Each edit that is recorded by a _FileEditRecorder. Fields: @@ -178,8 +179,7 @@ class _ASTCallVisitor(ast.NodeVisitor): 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), + 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 @@ -226,7 +226,7 @@ class _ASTCallVisitor(ast.NodeVisitor): # loop over lines while 1: # Reverse the text to and regular expression search for whitespace - text = self._lines[line-1] + 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. @@ -235,8 +235,8 @@ class _ASTCallVisitor(ast.NodeVisitor): new_col_offset = col - m.start(1) - 1 return line, new_col_offset else: - if (reversed_preceding_text=="" or - reversed_preceding_text.isspace()): + if (reversed_preceding_text == "" or + reversed_preceding_text.isspace()): line = line - 1 prev_line = self._lines[line - 1] # TODO(aselle): @@ -247,8 +247,8 @@ class _ASTCallVisitor(ast.NodeVisitor): # 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 + if comment_start == -1: + col = len(prev_line) - 1 elif find_string_chars.search(prev_line[comment_start:]) is None: col = comment_start else: @@ -259,7 +259,6 @@ class _ASTCallVisitor(ast.NodeVisitor): # 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. @@ -267,7 +266,6 @@ class _ASTCallVisitor(ast.NodeVisitor): node: Current Node """ - # Find a simple attribute name path e.g. "tf.foo.bar" full_name = self._get_attribute_full_path(node.func) @@ -292,18 +290,21 @@ class _ASTCallVisitor(ast.NodeVisitor): 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, - "", "", + "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 + "=") + 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 @@ -321,11 +322,11 @@ class _ASTCallVisitor(ast.NodeVisitor): # 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 + "="): + 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, + (argkey, + renamed_keywords[argkey]), argval_lineno, argval_col_offset - len(argkey) - 1, argkey + "=", renamed_keywords[argkey] + "=") continue @@ -334,7 +335,8 @@ class _ASTCallVisitor(ast.NodeVisitor): (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) @@ -351,7 +353,7 @@ class _ASTCallVisitor(ast.NodeVisitor): 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), + 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) @@ -379,8 +381,8 @@ class ASTCodeUpgrader(object): # 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) + ret = self.process_opened_file(in_filename, in_file, out_filename, + temp_file) shutil.move(temp_file.name, out_filename) return ret @@ -423,6 +425,7 @@ class ASTCodeUpgrader(object): 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, @@ -443,16 +446,16 @@ class ASTCodeUpgrader(object): # 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)) + 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)) + 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 @@ -464,14 +467,16 @@ class ASTCodeUpgrader(object): 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)) + 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)) + fullpath_output = os.path.join(output_root_directory, + os.path.relpath( + fullpath, root_directory)) files_to_copy.append((fullpath, fullpath_output)) file_count = 0 diff --git a/tensorflow/tools/compatibility/tf_upgrade.py b/tensorflow/tools/compatibility/tf_upgrade.py index fa1cc73905..f678681dac 100644 --- a/tensorflow/tools/compatibility/tf_upgrade.py +++ b/tensorflow/tools/compatibility/tf_upgrade.py @@ -236,8 +236,8 @@ class _ASTCallVisitor(ast.NodeVisitor): new_col_offset = col - m.start(1) - 1 return line, new_col_offset else: - if (reversed_preceding_text=="" or - reversed_preceding_text.isspace()): + if (reversed_preceding_text == "" or + reversed_preceding_text.isspace()): line = line - 1 prev_line = self._lines[line - 1] # TODO(aselle): -- GitLab From 111a466ea14cb3ca9cd2b9c76b459bf8472ca98f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 12:24:58 -0800 Subject: [PATCH 057/423] Automated g4 rollback of changelist 183251689 PiperOrigin-RevId: 183273334 --- tensorflow/python/ops/rnn.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index a10e1963d1..a1008f1c83 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -812,10 +812,7 @@ def _dynamic_rnn_loop(cell, return (time + 1, output_ta_t, new_state) if in_graph_mode: - # Make sure that we run at least 1 step, if necessary, to ensure - # the TensorArrays pick up the dynamic shape. - loop_bound = math_ops.minimum( - time_steps, math_ops.maximum(1, max_sequence_length)) + loop_bound = max_sequence_length else: # Using max_sequence_length isn't currently supported in the Eager branch. loop_bound = time_steps -- GitLab From dce14f5d38a19b3a19c834a877acc0042f1e9fbd Mon Sep 17 00:00:00 2001 From: Viraj Navkal Date: Thu, 25 Jan 2018 13:11:40 -0800 Subject: [PATCH 058/423] Remove calculation of unnecessary matrix columns in SVD gradient (#15801) * remove calculation of unnecessary matrix columns in svd gradient * simplify expression for svd gradient when compute_uv=False * assign Operation attribute to local variable --- tensorflow/python/ops/linalg_grad.py | 59 ++++++++++++---------------- 1 file changed, 26 insertions(+), 33 deletions(-) diff --git a/tensorflow/python/ops/linalg_grad.py b/tensorflow/python/ops/linalg_grad.py index 13a32c83d9..3cbbf3412a 100644 --- a/tensorflow/python/ops/linalg_grad.py +++ b/tensorflow/python/ops/linalg_grad.py @@ -277,20 +277,28 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): # https://j-towns.github.io/papers/svd-derivative.pdf a = op.inputs[0] a_shape = a.get_shape().with_rank_at_least(2) + grad_s_mat = array_ops.matrix_diag(grad_s) - if op.get_attr("compute_uv"): - # TODO(rmlarsen): Make this work with complex types. - if a.dtype.is_complex: - raise NotImplementedError( - "SVD gradient is not implemented for complex types and " - "compute_uv=True.") - grad_u_shape = grad_u.get_shape().with_rank_at_least(2) - grad_v_shape = grad_v.get_shape().with_rank_at_least(2) - m = a_shape[-2].merge_with(grad_u_shape[-2]) - n = a_shape[-1].merge_with(grad_v_shape[-2]) - batch_shape = a_shape[:-2].merge_with(grad_u_shape[:-2]).merge_with( - grad_v_shape[:-2]) - a_shape = batch_shape.concatenate([m, n]) + if not op.get_attr("compute_uv"): + s, u, v = linalg_ops.svd(a, compute_uv=True) + grad_a = math_ops.matmul(u, math_ops.matmul(grad_s_mat, v, adjoint_b=True)) + grad_a.set_shape(a_shape) + return grad_a + + full_matrices = op.get_attr("full_matrices") + + # TODO(rmlarsen): Make this work with complex types. + if a.dtype.is_complex: + raise NotImplementedError( + "SVD gradient is not implemented for complex types and " + "compute_uv=True.") + grad_u_shape = grad_u.get_shape().with_rank_at_least(2) + grad_v_shape = grad_v.get_shape().with_rank_at_least(2) + m = a_shape[-2].merge_with(grad_u_shape[-2]) + n = a_shape[-1].merge_with(grad_v_shape[-2]) + batch_shape = a_shape[:-2].merge_with(grad_u_shape[:-2]).merge_with( + grad_v_shape[:-2]) + a_shape = batch_shape.concatenate([m, n]) m = a_shape[-2].value n = a_shape[-1].value @@ -300,12 +308,9 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): "SVD gradient has not been implemented for input with unknown " "inner matrix shape.") - if not op.get_attr("compute_uv"): - s, u, v = linalg_ops.svd(a, compute_uv=True, full_matrices=True) - else: - s = op.outputs[0] - u = op.outputs[1] - v = op.outputs[2] + s = op.outputs[0] + u = op.outputs[1] + v = op.outputs[2] use_adjoint = False if m > n: @@ -317,19 +322,7 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): grad_u, grad_v = grad_v, grad_u with ops.control_dependencies([grad_s, grad_u, grad_v]): - grad_s_mat = array_ops.matrix_diag(grad_s) - if not op.get_attr("compute_uv"): - if use_adjoint: - grad_a = math_ops.matmul( - v[..., :, :m], math_ops.matmul(u, grad_s_mat), adjoint_b=True) - else: - grad_a = math_ops.matmul(u, - math_ops.matmul( - grad_s_mat, v[..., :, :m], adjoint_b=True)) - grad_a.set_shape(a_shape) - return grad_a - - if op.get_attr("full_matrices") and abs(m - n) > 1: + if full_matrices and abs(m - n) > 1: raise NotImplementedError( "svd gradient is not implemented for abs(m - n) > 1 " "when full_matrices is True") @@ -371,7 +364,7 @@ def _SvdGrad(op, grad_s, grad_u, grad_v): gv1t_v1 = math_ops.matmul(gv1t, v1) term2_nous = gv1t - math_ops.matmul(gv1t_v1, v1, adjoint_b=True) - if op.get_attr("full_matrices"): + if full_matrices: v2 = v[..., :, m:n] grad_v2 = grad_v[..., :, m:n] -- GitLab From 97cdb0625f45b02d2f1eb71da8ac001141851438 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Thu, 25 Jan 2018 13:21:24 -0800 Subject: [PATCH 059/423] VLOG shape inference and annotation return status. PiperOrigin-RevId: 183280222 --- tensorflow/core/grappler/optimizers/layout_optimizer.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc index 50e6ba4a64..735d78e7ee 100644 --- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc @@ -2076,6 +2076,7 @@ Status LayoutOptimizer::Tune(const GrapplerItem& item, const TuningConfig& config, GraphDef* output) { auto status = graph_properties.AnnotateOutputShapes(output); if (!status.ok()) { + VLOG(1) << "Annotate shape return status: " << status.ToString(); *output = item.graph; return status; } @@ -2100,6 +2101,7 @@ Status LayoutOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, GraphProperties graph_properties(item); auto status = graph_properties.InferStatically(false); if (!status.ok()) { + VLOG(1) << "Infer shape return status: " << status.ToString(); *output = item.graph; return status; } -- GitLab From dbfa5f559448a9466187be472d8a54eee1f69b46 Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Thu, 25 Jan 2018 13:11:07 -0800 Subject: [PATCH 060/423] Update tensorboard dep to >= 1.5.0, < 1.6.0 --- 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 cc5c6f2afc..45159fbfc9 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -36,7 +36,7 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.12.1', 'six >= 1.10.0', 'protobuf >= 3.4.0', - 'tensorflow-tensorboard >= 0.4.0', + 'tensorflow-tensorboard >= 1.5.0, < 1.6.0', ] project_name = 'tensorflow' -- GitLab From 45a2fe1e98c679e02ba037b4fb1933e16a1e3256 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 13:28:55 -0800 Subject: [PATCH 061/423] Windows: Add missing dependencies in lib_proto_parsing --- tensorflow/core/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 497281041f..1eb51370f3 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -274,6 +274,7 @@ cc_library( "platform/platform.h", "platform/protobuf.h", "platform/types.h", + "platform/windows/cpu_info.h", "lib/bfloat16/bfloat16.h", ] + tf_additional_proto_hdrs() + glob(tf_env_time_hdrs()), copts = tf_copts(), -- GitLab From 48d6280da20836f253f50485ce52eef91d4b5f50 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 13:46:54 -0800 Subject: [PATCH 062/423] Remove no longer used param fields from TfLiteResizeBilinerParams PiperOrigin-RevId: 183284937 --- tensorflow/contrib/lite/builtin_op_data.h | 2 -- tensorflow/contrib/lite/model.cc | 2 -- 2 files changed, 4 deletions(-) diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index 0b48ef4741..8338fde8ac 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -167,8 +167,6 @@ typedef struct { } TfLiteLSTMParams; typedef struct { - int new_height; - int new_width; } TfLiteResizeBilinearParams; typedef struct { diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 95949be9e6..ba29a2f4d1 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -479,8 +479,6 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, auto* params = MallocPOD(); if (auto* schema_params = op->builtin_options_as_ResizeBilinearOptions()) { - params->new_height = schema_params->new_height(); - params->new_width = schema_params->new_width(); } builtin_data = reinterpret_cast(params); break; -- GitLab From 6124dfd8f54fbc362914b562603b2b1c8a411df2 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Thu, 25 Jan 2018 14:18:45 -0800 Subject: [PATCH 063/423] Deprecation warning on Variables's += methods PiperOrigin-RevId: 183290246 --- .../python/ops/resource_variable_ops.py | 34 ++++++++--- tensorflow/python/ops/variables.py | 56 +++++++++++++++++++ 2 files changed, 83 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index 879c206313..f727a29233 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -835,25 +835,45 @@ class ResourceVariable(variables.Variable): return self.value() def __iadd__(self, unused_other): - raise RuntimeError("Variable += value not supported.") + raise RuntimeError("Variable += value not supported. Use " + "variable.assign_add(value) to modify the variable " + "value and variable = variable + value to get a new " + "Tensor object.") def __isub__(self, unused_other): - raise RuntimeError("Variable -= value not supported.") + raise RuntimeError("Variable -= value not supported. Use " + "variable.assign_sub(value) to modify the variable " + "value and variable = variable - value to get a new " + "Tensor object.") def __imul__(self, unused_other): - raise RuntimeError("Variable *= value not supported.") + raise RuntimeError("Variable *= value not supported. Use " + "variable.assign_mul(value) to modify the variable " + "value and variable = variable * value to get a new " + "Tensor object.") def __idiv__(self, unused_other): - raise RuntimeError("Variable /= value not supported.") + raise RuntimeError("Variable /= value not supported. Use " + "variable.assign_div(value) to modify the variable " + "value and variable = variable / value to get a new " + "Tensor object.") def __itruediv__(self, unused_other): - raise RuntimeError("Variable /= value not supported.") + raise RuntimeError("Variable /= value not supported. Use " + "variable.assign_div(value) to modify the variable " + "value and variable = variable / value to get a new " + "Tensor object.") def __irealdiv__(self, unused_other): - raise RuntimeError("Variable /= value not supported.") + raise RuntimeError("Variable /= value not supported. Use " + "variable.assign_div(value) to modify the variable " + "value and variable = variable / value to get a new " + "Tensor object.") def __ipow__(self, unused_other): - raise RuntimeError("Variable **= value not supported.") + raise RuntimeError("Variable **= value not supported. Use " + "value and variable = variable ** value to get a new " + "Tensor object.") def _dense_var_to_tensor(var, dtype=None, name=None, as_ref=False): diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index 7d7fa646c0..ff19612383 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops +from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat from tensorflow.python.util import tf_should_use from tensorflow.python.util.deprecation import deprecated @@ -1021,6 +1022,61 @@ class Variable(object): return Variable(variable_def=variable_def, import_scope=import_scope) + def __iadd__(self, other): + logging.log_first_n( + logging.WARN, + "Variable += will be deprecated. Use variable.assign_add" + " if you want assignment to the variable value or 'x = x + y'" + " if you want a new python Tensor object.", 1) + return self + other + + def __isub__(self, other): + logging.log_first_n( + logging.WARN, + "Variable -= will be deprecated. Use variable.assign_sub" + " if you want assignment to the variable value or 'x = x - y'" + " if you want a new python Tensor object.", 1) + return self - other + + def __imul__(self, other): + logging.log_first_n( + logging.WARN, + "Variable *= will be deprecated. Use variable.assign_mul" + " if you want assignment to the variable value or 'x = x * y'" + " if you want a new python Tensor object.", 1) + return self * other + + def __idiv__(self, other): + logging.log_first_n( + logging.WARN, + "Variable /= will be deprecated. Use variable.assign_div" + " if you want assignment to the variable value or 'x = x / y'" + " if you want a new python Tensor object.", 1) + return self / other + + def __itruediv__(self, other): + logging.log_first_n( + logging.WARN, + "Variable /= will be deprecated. Use variable.assign_div" + " if you want assignment to the variable value or 'x = x / y'" + " if you want a new python Tensor object.", 1) + return self / other + + def __irealdiv__(self, other): + logging.log_first_n( + logging.WARN, + "Variable /= will be deprecated. Use variable.assign_div" + " if you want assignment to the variable value or 'x = x / y'" + " if you want a new python Tensor object.", 1) + return self / other + + def __ipow__(self, other): + logging.log_first_n( + logging.WARN, + "Variable **= will be deprecated. Use 'x = x ** y'" + " if you want a new python Tensor object.", 1) + return self ** other + class SaveSliceInfo(object): """Information on how to save this Variable as a slice. -- GitLab From 119b993e00a2a138d1ef2f4886e39ca528bad1c3 Mon Sep 17 00:00:00 2001 From: "freedom\" Koan-Sin Tan" Date: Fri, 26 Jan 2018 06:24:23 +0800 Subject: [PATCH 064/423] make label_image for tflite build again (#16206) * make label_image for tflite build again 1. add namespace to label_image.h to make label_image for tflite build again 2. add --config monolithic and mention NDK settings in label_image.md 3. fix a typo in display_usage() * relies on tensor type info to switch types use tensor types of input and output tensors instead of command line flag from user * reformatted according to review --- .../lite/examples/label_image/label_image.cc | 55 +++++++++++-------- .../lite/examples/label_image/label_image.h | 7 ++- .../lite/examples/label_image/label_image.md | 12 ++-- 3 files changed, 46 insertions(+), 28 deletions(-) diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index 4d2e1ce0bc..d7f49ad875 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -148,14 +148,22 @@ void RunInference(Settings* s) { int wanted_width = dims->data[2]; int wanted_channels = dims->data[3]; - if (s->input_floating) { - downsize(interpreter->typed_tensor(input), in, image_height, - image_width, image_channels, wanted_height, wanted_width, - wanted_channels, s); - } else { - downsize(interpreter->typed_tensor(input), in, - image_height, image_width, image_channels, wanted_height, - wanted_width, wanted_channels, s); + switch (interpreter->tensor(input)->type) { + case kTfLiteFloat32: + s->input_floating = true; + downsize(interpreter->typed_tensor(input), in, + image_height, image_width, image_channels, + wanted_height, wanted_width, wanted_channels, s); + break; + case kTfLiteUInt8: + downsize(interpreter->typed_tensor(input), in, + image_height, image_width, image_channels, + wanted_height, wanted_width, wanted_channels, s); + break; + default: + LOG(FATAL) << "cannot handle input type " + << interpreter->tensor(input)->type << " yet"; + exit(-1); } struct timeval start_time, stop_time; @@ -177,13 +185,22 @@ void RunInference(Settings* s) { std::vector> top_results; - if (s->input_floating) { - get_top_n(interpreter->typed_output_tensor(0), output_size, - num_results, threshold, &top_results, s->input_floating); - } else { - get_top_n(interpreter->typed_output_tensor(0), + int output = interpreter->outputs()[0]; + switch (interpreter->tensor(output)->type) { + case kTfLiteFloat32: + get_top_n(interpreter->typed_output_tensor(0), output_size, num_results, threshold, &top_results, - s->input_floating); + true); + break; + case kTfLiteUInt8: + get_top_n(interpreter->typed_output_tensor(0), + output_size, num_results, threshold, &top_results, + false); + break; + default: + LOG(FATAL) << "cannot handle output type " + << interpreter->tensor(input)->type << " yet"; + exit(-1); } std::vector labels; @@ -203,13 +220,11 @@ void display_usage() { LOG(INFO) << "label_image\n" << "--accelerated, -a: [0|1], use Android NNAPI or note\n" << "--count, -c: loop interpreter->Invoke() for certain times\n" - << "--input_floating, -f: [0|1] type of input layer is floating " - "point numbers\n" << "--input_mean, -b: input mean\n" << "--input_std, -s: input standard deviation\n" << "--image, -i: image_name.bmp\n" << "--labels, -l: labels for the model\n" - << "--tflite_mode, -m: model_name.tflite\n" + << "--tflite_model, -m: model_name.tflite\n" << "--threads, -t: number of threads\n" << "--verbose, -v: [0|1] print more information\n" << "\n"; @@ -223,7 +238,6 @@ int Main(int argc, char** argv) { static struct option long_options[] = { {"accelerated", required_argument, 0, 'a'}, {"count", required_argument, 0, 'c'}, - {"input_floating", required_argument, 0, 'f'}, {"verbose", required_argument, 0, 'v'}, {"image", required_argument, 0, 'i'}, {"labels", required_argument, 0, 'l'}, @@ -254,11 +268,6 @@ int Main(int argc, char** argv) { s.loop_count = strtol( // NOLINT(runtime/deprecated_fn) optarg, (char**)NULL, 10); break; - case 'f': - s.input_floating = strtol( // NOLINT(runtime/deprecated_fn) - optarg, (char**)NULL, 10); - s.input_layer_type = "float"; - break; case 'i': s.input_bmp_name = optarg; break; diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.h b/tensorflow/contrib/lite/examples/label_image/label_image.h index ce98e06fc1..4de32e33fb 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.h +++ b/tensorflow/contrib/lite/examples/label_image/label_image.h @@ -16,9 +16,11 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_LABEL_IMAGE_H #define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_LABEL_IMAGE_H -#include #include "tensorflow/contrib/lite/string.h" +namespace tflite { +namespace label_image { + struct Settings { bool verbose = false; bool accel = false; @@ -33,4 +35,7 @@ struct Settings { int number_of_threads = 4; }; +} // namespace label_image +} // namespace tflite + #endif // TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_LABEL_IMAGE_H diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.md b/tensorflow/contrib/lite/examples/label_image/label_image.md index d6019d673f..9ce32cf101 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.md +++ b/tensorflow/contrib/lite/examples/label_image/label_image.md @@ -1,8 +1,12 @@ label_image for TensorFlow Lite inspired by TensorFlow's label_image. + +To build label_image for Android, run $TENSORFLOW_ROOT/configure +and set Android NDK or configure NDK setting in +$TENSORFLOW_ROOT/WORKSPACE first. To build it for android ARMv8: ``` -> bazel build --cxxopt=-std=c++11 \ +> bazel build --config monolithic --cxxopt=-std=c++11 \ --crosstool_top=//external:android/crosstool \ --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \ --cpu=arm64-v8a \ @@ -10,13 +14,13 @@ To build it for android ARMv8: ``` or ``` -> bazel build --config android_arm64 --cxxopt=-std=c++11 \ +> bazel build --config android_arm64 --config monolithic --cxxopt=-std=c++11 \ //tensorflow/contrib/lite/examples/label_image:label_image ``` To build it for android arm-v7a: ``` -> bazel build --cxxopt=-std=c++11 \ +> bazel build --config monolithic --cxxopt=-std=c++11 \ --crosstool_top=//external:android/crosstool \ --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \ --cpu=armeabi-v7a \ @@ -24,7 +28,7 @@ To build it for android arm-v7a: ``` or ``` -> bazel build --config android_arm --cxxopt=-std=c++11 \ +> bazel build --config android_arm --config monolithic --cxxopt=-std=c++11 \ //tensorflow/contrib/lite/examples/label_image:label_image ``` -- GitLab From 9019ace82139058538fcbee1bbcea765881d5724 Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Thu, 25 Jan 2018 14:28:41 -0800 Subject: [PATCH 065/423] Automated g4 rollback of changelist 183273334 PiperOrigin-RevId: 183291956 --- tensorflow/python/ops/rnn.py | 5 ++++- tensorflow/python/profiler/model_analyzer_test.py | 10 +++++----- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index a1008f1c83..a10e1963d1 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -812,7 +812,10 @@ def _dynamic_rnn_loop(cell, return (time + 1, output_ta_t, new_state) if in_graph_mode: - loop_bound = max_sequence_length + # Make sure that we run at least 1 step, if necessary, to ensure + # the TensorArrays pick up the dynamic shape. + loop_bound = math_ops.minimum( + time_steps, math_ops.maximum(1, max_sequence_length)) else: # Using max_sequence_length isn't currently supported in the Eager branch. loop_bound = time_steps diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py index 9153855588..04ba28c219 100644 --- a/tensorflow/python/profiler/model_analyzer_test.py +++ b/tensorflow/python/profiler/model_analyzer_test.py @@ -224,15 +224,15 @@ class PrintModelAnalysisTest(test.TestCase): # pylint: disable=line-too-long with gfile.Open(outfile, 'r') as f: lines = f.read().split('\n') + self.assertGreater(len(lines), 5) result = '\n'.join([l[:min(len(l), 80)] for l in lines]) - self.assertEqual( - compat.as_bytes( - 'node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/168.86k flops)\n model_analyzer_testlib.py:63:BuildFullModel (0/1.80k params, 0/45.37k flops)\n model_analyzer_testlib.py:40:BuildSmallModel (0/0 params, 0/0 flops)\n model_analyzer_testlib.py:44:BuildSmallModel (0/4 params, 0/8 flops)\n model_analyzer_testlib.py:48:BuildSmallModel (0/648 params, 0/1.30k flops)\n model_analyzer_testlib.py:49:BuildSmallModel (0/0 params, 0/23.33k flops)\n model_analyzer_testlib.py:53:BuildSmallModel (0/1.15k params, 0/2.30k flops)\n model_analyzer_testlib.py:54:BuildSmallModel (0/0 params, 0/18.43k flops)\n model_analyzer_testlib.py:63:BuildFullModel (gradient) (0/0 params, 0/67.39k f\n model_analyzer_testlib.py:49:BuildSmallModel (gradient) (0/0 params, 0/46.66\n model_analyzer_testlib.py:54:BuildSmallModel (gradient) (0/0 params, 0/20.74\n model_analyzer_testlib.py:67:BuildFullModel (0/1.04k params, 0/18.58k flops)\n model_analyzer_testlib.py:67:BuildFullModel (gradient) (0/0 params, 0/37.00k f\n model_analyzer_testlib.py:69:BuildFullModel (0/0 params, 0/0 flops)\n model_analyzer_testlib.py:70:BuildFullModel (0/0 params, 0/258 flops)\n model_analyzer_testlib.py:70:BuildFullModel (gradient) (0/0 params, 0/129 flop\n model_analyzer_testlib.py:72:BuildFullModel (0/0 params, 0/141 flops)\n' - ), compat.as_bytes(lib.CheckAndRemoveDoc(result))) + self.assertTrue( + compat.as_text(lib.CheckAndRemoveDoc(result)) + .startswith('node name | # parameters | # float_ops')) self.assertLess(0, tfprof_node.total_exec_micros) self.assertEqual(2844, tfprof_node.total_parameters) - self.assertEqual(168863, tfprof_node.total_float_ops) + self.assertLess(168800, tfprof_node.total_float_ops) self.assertEqual(8, len(tfprof_node.children)) self.assertEqual('_TFProfRoot', tfprof_node.name) self.assertEqual( -- GitLab From 46a78c429b689e08fa25fcebe6656884bcd6a45e Mon Sep 17 00:00:00 2001 From: Yunxing Dai Date: Thu, 25 Jan 2018 14:30:15 -0800 Subject: [PATCH 066/423] Make select_and_scatter_test optonly - Make select_and_scatter_test optonly. - Reduce number of shards. PiperOrigin-RevId: 183292230 --- tensorflow/compiler/xla/tests/BUILD | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index ac11081699..85ac28533d 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -1035,8 +1035,10 @@ xla_test( name = "select_and_scatter_test", timeout = "long", srcs = ["select_and_scatter_test.cc"], - shard_count = 40, - tags = ["enable_for_xla_interpreter"], + tags = [ + "enable_for_xla_interpreter", + "optonly", + ], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:literal_util", -- GitLab From f4b5d26f7d22d710c5bf8815ea97cdc00d979fce Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 14:38:57 -0800 Subject: [PATCH 067/423] Add OPENSOURCE extension to schema_generated.h PiperOrigin-RevId: 183293637 --- tensorflow/contrib/lite/schema/schema_generated.h | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 tensorflow/contrib/lite/schema/schema_generated.h diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h old mode 100644 new mode 100755 -- GitLab From 73b4b1502924acd461013d4ecf9825aedd3a3968 Mon Sep 17 00:00:00 2001 From: Jonathan Hseu Date: Thu, 25 Jan 2018 14:39:44 -0800 Subject: [PATCH 068/423] Use LookupOrCreateResource when creating summary writers. The resource may already exist because it's associated with the device. PiperOrigin-RevId: 183293761 --- tensorflow/core/kernels/summary_kernels.cc | 41 ++++++++++++++-------- 1 file changed, 26 insertions(+), 15 deletions(-) diff --git a/tensorflow/core/kernels/summary_kernels.cc b/tensorflow/core/kernels/summary_kernels.cc index 41cbece1d6..da3644779d 100644 --- a/tensorflow/core/kernels/summary_kernels.cc +++ b/tensorflow/core/kernels/summary_kernels.cc @@ -42,11 +42,16 @@ class CreateSummaryFileWriterOp : public OpKernel { const int32 flush_millis = tmp->scalar()(); OP_REQUIRES_OK(ctx, ctx->input("filename_suffix", &tmp)); const string filename_suffix = tmp->scalar()(); - SummaryWriterInterface* s; - OP_REQUIRES_OK(ctx, - CreateSummaryFileWriter(max_queue, flush_millis, logdir, - filename_suffix, ctx->env(), &s)); - OP_REQUIRES_OK(ctx, CreateResource(ctx, HandleFromInput(ctx, 0), s)); + + SummaryWriterInterface* s = nullptr; + OP_REQUIRES_OK(ctx, LookupOrCreateResource( + ctx, HandleFromInput(ctx, 0), &s, + [max_queue, flush_millis, logdir, filename_suffix, + ctx](SummaryWriterInterface** s) { + return CreateSummaryFileWriter( + max_queue, flush_millis, logdir, + filename_suffix, ctx->env(), s); + })); } }; REGISTER_KERNEL_BUILDER(Name("CreateSummaryFileWriter").Device(DEVICE_CPU), @@ -66,17 +71,23 @@ class CreateSummaryDbWriterOp : public OpKernel { const string run_name = tmp->scalar()(); OP_REQUIRES_OK(ctx, ctx->input("user_name", &tmp)); const string user_name = tmp->scalar()(); - SummaryWriterInterface* s; - Sqlite* db; - OP_REQUIRES_OK(ctx, Sqlite::Open(db_uri, - SQLITE_OPEN_READWRITE | SQLITE_OPEN_CREATE, - &db)); - core::ScopedUnref unref(db); - OP_REQUIRES_OK(ctx, SetupTensorboardSqliteDb(db)); + + SummaryWriterInterface* s = nullptr; OP_REQUIRES_OK( - ctx, CreateSummaryDbWriter(db, experiment_name, - run_name, user_name, ctx->env(), &s)); - OP_REQUIRES_OK(ctx, CreateResource(ctx, HandleFromInput(ctx, 0), s)); + ctx, + LookupOrCreateResource( + ctx, HandleFromInput(ctx, 0), &s, + [db_uri, experiment_name, run_name, user_name, + ctx](SummaryWriterInterface** s) { + Sqlite* db; + TF_RETURN_IF_ERROR(Sqlite::Open( + db_uri, SQLITE_OPEN_READWRITE | SQLITE_OPEN_CREATE, &db)); + core::ScopedUnref unref(db); + TF_RETURN_IF_ERROR(SetupTensorboardSqliteDb(db)); + TF_RETURN_IF_ERROR(CreateSummaryDbWriter( + db, experiment_name, run_name, user_name, ctx->env(), s)); + return Status::OK(); + })); } }; REGISTER_KERNEL_BUILDER(Name("CreateSummaryDbWriter").Device(DEVICE_CPU), -- GitLab From b998b7b456066530dd27ef532dae195d27505266 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 14:42:03 -0800 Subject: [PATCH 069/423] Drop the manually_create field from RnnState. Initially, I thought that the shape of RNN state arrays could always be determined by shape propagation. Then I came across some graphs where this wasn't so easy to infer, so I introduced manually_create thinking of it as a hack. Today I took another look at dropping that hack, and had a "D'oh" moment when I realized that the cyclic nature of RNN graphs makes it impossible to infer the shapes of all arrays by usual propagation. For example, in a LSTM cell, the input array is concatenated with a state array, so if we don't already know the shape of that state array, shape propagation stops there. Thus, this change removes manually_create by making toco always behave as if manually_create=true, i.e. early-creating all RNN state arrays with the shape explicitly specified by the user. The next TODO item here (see model_flags.proto) is to introduce a generic 'shape' field, so far the current 'size' field only allows specifying 1-D shapes. PiperOrigin-RevId: 183294102 --- .../lite/toco/allocate_transient_arrays.cc | 4 +--- .../contrib/lite/toco/model_cmdline_flags.cc | 3 --- tensorflow/contrib/lite/toco/model_flags.proto | 15 +++------------ tensorflow/contrib/lite/toco/tooling_util.cc | 13 ++++++------- 4 files changed, 10 insertions(+), 25 deletions(-) diff --git a/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc b/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc index 5961d30bf5..49cc1fc2aa 100644 --- a/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc +++ b/tensorflow/contrib/lite/toco/allocate_transient_arrays.cc @@ -158,9 +158,7 @@ std::size_t TransientArraySize(const Model& model, const string& array_name, LOG(FATAL) << "A RNN state array, " << array_name << ", still does not " << "have a known data type after all graph transformations have " - << "run. That's mostly a toco bug --- sorry. For now, you can " - << "work around this issue by adding manually_create:true in the " - << "--rnn_state description of this RNN state."; + << "run."; } } LOG(FATAL) << "An array, " << array_name << ", still does not " diff --git a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc index 790b3443ce..36520d9c55 100644 --- a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc @@ -327,9 +327,6 @@ void ReadModelFlagsFromCommandLineFlags( CHECK(absl::SimpleAtoi(value, &size)); CHECK_GT(size, 0); rnn_state_proto->set_size(size); - } else if (key == "manually_create") { - CHECK_EQ(absl::AsciiStrToLower(value), "true"); - rnn_state_proto->set_manually_create(true); } else { LOG(FATAL) << "Unknown key '" << key << "' in --rnn_states"; } diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto index 13fea29a07..9070ddc883 100644 --- a/tensorflow/contrib/lite/toco/model_flags.proto +++ b/tensorflow/contrib/lite/toco/model_flags.proto @@ -81,19 +81,10 @@ message RnnState { optional string state_array = 1; optional string back_edge_source_array = 2; optional bool discardable = 5; - // TODO(benoitjacob): drop the 'size' field. Should be redundant with - // --input_shapes and shapes propagation. + // size allows to specify a 1-D shape for the RNN state array. + // Will be expanded with 1's to fit the model. + // TODO(benoitjacob): should allow a generic, explicit shape. optional int32 size = 3; - // TODO(benoitjacob): manually_create is a temporary hack: - // due to discrepancies between the current toco dims tracking and - // TensorFlow shapes, for some models we need to manually create RNN state - // arrays with a specified shape. - // Maybe we should actually implement back-edges as operators of their own, - // which would remove the need for much special-casing, including here, - // we could probably consistently let PropagateFixedSizes handle state - // arrays. - // TODO(benoitjacob): should really drop manually_create now. - optional bool manually_create = 4; } // ModelFlags encodes properties of a model that, depending on the file diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 99a54a300b..df785a5102 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -958,7 +958,9 @@ void CheckModelCounts(const Model& model) { void MakeArrayDims(int num_dims, int batch, int height, int width, int depth, std::vector* out_dims) { CHECK(out_dims->empty()); - if (num_dims == 1) { + if (num_dims == 0) { + return; + } else if (num_dims == 1) { CHECK_EQ(batch, 1); *out_dims = {depth}; } else if (num_dims == 2) { @@ -990,13 +992,13 @@ void CreateOrCheckRnnStateArray(const string& name, int size, Model* model) { if (array.has_shape()) { num_dims = array.shape().dimensions_count(); } - std::vector dims; - MakeArrayDims(num_dims, batch, 1, 1, size, &dims); CHECK(array.data_type == ArrayDataType::kFloat || array.data_type == ArrayDataType::kNone); array.data_type = ArrayDataType::kFloat; - if (!array.has_shape()) { + if (!array.has_shape() && num_dims >= 0) { Shape* shape = array.mutable_shape(); + std::vector dims; + MakeArrayDims(num_dims, batch, 1, 1, size, &dims); *shape->mutable_dims() = dims; } } @@ -1185,9 +1187,6 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { } // Creation of the RNN state arrays for (const auto& rnn_state : model->flags.rnn_states()) { - if (!rnn_state.manually_create()) { - continue; - } CreateOrCheckRnnStateArray(rnn_state.state_array(), rnn_state.size(), model); } -- GitLab From ad5c04c9e1151c4de71288520d45f3b3142299fb Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Thu, 25 Jan 2018 14:53:37 -0800 Subject: [PATCH 070/423] Whitelist "bool" as a valid TPU infeed type. PiperOrigin-RevId: 183296017 --- tensorflow/contrib/tpu/python/ops/tpu_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tpu/python/ops/tpu_ops.py b/tensorflow/contrib/tpu/python/ops/tpu_ops.py index a49a3dcf29..1c970655d0 100644 --- a/tensorflow/contrib/tpu/python/ops/tpu_ops.py +++ b/tensorflow/contrib/tpu/python/ops/tpu_ops.py @@ -47,7 +47,7 @@ if platform.system() != "Windows": # types are supported. _SUPPORTED_INFEED_DTYPES = set([ - dtypes.int32, dtypes.bfloat16, dtypes.float32 + dtypes.bool, dtypes.int32, dtypes.bfloat16, dtypes.float32 ]) def infeed_dequeue(dtype, shape, name=None): -- GitLab From 022890f6ac03bb87cc7b4f1a5b722cd6b058e616 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Thu, 25 Jan 2018 14:56:38 -0800 Subject: [PATCH 071/423] [XLA] Add HLO matcher for CustomCall that accepts a call target. PiperOrigin-RevId: 183296506 --- .../compiler/xla/service/hlo_matchers.cc | 24 +++++++++ .../compiler/xla/service/hlo_matchers.h | 53 +++++++++++++++++-- .../compiler/xla/service/hlo_matchers_test.cc | 33 ++++++++++++ 3 files changed, 107 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_matchers.cc b/tensorflow/compiler/xla/service/hlo_matchers.cc index 4255d60866..fe1bf61e97 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers.cc @@ -102,6 +102,30 @@ bool HloGetTupleElementMatcher::MatchAndExplain( return true; } +void HloCustomCallMatcher::DescribeTo(std::ostream* os) const { + HloMatcher::DescribeTo(os); + *os << " with call target that " + << ::testing::DescribeMatcher(call_target_matcher_); +} + +bool HloCustomCallMatcher::MatchAndExplain( + const HloInstruction* instruction, + ::testing::MatchResultListener* listener) const { + if (!HloMatcher::MatchAndExplain(instruction, listener)) { + return false; + } + ::testing::StringMatchResultListener sub_listener; + bool result = ExplainMatchResult( + call_target_matcher_, instruction->custom_call_target(), &sub_listener); + if (sub_listener.str().empty()) { + sub_listener << " that " + << ::testing::DescribeMatcher(call_target_matcher_, + /*negation=*/!result); + } + *listener << "custom-call with call target" << sub_listener.str(); + return result; +} + } // namespace testing void PrintTo(const HloInstruction* inst, ::std::ostream* os) { diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h index 9206cdac05..103f04a2cb 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.h +++ b/tensorflow/compiler/xla/service/hlo_matchers.h @@ -56,8 +56,8 @@ class HloParameterMatcher : public HloMatcher { // index to match. class HloGetTupleElementMatcher : public HloMatcher { public: - explicit HloGetTupleElementMatcher( - ::testing::Matcher operand, int64 tuple_index) + HloGetTupleElementMatcher(::testing::Matcher operand, + int64 tuple_index) : HloMatcher(HloOpcode::kGetTupleElement, /*operands=*/{operand}), tuple_index_(tuple_index) {} @@ -68,6 +68,24 @@ class HloGetTupleElementMatcher : public HloMatcher { int64 tuple_index_; }; +// Custom matcher for custom-call instructions, which accepts a matcher for its +// call target. +class HloCustomCallMatcher : public HloMatcher { + public: + HloCustomCallMatcher( + ::testing::Matcher call_target_matcher, + std::vector<::testing::Matcher> operands) + : HloMatcher(HloOpcode::kCustomCall, operands), + call_target_matcher_(call_target_matcher) {} + + bool MatchAndExplain(const HloInstruction* instruction, + ::testing::MatchResultListener* listener) const override; + void DescribeTo(std::ostream* os) const override; + + private: + ::testing::Matcher call_target_matcher_; +}; + // HloInstruction* matchers for opcode and operands. Example: // namespace op = xla::opcode_matchers; // EXPECT_THAT(instruction, @@ -94,7 +112,6 @@ HLO_MATCHER(Convert); HLO_MATCHER(Convolution); HLO_MATCHER(Copy); HLO_MATCHER(CrossReplicaSum); -HLO_MATCHER(CustomCall); HLO_MATCHER(Divide); HLO_MATCHER(Dot); HLO_MATCHER(DynamicSlice); @@ -184,6 +201,36 @@ inline ::testing::Matcher GetTupleElement() { new ::xla::testing::HloMatcher(HloOpcode::kGetTupleElement, {})); } +// - CustomCall(T, operand1, ..., operandN) matches a CustomCall with call +// target T and the given operands. +// +// - CustomCall(operand1, ..., operandN) matches any CustomCall HLO with the +// given operands. +// +// - CustomCall() matches any CustomCall HLO at all. +template +inline ::testing::Matcher CustomCall( + ::testing::Matcher call_target_matcher, M... operands) { + return ::testing::MakeMatcher(new ::xla::testing::HloCustomCallMatcher( + call_target_matcher, {operands...})); +} +// This overload of CustomCall(A, B, C, ...) exists iff A is not convertible to +// ::testing::Matcher. In that case, we want to prefer the overload +// above. +template >::value, + void>::type*> +inline ::testing::Matcher CustomCall( + FirstM operands_first, M... operands_rest) { + return ::testing::MakeMatcher(new ::xla::testing::HloMatcher( + HloOpcode::kCustomCall, {operands_first, operands_rest...})); +} +inline ::testing::Matcher CustomCall() { + return ::testing::MakeMatcher( + new ::xla::testing::HloMatcher(HloOpcode::kCustomCall, {})); +} + #undef HLO_MATCHER } // namespace opcode_matchers diff --git a/tensorflow/compiler/xla/service/hlo_matchers_test.cc b/tensorflow/compiler/xla/service/hlo_matchers_test.cc index 1465d1cacd..1c21703a45 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers_test.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers_test.cc @@ -23,6 +23,12 @@ using ::testing::Eq; namespace xla { namespace { +string DescribeHloMatcher(const ::testing::Matcher& m) { + std::stringstream ss; + m.DescribeTo(&ss); + return ss.str(); +} + template string Explain(const T& t, const M& m) { ::testing::StringMatchResultListener listener; @@ -67,5 +73,32 @@ TEST(HloMatchersTest, Test) { "add")); } +TEST(HloMatchersTest, CustomCallMatcher) { + auto c1 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); + auto c2 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); + auto call = HloInstruction::CreateCustomCall( + ShapeUtil::MakeShape(F32, {1}), {c1.get(), c2.get()}, "foo_target"); + + EXPECT_THAT(call.get(), op::CustomCall()); + EXPECT_THAT(call.get(), op::CustomCall(c1.get(), c2.get())); + EXPECT_THAT(call.get(), op::CustomCall("foo_target")); + EXPECT_THAT(call.get(), op::CustomCall("foo_target", c1.get(), c2.get())); + EXPECT_THAT(call.get(), op::CustomCall(::testing::StartsWith("foo"))); + EXPECT_THAT(call.get(), + op::CustomCall(::testing::Not(::testing::StartsWith("bar")))); + + // Wrong number of operands. + EXPECT_THAT(call.get(), ::testing::Not(op::CustomCall(c1.get()))); + + // Call target does not match. + EXPECT_THAT(call.get(), + ::testing::Not(op::CustomCall(::testing::StartsWith("bar")))); + + EXPECT_THAT(Explain(call.get(), op::CustomCall("bar")), + R"(custom-call with call target that isn't equal to "bar")"); + EXPECT_THAT(DescribeHloMatcher(op::CustomCall("foo_target")), + R"(custom-call with call target that is equal to "foo_target")"); +} + } // namespace } // namespace xla -- GitLab From dfb59da4ede1daf163a167da590ac70c447eb41a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 15:01:30 -0800 Subject: [PATCH 072/423] [XLA:GPU] Implement conditional as a sequence of thunks in the GPU backend. This also includes the following fixes: (1) Update buffer assignment for conditionals so that the buffers corresponding to the true operand and the true computation parameter are colocated, and similarly, the buffers corresponding to the false operand and the false computation parameter are colocated. (2) Update GPU copy insertion pass to insert copies when constants appear as operands of conditional instructions. PiperOrigin-RevId: 183297282 --- .../compiler/xla/service/buffer_assignment.cc | 37 ++++++ tensorflow/compiler/xla/service/gpu/BUILD | 2 + .../xla/service/gpu/conditional_thunk.cc | 72 +++++++++++ .../xla/service/gpu/conditional_thunk.h | 65 ++++++++++ .../xla/service/gpu/gpu_copy_insertion.cc | 43 ++++--- .../xla/service/gpu/gpu_copy_insertion.h | 4 +- .../compiler/xla/service/gpu/ir_emitter.cc | 31 ----- .../compiler/xla/service/gpu/ir_emitter.h | 6 +- .../xla/service/gpu/ir_emitter_unnested.cc | 120 ++++++++++++++---- tensorflow/compiler/xla/service/gpu/thunk.h | 1 + .../compiler/xla/tests/conditional_test.cc | 36 +++++- 11 files changed, 334 insertions(+), 83 deletions(-) create mode 100644 tensorflow/compiler/xla/service/gpu/conditional_thunk.cc create mode 100644 tensorflow/compiler/xla/service/gpu/conditional_thunk.h diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 323620c131..d5594dc07c 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -1358,6 +1358,43 @@ void BufferAssigner::BuildColocatedBufferSets( index, points_to_analysis, &colocated_set); AddSetToColocatedBufferSets(colocated_set, colocated_buffer_sets); }); + + // Add true_operand and conditional.true_computation.parameter(0) as a + // colocated buffer set. Note that this has to be done for each subshape + // in the true_operand of the conditional. + ShapeUtil::ForEachSubshape( + conditional_hlo->operand(1)->shape(), + [this, conditional_hlo, &points_to_analysis, colocated_buffer_sets]( + const Shape& /*subshape*/, const ShapeIndex& index) { + std::vector true_set; + // Add conditional.true_operand. + AddBufferToColocatedSet(conditional_hlo->operand(1), index, + points_to_analysis, &true_set); + // Add conditional.true_computation.parameter_instruction(0). + AddBufferToColocatedSet( + conditional_hlo->true_computation()->parameter_instruction(0), + index, points_to_analysis, &true_set); + AddSetToColocatedBufferSets(true_set, colocated_buffer_sets); + }); + + // Add false_operand and conditional.false_computation.parameter(0) as a + // colocated buffer set. Note that this has to be done for each subshape + // in the false_operand of the conditional. + ShapeUtil::ForEachSubshape( + conditional_hlo->operand(2)->shape(), + [this, conditional_hlo, &points_to_analysis, colocated_buffer_sets]( + const Shape& /*subshape*/, const ShapeIndex& index) { + std::vector false_set; + // Add conditional.false_operand. + AddBufferToColocatedSet(conditional_hlo->operand(2), index, + points_to_analysis, &false_set); + // Add conditional.false_computation.parameter_instruction(0). + AddBufferToColocatedSet( + conditional_hlo->false_computation()->parameter_instruction( + 0), + index, points_to_analysis, &false_set); + AddSetToColocatedBufferSets(false_set, colocated_buffer_sets); + }); } } } diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index df5e2e35f8..3c3328b9cd 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -228,6 +228,7 @@ cc_library( cc_library( name = "gpu_executable", srcs = [ + "conditional_thunk.cc", "convolution_thunk.cc", "copy_thunk.cc", "cudnn_batchnorm_thunk.cc", @@ -243,6 +244,7 @@ cc_library( "while_thunk.cc", ], hdrs = [ + "conditional_thunk.h", "convolution_thunk.h", "copy_thunk.h", "cudnn_batchnorm_thunk.h", diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc new file mode 100644 index 0000000000..790ca535b1 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc @@ -0,0 +1,72 @@ +/* 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/conditional_thunk.h" + +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" + +namespace xla { +namespace gpu { + +ConditionalThunk::ConditionalThunk( + const BufferAllocation::Slice& predicate_buffer_index, + const BufferAllocation::Slice& true_operand_buffer_index, + const BufferAllocation::Slice& false_operand_buffer_index, + ThunkSequence true_thunk_sequence, ThunkSequence false_thunk_sequence, + const HloInstruction* hlo) + : Thunk(Kind::kConditional, hlo), + predicate_buffer_index_(predicate_buffer_index), + true_operand_buffer_index_(true_operand_buffer_index), + false_operand_buffer_index_(false_operand_buffer_index), + true_thunk_(std::move(true_thunk_sequence), hlo), + false_thunk_(std::move(false_thunk_sequence), hlo) {} + +Status ConditionalThunk::Initialize(const GpuExecutable& executable) { + TF_RETURN_IF_ERROR(true_thunk_.Initialize(executable)); + TF_RETURN_IF_ERROR(false_thunk_.Initialize(executable)); + return Status::OK(); +} + +Status ConditionalThunk::ExecuteOnStream( + const BufferAllocations& buffer_allocations, + perftools::gputools::Stream* stream) { + // Copy the predicate value from device. + bool predicate; + perftools::gputools::DeviceMemoryBase predicate_address = + buffer_allocations.GetDeviceAddress(predicate_buffer_index_); + stream->ThenMemcpy(&predicate, predicate_address, sizeof(bool)); + + Status block_status = stream->BlockHostUntilDone(); + if (!block_status.ok()) { + return InternalError("Failed to retrieve predicate value on stream %p: %s.", + stream, block_status.error_message().c_str()); + } + + // Execute the true or the false computation depending on the value of the + // predicate. + if (predicate) { + TF_RETURN_IF_ERROR(true_thunk_.ExecuteOnStream(buffer_allocations, stream)); + } else { + TF_RETURN_IF_ERROR( + false_thunk_.ExecuteOnStream(buffer_allocations, stream)); + } + + return Status::OK(); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.h b/tensorflow/compiler/xla/service/gpu/conditional_thunk.h new file mode 100644 index 0000000000..7725c46a3b --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.h @@ -0,0 +1,65 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONDITIONAL_THUNK_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONDITIONAL_THUNK_H_ + +#include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/sequential_thunk.h" +#include "tensorflow/compiler/xla/service/gpu/thunk.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace gpu { + +// ConditionalThunk implements the conditional instruction on GPU by reading the +// predicate of the conditional and executing the true or the false computation +// depending on the value of the predicate. +// +// ConditionalThunk assumes that the buffers of the conditional result and the +// result of the true and false computations share the same allocation. Also, +// the buffers of the true operand of the conditional and that of the parameter +// instruction of the true computation share the same allocation. Similarly, the +// buffers of the false operand and that of the parameter instruction of the +// false computation share the same allocation. +class ConditionalThunk : public Thunk { + public: + ConditionalThunk(const BufferAllocation::Slice& predicate_buffer_index, + const BufferAllocation::Slice& true_operand_buffer_index, + const BufferAllocation::Slice& false_operand_buffer_index, + ThunkSequence true_thunk_sequence, + ThunkSequence false_thunk_sequence, + const HloInstruction* hlo); + + ConditionalThunk(const ConditionalThunk&) = delete; + ConditionalThunk& operator=(const ConditionalThunk&) = delete; + + Status Initialize(const GpuExecutable& executable) override; + Status ExecuteOnStream(const BufferAllocations& buffer_allocations, + perftools::gputools::Stream* stream) override; + + private: + BufferAllocation::Slice predicate_buffer_index_; + BufferAllocation::Slice true_operand_buffer_index_; + BufferAllocation::Slice false_operand_buffer_index_; + SequentialThunk true_thunk_; + SequentialThunk false_thunk_; +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONDITIONAL_THUNK_H_ diff --git a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc index e67087d822..e3b493c663 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc @@ -36,7 +36,7 @@ namespace gpu { StatusOr GpuCopyInsertion::FindOrInsertCopy( HloInstruction* hlo) { - HloInstruction*& copy = inserted_copies_[hlo]; + HloInstruction*& copy = hlo_to_copy_map_[hlo]; if (copy == nullptr) { TF_ASSIGN_OR_RETURN(copy, hlo->parent()->DeepCopyInstruction(hlo)); } @@ -86,27 +86,34 @@ StatusOr GpuCopyInsertion::Run(HloModule* module) { } } - // Init values of a while node cannot be constants. Insert copies for any - // constants found at the operand of a while. - tensorflow::gtl::FlatSet copied_constants; + // 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; for (HloComputation* computation : module->computations()) { for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() != HloOpcode::kWhile) { + if (instruction->opcode() != HloOpcode::kWhile && + instruction->opcode() != HloOpcode::kConditional) { continue; } - for (auto& pair : - dataflow->GetInstructionValueSet(instruction->operand(0))) { - const HloValueSet& value_set = pair.second; - for (const HloValue* value : value_set.values()) { - if (value->defining_instruction()->opcode() == - HloOpcode::kConstant && - !ContainsKey(copied_constants, value->defining_instruction())) { - HloInstruction* constant = value->defining_instruction(); - TF_ASSIGN_OR_RETURN(HloInstruction * copy, - FindOrInsertCopy(constant)); - TF_RETURN_IF_ERROR(constant->ReplaceAllUsesWith(copy)); - copied_constants.insert(constant); - changed = true; + for (auto operand : instruction->operands()) { + // 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)) { + continue; + } + 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; + } } } } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.h b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.h index 4d77f337e6..0c6f9b511f 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.h @@ -32,13 +32,13 @@ class GpuCopyInsertion : public HloPassInterface { StatusOr Run(HloModule* module) override; protected: - // Returns a copy of `hlo`. Looks in inserted_copies_ first to avoid making + // Returns a copy of `hlo`. Looks in hlo_to_copy_map_ first to avoid making // duplicate copies. StatusOr FindOrInsertCopy(HloInstruction* hlo); // A map containing all copies inserted to materialize operands of library // calls. The key is the copied instruction and the value is the copy. - tensorflow::gtl::FlatMap inserted_copies_; + tensorflow::gtl::FlatMap hlo_to_copy_map_; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 095c3df3bf..23b72c3f71 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -758,37 +758,6 @@ Status IrEmitter::HandleBatchNormGrad(HloInstruction*) { "to a cudnn CustomCall using CudnnBatchNormRewriter."); } -Status IrEmitter::HandleConditional(HloInstruction* conditional) { - auto pred = conditional->operand(0); - auto true_arg = conditional->operand(1); - auto false_arg = conditional->operand(2); - - llvm::Value* conditional_result = GetBasePointer(*conditional); - - llvm::LoadInst* pred_value = ir_builder_.CreateLoad( - GetBasePointer(*pred), - llvm_ir::AsStringRef(IrName(conditional, "load_predicate_value"))); - llvm::Value* pred_cond = ir_builder_.CreateICmpNE( - pred_value, - llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType(PRED, module_), 0), - llvm_ir::AsStringRef(IrName(conditional, "boolean_predicate"))); - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - pred_cond, IrName(conditional, "if_then_else"), &ir_builder_); - - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *conditional->true_computation(), {GetBasePointer(*true_arg)}, - conditional_result)); - - SetToFirstInsertPoint(if_data.false_block, &ir_builder_); - TF_RETURN_IF_ERROR(EmitCallToNestedComputation( - *conditional->false_computation(), {GetBasePointer(*false_arg)}, - conditional_result)); - - SetToFirstInsertPoint(if_data.after_block, &ir_builder_); - return Status::OK(); -} - llvm_ir::IrArray::Index IrEmitter::EmitOperandArrayLoopNest( const llvm_ir::IrArray& operand_array, int64 reduction_dimension, tensorflow::StringPiece name_suffix, llvm_ir::ForLoopNest* loop_nest) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h index 39bafaa346..3aa178410f 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h @@ -96,7 +96,6 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleCall(HloInstruction* call) override; Status HandleCustomCall(HloInstruction* custom_call) override; Status HandleRng(HloInstruction* random) override; - Status HandleConditional(HloInstruction* conditional) override; Status HandleBatchNormInference(HloInstruction* batch_norm) override; Status HandleBatchNormTraining(HloInstruction* batch_norm) override; Status HandleBatchNormGrad(HloInstruction* batch_norm) override; @@ -367,6 +366,11 @@ class IrEmitterUnnested : public IrEmitter { std::unique_ptr BuildForThunk(const HloInstruction* hlo, const int64 loop_limit); + // Returns a ConditionalThunk that executes the thunk sequence for + // 'true_computation' or 'false_computation' depending on the value of the + // predicate in the given conditional instruction. + std::unique_ptr BuildConditionalThunk(const HloInstruction* hlo); + Status Postprocess(HloInstruction* hlo) override; // Returns the last generated thunk. diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index be35351e87..fc8783e753 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -28,6 +28,7 @@ limitations under the License. #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor.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" #include "tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.h" @@ -272,8 +273,8 @@ Status IrEmitterUnnested::HandleDot(HloInstruction* dot) { } Status IrEmitterUnnested::HandleConditional(HloInstruction* conditional) { - thunk_sequence_->push_back(BuildKernelThunk(conditional)); - return IrEmitter::HandleConditional(conditional); + thunk_sequence_->emplace_back(BuildConditionalThunk(conditional)); + return Status::OK(); } Status IrEmitterUnnested::HandleConvolution(HloInstruction* convolution) { @@ -2102,6 +2103,24 @@ Status IrEmitterUnnested::EmitInitializer(const HloInstruction* hlo, namespace { +// Checks that the buffers corresponding to the given two HLOs share the same +// allocation. +Status CheckHloBuffersShareAllocation( + const HloInstruction* a, const HloInstruction* b, const ShapeIndex& index, + const BufferAssignment& buffer_assignment) { + const BufferAllocation::Slice slice_a = + buffer_assignment.GetUniqueSlice(a, index).ConsumeValueOrDie(); + const BufferAllocation::Slice slice_b = + buffer_assignment.GetUniqueSlice(b, index).ConsumeValueOrDie(); + if (slice_a != slice_b) { + return InternalError( + "instruction %s %s does not share allocation with instruction %s %s", + a->ToString().c_str(), slice_a.ToString().c_str(), + b->ToString().c_str(), slice_b.ToString().c_str()); + } + return Status::OK(); +} + // Checks that all buffers used during while loop iteration share the same // buffer allocation. This includes buffers for while result, while init // operand, condition parameter, body parameter and body result. @@ -2111,37 +2130,65 @@ Status CheckWhileBuffersShareAllocation( const BufferAssignment& buffer_assignment) { return ShapeUtil::ForEachSubshapeWithStatus( xla_while->shape(), - [&buffer_assignment, &xla_while](const Shape& /*subshape*/, - const ShapeIndex& index) -> Status { - auto check = [&buffer_assignment](const HloInstruction* a, - const HloInstruction* b, - const ShapeIndex& index) -> Status { - const BufferAllocation::Slice slice_a = - buffer_assignment.GetUniqueSlice(a, index).ConsumeValueOrDie(); - const BufferAllocation::Slice slice_b = - buffer_assignment.GetUniqueSlice(b, index).ConsumeValueOrDie(); - if (slice_a != slice_b) { - return InternalError( - "instruction %s %s does not share allocation with " - "instruction %s %s", - a->ToString().c_str(), slice_a.ToString().c_str(), - b->ToString().c_str(), slice_b.ToString().c_str()); - } - return Status::OK(); - }; + [&](const Shape& /*subshape*/, const ShapeIndex& index) -> Status { const HloInstruction* condition_parameter = xla_while->while_condition()->parameter_instruction(0); const HloComputation* body = xla_while->while_body(); const HloInstruction* body_parameter = body->parameter_instruction(0); const HloInstruction* body_result = body->root_instruction(); - TF_RETURN_IF_ERROR(check(xla_while, xla_while->operand(0), index)); - TF_RETURN_IF_ERROR(check(xla_while, condition_parameter, index)); - TF_RETURN_IF_ERROR(check(xla_while, body_parameter, index)); - TF_RETURN_IF_ERROR(check(xla_while, body_result, index)); + TF_RETURN_IF_ERROR(CheckHloBuffersShareAllocation( + xla_while, xla_while->operand(0), index, buffer_assignment)); + TF_RETURN_IF_ERROR(CheckHloBuffersShareAllocation( + xla_while, condition_parameter, index, buffer_assignment)); + TF_RETURN_IF_ERROR(CheckHloBuffersShareAllocation( + xla_while, body_parameter, index, buffer_assignment)); + TF_RETURN_IF_ERROR(CheckHloBuffersShareAllocation( + xla_while, body_result, index, buffer_assignment)); return Status::OK(); }); } +// Checks that the buffers used in a conditional instruction are shared with the +// operands and result as follows: +// * The result buffer of the conditional should share the allocation with the +// result buffers of the true and false computations. +// * The buffer of operand 1 should share the allocation with the buffer of +// the parameter 0 instruction of the true computation. +// * The buffer of operand 2 should share the allocation with the buffer of +// the parameter 0 instruction of the false computation. +Status CheckConditionalBuffersShareAllocation( + const HloInstruction* conditional, + const BufferAssignment& buffer_assignment) { + TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( + conditional->shape(), + [&](const Shape& /*subshape*/, const ShapeIndex& index) -> Status { + TF_RETURN_IF_ERROR(CheckHloBuffersShareAllocation( + conditional, conditional->true_computation()->root_instruction(), + index, buffer_assignment)); + TF_RETURN_IF_ERROR(CheckHloBuffersShareAllocation( + conditional, conditional->false_computation()->root_instruction(), + index, buffer_assignment)); + return Status::OK(); + })); + TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( + conditional->operand(1)->shape(), + [&](const Shape& /*subshape*/, const ShapeIndex& index) -> Status { + return CheckHloBuffersShareAllocation( + conditional->operand(1), + conditional->true_computation()->parameter_instruction(0), index, + buffer_assignment); + })); + TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( + conditional->operand(2)->shape(), + [&](const Shape& /*subshape*/, const ShapeIndex& index) -> Status { + return CheckHloBuffersShareAllocation( + conditional->operand(2), + conditional->false_computation()->parameter_instruction(0), index, + buffer_assignment); + })); + return Status::OK(); +} + } // namespace std::unique_ptr IrEmitterUnnested::BuildWhileThunk( @@ -2184,6 +2231,31 @@ std::unique_ptr IrEmitterUnnested::BuildForThunk( ir_emitter_body.ConsumeThunkSequence(), hlo); } +std::unique_ptr IrEmitterUnnested::BuildConditionalThunk( + const HloInstruction* hlo) { + // Check that the buffers used in conditional are shared with the operands and + // result appropriately. + TF_CHECK_OK(CheckConditionalBuffersShareAllocation( + hlo, ir_emitter_context_->buffer_assignment())); + + 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)); + + 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)); + + return MakeUnique( + GetAllocationSlice(*hlo->operand(0)), + GetAllocationSlice(*hlo->operand(1)), + GetAllocationSlice(*hlo->operand(2)), + std::move(*ir_emitter_true.ConsumeThunkSequence()), + std::move(*ir_emitter_false.ConsumeThunkSequence()), hlo); +} + Status IrEmitterUnnested::EmitTargetElementLoopInThunk( const HloInstruction& hlo, const llvm_ir::ElementGenerator& element_generator, KernelThunk* thunk) { diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h index 625c3f8bea..2c3032d79b 100644 --- a/tensorflow/compiler/xla/service/gpu/thunk.h +++ b/tensorflow/compiler/xla/service/gpu/thunk.h @@ -41,6 +41,7 @@ class GpuExecutable; class Thunk { public: enum class Kind { + kConditional, kConvolution, kCopy, kCudnnBatchNormBackward, diff --git a/tensorflow/compiler/xla/tests/conditional_test.cc b/tensorflow/compiler/xla/tests/conditional_test.cc index 0016b6cc61..bc82167482 100644 --- a/tensorflow/compiler/xla/tests/conditional_test.cc +++ b/tensorflow/compiler/xla/tests/conditional_test.cc @@ -355,8 +355,7 @@ XLA_TEST_F(ConditionalOpTest, ReturnTupleOfScalars) { } // Test true and false computations that return a tuple of arrays. -// TODO(b/71715476): Returning tuples from Conditional fails in GPU backend. -XLA_TEST_F(ConditionalOpTest, DISABLED_ON_GPU(ReturnTupleOfArrays)) { +XLA_TEST_F(ConditionalOpTest, ReturnTupleOfArrays) { ComputationBuilder builder(client_, TestName()); auto pred = builder.ConstantR0(true); auto operands = builder.Tuple({builder.ConstantR1({12.2f, 15.8f}), @@ -373,9 +372,7 @@ XLA_TEST_F(ConditionalOpTest, DISABLED_ON_GPU(ReturnTupleOfArrays)) { // Test true and false computations that return a tuple of a predicate, a // scalar, and an array. -// TODO(b/71715476): Returning tuples from Conditional fails in GPU backend. -XLA_TEST_F(ConditionalOpTest, - DISABLED_ON_GPU(ReturnTupleofPredicateScalarArray)) { +XLA_TEST_F(ConditionalOpTest, ReturnTupleofPredicateScalarArray) { ComputationBuilder true_builder(client_, TestName() + ".true"); { true_builder.Parameter(0, empty_tuple_, "tuple"); @@ -413,8 +410,7 @@ XLA_TEST_F(ConditionalOpTest, } // Test true and false computations that return a nested tuple. -// TODO(b/71715476): Returning tuples from Conditional fails in GPU backend. -XLA_TEST_F(ConditionalOpTest, DISABLED_ON_GPU(ReturnNestedTuple)) { +XLA_TEST_F(ConditionalOpTest, ReturnNestedTuple) { ComputationBuilder true_builder(client_, TestName() + ".true"); { true_builder.Parameter(0, empty_tuple_, "tuple"); @@ -532,6 +528,32 @@ XLA_TEST_F(ConditionalOpTest, NestedConditionals) { ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); } +XLA_TEST_F(ConditionalOpTest, ConditionalInNestedComputation) { + ComputationBuilder inner_builder(client_, TestName() + ".inner_conditional"); + { + Shape r0bool = ShapeUtil::MakeShape(PRED, {}); + Shape tuple_shape = ShapeUtil::MakeTupleShape({r0bool, r0f32_, r0f32_}); + auto param0 = inner_builder.Parameter(0, tuple_shape, "param0"); + auto pred_cond = inner_builder.GetTupleElement(param0, 0); + auto true_operand = inner_builder.GetTupleElement(param0, 1); + auto false_operand = inner_builder.GetTupleElement(param0, 2); + inner_builder.Conditional(pred_cond, true_operand, + CreateR0CeilComputation(), false_operand, + CreateR0FloorComputation()); + } + auto inner_builder_result = inner_builder.Build(); + EXPECT_IS_OK(inner_builder_result.status()); + + ComputationBuilder builder(client_, TestName()); + auto pred2 = builder.ConstantR0(false); + auto operand1 = builder.ConstantR0(1.1f); + auto operand2 = builder.ConstantR0(12.2f); + auto tuple_operand = builder.Tuple({pred2, operand1, operand2}); + builder.Call(inner_builder_result.ConsumeValueOrDie(), {tuple_operand}); + + ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); +} + // Test a mismatch in the shape of the true operand and true computation. XLA_TEST_F(ConditionalOpTest, ShapeMismatch) { ComputationBuilder builder(client_, TestName()); -- GitLab From 3942de820958e75792c86a50084f9312b5edd3ba Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 15:25:58 -0800 Subject: [PATCH 073/423] Adds loss_fn argument in remaining heads. PiperOrigin-RevId: 183301479 --- tensorflow/contrib/estimator/BUILD | 3 +- .../estimator/python/estimator/head.py | 89 +++---- .../estimator/python/estimator/head_test.py | 4 +- tensorflow/python/estimator/BUILD | 1 + tensorflow/python/estimator/canned/head.py | 134 +++++++++- .../python/estimator/canned/head_test.py | 251 ++++++++++++++++++ 6 files changed, 412 insertions(+), 70 deletions(-) diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index cdbe05e4d2..6cdbed5b89 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -163,7 +163,7 @@ py_library( srcs_version = "PY2AND3", deps = [ "//tensorflow/python:array_ops", - "//tensorflow/python:control_flow_ops", + "//tensorflow/python:check_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", "//tensorflow/python:lookup_ops", @@ -177,7 +177,6 @@ py_library( "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/estimator:util", "//tensorflow/python/ops/losses", "//tensorflow/python/saved_model:signature_constants", ], diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py index fd0994490a..238cf287b7 100644 --- a/tensorflow/contrib/estimator/python/estimator/head.py +++ b/tensorflow/contrib/estimator/python/estimator/head.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function from tensorflow.python.estimator import model_fn -from tensorflow.python.estimator import util from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.canned import metric_keys from tensorflow.python.estimator.canned import prediction_keys @@ -29,7 +28,6 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops -from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_lib @@ -45,6 +43,7 @@ def multi_class_head(n_classes, weight_column=None, label_vocabulary=None, loss_reduction=losses.Reduction.SUM, + loss_fn=None, name=None): """Creates a `_Head` for multi class classification. @@ -65,6 +64,12 @@ def multi_class_head(n_classes, labels have shape `[batch_size, 1]`, the loss is the weighted sum over `batch_size`. + Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + `(labels, logits, features)` as arguments and returns unreduced loss with + shape `[D0, D1, ... DN, 1]`. `loss_fn` must support integer `labels` with + shape `[D0, D1, ... DN, 1]`. Namely, the head applies `label_vocabulary` to + the input labels before passing them to `loss_fn`. + Args: n_classes: Number of classes, must be greater than 2 (for 2 classes, use `binary_classification_head`). @@ -79,6 +84,7 @@ def multi_class_head(n_classes, `label_vocabulary` is not provided but labels are strings. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + loss_fn: Optional loss function. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -94,12 +100,17 @@ def multi_class_head(n_classes, weight_column=weight_column, label_vocabulary=label_vocabulary, loss_reduction=loss_reduction, + loss_fn=loss_fn, name=name) def binary_classification_head( - weight_column=None, thresholds=None, label_vocabulary=None, - loss_reduction=losses.Reduction.SUM, name=None): + weight_column=None, + thresholds=None, + label_vocabulary=None, + loss_reduction=losses.Reduction.SUM, + loss_fn=None, + name=None): """Creates a `_Head` for single label binary classification. This head uses `sigmoid_cross_entropy_with_logits` loss. @@ -119,6 +130,12 @@ def binary_classification_head( labels have shape `[batch_size, 1]`, the loss is the weighted sum over `batch_size`. + Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + `(labels, logits, features)` as arguments and returns unreduced loss with + shape `[D0, D1, ... DN, 1]`. `loss_fn` must support float `labels` with + shape `[D0, D1, ... DN, 1]`. Namely, the head applies `label_vocabulary` to + the input labels before passing them to `loss_fn`. + Args: weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing @@ -136,6 +153,7 @@ def binary_classification_head( is not provided but labels are strings. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + loss_fn: Optional loss function. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -151,12 +169,14 @@ def binary_classification_head( thresholds=thresholds, label_vocabulary=label_vocabulary, loss_reduction=loss_reduction, + loss_fn=loss_fn, name=name) def regression_head(weight_column=None, label_dimension=1, loss_reduction=losses.Reduction.SUM, + loss_fn=None, name=None): """Creates a `_Head` for regression using the `mean_squared_error` loss. @@ -175,6 +195,10 @@ def regression_head(weight_column=None, `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or `[D0, D1, ... DN, label_dimension]`. + Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + `(labels, logits, features)` as arguments and returns unreduced loss with + shape `[D0, D1, ... DN, label_dimension]`. + Args: weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing @@ -185,6 +209,7 @@ def regression_head(weight_column=None, `[batch_size, label_dimension]`). loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + loss_fn: Optional loss function. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -198,6 +223,7 @@ def regression_head(weight_column=None, weight_column=weight_column, label_dimension=label_dimension, loss_reduction=loss_reduction, + loss_fn=loss_fn, name=name) @@ -287,7 +313,7 @@ def multi_label_head(n_classes, 'Length of label_vocabulary must be n_classes ({}). ' 'Given: {}'.format(n_classes, len(label_vocabulary))) if loss_fn: - _validate_loss_fn_args(loss_fn) + head_lib._validate_loss_fn_args(loss_fn) # pylint:disable=protected-access if (loss_reduction not in losses.Reduction.all() or loss_reduction == losses.Reduction.NONE): raise ValueError('Invalid loss_reduction: {}'.format(loss_reduction)) @@ -371,9 +397,9 @@ class _MultiLabelHead(head_lib._Head): # pylint:disable=protected-access labels=processed_labels, logits=logits, expected_labels_dimension=self.logits_dimension) if self._loss_fn: - unweighted_loss = _call_loss_fn( + unweighted_loss = head_lib._call_loss_fn( # pylint:disable=protected-access loss_fn=self._loss_fn, labels=processed_labels, logits=logits, - features=features) + features=features, expected_loss_dim=1) else: unweighted_loss = losses.sigmoid_cross_entropy( multi_class_labels=processed_labels, logits=logits, @@ -555,52 +581,3 @@ class _MultiLabelHead(head_lib._Head): # pylint:disable=protected-access threshold=threshold, name=recall_key)) return metric_ops - - -def _validate_loss_fn_args(loss_fn): - """Validates loss_fn arguments. - - Required arguments: labels, logits. - Optional arguments: features. - - Args: - loss_fn: The loss function. - Raises: - ValueError: If the signature is unexpected. - """ - loss_fn_args = util.fn_args(loss_fn) - for required_arg in ['labels', 'logits']: - if required_arg not in loss_fn_args: - raise ValueError( - 'loss_fn must contain argument: {}. ' - 'Given arguments: {}'.format(required_arg, loss_fn_args)) - invalid_args = list(set(loss_fn_args) - set(['labels', 'logits', 'features'])) - if invalid_args: - raise ValueError('loss_fn has unexpected args: {}'.format(invalid_args)) - - -def _call_loss_fn(loss_fn, labels, logits, features): - """Calls loss_fn and checks the returned shape. - - Args: - loss_fn: The loss function. - labels: Processed labels Tensor. - logits: Logits Tensor of shape [batch_size, logits_dimension]. - features: Features dict. - Returns: - Loss Tensor with shape [batch_size, 1]. - """ - loss_fn_args = util.fn_args(loss_fn) - kwargs = {} - if 'features' in loss_fn_args: - kwargs['features'] = features - unweighted_loss = loss_fn(labels=labels, logits=logits, **kwargs) - batch_size = array_ops.shape(logits)[0] - loss_shape = array_ops.shape(unweighted_loss) - check_shape_op = control_flow_ops.Assert( - math_ops.reduce_all(math_ops.equal(loss_shape, [batch_size, 1])), - data=[ - 'loss_fn must return Tensor of shape [batch_size, 1]. Given: ', - loss_shape]) - with ops.control_dependencies([check_shape_op]): - return array_ops.identity(unweighted_loss) diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py index 1adbd6f0fe..43cdfec968 100644 --- a/tensorflow/contrib/estimator/python/estimator/head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/head_test.py @@ -381,8 +381,8 @@ class MultiLabelHead(test.TestCase): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, - r'loss_fn must return Tensor of shape \[batch_size, 1\]\. ' - r'Given: \] \[2\]'): + r'\[loss_fn must return Tensor of shape \[D0, D1, ... DN, 1\]\. \] ' + r'\[logits_shape: \] \[2 2\] \[loss_shape: \] \[2\]'): actual_training_loss.eval() def test_eval_labels_none(self): diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 41f55b12af..c519fd557a 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -604,6 +604,7 @@ py_library( ":metric_keys", ":model_fn", ":prediction_keys", + ":util", "//tensorflow/python:array_ops", "//tensorflow/python:check_ops", "//tensorflow/python:control_flow_ops", diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py index 94a5d3a342..cb9e3fc6ca 100644 --- a/tensorflow/python/estimator/canned/head.py +++ b/tensorflow/python/estimator/canned/head.py @@ -24,6 +24,7 @@ import collections import six from tensorflow.python.estimator import model_fn +from tensorflow.python.estimator import util from tensorflow.python.estimator.canned import metric_keys from tensorflow.python.estimator.canned import prediction_keys from tensorflow.python.estimator.export import export_output @@ -371,6 +372,64 @@ def _check_logits_final_dim(logits, expected_logits_dimension): return array_ops.identity(logits, name=scope) +def _validate_loss_fn_args(loss_fn): + """Validates loss_fn arguments. + + Required arguments: labels, logits. + Optional arguments: features. + + Args: + loss_fn: The loss function. + Raises: + ValueError: If the signature is unexpected. + """ + loss_fn_args = util.fn_args(loss_fn) + for required_arg in ['labels', 'logits']: + if required_arg not in loss_fn_args: + raise ValueError( + 'loss_fn must contain argument: {}. ' + 'Given arguments: {}'.format(required_arg, loss_fn_args)) + invalid_args = list(set(loss_fn_args) - set(['labels', 'logits', 'features'])) + if invalid_args: + raise ValueError('loss_fn has unexpected args: {}'.format(invalid_args)) + + +def _call_loss_fn(loss_fn, labels, logits, features, expected_loss_dim=1): + """Calls loss_fn and checks the returned shape. + + Args: + loss_fn: The loss function. + labels: Processed labels Tensor. + logits: Logits Tensor of shape [D0, D1, ... DN, logits_dimension]. + features: Features dict. + expected_loss_dim: The expected last dimension of loss Tensor. + Returns: + Loss Tensor with shape [D0, D1, ... DN, expected_loss_dim]. + """ + loss_fn_args = util.fn_args(loss_fn) + kwargs = {} + if 'features' in loss_fn_args: + kwargs['features'] = features + with ops.name_scope( + None, 'call_loss_fn', + values=[labels, logits] + list(six.itervalues(features))): + unweighted_loss = loss_fn(labels=labels, logits=logits, **kwargs) + logits_shape = array_ops.shape(logits, name='logits_shape') + expected_loss_shape = array_ops.concat( + [logits_shape[:-1], [expected_loss_dim]], axis=0, + name='expected_loss_shape') + loss_shape = array_ops.shape(unweighted_loss, name='loss_shape') + check_loss_shape_op = control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(loss_shape, expected_loss_shape)), + data=[ + 'loss_fn must return Tensor of shape ' + '[D0, D1, ... DN, {}]. '.format(expected_loss_dim), + 'logits_shape: ', logits_shape, 'loss_shape: ', loss_shape], + name='check_loss_shape') + with ops.control_dependencies([check_loss_shape_op]): + return array_ops.identity(unweighted_loss) + + def _indicator_labels_mean(labels, weights=None, name=None): with ops.name_scope(name, 'labels_mean', (labels, weights)) as scope: labels = math_ops.to_float(labels, name='labels') @@ -467,6 +526,7 @@ def _multi_class_head_with_softmax_cross_entropy_loss( weight_column=None, label_vocabulary=None, loss_reduction=losses.Reduction.SUM, + loss_fn=None, name=None): """Creates a '_Head' for multi class classification. @@ -485,6 +545,12 @@ def _multi_class_head_with_softmax_cross_entropy_loss( labels have shape `[batch_size, 1]`, the loss is the weighted sum over `batch_size`. + Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + `(labels, logits, features)` as arguments and returns unreduced loss with + shape `[D0, D1, ... DN, 1]`. `loss_fn` must support integer `labels` with + shape `[D0, D1, ... DN, 1]`. Namely, the head applies `label_vocabulary` to + the input labels before passing them to `loss_fn`. + Args: n_classes: Number of classes, must be greater than 2 (for 2 classes, use `_BinaryLogisticHeadWithSigmoidCrossEntropyLoss`). @@ -499,6 +565,7 @@ def _multi_class_head_with_softmax_cross_entropy_loss( `label_vocabulary` is not provided but labels are strings. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + loss_fn: Optional loss function. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -517,11 +584,14 @@ def _multi_class_head_with_softmax_cross_entropy_loss( if (loss_reduction not in losses.Reduction.all() or loss_reduction == losses.Reduction.NONE): raise ValueError('Invalid loss_reduction: {}'.format(loss_reduction)) + if loss_fn: + _validate_loss_fn_args(loss_fn) return _MultiClassHeadWithSoftmaxCrossEntropyLoss( n_classes=n_classes, weight_column=weight_column, label_vocabulary=label_vocabulary, loss_reduction=loss_reduction, + loss_fn=loss_fn, name=name) @@ -533,6 +603,7 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head): weight_column=None, label_vocabulary=None, loss_reduction=losses.Reduction.SUM, + loss_fn=None, name=None): if (n_classes is None) or (n_classes <= 2): raise ValueError('n_classes must be > 2: %s.' % n_classes) @@ -540,6 +611,7 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head): self._weight_column = weight_column self._label_vocabulary = label_vocabulary self._loss_reduction = loss_reduction + self._loss_fn = loss_fn self._name = name @property @@ -602,10 +674,15 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head): labels = _check_dense_labels_match_logits_and_reshape( labels=labels, logits=logits, expected_labels_dimension=1) label_ids = self._label_ids(labels) - unweighted_loss = losses.sparse_softmax_cross_entropy( - labels=label_ids, logits=logits, reduction=losses.Reduction.NONE) - # Restore the squeezed dim, so unweighted_loss matches the weights shape. - unweighted_loss = array_ops.expand_dims(unweighted_loss, axis=-1) + if self._loss_fn: + unweighted_loss = _call_loss_fn( + loss_fn=self._loss_fn, labels=label_ids, logits=logits, + features=features, expected_loss_dim=1) + else: + unweighted_loss = losses.sparse_softmax_cross_entropy( + labels=label_ids, logits=logits, reduction=losses.Reduction.NONE) + # Restore the squeezed dim, so unweighted_loss matches the weights shape. + unweighted_loss = array_ops.expand_dims(unweighted_loss, axis=-1) weights = _get_weights_and_check_match_logits( features=features, weight_column=self._weight_column, logits=logits) training_loss = losses.compute_weighted_loss( @@ -734,8 +811,12 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(_Head): def _binary_logistic_head_with_sigmoid_cross_entropy_loss( - weight_column=None, thresholds=None, label_vocabulary=None, - loss_reduction=losses.Reduction.SUM, name=None): + weight_column=None, + thresholds=None, + label_vocabulary=None, + loss_reduction=losses.Reduction.SUM, + loss_fn=None, + name=None): """Creates a `_Head` for single label binary classification. This head uses `sigmoid_cross_entropy_with_logits` loss. @@ -755,6 +836,12 @@ def _binary_logistic_head_with_sigmoid_cross_entropy_loss( labels have shape `[batch_size, 1]`, the loss is the weighted sum over `batch_size`. + Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + `(labels, logits, features)` as arguments and returns unreduced loss with + shape `[D0, D1, ... DN, 1]`. `loss_fn` must support float `labels` with + shape `[D0, D1, ... DN, 1]`. Namely, the head applies `label_vocabulary` to + the input labels before passing them to `loss_fn`. + Args: weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing @@ -772,6 +859,7 @@ def _binary_logistic_head_with_sigmoid_cross_entropy_loss( is not provided but labels are strings. loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + loss_fn: Optional loss function. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -795,11 +883,14 @@ def _binary_logistic_head_with_sigmoid_cross_entropy_loss( if (loss_reduction not in losses.Reduction.all() or loss_reduction == losses.Reduction.NONE): raise ValueError('Invalid loss_reduction: {}'.format(loss_reduction)) + if loss_fn: + _validate_loss_fn_args(loss_fn) return _BinaryLogisticHeadWithSigmoidCrossEntropyLoss( weight_column=weight_column, thresholds=thresholds, label_vocabulary=label_vocabulary, loss_reduction=loss_reduction, + loss_fn=loss_fn, name=name) @@ -811,11 +902,13 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(_Head): thresholds=None, label_vocabulary=None, loss_reduction=losses.Reduction.SUM, + loss_fn=None, name=None): self._weight_column = weight_column self._thresholds = thresholds self._label_vocabulary = label_vocabulary self._loss_reduction = loss_reduction + self._loss_fn = loss_fn self._name = name @property @@ -916,8 +1009,13 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(_Head): name='class_id_lookup').lookup(labels) labels = math_ops.to_float(labels) labels = _assert_range(labels, 2) - unweighted_loss = nn.sigmoid_cross_entropy_with_logits( - labels=labels, logits=logits) + if self._loss_fn: + unweighted_loss = _call_loss_fn( + loss_fn=self._loss_fn, labels=labels, logits=logits, + features=features, expected_loss_dim=1) + else: + unweighted_loss = nn.sigmoid_cross_entropy_with_logits( + labels=labels, logits=logits) weights = _get_weights_and_check_match_logits( features=features, weight_column=self._weight_column, logits=logits) training_loss = losses.compute_weighted_loss( @@ -1057,6 +1155,7 @@ def _regression_head_with_mean_squared_error_loss( weight_column=None, label_dimension=1, loss_reduction=losses.Reduction.SUM, + loss_fn=None, name=None): """Creates a `_Head` for regression using the `mean_squared_error` loss. @@ -1075,6 +1174,10 @@ def _regression_head_with_mean_squared_error_loss( `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or `[D0, D1, ... DN, label_dimension]`. + Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or + `(labels, logits, features)` as arguments and returns unreduced loss with + shape `[D0, D1, ... DN, label_dimension]`. + Args: weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing @@ -1085,6 +1188,7 @@ def _regression_head_with_mean_squared_error_loss( `[batch_size, label_dimension]`). loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. Defaults to `SUM`. + loss_fn: Optional loss function. name: name of the head. If provided, summary and metrics keys will be suffixed by `"/" + name`. Also used as `name_scope` when creating ops. @@ -1097,10 +1201,13 @@ def _regression_head_with_mean_squared_error_loss( if (loss_reduction not in losses.Reduction.all() or loss_reduction == losses.Reduction.NONE): raise ValueError('Invalid loss_reduction: {}'.format(loss_reduction)) + if loss_fn: + _validate_loss_fn_args(loss_fn) return _RegressionHeadWithMeanSquaredErrorLoss( weight_column=weight_column, label_dimension=label_dimension, loss_reduction=loss_reduction, + loss_fn=loss_fn, name=name) @@ -1112,6 +1219,7 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head): label_dimension, weight_column=None, loss_reduction=losses.Reduction.SUM, + loss_fn=None, name=None): """`Head` for regression.""" if label_dimension < 1: @@ -1119,6 +1227,7 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head): self._logits_dimension = label_dimension self._weight_column = weight_column self._loss_reduction = loss_reduction + self._loss_fn = loss_fn self._name = name @property @@ -1137,8 +1246,13 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head): labels=labels, logits=logits, expected_labels_dimension=self._logits_dimension) labels = math_ops.to_float(labels) - unweighted_loss = losses.mean_squared_error( - labels=labels, predictions=logits, reduction=losses.Reduction.NONE) + if self._loss_fn: + unweighted_loss = _call_loss_fn( + loss_fn=self._loss_fn, labels=labels, logits=logits, + features=features, expected_loss_dim=self._logits_dimension) + else: + unweighted_loss = losses.mean_squared_error( + labels=labels, predictions=logits, reduction=losses.Reduction.NONE) weights = _get_weights_and_check_match_logits( features=features, weight_column=self._weight_column, logits=logits, allow_per_logit_weights=True) diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py index 4e871e8f37..3a03770af4 100644 --- a/tensorflow/python/estimator/canned/head_test.py +++ b/tensorflow/python/estimator/canned/head_test.py @@ -111,6 +111,41 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): head_lib._multi_class_head_with_softmax_cross_entropy_loss( n_classes=3, loss_reduction=losses.Reduction.NONE) + def test_loss_fn_arg_labels_missing(self): + def _loss_fn(logits): + del logits # Unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn must contain argument: labels\. ' + r'Given arguments: \(\'logits\',\)'): + head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_fn=_loss_fn) + + def test_loss_fn_arg_logits_missing(self): + def _loss_fn(labels): + del labels # unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn must contain argument: logits\. ' + r'Given arguments: \(\'labels\',\)'): + head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_fn=_loss_fn) + + def test_loss_fn_arg_features_ok(self): + def _loss_fn(labels, logits, features): + del labels, logits, features # Unused + head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_fn=_loss_fn) + + def test_loss_fn_arg_invalid(self): + def _loss_fn(labels, logits, name=None): + del labels, logits, name # Unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn has unexpected args: \[\'name\'\]'): + head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_fn=_loss_fn) + def test_invalid_logits_shape(self): n_classes = 3 head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(n_classes) @@ -406,6 +441,56 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) + def test_eval_create_loss_loss_fn(self): + """Tests head.create_loss for eval mode and custom loss_fn.""" + loss = np.array([[1.], [2.]], dtype=np.float32) + logits_input = np.array([[-10., 10., 0.], [-15., 10., 0]], dtype=np.float32) + labels_input = np.array([[1], [2]], dtype=np.int64) + def _loss_fn(labels, logits): + check_labels = control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(labels, labels_input)), + data=[labels]) + check_logits = control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(logits, logits_input)), + data=[logits]) + with ops.control_dependencies([check_labels, check_logits]): + return constant_op.constant(loss) + head = head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_fn=_loss_fn) + + actual_training_loss = head.create_loss( + features={'x': np.array(((42,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.EVAL, + logits=logits_input, + labels=labels_input)[0] + with self.test_session(): + _initialize_variables(self, monitored_session.Scaffold()) + self.assertAllClose(np.sum(loss), actual_training_loss.eval()) + + def test_eval_create_loss_loss_fn_wrong_shape(self): + """Tests custom loss_fn that returns Tensor of unexpected shape.""" + loss = np.array([1., 2.], dtype=np.float32) + def _loss_fn(labels, logits): + del labels, logits # Unused + return constant_op.constant(loss) + head = head_lib._multi_class_head_with_softmax_cross_entropy_loss( + n_classes=3, loss_fn=_loss_fn) + + logits = np.array([[-10., 10., 0.], [-15., 10., 0.]], dtype=np.float32) + labels = np.array([[1], [2]], dtype=np.int64) + actual_training_loss = head.create_loss( + features={'x': np.array(((42,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.EVAL, + logits=logits, + labels=labels)[0] + with self.test_session(): + _initialize_variables(self, monitored_session.Scaffold()) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'\[loss_fn must return Tensor of shape \[D0, D1, ... DN, 1\]\. \] ' + r'\[logits_shape: \] \[2 3\] \[loss_shape: \] \[2\]'): + actual_training_loss.eval() + def test_eval_labels_none(self): """Tests that error is raised when labels is None.""" head = head_lib._multi_class_head_with_softmax_cross_entropy_loss( @@ -1204,6 +1289,41 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( loss_reduction=losses.Reduction.NONE) + def test_loss_fn_arg_labels_missing(self): + def _loss_fn(logits): + del logits # Unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn must contain argument: labels\. ' + r'Given arguments: \(\'logits\',\)'): + head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_fn=_loss_fn) + + def test_loss_fn_arg_logits_missing(self): + def _loss_fn(labels): + del labels # unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn must contain argument: logits\. ' + r'Given arguments: \(\'labels\',\)'): + head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_fn=_loss_fn) + + def test_loss_fn_arg_features_ok(self): + def _loss_fn(labels, logits, features): + del labels, logits, features # Unused + head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_fn=_loss_fn) + + def test_loss_fn_arg_invalid(self): + def _loss_fn(labels, logits, name=None): + del labels, logits, name # Unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn has unexpected args: \[\'name\'\]'): + head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_fn=_loss_fn) + def test_invalid_logits_shape(self): head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss() self.assertEqual(1, head.logits_dimension) @@ -1699,6 +1819,56 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): self.assertAllClose(expected_unreduced_loss, unreduced_loss.eval()) self.assertAllClose(expected_weights, actual_weights) + def test_eval_create_loss_loss_fn(self): + """Tests head.create_loss for eval mode and custom loss_fn.""" + loss = np.array([[1.], [2.]], dtype=np.float32) + logits_input = np.array([[-10.], [10.]], dtype=np.float32) + labels_input = np.array([[1], [0]], dtype=np.int64) + def _loss_fn(labels, logits): + check_labels = control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(labels, labels_input)), + data=[labels]) + check_logits = control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(logits, logits_input)), + data=[logits]) + with ops.control_dependencies([check_labels, check_logits]): + return constant_op.constant(loss) + head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_fn=_loss_fn) + + actual_training_loss = head.create_loss( + features={'x': np.array(((42,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.EVAL, + logits=logits_input, + labels=labels_input)[0] + with self.test_session(): + _initialize_variables(self, monitored_session.Scaffold()) + self.assertAllClose(np.sum(loss), actual_training_loss.eval()) + + def test_eval_create_loss_loss_fn_wrong_shape(self): + """Tests custom loss_fn that returns Tensor of unexpected shape.""" + loss = np.array([1., 2.], dtype=np.float32) + def _loss_fn(labels, logits): + del labels, logits # Unused + return constant_op.constant(loss) + head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_fn=_loss_fn) + + logits = np.array([[-10.], [10.]], dtype=np.float32) + labels = np.array([[1], [0]], dtype=np.int64) + actual_training_loss = head.create_loss( + features={'x': np.array(((42,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.EVAL, + logits=logits, + labels=labels)[0] + with self.test_session(): + _initialize_variables(self, monitored_session.Scaffold()) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'\[loss_fn must return Tensor of shape \[D0, D1, ... DN, 1\]\. \] ' + r'\[logits_shape: \] \[2 1\] \[loss_shape: \] \[2\]'): + actual_training_loss.eval() + def test_train_labels_none(self): """Tests that error is raised when labels is None.""" head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss() @@ -2355,6 +2525,37 @@ class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase): head_lib._regression_head_with_mean_squared_error_loss( loss_reduction=losses.Reduction.NONE) + def test_loss_fn_arg_labels_missing(self): + def _loss_fn(logits): + del logits # Unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn must contain argument: labels\. ' + r'Given arguments: \(\'logits\',\)'): + head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn) + + def test_loss_fn_arg_logits_missing(self): + def _loss_fn(labels): + del labels # unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn must contain argument: logits\. ' + r'Given arguments: \(\'labels\',\)'): + head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn) + + def test_loss_fn_arg_features_ok(self): + def _loss_fn(labels, logits, features): + del labels, logits, features # Unused + head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn) + + def test_loss_fn_arg_invalid(self): + def _loss_fn(labels, logits, name=None): + del labels, logits, name # Unused + with self.assertRaisesRegexp( + ValueError, + r'loss_fn has unexpected args: \[\'name\'\]'): + head_lib._regression_head_with_mean_squared_error_loss(loss_fn=_loss_fn) + def test_invalid_logits(self): head = head_lib._regression_head_with_mean_squared_error_loss( label_dimension=3) @@ -2530,6 +2731,56 @@ class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase): # loss = [(43-45)^2, (44-41)] = [4, 9] self.assertAllClose(13., training_loss.eval()) + def test_eval_create_loss_loss_fn(self): + """Tests head.create_loss for eval mode and custom loss_fn.""" + loss = np.array([[0., 1.], [2., 3.]], dtype=np.float32) + logits_input = np.array([[-1., 1.], [-2., 2.]], dtype=np.float32) + labels_input = np.array([[1., 0.], [2., -1.]], dtype=np.float32) + def _loss_fn(labels, logits): + check_labels = control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(labels, labels_input)), + data=[labels]) + check_logits = control_flow_ops.Assert( + math_ops.reduce_all(math_ops.equal(logits, logits_input)), + data=[logits]) + with ops.control_dependencies([check_labels, check_logits]): + return constant_op.constant(loss) + head = head_lib._regression_head_with_mean_squared_error_loss( + label_dimension=2, loss_fn=_loss_fn) + + actual_training_loss = head.create_loss( + features={'x': np.array(((42,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.EVAL, + logits=logits_input, + labels=labels_input)[0] + with self.test_session(): + _initialize_variables(self, monitored_session.Scaffold()) + self.assertAllClose(np.sum(loss), actual_training_loss.eval()) + + def test_eval_create_loss_loss_fn_wrong_shape(self): + """Tests custom loss_fn that returns Tensor of unexpected shape.""" + loss = np.array([[1.], [2.]], dtype=np.float32) + def _loss_fn(labels, logits): + del labels, logits # Unused + return constant_op.constant(loss) + head = head_lib._regression_head_with_mean_squared_error_loss( + label_dimension=2, loss_fn=_loss_fn) + + logits = np.array([[-1., 1.], [-2., 2.]], dtype=np.float32) + labels = np.array([[1., 0.], [2., -1.]], dtype=np.float32) + actual_training_loss = head.create_loss( + features={'x': np.array(((42,),), dtype=np.int32)}, + mode=model_fn.ModeKeys.EVAL, + logits=logits, + labels=labels)[0] + with self.test_session(): + _initialize_variables(self, monitored_session.Scaffold()) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + r'\[loss_fn must return Tensor of shape \[D0, D1, ... DN, 2\]\. \] ' + r'\[logits_shape: \] \[2 2\] \[loss_shape: \] \[2 1\]'): + actual_training_loss.eval() + def test_eval_labels_none(self): """Tests that error is raised when labels is None.""" head = head_lib._regression_head_with_mean_squared_error_loss() -- GitLab From 11905a5da2785dba666c23762bd82c1f0f9d583b Mon Sep 17 00:00:00 2001 From: Ashish Kumar Ram Date: Fri, 26 Jan 2018 00:31:25 +0100 Subject: [PATCH 074/423] Add missing library in Dockerfile (#16417) The local Dockerfile does not have all the dependencies for running the exercise notebooks in udacity assignments. --- tensorflow/examples/udacity/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/examples/udacity/Dockerfile b/tensorflow/examples/udacity/Dockerfile index 3ca58566c1..00eb853e52 100644 --- a/tensorflow/examples/udacity/Dockerfile +++ b/tensorflow/examples/udacity/Dockerfile @@ -8,7 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ apt-get clean && \ rm -rf /var/lib/apt/lists/* -RUN pip install scikit-learn pyreadline Pillow +RUN pip install scikit-learn pyreadline Pillow imageio RUN rm -rf /notebooks/* ADD *.ipynb /notebooks/ WORKDIR /notebooks -- GitLab From 8defb1047db9dc0444d30f3e4be7ac2580f426c2 Mon Sep 17 00:00:00 2001 From: Jian Lin Date: Fri, 26 Jan 2018 07:32:10 +0800 Subject: [PATCH 075/423] add URLEncode for the CopyObjectRequest of S3 Rename function (#16415) --- tensorflow/core/platform/s3/s3_file_system.cc | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/platform/s3/s3_file_system.cc b/tensorflow/core/platform/s3/s3_file_system.cc index 2c0babe098..1e89fa77c1 100644 --- a/tensorflow/core/platform/s3/s3_file_system.cc +++ b/tensorflow/core/platform/s3/s3_file_system.cc @@ -24,6 +24,7 @@ limitations under the License. #include #include #include +#include #include #include #include @@ -607,7 +608,8 @@ Status S3FileSystem::RenameFile(const string& src, const string& target) { Aws::String src_key = object.GetKey(); Aws::String target_key = src_key; target_key.replace(0, src_object.length(), target_object.c_str()); - Aws::String source = Aws::String(src_bucket.c_str()) + "/" + src_key; + Aws::String source = Aws::String(src_bucket.c_str()) + "/" + + Aws::Utils::StringUtils::URLEncode(src_key.c_str()); copyObjectRequest.SetBucket(target_bucket.c_str()); copyObjectRequest.SetKey(target_key); -- GitLab From aa1e754d4c3f7f03abf6a1e13ce92e71c1ff80c4 Mon Sep 17 00:00:00 2001 From: Sayed Hadi Hashemi Date: Thu, 25 Jan 2018 17:32:44 -0600 Subject: [PATCH 076/423] Implement LoggingAsync for GRPC Worker Services (#14604) * Implement LoggingAsync for GRPC Worker. * Add nullptr checks * Fix BUILD file format * Change LoggingAsync implementation - Revert changes to *_rendezvous_mgr - Implement logging primitives in session_mgr instead - Implement ClearLogs - Fixed C++ formating * Check for nullptr * Better handling of the case when both "clear" and "retrieve" flags are sent to "LogAsync." nullptr check on default_worker_cache_. * Fix formatting * Updata session_mgr.cc to address changes in 619792f --- tensorflow/core/distributed_runtime/BUILD | 1 + .../distributed_runtime/master_session.cc | 4 + .../rpc/grpc_worker_service.cc | 18 ++++ .../rpc/grpc_worker_service.h | 3 + .../core/distributed_runtime/session_mgr.cc | 82 ++++++++++++++++++- .../core/distributed_runtime/session_mgr.h | 9 ++ 6 files changed, 115 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD index f4ee841032..9e152aa082 100644 --- a/tensorflow/core/distributed_runtime/BUILD +++ b/tensorflow/core/distributed_runtime/BUILD @@ -145,6 +145,7 @@ cc_library( "//tensorflow/core:core_cpu_internal", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:worker_proto_cc", ], ) diff --git a/tensorflow/core/distributed_runtime/master_session.cc b/tensorflow/core/distributed_runtime/master_session.cc index dcc25e4426..9d4a1eb8a1 100644 --- a/tensorflow/core/distributed_runtime/master_session.cc +++ b/tensorflow/core/distributed_runtime/master_session.cc @@ -1448,6 +1448,8 @@ Status MasterSession::DoPartialRun(CallOptions* opts, const auto count = run_state->count; pss.collect_timeline = req.options().trace_level() == RunOptions::FULL_TRACE; + pss.collect_rpcs = + req.options().trace_level() == RunOptions::FULL_TRACE; pss.report_tensor_allocations_upon_oom = req.options().report_tensor_allocations_upon_oom(); @@ -1610,6 +1612,8 @@ Status MasterSession::DoRunWithLocalExecution( TRACEPRINTF("stepid %llu", step_id); pss.collect_timeline = req.options().trace_level() == RunOptions::FULL_TRACE; + pss.collect_rpcs = + req.options().trace_level() == RunOptions::FULL_TRACE; pss.report_tensor_allocations_upon_oom = req.options().report_tensor_allocations_upon_oom(); // Build the cost model every 'build_cost_model_every' steps after skipping an diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc index 95811476f7..b20e744a97 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc @@ -444,6 +444,24 @@ void GrpcWorker::GrpcRecvTensorAsync(CallOptions* opts, }); } +void GrpcWorker::LoggingAsync(const LoggingRequest* request, + LoggingResponse* response, StatusCallback done) { + auto env = this->env(); + if (env) { + auto session_mgr = (SessionMgr*)env->session_mgr; + if (session_mgr) { + session_mgr->SetLogging(request->rpc_logging()); + for (const auto& step_id : request->fetch_step_id()) { + session_mgr->RetrieveLogs(step_id, response); + } + if (request->clear()) { + session_mgr->ClearLogs(); + } + } + } + done(Status::OK()); +} + WorkerEnv* GrpcWorker::env() { return env_; } std::unique_ptr NewGrpcWorker(WorkerEnv* env) { diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h index 78a21fd9f6..3954af8ad8 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.h @@ -40,6 +40,9 @@ class GrpcWorker : public Worker { ::grpc::ByteBuffer* response, StatusCallback done); + virtual void LoggingAsync(const LoggingRequest* request, + LoggingResponse* response, StatusCallback done); + WorkerEnv* env(); private: diff --git a/tensorflow/core/distributed_runtime/session_mgr.cc b/tensorflow/core/distributed_runtime/session_mgr.cc index 8db49e7f15..51b9547f53 100644 --- a/tensorflow/core/distributed_runtime/session_mgr.cc +++ b/tensorflow/core/distributed_runtime/session_mgr.cc @@ -43,8 +43,8 @@ SessionMgr::SessionMgr( worker_cache_factory_(std::move(worker_cache_factory)) {} string SessionMgr::WorkerNameFromServerDef(const ServerDef& server_def) { - return strings::StrCat("/job:", server_def.job_name(), - "/replica:0/task:", server_def.task_index()); + return strings::StrCat("/job:", server_def.job_name(), "/replica:0/task:", + server_def.task_index()); } Status SessionMgr::CreateSession(const string& session, @@ -64,8 +64,13 @@ Status SessionMgr::CreateSession(const string& session, TF_RETURN_IF_ERROR(worker_cache_factory_(server_def, &worker_cache)); } + if (worker_cache != nullptr & default_worker_cache_.get() != nullptr) { + worker_cache->SetLogging(this->is_logging_active_); + } + CHECK(!worker_env_->local_devices.empty()) << "The WorkerEnv must have at least one device in `local_devices`."; + std::vector renamed_devices; for (Device* d : worker_env_->local_devices) { renamed_devices.push_back(RenamedDevice::NewRenamedDevice( @@ -113,4 +118,77 @@ std::shared_ptr SessionMgr::LegacySession() { return legacy_session_; } +void SessionMgr::SetLogging(bool active) { + mutex_lock l(mu_); + this->is_logging_active_ = active; + // Legacy Session + if (legacy_session_) { + auto* worker_cache = legacy_session_->worker_cache.get(); + if (worker_cache) { + worker_cache->SetLogging(active); + } + } + + for (const auto& session_kv : sessions_) { + auto session = session_kv.second.get(); + if (session) { + auto* worker_cache = session->worker_cache.get(); + if (worker_cache) { + worker_cache->SetLogging(active); + } + } + } +} + +void SessionMgr::RetrieveLogs(tensorflow::int64 step_id, + LoggingResponse* response) { + mutex_lock l(mu_); + // Legacy Session + if (legacy_session_) { + auto* worker_cache = legacy_session_->worker_cache.get(); + if (worker_cache) { + auto step_stats = StepStats(); + if (worker_cache->RetrieveLogs(step_id, &step_stats)) { + auto* labeled_step_stats = response->add_step(); + labeled_step_stats->set_step_id(step_id); + labeled_step_stats->mutable_step_stats()->Swap(&step_stats); + } + } + } + for (const auto& session_kv : sessions_) { + auto session = session_kv.second.get(); + if (session) { + auto* worker_cache = session->worker_cache.get(); + if (worker_cache) { + auto step_stats = StepStats(); + if (worker_cache->RetrieveLogs(step_id, &step_stats)) { + auto* labeled_step_stats = response->add_step(); + labeled_step_stats->set_step_id(step_id); + labeled_step_stats->mutable_step_stats()->Swap(&step_stats); + } + } + } + } +} + +void SessionMgr::ClearLogs() { + mutex_lock l(mu_); + // Legacy Session + if (legacy_session_) { + auto* worker_cache = legacy_session_->worker_cache.get(); + if (worker_cache) { + worker_cache->ClearLogs(); + } + } + + for (const auto& session_kv : sessions_) { + auto session = session_kv.second.get(); + if (session) { + auto* worker_cache = session->worker_cache.get(); + if (worker_cache) { + worker_cache->ClearLogs(); + } + } + } +} } // namespace tensorflow diff --git a/tensorflow/core/distributed_runtime/session_mgr.h b/tensorflow/core/distributed_runtime/session_mgr.h index 3ce260d12e..4c9702d522 100644 --- a/tensorflow/core/distributed_runtime/session_mgr.h +++ b/tensorflow/core/distributed_runtime/session_mgr.h @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/protobuf/tensorflow_server.pb.h" +#include "tensorflow/core/protobuf/worker.pb.h" namespace tensorflow { @@ -56,6 +57,12 @@ class SessionMgr { static string WorkerNameFromServerDef(const ServerDef& server_def); + void SetLogging(bool active); + + void RetrieveLogs(tensorflow::int64 step_id, LoggingResponse* response); + + void ClearLogs(); + private: const WorkerEnv* const worker_env_; // Not owned. @@ -75,6 +82,8 @@ class SessionMgr { std::unique_ptr default_worker_cache_; std::shared_ptr legacy_session_; + bool is_logging_active_ = false; + const WorkerCacheFactory worker_cache_factory_; std::shared_ptr WorkerSessionForSessionUnlocked( -- GitLab From 4f9ef6c625913947b8c83cdaef957b23b0bada62 Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Thu, 25 Jan 2018 15:59:12 -0800 Subject: [PATCH 077/423] In the TF cost model, ensure when id grows, records for each op's outputs match the number of outputs. PiperOrigin-RevId: 183306267 --- tensorflow/core/graph/costmodel.cc | 53 +++++++++++++++++++----------- tensorflow/core/graph/costmodel.h | 4 +-- 2 files changed, 36 insertions(+), 21 deletions(-) diff --git a/tensorflow/core/graph/costmodel.cc b/tensorflow/core/graph/costmodel.cc index b1e6cf64e8..4118f14f8b 100644 --- a/tensorflow/core/graph/costmodel.cc +++ b/tensorflow/core/graph/costmodel.cc @@ -57,10 +57,10 @@ void CostModel::MergeFromLocal(const Graph& g, const CostModel& cm) { const int local_id = cm.Id(n); const int global_id = Id(n); if (local_id < 0 || global_id < 0) continue; - Ensure(global_id); + int num_slots = cm.slot_bytes_[local_id].size(); + Ensure(global_id, num_slots); count_[global_id] += cm.count_[local_id]; time_[global_id] += cm.time_[local_id]; - int num_slots = cm.slot_bytes_[local_id].size(); if (num_slots > 0) { if (slot_bytes_[global_id].empty()) { slot_bytes_[global_id].resize(num_slots); @@ -78,11 +78,11 @@ void CostModel::MergeFromGlobal(const CostModel& cm) { CHECK(is_global_); CHECK_EQ(true, cm.is_global()); const int num_nodes = cm.count_.size(); - Ensure(num_nodes); - for (int i = 0; i < num_nodes; ++i) { + for (int i = num_nodes - 1; i >= 0; --i) { count_[i] += cm.count_[i]; time_[i] += cm.time_[i]; int num_slots = cm.slot_bytes_[i].size(); + Ensure(i, num_slots); if (num_slots > 0) { if (slot_bytes_[i].empty()) { slot_bytes_[i].resize(num_slots); @@ -106,7 +106,7 @@ void CostModel::MergeFromStats(const NodeNameToCostIdMap& map, // copy/send/recv nodes, feed/fetch, etc. if (iter == map.end()) continue; int32 global_id = iter->second; - Ensure(global_id); + Ensure(global_id, ns.output_size()); int64 elapsed_micros = ns.op_end_rel_micros() - ns.op_start_rel_micros(); count_[global_id]++; time_[global_id] += elapsed_micros; @@ -122,7 +122,7 @@ void CostModel::MergeFromStats(const NodeNameToCostIdMap& map, } } -void CostModel::Ensure(int id) { +void CostModel::Ensure(int id, int num_outputs) { if (slot_bytes_.size() <= static_cast(id)) { slot_bytes_.resize(id + 1); count_.resize(id + 1); @@ -131,25 +131,37 @@ void CostModel::Ensure(int id) { max_exec_time_.resize(id + 1); output_port_alloc_ids_.resize(id + 1); } + if (num_outputs > 0) { + auto perslot = &slot_bytes_[id]; + auto output_port_alloc_ids = &output_port_alloc_ids_[id]; + auto max_mem_usage = &max_mem_usage_[id]; + + CHECK_LE(perslot->size(), num_outputs); + DCHECK_EQ(output_port_alloc_ids->size(), perslot->size()); + DCHECK_EQ(max_mem_usage->output_port_mem.size(), perslot->size()); + DCHECK_EQ(max_mem_usage->output_port_shape.size(), perslot->size()); + DCHECK_EQ(max_mem_usage->output_port_type.size(), perslot->size()); + + perslot->resize(num_outputs, Bytes(-1)); + output_port_alloc_ids->resize(num_outputs, -1); + max_mem_usage->output_port_mem.resize(num_outputs, Bytes(-1)); + max_mem_usage->output_port_shape.resize(num_outputs, unknown_shape_); + max_mem_usage->output_port_type.resize(num_outputs, DT_INVALID); + } } void CostModel::SetNumOutputs(const Node* node, int num_outputs) { const int id = Id(node); if (id < 0) return; - Ensure(id); + // Do not resize the number of slots before checking its existing number of + // slots. + Ensure(id, 0); auto perslot = &slot_bytes_[id]; - auto max_mem_usage = &max_mem_usage_[id]; - auto output_port_alloc_ids = &output_port_alloc_ids_[id]; if (!perslot->empty()) { CHECK_EQ(num_outputs, perslot->size()) << "Cannot resize slot_bytes, node=" << node->name(); - } else { - perslot->resize(num_outputs, Bytes(-1)); - output_port_alloc_ids->resize(num_outputs, -1); - max_mem_usage->output_port_mem.resize(num_outputs, Bytes(-1)); - max_mem_usage->output_port_shape.resize(num_outputs, unknown_shape_); - max_mem_usage->output_port_type.resize(num_outputs, DT_INVALID); } + Ensure(id, num_outputs); } void CostModel::RecordCount(const Node* node, int count) { @@ -198,7 +210,7 @@ void CostModel::RecordTime(const Node* node, Microseconds time) { const int id = Id(node); if (id < 0) return; DCHECK(node->IsOp()) << node->DebugString(); - Ensure(id); + Ensure(id, node->num_outputs()); time_[id] += time; } @@ -240,7 +252,10 @@ void CostModel::RecordMaxMemorySize(const Node* node, int output_slot, const DataType& dtype) { const int id = Id(node); if (id < 0) return; - Ensure(id); + CHECK_LT(output_slot, node->num_outputs()) + << "Unexpected output slot for node " << node->DebugString() << ". Got " + << output_slot << " but its num_outputs is " << node->num_outputs(); + Ensure(id, node->num_outputs()); auto& current_max = max_mem_usage_[id].output_port_mem[output_slot]; // If the memory allocator doesn't track memory usage, let's infer a lower // bound from the tensor shape and its data type. @@ -316,7 +331,7 @@ void CostModel::RecordMemoryStats(const Node* node, void CostModel::RecordMaxExecutionTime(const Node* node, Microseconds time) { const int id = Id(node); if (id < 0) return; - Ensure(id); + Ensure(id, node->num_outputs()); max_exec_time_[id] = std::max(max_exec_time_[id], time); } @@ -332,7 +347,7 @@ void CostModel::RecordAllocationId(const Node* node, int output_slot, int64 alloc_id) { const int id = Id(node); if (id < 0) return; - Ensure(id); + Ensure(id, node->num_outputs()); output_port_alloc_ids_[id][output_slot] = alloc_id; } diff --git a/tensorflow/core/graph/costmodel.h b/tensorflow/core/graph/costmodel.h index 081eb2ff4c..c60a946c2c 100644 --- a/tensorflow/core/graph/costmodel.h +++ b/tensorflow/core/graph/costmodel.h @@ -183,8 +183,8 @@ class CostModel { const bool is_global_; - // Resizes vectors so that they are large enough for "id". - void Ensure(int id); + // Resizes vectors so that they are large enough for "id" and id's outputs. + void Ensure(int id, int num_outputs); // Nodes and Edges whose count is < this value // get type/byte estimates of 0. -- GitLab From 9581462f8743ee92f39d46263d68fc1283082b44 Mon Sep 17 00:00:00 2001 From: Max Galkin Date: Thu, 25 Jan 2018 16:08:13 -0800 Subject: [PATCH 078/423] Show friendlier error message on failure in tf_optimizer.i Without it we trigger a segmentation fault, but later in a different stack, which is not so helpful. PiperOrigin-RevId: 183307729 --- tensorflow/python/grappler/tf_optimizer.i | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tensorflow/python/grappler/tf_optimizer.i b/tensorflow/python/grappler/tf_optimizer.i index f0dd4483a6..1b657983a4 100644 --- a/tensorflow/python/grappler/tf_optimizer.i +++ b/tensorflow/python/grappler/tf_optimizer.i @@ -103,6 +103,11 @@ PyObject* TF_OptimizeGraph( std::unique_ptr grappler_item = tensorflow::grappler::GrapplerItemFromMetaGraphDef(graph_id, metagraph, item_config); + if (!grappler_item) { + TF_SetStatus(out_status, TF_INVALID_ARGUMENT, "Failed to import metagraph, check error log for more info."); + return nullptr; + } + tensorflow::DeviceBase* cpu_device = nullptr; tensorflow::GraphDef out_graph; tensorflow::grappler::MetaOptimizer optimizer(cpu_device, rewriter_config); -- GitLab From 2564d5e90e12b4ec3dbd01b442b42a2a8ac7f8f6 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Thu, 25 Jan 2018 16:15:50 -0800 Subject: [PATCH 079/423] Make batch_sequences_with_states_test.py work with C API enabled, take 2. This fixes the original rollback by using placeholders for the SparseTensor shapes. The flakiness was caused by the nondeterministic ordering of the sequences dict. PiperOrigin-RevId: 183308774 --- .../batch_sequences_with_states_test.py | 30 +++++++++++++++++-- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py b/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py index 2a0ef0e6b3..dbdbb08a82 100644 --- a/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py +++ b/tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py @@ -53,7 +53,7 @@ class BatchSequencesWithStatesTest(test.TestCase): sp_tensor1 = sparse_tensor.SparseTensor( array_ops.constant(ind1, dtypes.int64), array_ops.constant(val1, dtypes.int64), - array_ops.constant(shape1, dtypes.int64)) + array_ops.placeholder_with_default(shape1, shape=[2])) ind2 = np.array([ [0, 0, 1], [0, 1, 0], @@ -68,7 +68,7 @@ class BatchSequencesWithStatesTest(test.TestCase): sp_tensor2 = sparse_tensor.SparseTensor( array_ops.constant(ind2, dtypes.int64), array_ops.constant(val2, dtypes.int64), - array_ops.constant(shape2, dtypes.int64)) + array_ops.placeholder_with_default(shape2, shape=[3])) sp_tensor3 = sparse_tensor.SparseTensor( array_ops.constant([[1, 9], [2, 2], [2, 10]], dtypes.int64), array_ops.constant([7, 15, 2], dtypes.int64), @@ -320,6 +320,18 @@ class BatchSequencesWithStatesTest(test.TestCase): def testNotAMultiple(self): num_unroll = 3 # Not a divisor of value_length - # so padding would have been necessary. + + # Use placeholder_with_default in sequences to make sure we get runtime + # error instead of shape inference error + sequences = { + "seq1": array_ops.placeholder_with_default(self.sequences["seq1"], + shape=(None, 5)), + "seq2": array_ops.placeholder_with_default(self.sequences["seq2"], + shape=(None, 4, 2)), + "seq3": self.sequences["seq3"], + "seq4": self.sequences["seq4"], + } + with self.test_session() as sess: with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, ".*should be a multiple of: 3, but saw " @@ -330,7 +342,7 @@ class BatchSequencesWithStatesTest(test.TestCase): with coord.stop_on_exception(): next_batch = sqss.batch_sequences_with_states( input_key=self.key, - input_sequences=self.sequences, + input_sequences=sequences, input_context=self.context, input_length=3, initial_states=self.initial_states, @@ -493,6 +505,18 @@ class BatchSequencesWithStatesTest(test.TestCase): expected_seq4_batch2=expected_seq4_batch2) +class BatchSequencesWithStatesTestWithCApi(BatchSequencesWithStatesTest): + + def setUp(self): + self._prev_value = ops._USE_C_API + ops._USE_C_API = True + super(BatchSequencesWithStatesTestWithCApi, self).setUp() + + def tearDown(self): + super(BatchSequencesWithStatesTestWithCApi, self).tearDown() + ops._USE_C_API = self._prev_value + + class PaddingTest(test.TestCase): def testPaddingInvalidLengths(self): -- GitLab From 8a87518ae7074d5a0da779089e7024cd0920bba4 Mon Sep 17 00:00:00 2001 From: cclauss Date: Fri, 26 Jan 2018 01:31:41 +0100 Subject: [PATCH 080/423] import tensorflow as tf (#16318) * import tensorflow as tf * import tensorflow as tf * from contextlib import contextmanager * remove the last remaining change to py2tf --- tensorflow/compiler/tests/binary_ops_test.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 16856bd736..c95fb1c515 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -774,15 +774,15 @@ class BinaryOpsTest(XLATestCase): def DISABLED_testSparseMatMul(self): # Binary wrappers for sparse_matmul with different hints def SparseMatmulWrapperTF(a, b): - return tf.sparse_matmul(a, b, a_is_sparse=True) + return math_ops.sparse_matmul(a, b, a_is_sparse=True) def SparseMatmulWrapperFT(a, b): - return tf.sparse_matmul(a, b, b_is_sparse=True) + return math_ops.sparse_matmul(a, b, b_is_sparse=True) def SparseMatmulWrapperTT(a, b): - return tf.sparse_matmul(a, b, a_is_sparse=True, b_is_sparse=True) + return math_ops.sparse_matmul(a, b, a_is_sparse=True, b_is_sparse=True) - self._testMatMul(tf.sparse_matmul) + self._testMatMul(math_ops.sparse_matmul) self._testMatMul(SparseMatmulWrapperTF) self._testMatMul(SparseMatmulWrapperFT) self._testMatMul(SparseMatmulWrapperTT) -- GitLab From 1b44a76921295fa5dfa7271f3a486b0ceaa8b3e1 Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Thu, 25 Jan 2018 16:45:04 -0800 Subject: [PATCH 081/423] Deleted unused data fields PiperOrigin-RevId: 183312596 --- tensorflow/core/grappler/costs/cost_estimator.h | 3 --- tensorflow/core/grappler/costs/measuring_cost_estimator.cc | 4 ---- 2 files changed, 7 deletions(-) diff --git a/tensorflow/core/grappler/costs/cost_estimator.h b/tensorflow/core/grappler/costs/cost_estimator.h index 852e69737b..b7eaf8dc63 100644 --- a/tensorflow/core/grappler/costs/cost_estimator.h +++ b/tensorflow/core/grappler/costs/cost_estimator.h @@ -85,10 +85,7 @@ struct Costs { typedef NanoSeconds Duration; // Overall cost of running the graph; latency. - // Mean Duration execution_time; - Duration min_execution_time; - Duration max_execution_time; // Computation cost of running the graph. Duration compute_time; diff --git a/tensorflow/core/grappler/costs/measuring_cost_estimator.cc b/tensorflow/core/grappler/costs/measuring_cost_estimator.cc index 8fd1801863..ea4320687a 100644 --- a/tensorflow/core/grappler/costs/measuring_cost_estimator.cc +++ b/tensorflow/core/grappler/costs/measuring_cost_estimator.cc @@ -117,8 +117,6 @@ Status MeasuringCostEstimator::PredictCosts(const GraphDef& optimized_graph, LOG(ERROR) << "Failed to measure graph performance: " << status.error_message(); costs->execution_time = Costs::Duration::max(); - costs->max_execution_time = Costs::Duration::max(); - costs->min_execution_time = 0; return status; } @@ -126,8 +124,6 @@ Status MeasuringCostEstimator::PredictCosts(const GraphDef& optimized_graph, // to filter out outliers. RobustStats stats(times); costs->execution_time = Costs::Duration(stats.mean()); - costs->max_execution_time = Costs::Duration(stats.hi()); - costs->min_execution_time = Costs::Duration(stats.lo()); return Status::OK(); } -- GitLab From 879ea264780e6867ac48f507cddcdd9cb1e9b544 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Thu, 25 Jan 2018 16:45:52 -0800 Subject: [PATCH 082/423] Automated g4 rollback of changelist 183296506 PiperOrigin-RevId: 183312680 --- .../compiler/xla/service/hlo_matchers.cc | 24 --------- .../compiler/xla/service/hlo_matchers.h | 53 ++----------------- .../compiler/xla/service/hlo_matchers_test.cc | 33 ------------ 3 files changed, 3 insertions(+), 107 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_matchers.cc b/tensorflow/compiler/xla/service/hlo_matchers.cc index fe1bf61e97..4255d60866 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers.cc @@ -102,30 +102,6 @@ bool HloGetTupleElementMatcher::MatchAndExplain( return true; } -void HloCustomCallMatcher::DescribeTo(std::ostream* os) const { - HloMatcher::DescribeTo(os); - *os << " with call target that " - << ::testing::DescribeMatcher(call_target_matcher_); -} - -bool HloCustomCallMatcher::MatchAndExplain( - const HloInstruction* instruction, - ::testing::MatchResultListener* listener) const { - if (!HloMatcher::MatchAndExplain(instruction, listener)) { - return false; - } - ::testing::StringMatchResultListener sub_listener; - bool result = ExplainMatchResult( - call_target_matcher_, instruction->custom_call_target(), &sub_listener); - if (sub_listener.str().empty()) { - sub_listener << " that " - << ::testing::DescribeMatcher(call_target_matcher_, - /*negation=*/!result); - } - *listener << "custom-call with call target" << sub_listener.str(); - return result; -} - } // namespace testing void PrintTo(const HloInstruction* inst, ::std::ostream* os) { diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h index 103f04a2cb..9206cdac05 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.h +++ b/tensorflow/compiler/xla/service/hlo_matchers.h @@ -56,8 +56,8 @@ class HloParameterMatcher : public HloMatcher { // index to match. class HloGetTupleElementMatcher : public HloMatcher { public: - HloGetTupleElementMatcher(::testing::Matcher operand, - int64 tuple_index) + explicit HloGetTupleElementMatcher( + ::testing::Matcher operand, int64 tuple_index) : HloMatcher(HloOpcode::kGetTupleElement, /*operands=*/{operand}), tuple_index_(tuple_index) {} @@ -68,24 +68,6 @@ class HloGetTupleElementMatcher : public HloMatcher { int64 tuple_index_; }; -// Custom matcher for custom-call instructions, which accepts a matcher for its -// call target. -class HloCustomCallMatcher : public HloMatcher { - public: - HloCustomCallMatcher( - ::testing::Matcher call_target_matcher, - std::vector<::testing::Matcher> operands) - : HloMatcher(HloOpcode::kCustomCall, operands), - call_target_matcher_(call_target_matcher) {} - - bool MatchAndExplain(const HloInstruction* instruction, - ::testing::MatchResultListener* listener) const override; - void DescribeTo(std::ostream* os) const override; - - private: - ::testing::Matcher call_target_matcher_; -}; - // HloInstruction* matchers for opcode and operands. Example: // namespace op = xla::opcode_matchers; // EXPECT_THAT(instruction, @@ -112,6 +94,7 @@ HLO_MATCHER(Convert); HLO_MATCHER(Convolution); HLO_MATCHER(Copy); HLO_MATCHER(CrossReplicaSum); +HLO_MATCHER(CustomCall); HLO_MATCHER(Divide); HLO_MATCHER(Dot); HLO_MATCHER(DynamicSlice); @@ -201,36 +184,6 @@ inline ::testing::Matcher GetTupleElement() { new ::xla::testing::HloMatcher(HloOpcode::kGetTupleElement, {})); } -// - CustomCall(T, operand1, ..., operandN) matches a CustomCall with call -// target T and the given operands. -// -// - CustomCall(operand1, ..., operandN) matches any CustomCall HLO with the -// given operands. -// -// - CustomCall() matches any CustomCall HLO at all. -template -inline ::testing::Matcher CustomCall( - ::testing::Matcher call_target_matcher, M... operands) { - return ::testing::MakeMatcher(new ::xla::testing::HloCustomCallMatcher( - call_target_matcher, {operands...})); -} -// This overload of CustomCall(A, B, C, ...) exists iff A is not convertible to -// ::testing::Matcher. In that case, we want to prefer the overload -// above. -template >::value, - void>::type*> -inline ::testing::Matcher CustomCall( - FirstM operands_first, M... operands_rest) { - return ::testing::MakeMatcher(new ::xla::testing::HloMatcher( - HloOpcode::kCustomCall, {operands_first, operands_rest...})); -} -inline ::testing::Matcher CustomCall() { - return ::testing::MakeMatcher( - new ::xla::testing::HloMatcher(HloOpcode::kCustomCall, {})); -} - #undef HLO_MATCHER } // namespace opcode_matchers diff --git a/tensorflow/compiler/xla/service/hlo_matchers_test.cc b/tensorflow/compiler/xla/service/hlo_matchers_test.cc index 1c21703a45..1465d1cacd 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers_test.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers_test.cc @@ -23,12 +23,6 @@ using ::testing::Eq; namespace xla { namespace { -string DescribeHloMatcher(const ::testing::Matcher& m) { - std::stringstream ss; - m.DescribeTo(&ss); - return ss.str(); -} - template string Explain(const T& t, const M& m) { ::testing::StringMatchResultListener listener; @@ -73,32 +67,5 @@ TEST(HloMatchersTest, Test) { "add")); } -TEST(HloMatchersTest, CustomCallMatcher) { - auto c1 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); - auto c2 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); - auto call = HloInstruction::CreateCustomCall( - ShapeUtil::MakeShape(F32, {1}), {c1.get(), c2.get()}, "foo_target"); - - EXPECT_THAT(call.get(), op::CustomCall()); - EXPECT_THAT(call.get(), op::CustomCall(c1.get(), c2.get())); - EXPECT_THAT(call.get(), op::CustomCall("foo_target")); - EXPECT_THAT(call.get(), op::CustomCall("foo_target", c1.get(), c2.get())); - EXPECT_THAT(call.get(), op::CustomCall(::testing::StartsWith("foo"))); - EXPECT_THAT(call.get(), - op::CustomCall(::testing::Not(::testing::StartsWith("bar")))); - - // Wrong number of operands. - EXPECT_THAT(call.get(), ::testing::Not(op::CustomCall(c1.get()))); - - // Call target does not match. - EXPECT_THAT(call.get(), - ::testing::Not(op::CustomCall(::testing::StartsWith("bar")))); - - EXPECT_THAT(Explain(call.get(), op::CustomCall("bar")), - R"(custom-call with call target that isn't equal to "bar")"); - EXPECT_THAT(DescribeHloMatcher(op::CustomCall("foo_target")), - R"(custom-call with call target that is equal to "foo_target")"); -} - } // namespace } // namespace xla -- GitLab From 68f55a1b061b821866ae4d4aa8b43da628d0e18a Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Thu, 25 Jan 2018 16:50:24 -0800 Subject: [PATCH 083/423] Record requested cpu cores in OpPerformance. PiperOrigin-RevId: 183313321 --- tensorflow/core/grappler/costs/op_performance_data.proto | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tensorflow/core/grappler/costs/op_performance_data.proto b/tensorflow/core/grappler/costs/op_performance_data.proto index 1d623b8db8..37f9ebd6a1 100644 --- a/tensorflow/core/grappler/costs/op_performance_data.proto +++ b/tensorflow/core/grappler/costs/op_performance_data.proto @@ -58,11 +58,18 @@ message LogNormalDistribution { double sigma = 2; } +message SessionInfo { + int64 intra_op_parallelism = 1; +} + // Performance data for tensorflow operations message OpPerformance { // The op OpInfo op = 1; + // Information about the session configs. + SessionInfo session_info = 12; + // The node name (optional). Makes it easier to associate the performance data // with a specific graph node. string node = 5; -- GitLab From c89c452cd7a1675a6e2332d09379469320197a8c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 17:08:50 -0800 Subject: [PATCH 084/423] [XLA] Disable half_test_cpu as it is flaky PiperOrigin-RevId: 183315762 --- tensorflow/compiler/xla/tests/BUILD | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 85ac28533d..4410647f84 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -849,7 +849,8 @@ xla_test( name = "half_test", srcs = ["half_test.cc"], backends = [ - "cpu", + # TODO(b/72509305): Flaky (fails with SEGV) as of 2018-01-25 + # "cpu", "gpu", ], deps = [ -- GitLab From fbd3e8a2c01d83a6aa6cca044fe5678d20035451 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Thu, 25 Jan 2018 17:20:07 -0800 Subject: [PATCH 085/423] Make kernel_tests/scalar_test.py work with the C API enabled. This also moves the set_producer_version function from a specific test file to test_util.py, since it's needed in two test files now. PiperOrigin-RevId: 183316990 --- tensorflow/python/framework/test_util.py | 12 ++++++++++++ tensorflow/python/kernel_tests/scalar_test.py | 4 +++- tensorflow/python/ops/nn_batchnorm_test.py | 15 ++------------- 3 files changed, 17 insertions(+), 14 deletions(-) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 0133318456..6a7e1d0c89 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -53,6 +53,7 @@ from tensorflow.python.eager import tape from tensorflow.python.framework import device as pydev from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.framework import importer from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import versions @@ -1460,3 +1461,14 @@ def get_node_def_from_graph(node_name, graph_def): if node_def.name == node_name: return node_def return None + + +def set_producer_version(graph, producer_version): + """Sets graph.graph_def_versions.producer to `producer_version`.""" + # The C API doesn't expose altering GraphDefVersions. We can indirectly set + # it via import_graph_def though. + graph_def = graph_pb2.GraphDef() + graph_def.versions.producer = producer_version + with graph.as_default(): + importer.import_graph_def(graph_def) + assert graph.graph_def_versions.producer, producer_version diff --git a/tensorflow/python/kernel_tests/scalar_test.py b/tensorflow/python/kernel_tests/scalar_test.py index b34426cc21..e65241981e 100644 --- a/tensorflow/python/kernel_tests/scalar_test.py +++ b/tensorflow/python/kernel_tests/scalar_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_io_ops from tensorflow.python.ops import math_ops @@ -30,6 +31,7 @@ import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test +@test_util.with_c_api class ScalarTest(test.TestCase): def check(self, op, args, error, correct=None): @@ -51,7 +53,7 @@ class ScalarTest(test.TestCase): # Test various GraphDef versions for version in strict + lenient: with ops.Graph().as_default() as g: - g.graph_def_versions.producer = version + test_util.set_producer_version(g, version) with self.test_session(graph=g) as sess: feed = {} xs = placeholders(args, feed) diff --git a/tensorflow/python/ops/nn_batchnorm_test.py b/tensorflow/python/ops/nn_batchnorm_test.py index fc013b565b..eebfb17085 100644 --- a/tensorflow/python/ops/nn_batchnorm_test.py +++ b/tensorflow/python/ops/nn_batchnorm_test.py @@ -21,10 +21,8 @@ from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin -from tensorflow.core.framework import graph_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 @@ -40,15 +38,6 @@ from tensorflow.python.platform import test @test_util.with_c_api class BatchNormalizationTest(test.TestCase): - def SetProducerVersion(self, graph, producer_version): - # The C API doesn't expose altering GraphDefVersions. We can indirectly set - # it via import_graph_def though. - graph_def = graph_pb2.GraphDef() - graph_def.versions.producer = producer_version - with graph.as_default(): - importer.import_graph_def(graph_def) - assert graph.graph_def_versions.producer, producer_version - def _npBatchNorm(self, x, m, v, beta, gamma, epsilon, scale_after_normalization, shift_after_normalization): y = (x - m) / np.sqrt(v + epsilon) @@ -65,7 +54,7 @@ class BatchNormalizationTest(test.TestCase): def _tfBatchNormV1(self, x, m, v, beta, gamma, epsilon, scale_after_normalization): """Original implementation.""" - self.SetProducerVersion(ops.get_default_graph(), 8) + test_util.set_producer_version(ops.get_default_graph(), 8) return gen_nn_ops._batch_norm_with_global_normalization( x, m, v, beta, gamma, epsilon, scale_after_normalization) # pylint: enable=protected-access @@ -233,7 +222,7 @@ class BatchNormalizationTest(test.TestCase): epsilon = 0.001 for scale_after_normalization in [True, False]: # _batch_norm_with_global_normalization_grad is deprecated in v9 - self.SetProducerVersion(ops.get_default_graph(), 8) + test_util.set_producer_version(ops.get_default_graph(), 8) grad = gen_nn_ops._batch_norm_with_global_normalization_grad( x, m, v, gamma, backprop, epsilon, scale_after_normalization) dx, dm, dv, db, dg = grad -- GitLab From 8220c228ab066d23ddf506a58bb08b1694239a34 Mon Sep 17 00:00:00 2001 From: Yao Zhang Date: Thu, 25 Jan 2018 17:27:42 -0800 Subject: [PATCH 086/423] Add an option to input a GraphDef. PiperOrigin-RevId: 183317862 --- .../python/grappler/cost_analyzer_tool.py | 41 +++++++++++++++++-- 1 file changed, 37 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py index 146bb4311c..61dc4e2afb 100644 --- a/tensorflow/python/grappler/cost_analyzer_tool.py +++ b/tensorflow/python/grappler/cost_analyzer_tool.py @@ -23,18 +23,33 @@ import sys from google.protobuf import text_format +from tensorflow.core.framework import graph_pb2 from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops from tensorflow.python.grappler import cost_analyzer from tensorflow.python.grappler import tf_optimizer from tensorflow.python.platform import app from tensorflow.python.platform import gfile +from tensorflow.python.training import saver def main(_): - with gfile.GFile(FLAGS.input) as input_file: - metagraph = meta_graph_pb2.MetaGraphDef() - metagraph.ParseFromString(input_file.read()) + if FLAGS.metagraphdef: + with gfile.GFile(FLAGS.metagraphdef) as meta_file: + metagraph = meta_graph_pb2.MetaGraphDef() + metagraph.ParseFromString(meta_file.read()) + else: + with gfile.GFile(FLAGS.graphdef) as graph_file: + graph_def = graph_pb2.GraphDef() + graph_def.ParseFromString(graph_file.read()) + importer.import_graph_def(graph_def, name="") + graph = ops.get_default_graph() + fetch = graph.get_operation_by_name(FLAGS.fetch) + graph.add_to_collection("train_op", fetch) + metagraph = saver.export_meta_graph( + graph_def=graph.as_graph_def(), graph=graph) if FLAGS.rewriter_config is not None: rewriter_config = rewriter_config_pb2.RewriterConfig() @@ -49,7 +64,25 @@ def main(_): if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( - "--input", type=str, default=None, help="Input .meta file path.") + "--metagraphdef", + type=str, + default=None, + help="Input .meta MetaGraphDef file path.") + parser.add_argument( + "--graphdef", + type=str, + default=None, + help="Input .pb GraphDef file path.") + # Consider making flag fetch work together with flag metagraphdef. As some + # MetaGraphDef files don't have collection train_op. + parser.add_argument( + "--fetch", + type=str, + default=None, + help= + "The name of the fetch node. This flag is ignored if flag " + "metagraphdef is used." + ) parser.add_argument( "--rewriter_config", type=str, -- GitLab From 32a6eb80dcde4d107f21e41097fddd8341a725b9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 17:32:12 -0800 Subject: [PATCH 087/423] Move flatbuffer verifier to a separate lib PiperOrigin-RevId: 183318384 --- tensorflow/contrib/lite/model.cc | 15 +- tensorflow/contrib/lite/model_test.cc | 9 -- tensorflow/contrib/lite/tools/BUILD | 25 ++++ tensorflow/contrib/lite/tools/verifier.cc | 43 ++++++ tensorflow/contrib/lite/tools/verifier.h | 31 ++++ .../contrib/lite/tools/verifier_test.cc | 136 ++++++++++++++++++ 6 files changed, 237 insertions(+), 22 deletions(-) create mode 100644 tensorflow/contrib/lite/tools/verifier.cc create mode 100644 tensorflow/contrib/lite/tools/verifier.h create mode 100644 tensorflow/contrib/lite/tools/verifier_test.cc diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index ba29a2f4d1..415d984ad8 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -30,17 +30,6 @@ limitations under the License. namespace tflite { -namespace { -inline const tflite::Model* VerifyAndGetModel(const void* buf, size_t len) { - ::flatbuffers::Verifier verifier(static_cast(buf), len); - if (VerifyModelBuffer(verifier)) { - return ::tflite::GetModel(buf); - } else { - return nullptr; - } -} -} // namespace - const char* kEmptyTensorName = ""; std::unique_ptr FlatBufferModel::BuildFromFile( @@ -82,7 +71,7 @@ FlatBufferModel::FlatBufferModel(const char* filename, bool mmap_file, } if (!allocation_->valid() || !CheckModelIdentifier()) return; - model_ = VerifyAndGetModel(allocation_->base(), allocation_->bytes()); + model_ = ::tflite::GetModel(allocation_->base()); } bool FlatBufferModel::CheckModelIdentifier() const { @@ -103,7 +92,7 @@ FlatBufferModel::FlatBufferModel(const char* ptr, size_t num_bytes, allocation_ = new MemoryAllocation(ptr, num_bytes, error_reporter); if (!allocation_->valid()) return; - model_ = VerifyAndGetModel(allocation_->base(), allocation_->bytes()); + model_ = ::tflite::GetModel(allocation_->base()); } FlatBufferModel::FlatBufferModel(const Model* model, diff --git a/tensorflow/contrib/lite/model_test.cc b/tensorflow/contrib/lite/model_test.cc index 5330c8f594..66f22fd66a 100644 --- a/tensorflow/contrib/lite/model_test.cc +++ b/tensorflow/contrib/lite/model_test.cc @@ -20,7 +20,6 @@ limitations under the License. #include #include #include -#include #include "tensorflow/contrib/lite/model.h" @@ -247,14 +246,6 @@ TEST(BasicFlatBufferModel, TestNullErrorReporter) { ASSERT_NE(interpreter->Invoke(), kTfLiteOk); } -// Test what happens if we cannot bind any of the ops. -TEST(BasicFlatBufferModel, TestBuildModelFromCorruptedData) { - std::string corrupted_data = "123"; - auto model = FlatBufferModel::BuildFromBuffer(corrupted_data.c_str(), - corrupted_data.length()); - ASSERT_FALSE(model); -} - // Test that loading model directly from a Model flatbuffer works. TEST(BasicFlatBufferModel, TestBuildFromModel) { TestErrorReporter reporter; diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index 20df905270..1bffcfb987 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -93,3 +93,28 @@ filegroup( ), visibility = ["//tensorflow:__subpackages__"], ) + +cc_library( + name = "verifier", + srcs = ["verifier.cc"], + hdrs = ["verifier.h"], + deps = [ + "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite/schema:schema_fbs", + ], +) + +cc_test( + name = "verifier_test", + size = "small", + srcs = ["verifier_test.cc"], + deps = [ + ":verifier", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite/schema:schema_fbs", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + "@flatbuffers", + ], +) diff --git a/tensorflow/contrib/lite/tools/verifier.cc b/tensorflow/contrib/lite/tools/verifier.cc new file mode 100644 index 0000000000..95a0895379 --- /dev/null +++ b/tensorflow/contrib/lite/tools/verifier.cc @@ -0,0 +1,43 @@ +/* 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/tools/verifier.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" +#include "tensorflow/contrib/lite/version.h" + +namespace tflite { + +namespace { + +const Model* VerifyFlatbufferAndGetModel(const void* buf, size_t len) { + ::flatbuffers::Verifier verifier(static_cast(buf), len); + if (VerifyModelBuffer(verifier)) { + return ::tflite::GetModel(buf); + } else { + return nullptr; + } +} + +} // namespace + +bool Verify(const void* buf, size_t len) { + const Model* model = VerifyFlatbufferAndGetModel(buf, len); + if (model == nullptr) { + return false; + } + + return model->version() == TFLITE_SCHEMA_VERSION; +} +} // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier.h b/tensorflow/contrib/lite/tools/verifier.h new file mode 100644 index 0000000000..03e1f22b7e --- /dev/null +++ b/tensorflow/contrib/lite/tools/verifier.h @@ -0,0 +1,31 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_LITE_TOOLS_VERIFIER_H_ +#define TENSORFLOW_CONTRIB_LITE_TOOLS_VERIFIER_H_ + +#include + +namespace tflite { + +// Verifies the integrity of a Tensorflow Lite flatbuffer model file. +// Currently, it verifies: +// * The file is following a legit flatbuffer schema. +// * The model is in supported version. +bool Verify(const void* buf, size_t len); + +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_TOOLS_VERIFIER_H_ diff --git a/tensorflow/contrib/lite/tools/verifier_test.cc b/tensorflow/contrib/lite/tools/verifier_test.cc new file mode 100644 index 0000000000..0481a55a78 --- /dev/null +++ b/tensorflow/contrib/lite/tools/verifier_test.cc @@ -0,0 +1,136 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/tools/verifier.h" +#include "flatbuffers/flatbuffers.h" +#include "flatbuffers/util.h" +#include +#include "tensorflow/contrib/lite/allocation.h" +#include "tensorflow/contrib/lite/error_reporter.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" +#include "tensorflow/contrib/lite/testing/util.h" +#include "tensorflow/contrib/lite/version.h" + +namespace tflite { + +using flatbuffers::FlatBufferBuilder; +using flatbuffers::Offset; +using flatbuffers::Vector; + +// Class that abstracts the list of buffers at the end of the TF Lite structure +class DeferredBufferWriter { + public: + DeferredBufferWriter() { + data_.push_back({}); // sentinel empty buffer. + } + + Offset>> BuildBuffers(FlatBufferBuilder *builder) { + std::vector> buffer_vector; + for (const auto &vec : data_) { + auto data_buffer = builder->CreateVector(vec.data(), vec.size()); + buffer_vector.push_back(tflite::CreateBuffer(*builder, data_buffer)); + } + return builder->CreateVector(buffer_vector); + } + + // Registers a buffer index and takes ownership of the data to write to it. + int Record(std::vector data) { + int buffer_index = data_.size(); + data_.emplace_back(std::move(data)); + return buffer_index; + } + + private: + std::vector> data_; +}; + +TEST(VerifyModel, TestEmptyModel) { + FlatBufferBuilder builder; + auto model = CreateModel(builder, /*version=*/TFLITE_SCHEMA_VERSION, + /*operator_codes=*/0, /*subgraphs=*/0, + /*description=*/0, /*buffers=*/0); + ::tflite::FinishModelBuffer(builder, model); + + ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); +} + +TEST(VerifyModel, TestSimpleModel) { + FlatBufferBuilder builder; + auto inputs = builder.CreateVector({0}); + auto outputs = builder.CreateVector({1}); + auto operator_codes = builder.CreateVector(std::vector>{ + CreateOperatorCodeDirect(builder, BuiltinOperator_CUSTOM, "test")}); + auto operators = + builder.CreateVector(std::vector>{CreateOperator( + builder, /*opcode_index=*/0, + /*inputs=*/builder.CreateVector({0}), + /*outputs=*/builder.CreateVector({1}), BuiltinOptions_NONE, + /*builtin_options=*/0, + /*custom_options=*/0, ::tflite::CustomOptionsFormat_FLEXBUFFERS)}); + std::vector shape; + auto tensors = builder.CreateVector(std::vector>{ + CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, + "input", /*quantization=*/0), + CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, + "output", /*quantization=*/0)}); + auto subgraph = std::vector>( + {CreateSubGraph(builder, tensors, inputs, outputs, operators, + builder.CreateString("Main"))}); + + auto model = CreateModel(builder, TFLITE_SCHEMA_VERSION, operator_codes, + builder.CreateVector(subgraph), + builder.CreateString("SmartReply"), /*buffers=*/0); + + ::tflite::FinishModelBuffer(builder, model); + ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); +} + +TEST(VerifyModel, TestCorruptedData) { + string model = "123"; + ASSERT_FALSE(Verify(model.data(), model.size())); +} + +TEST(VerifyModel, TestUnsupportedVersion) { + FlatBufferBuilder builder; + auto model = CreateModel(builder, /*version=*/1, /*operator_codes=*/0, + /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); + ::tflite::FinishModelBuffer(builder, model); + ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize())); +} + +TEST(VerifyModel, TestRandomModificationIsNotAllowed) { + FlatBufferBuilder builder; + auto model = CreateModel(builder, /*version=*/TFLITE_SCHEMA_VERSION, + /*operator_codes=*/0, + /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); + ::tflite::FinishModelBuffer(builder, model); + + string model_content(reinterpret_cast(builder.GetBufferPointer()), + builder.GetSize()); + for (int i = 0; i < model_content.size(); i++) { + model_content[i] = (model_content[i] + 137) % 255; + EXPECT_FALSE(Verify(model_content.data(), model_content.size())) + << "Fail at position: " << i; + } +} + +// TODO(yichengfan): make up malicious files to test with. + +} // namespace tflite + +int main(int argc, char **argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} -- GitLab From c8f1a34ba6f9bbde7e3fbb106158661ded98f0a0 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Thu, 25 Jan 2018 17:50:40 -0800 Subject: [PATCH 088/423] Replace instances of `control_dependencies(None)` with `init_scope` when initializing variables. Today, when variables are constructed (Resource and otherwise), we lift certain operations, including the VarHandleOp and initialization ops, out of all control flow contexts; the mechanism for doing so is entering the context manager returned by `control_dependencies(None)`. This change replaces various instances of this mechanism with `init_scope`, which clears control dependencies, lifts ops out of function-building graphs, and pauses the gradient tape. As a result, variables that are created inside graph functions will be automatically hoisted into an outer context. PiperOrigin-RevId: 183320576 --- tensorflow/python/layers/base.py | 118 +++++++++--------- .../python/ops/resource_variable_ops.py | 4 +- tensorflow/python/ops/variable_scope.py | 4 +- tensorflow/python/ops/variables.py | 7 +- tensorflow/python/training/moving_averages.py | 4 +- tensorflow/python/training/optimizer.py | 2 +- 6 files changed, 73 insertions(+), 66 deletions(-) diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index 00faf3faa1..d892654ebe 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -500,13 +500,30 @@ class Layer(object): instance is returned. Raises: - RuntimeError: If called in Eager mode with partioned variable - regularization. + RuntimeError: If called with partioned variable regularization and + eager execution is enabled. """ - in_graph_mode = context.in_graph_mode() - if in_graph_mode: - existing_variables = set(tf_variables.global_variables()) + # `init_graph` should point to the graph in which variable initialization + # will occur; it should be None if and only if initialization will take + # place in the eager context. + init_graph = None + if context.in_graph_mode(): + default_graph = ops.get_default_graph() + if default_graph.building_function: + with ops.init_scope(): + # Retrieve the variables from the graph into which variables + # will be lifted; if initialization ops will be lifted into + # the eager context, then there is nothing to retrieve, since variable + # collections are not supported when eager execution is enabled. + if context.in_graph_mode(): + init_graph = ops.get_default_graph() + existing_variables = set(tf_variables.global_variables()) + else: + # Initialization ops will not be lifted out of the default graph. + init_graph = default_graph + existing_variables = set(tf_variables.global_variables()) + if dtype is None: dtype = self.dtype or dtypes.float32 @@ -523,54 +540,51 @@ class Layer(object): trainable=trainable and self.trainable, partitioner=partitioner) - if in_graph_mode: - if (trainable and self.trainable - and variable not in tf_variables.trainable_variables()): - # A custom getter / variable scope overrode the trainable flag. - trainable = False + if init_graph is not None: # pylint: disable=protected-access + # The variable was created and initialized in a graph. + if variable in existing_variables: # To match the behavior of tf.get_variable(), we only apply # regularization if the variable is newly created. return variable - if regularizer: - def regularizer_factory(): - if context.in_graph_mode(): - with vs.variable_scope(scope, reuse=reuse, - auxiliary_name_scope=False): - with ops.name_scope(self._name_scope_name(scope)): - if isinstance(variable, tf_variables.PartitionedVariable): - for v in variable: - with ops.colocate_with(v.op): - with ops.name_scope(name + '/Regularizer'): - regularization = regularizer(v) - if regularization is not None: - self.add_loss(regularization) - else: - with ops.colocate_with(variable.op): - with ops.name_scope(name + '/Regularizer'): - regularization = regularizer(variable) - if regularization is not None: - self.add_loss(regularization) + with init_graph.as_default(): + trainable_variables = tf_variables.trainable_variables() + if (trainable and self.trainable and + variable not in trainable_variables): + # A custom getter / variable scope overrode the trainable flag. + trainable = False + + if regularizer: + if isinstance(variable, tf_variables.PartitionedVariable): + for v in variable: + with ops.colocate_with(v.op): + with ops.name_scope(name + '/Regularizer'): + regularization = regularizer(v) + if regularization is not None: + self.add_loss(regularization) else: - if isinstance(variable, tf_variables.PartitionedVariable): - raise RuntimeError( - 'Partitioned variable regularization is not yet ' - 'supported when executing eagerly. File a feature request' - 'if this is important to you.') - # Save a zero-argument lambda which runs the regularizer on the - # variable, to be executed when `Layer.losses` is requested. - # This makes losses responsive to variable updates when - # executing eagerly. - self._losses.append(lambda: regularizer(variable)) - - if hasattr(self, '_defer_regularizers') and self._defer_regularizers: - # _defer_regularizers exists and is set to True if `build` was - # invoked in `__call__`: deferring regularizer construction - # prevents the regularizer from being created in an `init_scope`. - self._get_regularizer_factories().append(regularizer_factory) - else: - regularizer_factory() + with ops.colocate_with(variable.op): + with ops.name_scope(name + '/Regularizer'): + regularization = regularizer(variable) + if regularization is not None: + self.add_loss(regularization) + elif regularizer: # and initialization took place in an eager context + if isinstance(variable, tf_variables.PartitionedVariable): + raise RuntimeError( + 'Partitioned variable regularization is not yet ' + 'supported when executing eagerly. File a feature request' + 'if this is important to you.') + # Save a zero-argument lambda which runs the regularizer on the + # variable, to be executed when `Layer.losses` is requested. + # This makes losses responsive to variable updates when executing + # eagerly. + # + # TODO(akshayka): Do the same for graphs as well, so that losses + # collected in a while_loop can be run outside its control flow + # context and so that losses won't be swallowed up by graph functions + # (i.e., `.losses()` should always create regularizers). + self._losses.append(lambda: regularizer(variable)) if trainable: self._trainable_weights.append(variable) @@ -670,15 +684,7 @@ class Layer(object): except AttributeError: pass input_shapes = nest.map_structure(lambda x: x.get_shape(), inputs) - - # Signal to `add_variable` that regularizer construction should be - # deferred. - self._defer_regularizers = True - with ops.init_scope(): - self.build(input_shapes) - # Create any regularizers added by `build`. - self._maybe_create_variable_regularizers() - self._defer_regularizers = False + self.build(input_shapes) try: # Note: not all sub-classes of Layer call Layer.__init__ (especially # the ones under tensorflow/python/keras). Hence we recompute this diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index f727a29233..bdf41cd75d 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -348,11 +348,11 @@ class ResourceVariable(variables.Variable): if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections: collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES] self._save_slice_info = None - self._in_graph_mode = context.in_graph_mode() # Save the graph's container prefix for error checking. Reading the value of # the ResourceVariable from another Graph in Eager mode is an error. self._container_prefix = ops.get_default_graph()._container_prefix # pylint: disable=protected-access - with ops.control_dependencies(None): + with ops.init_scope(): + self._in_graph_mode = context.in_graph_mode() with ops.name_scope(name, "Variable", [] if init_from_fn else [initial_value]) as name: # pylint: disable=protected-access diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index db594ac6a0..81565a6377 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -771,8 +771,8 @@ class _VariableStore(object): if initializer is None: initializer, initializing_from_value = self._get_default_initializer( name=name, shape=shape, dtype=dtype) - # Clear control dependencies while creating the initializer. - with ops.control_dependencies(None): + # Enter an init scope when creating the initializer. + with ops.init_scope(): if initializing_from_value: init_val = initializer variable_dtype = None diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index ff19612383..19e3298e40 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -212,6 +212,7 @@ class Variable(object): if not context.in_graph_mode(): raise RuntimeError("tf.Variable not supported in Eager mode. " "Please use tfe.Variable instead") + self._in_graph_mode = context.in_graph_mode() if variable_def: # If variable_def is provided, recreates the variable from its fields. if initial_value: @@ -307,7 +308,7 @@ class Variable(object): if trainable and ops.GraphKeys.TRAINABLE_VARIABLES not in collections: collections = list(collections) + [ops.GraphKeys.TRAINABLE_VARIABLES] - with ops.control_dependencies(None): + with ops.init_scope(): with ops.name_scope(name, "Variable", [] if init_from_fn else [initial_value]) as name: @@ -378,8 +379,8 @@ class Variable(object): else: with ops.colocate_with(self._variable.op): self._snapshot = array_ops.identity(self._variable, name="read") + ops.add_to_collections(collections, self) - ops.add_to_collections(collections, self) self._caching_device = caching_device self._save_slice_info = None self._constraint = constraint @@ -553,7 +554,7 @@ class Variable(object): A `Tensor` holding the value of this variable after its initializer has run. """ - with ops.control_dependencies(None): + with ops.init_scope(): return control_flow_ops.cond(is_variable_initialized(self), self.read_value, lambda: self.initial_value) diff --git a/tensorflow/python/training/moving_averages.py b/tensorflow/python/training/moving_averages.py index e34c759e89..43ed1ac170 100644 --- a/tensorflow/python/training/moving_averages.py +++ b/tensorflow/python/training/moving_averages.py @@ -187,7 +187,7 @@ def _zero_debias(unbiased_var, value, decay): with variable_scope.variable_scope( unbiased_var.op.name, values=[unbiased_var, value, decay]) as scope: with ops.colocate_with(unbiased_var): - with ops.control_dependencies(None): + with ops.init_scope(): biased_initializer = init_ops.zeros_initializer( dtype=unbiased_var.dtype)(unbiased_var.get_shape()) local_step_initializer = init_ops.zeros_initializer() @@ -385,7 +385,7 @@ class ExponentialMovingAverage(object): # For variables: to lower communication bandwidth across devices we keep # the moving averages on the same device as the variables. For other # tensors, we rely on the existing device allocation mechanism. - with ops.control_dependencies(None): + with ops.init_scope(): if isinstance(var, variables.Variable): avg = slot_creator.create_slot(var, var.initialized_value(), diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index 038469b1ba..719b83e5ca 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -514,7 +514,7 @@ class Optimizer(object): if not var_list: raise ValueError("No gradients provided for any variable: %s." % ([str(v) for _, _, v in converted_grads_and_vars],)) - with ops.control_dependencies(None): + with ops.init_scope(): self._create_slots([_get_variable_for(v) for v in var_list]) update_ops = [] with ops.name_scope(name, self._name) as name: -- GitLab From 642454bd3296959f0025e1fb1730cdd95c36713f Mon Sep 17 00:00:00 2001 From: Adam Roberts Date: Thu, 25 Jan 2018 17:58:48 -0800 Subject: [PATCH 089/423] Add input_shape to seq2seq helpers. PiperOrigin-RevId: 183321394 --- .../contrib/seq2seq/python/ops/helper.py | 36 +++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/tensorflow/contrib/seq2seq/python/ops/helper.py b/tensorflow/contrib/seq2seq/python/ops/helper.py index ef3722ee41..6d8f786223 100644 --- a/tensorflow/contrib/seq2seq/python/ops/helper.py +++ b/tensorflow/contrib/seq2seq/python/ops/helper.py @@ -72,6 +72,14 @@ class Helper(object): """ raise NotImplementedError("batch_size has not been implemented") + @abc.abstractproperty + def input_shape(self): + """Shape of each input element in batch. + + Returns a `TensorShape`. + """ + raise NotImplementedError("input_shape has not been implemented") + @abc.abstractproperty def sample_ids_shape(self): """Shape of tensor returned by `sample`, excluding the batch dimension. @@ -127,6 +135,7 @@ class CustomHelper(Helper): self._sample_fn = sample_fn self._next_inputs_fn = next_inputs_fn self._batch_size = None + self._input_shape = None self._sample_ids_shape = tensor_shape.TensorShape(sample_ids_shape or []) self._sample_ids_dtype = sample_ids_dtype or dtypes.int32 @@ -149,6 +158,8 @@ class CustomHelper(Helper): (finished, next_inputs) = self._initialize_fn() if self._batch_size is None: self._batch_size = array_ops.size(finished) + if self._input_shape is None: + self._input_shape = next_inputs.shape[1:] return (finished, next_inputs) def sample(self, time, outputs, state, name=None): @@ -184,6 +195,7 @@ class TrainingHelper(Helper): """ with ops.name_scope(name, "TrainingHelper", [inputs, sequence_length]): inputs = ops.convert_to_tensor(inputs, name="inputs") + self._inputs = inputs if not time_major: inputs = nest.map_structure(_transpose_batch_time, inputs) @@ -199,11 +211,16 @@ class TrainingHelper(Helper): lambda inp: array_ops.zeros_like(inp[0, :]), inputs) self._batch_size = array_ops.size(sequence_length) + self._input_shape = inputs.shape[2:] @property def batch_size(self): return self._batch_size + @property + def input_shape(self): + return self._input_shape + @property def sample_ids_shape(self): return tensor_shape.TensorShape([]) @@ -212,6 +229,14 @@ class TrainingHelper(Helper): def sample_ids_dtype(self): return dtypes.int32 + @property + def inputs(self): + return self._inputs + + @property + def sequence_length(self): + return self._sequence_length + def initialize(self, name=None): with ops.name_scope(name, "TrainingHelperInitialize"): finished = math_ops.equal(0, self._sequence_length) @@ -516,11 +541,16 @@ class GreedyEmbeddingHelper(Helper): if self._end_token.get_shape().ndims != 0: raise ValueError("end_token must be a scalar") self._start_inputs = self._embedding_fn(self._start_tokens) + self._input_shape = self._start_inputs.shape[1:] @property def batch_size(self): return self._batch_size + @property + def input_shape(self): + return self._input_shape + @property def sample_ids_shape(self): return tensor_shape.TensorShape([]) @@ -632,6 +662,8 @@ class InferenceHelper(Helper): self._sample_dtype = sample_dtype self._next_inputs_fn = next_inputs_fn self._batch_size = array_ops.shape(start_inputs)[0] + self._input_shape = start_inputs.shape[1:] + self._start_inputs = ops.convert_to_tensor( start_inputs, name="start_inputs") @@ -639,6 +671,10 @@ class InferenceHelper(Helper): def batch_size(self): return self._batch_size + @property + def input_shape(self): + return self._input_shape + @property def sample_ids_shape(self): return self._sample_shape -- GitLab From 2b7d03c91d092cda88e6db345705fff3cd5b7b77 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 18:42:44 -0800 Subject: [PATCH 090/423] Allow passing dummy/custom minmax information on a per-array basis, unlike the existing --default_ranges_{min,max} flags which only allowed to set a single global value for all arrays. This takes the form of a new embedded message in ModelFlags, which is its own message so that it can be serialized separately. The command-line interface is --arrays_extra_info_file=some_proto.pbtxt, i.e. we don't try to make a command-line-flags-only interface, we mandate putting the info in a file. The rationale is that users may want to specify custom minmax for hundreds of arrays, so it would be cumbersome to have that all in a command line. This should be considered an experimental feature, in the sense that in properly quantized models, minmax information is already embedded in the graph (e.g. in FakeQuant nodes). This is an extension of the existing --default_ranges_{min,max} feature which had turned out to be too restrictive for many users. PiperOrigin-RevId: 183326000 --- .../internal/optimized/depthwiseconv_uint8.h | 2 +- tensorflow/contrib/lite/toco/BUILD | 1 + tensorflow/contrib/lite/toco/args.h | 1 + .../contrib/lite/toco/model_cmdline_flags.cc | 15 +++++++++++++ .../contrib/lite/toco/model_flags.proto | 22 ++++++++++++++++++- tensorflow/contrib/lite/toco/toco_port.h | 21 ++++++++++++++++++ tensorflow/contrib/lite/toco/toco_tooling.cc | 2 ++ tensorflow/contrib/lite/toco/tooling_util.cc | 14 ++++++++++++ tensorflow/contrib/lite/toco/tooling_util.h | 22 ++----------------- 9 files changed, 78 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h index f993fd6a00..fc58978964 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h @@ -1504,7 +1504,7 @@ inline void QuantizedDepthwiseConvAccumRowGeneric( << "*\n" << "* If you would like to carry on with the slow code, compile\n" << "* with this preprocessor token defined:\n" - << "* TFLITE_ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK.\n" + << "* ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK.\n" << "*\n" << "* The right thing to do, if you care about performance, is to add\n" << "* a new DepthwiseConv kernel to tfmini to cover your case.\n" diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 041e248790..6fc7e5e3fd 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -160,6 +160,7 @@ cc_library( ], deps = [ # Placeholder for internal file dependency. + "@protobuf_archive//:protobuf_headers", "//tensorflow/core:framework_lite", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", diff --git a/tensorflow/contrib/lite/toco/args.h b/tensorflow/contrib/lite/toco/args.h index 8004a1a37a..b97a4720a7 100644 --- a/tensorflow/contrib/lite/toco/args.h +++ b/tensorflow/contrib/lite/toco/args.h @@ -208,6 +208,7 @@ struct ParsedModelFlags { Arg dump_graphviz_video = Arg(false); Arg allow_nonexistent_arrays = Arg(false); Arg allow_nonascii_arrays = Arg(false); + Arg arrays_extra_info_file; }; // Flags that describe the operation you would like to do (what conversion diff --git a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc index 36520d9c55..4e2dec15a5 100644 --- a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc @@ -148,6 +148,12 @@ bool ParseModelFlagsFromCommandLineFlags( "ranging from 32 to 127. This is disallowed by default so as to " "catch common copy-and-paste issues where invisible unicode " "characters are unwittingly added to these strings."), + Flag( + "arrays_extra_info_file", parsed_flags.arrays_extra_info_file.bind(), + parsed_flags.arrays_extra_info_file.default_value(), + "Path to an optional file containing a serialized ArraysExtraInfo " + "proto allowing to pass extra information about arrays not specified " + "in the input model file, such as extra MinMax information."), }; bool asked_for_help = *argc == 2 && (!strcmp(argv[1], "--help") || !strcmp(argv[1], "-help")); @@ -365,6 +371,15 @@ void ReadModelFlagsFromCommandLineFlags( parsed_model_flags.allow_nonascii_arrays.value()); model_flags->set_allow_nonexistent_arrays( parsed_model_flags.allow_nonexistent_arrays.value()); + + if (parsed_model_flags.arrays_extra_info_file.specified()) { + string arrays_extra_info_file_contents; + port::file::GetContents(parsed_model_flags.arrays_extra_info_file.value(), + &arrays_extra_info_file_contents, + port::file::Defaults()); + ParseFromStringEitherTextOrBinary(arrays_extra_info_file_contents, + model_flags->mutable_arrays_extra_info()); + } } ParsedModelFlags* UncheckedGlobalParsedModelFlags(bool must_already_exist) { diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto index 9070ddc883..e4b39b34e8 100644 --- a/tensorflow/contrib/lite/toco/model_flags.proto +++ b/tensorflow/contrib/lite/toco/model_flags.proto @@ -87,6 +87,22 @@ message RnnState { optional int32 size = 3; } +// An ArraysExtraInfo message stores a collection of additional Information +// about arrays in a model, complementing the information in the model itself. +// It is intentionally a separate message so that it may be serialized and +// passed separately from the model. See --arrays_extra_info_file. +// +// A typical use case is to manually specify MinMax for specific arrays in a +// model that does not already contain such MinMax information. +message ArraysExtraInfo { + message Entry { + optional string name = 1; + optional float min = 2; + optional float max = 3; + } + repeated Entry entries = 1; +} + // ModelFlags encodes properties of a model that, depending on the file // format, may or may not be recorded in the model file. The purpose of // representing these properties in ModelFlags is to allow passing them @@ -108,7 +124,7 @@ message RnnState { // optional int32 input_dims = 11 [ default = 4]; // repeated int32 input_shape = 13; // -// Next ID to USE: 18. +// Next ID to USE: 19. message ModelFlags { // Information about the input arrays, i.e. the arrays from which input // activations will be read. @@ -151,4 +167,8 @@ message ModelFlags { // catch common copy-and-paste issues where invisible unicode // characters are unwittingly added to these strings. optional bool allow_nonascii_arrays = 17; + + // If set, this ArraysExtraInfo allows to pass extra information about arrays + // not specified in the input model file, such as extra MinMax information. + optional ArraysExtraInfo arrays_extra_info = 18; } diff --git a/tensorflow/contrib/lite/toco/toco_port.h b/tensorflow/contrib/lite/toco/toco_port.h index 0572848cb5..4be3b5a0bf 100644 --- a/tensorflow/contrib/lite/toco/toco_port.h +++ b/tensorflow/contrib/lite/toco/toco_port.h @@ -19,6 +19,7 @@ limitations under the License. // can build and use on google internal environments and on OSX. #include +#include "google/protobuf/text_format.h" #include "tensorflow/contrib/lite/toco/format_port.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/platform.h" @@ -75,6 +76,26 @@ void CopyToBuffer(const ::Cord& src, char* dest); #endif // PLATFORM_GOOGLE void CopyToBuffer(const string& src, char* dest); } // namespace port + +inline bool ParseFromStringOverload(const std::string& in, + TFLITE_PROTO_NS::Message* proto) { + return TFLITE_PROTO_NS::TextFormat::ParseFromString(in, proto); +} + +template +bool ParseFromStringEitherTextOrBinary(const std::string& input_file_contents, + Proto* proto) { + if (proto->ParseFromString(input_file_contents)) { + return true; + } + + if (ParseFromStringOverload(input_file_contents, proto)) { + return true; + } + + return false; +} + } // namespace toco #endif // TENSORFLOW_CONTRIB_LITE_TOCO_TOCO_PORT_H_ diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index 720c33777d..727df1cc76 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -193,6 +193,7 @@ void Transform(const TocoFlags& toco_flags, Model* model) { } SetFinalDataTypeOnInputs(toco_flags, model); + UseArraysExtraInfo(model); // Remove unused ops before performing any other optimizations. This is to // stop optimizations from crossing the input/output boundaries. For example @@ -232,6 +233,7 @@ void Transform(const TocoFlags& toco_flags, Model* model) { transformations.Add(new ResolveConstantConcatenation); RunGraphTransformations(model, "general graph transformations", transformations); + if (quantize_output) { RunGraphTransformations(model, "pre-quantization graph transformations", {new HardcodeMinMax, new DropFakeQuant}); diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index df785a5102..3728d48659 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -1200,6 +1200,9 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { model->flags.set_allow_nonascii_arrays(model_flags.allow_nonascii_arrays()); model->flags.set_allow_nonexistent_arrays( model_flags.allow_nonexistent_arrays()); + + CHECK(!model->flags.has_arrays_extra_info()); + *model->flags.mutable_arrays_extra_info() = model_flags.arrays_extra_info(); } void CheckIsReadyForQuantization(const Model& model) { @@ -1711,4 +1714,15 @@ ArrayDataType ConvertIODataTypeToArrayDataType(IODataType type) { } } +void UseArraysExtraInfo(Model* model) { + for (const auto& entry : model->flags.arrays_extra_info().entries()) { + QCHECK(model->HasArray(entry.name())) + << "ArraysExtraInfo refers to non-existent array name: " + << entry.name(); + auto& minmax = model->GetArray(entry.name()).GetOrCreateMinMax(); + minmax.min = entry.min(); + minmax.max = entry.max(); + } +} + } // namespace toco diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index 5986d63649..2ac51c7e5b 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -23,7 +23,6 @@ limitations under the License. #include #include -#include "google/protobuf/text_format.h" #include "tensorflow/core/platform/logging.h" #if TOCO_SUPPORT_PORTABLE_PROTOS #include "third_party/protobuf/src/google/protobuf/text_format.h" @@ -84,25 +83,6 @@ void DumpGraphvizVideoFrame(const Model& model); void LogDump(int log_level, const string& message, const Model& model); void LogSummary(int log_level, const string& message, const Model& model); -inline bool ParseFromStringOverload(const std::string& in, - TFLITE_PROTO_NS::Message* proto) { - return TFLITE_PROTO_NS::TextFormat::ParseFromString(in, proto); -} - -template -bool ParseFromStringEitherTextOrBinary(const std::string& input_file_contents, - Proto* proto) { - if (proto->ParseFromString(input_file_contents)) { - return true; - } - - if (ParseFromStringOverload(input_file_contents, proto)) { - return true; - } - - return false; -} - // TODO(b/36075966): Clean up when dims superseded by array shape. void ExtendShape(Shape* shape, int new_shape_size); @@ -298,6 +278,8 @@ void CheckFinalDataTypesSatisfied(const Model& model); ArrayDataType ConvertIODataTypeToArrayDataType(IODataType type); +void UseArraysExtraInfo(Model* model); + } // namespace toco #endif // TENSORFLOW_CONTRIB_LITE_TOCO_TOOLING_UTIL_H_ -- GitLab From c2ea7a710c5ec478fcc0150ec0117250c54e601e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 18:46:51 -0800 Subject: [PATCH 091/423] Set size of test //third_party/tensorflow/python/data/kernel_tests:dataset_from_generator_op_test to medium It sometimes takes longer than a minute, and thus gets flaky timeouts. PiperOrigin-RevId: 183326334 --- tensorflow/python/data/kernel_tests/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/data/kernel_tests/BUILD b/tensorflow/python/data/kernel_tests/BUILD index 5fb389cf92..43cbde69d9 100644 --- a/tensorflow/python/data/kernel_tests/BUILD +++ b/tensorflow/python/data/kernel_tests/BUILD @@ -59,7 +59,7 @@ tf_py_test( tf_py_test( name = "dataset_from_generator_op_test", - size = "small", + size = "medium", srcs = ["dataset_from_generator_op_test.py"], additional_deps = [ "//third_party/py/numpy", -- GitLab From 8dba939e34416b15f21dfcb41e9db30b6d46bcb2 Mon Sep 17 00:00:00 2001 From: cclauss Date: Fri, 26 Jan 2018 03:51:55 +0100 Subject: [PATCH 092/423] import contextmanager in side_effect_guards.py (#16426) --- tensorflow/contrib/py2tf/converters/side_effect_guards.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/py2tf/converters/side_effect_guards.py b/tensorflow/contrib/py2tf/converters/side_effect_guards.py index 1f25303fba..83d0720b6b 100644 --- a/tensorflow/contrib/py2tf/converters/side_effect_guards.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards.py @@ -34,6 +34,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from contextlib import contextmanager + import gast from tensorflow.contrib.py2tf.pyct import anno @@ -123,7 +125,7 @@ class SideEffectGuardTransformer(gast.NodeTransformer): temp_result = (temp_result,) ctx = tf.control_dependencies(temp_result) # pylint:disable=undefined-variable else: - ctx = contextmanager(lambda: (yield))() # pylint:disable=undefined-variable + ctx = contextmanager(lambda: (yield))() with ctx: # TODO(mdan): Also insert ops to re-fetch if variables are involved. pass # Will be removed below. -- GitLab From b0048a926e35311a107d93dd4df3132d16271d66 Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Fri, 26 Jan 2018 11:52:18 +0900 Subject: [PATCH 093/423] Fix typo (#16425) * fix typos * fix typos --- tensorflow/core/platform/profile_utils/cpu_utils.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h index 5d215b4804..e95843b80a 100644 --- a/tensorflow/core/platform/profile_utils/cpu_utils.h +++ b/tensorflow/core/platform/profile_utils/cpu_utils.h @@ -42,7 +42,7 @@ namespace profile_utils { class CpuUtils { public: // Constant for invalid frequency. - // This value is returned when the furequency is not obtained somehow. + // This value is returned when the frequency is not obtained somehow. static constexpr int64 INVALID_FREQUENCY = -1; static constexpr uint64 DUMMY_CYCLE_CLOCK = 1; @@ -103,7 +103,7 @@ class CpuUtils { static int64 GetCycleCounterFrequency(); #endif - // Return micro secound per each clock + // Return micro second per each clock // As this method caches the cpu frequency internally, // the first call will incur overhead, but not subsequent calls. static double GetMicroSecPerClock(); -- GitLab From 81c36a6987bc074a4cbcbeeb7baa93b31c960b9e Mon Sep 17 00:00:00 2001 From: Stanislav Levental Date: Thu, 25 Jan 2018 18:52:59 -0800 Subject: [PATCH 094/423] Including common.h with NEON_2_SSE.h (#15743) * Including common.h with NEON_2_SSE.h Including common.h to make sure that USE_NEON is defined in case of NEON_2_SSE.h is used; otherwise USE_NEON will not be propagated to this file and `portable_tensor_utils.h` will be used * Removing spaces * Using neon is USE_NEON is defined * Using common.h to set USE_NEON if applicable * Adding required dependencies Since tests are more likely compiled on sse4-enabled machine - if they are run with common.h - it will enable sse4, there are two ways - just enable it and let tests using sse4, or add extra flag check which would require USE_SSE4 flag to be defined. I've added all required dependencies for tests to pass, so it could work. * Rearranging dependencies according to linter * Fixing sanity check * trying to rerun, could be cached * Triggering build * triggering build - looks like it stuck no status for more than 24h * including x86_64 since NEON_2_SSE4 convertion will allow neon to run on x86 with SSE3,4 * Fixing name * using darwing for MacOS * Fixing header inclusion rules * Fix syntax * Depend on includes We depend on both types and compatibility. That's in the "types" rule. --- .../contrib/lite/kernels/internal/BUILD | 21 +++++++++++++++++++ .../kernels/internal/optimized/cpu_check.h | 2 +- .../internal/optimized/neon_tensor_utils.cc | 2 +- .../lite/kernels/internal/tensor_utils.cc | 1 + 4 files changed, 24 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index 21118fc96d..38b032c6de 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -267,6 +267,8 @@ cc_library( "optimized/neon_tensor_utils.cc", ], hdrs = [ + "common.h", + "optimized/cpu_check.h", "optimized/neon_tensor_utils.h", "optimized/tensor_utils_impl.h", ], @@ -274,8 +276,11 @@ cc_library( deps = [ ":cpu_check", ":portable_tensor_utils", + ":types", "//tensorflow/contrib/lite:builtin_op_data", "//tensorflow/contrib/lite/kernels:activation_functor", + "@arm_neon_2_x86_sse", + "@gemmlowp//:gemmlowp", ], ) @@ -285,14 +290,21 @@ cc_library( "tensor_utils.cc", ], hdrs = [ + "common.h", + "compatibility.h", + "optimized/cpu_check.h", + "optimized/neon_tensor_utils.h", "optimized/tensor_utils_impl.h", "reference/portable_tensor_utils.h", "tensor_utils.h", + "types.h", ], copts = NEON_FLAGS_IF_APPLICABLE, deps = [ "//tensorflow/contrib/lite/kernels:activation_functor", "//tensorflow/contrib/lite:builtin_op_data", + "@arm_neon_2_x86_sse", + "@gemmlowp//:gemmlowp", ] + select({ ":arm": [ ":neon_tensor_utils", @@ -312,6 +324,15 @@ cc_library( ":ios_arm64": [ ":neon_tensor_utils", ], + ":x86_64": [ + ":neon_tensor_utils", + ], + ":x86": [ + ":neon_tensor_utils", + ], + ":darwin": [ + ":neon_tensor_utils", + ], "//conditions:default": [ ":portable_tensor_utils", ], diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h index dea46cc120..629783d7e5 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h @@ -34,7 +34,7 @@ inline bool TestCPUFeatureNeon() { #endif // __aarch64__ } -#elif __ARM_NEON +#elif defined USE_NEON || defined __ARM_NEON inline bool TestCPUFeatureNeon() { return true; diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc index bf0bdfb1fb..ea8502ae33 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc @@ -15,12 +15,12 @@ limitations under the License. #include #include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h" #ifdef USE_NEON -#include #define kFloatWeightsPerNeonLane 4 namespace tflite { diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc index 904a97803a..f4181b18a8 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" #ifndef USE_NEON #if defined(__ARM_NEON__) || defined(__ARM_NEON) -- GitLab From 0bd0bf02aa15a3238b77053a2f0ad6fe373c7d1c Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Thu, 25 Jan 2018 19:05:28 -0800 Subject: [PATCH 095/423] Update tf.keras to the Keras 2.1.3 API. PiperOrigin-RevId: 183328052 --- tensorflow/contrib/cmake/python_modules.txt | 2 + tensorflow/python/keras/BUILD | 32 +- .../python/keras/_impl/keras/__init__.py | 2 +- .../python/keras/_impl/keras/activations.py | 10 +- .../_impl/keras/applications/__init__.py | 5 + .../_impl/keras/applications/densenet.py | 346 ++++++++ .../_impl/keras/applications/densenet_test.py | 101 +++ .../keras/applications/imagenet_utils.py | 156 ++-- .../keras/applications/inception_resnet_v2.py | 19 +- .../_impl/keras/applications/inception_v3.py | 21 +- .../_impl/keras/applications/mobilenet.py | 70 +- .../keras/_impl/keras/applications/nasnet.py | 783 ++++++++++++++++++ .../_impl/keras/applications/nasnet_test.py | 76 ++ .../_impl/keras/applications/resnet50.py | 51 +- .../keras/_impl/keras/applications/vgg16.py | 60 +- .../keras/_impl/keras/applications/vgg19.py | 73 +- .../_impl/keras/applications/xception.py | 90 +- .../python/keras/_impl/keras/backend.py | 289 +++++-- .../python/keras/_impl/keras/backend_test.py | 12 - .../python/keras/_impl/keras/callbacks.py | 132 +-- .../python/keras/_impl/keras/constraints.py | 22 +- .../_impl/keras/datasets/boston_housing.py | 10 +- .../keras/_impl/keras/datasets/cifar.py | 3 +- .../keras/_impl/keras/datasets/cifar10.py | 4 +- .../keras/_impl/keras/datasets/cifar100.py | 6 +- .../_impl/keras/datasets/fashion_mnist.py | 7 +- .../python/keras/_impl/keras/datasets/imdb.py | 63 +- .../keras/_impl/keras/datasets/mnist.py | 10 +- .../keras/_impl/keras/datasets/reuters.py | 58 +- .../keras/_impl/keras/engine/topology.py | 61 +- .../keras/_impl/keras/engine/training.py | 588 +++++++------ .../keras/_impl/keras/engine/training_test.py | 87 +- .../keras/layers/advanced_activations.py | 68 +- .../keras/layers/advanced_activations_test.py | 6 + .../keras/_impl/keras/layers/convolutional.py | 139 ++++ .../keras/layers/convolutional_recurrent.py | 44 +- .../_impl/keras/layers/convolutional_test.py | 66 ++ .../keras/_impl/keras/layers/embeddings.py | 26 +- .../python/keras/_impl/keras/layers/local.py | 110 ++- .../python/keras/_impl/keras/layers/merge.py | 110 ++- .../python/keras/_impl/keras/layers/noise.py | 49 +- .../keras/_impl/keras/layers/recurrent.py | 650 ++++++++------- .../_impl/keras/layers/recurrent_test.py | 99 +++ .../keras/_impl/keras/layers/wrappers.py | 58 +- .../keras/_impl/keras/layers/wrappers_test.py | 125 +++ tensorflow/python/keras/_impl/keras/losses.py | 20 +- .../python/keras/_impl/keras/metrics.py | 9 +- tensorflow/python/keras/_impl/keras/models.py | 4 +- .../python/keras/_impl/keras/models_test.py | 29 + .../python/keras/_impl/keras/optimizers.py | 125 +-- .../keras/_impl/keras/optimizers_test.py | 1 + .../keras/_impl/keras/preprocessing/image.py | 164 ++-- .../_impl/keras/preprocessing/sequence.py | 25 +- .../keras/_impl/keras/preprocessing/text.py | 51 +- .../_impl/keras/preprocessing/text_test.py | 16 + .../python/keras/_impl/keras/regularizers.py | 2 +- .../keras/_impl/keras/utils/data_utils.py | 213 +++-- .../keras/_impl/keras/utils/generic_utils.py | 6 +- .../keras/_impl/keras/utils/io_utils.py | 18 +- .../keras/_impl/keras/utils/layer_utils.py | 35 +- .../keras/_impl/keras/utils/np_utils.py | 5 +- .../keras/_impl/keras/utils/vis_utils.py | 35 +- .../_impl/keras/wrappers/scikit_learn.py | 71 +- .../python/keras/applications/__init__.py | 7 + .../keras/applications/densenet/__init__.py | 29 + .../keras/applications/nasnet/__init__.py | 28 + tensorflow/python/keras/layers/__init__.py | 3 + tensorflow/python/layers/base.py | 21 + tensorflow/python/layers/network.py | 7 + .../api/golden/tensorflow.keras.-model.pbtxt | 8 +- ...nsorflow.keras.applications.densenet.pbtxt | 23 + ...tensorflow.keras.applications.nasnet.pbtxt | 19 + .../tensorflow.keras.applications.pbtxt | 28 + .../api/golden/tensorflow.keras.backend.pbtxt | 2 +- ...s.callbacks.-learning-rate-scheduler.pbtxt | 2 +- ...orflow.keras.datasets.boston_housing.pbtxt | 2 +- .../tensorflow.keras.datasets.imdb.pbtxt | 2 +- .../tensorflow.keras.datasets.reuters.pbtxt | 2 +- .../golden/tensorflow.keras.layers.-add.pbtxt | 4 +- ...nsorflow.keras.layers.-alpha-dropout.pbtxt | 2 +- .../tensorflow.keras.layers.-average.pbtxt | 4 +- ...nsorflow.keras.layers.-bidirectional.pbtxt | 4 +- ...tensorflow.keras.layers.-concatenate.pbtxt | 4 +- ...orflow.keras.layers.-conv-l-s-t-m2-d.pbtxt | 4 +- .../golden/tensorflow.keras.layers.-dot.pbtxt | 4 +- .../tensorflow.keras.layers.-e-l-u.pbtxt | 2 +- .../tensorflow.keras.layers.-embedding.pbtxt | 4 +- .../tensorflow.keras.layers.-g-r-u-cell.pbtxt | 2 +- .../tensorflow.keras.layers.-g-r-u.pbtxt | 4 +- ...rflow.keras.layers.-gaussian-dropout.pbtxt | 2 +- ...sorflow.keras.layers.-gaussian-noise.pbtxt | 2 +- ...ensorflow.keras.layers.-l-s-t-m-cell.pbtxt | 2 +- .../tensorflow.keras.layers.-l-s-t-m.pbtxt | 4 +- ...ensorflow.keras.layers.-leaky-re-l-u.pbtxt | 2 +- ...w.keras.layers.-locally-connected1-d.pbtxt | 4 +- ...w.keras.layers.-locally-connected2-d.pbtxt | 4 +- .../tensorflow.keras.layers.-maximum.pbtxt | 4 +- .../tensorflow.keras.layers.-multiply.pbtxt | 4 +- .../tensorflow.keras.layers.-p-re-l-u.pbtxt | 4 +- .../tensorflow.keras.layers.-r-n-n.pbtxt | 6 +- ...flow.keras.layers.-separable-conv1-d.pbtxt | 186 +++++ ...ras.layers.-separable-convolution1-d.pbtxt | 186 +++++ ...flow.keras.layers.-simple-r-n-n-cell.pbtxt | 2 +- ...ensorflow.keras.layers.-simple-r-n-n.pbtxt | 4 +- .../tensorflow.keras.layers.-softmax.pbtxt | 183 ++++ ...ow.keras.layers.-stacked-r-n-n-cells.pbtxt | 2 +- ...low.keras.layers.-thresholded-re-l-u.pbtxt | 2 +- .../api/golden/tensorflow.keras.layers.pbtxt | 12 + .../tensorflow.keras.models.-model.pbtxt | 8 +- ...ensorflow.keras.optimizers.-adadelta.pbtxt | 2 +- ...tensorflow.keras.optimizers.-adagrad.pbtxt | 2 +- .../tensorflow.keras.optimizers.-adam.pbtxt | 2 +- .../tensorflow.keras.optimizers.-adamax.pbtxt | 2 +- .../tensorflow.keras.optimizers.-nadam.pbtxt | 2 +- ...nsorflow.keras.optimizers.-r-m-sprop.pbtxt | 2 +- ....keras.preprocessing.text.-tokenizer.pbtxt | 2 +- 116 files changed, 4870 insertions(+), 1616 deletions(-) create mode 100644 tensorflow/python/keras/_impl/keras/applications/densenet.py create mode 100644 tensorflow/python/keras/_impl/keras/applications/densenet_test.py create mode 100644 tensorflow/python/keras/_impl/keras/applications/nasnet.py create mode 100644 tensorflow/python/keras/_impl/keras/applications/nasnet_test.py create mode 100644 tensorflow/python/keras/applications/densenet/__init__.py create mode 100644 tensorflow/python/keras/applications/nasnet/__init__.py create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.applications.densenet.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.applications.nasnet.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 7db454bd83..9ce8b3cc9c 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -33,9 +33,11 @@ tensorflow/python/grappler tensorflow/python/keras tensorflow/python/keras/activations tensorflow/python/keras/applications +tensorflow/python/keras/applications/densenet tensorflow/python/keras/applications/inception_resnet_v2 tensorflow/python/keras/applications/inception_v3 tensorflow/python/keras/applications/mobilenet +tensorflow/python/keras/applications/nasnet tensorflow/python/keras/applications/resnet50 tensorflow/python/keras/applications/vgg16 tensorflow/python/keras/applications/vgg19 diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 1f20b3ae0e..6125755775 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -14,10 +14,12 @@ py_library( "_impl/keras/__init__.py", "_impl/keras/activations.py", "_impl/keras/applications/__init__.py", + "_impl/keras/applications/densenet.py", "_impl/keras/applications/imagenet_utils.py", "_impl/keras/applications/inception_resnet_v2.py", "_impl/keras/applications/inception_v3.py", "_impl/keras/applications/mobilenet.py", + "_impl/keras/applications/nasnet.py", "_impl/keras/applications/resnet50.py", "_impl/keras/applications/vgg16.py", "_impl/keras/applications/vgg19.py", @@ -76,9 +78,11 @@ py_library( "_impl/keras/wrappers/scikit_learn.py", "activations/__init__.py", "applications/__init__.py", + "applications/densenet/__init__.py", "applications/inception_resnet_v2/__init__.py", "applications/inception_v3/__init__.py", "applications/mobilenet/__init__.py", + "applications/nasnet/__init__.py", "applications/resnet50/__init__.py", "applications/vgg16/__init__.py", "applications/vgg19/__init__.py", @@ -256,6 +260,18 @@ py_test( ], ) +py_test( + name = "densenet_test", + size = "large", + srcs = ["_impl/keras/applications/densenet_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":keras", + "//tensorflow/python:client_testlib", + "//third_party/py/numpy", + ], +) + py_test( name = "inception_resnet_v2_test", size = "medium", @@ -292,6 +308,18 @@ py_test( ], ) +py_test( + name = "nasnet_test", + size = "large", + srcs = ["_impl/keras/applications/nasnet_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":keras", + "//tensorflow/python:client_testlib", + "//third_party/py/numpy", + ], +) + py_test( name = "resnet50_test", size = "small", @@ -504,7 +532,7 @@ py_test( py_test( name = "recurrent_test", - size = "small", + size = "medium", srcs = ["_impl/keras/layers/recurrent_test.py"], srcs_version = "PY2AND3", deps = [ @@ -527,7 +555,7 @@ py_test( py_test( name = "wrappers_test", - size = "small", + size = "medium", srcs = ["_impl/keras/layers/wrappers_test.py"], srcs_version = "PY2AND3", tags = ["notsan"], diff --git a/tensorflow/python/keras/_impl/keras/__init__.py b/tensorflow/python/keras/_impl/keras/__init__.py index a70250d796..7311353932 100644 --- a/tensorflow/python/keras/_impl/keras/__init__.py +++ b/tensorflow/python/keras/_impl/keras/__init__.py @@ -40,4 +40,4 @@ from tensorflow.python.keras._impl.keras.layers import Input from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.models import Sequential -__version__ = '2.1.2-tf' +__version__ = '2.1.3-tf' diff --git a/tensorflow/python/keras/_impl/keras/activations.py b/tensorflow/python/keras/_impl/keras/activations.py index f017d2ae85..4852b8c36a 100644 --- a/tensorflow/python/keras/_impl/keras/activations.py +++ b/tensorflow/python/keras/_impl/keras/activations.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Keras built-in activation functions. +"""Built-in activation functions. """ from __future__ import absolute_import from __future__ import division @@ -61,10 +61,12 @@ def selu(x): x: A tensor or variable to compute the activation function for. Returns: - Tensor with the same shape and dtype as `x`. + Tensor with the same shape and dtype as `x`. + + # Note + - To be used together with the initialization "lecun_normal". + - To be used together with the dropout variant "AlphaDropout". - References: - - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 diff --git a/tensorflow/python/keras/_impl/keras/applications/__init__.py b/tensorflow/python/keras/_impl/keras/applications/__init__.py index c11c52b71e..206a769b37 100644 --- a/tensorflow/python/keras/_impl/keras/applications/__init__.py +++ b/tensorflow/python/keras/_impl/keras/applications/__init__.py @@ -18,9 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.keras._impl.keras.applications.densenet import DenseNet121 +from tensorflow.python.keras._impl.keras.applications.densenet import DenseNet169 +from tensorflow.python.keras._impl.keras.applications.densenet import DenseNet201 from tensorflow.python.keras._impl.keras.applications.inception_resnet_v2 import InceptionResNetV2 from tensorflow.python.keras._impl.keras.applications.inception_v3 import InceptionV3 from tensorflow.python.keras._impl.keras.applications.mobilenet import MobileNet +from tensorflow.python.keras._impl.keras.applications.nasnet import NASNetLarge +from tensorflow.python.keras._impl.keras.applications.nasnet import NASNetMobile from tensorflow.python.keras._impl.keras.applications.resnet50 import ResNet50 from tensorflow.python.keras._impl.keras.applications.vgg16 import VGG16 from tensorflow.python.keras._impl.keras.applications.vgg19 import VGG19 diff --git a/tensorflow/python/keras/_impl/keras/applications/densenet.py b/tensorflow/python/keras/_impl/keras/applications/densenet.py new file mode 100644 index 0000000000..9e40d34930 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/applications/densenet.py @@ -0,0 +1,346 @@ +# 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=invalid-name +# pylint: disable=unused-import +"""DenseNet models for Keras. + +# Reference paper + +- [Densely Connected Convolutional Networks] + (https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras.applications import imagenet_utils +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions +from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.layers import Activation +from tensorflow.python.keras._impl.keras.layers import AveragePooling2D +from tensorflow.python.keras._impl.keras.layers import BatchNormalization +from tensorflow.python.keras._impl.keras.layers import Concatenate +from tensorflow.python.keras._impl.keras.layers import Conv2D +from tensorflow.python.keras._impl.keras.layers import Dense +from tensorflow.python.keras._impl.keras.layers import GlobalAveragePooling2D +from tensorflow.python.keras._impl.keras.layers import GlobalMaxPooling2D +from tensorflow.python.keras._impl.keras.layers import Input +from tensorflow.python.keras._impl.keras.layers import MaxPooling2D +from tensorflow.python.keras._impl.keras.layers import ZeroPadding2D +from tensorflow.python.keras._impl.keras.models import Model +from tensorflow.python.keras._impl.keras.utils.data_utils import get_file + + +DENSENET121_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels.h5' +DENSENET121_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5' +DENSENET169_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels.h5' +DENSENET169_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5' +DENSENET201_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels.h5' +DENSENET201_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5' + + +def dense_block(x, blocks, name): + """A dense block. + + Arguments: + x: input tensor. + blocks: integer, the number of building blocks. + name: string, block label. + + Returns: + output tensor for the block. + """ + for i in range(blocks): + x = conv_block(x, 32, name=name + '_block' + str(i + 1)) + return x + + +def transition_block(x, reduction, name): + """A transition block. + + Arguments: + x: input tensor. + reduction: float, compression rate at transition layers. + name: string, block label. + + Returns: + output tensor for the block. + """ + bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 + x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) + x = Activation('relu', name=name + '_relu')(x) + x = Conv2D( + int(K.int_shape(x)[bn_axis] * reduction), + 1, + use_bias=False, + name=name + '_conv')( + x) + x = AveragePooling2D(2, strides=2, name=name + '_pool')(x) + return x + + +def conv_block(x, growth_rate, name): + """A building block for a dense block. + + Arguments: + x: input tensor. + growth_rate: float, growth rate at dense layers. + name: string, block label. + + Returns: + output tensor for the block. + """ + bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 + x1 = BatchNormalization( + axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')( + x) + x1 = Activation('relu', name=name + '_0_relu')(x1) + x1 = Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1) + x1 = BatchNormalization( + axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')( + x1) + x1 = Activation('relu', name=name + '_1_relu')(x1) + x1 = Conv2D( + growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')( + x1) + x = Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) + return x + + +def DenseNet(blocks, + include_top=True, + weights='imagenet', + input_tensor=None, + input_shape=None, + pooling=None, + classes=1000): + """Instantiates the DenseNet architecture. + + Optionally loads weights pre-trained + on ImageNet. Note that when using TensorFlow, + for best performance you should set + `image_data_format='channels_last'` in your Keras config + at ~/.keras/keras.json. + + The model and the weights are compatible with + TensorFlow, Theano, and CNTK. The data format + convention used by the model is the one + specified in your Keras config file. + + Arguments: + blocks: numbers of building blocks for the four dense layers. + include_top: whether to include the fully-connected + layer at the top of the network. + weights: one of `None` (random initialization), + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. + input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) + to use as image input for the model. + input_shape: optional shape tuple, only to be specified + if `include_top` is False (otherwise the input shape + has to be `(224, 224, 3)` (with `channels_last` data format) + or `(3, 224, 224)` (with `channels_first` data format). + It should have exactly 3 inputs channels. + pooling: optional pooling mode for feature extraction + when `include_top` is `False`. + - `None` means that the output of the model will be + the 4D tensor output of the + last convolutional layer. + - `avg` means that global average pooling + will be applied to the output of the + last convolutional layer, and thus + the output of the model will be a 2D tensor. + - `max` means that global max pooling will + be applied. + classes: optional number of classes to classify images + into, only to be specified if `include_top` is True, and + if no `weights` argument is specified. + + Returns: + A Keras model instance. + + Raises: + ValueError: in case of invalid argument for `weights`, + or invalid input shape. + """ + if not (weights in {'imagenet', None} or os.path.exists(weights)): + raise ValueError('The `weights` argument should be either ' + '`None` (random initialization), `imagenet` ' + '(pre-training on ImageNet), ' + 'or the path to the weights file to be loaded.') + + if weights == 'imagenet' and include_top and classes != 1000: + raise ValueError('If using `weights` as imagenet with `include_top`' + ' as true, `classes` should be 1000') + + # Determine proper input shape + input_shape = _obtain_input_shape( + input_shape, + default_size=224, + min_size=221, + data_format=K.image_data_format(), + require_flatten=include_top, + weights=weights) + + if input_tensor is None: + img_input = Input(shape=input_shape) + else: + if not K.is_keras_tensor(input_tensor): + img_input = Input(tensor=input_tensor, shape=input_shape) + else: + img_input = input_tensor + + bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 + + x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input) + x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) + x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x) + x = Activation('relu', name='conv1/relu')(x) + x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x) + x = MaxPooling2D(3, strides=2, name='pool1')(x) + + x = dense_block(x, blocks[0], name='conv2') + x = transition_block(x, 0.5, name='pool2') + x = dense_block(x, blocks[1], name='conv3') + x = transition_block(x, 0.5, name='pool3') + x = dense_block(x, blocks[2], name='conv4') + x = transition_block(x, 0.5, name='pool4') + x = dense_block(x, blocks[3], name='conv5') + + x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x) + + if include_top: + x = GlobalAveragePooling2D(name='avg_pool')(x) + x = Dense(classes, activation='softmax', name='fc1000')(x) + else: + if pooling == 'avg': + x = GlobalAveragePooling2D(name='avg_pool')(x) + elif pooling == 'max': + x = GlobalMaxPooling2D(name='max_pool')(x) + + # Ensure that the model takes into account + # any potential predecessors of `input_tensor`. + if input_tensor is not None: + inputs = get_source_inputs(input_tensor) + else: + inputs = img_input + + # Create model. + if blocks == [6, 12, 24, 16]: + model = Model(inputs, x, name='densenet121') + elif blocks == [6, 12, 32, 32]: + model = Model(inputs, x, name='densenet169') + elif blocks == [6, 12, 48, 32]: + model = Model(inputs, x, name='densenet201') + else: + model = Model(inputs, x, name='densenet') + + # Load weights. + if weights == 'imagenet': + if include_top: + if blocks == [6, 12, 24, 16]: + weights_path = get_file( + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5', + DENSENET121_WEIGHT_PATH, + cache_subdir='models', + file_hash='0962ca643bae20f9b6771cb844dca3b0') + elif blocks == [6, 12, 32, 32]: + weights_path = get_file( + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5', + DENSENET169_WEIGHT_PATH, + cache_subdir='models', + file_hash='bcf9965cf5064a5f9eb6d7dc69386f43') + elif blocks == [6, 12, 48, 32]: + weights_path = get_file( + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5', + DENSENET201_WEIGHT_PATH, + cache_subdir='models', + file_hash='7bb75edd58cb43163be7e0005fbe95ef') + else: + if blocks == [6, 12, 24, 16]: + weights_path = get_file( + 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5', + DENSENET121_WEIGHT_PATH_NO_TOP, + cache_subdir='models', + file_hash='4912a53fbd2a69346e7f2c0b5ec8c6d3') + elif blocks == [6, 12, 32, 32]: + weights_path = get_file( + 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5', + DENSENET169_WEIGHT_PATH_NO_TOP, + cache_subdir='models', + file_hash='50662582284e4cf834ce40ab4dfa58c6') + elif blocks == [6, 12, 48, 32]: + weights_path = get_file( + 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5', + DENSENET201_WEIGHT_PATH_NO_TOP, + cache_subdir='models', + file_hash='1c2de60ee40562448dbac34a0737e798') + model.load_weights(weights_path) + elif weights is not None: + model.load_weights(weights) + + return model + + +def DenseNet121(include_top=True, + weights='imagenet', + input_tensor=None, + input_shape=None, + pooling=None, + classes=1000): + return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor, + input_shape, pooling, classes) + + +def DenseNet169(include_top=True, + weights='imagenet', + input_tensor=None, + input_shape=None, + pooling=None, + classes=1000): + return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor, + input_shape, pooling, classes) + + +def DenseNet201(include_top=True, + weights='imagenet', + input_tensor=None, + input_shape=None, + pooling=None, + classes=1000): + return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor, + input_shape, pooling, classes) + + +def preprocess_input(x, data_format=None): + """Preprocesses a numpy array encoding a batch of images. + + Arguments: + x: a 3D or 4D numpy array consists of RGB values within [0, 255]. + data_format: data format of the image tensor. + + Returns: + Preprocessed array. + """ + return imagenet_utils.preprocess_input(x, data_format, mode='torch') + + +setattr(DenseNet121, '__doc__', DenseNet.__doc__) +setattr(DenseNet169, '__doc__', DenseNet.__doc__) +setattr(DenseNet201, '__doc__', DenseNet.__doc__) diff --git a/tensorflow/python/keras/_impl/keras/applications/densenet_test.py b/tensorflow/python/keras/_impl/keras/applications/densenet_test.py new file mode 100644 index 0000000000..3b92287a1e --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/applications/densenet_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. +# ============================================================================== +"""Tests for DenseNet application.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.keras._impl import keras +from tensorflow.python.platform import test + + +class DenseNet121Test(test.TestCase): + + def test_with_top(self): + model = keras.applications.DenseNet121(weights=None) + self.assertEqual(model.output_shape, (None, 1000)) + + def test_no_top(self): + model = keras.applications.DenseNet121(weights=None, include_top=False) + self.assertEqual(model.output_shape, (None, None, None, 1024)) + + def test_with_pooling(self): + model = keras.applications.DenseNet121(weights=None, + include_top=False, + pooling='avg') + self.assertEqual(model.output_shape, (None, 1024)) + + def test_weight_loading(self): + with self.assertRaises(ValueError): + keras.applications.DenseNet121(weights='unknown', + include_top=False) + with self.assertRaises(ValueError): + keras.applications.DenseNet121(weights='imagenet', + classes=2000) + + +class DenseNet169Test(test.TestCase): + + def test_with_top(self): + model = keras.applications.DenseNet169(weights=None) + self.assertEqual(model.output_shape, (None, 1000)) + + def test_no_top(self): + model = keras.applications.DenseNet169(weights=None, include_top=False) + self.assertEqual(model.output_shape, (None, None, None, 1664)) + + def test_with_pooling(self): + model = keras.applications.DenseNet169(weights=None, + include_top=False, + pooling='max') + self.assertEqual(model.output_shape, (None, 1664)) + + def test_weight_loading(self): + with self.assertRaises(ValueError): + keras.applications.DenseNet169(weights='unknown', + include_top=False) + with self.assertRaises(ValueError): + keras.applications.DenseNet169(weights='imagenet', + classes=2000) + + +class DenseNet201(test.TestCase): + + def test_with_top(self): + model = keras.applications.DenseNet201(weights=None) + self.assertEqual(model.output_shape, (None, 1000)) + + def test_no_top(self): + model = keras.applications.DenseNet201(weights=None, include_top=False) + self.assertEqual(model.output_shape, (None, None, None, 1920)) + + def test_with_pooling(self): + model = keras.applications.DenseNet201(weights=None, + include_top=False, + pooling='avg') + self.assertEqual(model.output_shape, (None, 1920)) + + def test_weight_loading(self): + with self.assertRaises(ValueError): + keras.applications.DenseNet201(weights='unknown', + include_top=False) + with self.assertRaises(ValueError): + keras.applications.DenseNet201(weights='imagenet', + classes=2000) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py b/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py index 63ee83cb51..f1f20f12a8 100644 --- a/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py +++ b/tensorflow/python/keras/_impl/keras/applications/imagenet_utils.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities used by models pre-trained on ImageNet. +"""Utilities for ImageNet data preprocessing & prediction decoding. """ from __future__ import absolute_import from __future__ import division @@ -35,63 +35,92 @@ _IMAGENET_MEAN = None def _preprocess_numpy_input(x, data_format, mode): - """Preprocesses a image tensor as a Numpy array. + """Preprocesses a Numpy array encoding a batch of images. Arguments: - x: input Numpy, 3D or 4D. - data_format: data format of the image tensor. - mode: One of "caffe", "tf". + x: Input array, 3D or 4D. + data_format: Data format of the image array. + mode: One of "caffe", "tf" or "torch". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. - tf: will scale pixels between -1 and 1, sample-wise. + - torch: will scale pixels between 0 and 1 and then + will normalize each channel with respect to the + ImageNet dataset. Returns: - Preprocessed array. + Preprocessed Numpy array. """ if mode == 'tf': x /= 127.5 x -= 1. return x + if mode == 'torch': + x /= 255. + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + else: + if data_format == 'channels_first': + # 'RGB'->'BGR' + if x.ndim == 3: + x = x[::-1, ...] + else: + x = x[:, ::-1, ...] + else: + # 'RGB'->'BGR' + x = x[..., ::-1] + mean = [103.939, 116.779, 123.68] + std = None + + # Zero-center by mean pixel if data_format == 'channels_first': if x.ndim == 3: - # 'RGB'->'BGR' - x = x[::-1, ...] - # Zero-center by mean pixel - x[0, :, :] -= 103.939 - x[1, :, :] -= 116.779 - x[2, :, :] -= 123.68 + x[0, :, :] -= mean[0] + x[1, :, :] -= mean[1] + x[2, :, :] -= mean[2] + if std is not None: + x[0, :, :] /= std[0] + x[1, :, :] /= std[1] + x[2, :, :] /= std[2] else: - x = x[:, ::-1, ...] - x[:, 0, :, :] -= 103.939 - x[:, 1, :, :] -= 116.779 - x[:, 2, :, :] -= 123.68 + x[:, 0, :, :] -= mean[0] + x[:, 1, :, :] -= mean[1] + x[:, 2, :, :] -= mean[2] + if std is not None: + x[:, 0, :, :] /= std[0] + x[:, 1, :, :] /= std[1] + x[:, 2, :, :] /= std[2] else: - # 'RGB'->'BGR' - x = x[..., ::-1] - # Zero-center by mean pixel - x[..., 0] -= 103.939 - x[..., 1] -= 116.779 - x[..., 2] -= 123.68 + x[..., 0] -= mean[0] + x[..., 1] -= mean[1] + x[..., 2] -= mean[2] + if std is not None: + x[..., 0] /= std[0] + x[..., 1] /= std[1] + x[..., 2] /= std[2] return x def _preprocess_symbolic_input(x, data_format, mode): - """Preprocesses a symbolic image tensor. + """Preprocesses a tensor encoding a batch of images. Arguments: - x: symoblic tensor, 3D or 4D. - data_format: data format of the image tensor. - mode: One of "caffe", "tf". + x: Input tensor, 3D or 4D. + data_format: Data format of the image tensor. + mode: One of "caffe", "tf" or "torch". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. - tf: will scale pixels between -1 and 1, sample-wise. + - torch: will scale pixels between 0 and 1 and then + will normalize each channel with respect to the + ImageNet dataset. Returns: Preprocessed tensor. @@ -103,32 +132,42 @@ def _preprocess_symbolic_input(x, data_format, mode): x -= 1. return x - if data_format == 'channels_first': - # 'RGB'->'BGR' - if K.ndim(x) == 3: - x = x[::-1, ...] - else: - x = x[:, ::-1, ...] + if mode == 'torch': + x /= 255. + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] else: - # 'RGB'->'BGR' - x = x[..., ::-1] + if data_format == 'channels_first': + # 'RGB'->'BGR' + if K.ndim(x) == 3: + x = x[::-1, ...] + else: + x = x[:, ::-1, ...] + else: + # 'RGB'->'BGR' + x = x[..., ::-1] + mean = [103.939, 116.779, 123.68] + std = None if _IMAGENET_MEAN is None: - _IMAGENET_MEAN = K.constant(-np.array([103.939, 116.779, 123.68])) + _IMAGENET_MEAN = K.constant(-np.array(mean)) + # Zero-center by mean pixel if K.dtype(x) != K.dtype(_IMAGENET_MEAN): x = K.bias_add(x, K.cast(_IMAGENET_MEAN, K.dtype(x)), data_format) else: x = K.bias_add(x, _IMAGENET_MEAN, data_format) + if std is not None: + x /= std return x def preprocess_input(x, data_format=None, mode='caffe'): - """Preprocesses a tensor encoding a batch of images. + """Preprocesses a tensor or Numpy array encoding a batch of images. Arguments: - x: input Numpy or symoblic tensor, 3D or 4D. - data_format: data format of the image tensor. + x: Input Numpy or symbolic tensor, 3D or 4D. + data_format: Data format of the image tensor/array. mode: One of "caffe", "tf". - caffe: will convert the images from RGB to BGR, then will zero-center each color channel with @@ -138,10 +177,10 @@ def preprocess_input(x, data_format=None, mode='caffe'): sample-wise. Returns: - Preprocessed tensor. + Preprocessed tensor or Numpy array. Raises: - ValueError: in case of incorrect data_format. + ValueError: In case of unknown `data_format` argument. """ if data_format is None: data_format = K.image_data_format() @@ -159,7 +198,7 @@ def decode_predictions(preds, top=5): Arguments: preds: Numpy tensor encoding a batch of predictions. - top: integer, how many top-guesses to return. + top: Integer, how many top-guesses to return. Returns: A list of lists of top class prediction tuples @@ -167,7 +206,7 @@ def decode_predictions(preds, top=5): One list of tuples per sample in batch input. Raises: - ValueError: in case of invalid shape of the `pred` array + ValueError: In case of invalid shape of the `pred` array (must be 2D). """ global CLASS_INDEX @@ -177,10 +216,11 @@ def decode_predictions(preds, top=5): '(i.e. a 2D array of shape (samples, 1000)). ' 'Found array with shape: ' + str(preds.shape)) if CLASS_INDEX is None: - fpath = get_file('imagenet_class_index.json', - CLASS_INDEX_PATH, - cache_subdir='models', - file_hash='c2c37ea517e94d9795004a39431a14cb') + fpath = get_file( + 'imagenet_class_index.json', + CLASS_INDEX_PATH, + cache_subdir='models', + file_hash='c2c37ea517e94d9795004a39431a14cb') CLASS_INDEX = json.load(open(fpath)) results = [] for pred in preds: @@ -197,17 +237,17 @@ def _obtain_input_shape(input_shape, data_format, require_flatten, weights=None): - """Internal utility to compute/validate an ImageNet model's input shape. + """Internal utility to compute/validate a model's input shape. Arguments: - input_shape: either None (will return the default network input shape), + input_shape: Either None (will return the default network input shape), or a user-provided shape to be validated. - default_size: default input width/height for the model. - min_size: minimum input width/height accepted by the model. - data_format: image data format to use. - require_flatten: whether the model is expected to + default_size: Default input width/height for the model. + min_size: Minimum input width/height accepted by the model. + data_format: Image data format to use. + require_flatten: Whether the model is expected to be linked to a classifier via a Flatten layer. - weights: one of `None` (random initialization) + weights: One of `None` (random initialization) or 'imagenet' (pre-training on ImageNet). If weights='imagenet' input channels must be equal to 3. @@ -215,7 +255,7 @@ def _obtain_input_shape(input_shape, An integer shape tuple (may include None entries). Raises: - ValueError: in case of invalid argument values. + ValueError: In case of invalid argument values. """ if weights != 'imagenet' and input_shape and len(input_shape) == 3: if data_format == 'channels_first': @@ -252,8 +292,8 @@ def _obtain_input_shape(input_shape, '`input_shape=' + str(input_shape) + '`') if ((input_shape[1] is not None and input_shape[1] < min_size) or (input_shape[2] is not None and input_shape[2] < min_size)): - raise ValueError('Input size must be at least ' + str(min_size) + 'x' - + str(min_size) + '; got ' + raise ValueError('Input size must be at least ' + str(min_size) + + 'x' + str(min_size) + '; got ' '`input_shape=' + str(input_shape) + '`') else: if input_shape is not None: @@ -264,8 +304,8 @@ def _obtain_input_shape(input_shape, '`input_shape=' + str(input_shape) + '`') if ((input_shape[0] is not None and input_shape[0] < min_size) or (input_shape[1] is not None and input_shape[1] < min_size)): - raise ValueError('Input size must be at least ' + str(min_size) + 'x' - + str(min_size) + '; got ' + raise ValueError('Input size must be at least ' + str(min_size) + + 'x' + str(min_size) + '; got ' '`input_shape=' + str(input_shape) + '`') else: if require_flatten: diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py index 2e73cefb6c..1dc15b5b34 100644 --- a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py +++ b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# pylint: disable=invalid-name +# pylint: disable=unused-import """Inception-ResNet V2 model for Keras. # Reference @@ -28,7 +30,7 @@ import os from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D @@ -43,6 +45,8 @@ from tensorflow.python.keras._impl.keras.layers import Lambda from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.platform import tf_logging as logging + BASE_WEIGHT_URL = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.7/' @@ -116,7 +120,8 @@ def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): scale: scaling factor to scale the residuals (i.e., the output of passing `x` through an inception module) before adding them to the shortcut branch. Let `r` be the output from the residual - branch, the output of this block will be `x + scale * r`. + branch, + the output of this block will be `x + scale * r`. block_type: `'block35'`, `'block17'` or `'block8'`, determines the network structure in the residual branch. block_idx: an `int` used for generating layer names. The Inception-ResNet @@ -128,8 +133,7 @@ def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): will have `block_type='block35', block_idx=0`, ane the layer names will have a common prefix `'block35_0'`. - activation: activation function to use at the end of the block - (see [activations](../activations.md)). + activation: activation function to use at the end of the block. When `activation=None`, no activation is applied (i.e., "linear" activation: `a(x) = x`). @@ -178,6 +182,7 @@ def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): x = Lambda( lambda inputs, scale: inputs[0] + inputs[1] * scale, + output_shape=K.int_shape(x)[1:], arguments={'scale': scale}, name=block_name)([x, up]) if activation is not None: @@ -185,7 +190,7 @@ def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): return x -def InceptionResNetV2(include_top=True, # pylint: disable=invalid-name +def InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, @@ -211,8 +216,8 @@ def InceptionResNetV2(include_top=True, # pylint: disable=invalid-name include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py index 4424b92804..ff57116f2d 100644 --- a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py +++ b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py @@ -13,6 +13,7 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name +# pylint: disable=unused-import """Inception V3 model for Keras. Note that the input image format for this model is different than for @@ -35,7 +36,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D @@ -48,6 +49,7 @@ from tensorflow.python.keras._impl.keras.layers import Input from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.platform import tf_logging as logging WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels.h5' @@ -92,7 +94,8 @@ def conv2d_bn(x, strides=strides, padding=padding, use_bias=False, - name=conv_name)(x) + name=conv_name)( + x) x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) x = Activation('relu', name=name)(x) return x @@ -109,7 +112,7 @@ def InceptionV3(include_top=True, Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set - `image_data_format="channels_last"` in your Keras config + `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both TensorFlow and Theano. The data format @@ -121,15 +124,15 @@ def InceptionV3(include_top=True, include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), - "imagenet" (pre-training on ImageNet), - or the path to the weights file to be loaded. + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(299, 299, 3)` (with `channels_last` data format) or `(3, 299, 299)` (with `channels_first` data format). - It should have exactly 3 input channels, + It should have exactly 3 inputs channels, and width and height should be no smaller than 139. E.g. `(150, 150, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction @@ -176,7 +179,10 @@ def InceptionV3(include_top=True, if input_tensor is None: img_input = Input(shape=input_shape) else: - img_input = Input(tensor=input_tensor, shape=input_shape) + if not K.is_keras_tensor(input_tensor): + img_input = Input(tensor=input_tensor, shape=input_shape) + else: + img_input = input_tensor if K.image_data_format() == 'channels_first': channel_axis = 1 @@ -389,6 +395,7 @@ def InceptionV3(include_top=True, model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) + return model diff --git a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py index 5f97c138fc..790bf8cead 100644 --- a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py +++ b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# pylint: disable=invalid-name +# pylint: disable=unused-import """MobileNet v1 models for Keras. MobileNet is a general architecture and can be used for multiple use cases. @@ -56,7 +58,7 @@ the 100 % MobileNet on various input sizes: ------------------------------------------------------------------------ The weights for all 16 models are obtained and translated -from Tensorflow checkpoints found at +from TensorFlow checkpoints found at https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md # Reference @@ -75,9 +77,10 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization from tensorflow.python.keras._impl.keras.layers import Conv2D @@ -91,6 +94,7 @@ from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging + BASE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.6/' @@ -130,7 +134,7 @@ class DepthwiseConv2D(Conv2D): all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. - padding: one of `"valid"` or `"same"` (case-insensitive). + padding: one of `'valid'` or `'same'` (case-insensitive). depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output @@ -144,29 +148,21 @@ class DepthwiseConv2D(Conv2D): `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. - If you never set it, then it will be "channels_last". - activation: Activation function to use - (see [activations](../activations.md)). + If you never set it, then it will be 'channels_last'. + activation: Activation function to use. If you don't specify anything, no activation is applied - (ie. "linear" activation: `a(x) = x`). + (ie. 'linear' activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. - depthwise_initializer: Initializer for the depthwise kernel matrix - (see [initializers](../initializers.md)). - bias_initializer: Initializer for the bias vector - (see [initializers](../initializers.md)). + depthwise_initializer: Initializer for the depthwise kernel matrix. + bias_initializer: Initializer for the bias vector. depthwise_regularizer: Regularizer function applied to - the depthwise kernel matrix - (see [regularizer](../regularizers.md)). - bias_regularizer: Regularizer function applied to the bias vector - (see [regularizer](../regularizers.md)). + the depthwise kernel matrix. + bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to - the output of the layer (its "activation"). - (see [regularizer](../regularizers.md)). + the output of the layer (its 'activation').. depthwise_constraint: Constraint function applied to - the depthwise kernel matrix - (see [constraints](../constraints.md)). - bias_constraint: Constraint function applied to the bias vector - (see [constraints](../constraints.md)). + the depthwise kernel matrix. + bias_constraint: Constraint function applied to the bias vector. Input shape: 4D tensor with shape: @@ -216,6 +212,7 @@ class DepthwiseConv2D(Conv2D): self.depthwise_constraint = constraints.get(depthwise_constraint) self.bias_initializer = initializers.get(bias_initializer) + @shape_type_conversion def build(self, input_shape): if len(input_shape) < 4: raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. ' @@ -269,6 +266,7 @@ class DepthwiseConv2D(Conv2D): return outputs + @shape_type_conversion def compute_output_shape(self, input_shape): if self.data_format == 'channels_first': rows = input_shape[2] @@ -305,7 +303,7 @@ class DepthwiseConv2D(Conv2D): return config -def MobileNet(input_shape=None, # pylint: disable=invalid-name +def MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, @@ -334,7 +332,7 @@ def MobileNet(input_shape=None, # pylint: disable=invalid-name if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or (3, 224, 224) (with `channels_first` data format). - It should have exactly 3 input channels, + It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. alpha: controls the width of the network. @@ -350,8 +348,8 @@ def MobileNet(input_shape=None, # pylint: disable=invalid-name include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. @@ -380,6 +378,12 @@ def MobileNet(input_shape=None, # pylint: disable=invalid-name RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ + + if K.backend() != 'tensorflow': + raise RuntimeError('Only TensorFlow backend is currently supported, ' + 'as other backends do not support ' + 'depthwise convolution.') + if not (weights in {'imagenet', None} or os.path.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' @@ -390,7 +394,7 @@ def MobileNet(input_shape=None, # pylint: disable=invalid-name raise ValueError('If using `weights` as ImageNet with `include_top` ' 'as true, `classes` should be 1000') - # Determine proper input shape. + # Determine proper input shape and default size. if input_shape is None: default_size = 224 else: @@ -400,10 +404,12 @@ def MobileNet(input_shape=None, # pylint: disable=invalid-name else: rows = input_shape[0] cols = input_shape[1] + if rows == cols and rows in [128, 160, 192, 224]: default_size = rows else: default_size = 224 + input_shape = _obtain_input_shape( input_shape, default_size=default_size, @@ -411,6 +417,7 @@ def MobileNet(input_shape=None, # pylint: disable=invalid-name data_format=K.image_data_format(), require_flatten=include_top, weights=weights) + if K.image_data_format() == 'channels_last': row_axis, col_axis = (0, 1) else: @@ -536,8 +543,6 @@ def MobileNet(input_shape=None, # pylint: disable=invalid-name if old_data_format: K.set_image_data_format(old_data_format) - elif weights is not None: - model.load_weights(weights) return model @@ -595,7 +600,8 @@ def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)): padding='same', use_bias=False, strides=strides, - name='conv1')(inputs) + name='conv1')( + inputs) x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x) return Activation(relu6, name='conv1_relu')(x) @@ -662,7 +668,8 @@ def _depthwise_conv_block(inputs, depth_multiplier=depth_multiplier, strides=strides, use_bias=False, - name='conv_dw_%d' % block_id)(inputs) + name='conv_dw_%d' % block_id)( + inputs) x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x) x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x) @@ -671,6 +678,7 @@ def _depthwise_conv_block(inputs, padding='same', use_bias=False, strides=(1, 1), - name='conv_pw_%d' % block_id)(x) + name='conv_pw_%d' % block_id)( + x) x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x) return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x) diff --git a/tensorflow/python/keras/_impl/keras/applications/nasnet.py b/tensorflow/python/keras/_impl/keras/applications/nasnet.py new file mode 100644 index 0000000000..5dd038c096 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/applications/nasnet.py @@ -0,0 +1,783 @@ +# 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 +# pylint: disable=invalid-name +# pylint: disable=unused-import +"""NASNet-A models for Keras. + +NASNet refers to Neural Architecture Search Network, a family of models +that were designed automatically by learning the model architectures +directly on the dataset of interest. + +Here we consider NASNet-A, the highest performance model that was found +for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset, +obtaining state of the art performance on CIFAR-10 and ImageNet 2012. +Only the NASNet-A models, and their respective weights, which are suited +for ImageNet 2012 are provided. + +The below table describes the performance on ImageNet 2012: +-------------------------------------------------------------------------------- + Architecture | Top-1 Acc | Top-5 Acc | Multiply-Adds | Params (M) +-------------------------------------------------------------------------------- +| NASNet-A (4 @ 1056) | 74.0 % | 91.6 % | 564 M | 5.3 | +| NASNet-A (6 @ 4032) | 82.7 % | 96.2 % | 23.8 B | 88.9 | +-------------------------------------------------------------------------------- + +References: + - [Learning Transferable Architectures for Scalable Image Recognition] + (https://arxiv.org/abs/1707.07012) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions +from tensorflow.python.keras._impl.keras.applications.inception_v3 import preprocess_input +from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs +from tensorflow.python.keras._impl.keras.layers import Activation +from tensorflow.python.keras._impl.keras.layers import add +from tensorflow.python.keras._impl.keras.layers import AveragePooling2D +from tensorflow.python.keras._impl.keras.layers import BatchNormalization +from tensorflow.python.keras._impl.keras.layers import concatenate +from tensorflow.python.keras._impl.keras.layers import Conv2D +from tensorflow.python.keras._impl.keras.layers import Cropping2D +from tensorflow.python.keras._impl.keras.layers import Dense +from tensorflow.python.keras._impl.keras.layers import GlobalAveragePooling2D +from tensorflow.python.keras._impl.keras.layers import GlobalMaxPooling2D +from tensorflow.python.keras._impl.keras.layers import Input +from tensorflow.python.keras._impl.keras.layers import MaxPooling2D +from tensorflow.python.keras._impl.keras.layers import SeparableConv2D +from tensorflow.python.keras._impl.keras.layers import ZeroPadding2D +from tensorflow.python.keras._impl.keras.models import Model +from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.platform import tf_logging as logging + + +NASNET_MOBILE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-mobile.h5' +NASNET_MOBILE_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-mobile-no-top.h5' +NASNET_LARGE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-large.h5' +NASNET_LARGE_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-large-no-top.h5' + + +def NASNet(input_shape=None, + penultimate_filters=4032, + num_blocks=6, + stem_block_filters=96, + skip_reduction=True, + filter_multiplier=2, + include_top=True, + weights=None, + input_tensor=None, + pooling=None, + classes=1000, + default_size=None): + """Instantiates a NASNet model. + + Note that only TensorFlow is supported for now, + therefore it only works with the data format + `image_data_format='channels_last'` in your Keras config + at `~/.keras/keras.json`. + + Arguments: + input_shape: Optional shape tuple, only to be specified + if `include_top` is False (otherwise the input shape + has to be `(331, 331, 3)` for NASNetLarge or + `(224, 224, 3)` for NASNetMobile + It should have exactly 3 inputs channels, + and width and height should be no smaller than 32. + E.g. `(224, 224, 3)` would be one valid value. + penultimate_filters: Number of filters in the penultimate layer. + NASNet models use the notation `NASNet (N @ P)`, where: + - N is the number of blocks + - P is the number of penultimate filters + num_blocks: Number of repeated blocks of the NASNet model. + NASNet models use the notation `NASNet (N @ P)`, where: + - N is the number of blocks + - P is the number of penultimate filters + stem_block_filters: Number of filters in the initial stem block + skip_reduction: Whether to skip the reduction step at the tail + end of the network. Set to `False` for CIFAR models. + filter_multiplier: Controls the width of the network. + - If `filter_multiplier` < 1.0, proportionally decreases the number + of filters in each layer. + - If `filter_multiplier` > 1.0, proportionally increases the number + of filters in each layer. + - If `filter_multiplier` = 1, default number of filters from the + paper are used at each layer. + include_top: Whether to include the fully-connected + layer at the top of the network. + weights: `None` (random initialization) or + `imagenet` (ImageNet weights) + input_tensor: Optional Keras tensor (i.e. output of + `layers.Input()`) + to use as image input for the model. + pooling: Optional pooling mode for feature extraction + when `include_top` is `False`. + - `None` means that the output of the model + will be the 4D tensor output of the + last convolutional layer. + - `avg` means that global average pooling + will be applied to the output of the + last convolutional layer, and thus + the output of the model will be a + 2D tensor. + - `max` means that global max pooling will + be applied. + classes: Optional number of classes to classify images + into, only to be specified if `include_top` is True, and + if no `weights` argument is specified. + default_size: Specifies the default image size of the model + + Returns: + A Keras model instance. + + Raises: + ValueError: In case of invalid argument for `weights`, + invalid input shape or invalid `penultimate_filters` value. + RuntimeError: If attempting to run this model with a + backend that does not support separable convolutions. + """ + if K.backend() != 'tensorflow': + raise RuntimeError('Only Tensorflow backend is currently supported, ' + 'as other backends do not support ' + 'separable convolution.') + + if not (weights in {'imagenet', None} or os.path.exists(weights)): + raise ValueError('The `weights` argument should be either ' + '`None` (random initialization), `imagenet` ' + '(pre-training on ImageNet), ' + 'or the path to the weights file to be loaded.') + + if weights == 'imagenet' and include_top and classes != 1000: + raise ValueError('If using `weights` as ImageNet with `include_top` ' + 'as true, `classes` should be 1000') + + if default_size is None: + default_size = 331 + + # Determine proper input shape and default size. + input_shape = _obtain_input_shape( + input_shape, + default_size=default_size, + min_size=32, + data_format=K.image_data_format(), + require_flatten=include_top or weights, + weights=weights) + + if K.image_data_format() != 'channels_last': + logging.warning('The NASNet family of models is only available ' + 'for the input data format "channels_last" ' + '(width, height, channels). ' + 'However your settings specify the default ' + 'data format "channels_first" (channels, width, height).' + ' You should set `image_data_format="channels_last"` ' + 'in your Keras config located at ~/.keras/keras.json. ' + 'The model being returned right now will expect inputs ' + 'to follow the "channels_last" data format.') + K.set_image_data_format('channels_last') + old_data_format = 'channels_first' + else: + old_data_format = None + + if input_tensor is None: + img_input = Input(shape=input_shape) + else: + if not K.is_keras_tensor(input_tensor): + img_input = Input(tensor=input_tensor, shape=input_shape) + else: + img_input = input_tensor + + if penultimate_filters % 24 != 0: + raise ValueError( + 'For NASNet-A models, the value of `penultimate_filters` ' + 'needs to be divisible by 24. Current value: %d' % penultimate_filters) + + channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 + filters = penultimate_filters // 24 + + if not skip_reduction: + x = Conv2D( + stem_block_filters, (3, 3), + strides=(2, 2), + padding='valid', + use_bias=False, + name='stem_conv1', + kernel_initializer='he_normal')( + img_input) + else: + x = Conv2D( + stem_block_filters, (3, 3), + strides=(1, 1), + padding='same', + use_bias=False, + name='stem_conv1', + kernel_initializer='he_normal')( + img_input) + + x = BatchNormalization( + axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')( + x) + + p = None + if not skip_reduction: # imagenet / mobile mode + x, p = _reduction_a_cell( + x, p, filters // (filter_multiplier**2), block_id='stem_1') + x, p = _reduction_a_cell( + x, p, filters // filter_multiplier, block_id='stem_2') + + for i in range(num_blocks): + x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i)) + + x, p0 = _reduction_a_cell( + x, p, filters * filter_multiplier, block_id='reduce_%d' % (num_blocks)) + + p = p0 if not skip_reduction else p + + for i in range(num_blocks): + x, p = _normal_a_cell( + x, p, filters * filter_multiplier, block_id='%d' % (num_blocks + i + 1)) + + x, p0 = _reduction_a_cell( + x, + p, + filters * filter_multiplier**2, + block_id='reduce_%d' % (2 * num_blocks)) + + p = p0 if not skip_reduction else p + + for i in range(num_blocks): + x, p = _normal_a_cell( + x, + p, + filters * filter_multiplier**2, + block_id='%d' % (2 * num_blocks + i + 1)) + + x = Activation('relu')(x) + + if include_top: + x = GlobalAveragePooling2D()(x) + x = Dense(classes, activation='softmax', name='predictions')(x) + else: + if pooling == 'avg': + x = GlobalAveragePooling2D()(x) + elif pooling == 'max': + x = GlobalMaxPooling2D()(x) + + # Ensure that the model takes into account + # any potential predecessors of `input_tensor`. + if input_tensor is not None: + inputs = get_source_inputs(input_tensor) + else: + inputs = img_input + + model = Model(inputs, x, name='NASNet') + + # load weights + if weights == 'imagenet': + if default_size == 224: # mobile version + if include_top: + weight_path = NASNET_MOBILE_WEIGHT_PATH + model_name = 'nasnet_mobile.h5' + else: + weight_path = NASNET_MOBILE_WEIGHT_PATH_NO_TOP + model_name = 'nasnet_mobile_no_top.h5' + + weights_file = get_file(model_name, weight_path, cache_subdir='models') + model.load_weights(weights_file) + + elif default_size == 331: # large version + if include_top: + weight_path = NASNET_LARGE_WEIGHT_PATH + model_name = 'nasnet_large.h5' + else: + weight_path = NASNET_LARGE_WEIGHT_PATH_NO_TOP + model_name = 'nasnet_large_no_top.h5' + + weights_file = get_file(model_name, weight_path, cache_subdir='models') + model.load_weights(weights_file) + else: + raise ValueError('ImageNet weights can only be loaded with NASNetLarge' + ' or NASNetMobile') + elif weights is not None: + model.load_weights(weights) + + if old_data_format: + K.set_image_data_format(old_data_format) + + return model + + +def NASNetLarge(input_shape=None, + include_top=True, + weights='imagenet', + input_tensor=None, + pooling=None, + classes=1000): + """Instantiates a NASNet model in ImageNet mode. + + Note that only TensorFlow is supported for now, + therefore it only works with the data format + `image_data_format='channels_last'` in your Keras config + at `~/.keras/keras.json`. + + Arguments: + input_shape: Optional shape tuple, only to be specified + if `include_top` is False (otherwise the input shape + has to be `(331, 331, 3)` for NASNetLarge. + It should have exactly 3 inputs channels, + and width and height should be no smaller than 32. + E.g. `(224, 224, 3)` would be one valid value. + include_top: Whether to include the fully-connected + layer at the top of the network. + weights: `None` (random initialization) or + `imagenet` (ImageNet weights) + input_tensor: Optional Keras tensor (i.e. output of + `layers.Input()`) + to use as image input for the model. + pooling: Optional pooling mode for feature extraction + when `include_top` is `False`. + - `None` means that the output of the model + will be the 4D tensor output of the + last convolutional layer. + - `avg` means that global average pooling + will be applied to the output of the + last convolutional layer, and thus + the output of the model will be a + 2D tensor. + - `max` means that global max pooling will + be applied. + classes: Optional number of classes to classify images + into, only to be specified if `include_top` is True, and + if no `weights` argument is specified. + + Returns: + A Keras model instance. + + Raises: + ValueError: in case of invalid argument for `weights`, + or invalid input shape. + RuntimeError: If attempting to run this model with a + backend that does not support separable convolutions. + """ + return NASNet( + input_shape, + penultimate_filters=4032, + num_blocks=6, + stem_block_filters=96, + skip_reduction=False, + filter_multiplier=2, + include_top=include_top, + weights=weights, + input_tensor=input_tensor, + pooling=pooling, + classes=classes, + default_size=331) + + +def NASNetMobile(input_shape=None, + include_top=True, + weights='imagenet', + input_tensor=None, + pooling=None, + classes=1000): + """Instantiates a Mobile NASNet model in ImageNet mode. + + Note that only TensorFlow is supported for now, + therefore it only works with the data format + `image_data_format='channels_last'` in your Keras config + at `~/.keras/keras.json`. + + Arguments: + input_shape: Optional shape tuple, only to be specified + if `include_top` is False (otherwise the input shape + has to be `(224, 224, 3)` for NASNetMobile + It should have exactly 3 inputs channels, + and width and height should be no smaller than 32. + E.g. `(224, 224, 3)` would be one valid value. + include_top: Whether to include the fully-connected + layer at the top of the network. + weights: `None` (random initialization) or + `imagenet` (ImageNet weights) + input_tensor: Optional Keras tensor (i.e. output of + `layers.Input()`) + to use as image input for the model. + pooling: Optional pooling mode for feature extraction + when `include_top` is `False`. + - `None` means that the output of the model + will be the 4D tensor output of the + last convolutional layer. + - `avg` means that global average pooling + will be applied to the output of the + last convolutional layer, and thus + the output of the model will be a + 2D tensor. + - `max` means that global max pooling will + be applied. + classes: Optional number of classes to classify images + into, only to be specified if `include_top` is True, and + if no `weights` argument is specified. + + Returns: + A Keras model instance. + + Raises: + ValueError: In case of invalid argument for `weights`, + or invalid input shape. + RuntimeError: If attempting to run this model with a + backend that does not support separable convolutions. + """ + return NASNet( + input_shape, + penultimate_filters=1056, + num_blocks=4, + stem_block_filters=32, + skip_reduction=False, + filter_multiplier=2, + include_top=include_top, + weights=weights, + input_tensor=input_tensor, + pooling=pooling, + classes=classes, + default_size=224) + + +def _separable_conv_block(ip, + filters, + kernel_size=(3, 3), + strides=(1, 1), + block_id=None): + """Adds 2 blocks of [relu-separable conv-batchnorm]. + + Arguments: + ip: Input tensor + filters: Number of output filters per layer + kernel_size: Kernel size of separable convolutions + strides: Strided convolution for downsampling + block_id: String block_id + + Returns: + A Keras tensor + """ + channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 + + with K.name_scope('separable_conv_block_%s' % block_id): + x = Activation('relu')(ip) + x = SeparableConv2D( + filters, + kernel_size, + strides=strides, + name='separable_conv_1_%s' % block_id, + padding='same', + use_bias=False, + kernel_initializer='he_normal')( + x) + x = BatchNormalization( + axis=channel_dim, + momentum=0.9997, + epsilon=1e-3, + name='separable_conv_1_bn_%s' % (block_id))( + x) + x = Activation('relu')(x) + x = SeparableConv2D( + filters, + kernel_size, + name='separable_conv_2_%s' % block_id, + padding='same', + use_bias=False, + kernel_initializer='he_normal')( + x) + x = BatchNormalization( + axis=channel_dim, + momentum=0.9997, + epsilon=1e-3, + name='separable_conv_2_bn_%s' % (block_id))( + x) + return x + + +def _adjust_block(p, ip, filters, block_id=None): + """Adjusts the input `previous path` to match the shape of the `input`. + + Used in situations where the output number of filters needs to be changed. + + Arguments: + p: Input tensor which needs to be modified + ip: Input tensor whose shape needs to be matched + filters: Number of output filters to be matched + block_id: String block_id + + Returns: + Adjusted Keras tensor + """ + channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 + img_dim = 2 if K.image_data_format() == 'channels_first' else -2 + + ip_shape = K.int_shape(ip) + + if p is not None: + p_shape = K.int_shape(p) + + with K.name_scope('adjust_block'): + if p is None: + p = ip + + elif p_shape[img_dim] != ip_shape[img_dim]: + with K.name_scope('adjust_reduction_block_%s' % block_id): + p = Activation('relu', name='adjust_relu_1_%s' % block_id)(p) + + p1 = AveragePooling2D( + (1, 1), + strides=(2, 2), + padding='valid', + name='adjust_avg_pool_1_%s' % block_id)( + p) + p1 = Conv2D( + filters // 2, (1, 1), + padding='same', + use_bias=False, + name='adjust_conv_1_%s' % block_id, + kernel_initializer='he_normal')( + p1) + + p2 = ZeroPadding2D(padding=((0, 1), (0, 1)))(p) + p2 = Cropping2D(cropping=((1, 0), (1, 0)))(p2) + p2 = AveragePooling2D( + (1, 1), + strides=(2, 2), + padding='valid', + name='adjust_avg_pool_2_%s' % block_id)( + p2) + p2 = Conv2D( + filters // 2, (1, 1), + padding='same', + use_bias=False, + name='adjust_conv_2_%s' % block_id, + kernel_initializer='he_normal')( + p2) + + p = concatenate([p1, p2], axis=channel_dim) + p = BatchNormalization( + axis=channel_dim, + momentum=0.9997, + epsilon=1e-3, + name='adjust_bn_%s' % block_id)( + p) + + elif p_shape[channel_dim] != filters: + with K.name_scope('adjust_projection_block_%s' % block_id): + p = Activation('relu')(p) + p = Conv2D( + filters, (1, 1), + strides=(1, 1), + padding='same', + name='adjust_conv_projection_%s' % block_id, + use_bias=False, + kernel_initializer='he_normal')( + p) + p = BatchNormalization( + axis=channel_dim, + momentum=0.9997, + epsilon=1e-3, + name='adjust_bn_%s' % block_id)( + p) + return p + + +def _normal_a_cell(ip, p, filters, block_id=None): + """Adds a Normal cell for NASNet-A (Fig. 4 in the paper). + + Arguments: + ip: Input tensor `x` + p: Input tensor `p` + filters: Number of output filters + block_id: String block_id + + Returns: + A Keras tensor + """ + channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 + + with K.name_scope('normal_A_block_%s' % block_id): + p = _adjust_block(p, ip, filters, block_id) + + h = Activation('relu')(ip) + h = Conv2D( + filters, (1, 1), + strides=(1, 1), + padding='same', + name='normal_conv_1_%s' % block_id, + use_bias=False, + kernel_initializer='he_normal')( + h) + h = BatchNormalization( + axis=channel_dim, + momentum=0.9997, + epsilon=1e-3, + name='normal_bn_1_%s' % block_id)( + h) + + with K.name_scope('block_1'): + x1_1 = _separable_conv_block( + h, filters, kernel_size=(5, 5), block_id='normal_left1_%s' % block_id) + x1_2 = _separable_conv_block( + p, filters, block_id='normal_right1_%s' % block_id) + x1 = add([x1_1, x1_2], name='normal_add_1_%s' % block_id) + + with K.name_scope('block_2'): + x2_1 = _separable_conv_block( + p, filters, (5, 5), block_id='normal_left2_%s' % block_id) + x2_2 = _separable_conv_block( + p, filters, (3, 3), block_id='normal_right2_%s' % block_id) + x2 = add([x2_1, x2_2], name='normal_add_2_%s' % block_id) + + with K.name_scope('block_3'): + x3 = AveragePooling2D( + (3, 3), + strides=(1, 1), + padding='same', + name='normal_left3_%s' % (block_id))( + h) + x3 = add([x3, p], name='normal_add_3_%s' % block_id) + + with K.name_scope('block_4'): + x4_1 = AveragePooling2D( + (3, 3), + strides=(1, 1), + padding='same', + name='normal_left4_%s' % (block_id))( + p) + x4_2 = AveragePooling2D( + (3, 3), + strides=(1, 1), + padding='same', + name='normal_right4_%s' % (block_id))( + p) + x4 = add([x4_1, x4_2], name='normal_add_4_%s' % block_id) + + with K.name_scope('block_5'): + x5 = _separable_conv_block( + h, filters, block_id='normal_left5_%s' % block_id) + x5 = add([x5, h], name='normal_add_5_%s' % block_id) + + x = concatenate( + [p, x1, x2, x3, x4, x5], + axis=channel_dim, + name='normal_concat_%s' % block_id) + return x, ip + + +def _reduction_a_cell(ip, p, filters, block_id=None): + """Adds a Reduction cell for NASNet-A (Fig. 4 in the paper). + + Arguments: + ip: Input tensor `x` + p: Input tensor `p` + filters: Number of output filters + block_id: String block_id + + Returns: + A Keras tensor + """ + channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 + + with K.name_scope('reduction_A_block_%s' % block_id): + p = _adjust_block(p, ip, filters, block_id) + + h = Activation('relu')(ip) + h = Conv2D( + filters, (1, 1), + strides=(1, 1), + padding='same', + name='reduction_conv_1_%s' % block_id, + use_bias=False, + kernel_initializer='he_normal')( + h) + h = BatchNormalization( + axis=channel_dim, + momentum=0.9997, + epsilon=1e-3, + name='reduction_bn_1_%s' % block_id)( + h) + + with K.name_scope('block_1'): + x1_1 = _separable_conv_block( + h, + filters, (5, 5), + strides=(2, 2), + block_id='reduction_left1_%s' % block_id) + x1_2 = _separable_conv_block( + p, + filters, (7, 7), + strides=(2, 2), + block_id='reduction_1_%s' % block_id) + x1 = add([x1_1, x1_2], name='reduction_add_1_%s' % block_id) + + with K.name_scope('block_2'): + x2_1 = MaxPooling2D( + (3, 3), + strides=(2, 2), + padding='same', + name='reduction_left2_%s' % block_id)( + h) + x2_2 = _separable_conv_block( + p, + filters, (7, 7), + strides=(2, 2), + block_id='reduction_right2_%s' % block_id) + x2 = add([x2_1, x2_2], name='reduction_add_2_%s' % block_id) + + with K.name_scope('block_3'): + x3_1 = AveragePooling2D( + (3, 3), + strides=(2, 2), + padding='same', + name='reduction_left3_%s' % block_id)( + h) + x3_2 = _separable_conv_block( + p, + filters, (5, 5), + strides=(2, 2), + block_id='reduction_right3_%s' % block_id) + x3 = add([x3_1, x3_2], name='reduction_add3_%s' % block_id) + + with K.name_scope('block_4'): + x4 = AveragePooling2D( + (3, 3), + strides=(1, 1), + padding='same', + name='reduction_left4_%s' % block_id)( + x1) + x4 = add([x2, x4]) + + with K.name_scope('block_5'): + x5_1 = _separable_conv_block( + x1, filters, (3, 3), block_id='reduction_left4_%s' % block_id) + x5_2 = MaxPooling2D( + (3, 3), + strides=(2, 2), + padding='same', + name='reduction_right5_%s' % block_id)( + h) + x5 = add([x5_1, x5_2], name='reduction_add4_%s' % block_id) + + x = concatenate( + [x2, x3, x4, x5], + axis=channel_dim, + name='reduction_concat_%s' % block_id) + return x, ip diff --git a/tensorflow/python/keras/_impl/keras/applications/nasnet_test.py b/tensorflow/python/keras/_impl/keras/applications/nasnet_test.py new file mode 100644 index 0000000000..aa1dec670c --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/applications/nasnet_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. +# ============================================================================== +"""Tests for Nasnet application.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.keras._impl import keras +from tensorflow.python.platform import test + + +class NASNetMobileTest(test.TestCase): + + def test_with_top(self): + model = keras.applications.NASNetMobile(weights=None) + self.assertEqual(model.output_shape, (None, 1000)) + + def test_no_top(self): + model = keras.applications.NASNetMobile(weights=None, include_top=False) + self.assertEqual(model.output_shape, (None, None, None, 1056)) + + def test_with_pooling(self): + model = keras.applications.NASNetMobile(weights=None, + include_top=False, + pooling='avg') + self.assertEqual(model.output_shape, (None, 1056)) + + def test_weight_loading(self): + with self.assertRaises(ValueError): + keras.applications.NASNetMobile(weights='unknown', + include_top=False) + with self.assertRaises(ValueError): + keras.applications.NASNetMobile(weights='imagenet', + classes=2000) + + +class NASNetLargeTest(test.TestCase): + + def test_with_top(self): + model = keras.applications.NASNetLarge(weights=None) + self.assertEqual(model.output_shape, (None, 1000)) + + def test_no_top(self): + model = keras.applications.NASNetLarge(weights=None, include_top=False) + self.assertEqual(model.output_shape, (None, None, None, 4032)) + + def test_with_pooling(self): + model = keras.applications.NASNetLarge(weights=None, + include_top=False, + pooling='avg') + self.assertEqual(model.output_shape, (None, 4032)) + + def test_weight_loading(self): + with self.assertRaises(ValueError): + keras.applications.NASNetLarge(weights='unknown', + include_top=False) + with self.assertRaises(ValueError): + keras.applications.NASNetLarge(weights='imagenet', + classes=2000) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/keras/_impl/keras/applications/resnet50.py b/tensorflow/python/keras/_impl/keras/applications/resnet50.py index 8ab46693aa..5705b3481a 100644 --- a/tensorflow/python/keras/_impl/keras/applications/resnet50.py +++ b/tensorflow/python/keras/_impl/keras/applications/resnet50.py @@ -13,6 +13,7 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name +# pylint: disable=unused-import """ResNet50 model for Keras. # Reference: @@ -31,8 +32,8 @@ import os from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input # pylint: disable=unused-import +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import AveragePooling2D @@ -45,7 +46,9 @@ from tensorflow.python.keras._impl.keras.layers import GlobalMaxPooling2D from tensorflow.python.keras._impl.keras.layers import Input from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.models import Model +from tensorflow.python.keras._impl.keras.utils import layer_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.platform import tf_logging as logging WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5' @@ -78,7 +81,8 @@ def identity_block(input_tensor, kernel_size, filters, stage, block): x = Activation('relu')(x) x = Conv2D( - filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) + filters2, kernel_size, padding='same', name=conv_name_base + '2b')( + x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) @@ -92,7 +96,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block): def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): - """conv_block is the block that has a conv layer at shortcut. + """A block that has a conv layer at shortcut. Arguments: input_tensor: input tensor @@ -100,14 +104,14 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, filters: list of integers, the filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names - strides: Tuple of integers. + strides: Strides for the first conv layer in the block. Returns: Output tensor for the block. - Note that from stage 3, the first conv layer at main path is with - strides=(2,2) - And the shortcut should have strides=(2,2) as well + Note that from stage 3, + the first conv layer at main path is with strides=(2, 2) + And the shortcut should have strides=(2, 2) as well """ filters1, filters2, filters3 = filters if K.image_data_format() == 'channels_last': @@ -118,13 +122,14 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, bn_name_base = 'bn' + str(stage) + block + '_branch' x = Conv2D( - filters1, (1, 1), strides=strides, - name=conv_name_base + '2a')(input_tensor) + filters1, (1, 1), strides=strides, name=conv_name_base + '2a')( + input_tensor) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Conv2D( - filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x) + filters2, kernel_size, padding='same', name=conv_name_base + '2b')( + x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) @@ -132,8 +137,8 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) shortcut = Conv2D( - filters3, (1, 1), strides=strides, - name=conv_name_base + '1')(input_tensor) + filters3, (1, 1), strides=strides, name=conv_name_base + '1')( + input_tensor) shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) x = layers.add([x, shortcut]) @@ -152,7 +157,7 @@ def ResNet50(include_top=True, Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set - `image_data_format="channels_last"` in your Keras config + `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both @@ -164,15 +169,15 @@ def ResNet50(include_top=True, include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `channels_first` data format). - It should have exactly 3 input channels, + It should have exactly 3 inputs channels, and width and height should be no smaller than 197. E.g. `(200, 200, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction @@ -219,15 +224,18 @@ def ResNet50(include_top=True, if input_tensor is None: img_input = Input(shape=input_shape) else: - img_input = Input(tensor=input_tensor, shape=input_shape) - + if not K.is_keras_tensor(input_tensor): + img_input = Input(tensor=input_tensor, shape=input_shape) + else: + img_input = input_tensor if K.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 - x = Conv2D(64, (7, 7), - strides=(2, 2), padding='same', name='conv1')(img_input) + x = Conv2D( + 64, (7, 7), strides=(2, 2), padding='same', name='conv1')( + img_input) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) @@ -289,4 +297,5 @@ def ResNet50(include_top=True, model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) + return model diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg16.py b/tensorflow/python/keras/_impl/keras/applications/vgg16.py index 38dbbdc809..c91c24e6fb 100644 --- a/tensorflow/python/keras/_impl/keras/applications/vgg16.py +++ b/tensorflow/python/keras/_impl/keras/applications/vgg16.py @@ -13,6 +13,7 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name +# pylint: disable=unused-import """VGG16 model for Keras. # Reference @@ -29,8 +30,8 @@ import os from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input # pylint: disable=unused-import +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dense @@ -42,6 +43,7 @@ from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import layer_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.platform import tf_logging as logging WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5' @@ -59,7 +61,7 @@ def VGG16(include_top=True, Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set - `image_data_format="channels_last"` in your Keras config + `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both @@ -71,8 +73,8 @@ def VGG16(include_top=True, include_top: whether to include the 3 fully-connected layers at the top of the network. weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified @@ -125,48 +127,62 @@ def VGG16(include_top=True, if input_tensor is None: img_input = Input(shape=input_shape) else: - img_input = Input(tensor=input_tensor, shape=input_shape) - + if not K.is_keras_tensor(input_tensor): + img_input = Input(tensor=input_tensor, shape=input_shape) + else: + img_input = input_tensor # Block 1 x = Conv2D( - 64, (3, 3), activation='relu', padding='same', - name='block1_conv1')(img_input) + 64, (3, 3), activation='relu', padding='same', name='block1_conv1')( + img_input) x = Conv2D( - 64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) + 64, (3, 3), activation='relu', padding='same', name='block1_conv2')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x) + 128, (3, 3), activation='relu', padding='same', name='block2_conv1')( + x) x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x) + 128, (3, 3), activation='relu', padding='same', name='block2_conv2')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x) + 256, (3, 3), activation='relu', padding='same', name='block3_conv1')( + x) x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x) + 256, (3, 3), activation='relu', padding='same', name='block3_conv2')( + x) x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x) + 256, (3, 3), activation='relu', padding='same', name='block3_conv3')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x) + 512, (3, 3), activation='relu', padding='same', name='block4_conv1')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x) + 512, (3, 3), activation='relu', padding='same', name='block4_conv2')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x) + 512, (3, 3), activation='relu', padding='same', name='block4_conv3')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x) + 512, (3, 3), activation='relu', padding='same', name='block5_conv1')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x) + 512, (3, 3), activation='relu', padding='same', name='block5_conv2')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x) + 512, (3, 3), activation='relu', padding='same', name='block5_conv3')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) if include_top: @@ -215,6 +231,8 @@ def VGG16(include_top=True, dense = model.get_layer(name='fc1') layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first') + elif weights is not None: model.load_weights(weights) + return model diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg19.py b/tensorflow/python/keras/_impl/keras/applications/vgg19.py index 126c64260b..223cd79d7b 100644 --- a/tensorflow/python/keras/_impl/keras/applications/vgg19.py +++ b/tensorflow/python/keras/_impl/keras/applications/vgg19.py @@ -13,6 +13,7 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name +# pylint: disable=unused-import """VGG19 model for Keras. # Reference @@ -29,8 +30,8 @@ import os from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input # pylint: disable=unused-import +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dense @@ -42,6 +43,7 @@ from tensorflow.python.keras._impl.keras.layers import MaxPooling2D from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import layer_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file +from tensorflow.python.platform import tf_logging as logging WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5' @@ -59,7 +61,7 @@ def VGG19(include_top=True, Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set - `image_data_format="channels_last"` in your Keras config + `image_data_format='channels_last'` in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with both @@ -71,15 +73,15 @@ def VGG19(include_top=True, include_top: whether to include the 3 fully-connected layers at the top of the network. weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `channels_first` data format). - It should have exactly 3 input channels, + It should have exactly 3 inputs channels, and width and height should be no smaller than 48. E.g. `(200, 200, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction @@ -125,54 +127,71 @@ def VGG19(include_top=True, if input_tensor is None: img_input = Input(shape=input_shape) else: - img_input = Input(tensor=input_tensor, shape=input_shape) - + if not K.is_keras_tensor(input_tensor): + img_input = Input(tensor=input_tensor, shape=input_shape) + else: + img_input = input_tensor # Block 1 x = Conv2D( - 64, (3, 3), activation='relu', padding='same', - name='block1_conv1')(img_input) + 64, (3, 3), activation='relu', padding='same', name='block1_conv1')( + img_input) x = Conv2D( - 64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) + 64, (3, 3), activation='relu', padding='same', name='block1_conv2')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x) + 128, (3, 3), activation='relu', padding='same', name='block2_conv1')( + x) x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x) + 128, (3, 3), activation='relu', padding='same', name='block2_conv2')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x) + 256, (3, 3), activation='relu', padding='same', name='block3_conv1')( + x) x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x) + 256, (3, 3), activation='relu', padding='same', name='block3_conv2')( + x) x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x) + 256, (3, 3), activation='relu', padding='same', name='block3_conv3')( + x) x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv4')(x) + 256, (3, 3), activation='relu', padding='same', name='block3_conv4')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x) + 512, (3, 3), activation='relu', padding='same', name='block4_conv1')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x) + 512, (3, 3), activation='relu', padding='same', name='block4_conv2')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x) + 512, (3, 3), activation='relu', padding='same', name='block4_conv3')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv4')(x) + 512, (3, 3), activation='relu', padding='same', name='block4_conv4')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x) + 512, (3, 3), activation='relu', padding='same', name='block5_conv1')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x) + 512, (3, 3), activation='relu', padding='same', name='block5_conv2')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x) + 512, (3, 3), activation='relu', padding='same', name='block5_conv3')( + x) x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv4')(x) + 512, (3, 3), activation='relu', padding='same', name='block5_conv4')( + x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) if include_top: @@ -211,6 +230,8 @@ def VGG19(include_top=True, cache_subdir='models', file_hash='253f8cb515780f3b799900260a226db6') model.load_weights(weights_path) + if K.backend() == 'theano': + layer_utils.convert_all_kernels_in_model(model) if K.image_data_format() == 'channels_first': if include_top: @@ -219,6 +240,8 @@ def VGG19(include_top=True, dense = model.get_layer(name='fc1') layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first') + elif weights is not None: model.load_weights(weights) + return model diff --git a/tensorflow/python/keras/_impl/keras/applications/xception.py b/tensorflow/python/keras/_impl/keras/applications/xception.py index 8219831408..0a6eb4953a 100644 --- a/tensorflow/python/keras/_impl/keras/applications/xception.py +++ b/tensorflow/python/keras/_impl/keras/applications/xception.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,6 +13,7 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name +# pylint: disable=unused-import """Xception V1 model for Keras. On ImageNet, this model gets to a top-1 validation accuracy of 0.790 @@ -42,7 +43,7 @@ from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import layers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import +from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization @@ -74,7 +75,7 @@ def Xception(include_top=True, on ImageNet. This model is available for TensorFlow only, and can only be used with inputs following the TensorFlow data format `(width, height, channels)`. - You should set `image_data_format="channels_last"` in your Keras config + You should set `image_data_format='channels_last'` in your Keras config located at ~/.keras/keras.json. Note that the default input image size for this model is 299x299. @@ -83,14 +84,14 @@ def Xception(include_top=True, include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. + 'imagenet' (pre-training on ImageNet), + or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(299, 299, 3)`. - It should have exactly 3 input channels, + It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g. `(150, 150, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction @@ -155,11 +156,14 @@ def Xception(include_top=True, if input_tensor is None: img_input = Input(shape=input_shape) else: - img_input = Input(tensor=input_tensor, shape=input_shape) + if not K.is_keras_tensor(input_tensor): + img_input = Input(tensor=input_tensor, shape=input_shape) + else: + img_input = input_tensor x = Conv2D( - 32, (3, 3), strides=(2, 2), use_bias=False, - name='block1_conv1')(img_input) + 32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')( + img_input) x = BatchNormalization(name='block1_conv1_bn')(x) x = Activation('relu', name='block1_conv1_act')(x) x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) @@ -167,53 +171,65 @@ def Xception(include_top=True, x = Activation('relu', name='block1_conv2_act')(x) residual = Conv2D( - 128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + 128, (1, 1), strides=(2, 2), padding='same', use_bias=False)( + x) residual = BatchNormalization()(residual) x = SeparableConv2D( - 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x) + 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')( + x) x = BatchNormalization(name='block2_sepconv1_bn')(x) x = Activation('relu', name='block2_sepconv2_act')(x) x = SeparableConv2D( - 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x) + 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')( + x) x = BatchNormalization(name='block2_sepconv2_bn')(x) x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block2_pool')(x) + (3, 3), strides=(2, 2), padding='same', name='block2_pool')( + x) x = layers.add([x, residual]) residual = Conv2D( - 256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + 256, (1, 1), strides=(2, 2), padding='same', use_bias=False)( + x) residual = BatchNormalization()(residual) x = Activation('relu', name='block3_sepconv1_act')(x) x = SeparableConv2D( - 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x) + 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')( + x) x = BatchNormalization(name='block3_sepconv1_bn')(x) x = Activation('relu', name='block3_sepconv2_act')(x) x = SeparableConv2D( - 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x) + 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')( + x) x = BatchNormalization(name='block3_sepconv2_bn')(x) x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block3_pool')(x) + (3, 3), strides=(2, 2), padding='same', name='block3_pool')( + x) x = layers.add([x, residual]) residual = Conv2D( - 728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + 728, (1, 1), strides=(2, 2), padding='same', use_bias=False)( + x) residual = BatchNormalization()(residual) x = Activation('relu', name='block4_sepconv1_act')(x) x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x) + 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')( + x) x = BatchNormalization(name='block4_sepconv1_bn')(x) x = Activation('relu', name='block4_sepconv2_act')(x) x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x) + 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')( + x) x = BatchNormalization(name='block4_sepconv2_bn')(x) x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block4_pool')(x) + (3, 3), strides=(2, 2), padding='same', name='block4_pool')( + x) x = layers.add([x, residual]) for i in range(8): @@ -222,46 +238,52 @@ def Xception(include_top=True, x = Activation('relu', name=prefix + '_sepconv1_act')(x) x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, - name=prefix + '_sepconv1')(x) + 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')( + x) x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) x = Activation('relu', name=prefix + '_sepconv2_act')(x) x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, - name=prefix + '_sepconv2')(x) + 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')( + x) x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) x = Activation('relu', name=prefix + '_sepconv3_act')(x) x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, - name=prefix + '_sepconv3')(x) + 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')( + x) x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) x = layers.add([x, residual]) residual = Conv2D( - 1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x) + 1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)( + x) residual = BatchNormalization()(residual) x = Activation('relu', name='block13_sepconv1_act')(x) x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x) + 728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')( + x) x = BatchNormalization(name='block13_sepconv1_bn')(x) x = Activation('relu', name='block13_sepconv2_act')(x) x = SeparableConv2D( - 1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x) + 1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')( + x) x = BatchNormalization(name='block13_sepconv2_bn')(x) x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block13_pool')(x) + (3, 3), strides=(2, 2), padding='same', name='block13_pool')( + x) x = layers.add([x, residual]) x = SeparableConv2D( - 1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x) + 1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')( + x) x = BatchNormalization(name='block14_sepconv1_bn')(x) x = Activation('relu', name='block14_sepconv1_act')(x) x = SeparableConv2D( - 2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x) + 2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')( + x) x = BatchNormalization(name='block14_sepconv2_bn')(x) x = Activation('relu', name='block14_sepconv2_act')(x) @@ -303,8 +325,6 @@ def Xception(include_top=True, if old_data_format: K.set_image_data_format(old_data_format) - elif weights is not None: - model.load_weights(weights) return model diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py index 9476085bd8..460c0dc5f3 100644 --- a/tensorflow/python/keras/_impl/keras/backend.py +++ b/tensorflow/python/keras/_impl/keras/backend.py @@ -85,7 +85,7 @@ _MANUAL_VAR_INIT = False _FLOATX = 'float32' # Epsilon fuzz factor used throughout the codebase. -_EPSILON = 10e-8 +_EPSILON = 1e-7 # Default image data format, one of "channels_last", "channels_first". _IMAGE_DATA_FORMAT = 'channels_last' @@ -116,7 +116,7 @@ def epsilon(): Example: ```python >>> keras.backend.epsilon() - 1e-08 + 1e-07 ``` """ return _EPSILON @@ -132,7 +132,7 @@ def set_epsilon(value): ```python >>> from keras import backend as K >>> K.epsilon() - 1e-08 + 1e-07 >>> K.set_epsilon(1e-05) >>> K.epsilon() 1e-05 @@ -295,7 +295,8 @@ def clear_session(): ops.reset_default_graph() reset_uids() _SESSION = None - phase = array_ops.placeholder(dtype='bool', name='keras_learning_phase') + phase = array_ops.placeholder_with_default( + False, shape=(), name='keras_learning_phase') _GRAPH_LEARNING_PHASES = {} _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = phase @@ -328,7 +329,8 @@ def learning_phase(): """ graph = ops.get_default_graph() if graph not in _GRAPH_LEARNING_PHASES: - phase = array_ops.placeholder(dtype='bool', name='keras_learning_phase') + phase = array_ops.placeholder_with_default( + False, shape=(), name='keras_learning_phase') _GRAPH_LEARNING_PHASES[graph] = phase return _GRAPH_LEARNING_PHASES[graph] @@ -876,6 +878,8 @@ def zeros(shape, dtype=None, name=None): Returns: A variable (including Keras metadata), filled with `0.0`. + Note that if `shape` was symbolic, we cannot return a variable, + and will return a dynamically-shaped tensor instead. Example: ```python @@ -890,12 +894,14 @@ def zeros(shape, dtype=None, name=None): if dtype is None: dtype = floatx() tf_dtype = dtypes_module.as_dtype(dtype) - return variable( - init_ops.constant_initializer(0., dtype=tf_dtype)(shape), dtype, name) + v = array_ops.zeros(shape=shape, dtype=tf_dtype, name=name) + if py_all(v.get_shape().as_list()): + return variable(v, dtype=dtype, name=name) + return v def ones(shape, dtype=None, name=None): - """Instantiates an all-ones tensor variable and returns it. + """Instantiates an all-ones variable and returns it. Arguments: shape: Tuple of integers, shape of returned Keras variable. @@ -904,6 +910,8 @@ def ones(shape, dtype=None, name=None): Returns: A Keras variable, filled with `1.0`. + Note that if `shape` was symbolic, we cannot return a variable, + and will return a dynamically-shaped tensor instead. Example: ```python @@ -918,8 +926,10 @@ def ones(shape, dtype=None, name=None): if dtype is None: dtype = floatx() tf_dtype = dtypes_module.as_dtype(dtype) - return variable( - init_ops.constant_initializer(1., dtype=tf_dtype)(shape), dtype, name) + v = array_ops.ones(shape=shape, dtype=tf_dtype, name=name) + if py_all(v.get_shape().as_list()): + return variable(v, dtype=dtype, name=name) + return v def eye(size, dtype=None, name=None): @@ -1185,7 +1195,7 @@ def moving_average_update(x, value, momentum): An Operation to update the variable. """ return moving_averages.assign_moving_average( - x, value, momentum, zero_debias=False) + x, value, momentum, zero_debias=True) # LINEAR ALGEBRA @@ -1419,7 +1429,7 @@ def max(x, axis=None, keepdims=False): Returns: A tensor with maximum values of `x`. """ - return math_ops.reduce_max(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_max(x, axis, keepdims) def min(x, axis=None, keepdims=False): @@ -1436,7 +1446,7 @@ def min(x, axis=None, keepdims=False): Returns: A tensor with miminum values of `x`. """ - return math_ops.reduce_min(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_min(x, axis, keepdims) def sum(x, axis=None, keepdims=False): @@ -1453,7 +1463,7 @@ def sum(x, axis=None, keepdims=False): Returns: A tensor with sum of `x`. """ - return math_ops.reduce_sum(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_sum(x, axis, keepdims) def prod(x, axis=None, keepdims=False): @@ -1470,7 +1480,7 @@ def prod(x, axis=None, keepdims=False): Returns: A tensor with the product of elements of `x`. """ - return math_ops.reduce_prod(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_prod(x, axis, keepdims) def cumsum(x, axis=0): @@ -1515,10 +1525,10 @@ def var(x, axis=None, keepdims=False): """ if x.dtype.base_dtype == dtypes_module.bool: x = math_ops.cast(x, floatx()) - m = math_ops.reduce_mean(x, axis=axis, keep_dims=True) + m = math_ops.reduce_mean(x, axis, True) devs_squared = math_ops.square(x - m) return math_ops.reduce_mean( - devs_squared, axis=axis, keep_dims=keepdims) + devs_squared, axis, keepdims) def std(x, axis=None, keepdims=False): @@ -1546,7 +1556,7 @@ def mean(x, axis=None, keepdims=False): axis: A list of integer. Axes to compute the mean. keepdims: A boolean, whether to keep the dimensions or not. If `keepdims` is `False`, the rank of the tensor is reduced - by 1 for each entry in `axis`. If `keep_dims` is `True`, + by 1 for each entry in `axis`. If `keepdims` is `True`, the reduced dimensions are retained with length 1. Returns: @@ -1554,7 +1564,7 @@ def mean(x, axis=None, keepdims=False): """ if x.dtype.base_dtype == dtypes_module.bool: x = math_ops.cast(x, floatx()) - return math_ops.reduce_mean(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_mean(x, axis, keepdims) def any(x, axis=None, keepdims=False): @@ -1569,7 +1579,7 @@ def any(x, axis=None, keepdims=False): A uint8 tensor (0s and 1s). """ x = math_ops.cast(x, dtypes_module.bool) - return math_ops.reduce_any(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_any(x, axis, keepdims) def all(x, axis=None, keepdims=False): @@ -1584,7 +1594,7 @@ def all(x, axis=None, keepdims=False): A uint8 tensor (0s and 1s). """ x = math_ops.cast(x, dtypes_module.bool) - return math_ops.reduce_all(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_all(x, axis, keepdims) def argmax(x, axis=-1): @@ -1694,7 +1704,7 @@ def logsumexp(x, axis=None, keepdims=False): Returns: The reduced tensor. """ - return math_ops.reduce_logsumexp(x, axis=axis, keep_dims=keepdims) + return math_ops.reduce_logsumexp(x, axis, keepdims) def round(x): @@ -1884,6 +1894,108 @@ def cos(x): return math_ops.cos(x) +def _regular_normalize_batch_in_training(x, + gamma, + beta, + reduction_axes, + epsilon=1e-3): + """Non-fused version of `normalize_batch_in_training`. + + Arguments: + x: Input tensor or variable. + gamma: Tensor by which to scale the input. + beta: Tensor with which to center the input. + reduction_axes: iterable of integers, + axes over which to normalize. + epsilon: Fuzz factor. + + Returns: + A tuple length of 3, `(normalized_tensor, mean, variance)`. + """ + mean, var = nn.moments(x, reduction_axes, None, None, False) + normed = nn.batch_normalization(x, mean, var, beta, gamma, epsilon) + return normed, mean, var + + +def _broadcast_normalize_batch_in_training(x, + gamma, + beta, + reduction_axes, + epsilon=1e-3): + """Non-fused, broadcast version of `normalize_batch_in_training`. + + Arguments: + x: Input tensor or variable. + gamma: Tensor by which to scale the input. + beta: Tensor with which to center the input. + reduction_axes: iterable of integers, + axes over which to normalize. + epsilon: Fuzz factor. + + Returns: + A tuple length of 3, `(normalized_tensor, mean, variance)`. + """ + mean, var = nn.moments(x, reduction_axes, None, None, False) + target_shape = [] + for axis in range(ndim(x)): + if axis in reduction_axes: + target_shape.append(1) + else: + target_shape.append(array_ops.shape(x)[axis]) + target_shape = array_ops.stack(target_shape) + + broadcast_mean = array_ops.reshape(mean, target_shape) + broadcast_var = array_ops.reshape(var, target_shape) + if gamma is None: + broadcast_gamma = None + else: + broadcast_gamma = array_ops.reshape(gamma, target_shape) + if beta is None: + broadcast_beta = None + else: + broadcast_beta = array_ops.reshape(beta, target_shape) + + normed = nn.batch_normalization(x, broadcast_mean, broadcast_var, + broadcast_beta, broadcast_gamma, epsilon) + return normed, mean, var + + +def _fused_normalize_batch_in_training(x, + gamma, + beta, + reduction_axes, + epsilon=1e-3): + """Fused version of `normalize_batch_in_training`. + + Arguments: + x: Input tensor or variable. + gamma: Tensor by which to scale the input. + beta: Tensor with which to center the input. + reduction_axes: iterable of integers, + axes over which to normalize. + epsilon: Fuzz factor. + + Returns: + A tuple length of 3, `(normalized_tensor, mean, variance)`. + """ + if list(reduction_axes) == [0, 1, 2]: + normalization_axis = 3 + tf_data_format = 'NHWC' + else: + normalization_axis = 1 + tf_data_format = 'NCHW' + + if gamma is None: + gamma = constant_op.constant( + 1.0, dtype=x.dtype, shape=[x.get_shape()[normalization_axis]]) + if beta is None: + beta = constant_op.constant( + 0.0, dtype=x.dtype, shape=[x.get_shape()[normalization_axis]]) + + return nn.fused_batch_norm( + x, gamma, beta, epsilon=epsilon, data_format=tf_data_format) + + def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): """Computes mean and std for batch then apply batch_normalization on batch. @@ -1898,33 +2010,19 @@ def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): Returns: A tuple length of 3, `(normalized_tensor, mean, variance)`. """ - mean, var = nn.moments( - x, reduction_axes, shift=None, name=None, keep_dims=False) - if sorted(reduction_axes) == list(range(ndim(x)))[:-1]: - normed = nn.batch_normalization(x, mean, var, beta, gamma, epsilon) + if ndim(x) == 4 and list(reduction_axes) in [[0, 1, 2], [0, 2, 3]]: + if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]: + return _broadcast_normalize_batch_in_training( + x, gamma, beta, reduction_axes, epsilon=epsilon) + return _fused_normalize_batch_in_training( + x, gamma, beta, reduction_axes, epsilon=epsilon) else: - # need broadcasting - target_shape = [] - for axis in range(ndim(x)): - if axis in reduction_axes: - target_shape.append(1) - else: - target_shape.append(array_ops.shape(x)[axis]) - target_shape = array_ops.stack(target_shape) - - broadcast_mean = array_ops.reshape(mean, target_shape) - broadcast_var = array_ops.reshape(var, target_shape) - if gamma is None: - broadcast_gamma = None + if sorted(reduction_axes) == list(range(ndim(x)))[:-1]: + return _regular_normalize_batch_in_training( + x, gamma, beta, reduction_axes, epsilon=epsilon) else: - broadcast_gamma = array_ops.reshape(gamma, target_shape) - if beta is None: - broadcast_beta = None - else: - broadcast_beta = array_ops.reshape(beta, target_shape) - normed = nn.batch_normalization(x, broadcast_mean, broadcast_var, - broadcast_beta, broadcast_gamma, epsilon) - return normed, mean, var + return _broadcast_normalize_batch_in_training( + x, gamma, beta, reduction_axes, epsilon=epsilon) def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3): @@ -2619,7 +2717,8 @@ def rnn(step_function, go_backwards=False, mask=None, constants=None, - unroll=False): + unroll=False, + input_length=None): """Iterates over the time dimension of a tensor. Arguments: @@ -2648,6 +2747,7 @@ def rnn(step_function, constants: a list of constant values passed at each step. unroll: whether to unroll the RNN or to use a symbolic loop (`while_loop` or `scan` depending on backend). + input_length: Unused; exists for API compatibility. Returns: A tuple, `(last_output, outputs, new_states)`. @@ -2665,6 +2765,7 @@ def rnn(step_function, ValueError: if `mask` is provided (not `None`) but states is not provided (`len(states)` == 0). """ + del input_length ndim = len(inputs.get_shape()) if ndim < 3: raise ValueError('Input should be at least 3D.') @@ -3016,7 +3117,7 @@ def elu(x, alpha=1.): Arguments: x: A tensor or variable to compute the activation function for. - alpha: A scalar, slope of positive section. + alpha: A scalar, slope of negative section. Returns: A tensor. @@ -3083,7 +3184,7 @@ def categorical_crossentropy(target, output, from_logits=False): if not from_logits: # scale preds so that the class probas of each sample sum to 1 output /= math_ops.reduce_sum( - output, axis=len(output.get_shape()) - 1, keep_dims=True) + output, len(output.get_shape()) - 1, True) # manual computation of crossentropy epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype) output = clip_ops.clip_by_value(output, epsilon_, 1. - epsilon_) @@ -3248,6 +3349,25 @@ def in_top_k(predictions, targets, k): # CONVOLUTIONS +def _preprocess_conv1d_input(x, data_format): + """Transpose and cast the input before the conv1d. + + Arguments: + x: input tensor. + data_format: string, `"channels_last"` or `"channels_first"`. + + Returns: + A tensor. + """ + tf_data_format = 'NHWC' # to pass TF Conv2dNative operations + if data_format == 'channels_first': + if not _has_nchw_support(): + x = array_ops.transpose(x, (0, 2, 1)) # NCW -> NWC + else: + tf_data_format = 'NCHW' + return x, tf_data_format + + def _preprocess_conv2d_input(x, data_format): """Transpose and cast the input before the conv2d. @@ -3461,6 +3581,66 @@ def conv2d_transpose(x, return x +def separable_conv1d(x, + depthwise_kernel, + pointwise_kernel, + strides=1, + padding='valid', + data_format=None, + dilation_rate=1): + """1D convolution with separable filters. + + Arguments: + x: input tensor + depthwise_kernel: convolution kernel for the depthwise convolution. + pointwise_kernel: kernel for the 1x1 convolution. + strides: stride integer. + padding: string, `"same"` or `"valid"`. + data_format: string, `"channels_last"` or `"channels_first"`. + dilation_rate: integer dilation rate. + + Returns: + Output tensor. + + Raises: + ValueError: if `data_format` is neither `channels_last` or + `channels_first`. + """ + if data_format is None: + data_format = image_data_format() + if data_format not in {'channels_first', 'channels_last'}: + raise ValueError('Unknown data_format ' + str(data_format)) + + x, tf_data_format = _preprocess_conv1d_input(x, data_format) + padding = _preprocess_padding(padding) + if tf_data_format == 'NHWC': + spatial_start_dim = 1 + strides = (1, 1) + strides + (1,) + else: + spatial_start_dim = 2 + strides = (1, 1, 1) + strides + x = array_ops.expand_dims(x, spatial_start_dim) + depthwise_kernel = array_ops.expand_dims(depthwise_kernel, 0) + pointwise_kernel = array_ops.expand_dims(pointwise_kernel, 0) + dilation_rate = (1,) + dilation_rate + + x = nn.separable_conv2d( + x, + depthwise_kernel, + pointwise_kernel, + strides=strides, + padding=padding, + rate=dilation_rate, + data_format=tf_data_format) + + x = array_ops.squeeze(x, [spatial_start_dim]) + + if data_format == 'channels_first' and tf_data_format == 'NHWC': + x = array_ops.transpose(x, (0, 2, 1)) # NWC -> NCW + + return x + + def separable_conv2d(x, depthwise_kernel, pointwise_kernel, @@ -3921,7 +4101,10 @@ def bias_add(x, bias, data_format=None): elif ndim(x) == 4: if data_format == 'channels_first': if len(bias_shape) == 1: - x += reshape(bias, (1, bias_shape[0], 1, 1)) + if _has_nchw_support(): + x = nn.bias_add(x, bias, data_format='NCHW') + else: + x += reshape(bias, (1, bias_shape[0], 1, 1)) else: x += reshape(bias, (1, bias_shape[2]) + bias_shape[:2]) elif data_format == 'channels_last': @@ -4113,7 +4296,7 @@ def ctc_batch_cost(y_true, y_pred, input_length, label_length): sparse_labels = math_ops.to_int32( ctc_label_dense_to_sparse(y_true, label_length)) - y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + 1e-8) + y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + epsilon()) return array_ops.expand_dims( ctc.ctc_loss( @@ -4148,7 +4331,7 @@ def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1): Tensor `(top_paths, )` that contains the log probability of each decoded sequence. """ - y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + 1e-8) + y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + epsilon()) input_length = math_ops.to_int32(input_length) if greedy: diff --git a/tensorflow/python/keras/_impl/keras/backend_test.py b/tensorflow/python/keras/_impl/keras/backend_test.py index e34f1b6926..27833e368d 100644 --- a/tensorflow/python/keras/_impl/keras/backend_test.py +++ b/tensorflow/python/keras/_impl/keras/backend_test.py @@ -954,7 +954,6 @@ class BackendNNOpsTest(test.TestCase): x = keras.backend.variable(val) reduction_axes = (0, 2, 3) - # case: need broadcasting g_val = np.random.random((3,)) b_val = np.random.random((3,)) gamma = keras.backend.variable(g_val) @@ -965,17 +964,6 @@ class BackendNNOpsTest(test.TestCase): self.assertEqual(mean.get_shape().as_list(), [3,]) self.assertEqual(var.get_shape().as_list(), [3,]) - # case: doesn't need broadcasting - g_val = np.random.random((1, 3, 1, 1)) - b_val = np.random.random((1, 3, 1, 1)) - gamma = keras.backend.variable(g_val) - beta = keras.backend.variable(b_val) - normed, mean, var = keras.backend.normalize_batch_in_training( - x, gamma, beta, reduction_axes, epsilon=1e-3) - self.assertEqual(normed.get_shape().as_list(), [10, 3, 10, 10]) - self.assertEqual(mean.get_shape().as_list(), [3,]) - self.assertEqual(var.get_shape().as_list(), [3,]) - # case: gamma=None gamma = None normed, mean, var = keras.backend.normalize_batch_in_training( diff --git a/tensorflow/python/keras/_impl/keras/callbacks.py b/tensorflow/python/keras/_impl/keras/callbacks.py index 8da3b85718..f0d9e0b0f5 100644 --- a/tensorflow/python/keras/_impl/keras/callbacks.py +++ b/tensorflow/python/keras/_impl/keras/callbacks.py @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Keras callbacks: utilities called at certain points during model training. +# pylint: disable=g-import-not-at-top +"""Callbacks: utilities called at certain points during model training. """ from __future__ import absolute_import from __future__ import division @@ -36,12 +37,10 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary as tf_summary -# pylint: disable=g-import-not-at-top try: import requests except ImportError: requests = None -# pylint: enable=g-import-not-at-top class CallbackList(object): @@ -109,9 +108,9 @@ class CallbackList(object): delta_t_median = np.median(self._delta_ts_batch_begin) if (self._delta_t_batch > 0. and delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1): - logging.warning( - 'Method on_batch_begin() is slow compared ' - 'to the batch update (%f). Check your callbacks.' % delta_t_median) + logging.warning('Method on_batch_begin() is slow compared ' + 'to the batch update (%f). Check your callbacks.', + delta_t_median) self._t_enter_batch = time.time() def on_batch_end(self, batch, logs=None): @@ -132,9 +131,9 @@ class CallbackList(object): delta_t_median = np.median(self._delta_ts_batch_end) if (self._delta_t_batch > 0. and (delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1)): - logging.warning( - 'Method on_batch_end() is slow compared ' - 'to the batch update (%f). Check your callbacks.' % delta_t_median) + logging.warning('Method on_batch_end() is slow compared ' + 'to the batch update (%f). Check your callbacks.', + delta_t_median) def on_train_begin(self, logs=None): """Called at the beginning of training. @@ -246,7 +245,8 @@ class BaseLogger(Callback): class TerminateOnNaN(Callback): - """Callback that terminates training when a NaN loss is encountered.""" + """Callback that terminates training when a NaN loss is encountered. + """ def __init__(self): super(TerminateOnNaN, self).__init__() @@ -396,7 +396,7 @@ class ModelCheckpoint(Callback): if mode not in ['auto', 'min', 'max']: logging.warning('ModelCheckpoint mode %s is unknown, ' - 'fallback to auto mode.' % mode) + 'fallback to auto mode.', (mode), RuntimeWarning) mode = 'auto' if mode == 'min': @@ -423,11 +423,11 @@ class ModelCheckpoint(Callback): current = logs.get(self.monitor) if current is None: logging.warning('Can save best model only with %s available, ' - 'skipping.' % (self.monitor)) + 'skipping.', self.monitor, RuntimeWarning) else: if self.monitor_op(current, self.best): if self.verbose > 0: - print('Epoch %05d: %s improved from %0.5f to %0.5f,' + print('\nEpoch %05d: %s improved from %0.5f to %0.5f,' ' saving model to %s' % (epoch + 1, self.monitor, self.best, current, filepath)) self.best = current @@ -437,11 +437,11 @@ class ModelCheckpoint(Callback): self.model.save(filepath, overwrite=True) else: if self.verbose > 0: - print('Epoch %05d: %s did not improve' % (epoch + 1, - self.monitor)) + print('\nEpoch %05d: %s did not improve' % (epoch + 1, + self.monitor)) else: if self.verbose > 0: - print('Epoch %05d: saving model to %s' % (epoch + 1, filepath)) + print('\nEpoch %05d: saving model to %s' % (epoch + 1, filepath)) if self.save_weights_only: self.model.save_weights(filepath, overwrite=True) else: @@ -486,7 +486,7 @@ class EarlyStopping(Callback): if mode not in ['auto', 'min', 'max']: logging.warning('EarlyStopping mode %s is unknown, ' - 'fallback to auto mode.' % mode) + 'fallback to auto mode.', mode, RuntimeWarning) mode = 'auto' if mode == 'min': @@ -514,8 +514,8 @@ class EarlyStopping(Callback): current = logs.get(self.monitor) if current is None: logging.warning('Early stopping conditioned on metric `%s` ' - 'which is not available. Available metrics are: %s' % - (self.monitor, ','.join(list(logs.keys())))) + 'which is not available. Available metrics are: %s', + self.monitor, ','.join(list(logs.keys())), RuntimeWarning) return if self.monitor_op(current - self.min_delta, self.best): self.best = current @@ -544,8 +544,6 @@ class RemoteMonitor(Callback): path: String; path relative to `root` to which the events will be sent. field: String; JSON field under which the data will be stored. headers: Dictionary; optional custom HTTP headers. - Defaults to: - `{'Accept': 'application/json', 'Content-Type': 'application/json'}` """ def __init__(self, @@ -554,11 +552,7 @@ class RemoteMonitor(Callback): field='data', headers=None): super(RemoteMonitor, self).__init__() - if headers is None: - headers = { - 'Accept': 'application/json', - 'Content-Type': 'application/json' - } + self.root = root self.path = path self.field = field @@ -588,11 +582,13 @@ class LearningRateScheduler(Callback): schedule: a function that takes an epoch index as input (integer, indexed from 0) and returns a new learning rate as output (float). + verbose: int. 0: quiet, 1: update messages. """ - def __init__(self, schedule): + def __init__(self, schedule, verbose=0): super(LearningRateScheduler, self).__init__() self.schedule = schedule + self.verbose = verbose def on_epoch_begin(self, epoch, logs=None): if not hasattr(self.model.optimizer, 'lr'): @@ -602,6 +598,9 @@ class LearningRateScheduler(Callback): raise ValueError('The output of the "schedule" function ' 'should be float.') K.set_value(self.model.optimizer.lr, lr) + if self.verbose > 0: + print('\nEpoch %05d: LearningRateScheduler reducing learning ' + 'rate to %s.' % (epoch + 1, lr)) class TensorBoard(Callback): @@ -842,7 +841,7 @@ class ReduceLROnPlateau(Callback): """ if self.mode not in ['auto', 'min', 'max']: logging.warning('Learning Rate Plateau Reducing mode %s is unknown, ' - 'fallback to auto mode.' % (self.mode)) + 'fallback to auto mode.', self.mode, RuntimeWarning) self.mode = 'auto' if (self.mode == 'min' or (self.mode == 'auto' and 'acc' not in self.monitor)): @@ -853,7 +852,6 @@ class ReduceLROnPlateau(Callback): self.best = -np.Inf self.cooldown_counter = 0 self.wait = 0 - self.lr_epsilon = self.min_lr * 1e-4 def on_train_begin(self, logs=None): self._reset() @@ -864,8 +862,9 @@ class ReduceLROnPlateau(Callback): current = logs.get(self.monitor) if current is None: logging.warning('Reduce LR on plateau conditioned on metric `%s` ' - 'which is not available. Available metrics are: %s' % - (self.monitor, ','.join(list(logs.keys())))) + 'which is not available. Available metrics are: %s', + self.monitor, ','.join(list(logs.keys())), RuntimeWarning) + else: if self.in_cooldown(): self.cooldown_counter -= 1 @@ -877,13 +876,13 @@ class ReduceLROnPlateau(Callback): elif not self.in_cooldown(): if self.wait >= self.patience: old_lr = float(K.get_value(self.model.optimizer.lr)) - if old_lr > self.min_lr + self.lr_epsilon: + if old_lr > self.min_lr: new_lr = old_lr * self.factor new_lr = max(new_lr, self.min_lr) K.set_value(self.model.optimizer.lr, new_lr) if self.verbose > 0: - print('\nEpoch %05d: reducing learning rate to %s.' % (epoch, - new_lr)) + print('\nEpoch %05d: ReduceLROnPlateau reducing learning ' + 'rate to %s.' % (epoch + 1, new_lr)) self.cooldown_counter = self.cooldown self.wait = 0 self.wait += 1 @@ -899,10 +898,11 @@ class CSVLogger(Callback): including 1D iterables such as np.ndarray. Example: - ```python - csv_logger = CSVLogger('training.log') - model.fit(X_train, Y_train, callbacks=[csv_logger]) - ``` + + ```python + csv_logger = CSVLogger('training.log') + model.fit(X_train, Y_train, callbacks=[csv_logger]) + ``` Arguments: filename: filename of the csv file, e.g. 'run/log.csv'. @@ -942,12 +942,14 @@ class CSVLogger(Callback): else: return k + if self.keys is None: + self.keys = sorted(logs.keys()) + if self.model.stop_training: # We set NA so that csv parsers do not fail for this last epoch. logs = dict([(k, logs[k]) if k in logs else (k, 'NA') for k in self.keys]) if not self.writer: - self.keys = sorted(logs.keys()) class CustomDialect(csv.excel): delimiter = self.sep @@ -993,32 +995,32 @@ class LambdaCallback(Callback): Example: - ```python - # Print the batch number at the beginning of every batch. - batch_print_callback = LambdaCallback( - on_batch_begin=lambda batch,logs: print(batch)) - - # Stream the epoch loss to a file in JSON format. The file content - # is not well-formed JSON but rather has a JSON object per line. - import json - json_log = open('loss_log.json', mode='wt', buffering=1) - json_logging_callback = LambdaCallback( - on_epoch_end=lambda epoch, logs: json_log.write( - json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'), - on_train_end=lambda logs: json_log.close() - ) - - # Terminate some processes after having finished model training. - processes = ... - cleanup_callback = LambdaCallback( - on_train_end=lambda logs: [ - p.terminate() for p in processes if p.is_alive()]) - - model.fit(..., - callbacks=[batch_print_callback, - json_logging_callback, - cleanup_callback]) - ``` + ```python + # Print the batch number at the beginning of every batch. + batch_print_callback = LambdaCallback( + on_batch_begin=lambda batch,logs: print(batch)) + + # Stream the epoch loss to a file in JSON format. The file content + # is not well-formed JSON but rather has a JSON object per line. + import json + json_log = open('loss_log.json', mode='wt', buffering=1) + json_logging_callback = LambdaCallback( + on_epoch_end=lambda epoch, logs: json_log.write( + json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'), + on_train_end=lambda logs: json_log.close() + ) + + # Terminate some processes after having finished model training. + processes = ... + cleanup_callback = LambdaCallback( + on_train_end=lambda logs: [ + p.terminate() for p in processes if p.is_alive()]) + + model.fit(..., + callbacks=[batch_print_callback, + json_logging_callback, + cleanup_callback]) + ``` """ def __init__(self, diff --git a/tensorflow/python/keras/_impl/keras/constraints.py b/tensorflow/python/keras/_impl/keras/constraints.py index e58e3b0377..4b051c93f3 100644 --- a/tensorflow/python/keras/_impl/keras/constraints.py +++ b/tensorflow/python/keras/_impl/keras/constraints.py @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Constraints: functions that impose constraints on weights values. +# pylint: disable=invalid-name +"""Constraints: functions that impose constraints on weight values. """ from __future__ import absolute_import from __future__ import division @@ -54,10 +55,6 @@ class MaxNorm(Constraint): to constrain the weights of each filter tensor of size `(rows, cols, input_depth)`. - References: - - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting - Srivastava, Hinton, et al. - 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) """ def __init__(self, max_value=2, axis=0): @@ -79,7 +76,7 @@ class NonNeg(Constraint): """ def __call__(self, w): - w *= K.cast(w >= 0., K.floatx()) + w *= K.cast(K.greater_equal(w, 0.), K.floatx()) return w @@ -132,7 +129,7 @@ class MinMaxNorm(Constraint): has shape `(input_dim, output_dim)`, set `axis` to `0` to constrain each weight vector of length `(input_dim,)`. - In a `Conv2D` layer with `dim_ordering="channels_last"`, + In a `Conv2D` layer with `data_format="channels_last"`, the weight tensor has shape `(rows, cols, input_depth, output_depth)`, set `axis` to `[0, 1, 2]` @@ -148,8 +145,9 @@ class MinMaxNorm(Constraint): def __call__(self, w): norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True)) - desired = (self.rate * K.clip(norms, self.min_value, self.max_value) + - (1 - self.rate) * norms) + desired = ( + self.rate * K.clip(norms, self.min_value, self.max_value) + + (1 - self.rate) * norms) w *= (desired / (K.epsilon() + norms)) return w @@ -164,13 +162,15 @@ class MinMaxNorm(Constraint): # Aliases. -# pylint: disable=invalid-name max_norm = MaxNorm non_neg = NonNeg unit_norm = UnitNorm min_max_norm = MinMaxNorm -# pylint: enable=invalid-name +# Legacy aliases. +maxnorm = max_norm +nonneg = non_neg +unitnorm = unit_norm def serialize(constraint): diff --git a/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py b/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py index 0570e9bc0c..cfd7df61d5 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py +++ b/tensorflow/python/keras/_impl/keras/datasets/boston_housing.py @@ -21,29 +21,27 @@ from __future__ import print_function import numpy as np from tensorflow.python.keras._impl.keras.utils.data_utils import get_file -from tensorflow.python.util.tf_export import tf_export -@tf_export('keras.datasets.boston_housing.load_data') -def load_data(path='boston_housing.npz', seed=113, test_split=0.2): +def load_data(path='boston_housing.npz', test_split=0.2, seed=113): """Loads the Boston Housing dataset. Arguments: path: path where to cache the dataset locally (relative to ~/.keras/datasets). + test_split: fraction of the data to reserve as test set. seed: Random seed for shuffling the data before computing the test split. - test_split: fraction of the data to reserve as test set. Returns: Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. """ assert 0 <= test_split < 1 - fh = 'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5' path = get_file( path, origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz', - file_hash=fh) + file_hash= + 'f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5') f = np.load(path) x = f['x'] y = f['y'] diff --git a/tensorflow/python/keras/_impl/keras/datasets/cifar.py b/tensorflow/python/keras/_impl/keras/datasets/cifar.py index 564709c0ee..7ada3340a5 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/cifar.py +++ b/tensorflow/python/keras/_impl/keras/datasets/cifar.py @@ -12,9 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities used by the CIFAR10 and CIFAR100 datasets. +"""Utilities common to CIFAR10 and CIFAR100 datasets. """ - from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/tensorflow/python/keras/_impl/keras/datasets/cifar10.py b/tensorflow/python/keras/_impl/keras/datasets/cifar10.py index 1971f434b9..fb9d98d42c 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/cifar10.py +++ b/tensorflow/python/keras/_impl/keras/datasets/cifar10.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""CIFAR10 small image classification dataset. +"""CIFAR10 small images classification dataset. """ from __future__ import absolute_import from __future__ import division @@ -25,10 +25,8 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.datasets.cifar import load_batch from tensorflow.python.keras._impl.keras.utils.data_utils import get_file -from tensorflow.python.util.tf_export import tf_export -@tf_export('keras.datasets.cifar10.load_data') def load_data(): """Loads CIFAR10 dataset. diff --git a/tensorflow/python/keras/_impl/keras/datasets/cifar100.py b/tensorflow/python/keras/_impl/keras/datasets/cifar100.py index f4039e9350..95aace599a 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/cifar100.py +++ b/tensorflow/python/keras/_impl/keras/datasets/cifar100.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""CIFAR100 small image classification dataset. +"""CIFAR100 small images classification dataset. """ from __future__ import absolute_import from __future__ import division @@ -25,10 +25,8 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.datasets.cifar import load_batch from tensorflow.python.keras._impl.keras.utils.data_utils import get_file -from tensorflow.python.util.tf_export import tf_export -@tf_export('keras.datasets.cifar100.load_data') def load_data(label_mode='fine'): """Loads CIFAR100 dataset. @@ -42,7 +40,7 @@ def load_data(label_mode='fine'): ValueError: in case of invalid `label_mode`. """ if label_mode not in ['fine', 'coarse']: - raise ValueError('label_mode must be one of "fine" "coarse".') + raise ValueError('`label_mode` must be one of `"fine"`, `"coarse"`.') dirname = 'cifar-100-python' origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz' diff --git a/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py b/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py index 17be684e4f..b9ae41a0d4 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py +++ b/tensorflow/python/keras/_impl/keras/datasets/fashion_mnist.py @@ -20,7 +20,9 @@ from __future__ import print_function import gzip import os + import numpy as np + from tensorflow.python.keras._impl.keras.utils.data_utils import get_file @@ -38,9 +40,8 @@ def load_data(): ] paths = [] - for given_file in files: - paths.append( - get_file(given_file, origin=base + given_file, cache_subdir=dirname)) + for fname in files: + paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname)) with gzip.open(paths[0], 'rb') as lbpath: y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8) diff --git a/tensorflow/python/keras/_impl/keras/datasets/imdb.py b/tensorflow/python/keras/_impl/keras/datasets/imdb.py index 7946c46960..880c9c821b 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/imdb.py +++ b/tensorflow/python/keras/_impl/keras/datasets/imdb.py @@ -1,4 +1,4 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# 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. @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""IMDB movie review sentiment classification dataset. +"""IMDB sentiment classification dataset. """ from __future__ import absolute_import from __future__ import division @@ -21,13 +21,12 @@ from __future__ import print_function import json import numpy as np -from six.moves import zip # pylint: disable=redefined-builtin +from tensorflow.python.keras._impl.keras.preprocessing.sequence import _remove_long_seq from tensorflow.python.keras._impl.keras.utils.data_utils import get_file -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.platform import tf_logging as logging -@tf_export('keras.datasets.imdb.load_data') def load_data(path='imdb.npz', num_words=None, skip_top=0, @@ -35,7 +34,8 @@ def load_data(path='imdb.npz', seed=113, start_char=1, oov_char=2, - index_from=3): + index_from=3, + **kwargs): """Loads the IMDB dataset. Arguments: @@ -52,6 +52,7 @@ def load_data(path='imdb.npz', oov_char: words that were cut out because of the `num_words` or `skip_top` limit will be replaced with this character. index_from: index actual words with this index and higher. + **kwargs: Used for backwards compatibility. Returns: Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. @@ -66,14 +67,21 @@ def load_data(path='imdb.npz', Words that were not seen in the training set but are in the test set have simply been skipped. """ + # Legacy support + if 'nb_words' in kwargs: + logging.warning('The `nb_words` argument in `load_data` ' + 'has been renamed `num_words`.') + num_words = kwargs.pop('nb_words') + if kwargs: + raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) + path = get_file( path, origin='https://s3.amazonaws.com/text-datasets/imdb.npz', file_hash='599dadb1135973df5b59232a0e9a887c') - f = np.load(path) - x_train, labels_train = f['x_train'], f['y_train'] - x_test, labels_test = f['x_test'], f['y_test'] - f.close() + with np.load(path) as f: + x_train, labels_train = f['x_train'], f['y_train'] + x_test, labels_test = f['x_test'], f['y_test'] np.random.seed(seed) indices = np.arange(len(x_train)) @@ -95,14 +103,7 @@ def load_data(path='imdb.npz', xs = [[w + index_from for w in x] for x in xs] if maxlen: - new_xs = [] - new_labels = [] - for x, y in zip(xs, labels): - if len(x) < maxlen: - new_xs.append(x) - new_labels.append(y) - xs = new_xs - labels = new_labels + xs, labels = _remove_long_seq(maxlen, xs, labels) if not xs: raise ValueError('After filtering for sequences shorter than maxlen=' + str(maxlen) + ', no sequence was kept. ' @@ -114,28 +115,19 @@ def load_data(path='imdb.npz', # reserve 'index_from' (=3 by default) characters: # 0 (padding), 1 (start), 2 (OOV) if oov_char is not None: - xs = [[oov_char if (w >= num_words or w < skip_top) else w for w in x] - for x in xs] + xs = [ + [w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs + ] else: - new_xs = [] - for x in xs: - nx = [] - for w in x: - if skip_top <= w < num_words: - nx.append(w) - new_xs.append(nx) - xs = new_xs - - x_train = np.array(xs[:len(x_train)]) - y_train = np.array(labels[:len(x_train)]) + xs = [[w for w in x if skip_top <= w < num_words] for x in xs] - x_test = np.array(xs[len(x_train):]) - y_test = np.array(labels[len(x_train):]) + idx = len(x_train) + x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx]) + x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:]) return (x_train, y_train), (x_test, y_test) -@tf_export('keras.datasets.imdb.get_word_index') def get_word_index(path='imdb_word_index.json'): """Retrieves the dictionary mapping word indices back to words. @@ -147,7 +139,8 @@ def get_word_index(path='imdb_word_index.json'): """ path = get_file( path, - origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json') + origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json', + file_hash='bfafd718b763782e994055a2d397834f') f = open(path) data = json.load(f) f.close() diff --git a/tensorflow/python/keras/_impl/keras/datasets/mnist.py b/tensorflow/python/keras/_impl/keras/datasets/mnist.py index e9f5348015..ec12a31dcf 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/mnist.py +++ b/tensorflow/python/keras/_impl/keras/datasets/mnist.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""MNIST handwritten digits classification dataset. +"""MNIST handwritten digits dataset. """ from __future__ import absolute_import from __future__ import division @@ -21,10 +21,8 @@ from __future__ import print_function import numpy as np from tensorflow.python.keras._impl.keras.utils.data_utils import get_file -from tensorflow.python.util.tf_export import tf_export -@tf_export('keras.datasets.mnist.load_data') def load_data(path='mnist.npz'): """Loads the MNIST dataset. @@ -40,9 +38,7 @@ def load_data(path='mnist.npz'): origin='https://s3.amazonaws.com/img-datasets/mnist.npz', file_hash='8a61469f7ea1b51cbae51d4f78837e45') f = np.load(path) - x_train = f['x_train'] - y_train = f['y_train'] - x_test = f['x_test'] - y_test = f['y_test'] + x_train, y_train = f['x_train'], f['y_train'] + x_test, y_test = f['x_test'], f['y_test'] f.close() return (x_train, y_train), (x_test, y_test) diff --git a/tensorflow/python/keras/_impl/keras/datasets/reuters.py b/tensorflow/python/keras/_impl/keras/datasets/reuters.py index 6da5aa4b5e..95cf8852a9 100644 --- a/tensorflow/python/keras/_impl/keras/datasets/reuters.py +++ b/tensorflow/python/keras/_impl/keras/datasets/reuters.py @@ -12,9 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Reuters newswire topic classification dataset. +"""Reuters topic classification dataset. """ - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -22,13 +21,12 @@ from __future__ import print_function import json import numpy as np -from six.moves import zip # pylint: disable=redefined-builtin +from tensorflow.python.keras._impl.keras.preprocessing.sequence import _remove_long_seq from tensorflow.python.keras._impl.keras.utils.data_utils import get_file -from tensorflow.python.util.tf_export import tf_export +from tensorflow.python.platform import tf_logging as logging -@tf_export('keras.datasets.reuters.load_data') def load_data(path='reuters.npz', num_words=None, skip_top=0, @@ -37,7 +35,8 @@ def load_data(path='reuters.npz', seed=113, start_char=1, oov_char=2, - index_from=3): + index_from=3, + **kwargs): """Loads the Reuters newswire classification dataset. Arguments: @@ -55,6 +54,7 @@ def load_data(path='reuters.npz', oov_char: words that were cut out because of the `num_words` or `skip_top` limit will be replaced with this character. index_from: index actual words with this index and higher. + **kwargs: Used for backwards compatibility. Returns: Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. @@ -65,14 +65,20 @@ def load_data(path='reuters.npz', Words that were not seen in the training set but are in the test set have simply been skipped. """ + # Legacy support + if 'nb_words' in kwargs: + logging.warning('The `nb_words` argument in `load_data` ' + 'has been renamed `num_words`.') + num_words = kwargs.pop('nb_words') + if kwargs: + raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) + path = get_file( path, origin='https://s3.amazonaws.com/text-datasets/reuters.npz', file_hash='87aedbeb0cb229e378797a632c1997b6') - npzfile = np.load(path) - xs = npzfile['x'] - labels = npzfile['y'] - npzfile.close() + with np.load(path) as f: + xs, labels = f['x'], f['y'] np.random.seed(seed) indices = np.arange(len(xs)) @@ -80,22 +86,13 @@ def load_data(path='reuters.npz', xs = xs[indices] labels = labels[indices] - np.random.shuffle(labels) - if start_char is not None: xs = [[start_char] + [w + index_from for w in x] for x in xs] elif index_from: xs = [[w + index_from for w in x] for x in xs] if maxlen: - new_xs = [] - new_labels = [] - for x, y in zip(xs, labels): - if len(x) < maxlen: - new_xs.append(x) - new_labels.append(y) - xs = new_xs - labels = new_labels + xs, labels = _remove_long_seq(maxlen, xs, labels) if not num_words: num_words = max([max(x) for x in xs]) @@ -104,28 +101,17 @@ def load_data(path='reuters.npz', # reserve 'index_from' (=3 by default) characters: # 0 (padding), 1 (start), 2 (OOV) if oov_char is not None: - xs = [[oov_char if (w >= num_words or w < skip_top) else w for w in x] - for x in xs] + xs = [[w if skip_top <= w < num_words else oov_char for w in x] for x in xs] else: - new_xs = [] - for x in xs: - nx = [] - for w in x: - if skip_top <= w < num_words: - nx.append(w) - new_xs.append(nx) - xs = new_xs - - x_train = np.array(xs[:int(len(xs) * (1 - test_split))]) - y_train = np.array(labels[:int(len(xs) * (1 - test_split))]) + xs = [[w for w in x if skip_top <= w < num_words] for x in xs] - x_test = np.array(xs[int(len(xs) * (1 - test_split)):]) - y_test = np.array(labels[int(len(xs) * (1 - test_split)):]) + idx = int(len(xs) * (1 - test_split)) + x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx]) + x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:]) return (x_train, y_train), (x_test, y_test) -@tf_export('keras.datasets.reuters.get_word_index') def get_word_index(path='reuters_word_index.json'): """Retrieves the dictionary mapping word indices back to words. diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/topology.py index d6e0be8e43..64aa868f38 100644 --- a/tensorflow/python/keras/_impl/keras/engine/topology.py +++ b/tensorflow/python/keras/_impl/keras/engine/topology.py @@ -27,6 +27,7 @@ import numpy as np from six.moves import zip # pylint: disable=redefined-builtin from tensorflow.python.eager import context +from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers @@ -712,8 +713,8 @@ class Network(tf_network.GraphNetwork, Layer): for layer in self._output_layers: self.output_names.append(layer.name) - self.internal_input_shapes = [K.int_shape(x) for x in self.inputs] - self.internal_output_shapes = [K.int_shape(x) for x in self.outputs] + self._internal_input_shapes = [K.int_shape(x) for x in self.inputs] + self._internal_output_shapes = [K.int_shape(x) for x in self.outputs] @property def uses_learning_phase(self): @@ -1303,18 +1304,17 @@ def preprocess_weights_for_loading(layer, Returns: A list of weights values (Numpy arrays). """ - if original_keras_version == '1': - if layer.__class__.__name__ == 'Bidirectional': - num_weights_per_layer = len(weights) // 2 - - forward_weights = preprocess_weights_for_loading( - layer.forward_layer, weights[:num_weights_per_layer], - original_keras_version, original_backend) - backward_weights = preprocess_weights_for_loading( - layer.backward_layer, weights[num_weights_per_layer:], - original_keras_version, original_backend) - weights = forward_weights + backward_weights + if layer.__class__.__name__ == 'Bidirectional': + num_weights_per_layer = len(weights) // 2 + forward_weights = preprocess_weights_for_loading( + layer.forward_layer, weights[:num_weights_per_layer], + original_keras_version, original_backend) + backward_weights = preprocess_weights_for_loading( + layer.backward_layer, weights[num_weights_per_layer:], + original_keras_version, original_backend) + weights = forward_weights + backward_weights + if original_keras_version == '1': if layer.__class__.__name__ == 'TimeDistributed': weights = preprocess_weights_for_loading( layer.layer, weights, original_keras_version, original_backend) @@ -1418,7 +1418,7 @@ def preprocess_weights_for_loading(layer, conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D'] if layer.__class__.__name__ in conv_layers: - if original_backend and K.backend() != original_backend: + if original_backend == 'theano': weights[0] = conv_utils.convert_kernel(weights[0]) if layer.__class__.__name__ == 'ConvLSTM2D': weights[1] = conv_utils.convert_kernel(weights[1]) @@ -1427,10 +1427,9 @@ def preprocess_weights_for_loading(layer, if layer.__class__.__name__ == 'ConvLSTM2D': weights[1] = np.transpose(weights[1], (3, 2, 0, 1)) - # convert the weights of CuDNNLSTM so that they could be loaded into LSTM + # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM if layer.__class__.__name__ == 'LSTM' and len(weights) == 3: - # determine if we're loading a CuDNNLSTM layer from the number of bias - # weights: + # Determine if loading a CuDNNLSTM layer from the number of bias weights: # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4) # if there's no bias weight in the file, skip this conversion units = weights[1].shape[0] @@ -1572,3 +1571,31 @@ def load_weights_from_hdf5_group_by_name(f, layers): for i in range(len(weight_values)): weight_value_tuples.append((symbolic_weights[i], weight_values[i])) K.batch_set_value(weight_value_tuples) + + +def shape_type_conversion(fn): + """Decorator that handles tuple/TensorShape conversion. + + Used in `compute_output_shape` and `build`. + + Arguments: + fn: function to wrap. + + Returns: + Wrapped function. + """ + + def wrapper(instance, input_shape): + if input_shape is not None: + if isinstance(input_shape, list): + input_shape = [ + tuple(tensor_shape.TensorShape(x).as_list()) for x in input_shape] + else: + input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) + output_shape = fn(instance, input_shape) + if output_shape is not None: + if isinstance(output_shape, list): + return [tensor_shape.TensorShape(x) for x in output_shape] + return tensor_shape.TensorShape(output_shape) + + return wrapper diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py index debea2503e..699ae2edf0 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training.py +++ b/tensorflow/python/keras/_impl/keras/engine/training.py @@ -12,9 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Keras training and evaluation routines. +"""Training-related part of the Keras engine. """ - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -35,6 +34,11 @@ from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar from tensorflow.python.platform import tf_logging as logging +try: + from scipy.sparse import issparse # pylint: disable=g-import-not-at-top +except ImportError: + issparse = None + def _standardize_input_data(data, names, @@ -70,89 +74,72 @@ def _standardize_input_data(data, return [] if data is None: return [None for _ in range(len(names))] + if isinstance(data, dict): - for key, value in data.items(): - if value.__class__.__name__ == 'DataFrame': - data[key] = value.values - arrays = [] - for name in names: - if name not in data: - raise ValueError('No data provided for "' + name + - '". Need data for each key in: ' + str(names)) - arrays.append(data[name]) + try: + data = [ + data[x].values + if data[x].__class__.__name__ == 'DataFrame' else data[x] + for x in names + ] + data = [np.expand_dims(x, 1) if x.ndim == 1 else x for x in data] + except KeyError as e: + raise ValueError('No data provided for "' + e.args[0] + '". Need data ' + 'for each key in: ' + str(names)) elif isinstance(data, list): - for key, value in enumerate(data): - if value.__class__.__name__ == 'DataFrame': - data[key] = value.values - if len(data) != len(names): - if data and hasattr(data[0], 'shape'): - raise ValueError( - 'Error when checking model ' + exception_prefix + - ': the list of Numpy arrays ' - 'that you are passing to your model ' - 'is not the size the model expected. ' - 'Expected to see ' + str(len(names)) + ' array(s), but instead got ' - 'the following list of ' + str(len(data)) + ' arrays: ' + - str(data)[:200] + '...') - else: - if len(names) == 1: - data = [np.asarray(data)] - else: - raise ValueError('Error when checking model ' + exception_prefix + - ': you are passing a list as ' - 'input to your model, ' - 'but the model expects ' - 'a list of ' + str(len(names)) + - ' Numpy arrays instead. ' - 'The list you passed was: ' + str(data)[:200]) - arrays = data - elif data.__class__.__name__ == 'DataFrame': - # test if data is a DataFrame, without pandas installed - arrays = data.values + data = [ + x.values if x.__class__.__name__ == 'DataFrame' else x for x in data + ] + data = [ + np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x + for x in data + ] else: - if not hasattr(data, 'shape'): + data = data.values if data.__class__.__name__ == 'DataFrame' else data + data = [np.expand_dims(data, 1)] if data.ndim == 1 else [data] + + if len(data) != len(names): + if data and hasattr(data[0], 'shape'): + raise ValueError('Error when checking model ' + exception_prefix + + ': the list of Numpy arrays that you are passing to ' + 'your model is not the size the model expected. ' + 'Expected to see ' + str(len(names)) + ' array(s), ' + 'but instead got the following list of ' + + str(len(data)) + ' arrays: ' + str(data)[:200] + '...') + elif len(names) > 1: + raise ValueError( + 'Error when checking model ' + exception_prefix + + ': you are passing a list as input to your model, ' + 'but the model expects a list of ' + str(len(names)) + + ' Numpy arrays instead. The list you passed was: ' + str(data)[:200]) + elif len(data) == 1 and not hasattr(data[0], 'shape'): raise TypeError('Error when checking model ' + exception_prefix + - ': data should be a Numpy array, ' - 'or list/dict of Numpy arrays. ' - 'Found: ' + str(data)[:200] + '...') - if len(names) > 1: - # Case: model expects multiple inputs but only received - # a single Numpy array. - raise ValueError('The model expects ' + str(len(names)) + ' ' + - exception_prefix + - ' arrays, but only received one array. ' - 'Found: array with shape ' + str(data.shape)) - arrays = [data] - - # Make arrays at least 2D. - for i in range(len(names)): - array = arrays[i] - if len(array.shape) == 1: - array = np.expand_dims(array, 1) - arrays[i] = array + ': data should be a Numpy array, or list/dict of ' + 'Numpy arrays. Found: ' + str(data)[:200] + '...') + elif len(names) == 1: + data = [np.asarray(data)] # Check shapes compatibility. if shapes: for i in range(len(names)): - if shapes[i] is None: - continue - array = arrays[i] - if len(array.shape) != len(shapes[i]): - raise ValueError( - 'Error when checking ' + exception_prefix + ': expected ' + names[i] - + ' to have ' + str(len(shapes[i])) + - ' dimensions, but got array with shape ' + str(array.shape)) - for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])): - if not j and not check_batch_axis: - # skip the first axis - continue - if ref_dim: - if ref_dim != dim: - raise ValueError('Error when checking ' + exception_prefix + - ': expected ' + names[i] + ' to have shape ' + - str(shapes[i]) + ' but got array with shape ' + - str(array.shape)) - return arrays + if shapes[i] is not None: + data_shape = data[i].shape + shape = shapes[i] + if data[i].ndim != len(shape): + raise ValueError('Error when checking ' + exception_prefix + + ': expected ' + names[i] + ' to have ' + + str(len(shape)) + ' dimensions, but got array ' + 'with shape ' + str(data_shape)) + if not check_batch_axis: + data_shape = data_shape[1:] + shape = shape[1:] + for dim, ref_dim in zip(data_shape, shape): + if ref_dim != dim and ref_dim: + raise ValueError( + 'Error when checking ' + exception_prefix + ': expected ' + + names[i] + ' to have shape ' + str(shape) + + ' but got array with shape ' + str(data_shape)) + return data def _standardize_sample_or_class_weights(x_weight, output_names, weight_type): @@ -193,10 +180,10 @@ def _standardize_sample_or_class_weights(x_weight, output_names, weight_type): x_weights.append(x_weight.get(name)) return x_weights else: - raise TypeError('The model has multiple outputs, so `' + weight_type + '` ' - 'should be either a list or a dict. ' - 'Provided `' + weight_type + '` type not understood: ' + - str(x_weight)) + raise TypeError( + 'The model has multiple outputs, so `' + weight_type + '` ' + 'should be either a list or a dict. ' + 'Provided `' + weight_type + '` type not understood: ' + str(x_weight)) def _standardize_class_weights(class_weight, output_names): @@ -234,12 +221,12 @@ def _check_array_lengths(inputs, targets, weights=None): set_w = set_of_lengths(weights) if len(set_x) > 1: raise ValueError('All input arrays (x) should have ' - 'the same number of samples. Got array shapes: ' + str( - [x.shape for x in inputs])) + 'the same number of samples. Got array shapes: ' + + str([x.shape for x in inputs])) if len(set_y) > 1: raise ValueError('All target arrays (y) should have ' - 'the same number of samples. Got array shapes: ' + str( - [y.shape for y in targets])) + 'the same number of samples. Got array shapes: ' + + str([y.shape for y in targets])) if set_x and set_y and list(set_x)[0] != list(set_y)[0]: raise ValueError('Input arrays should have ' 'the same number of samples as target arrays. ' @@ -247,8 +234,8 @@ def _check_array_lengths(inputs, targets, weights=None): 'and ' + str(list(set_y)[0]) + ' target samples.') if len(set_w) > 1: raise ValueError('All sample_weight arrays should have ' - 'the same number of samples. Got array shapes: ' + str( - [w.shape for w in weights])) + 'the same number of samples. Got array shapes: ' + + str([w.shape for w in weights])) if set_y and set_w and list(set_y)[0] != list(set_w)[0]: raise ValueError('Sample_weight arrays should have ' 'the same number of samples as target arrays. Got ' + @@ -528,16 +515,16 @@ def _standardize_weights(y, if sample_weight is not None: if len(sample_weight.shape) > len(y.shape): - raise ValueError('Found a sample_weight with shape' + - str(sample_weight.shape) + '.' - 'Expected sample_weight with rank ' - 'less than or equal to ' + str(len(y.shape))) + raise ValueError( + 'Found a sample_weight with shape' + str(sample_weight.shape) + '.' + 'Expected sample_weight with rank ' + 'less than or equal to ' + str(len(y.shape))) if y.shape[:sample_weight.ndim] != sample_weight.shape: - raise ValueError('Found a sample_weight array with shape ' + - str(sample_weight.shape) + ' for an input with shape ' + - str(y.shape) + '. ' - 'sample_weight cannot be broadcast.') + raise ValueError( + 'Found a sample_weight array with shape ' + str(sample_weight.shape) + + ' for an input with shape ' + str(y.shape) + '. ' + 'sample_weight cannot be broadcast.') return sample_weight elif isinstance(class_weight, dict): if len(y.shape) > 2: @@ -632,7 +619,6 @@ class Model(Network): """ loss = loss or {} self.optimizer = optimizers.get(optimizer) - self.sample_weight_mode = sample_weight_mode self.loss = loss self.loss_weights = loss_weights self.sample_weight_mode = sample_weight_mode @@ -641,10 +627,10 @@ class Model(Network): if isinstance(loss, dict): for name in loss: if name not in self.output_names: - raise ValueError('Unknown entry in loss ' - 'dictionary: "' + name + '". ' - 'Only expected the following keys: ' + - str(self.output_names)) + raise ValueError( + 'Unknown entry in loss ' + 'dictionary: "' + name + '". ' + 'Only expected the following keys: ' + str(self.output_names)) loss_functions = [] for name in self.output_names: if name not in loss: @@ -657,7 +643,7 @@ class Model(Network): elif isinstance(loss, list): if len(loss) != len(self.outputs): raise ValueError('When passing a list as loss, ' - 'it should have one entry per model output. ' + 'it should have one entry per model outputs. ' 'The model has ' + str(len(self.outputs)) + ' outputs, but you passed loss=' + str(loss)) loss_functions = [losses.get(l) for l in loss] @@ -690,20 +676,20 @@ class Model(Network): elif isinstance(loss_weights, dict): for name in loss_weights: if name not in self.output_names: - raise ValueError('Unknown entry in loss_weights ' - 'dictionary: "' + name + '". ' - 'Only expected the following keys: ' + - str(self.output_names)) + raise ValueError( + 'Unknown entry in loss_weights ' + 'dictionary: "' + name + '". ' + 'Only expected the following keys: ' + str(self.output_names)) loss_weights_list = [] for name in self.output_names: loss_weights_list.append(loss_weights.get(name, 1.)) elif isinstance(loss_weights, list): if len(loss_weights) != len(self.outputs): - raise ValueError('When passing a list as loss_weights, ' - 'it should have one entry per model output. ' - 'The model has ' + str(len(self.outputs)) + - ' outputs, but you passed loss_weights=' + - str(loss_weights)) + raise ValueError( + 'When passing a list as loss_weights, ' + 'it should have one entry per model output. ' + 'The model has ' + str(len(self.outputs)) + + ' outputs, but you passed loss_weights=' + str(loss_weights)) loss_weights_list = loss_weights else: raise TypeError('Could not interpret loss_weights argument: ' + @@ -715,22 +701,22 @@ class Model(Network): if target_tensors is not None: if isinstance(target_tensors, list): if len(target_tensors) != len(self.outputs): - raise ValueError('When passing a list as `target_tensors`, ' - 'it should have one entry per model output. ' - 'The model has ' + str(len(self.outputs)) + - ' outputs, but you passed target_tensors=' + - str(target_tensors)) + raise ValueError( + 'When passing a list as `target_tensors`, ' + 'it should have one entry per model output. ' + 'The model has ' + str(len(self.outputs)) + + ' outputs, but you passed target_tensors=' + str(target_tensors)) elif isinstance(target_tensors, dict): for name in target_tensors: if name not in self.output_names: - raise ValueError('Unknown entry in `target_tensors` ' - 'dictionary: "' + name + '". ' - 'Only expected the following keys: ' + - str(self.output_names)) - target_tensors_ = [] + raise ValueError( + 'Unknown entry in `target_tensors` ' + 'dictionary: "' + name + '". ' + 'Only expected the following keys: ' + str(self.output_names)) + tmp_target_tensors = [] for name in self.output_names: - target_tensors_.append(target_tensors.get(name, None)) - target_tensors = target_tensors_ + tmp_target_tensors.append(target_tensors.get(name, None)) + target_tensors = tmp_target_tensors else: raise TypeError('Expected `target_tensors` to be ' 'a list or dict, but got:', target_tensors) @@ -738,7 +724,7 @@ class Model(Network): if i in skip_target_indices: self.targets.append(None) else: - shape = self.internal_output_shapes[i] + shape = self._internal_output_shapes[i] name = self.output_names[i] if target_tensors is not None: target = target_tensors[i] @@ -766,19 +752,19 @@ class Model(Network): if isinstance(sample_weight_mode, dict): for name in sample_weight_mode: if name not in self.output_names: - raise ValueError('Unknown entry in ' - 'sample_weight_mode dictionary: "' + name + '". ' - 'Only expected the following keys: ' + - str(self.output_names)) + raise ValueError( + 'Unknown entry in ' + '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') + 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') @@ -894,23 +880,36 @@ class Model(Network): metric_name_prefix = 'weighted_' if weights is not None else '' for metric in metrics: - if metric == 'accuracy' or metric == 'acc': - # custom handling of accuracy + if metric in ('accuracy', 'acc', 'crossentropy', 'ce'): + # custom handling of accuracy/crossentropy # (because of class mode duality) - output_shape = self.internal_output_shapes[i] + output_shape = self._internal_output_shapes[i] if (output_shape[-1] == 1 or self.loss_functions[i] == losses.binary_crossentropy): - # case: binary accuracy - acc_fn = metrics_module.binary_accuracy + # case: binary accuracy/crossentropy + if metric in ('accuracy', 'acc'): + acc_fn = metrics_module.binary_accuracy + elif metric in ('crossentropy', 'ce'): + acc_fn = metrics_module.binary_crossentropy elif self.loss_functions[ i] == losses.sparse_categorical_crossentropy: - # case: categorical accuracy with sparse targets - acc_fn = metrics_module.sparse_categorical_accuracy + # case: categorical accuracy/crossentropy with sparse targets + if metric in ('accuracy', 'acc'): + acc_fn = metrics_module.sparse_categorical_accuracy + elif metric in ('crossentropy', 'ce'): + acc_fn = metrics_module.sparse_categorical_crossentropy else: - acc_fn = metrics_module.categorical_accuracy - + # case: categorical accuracy/crossentropy + if metric in ('accuracy', 'acc'): + acc_fn = metrics_module.categorical_accuracy + elif metric in ('crossentropy', 'ce'): + acc_fn = metrics_module.categorical_crossentropy + if metric in ('accuracy', 'acc'): + suffix = 'acc' + elif metric in ('crossentropy', 'ce'): + suffix = 'ce' weighted_metric_fn = _weighted_masked_objective(acc_fn) - metric_name = metric_name_prefix + 'acc' + metric_name = metric_name_prefix + suffix else: metric_fn = metrics_module.get(metric) weighted_metric_fn = _weighted_masked_objective(metric_fn) @@ -949,7 +948,7 @@ class Model(Network): """Check trainable weights count consistency. This will raise a warning if `trainable_weights` and - `_collected_trainable_weights` are consistent (i.e. have the same + `_collected_trainable_weights` are inconsistent (i.e. have different number of parameters). Inconsistency will typically arise when one modifies `model.trainable` without calling `model.compile` again. @@ -959,9 +958,10 @@ class Model(Network): if len(self.trainable_weights) != len(self._collected_trainable_weights): logging.warning( - 'Discrepancy between trainable weights and collected trainable' - ' weights, did you set `model.trainable` without calling' - ' `model.compile` after ?') + UserWarning( + 'Discrepancy between trainable weights and collected trainable' + ' weights, did you set `model.trainable` without calling' + ' `model.compile` after ?')) def _make_train_function(self): if not hasattr(self, 'train_function'): @@ -1050,18 +1050,21 @@ class Model(Network): processed based on the size of the first dimension of the first input numpy array. When steps is not `None` and `batch_size` is `None`, returns `None`. + + Raises: + ValueError: In case of invalid arguments. """ if steps is not None: num_samples = None if batch_size is not None: - raise ValueError('If ' + steps_name + - ' is set, the `batch_size` must be None.') + raise ValueError( + 'If ' + steps_name + ' is set, the `batch_size` must be None.') elif ins and hasattr(ins[0], 'shape'): num_samples = ins[0].shape[0] else: - raise ValueError('Either the input data should have ' - 'a defined shape, or ' + steps_name + - ' should be specified.') + raise ValueError( + 'Either the input data should have ' + 'a defined shape, or ' + steps_name + ' should be specified.') return num_samples def _fit_loop(self, @@ -1104,31 +1107,33 @@ class Model(Network): steps_per_epoch: Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. Ignored with the default value of `None`. - validation_steps: Number of steps to run validation for (only if doing - validation from data tensors). Ignored with default value of `None`. + validation_steps: Number of steps to run validation for + (only if doing validation from data tensors). + Ignored with the default value of `None`. Returns: `History` object. Raises: - ValueError: In case of invalid argument values. + ValueError: in case of invalid arguments. """ do_validation = False if val_f and val_ins: do_validation = True - if (verbose and ins and - hasattr(ins[0], 'shape') and hasattr(val_ins[0], 'shape')): + if verbose and ins and hasattr(ins[0], 'shape') and hasattr( + val_ins[0], 'shape'): print('Train on %d samples, validate on %d samples' % (ins[0].shape[0], val_ins[0].shape[0])) if validation_steps: - if steps_per_epoch is None: - raise ValueError('Can only use `validation_steps` when doing step-wise ' - 'training, i.e. `steps_per_epoch` must be set.') do_validation = True + if steps_per_epoch is None: + raise ValueError('Can only use `validation_steps` ' + 'when doing step-wise ' + 'training, i.e. `steps_per_epoch` ' + 'must be set.') num_train_samples = self._check_num_samples( ins, batch_size, steps_per_epoch, 'steps_per_epoch') - if num_train_samples is not None: index_array = np.arange(num_train_samples) @@ -1165,6 +1170,13 @@ class Model(Network): for cbk in callbacks: cbk.validation_data = val_ins + # To prevent a slowdown, we find beforehand the arrays that need conversion. + feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights + indices_for_conversion_to_dense = [] + for i in range(len(feed)): + if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): + indices_for_conversion_to_dense.append(i) + for epoch in range(initial_epoch, epochs): callbacks.on_epoch_begin(epoch) epoch_logs = {} @@ -1220,6 +1232,9 @@ class Model(Network): batch_logs['batch'] = batch_index batch_logs['size'] = len(batch_ids) callbacks.on_batch_begin(batch_index, batch_logs) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + outs = f(ins_batch) if not isinstance(outs, list): outs = [outs] @@ -1268,6 +1283,13 @@ class Model(Network): progbar = Progbar(target=steps) else: progbar = Progbar(target=num_samples) + + indices_for_conversion_to_dense = [] + for i in range(len(self._feed_inputs)): + if (issparse is not None and issparse(ins[i]) and + not K.is_sparse(self._feed_inputs[i])): + indices_for_conversion_to_dense.append(i) + if steps is not None: # Step-based predictions. # Since we do not know how many samples @@ -1305,6 +1327,9 @@ class Model(Network): ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] else: ins_batch = _slice_arrays(ins, batch_ids) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() + batch_outs = f(ins_batch) if not isinstance(batch_outs, list): batch_outs = [batch_outs] @@ -1341,12 +1366,19 @@ class Model(Network): """ num_samples = self._check_num_samples(ins, batch_size, steps, 'steps') outs = [] - if verbose == 1: if steps is not None: progbar = Progbar(target=steps) else: progbar = Progbar(target=num_samples) + + # To prevent a slowdown, we find beforehand the arrays that need conversion. + feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights + indices_for_conversion_to_dense = [] + for i in range(len(feed)): + if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): + indices_for_conversion_to_dense.append(i) + if steps is not None: for step in range(steps): batch_outs = f(ins) @@ -1365,8 +1397,6 @@ class Model(Network): for i in range(len(outs)): outs[i] /= steps else: - if verbose == 1: - progbar = Progbar(target=num_samples) batches = _make_batches(num_samples, batch_size) index_array = np.arange(num_samples) for batch_index, (batch_start, batch_end) in enumerate(batches): @@ -1376,6 +1406,8 @@ class Model(Network): ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] else: ins_batch = _slice_arrays(ins, batch_ids) + for i in indices_for_conversion_to_dense: + ins_batch[i] = ins_batch[i].toarray() batch_outs = f(ins_batch) if isinstance(batch_outs, list): @@ -1484,7 +1516,8 @@ class Model(Network): sample_weight=None, initial_epoch=0, steps_per_epoch=None, - validation_steps=None): + validation_steps=None, + **kwargs): """Trains the model for a fixed number of epochs (iterations on a dataset). Arguments: @@ -1501,10 +1534,9 @@ class Model(Network): dictionary mapping output names to Numpy arrays. `y` can be `None` (default) if feeding from TensorFlow data tensors. - Can be `None` (default) if feeding from framework-native tensors. batch_size: Integer or `None`. Number of samples per gradient update. - If unspecified, it will default to 32. + If unspecified, `batch_size` will default to 32. epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided. @@ -1513,7 +1545,7 @@ class Model(Network): The model is not trained for a number of iterations given by `epochs`, but merely until the epoch of index `epochs` is reached. - verbose: 0, 1, or 2. Verbosity mode. + verbose: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. @@ -1530,7 +1562,7 @@ class Model(Network): `(x_val, y_val, val_sample_weights)` on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. - This will override `validation_split`. + `validation_data` will override `validation_split`. shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). 'batch' is a special option for dealing with the @@ -1553,17 +1585,20 @@ class Model(Network): to apply a different weight to every timestep of every sample. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. - initial_epoch: Epoch at which to start training + initial_epoch: Integer. + Epoch at which to start training (useful for resuming a previous training run). - steps_per_epoch: Total number of steps (batches of samples) + steps_per_epoch: Integer or `None`. + Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default `None` is equal to - the number of unique samples in your dataset divided by + the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. validation_steps: Only relevant if `steps_per_epoch` is specified. Total number of steps (batches of samples) to validate before stopping. + **kwargs: Used for backwards compatibility. Returns: A `History` object. Its `History.history` attribute is @@ -1572,12 +1607,21 @@ class Model(Network): and validation metrics values (if applicable). Raises: + RuntimeError: If the model was never compiled. ValueError: In case of mismatch between the provided input data and what the model expects. """ # Backwards compatibility if batch_size is None and steps_per_epoch is None: batch_size = 32 + # Legacy support + if 'nb_epoch' in kwargs: + logging.warning( + 'The `nb_epoch` argument in `fit` ' + 'has been renamed `epochs`.') + epochs = kwargs.pop('nb_epoch') + if kwargs: + raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) if x is None and y is None and steps_per_epoch is None: raise ValueError('If fitting from data tensors, ' 'you should specify the `steps_per_epoch` ' @@ -1590,10 +1634,8 @@ class Model(Network): class_weight=class_weight, check_batch_axis=False, batch_size=batch_size) - # Prepare validation data. do_validation = False - val_ins = [] if validation_data: do_validation = True if len(validation_data) == 2: @@ -1657,8 +1699,9 @@ class Model(Network): 'val_' + n for n in out_labels ] else: - val_f = None callback_metrics = copy.copy(out_labels) + val_f = None + val_ins = [] # Delegate logic to `_fit_loop`. return self._fit_loop( @@ -1694,14 +1737,14 @@ class Model(Network): If input layers in the model are named, you can also pass a dictionary mapping input names to Numpy arrays. `x` can be `None` (default) if feeding from - framework-native tensors (e.g. TensorFlow data tensors). + TensorFlow data tensors. y: Numpy array of target (label) data (if the model has a single output), or list of Numpy arrays (if the model has multiple outputs). If output layers in the model are named, you can also pass a dictionary mapping output names to Numpy arrays. `y` can be `None` (default) if feeding from - framework-native tensors (e.g. TensorFlow data tensors). + TensorFlow data tensors. batch_size: Integer or `None`. Number of samples per evaluation step. If unspecified, `batch_size` will default to 32. @@ -1721,8 +1764,7 @@ class Model(Network): steps: Integer or `None`. Total number of steps (batches of samples) before declaring the evaluation round finished. - The default `None` is equal to the number of unique samples in - your dataset divided by the batch size. + Ignored with the default value of `None`. Returns: Scalar test loss (if the model has a single output and no metrics) @@ -1731,7 +1773,7 @@ class Model(Network): the display labels for the scalar outputs. Raises: - ValueError: In case of invalid arguments. + ValueError: in case of invalid arguments. """ # Backwards compatibility. if batch_size is None and steps is None: @@ -1890,6 +1932,9 @@ class Model(Network): or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. + + Raises: + ValueError: in case of invalid arguments. """ x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, check_batch_axis=True) @@ -1937,8 +1982,7 @@ class Model(Network): workers=1, use_multiprocessing=False, shuffle=True, - initial_epoch=0, - **kwargs): + initial_epoch=0): """Fits the model on data yielded batch-by-batch by a Python generator. The generator is run in parallel to the model, for efficiency. @@ -1950,22 +1994,31 @@ class Model(Network): using `use_multiprocessing=True`. Arguments: - generator: A generator or an instance of Sequence (keras.utils.Sequence) - object in order to avoid duplicate data when using multiprocessing. + generator: A generator or an instance of `Sequence` + (`keras.utils.Sequence`) + object in order to avoid duplicate data + when using multiprocessing. The output of the generator must be either - - a tuple (inputs, targets) - - a tuple (inputs, targets, sample_weights). - All arrays should contain the same number of samples. + - a tuple `(inputs, targets)` + - a tuple `(inputs, targets, sample_weights)`. + This tuple (a single output of the generator) makes a single batch. + Therefore, all arrays in this tuple must have the same length (equal + to the size of this batch). Different batches may have different + sizes. + For example, the last batch of the epoch is commonly smaller than + the + others, if the size of the dataset is not divisible by the batch + size. The generator is expected to loop over its data indefinitely. An epoch finishes when `steps_per_epoch` batches have been seen by the model. steps_per_epoch: Total number of steps (batches of samples) to yield from `generator` before declaring one epoch finished and starting the next epoch. It should typically - be equal to the number of unique samples of your dataset + be equal to the number of samples of your dataset divided by the batch size. Optional for `Sequence`: if unspecified, will use - `len(generator)` as a number of steps. + the `len(generator)` as a number of steps. epochs: Integer, total number of iterations on the data. verbose: Verbosity mode, 0, 1, or 2. callbacks: List of callbacks to be called during training. @@ -1977,27 +2030,28 @@ class Model(Network): is a generator. Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use - `len(generator)` as a number of steps. + the `len(validation_data)` as a number of steps. class_weight: Dictionary mapping class indices to a weight for the class. - max_queue_size: Maximum size for the generator queue. + max_queue_size: Integer. Maximum size for the generator queue. + If unspecified, `max_queue_size` will default to 10. workers: Integer. Maximum number of processes to spin up when using process based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. - use_multiprocessing: If True, use process based threading. + use_multiprocessing: Boolean. If True, use process based threading. + If unspecified, `workers` will default to False. Note that because this implementation relies on multiprocessing, you should not pass non picklable arguments to the generator as they can't be passed easily to children processes. - shuffle: Whether to shuffle the data at the beginning of each - epoch. Only used with instances of `Sequence` - (`keras.utils.Sequence`). + shuffle: Whether to shuffle the order of the batches at + the beginning of each epoch. Only used with instances + of `Sequence` (keras.utils.Sequence). initial_epoch: Epoch at which to start training (useful for resuming a previous training run) - **kwargs: support for legacy arguments. Returns: A `History` object. @@ -2023,19 +2077,6 @@ class Model(Network): ValueError: In case the generator yields data in an invalid format. """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - wait_time = 0.01 # in seconds epoch = initial_epoch @@ -2046,10 +2087,11 @@ class Model(Network): is_sequence = isinstance(generator, Sequence) if not is_sequence and use_multiprocessing and workers > 1: - logging.warning('Using a generator with `use_multiprocessing=True`' + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' ' and multiple workers may duplicate your data.' ' Please consider using the`keras.utils.Sequence' - ' class.') + ' class.')) if steps_per_epoch is None: if is_sequence: steps_per_epoch = len(generator) @@ -2098,26 +2140,47 @@ class Model(Network): }) callbacks.on_train_begin() - if do_validation and not val_gen: - if len(validation_data) == 2: - val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence - val_sample_weight = None - elif len(validation_data) == 3: - val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence - else: - raise ValueError('`validation_data` should be a tuple ' - '`(val_x, val_y, val_sample_weight)` ' - 'or `(val_x, val_y)`. Found: ' + str(validation_data)) - val_x, val_y, val_sample_weights = self._standardize_user_data( - val_x, val_y, val_sample_weight) - val_data = val_x + val_y + val_sample_weights - if self.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_data += [0.] - for cbk in callbacks: - cbk.validation_data = val_data enqueuer = None + val_enqueuer = None try: + if do_validation: + if val_gen: + if workers > 0: + if isinstance(validation_data, Sequence): + val_enqueuer = OrderedEnqueuer( + validation_data, use_multiprocessing=use_multiprocessing) + if validation_steps is None: + validation_steps = len(validation_data) + else: + val_enqueuer = GeneratorEnqueuer( + validation_data, + use_multiprocessing=use_multiprocessing, + wait_time=wait_time) + val_enqueuer.start(workers=workers, max_queue_size=max_queue_size) + validation_generator = val_enqueuer.get() + else: + validation_generator = validation_data + else: + if len(validation_data) == 2: + val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence + val_sample_weight = None + elif len(validation_data) == 3: + val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence + else: + raise ValueError( + '`validation_data` should be a tuple ' + '`(val_x, val_y, val_sample_weight)` ' + 'or `(val_x, val_y)`. Found: ' + str(validation_data)) + val_x, val_y, val_sample_weights = self._standardize_user_data( + val_x, val_y, val_sample_weight) + val_data = val_x + val_y + val_sample_weights + if self.uses_learning_phase and not isinstance( + K.learning_phase(), int): + val_data += [0.] + for cbk in callbacks: + cbk.validation_data = val_data + if workers > 0: if is_sequence: enqueuer = OrderedEnqueuer( @@ -2135,6 +2198,8 @@ class Model(Network): output_generator = generator callback_model.stop_training = False + # Construct epoch logs. + epoch_logs = {} while epoch < epochs: callbacks.on_epoch_begin(epoch) steps_done = 0 @@ -2178,8 +2243,6 @@ class Model(Network): callbacks.on_batch_end(batch_index, batch_logs) - # Construct epoch logs. - epoch_logs = {} batch_index += 1 steps_done += 1 @@ -2187,11 +2250,7 @@ class Model(Network): if steps_done >= steps_per_epoch and do_validation: if val_gen: val_outs = self.evaluate_generator( - validation_data, - validation_steps, - max_queue_size=max_queue_size, - workers=workers, - use_multiprocessing=use_multiprocessing) + validation_generator, validation_steps, workers=0) else: # No need for try/except because # data has already been validated. @@ -2216,8 +2275,12 @@ class Model(Network): break finally: - if enqueuer is not None: - enqueuer.stop() + try: + if enqueuer is not None: + enqueuer.stop() + finally: + if val_enqueuer is not None: + val_enqueuer.stop() callbacks.on_train_end() return self.history @@ -2227,8 +2290,7 @@ class Model(Network): steps=None, max_queue_size=10, workers=1, - use_multiprocessing=False, - **kwargs): + use_multiprocessing=False): """Evaluates the model on a data generator. The generator should return the same kind of data @@ -2256,7 +2318,6 @@ class Model(Network): non picklable arguments to the generator as they can't be passed easily to children processes. - **kwargs: support for legacy arguments. Returns: Scalar test loss (if the model has a single output and no metrics) @@ -2264,23 +2325,13 @@ class Model(Network): and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. + Raises: + ValueError: in case of invalid arguments. + Raises: ValueError: In case the generator yields data in an invalid format. """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - if kwargs: - raise ValueError('Unrecognized keyword arguments: ' + str(kwargs)) - self._make_test_function() steps_done = 0 @@ -2289,10 +2340,11 @@ class Model(Network): batch_sizes = [] is_sequence = isinstance(generator, Sequence) if not is_sequence and use_multiprocessing and workers > 1: - logging.warning('Using a generator with `use_multiprocessing=True`' + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' ' and multiple workers may duplicate your data.' ' Please consider using the`keras.utils.Sequence' - ' class.') + ' class.')) if steps is None: if is_sequence: steps = len(generator) @@ -2368,8 +2420,7 @@ class Model(Network): max_queue_size=10, workers=1, use_multiprocessing=False, - verbose=0, - **kwargs): + verbose=0): """Generates predictions for the input samples from a data generator. The generator should return the same kind of data as accepted by @@ -2377,9 +2428,9 @@ class Model(Network): Arguments: generator: Generator yielding batches of input samples - or an instance of Sequence (keras.utils.Sequence) - object in order to avoid duplicate data - when using multiprocessing. + or an instance of Sequence (keras.utils.Sequence) + object in order to avoid duplicate data + when using multiprocessing. steps: Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use @@ -2397,7 +2448,6 @@ class Model(Network): as they can't be passed easily to children processes. verbose: verbosity mode, 0 or 1. - **kwargs: support for legacy arguments. Returns: Numpy array(s) of predictions. @@ -2406,17 +2456,6 @@ class Model(Network): ValueError: In case the generator yields data in an invalid format. """ - # Legacy support - if 'max_q_size' in kwargs: - max_queue_size = kwargs.pop('max_q_size') - logging.warning('The argument `max_q_size` has been renamed ' - '`max_queue_size`. Update your method calls accordingly.') - if 'pickle_safe' in kwargs: - use_multiprocessing = kwargs.pop('pickle_safe') - logging.warning('The argument `pickle_safe` has been renamed ' - '`use_multiprocessing`. ' - 'Update your method calls accordingly.') - self._make_predict_function() steps_done = 0 @@ -2424,10 +2463,11 @@ class Model(Network): all_outs = [] is_sequence = isinstance(generator, Sequence) if not is_sequence and use_multiprocessing and workers > 1: - logging.warn('Using a generator with `use_multiprocessing=True`' - ' and multiple workers may duplicate your data.' - ' Please consider using the`keras.utils.Sequence' - ' class.') + logging.warning( + UserWarning('Using a generator with `use_multiprocessing=True`' + ' and multiple workers may duplicate your data.' + ' Please consider using the`keras.utils.Sequence' + ' class.')) if steps is None: if is_sequence: steps = len(generator) @@ -2498,6 +2538,6 @@ class Model(Network): else: return np.concatenate(all_outs[0]) if steps_done == 1: - return [out for out in all_outs] + return [out[0] for out in all_outs] else: return [np.concatenate(out) for out in all_outs] diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py index 7650bfb6e8..5a033a04ad 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py @@ -28,6 +28,11 @@ from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.keras._impl.keras.engine.training import _weighted_masked_objective from tensorflow.python.platform import test +try: + import scipy.sparse as scipy_sparse # pylint: disable=g-import-not-at-top +except ImportError: + scipy_sparse = None + class TrainingTest(test.TestCase): @@ -169,7 +174,7 @@ class TrainingTest(test.TestCase): with self.assertRaises(ValueError): model.train_on_batch({'input_a': input_a_np}, [output_d_np, output_e_np]) - with self.assertRaises(TypeError): + with self.assertRaises(AttributeError): model.fit( [input_a_np, input_b_np], [output_d_np, output_e_np], epochs=1, @@ -177,7 +182,7 @@ class TrainingTest(test.TestCase): verbose=0) with self.assertRaises(ValueError): model.train_on_batch([input_a_np], [output_d_np, output_e_np]) - with self.assertRaises(TypeError): + with self.assertRaises(AttributeError): model.train_on_batch(1, [output_d_np, output_e_np]) with self.assertRaises(ValueError): model.train_on_batch(input_a_np, [output_d_np, output_e_np]) @@ -312,6 +317,63 @@ class TrainingTest(test.TestCase): model.compile(loss=None, optimizer='rmsprop') + def test_training_on_sparse_data_with_dense_placeholders(self): + if scipy_sparse is None: + return + + test_inputs = [ + scipy_sparse.random(6, 3, density=0.25).tocsr() for _ in range(2)] + test_outputs = [ + scipy_sparse.random(6, i, density=0.25).tocsr() for i in range(3, 5)] + in1 = keras.layers.Input(shape=(3,)) + in2 = keras.layers.Input(shape=(3,)) + out1 = keras.layers.Dropout(0.5, name='dropout')(in1) + out2 = keras.layers.Dense(4, name='dense_1')(in2) + model = keras.Model([in1, in2], [out1, out2]) + model.predict(test_inputs, batch_size=2) + model.compile('rmsprop', 'mse') + model.fit(test_inputs, test_outputs, + epochs=1, batch_size=2, validation_split=0.5) + model.evaluate(test_inputs, test_outputs, batch_size=2) + + def test_that_trainable_disables_updates(self): + val_a = np.random.random((10, 4)) + val_out = np.random.random((10, 4)) + + with self.test_session(): + a = keras.layers.Input(shape=(4,)) + layer = keras.layers.BatchNormalization(input_shape=(4,)) + b = layer(a) + model = keras.Model(a, b) + + model.trainable = False + assert not model.updates + + model.compile('sgd', 'mse') + assert not model.updates + + x1 = model.predict(val_a) + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + self.assertAllClose(x1, x2, atol=1e-7) + + model.trainable = True + model.compile('sgd', 'mse') + assert model.updates + + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + assert np.abs(np.sum(x1 - x2)) > 1e-5 + + layer.trainable = False + model.compile('sgd', 'mse') + assert not model.updates + + x1 = model.predict(val_a) + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + self.assertAllClose(x1, x2, atol=1e-7) + class LossWeightingTest(test.TestCase): @@ -869,25 +931,6 @@ class TestGeneratorMethods(test.TestCase): use_multiprocessing=False, workers=0) - # Test legacy API - model.fit_generator(custom_generator(), - steps_per_epoch=5, - epochs=1, - verbose=1, - max_q_size=10, - workers=4, - pickle_safe=True) - model.predict_generator(custom_generator(), - steps=5, - max_q_size=10, - workers=2, - pickle_safe=True) - model.evaluate_generator(custom_generator(), - steps=5, - max_q_size=10, - workers=2, - pickle_safe=True) - def test_generator_methods_with_sample_weights(self): arr_data = np.random.random((50, 2)) arr_labels = np.random.random((50,)) @@ -960,7 +1003,7 @@ class TestGeneratorMethods(test.TestCase): use_multiprocessing=False, validation_data=custom_generator(), validation_steps=10) - with self.assertRaises(TypeError): + with self.assertRaises(AttributeError): model.predict_generator(custom_generator(), steps=5, max_queue_size=10, diff --git a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py index e4b9afd38a..ffbf77c4b8 100644 --- a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py +++ b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py @@ -14,18 +14,18 @@ # ============================================================================== """Layers that act as activation functions. """ - from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.framework import tensor_shape +from tensorflow.python.keras._impl.keras import activations from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion class LeakyReLU(Layer): @@ -61,6 +61,7 @@ class LeakyReLU(Layer): base_config = super(LeakyReLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) + @shape_type_conversion def compute_output_shape(self, input_shape): return input_shape @@ -114,9 +115,9 @@ class PReLU(Layer): else: self.shared_axes = list(shared_axes) + @shape_type_conversion def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() - param_shape = input_shape[1:] + param_shape = list(input_shape[1:]) self.param_broadcast = [False] * len(param_shape) if self.shared_axes is not None: for i in self.shared_axes: @@ -140,15 +141,13 @@ class PReLU(Layer): def call(self, inputs, mask=None): pos = K.relu(inputs) if K.backend() == 'theano': - neg = (K.pattern_broadcast(self.alpha, self.param_broadcast) * - (inputs - K.abs(inputs)) * 0.5) + neg = ( + K.pattern_broadcast(self.alpha, self.param_broadcast) * + (inputs - K.abs(inputs)) * 0.5) else: neg = -self.alpha * K.relu(-inputs) return pos + neg - def compute_output_shape(self, input_shape): - return input_shape - def get_config(self): config = { 'alpha_initializer': initializers.serialize(self.alpha_initializer), @@ -159,6 +158,10 @@ class PReLU(Layer): base_config = super(PReLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) + @shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape + class ELU(Layer): """Exponential Linear Unit. @@ -188,14 +191,15 @@ class ELU(Layer): def call(self, inputs): return K.elu(inputs, self.alpha) - def compute_output_shape(self, input_shape): - return input_shape - def get_config(self): config = {'alpha': float(self.alpha)} base_config = super(ELU, self).get_config() return dict(list(base_config.items()) + list(config.items())) + @shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape + class ThresholdedReLU(Layer): """Thresholded Rectified Linear Unit. @@ -223,12 +227,46 @@ class ThresholdedReLU(Layer): self.theta = K.cast_to_floatx(theta) def call(self, inputs, mask=None): - return inputs * K.cast(inputs > self.theta, K.floatx()) + return inputs * K.cast(K.greater(inputs, self.theta), K.floatx()) + + def get_config(self): + config = {'theta': float(self.theta)} + base_config = super(ThresholdedReLU, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + @shape_type_conversion def compute_output_shape(self, input_shape): return input_shape + +class Softmax(Layer): + """Softmax activation function. + + Input shape: + Arbitrary. Use the keyword argument `input_shape` + (tuple of integers, does not include the samples axis) + when using this layer as the first layer in a model. + + Output shape: + Same shape as the input. + + Arguments: + axis: Integer, axis along which the softmax normalization is applied. + """ + + def __init__(self, axis=-1, **kwargs): + super(Softmax, self).__init__(**kwargs) + self.supports_masking = True + self.axis = axis + + def call(self, inputs): + return activations.softmax(inputs, axis=self.axis) + def get_config(self): - config = {'theta': float(self.theta)} - base_config = super(ThresholdedReLU, self).get_config() + config = {'axis': self.axis} + base_config = super(Softmax, self).get_config() return dict(list(base_config.items()) + list(config.items())) + + @shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape diff --git a/tensorflow/python/keras/_impl/keras/layers/advanced_activations_test.py b/tensorflow/python/keras/_impl/keras/layers/advanced_activations_test.py index 91efab30ed..343b7949ac 100644 --- a/tensorflow/python/keras/_impl/keras/layers/advanced_activations_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/advanced_activations_test.py @@ -56,6 +56,12 @@ class AdvancedActivationsTest(test.TestCase): kwargs={'theta': 0.5}, input_shape=(2, 3, 4)) + def test_softmax(self): + with self.test_session(): + testing_utils.layer_test(keras.layers.Softmax, + kwargs={'axis': 1}, + input_shape=(2, 3, 4)) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional.py b/tensorflow/python/keras/_impl/keras/layers/convolutional.py index 22496e8a76..b2ad4c4b65 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional.py @@ -711,6 +711,144 @@ class Conv3DTranspose(tf_convolutional_layers.Conv3D, Layer): return dict(list(base_config.items()) + list(config.items())) +class SeparableConv1D(tf_convolutional_layers.SeparableConv1D, Layer): + """Depthwise separable 1D convolution. + + This layer performs a depthwise convolution that acts separately on + channels, followed by a pointwise convolution that mixes channels. + If `use_bias` is True and a bias initializer is provided, + it adds a bias vector to the output. + It then optionally applies an activation function to produce the final output. + + Arguments: + filters: Integer, the dimensionality of the output space (i.e. the number + of filters in the convolution). + kernel_size: A single integer specifying the spatial + dimensions of the filters. + strides: A single integer specifying the strides + of the convolution. + Specifying any `stride` value != 1 is incompatible with specifying + any `dilation_rate` value != 1. + padding: One of `"valid"` or `"same"` (case-insensitive). + data_format: A string, one of `channels_last` (default) or `channels_first`. + The ordering of the dimensions in the inputs. + `channels_last` corresponds to inputs with shape + `(batch, length, channels)` while `channels_first` corresponds to + inputs with shape `(batch, channels, length)`. + dilation_rate: A single integer, specifying + the dilation rate to use for dilated convolution. + Currently, specifying any `dilation_rate` value != 1 is + incompatible with specifying any stride value != 1. + depth_multiplier: The number of depthwise convolution output channels for + each input channel. The total number of depthwise convolution output + channels will be equal to `num_filters_in * depth_multiplier`. + activation: Activation function. Set it to None to maintain a + linear activation. + use_bias: Boolean, whether the layer uses a bias. + depthwise_initializer: An initializer for the depthwise convolution kernel. + pointwise_initializer: An initializer for the pointwise convolution kernel. + bias_initializer: An initializer for the bias vector. If None, the default + initializer will be used. + depthwise_regularizer: Optional regularizer for the depthwise + convolution kernel. + pointwise_regularizer: Optional regularizer for the pointwise + convolution kernel. + bias_regularizer: Optional regularizer for the bias vector. + activity_regularizer: Optional regularizer function for the output. + depthwise_constraint: Optional projection function to be applied to the + depthwise kernel after being updated by an `Optimizer` (e.g. used for + norm constraints or value constraints for layer weights). The function + must take as input the unprojected variable and must return the + projected variable (which must have the same shape). Constraints are + not safe to use when doing asynchronous distributed training. + pointwise_constraint: Optional projection function to be applied to the + pointwise kernel after being updated by an `Optimizer`. + bias_constraint: Optional projection function to be applied to the + bias after being updated by an `Optimizer`. + trainable: Boolean, if `True` also add variables to the graph collection + `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). + name: A string, the name of the layer. + """ + + def __init__(self, + filters, + kernel_size, + strides=1, + padding='valid', + data_format=None, + dilation_rate=1, + depth_multiplier=1, + activation=None, + use_bias=True, + depthwise_initializer='glorot_uniform', + pointwise_initializer='glorot_uniform', + bias_initializer='zeros', + depthwise_regularizer=None, + pointwise_regularizer=None, + bias_regularizer=None, + activity_regularizer=None, + depthwise_constraint=None, + pointwise_constraint=None, + bias_constraint=None, + **kwargs): + if data_format is None: + data_format = K.image_data_format() + super(SeparableConv1D, self).__init__( + filters=filters, + kernel_size=kernel_size, + strides=strides, + padding=padding, + data_format=data_format, + dilation_rate=dilation_rate, + activation=activations.get(activation), + use_bias=use_bias, + depthwise_initializer=initializers.get(depthwise_initializer), + pointwise_initializer=initializers.get(pointwise_initializer), + bias_initializer=initializers.get(bias_initializer), + depthwise_regularizer=regularizers.get(depthwise_regularizer), + pointwise_regularizer=regularizers.get(pointwise_regularizer), + bias_regularizer=regularizers.get(bias_regularizer), + activity_regularizer=regularizers.get(activity_regularizer), + depthwise_constraint=constraints.get(depthwise_constraint), + pointwise_constraint=constraints.get(pointwise_constraint), + bias_constraint=constraints.get(bias_constraint), + **kwargs) + + def get_config(self): + config = { + 'filters': self.filters, + 'kernel_size': self.kernel_size, + 'strides': self.strides, + 'padding': self.padding, + 'data_format': self.data_format, + 'dilation_rate': self.dilation_rate, + 'activation': activations.serialize(self.activation), + 'use_bias': self.use_bias, + 'depthwise_initializer': + initializers.serialize(self.depthwise_initializer), + 'pointwise_initializer': + initializers.serialize(self.pointwise_initializer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'depthwise_regularizer': + regularizers.serialize(self.depthwise_regularizer), + 'pointwise_regularizer': + regularizers.serialize(self.pointwise_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), + 'activity_regularizer': + regularizers.serialize(self.activity_regularizer), + 'depthwise_constraint': + constraints.serialize(self.depthwise_constraint), + 'pointwise_constraint': + constraints.serialize(self.pointwise_constraint), + 'bias_constraint': + constraints.serialize(self.bias_constraint) + } + base_config = super(SeparableConv1D, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + class SeparableConv2D(tf_convolutional_layers.SeparableConv2D, Layer): """Depthwise separable 2D convolution. @@ -1663,6 +1801,7 @@ class Cropping3D(Layer): Convolution1D = Conv1D Convolution2D = Conv2D Convolution3D = Conv3D +SeparableConvolution1D = SeparableConv1D SeparableConvolution2D = SeparableConv2D Convolution2DTranspose = Conv2DTranspose Convolution3DTranspose = Conv3DTranspose diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py index 4f0e9fc691..565db19e41 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py @@ -20,13 +20,13 @@ from __future__ import print_function import numpy as np -from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import activations from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion from tensorflow.python.keras._impl.keras.layers.recurrent import Recurrent from tensorflow.python.keras._impl.keras.utils import conv_utils @@ -127,10 +127,10 @@ class ConvRecurrent2D(Recurrent): self.input_spec = [InputSpec(ndim=5)] self.state_spec = None + @shape_type_conversion def compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] - input_shape = tensor_shape.TensorShape(input_shape).as_list() if self.data_format == 'channels_first': rows = input_shape[3] cols = input_shape[4] @@ -151,30 +151,28 @@ class ConvRecurrent2D(Recurrent): dilation=self.dilation_rate[1]) if self.return_sequences: if self.data_format == 'channels_first': - output_shape = [input_shape[0], input_shape[1], - self.filters, rows, cols] + output_shape = (input_shape[0], input_shape[1], self.filters, rows, + cols) elif self.data_format == 'channels_last': - output_shape = [input_shape[0], input_shape[1], - rows, cols, self.filters] + output_shape = (input_shape[0], input_shape[1], rows, cols, + self.filters) else: if self.data_format == 'channels_first': - output_shape = [input_shape[0], self.filters, rows, cols] + output_shape = (input_shape[0], self.filters, rows, cols) elif self.data_format == 'channels_last': - output_shape = [input_shape[0], rows, cols, self.filters] + output_shape = (input_shape[0], rows, cols, self.filters) if self.return_state: if self.data_format == 'channels_first': - output_shapes = [output_shape] + [(input_shape[0], - self.filters, - rows, - cols) for _ in range(2)] + output_shape = [output_shape] + [ + (input_shape[0], self.filters, rows, cols) for _ in range(2) + ] elif self.data_format == 'channels_last': - output_shapes = [output_shape] + [(input_shape[0], - rows, - cols, - self.filters) for _ in range(2)] - return [tensor_shape.TensorShape(shape) for shape in output_shapes] - return tensor_shape.TensorShape(output_shape) + output_shape = [output_shape] + [ + (input_shape[0], rows, cols, self.filters) for _ in range(2) + ] + + return output_shape def get_config(self): config = { @@ -294,11 +292,6 @@ class ConvLSTM2D(ConvRecurrent2D): Raises: ValueError: in case of invalid constructor arguments. - References: - - [Convolutional LSTM Network: A Machine Learning Approach for - Precipitation Nowcasting](http://arxiv.org/abs/1506.04214v1) - The current implementation does not include the feedback loop on the - cells output """ def __init__(self, @@ -338,7 +331,6 @@ class ConvLSTM2D(ConvRecurrent2D): return_sequences=return_sequences, go_backwards=go_backwards, stateful=stateful, - activity_regularizer=regularizers.get(activity_regularizer), **kwargs) self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) @@ -352,6 +344,7 @@ class ConvLSTM2D(ConvRecurrent2D): self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) + self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) @@ -361,13 +354,12 @@ class ConvLSTM2D(ConvRecurrent2D): self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_spec = [InputSpec(ndim=4), InputSpec(ndim=4)] + @shape_type_conversion def build(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] - input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) batch_size = input_shape[0] if self.stateful else None self.input_spec[0] = InputSpec(shape=(batch_size, None) + input_shape[2:]) - if self.stateful: self.reset_states() else: diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py index be7da6f2b4..39c9d4f0fb 100644 --- a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py @@ -311,6 +311,72 @@ class Conv3DTransposeTest(test.TestCase): self.assertEqual(layer.bias.constraint, b_constraint) +class SeparableConv1DTest(test.TestCase): + + def test_separable_conv_1d(self): + num_samples = 2 + filters = 6 + stack_size = 3 + length = 7 + strides = 1 + + for padding in ['valid', 'same']: + for multiplier in [1, 2]: + if padding == 'same' and strides != 1: + continue + + with self.test_session(use_gpu=True): + testing_utils.layer_test( + keras.layers.SeparableConv1D, + kwargs={ + 'filters': filters, + 'kernel_size': 3, + 'padding': padding, + 'strides': strides, + 'depth_multiplier': multiplier + }, + input_shape=(num_samples, length, stack_size)) + + def test_separable_conv1d_regularizers(self): + kwargs = { + 'filters': 3, + 'kernel_size': 3, + 'padding': 'valid', + 'depthwise_regularizer': 'l2', + 'pointwise_regularizer': 'l2', + 'bias_regularizer': 'l2', + 'activity_regularizer': 'l2', + 'strides': 1 + } + with self.test_session(use_gpu=True): + layer = keras.layers.SeparableConv1D(**kwargs) + layer.build((None, 5, 2)) + self.assertEqual(len(layer.losses), 3) + layer(keras.backend.variable(np.ones((1, 5, 2)))) + self.assertEqual(len(layer.losses), 4) + + def test_separable_conv1d_constraints(self): + d_constraint = lambda x: x + p_constraint = lambda x: x + b_constraint = lambda x: x + + kwargs = { + 'filters': 3, + 'kernel_size': 3, + 'padding': 'valid', + 'pointwise_constraint': p_constraint, + 'depthwise_constraint': d_constraint, + 'bias_constraint': b_constraint, + 'strides': 1 + } + with self.test_session(use_gpu=True): + layer = keras.layers.SeparableConv1D(**kwargs) + layer.build((None, 5, 2)) + self.assertEqual(layer.depthwise_kernel.constraint, d_constraint) + self.assertEqual(layer.pointwise_kernel.constraint, p_constraint) + self.assertEqual(layer.bias.constraint, b_constraint) + + class SeparableConv2DTest(test.TestCase): def test_separable_conv_2d(self): diff --git a/tensorflow/python/keras/_impl/keras/layers/embeddings.py b/tensorflow/python/keras/_impl/keras/layers/embeddings.py index 51c520be38..f8e31068f8 100644 --- a/tensorflow/python/keras/_impl/keras/layers/embeddings.py +++ b/tensorflow/python/keras/_impl/keras/layers/embeddings.py @@ -18,12 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import Layer +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion class Embedding(Layer): @@ -58,13 +58,13 @@ class Embedding(Layer): output_dim: int >= 0. Dimension of the dense embedding. embeddings_initializer: Initializer for the `embeddings` matrix. embeddings_regularizer: Regularizer function applied to - the `embeddings` matrix. + the `embeddings` matrix. embeddings_constraint: Constraint function applied to - the `embeddings` matrix. + the `embeddings` matrix. mask_zero: Whether or not the input value 0 is a special "padding" value that should be masked out. - This is useful when using recurrent layers, - which may take variable length inputs. + This is useful when using recurrent layers + which may take variable length input. If this is `True` then all subsequent layers in the model need to support masking or an exception will be raised. If mask_zero is set to True, as a consequence, index 0 cannot be @@ -81,9 +81,6 @@ class Embedding(Layer): Output shape: 3D tensor with shape: `(batch_size, sequence_length, output_dim)`. - References: - - [A Theoretically Grounded Application of Dropout in Recurrent Neural - Networks](http://arxiv.org/abs/1512.05287) """ def __init__(self, @@ -101,19 +98,19 @@ class Embedding(Layer): kwargs['input_shape'] = (input_length,) else: kwargs['input_shape'] = (None,) - super(Embedding, self).__init__( - activity_regularizer=regularizers.get(activity_regularizer), **kwargs) + super(Embedding, self).__init__(**kwargs) self.input_dim = input_dim self.output_dim = output_dim self.embeddings_initializer = initializers.get(embeddings_initializer) self.embeddings_regularizer = regularizers.get(embeddings_regularizer) + self.activity_regularizer = regularizers.get(activity_regularizer) self.embeddings_constraint = constraints.get(embeddings_constraint) self.mask_zero = mask_zero self.input_length = input_length + @shape_type_conversion def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() self.embeddings = self.add_weight( shape=(self.input_dim, self.output_dim), initializer=self.embeddings_initializer, @@ -129,10 +126,10 @@ class Embedding(Layer): else: return K.not_equal(inputs, 0) + @shape_type_conversion def compute_output_shape(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() if self.input_length is None: - return tensor_shape.TensorShape(input_shape + [self.output_dim]) + return input_shape + (self.output_dim,) else: # input_length can be tuple if input is 3D or higher if isinstance(self.input_length, (list, tuple)): @@ -149,8 +146,7 @@ class Embedding(Layer): (str(self.input_length), str(input_shape))) elif s1 is None: in_lens[i] = s2 - return tensor_shape.TensorShape( - (input_shape[0],) + tuple(in_lens) + (self.output_dim,)) + return (input_shape[0],) + tuple(in_lens) + (self.output_dim,) def call(self, inputs): if K.dtype(inputs) != 'int32': diff --git a/tensorflow/python/keras/_impl/keras/layers/local.py b/tensorflow/python/keras/_impl/keras/layers/local.py index 0a31b87fb5..b844b071e0 100644 --- a/tensorflow/python/keras/_impl/keras/layers/local.py +++ b/tensorflow/python/keras/_impl/keras/layers/local.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import activations from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints @@ -26,6 +25,7 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion from tensorflow.python.keras._impl.keras.utils import conv_utils @@ -98,8 +98,7 @@ class LocallyConnected1D(Layer): kernel_constraint=None, bias_constraint=None, **kwargs): - super(LocallyConnected1D, self).__init__( - activity_regularizer=regularizers.get(activity_regularizer), **kwargs) + super(LocallyConnected1D, self).__init__(**kwargs) self.filters = filters self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, 1, 'strides') @@ -114,12 +113,13 @@ class LocallyConnected1D(Layer): self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) + self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.input_spec = InputSpec(ndim=3) + @shape_type_conversion def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() input_dim = input_shape[2] if input_dim is None: raise ValueError('Axis 2 of input should be fully-defined. ' @@ -146,15 +146,14 @@ class LocallyConnected1D(Layer): self.input_spec = InputSpec(ndim=3, axes={2: input_dim}) self.built = True + @shape_type_conversion def compute_output_shape(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() length = conv_utils.conv_output_length(input_shape[1], self.kernel_size[0], self.padding, self.strides[0]) - return tensor_shape.TensorShape([input_shape[0], length, self.filters]) + return (input_shape[0], length, self.filters) def call(self, inputs): output = K.local_conv1d(inputs, self.kernel, self.kernel_size, self.strides) - if self.use_bias: output = K.bias_add(output, self.bias) if self.activation is not None: @@ -163,20 +162,32 @@ class LocallyConnected1D(Layer): def get_config(self): config = { - 'filters': self.filters, - 'kernel_size': self.kernel_size, - 'strides': self.strides, - 'padding': self.padding, - 'activation': activations.serialize(self.activation), - 'use_bias': self.use_bias, - 'kernel_initializer': initializers.serialize(self.kernel_initializer), - 'bias_initializer': initializers.serialize(self.bias_initializer), - 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), - 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'filters': + self.filters, + 'kernel_size': + self.kernel_size, + 'strides': + self.strides, + 'padding': + self.padding, + 'activation': + activations.serialize(self.activation), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), - 'kernel_constraint': constraints.serialize(self.kernel_constraint), - 'bias_constraint': constraints.serialize(self.bias_constraint) + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), + 'bias_constraint': + constraints.serialize(self.bias_constraint) } base_config = super(LocallyConnected1D, self).get_config() return dict(list(base_config.items()) + list(config.items())) @@ -273,8 +284,7 @@ class LocallyConnected2D(Layer): kernel_constraint=None, bias_constraint=None, **kwargs): - super(LocallyConnected2D, self).__init__( - activity_regularizer=regularizers.get(activity_regularizer), **kwargs) + super(LocallyConnected2D, self).__init__(**kwargs) self.filters = filters self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, 2, 'strides') @@ -289,12 +299,13 @@ class LocallyConnected2D(Layer): self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) + self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.input_spec = InputSpec(ndim=4) + @shape_type_conversion def build(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() if self.data_format == 'channels_last': input_row, input_col = input_shape[1:-1] input_filter = input_shape[3] @@ -306,7 +317,6 @@ class LocallyConnected2D(Layer): ' a LocallyConnected2D layer ' 'should be fully-defined, but layer received ' 'the inputs shape ' + str(input_shape)) - output_row = conv_utils.conv_output_length(input_row, self.kernel_size[0], self.padding, self.strides[0]) output_col = conv_utils.conv_output_length(input_col, self.kernel_size[1], @@ -337,33 +347,30 @@ class LocallyConnected2D(Layer): self.input_spec = InputSpec(ndim=4, axes={-1: input_filter}) self.built = True + @shape_type_conversion def compute_output_shape(self, input_shape): - input_shape = tensor_shape.TensorShape(input_shape).as_list() if self.data_format == 'channels_first': rows = input_shape[2] cols = input_shape[3] elif self.data_format == 'channels_last': rows = input_shape[1] cols = input_shape[2] + rows = conv_utils.conv_output_length(rows, self.kernel_size[0], self.padding, self.strides[0]) cols = conv_utils.conv_output_length(cols, self.kernel_size[1], self.padding, self.strides[1]) if self.data_format == 'channels_first': - return tensor_shape.TensorShape( - [input_shape[0], self.filters, rows, cols]) + return (input_shape[0], self.filters, rows, cols) elif self.data_format == 'channels_last': - return tensor_shape.TensorShape( - [input_shape[0], rows, cols, self.filters]) + return (input_shape[0], rows, cols, self.filters) def call(self, inputs): - output = K.local_conv2d(inputs, - self.kernel, - self.kernel_size, - self.strides, + output = K.local_conv2d(inputs, self.kernel, self.kernel_size, self.strides, (self.output_row, self.output_col), self.data_format) + if self.use_bias: output = K.bias_add(output, self.bias, data_format=self.data_format) @@ -372,21 +379,34 @@ class LocallyConnected2D(Layer): def get_config(self): config = { - 'filters': self.filters, - 'kernel_size': self.kernel_size, - 'strides': self.strides, - 'padding': self.padding, - 'data_format': self.data_format, - 'activation': activations.serialize(self.activation), - 'use_bias': self.use_bias, - 'kernel_initializer': initializers.serialize(self.kernel_initializer), - 'bias_initializer': initializers.serialize(self.bias_initializer), - 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), - 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'filters': + self.filters, + 'kernel_size': + self.kernel_size, + 'strides': + self.strides, + 'padding': + self.padding, + 'data_format': + self.data_format, + 'activation': + activations.serialize(self.activation), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), - 'kernel_constraint': constraints.serialize(self.kernel_constraint), - 'bias_constraint': constraints.serialize(self.bias_constraint) + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), + 'bias_constraint': + constraints.serialize(self.bias_constraint) } base_config = super(LocallyConnected2D, self).get_config() return dict(list(base_config.items()) + list(config.items())) diff --git a/tensorflow/python/keras/_impl/keras/layers/merge.py b/tensorflow/python/keras/_impl/keras/layers/merge.py index 76eb03cf27..38b0b30297 100644 --- a/tensorflow/python/keras/_impl/keras/layers/merge.py +++ b/tensorflow/python/keras/_impl/keras/layers/merge.py @@ -14,15 +14,15 @@ # ============================================================================== # pylint: disable=not-callable # pylint: disable=redefined-builtin -"""Layers can merge several input tensors into a single output tensor. +"""Layers that can merge several inputs into one. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.engine.topology import Layer +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion class _Merge(Layer): @@ -73,12 +73,13 @@ class _Merge(Layer): output_shape.append(i) else: if i != j: - raise ValueError('Operands could not be broadcast ' - 'together with shapes ' + str(shape1) + ' ' + - str(shape2)) + raise ValueError( + 'Operands could not be broadcast ' + 'together with shapes ' + str(shape1) + ' ' + str(shape2)) output_shape.append(i) return tuple(output_shape) + @shape_type_conversion def build(self, input_shape): # Used purely for shape validation. if not isinstance(input_shape, list): @@ -87,14 +88,13 @@ class _Merge(Layer): raise ValueError('A merge layer should be called ' 'on a list of at least 2 inputs. ' 'Got ' + str(len(input_shape)) + ' inputs.') - input_shape = [tensor_shape.TensorShape(s).as_list() for s in input_shape] batch_sizes = [s[0] for s in input_shape if s is not None] batch_sizes = set(batch_sizes) batch_sizes -= set([None]) if len(batch_sizes) > 1: - raise ValueError('Can not merge tensors with different ' - 'batch sizes. Got tensors with shapes : ' + - str(input_shape)) + raise ValueError( + 'Can not merge tensors with different ' + 'batch sizes. Got tensors with shapes : ' + str(input_shape)) if input_shape[0] is None: output_shape = None else: @@ -111,9 +111,10 @@ class _Merge(Layer): self._reshape_required = False else: self._reshape_required = True - self.built = True def call(self, inputs): + if not isinstance(inputs, list): + raise ValueError('A merge layer should be called ' 'on a list of inputs.') if self._reshape_required: reshaped_inputs = [] input_ndims = list(map(K.ndim, inputs)) @@ -172,6 +173,7 @@ class _Merge(Layer): else: return self._merge_function(inputs) + @shape_type_conversion def compute_output_shape(self, input_shape): if input_shape[0] is None: output_shape = None @@ -214,6 +216,22 @@ class Add(_Merge): It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape). + + Examples: + + ```python + import keras + + input1 = keras.layers.Input(shape=(16,)) + x1 = keras.layers.Dense(8, activation='relu')(input1) + input2 = keras.layers.Input(shape=(32,)) + x2 = keras.layers.Dense(8, activation='relu')(input2) + added = keras.layers.Add()([x1, x2]) # equivalent to added = + keras.layers.add([x1, x2]) + + out = keras.layers.Dense(4)(added) + model = keras.models.Model(inputs=[input1, input2], outputs=out) + ``` """ def _merge_function(self, inputs): @@ -247,10 +265,17 @@ class Subtract(_Merge): ``` """ + @shape_type_conversion + def build(self, input_shape): + super(Subtract, self).build(input_shape) + if len(input_shape) != 2: + raise ValueError('A `Subtract` layer should be called ' + 'on exactly 2 inputs') + def _merge_function(self, inputs): if len(inputs) != 2: - raise ValueError('`Subtract` layer should be called ' - 'on exactly 2 inputs. Received: %s' % inputs) + raise ValueError('A `Subtract` layer should be called ' + 'on exactly 2 inputs') return inputs[0] - inputs[1] @@ -330,47 +355,43 @@ class Concatenate(_Merge): super(Concatenate, self).__init__(**kwargs) self.axis = axis self.supports_masking = True + self._reshape_required = False + @shape_type_conversion def build(self, input_shape): # Used purely for shape validation. - if not (isinstance(input_shape, list) and len(input_shape) > 1): - raise ValueError('`Concatenate` layer should be called ' - 'on a list containing at least two inputs') + if not isinstance(input_shape, list) or len(input_shape) < 2: + raise ValueError('A `Concatenate` layer should be called ' + 'on a list of at least 2 inputs') if all([shape is None for shape in input_shape]): return - reduced_inputs_shapes = [ - tensor_shape.TensorShape(shape).as_list() for shape in input_shape - ] + reduced_inputs_shapes = [list(shape) for shape in input_shape] shape_set = set() for i in range(len(reduced_inputs_shapes)): del reduced_inputs_shapes[i][self.axis] shape_set.add(tuple(reduced_inputs_shapes[i])) if len(shape_set) > 1: - raise ValueError('`Concatenate` layer requires ' + raise ValueError('A `Concatenate` layer requires ' 'inputs with matching shapes ' 'except for the concat axis. ' 'Got inputs shapes: %s' % (input_shape)) - self.built = True - def call(self, inputs): - if not isinstance(inputs, list): - raise ValueError('A `Concatenate` layer should be called ' - 'on a list of inputs.') + def _merge_function(self, inputs): return K.concatenate(inputs, axis=self.axis) + @shape_type_conversion def compute_output_shape(self, input_shape): if not isinstance(input_shape, list): raise ValueError('A `Concatenate` layer should be called ' 'on a list of inputs.') input_shapes = input_shape - output_shape = tensor_shape.TensorShape(input_shapes[0]).as_list() + output_shape = list(input_shapes[0]) for shape in input_shapes[1:]: - shape = tensor_shape.TensorShape(shape).as_list() if output_shape[self.axis] is None or shape[self.axis] is None: output_shape[self.axis] = None break output_shape[self.axis] += shape[self.axis] - return tensor_shape.TensorShape(output_shape) + return tuple(output_shape) def compute_mask(self, inputs, mask=None): if mask is None: @@ -390,7 +411,7 @@ class Concatenate(_Merge): masks = [] for input_i, mask_i in zip(inputs, mask): if mask_i is None: - # Input is unmasked. Append all 1s to masks + # Input is unmasked. Append all 1s to masks, masks.append(K.ones_like(input_i, dtype='bool')) elif K.ndim(mask_i) < K.ndim(input_i): # Mask is smaller than the input, expand it @@ -441,14 +462,16 @@ class Dot(_Merge): self.axes = axes self.normalize = normalize self.supports_masking = True + self._reshape_required = False + @shape_type_conversion def build(self, input_shape): # Used purely for shape validation. if not isinstance(input_shape, list) or len(input_shape) != 2: raise ValueError('A `Dot` layer should be called ' 'on a list of 2 inputs.') - shape1 = tensor_shape.TensorShape(input_shape[0]).as_list() - shape2 = tensor_shape.TensorShape(input_shape[1]).as_list() + shape1 = input_shape[0] + shape2 = input_shape[1] if shape1 is None or shape2 is None: return if isinstance(self.axes, int): @@ -462,9 +485,10 @@ class Dot(_Merge): raise ValueError('Dimension incompatibility ' '%s != %s. ' % (shape1[axes[0]], shape2[axes[1]]) + 'Layer shapes: %s, %s' % (shape1, shape2)) - self.built = True - def call(self, inputs): + def _merge_function(self, inputs): + if len(inputs) != 2: + raise ValueError('A `Dot` layer should be called ' 'on exactly 2 inputs') x1 = inputs[0] x2 = inputs[1] if isinstance(self.axes, int): @@ -485,12 +509,13 @@ class Dot(_Merge): output = K.batch_dot(x1, x2, axes) return output + @shape_type_conversion def compute_output_shape(self, input_shape): if not isinstance(input_shape, list) or len(input_shape) != 2: raise ValueError('A `Dot` layer should be called ' 'on a list of 2 inputs.') - shape1 = tensor_shape.TensorShape(input_shape[0]).as_list() - shape2 = tensor_shape.TensorShape(input_shape[1]).as_list() + shape1 = list(input_shape[0]) + shape2 = list(input_shape[1]) if isinstance(self.axes, int): if self.axes < 0: axes = [self.axes % len(shape1), self.axes % len(shape2)] @@ -504,7 +529,7 @@ class Dot(_Merge): output_shape = shape1 + shape2 if len(output_shape) == 1: output_shape += [1] - return tensor_shape.TensorShape(output_shape) + return tuple(output_shape) def compute_mask(self, inputs, mask=None): return None @@ -527,6 +552,21 @@ def add(inputs, **kwargs): Returns: A tensor, the sum of the inputs. + + Examples: + + ```python + import keras + + input1 = keras.layers.Input(shape=(16,)) + x1 = keras.layers.Dense(8, activation='relu')(input1) + input2 = keras.layers.Input(shape=(32,)) + x2 = keras.layers.Dense(8, activation='relu')(input2) + added = keras.layers.add([x1, x2]) + + out = keras.layers.Dense(4)(added) + model = keras.models.Model(inputs=[input1, input2], outputs=out) + ``` """ return Add(**kwargs)(inputs) diff --git a/tensorflow/python/keras/_impl/keras/layers/noise.py b/tensorflow/python/keras/_impl/keras/layers/noise.py index 459f13145f..04fffcc384 100644 --- a/tensorflow/python/keras/_impl/keras/layers/noise.py +++ b/tensorflow/python/keras/_impl/keras/layers/noise.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Layers for regularization models via the addition of noise. +"""Layers that operate regularization via the addition of noise. """ from __future__ import absolute_import from __future__ import division @@ -22,6 +22,7 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.engine import Layer +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion class GaussianNoise(Layer): @@ -59,14 +60,15 @@ class GaussianNoise(Layer): return K.in_train_phase(noised, inputs, training=training) - def compute_output_shape(self, input_shape): - return input_shape - def get_config(self): config = {'stddev': self.stddev} base_config = super(GaussianNoise, self).get_config() return dict(list(base_config.items()) + list(config.items())) + @shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape + class GaussianDropout(Layer): """Apply multiplicative 1-centered Gaussian noise. @@ -86,10 +88,6 @@ class GaussianDropout(Layer): Output shape: Same shape as input. - References: - - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting - Srivastava, Hinton, et al. - 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) """ def __init__(self, rate, **kwargs): @@ -108,14 +106,15 @@ class GaussianDropout(Layer): return K.in_train_phase(noised, inputs, training=training) return inputs - def compute_output_shape(self, input_shape): - return input_shape - def get_config(self): config = {'rate': self.rate} base_config = super(GaussianDropout, self).get_config() return dict(list(base_config.items()) + list(config.items())) + @shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape + class AlphaDropout(Layer): """Applies Alpha Dropout to the input. @@ -140,8 +139,6 @@ class AlphaDropout(Layer): Output shape: Same shape as input. - References: - - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) """ def __init__(self, rate, noise_shape=None, seed=None, **kwargs): @@ -157,26 +154,34 @@ class AlphaDropout(Layer): def call(self, inputs, training=None): if 0. < self.rate < 1.: noise_shape = self._get_noise_shape(inputs) - alpha = 1.6732632423543772848170429916717 - scale = 1.0507009873554804934193349852946 - def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed): + def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed): # pylint: disable=missing-docstring + alpha = 1.6732632423543772848170429916717 + scale = 1.0507009873554804934193349852946 alpha_p = -alpha * scale - kept_idx = K.greater_equal(K.random_uniform(noise_shape, seed=seed), - rate) + + kept_idx = K.greater_equal( + K.random_uniform(noise_shape, seed=seed), rate) kept_idx = K.cast(kept_idx, K.floatx()) - a = ((1 - rate) * (1 + rate * alpha_p ** 2)) ** -0.5 + + # Get affine transformation params + a = ((1 - rate) * (1 + rate * alpha_p**2))**-0.5 b = -a * alpha_p * rate + + # Apply mask x = inputs * kept_idx + alpha_p * (1 - kept_idx) + + # Do affine transformation return a * x + b return K.in_train_phase(dropped_inputs, inputs, training=training) return inputs - def compute_output_shape(self, input_shape): - return input_shape - def get_config(self): config = {'rate': self.rate} base_config = super(AlphaDropout, self).get_config() return dict(list(base_config.items()) + list(config.items())) + + @shape_type_conversion + def compute_output_shape(self, input_shape): + return input_shape diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py index 9ea21c9c36..1b0f6cb6cf 100644 --- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py +++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py @@ -1,4 +1,4 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# 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. @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================== # pylint: disable=protected-access -"""Recurrent layers. +"""Recurrent layers and their base classes. """ from __future__ import absolute_import from __future__ import division @@ -29,6 +29,7 @@ from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.platform import tf_logging as logging @@ -109,6 +110,7 @@ class StackedRNNCells(Layer): states += cell_states return inputs, states + @shape_type_conversion def build(self, input_shape): for cell in self.cells: if isinstance(cell, Layer): @@ -117,7 +119,7 @@ class StackedRNNCells(Layer): output_dim = cell.state_size[0] else: output_dim = cell.state_size - input_shape = (input_shape[0], input_shape[1], output_dim) + input_shape = (input_shape[0], output_dim) self.built = True def get_config(self): @@ -262,8 +264,7 @@ class RNN(Layer): (e.g. via the `input_shape` argument) Input shape: - 3D tensor with shape `(batch_size, timesteps, input_dim)`, - (Optional) 2D tensors with shape `(batch_size, output_dim)`. + 3D tensor with shape `(batch_size, timesteps, input_dim)`. Output shape: - if `return_state`: a list of tensors. The first tensor is @@ -370,7 +371,6 @@ class RNN(Layer): go_backwards=False, stateful=False, unroll=False, - activity_regularizer=None, **kwargs): if isinstance(cell, (list, tuple)): cell = StackedRNNCells(cell) @@ -382,8 +382,7 @@ class RNN(Layer): 'an attribute `state_size` ' '(tuple of integers, ' 'one integer per RNN state).') - super(RNN, self).__init__( - activity_regularizer=regularizers.get(activity_regularizer), **kwargs) + super(RNN, self).__init__(**kwargs) self.cell = cell self.return_sequences = return_sequences self.return_state = return_state @@ -412,15 +411,16 @@ class RNN(Layer): def states(self, states): self._states = states + @shape_type_conversion def compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] - input_shape = tensor_shape.TensorShape(input_shape).as_list() if hasattr(self.cell.state_size, '__len__'): - output_dim = self.cell.state_size[0] + state_size = self.cell.state_size else: - output_dim = self.cell.state_size + state_size = [self.cell.state_size] + output_dim = state_size[0] if self.return_sequences: output_shape = (input_shape[0], input_shape[1], output_dim) @@ -428,11 +428,10 @@ class RNN(Layer): output_shape = (input_shape[0], output_dim) if self.return_state: - state_shape = [(input_shape[0], output_dim) for _ in self.states] - output_shape = [output_shape] + state_shape + state_shape = [(input_shape[0], dim) for dim in state_size] + return [output_shape] + state_shape else: - output_shape = output_shape - return tensor_shape.TensorShape(output_shape) + return output_shape def compute_mask(self, inputs, mask): if isinstance(mask, list): @@ -444,6 +443,7 @@ class RNN(Layer): else: return output_mask + @shape_type_conversion def build(self, input_shape): # Note input_shape will be list of shapes of initial states and # constants if these are passed in __call__. @@ -454,7 +454,6 @@ class RNN(Layer): if isinstance(input_shape, list): input_shape = input_shape[0] - input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) batch_size = input_shape[0] if self.stateful else None input_dim = input_shape[-1] @@ -478,9 +477,9 @@ class RNN(Layer): # initial_state was passed in call, check compatibility if [spec.shape[-1] for spec in self.state_spec] != state_size: raise ValueError( - 'An initial_state was passed that is not compatible with ' + 'An `initial_state` was passed that is not compatible with ' '`cell.state_size`. Received `state_spec`={}; ' - 'However `cell.state_size` is ' + 'however `cell.state_size` is ' '{}'.format(self.state_spec, self.cell.state_size)) else: self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size] @@ -610,7 +609,8 @@ class RNN(Layer): constants=constants, go_backwards=self.go_backwards, mask=mask, - unroll=self.unroll) + unroll=self.unroll, + input_length=timesteps) if self.stateful: updates = [] for i in range(len(states)): @@ -625,6 +625,8 @@ class RNN(Layer): # Properly set learning phase if getattr(last_output, '_uses_learning_phase', False): output._uses_learning_phase = True + for state in states: + state._uses_learning_phase = True if self.return_state: if not isinstance(states, (list, tuple)): @@ -636,7 +638,7 @@ class RNN(Layer): return output def _standardize_args(self, inputs, initial_state, constants): - """Standardize `__call__` arguments to a single list of tensor inputs. + """Standardize `__call__` to a single list of tensor inputs. When running a model loaded from file, the input tensors `initial_state` and `constants` can be passed to `RNN.__call__` as part @@ -688,7 +690,7 @@ class RNN(Layer): 'a `batch_input_shape` ' 'argument to your first layer.\n' '- If using the functional API, specify ' - 'the time dimension by passing a ' + 'the batch size by passing a ' '`batch_shape` argument to your Input layer.') # initialize state if None if self.states[0] is None: @@ -788,37 +790,26 @@ class SimpleRNNCell(Layer): Arguments: units: Positive integer, dimensionality of the output space. - activation: Activation function to use - (see [activations](../activations.md)). - Default: hyperbolic tangent (`tanh`). - If you pass `None`, no activation is applied + activation: Activation function to use. + If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. - (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. - (see [initializers](../initializers.md)). - bias_initializer: Initializer for the bias vector - (see [initializers](../initializers.md)). + bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to - the `kernel` weights matrix - (see [regularizer](../regularizers.md)). + the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to - the `recurrent_kernel` weights matrix - (see [regularizer](../regularizers.md)). - bias_regularizer: Regularizer function applied to the bias vector - (see [regularizer](../regularizers.md)). + the `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. kernel_constraint: Constraint function applied to - the `kernel` weights matrix - (see [constraints](../constraints.md)). + the `kernel` weights matrix. recurrent_constraint: Constraint function applied to - the `recurrent_kernel` weights matrix - (see [constraints](../constraints.md)). - bias_constraint: Constraint function applied to the bias vector - (see [constraints](../constraints.md)). + the `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. @@ -866,6 +857,7 @@ class SimpleRNNCell(Layer): self._dropout_mask = None self._recurrent_dropout_mask = None + @shape_type_conversion def build(self, input_shape): self.kernel = self.add_weight( shape=(input_shape[-1], self.units), @@ -890,33 +882,21 @@ class SimpleRNNCell(Layer): self.bias = None self.built = True - def _generate_dropout_mask(self, inputs, training=None): - if 0 < self.dropout < 1: - ones = K.ones_like(K.squeeze(inputs[:, 0:1, :], axis=1)) - - def dropped_inputs(): - return K.dropout(ones, self.dropout) - - self._dropout_mask = K.in_train_phase( - dropped_inputs, ones, training=training) - else: - self._dropout_mask = None - - def _generate_recurrent_dropout_mask(self, inputs, training=None): - if 0 < self.recurrent_dropout < 1: - ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) - ones = K.tile(ones, (1, self.units)) - - def dropped_inputs(): - return K.dropout(ones, self.dropout) - - self._recurrent_dropout_mask = K.in_train_phase( - dropped_inputs, ones, training=training) - else: - self._recurrent_dropout_mask = None - def call(self, inputs, states, training=None): prev_output = states[0] + if 0 < self.dropout < 1 and self._dropout_mask is None: + self._dropout_mask = _generate_dropout_mask( + _generate_dropout_ones(inputs, + K.shape(inputs)[-1]), + self.dropout, + training=training) + if (0 < self.recurrent_dropout < 1 and + self._recurrent_dropout_mask is None): + self._recurrent_dropout_mask = _generate_dropout_mask( + _generate_dropout_ones(inputs, self.units), + self.recurrent_dropout, + training=training) + dp_mask = self._dropout_mask rec_dp_mask = self._recurrent_dropout_mask @@ -939,46 +919,68 @@ class SimpleRNNCell(Layer): output._uses_learning_phase = True return output, [output] + def get_config(self): + config = { + 'units': + self.units, + 'activation': + activations.serialize(self.activation), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), + 'recurrent_initializer': + initializers.serialize(self.recurrent_initializer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), + 'recurrent_regularizer': + regularizers.serialize(self.recurrent_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), + 'recurrent_constraint': + constraints.serialize(self.recurrent_constraint), + 'bias_constraint': + constraints.serialize(self.bias_constraint), + 'dropout': + self.dropout, + 'recurrent_dropout': + self.recurrent_dropout + } + base_config = super(SimpleRNNCell, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + class SimpleRNN(RNN): """Fully-connected RNN where the output is to be fed back to input. Arguments: units: Positive integer, dimensionality of the output space. - activation: Activation function to use - (see [activations](../activations.md)). - Default: hyperbolic tangent (`tanh`). - If you pass `None`, no activation is applied + activation: Activation function to use. + If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. - (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. - (see [initializers](../initializers.md)). - bias_initializer: Initializer for the bias vector - (see [initializers](../initializers.md)). + bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to - the `kernel` weights matrix - (see [regularizer](../regularizers.md)). + the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to - the `recurrent_kernel` weights matrix - (see [regularizer](../regularizers.md)). - bias_regularizer: Regularizer function applied to the bias vector - (see [regularizer](../regularizers.md)). + the `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to - the output of the layer (its "activation"). - (see [regularizer](../regularizers.md)). + the output of the layer (its "activation").. kernel_constraint: Constraint function applied to - the `kernel` weights matrix - (see [constraints](../constraints.md)). + the `kernel` weights matrix. recurrent_constraint: Constraint function applied to - the `recurrent_kernel` weights matrix - (see [constraints](../constraints.md)). - bias_constraint: Constraint function applied to the bias vector - (see [constraints](../constraints.md)). + the `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. @@ -1052,12 +1054,12 @@ class SimpleRNN(RNN): go_backwards=go_backwards, stateful=stateful, unroll=unroll, - activity_regularizer=regularizers.get(activity_regularizer), **kwargs) + self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): - self.cell._generate_dropout_mask(inputs, training=training) - self.cell._generate_recurrent_dropout_mask(inputs, training=training) + self.cell._dropout_mask = None + self.cell._recurrent_dropout_mask = None return super(SimpleRNN, self).call( inputs, mask=mask, training=training, initial_state=initial_state) @@ -1119,25 +1121,36 @@ class SimpleRNN(RNN): def get_config(self): config = { - 'units': self.units, - 'activation': activations.serialize(self.activation), - 'use_bias': self.use_bias, - 'kernel_initializer': initializers.serialize(self.kernel_initializer), + 'units': + self.units, + 'activation': + activations.serialize(self.activation), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), - 'bias_initializer': initializers.serialize(self.bias_initializer), - 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), - 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), - 'kernel_constraint': constraints.serialize(self.kernel_constraint), + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), - 'bias_constraint': constraints.serialize(self.bias_constraint), - 'dropout': self.dropout, - 'recurrent_dropout': self.recurrent_dropout + 'bias_constraint': + constraints.serialize(self.bias_constraint), + 'dropout': + self.dropout, + 'recurrent_dropout': + self.recurrent_dropout } base_config = super(SimpleRNN, self).get_config() del base_config['cell'] @@ -1155,43 +1168,28 @@ class GRUCell(Layer): Arguments: units: Positive integer, dimensionality of the output space. - activation: Activation function to use - (see [activations](../activations.md)). - Default: hyperbolic tangent (`tanh`). - If you pass `None`, no activation is applied + activation: Activation function to use. + If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use - for the recurrent step - (see [activations](../activations.md)). - Default: hard sigmoid (`hard_sigmoid`). - If you pass `None`, no activation is applied - (ie. "linear" activation: `a(x) = x`). + for the recurrent step. use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. - (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. - (see [initializers](../initializers.md)). - bias_initializer: Initializer for the bias vector - (see [initializers](../initializers.md)). + bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to - the `kernel` weights matrix - (see [regularizer](../regularizers.md)). + the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to - the `recurrent_kernel` weights matrix - (see [regularizer](../regularizers.md)). - bias_regularizer: Regularizer function applied to the bias vector - (see [regularizer](../regularizers.md)). + the `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. kernel_constraint: Constraint function applied to - the `kernel` weights matrix - (see [constraints](../constraints.md)). + the `kernel` weights matrix. recurrent_constraint: Constraint function applied to - the `recurrent_kernel` weights matrix - (see [constraints](../constraints.md)). - bias_constraint: Constraint function applied to the bias vector - (see [constraints](../constraints.md)). + the `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. @@ -1249,6 +1247,7 @@ class GRUCell(Layer): self._dropout_mask = None self._recurrent_dropout_mask = None + @shape_type_conversion def build(self, input_shape): input_dim = input_shape[-1] self.kernel = self.add_weight( @@ -1292,38 +1291,24 @@ class GRUCell(Layer): self.bias_h = None self.built = True - def _generate_dropout_mask(self, inputs, training=None): - if 0 < self.dropout < 1: - ones = K.ones_like(K.squeeze(inputs[:, 0:1, :], axis=1)) - - def dropped_inputs(): - return K.dropout(ones, self.dropout) - - self._dropout_mask = [ - K.in_train_phase(dropped_inputs, ones, training=training) - for _ in range(3) - ] - else: - self._dropout_mask = None - - def _generate_recurrent_dropout_mask(self, inputs, training=None): - if 0 < self.recurrent_dropout < 1: - ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) - ones = K.tile(ones, (1, self.units)) - - def dropped_inputs(): - return K.dropout(ones, self.dropout) - - self._recurrent_dropout_mask = [ - K.in_train_phase(dropped_inputs, ones, training=training) - for _ in range(3) - ] - else: - self._recurrent_dropout_mask = None - def call(self, inputs, states, training=None): h_tm1 = states[0] # previous memory + if 0 < self.dropout < 1 and self._dropout_mask is None: + self._dropout_mask = _generate_dropout_mask( + _generate_dropout_ones(inputs, + K.shape(inputs)[-1]), + self.dropout, + training=training, + count=3) + if (0 < self.recurrent_dropout < 1 and + self._recurrent_dropout_mask is None): + self._recurrent_dropout_mask = _generate_dropout_mask( + _generate_dropout_ones(inputs, self.units), + self.recurrent_dropout, + training=training, + count=3) + # dropout matrices for input units dp_mask = self._dropout_mask # dropout matrices for recurrent units @@ -1387,55 +1372,76 @@ class GRUCell(Layer): h._uses_learning_phase = True return h, [h] + def get_config(self): + config = { + 'units': + self.units, + 'activation': + activations.serialize(self.activation), + 'recurrent_activation': + activations.serialize(self.recurrent_activation), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), + 'recurrent_initializer': + initializers.serialize(self.recurrent_initializer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), + 'recurrent_regularizer': + regularizers.serialize(self.recurrent_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), + 'recurrent_constraint': + constraints.serialize(self.recurrent_constraint), + 'bias_constraint': + constraints.serialize(self.bias_constraint), + 'dropout': + self.dropout, + 'recurrent_dropout': + self.recurrent_dropout, + 'implementation': + self.implementation + } + base_config = super(GRUCell, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + class GRU(RNN): - # pylint: disable=line-too-long """Gated Recurrent Unit - Cho et al. 2014. Arguments: units: Positive integer, dimensionality of the output space. - activation: Activation function to use - (see [activations](../activations.md)). - Default: hyperbolic tangent (`tanh`). - If you pass `None`, no activation is applied + activation: Activation function to use. + If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use - for the recurrent step - (see [activations](../activations.md)). - Default: hard sigmoid (`hard_sigmoid`). - If you pass `None`, no activation is applied - (ie. "linear" activation: `a(x) = x`). + for the recurrent step. use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. - (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. - (see [initializers](../initializers.md)). - bias_initializer: Initializer for the bias vector - (see [initializers](../initializers.md)). + bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to - the `kernel` weights matrix - (see [regularizer](../regularizers.md)). + the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to - the `recurrent_kernel` weights matrix - (see [regularizer](../regularizers.md)). - bias_regularizer: Regularizer function applied to the bias vector - (see [regularizer](../regularizers.md)). + the `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to - the output of the layer (its "activation"). - (see [regularizer](../regularizers.md)). + the output of the layer (its "activation").. kernel_constraint: Constraint function applied to - the `kernel` weights matrix - (see [constraints](../constraints.md)). + the `kernel` weights matrix. recurrent_constraint: Constraint function applied to - the `recurrent_kernel` weights matrix - (see [constraints](../constraints.md)). - bias_constraint: Constraint function applied to the bias vector - (see [constraints](../constraints.md)). + the `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. @@ -1465,12 +1471,7 @@ class GRU(RNN): although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. - References: - - [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://arxiv.org/abs/1409.1259) - - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](http://arxiv.org/abs/1412.3555v1) - - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) """ - # pylint: enable=line-too-long def __init__(self, units, @@ -1528,8 +1529,8 @@ class GRU(RNN): self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): - self.cell._generate_dropout_mask(inputs, training=training) - self.cell._generate_recurrent_dropout_mask(inputs, training=training) + self.cell._dropout_mask = None + self.cell._recurrent_dropout_mask = None return super(GRU, self).call( inputs, mask=mask, training=training, initial_state=initial_state) @@ -1599,28 +1600,40 @@ class GRU(RNN): def get_config(self): config = { - 'units': self.units, - 'activation': activations.serialize(self.activation), + 'units': + self.units, + 'activation': + activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), - 'use_bias': self.use_bias, - 'kernel_initializer': initializers.serialize(self.kernel_initializer), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), - 'bias_initializer': initializers.serialize(self.bias_initializer), - 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), - 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), - 'kernel_constraint': constraints.serialize(self.kernel_constraint), + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), - 'bias_constraint': constraints.serialize(self.bias_constraint), - 'dropout': self.dropout, - 'recurrent_dropout': self.recurrent_dropout, - 'implementation': self.implementation + 'bias_constraint': + constraints.serialize(self.bias_constraint), + 'dropout': + self.dropout, + 'recurrent_dropout': + self.recurrent_dropout, + 'implementation': + self.implementation } base_config = super(GRU, self).get_config() del base_config['cell'] @@ -1638,48 +1651,33 @@ class LSTMCell(Layer): Arguments: units: Positive integer, dimensionality of the output space. - activation: Activation function to use - (see [activations](../activations.md)). - Default: hyperbolic tangent (`tanh`). - If you pass `None`, no activation is applied + activation: Activation function to use. + If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use - for the recurrent step - (see [activations](../activations.md)). - Default: hard sigmoid (`hard_sigmoid`). - If you pass `None`, no activation is applied - (ie. "linear" activation: `a(x) = x`). + for the recurrent step. use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. - (see [initializers](../initializers.md)). recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. - (see [initializers](../initializers.md)). - bias_initializer: Initializer for the bias vector - (see [initializers](../initializers.md)). + bias_initializer: Initializer for the bias vector. unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to - the `kernel` weights matrix - (see [regularizer](../regularizers.md)). + the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to - the `recurrent_kernel` weights matrix - (see [regularizer](../regularizers.md)). - bias_regularizer: Regularizer function applied to the bias vector - (see [regularizer](../regularizers.md)). + the `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. kernel_constraint: Constraint function applied to - the `kernel` weights matrix - (see [constraints](../constraints.md)). + the `kernel` weights matrix. recurrent_constraint: Constraint function applied to - the `recurrent_kernel` weights matrix - (see [constraints](../constraints.md)). - bias_constraint: Constraint function applied to the bias vector - (see [constraints](../constraints.md)). + the `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. @@ -1739,6 +1737,7 @@ class LSTMCell(Layer): self._dropout_mask = None self._recurrent_dropout_mask = None + @shape_type_conversion def build(self, input_shape): input_dim = input_shape[-1] self.kernel = self.add_weight( @@ -1798,36 +1797,22 @@ class LSTMCell(Layer): self.bias_o = None self.built = True - def _generate_dropout_mask(self, inputs, training=None): - if 0 < self.dropout < 1: - ones = K.ones_like(K.squeeze(inputs[:, 0:1, :], axis=1)) - - def dropped_inputs(): - return K.dropout(ones, self.dropout) - - self._dropout_mask = [ - K.in_train_phase(dropped_inputs, ones, training=training) - for _ in range(4) - ] - else: - self._dropout_mask = None - - def _generate_recurrent_dropout_mask(self, inputs, training=None): - if 0 < self.recurrent_dropout < 1: - ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) - ones = K.tile(ones, (1, self.units)) - - def dropped_inputs(): - return K.dropout(ones, self.dropout) - - self._recurrent_dropout_mask = [ - K.in_train_phase(dropped_inputs, ones, training=training) - for _ in range(4) - ] - else: - self._recurrent_dropout_mask = None - def call(self, inputs, states, training=None): + if 0 < self.dropout < 1 and self._dropout_mask is None: + self._dropout_mask = _generate_dropout_mask( + _generate_dropout_ones(inputs, + K.shape(inputs)[-1]), + self.dropout, + training=training, + count=4) + if (0 < self.recurrent_dropout < 1 and + self._recurrent_dropout_mask is None): + self._recurrent_dropout_mask = _generate_dropout_mask( + _generate_dropout_ones(inputs, self.units), + self.recurrent_dropout, + training=training, + count=4) + # dropout matrices for input units dp_mask = self._dropout_mask # dropout matrices for recurrent units @@ -1901,59 +1886,81 @@ class LSTMCell(Layer): h._uses_learning_phase = True return h, [h, c] + def get_config(self): + config = { + 'units': + self.units, + 'activation': + activations.serialize(self.activation), + 'recurrent_activation': + activations.serialize(self.recurrent_activation), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), + 'recurrent_initializer': + initializers.serialize(self.recurrent_initializer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'unit_forget_bias': + self.unit_forget_bias, + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), + 'recurrent_regularizer': + regularizers.serialize(self.recurrent_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), + 'recurrent_constraint': + constraints.serialize(self.recurrent_constraint), + 'bias_constraint': + constraints.serialize(self.bias_constraint), + 'dropout': + self.dropout, + 'recurrent_dropout': + self.recurrent_dropout, + 'implementation': + self.implementation + } + base_config = super(LSTMCell, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + class LSTM(RNN): - # pylint: disable=line-too-long """Long-Short Term Memory layer - Hochreiter 1997. Arguments: units: Positive integer, dimensionality of the output space. - activation: Activation function to use - (see [activations](../activations.md)). - Default: hyperbolic tangent (`tanh`). - If you pass `None`, no activation is applied + activation: Activation function to use. + If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use - for the recurrent step - (see [activations](../activations.md)). - Default: hyperbolic tangent (`tanh`). - Default: hard sigmoid (`hard_sigmoid`). - If you pass `None`, no activation is applied - (ie. "linear" activation: `a(x) = x`). + for the recurrent step. use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, - used for the linear transformation of the inputs. - (see [initializers](../initializers.md)). + used for the linear transformation of the inputs.. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, - used for the linear transformation of the recurrent state. - (see [initializers](../initializers.md)). - bias_initializer: Initializer for the bias vector - (see [initializers](../initializers.md)). + used for the linear transformation of the recurrent state.. + bias_initializer: Initializer for the bias vector. unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to - the `kernel` weights matrix - (see [regularizer](../regularizers.md)). + the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to - the `recurrent_kernel` weights matrix - (see [regularizer](../regularizers.md)). - bias_regularizer: Regularizer function applied to the bias vector - (see [regularizer](../regularizers.md)). + the `recurrent_kernel` weights matrix. + bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to - the output of the layer (its "activation"). - (see [regularizer](../regularizers.md)). + the output of the layer (its "activation").. kernel_constraint: Constraint function applied to - the `kernel` weights matrix - (see [constraints](../constraints.md)). + the `kernel` weights matrix. recurrent_constraint: Constraint function applied to - the `recurrent_kernel` weights matrix - (see [constraints](../constraints.md)). - bias_constraint: Constraint function applied to the bias vector - (see [constraints](../constraints.md)). + the `recurrent_kernel` weights matrix. + bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. @@ -1983,13 +1990,7 @@ class LSTM(RNN): although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. - References: - - [Long short-term memory](http://www.bioinf.jku.at/publications/older/2604.pdf) - - [Learning to forget: Continual prediction with LSTM](http://www.mitpressjournals.org/doi/pdf/10.1162/089976600300015015) - - [Supervised sequence labeling with recurrent neural networks](http://www.cs.toronto.edu/~graves/preprint.pdf) - - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) """ - # pylint: enable=line-too-long def __init__(self, units, @@ -2049,8 +2050,8 @@ class LSTM(RNN): self.activity_regularizer = regularizers.get(activity_regularizer) def call(self, inputs, mask=None, training=None, initial_state=None): - self.cell._generate_dropout_mask(inputs, training=training) - self.cell._generate_recurrent_dropout_mask(inputs, training=training) + self.cell._dropout_mask = None + self.cell._recurrent_dropout_mask = None return super(LSTM, self).call( inputs, mask=mask, training=training, initial_state=initial_state) @@ -2124,29 +2125,42 @@ class LSTM(RNN): def get_config(self): config = { - 'units': self.units, - 'activation': activations.serialize(self.activation), + 'units': + self.units, + 'activation': + activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), - 'use_bias': self.use_bias, - 'kernel_initializer': initializers.serialize(self.kernel_initializer), + 'use_bias': + self.use_bias, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), - 'bias_initializer': initializers.serialize(self.bias_initializer), - 'unit_forget_bias': self.unit_forget_bias, - 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'unit_forget_bias': + self.unit_forget_bias, + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), - 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), - 'kernel_constraint': constraints.serialize(self.kernel_constraint), + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), - 'bias_constraint': constraints.serialize(self.bias_constraint), - 'dropout': self.dropout, - 'recurrent_dropout': self.recurrent_dropout, - 'implementation': self.implementation + 'bias_constraint': + constraints.serialize(self.bias_constraint), + 'dropout': + self.dropout, + 'recurrent_dropout': + self.recurrent_dropout, + 'implementation': + self.implementation } base_config = super(LSTM, self).get_config() del base_config['cell'] @@ -2159,6 +2173,23 @@ class LSTM(RNN): return cls(**config) +def _generate_dropout_ones(inputs, dims): + return K.ones((K.shape(inputs)[0], dims)) + + +def _generate_dropout_mask(ones, rate, training=None, count=1): + + def dropped_inputs(): + return K.dropout(ones, rate) + + if count > 1: + return [ + K.in_train_phase(dropped_inputs, ones, training=training) + for _ in range(count) + ] + return K.in_train_phase(dropped_inputs, ones, training=training) + + class Recurrent(Layer): """Deprecated abstract base class for recurrent layers. @@ -2285,6 +2316,7 @@ class Recurrent(Layer): self.dropout = 0 self.recurrent_dropout = 0 + @shape_type_conversion def compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py b/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py index 7dc4c1db9b..a1407a24ea 100644 --- a/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/recurrent_test.py @@ -392,6 +392,105 @@ class RNNTest(test.TestCase): self.assertEqual(len(layer.trainable_weights), 3) self.assertEqual(len(layer.non_trainable_weights), 0) + def test_state_reuse_with_dropout(self): + layer_class = keras.layers.SimpleRNN + embedding_dim = 4 + units = 3 + timesteps = 2 + num_samples = 2 + + with self.test_session(): + input1 = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) + layer = layer_class(units, + return_state=True, + return_sequences=True, + dropout=0.2) + state = layer(input1)[1:] + + input2 = keras.Input(batch_shape=(num_samples, timesteps, embedding_dim)) + output = layer_class(units)(input2, initial_state=state) + model = keras.Model([input1, input2], output) + + inputs = [np.random.random((num_samples, timesteps, embedding_dim)), + np.random.random((num_samples, timesteps, embedding_dim))] + model.predict(inputs) + + def test_builtin_rnn_cell_serialization(self): + for cell_class in [keras.layers.SimpleRNNCell, + keras.layers.GRUCell, + keras.layers.LSTMCell]: + with self.test_session(): + # Test basic case. + x = keras.Input((None, 5)) + cell = cell_class(32) + layer = keras.layers.RNN(cell) + y = layer(x) + model = keras.models.Model(x, y) + model.compile(optimizer='rmsprop', loss='mse') + + # Test basic case serialization. + x_np = np.random.random((6, 5, 5)) + y_np = model.predict(x_np) + weights = model.get_weights() + config = layer.get_config() + layer = keras.layers.RNN.from_config(config) + y = layer(x) + model = keras.models.Model(x, y) + model.set_weights(weights) + y_np_2 = model.predict(x_np) + self.assertAllClose(y_np, y_np_2, atol=1e-4) + + # Test stacking. + cells = [cell_class(8), + cell_class(12), + cell_class(32)] + layer = keras.layers.RNN(cells) + y = layer(x) + model = keras.models.Model(x, y) + model.compile(optimizer='rmsprop', loss='mse') + + # Test stacked RNN serialization. + x_np = np.random.random((6, 5, 5)) + y_np = model.predict(x_np) + weights = model.get_weights() + config = layer.get_config() + layer = keras.layers.RNN.from_config(config) + y = layer(x) + model = keras.models.Model(x, y) + model.set_weights(weights) + y_np_2 = model.predict(x_np) + self.assertAllClose(y_np, y_np_2, atol=1e-4) + + def test_stacked_rnn_dropout(self): + cells = [keras.layers.LSTMCell(3, dropout=0.1, recurrent_dropout=0.1), + keras.layers.LSTMCell(3, dropout=0.1, recurrent_dropout=0.1)] + layer = keras.layers.RNN(cells) + + with self.test_session(): + x = keras.Input((None, 5)) + y = layer(x) + model = keras.models.Model(x, y) + model.compile('sgd', 'mse') + x_np = np.random.random((6, 5, 5)) + y_np = np.random.random((6, 3)) + model.train_on_batch(x_np, y_np) + + def test_stacked_rnn_compute_output_shape(self): + cells = [keras.layers.LSTMCell(3), + keras.layers.LSTMCell(6)] + embedding_dim = 4 + timesteps = 2 + layer = keras.layers.RNN(cells, return_state=True, return_sequences=True) + output_shape = layer.compute_output_shape((None, timesteps, embedding_dim)) + expected_output_shape = [(None, timesteps, 6), + (None, 6), + (None, 6), + (None, 3), + (None, 3)] + self.assertEqual( + [tuple(o.as_list()) for o in output_shape], + expected_output_shape) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers.py b/tensorflow/python/keras/_impl/keras/layers/wrappers.py index 452801b656..3667956f80 100644 --- a/tensorflow/python/keras/_impl/keras/layers/wrappers.py +++ b/tensorflow/python/keras/_impl/keras/layers/wrappers.py @@ -25,6 +25,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer +from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.layers import utils as tf_layers_util @@ -291,6 +292,7 @@ class Bidirectional(Wrapper): self.backward_layer.initial_weights = weights[nw // 2:] self.stateful = layer.stateful self.return_sequences = layer.return_sequences + self.return_state = layer.return_state self.supports_masking = True def get_weights(self): @@ -301,27 +303,54 @@ class Bidirectional(Wrapper): self.forward_layer.set_weights(weights[:nw // 2]) self.backward_layer.set_weights(weights[nw // 2:]) + @shape_type_conversion def compute_output_shape(self, input_shape): - input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) - if self.merge_mode in ['sum', 'ave', 'mul']: - return self.forward_layer.compute_output_shape(input_shape) - elif self.merge_mode == 'concat': - shape = self.forward_layer.compute_output_shape(input_shape).as_list() - shape[-1] *= 2 - return tensor_shape.TensorShape(shape) + output_shape = tuple(self.forward_layer.compute_output_shape( + input_shape).as_list()) + if self.return_state: + state_shape = output_shape[1:] + output_shape = output_shape[0] + + if self.merge_mode == 'concat': + output_shape = list(output_shape) + output_shape[-1] *= 2 + output_shape = tuple(output_shape) elif self.merge_mode is None: - shape = self.forward_layer.compute_output_shape(input_shape) - return [shape, copy.copy(shape)] + output_shape = [output_shape, copy.copy(output_shape)] - def call(self, inputs, training=None, mask=None): + if self.return_state: + if self.merge_mode is None: + return output_shape + state_shape + copy.copy(state_shape) + return [output_shape] + state_shape + copy.copy(state_shape) + return output_shape + + def call(self, inputs, training=None, mask=None, initial_state=None): kwargs = {} if has_arg(self.layer.call, 'training'): kwargs['training'] = training if has_arg(self.layer.call, 'mask'): kwargs['mask'] = mask - y = self.forward_layer.call(inputs, **kwargs) - y_rev = self.backward_layer.call(inputs, **kwargs) + if initial_state is not None and has_arg(self.layer.call, 'initial_state'): + if not isinstance(initial_state, list): + raise ValueError( + 'When passing `initial_state` to a Bidirectional RNN, the state ' + 'should be a list containing the states of the underlying RNNs. ' + 'Found: ' + str(initial_state)) + forward_state = initial_state[:len(initial_state) // 2] + backward_state = initial_state[len(initial_state) // 2:] + y = self.forward_layer.call(inputs, initial_state=forward_state, **kwargs) + y_rev = self.backward_layer.call( + inputs, initial_state=backward_state, **kwargs) + else: + y = self.forward_layer.call(inputs, **kwargs) + y_rev = self.backward_layer.call(inputs, **kwargs) + + if self.return_state: + states = y[1:] + y_rev[1:] + y = y[0] + y_rev = y_rev[0] + if self.return_sequences: y_rev = K.reverse(y_rev, 1) if self.merge_mode == 'concat': @@ -343,6 +372,11 @@ class Bidirectional(Wrapper): out._uses_learning_phase = True else: output._uses_learning_phase = True + + if self.return_state: + if self.merge_mode is None: + return output + states + return [output] + states return output def reset_states(self): diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py index 0866c4b0ae..f48c8919a1 100644 --- a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py @@ -238,6 +238,131 @@ class BidirectionalTest(test.TestCase): model.compile(loss='mse', optimizer='sgd') model.fit(x, y, epochs=1, batch_size=1) + def test_Bidirectional_merged_value(self): + rnn = keras.layers.LSTM + samples = 2 + dim = 5 + timesteps = 3 + units = 3 + x = [np.random.rand(samples, timesteps, dim)] + + with self.test_session(): + for merge_mode in ['sum', 'mul', 'ave', 'concat', None]: + if merge_mode == 'sum': + merge_func = lambda y, y_rev: y + y_rev + elif merge_mode == 'mul': + merge_func = lambda y, y_rev: y * y_rev + elif merge_mode == 'ave': + merge_func = lambda y, y_rev: (y + y_rev) / 2 + elif merge_mode == 'concat': + merge_func = lambda y, y_rev: np.concatenate((y, y_rev), axis=-1) + else: + merge_func = lambda y, y_rev: [y, y_rev] + + # basic case + inputs = keras.Input((timesteps, dim)) + layer = keras.layers.Bidirectional( + rnn(units, return_sequences=True), merge_mode=merge_mode) + f_merged = keras.backend.function([inputs], _to_list(layer(inputs))) + f_forward = keras.backend.function([inputs], + [layer.forward_layer.call(inputs)]) + f_backward = keras.backend.function( + [inputs], + [keras.backend.reverse(layer.backward_layer.call(inputs), 1)]) + + y_merged = f_merged(x) + y_expected = _to_list(merge_func(f_forward(x)[0], f_backward(x)[0])) + assert len(y_merged) == len(y_expected) + for x1, x2 in zip(y_merged, y_expected): + self.assertAllClose(x1, x2, atol=1e-5) + + # test return_state + inputs = keras.Input((timesteps, dim)) + layer = keras.layers.Bidirectional( + rnn(units, return_state=True), merge_mode=merge_mode) + f_merged = keras.backend.function([inputs], layer(inputs)) + f_forward = keras.backend.function([inputs], + layer.forward_layer.call(inputs)) + f_backward = keras.backend.function([inputs], + layer.backward_layer.call(inputs)) + n_states = len(layer.layer.states) + + y_merged = f_merged(x) + y_forward = f_forward(x) + y_backward = f_backward(x) + y_expected = _to_list(merge_func(y_forward[0], y_backward[0])) + assert len(y_merged) == len(y_expected) + n_states * 2 + for x1, x2 in zip(y_merged, y_expected): + self.assertAllClose(x1, x2, atol=1e-5) + + y_merged = y_merged[-n_states * 2:] + y_forward = y_forward[-n_states:] + y_backward = y_backward[-n_states:] + for state_birnn, state_inner in zip(y_merged, y_forward + y_backward): + self.assertAllClose(state_birnn, state_inner, atol=1e-5) + + def test_Bidirectional_dropout(self): + rnn = keras.layers.LSTM + samples = 2 + dim = 5 + timesteps = 3 + units = 3 + merge_mode = 'sum' + x = [np.random.rand(samples, timesteps, dim)] + + with self.test_session(): + inputs = keras.Input((timesteps, dim)) + wrapped = keras.layers.Bidirectional( + rnn(units, dropout=0.2, recurrent_dropout=0.2), merge_mode=merge_mode) + outputs = _to_list(wrapped(inputs, training=True)) + assert all(not getattr(x, '_uses_learning_phase') for x in outputs) + + inputs = keras.Input((timesteps, dim)) + wrapped = keras.layers.Bidirectional( + rnn(units, dropout=0.2, return_state=True), merge_mode=merge_mode) + outputs = _to_list(wrapped(inputs)) + assert all(x._uses_learning_phase for x in outputs) + + model = keras.Model(inputs, outputs) + assert model.uses_learning_phase + y1 = _to_list(model.predict(x)) + y2 = _to_list(model.predict(x)) + for x1, x2 in zip(y1, y2): + self.assertAllClose(x1, x2, atol=1e-5) + + def test_Bidirectional_state_reuse(self): + rnn = keras.layers.LSTM + samples = 2 + dim = 5 + timesteps = 3 + units = 3 + + with self.test_session(): + inputs = keras.Input((timesteps, dim)) + layer = keras.layers.Bidirectional( + rnn(units, return_state=True, return_sequences=True)) + outputs = layer(inputs) + output, state = outputs[0], outputs[1:] + + # test passing invalid initial_state: passing a tensor + with self.assertRaises(ValueError): + output = keras.layers.Bidirectional( + rnn(units))(output, initial_state=state[0]) + + # test valid usage: passing a list + output = keras.layers.Bidirectional( + rnn(units))(output, initial_state=state) + model = keras.Model(inputs, output) + inputs = np.random.rand(samples, timesteps, dim) + outputs = model.predict(inputs) + + +def _to_list(ls): + if isinstance(ls, list): + return ls + else: + return [ls] + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/losses.py b/tensorflow/python/keras/_impl/keras/losses.py index 1d6319abb1..fe0ef54360 100644 --- a/tensorflow/python/keras/_impl/keras/losses.py +++ b/tensorflow/python/keras/_impl/keras/losses.py @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Built-in Keras loss functions. +# pylint: disable=unused-import +"""Built-in loss functions. """ from __future__ import absolute_import from __future__ import division @@ -34,7 +35,6 @@ def mean_absolute_error(y_true, y_pred): def mean_absolute_percentage_error(y_true, y_pred): - # Equivalent to MAE, but sometimes easier to interpret. diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), None)) return 100. * K.mean(diff, axis=-1) @@ -56,10 +56,24 @@ def hinge(y_true, y_pred): def categorical_hinge(y_true, y_pred): pos = K.sum(y_true * y_pred, axis=-1) neg = K.max((1. - y_true) * y_pred, axis=-1) - return K.maximum(neg - pos + 1., 0.) + return K.maximum(0., neg - pos + 1.) def logcosh(y_true, y_pred): + """Logarithm of the hyperbolic cosine of the prediction error. + + `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and + to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly + like the mean squared error, but will not be so strongly affected by the + occasional wildly incorrect prediction. + + Arguments: + y_true: tensor of true targets. + y_pred: tensor of predicted targets. + + Returns: + Tensor with one scalar loss entry per sample. + """ def _logcosh(x): return x + K.softplus(-2. * x) - K.log(2.) diff --git a/tensorflow/python/keras/_impl/keras/metrics.py b/tensorflow/python/keras/_impl/keras/metrics.py index 202048f26d..3c18e68260 100644 --- a/tensorflow/python/keras/_impl/keras/metrics.py +++ b/tensorflow/python/keras/_impl/keras/metrics.py @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Built-in Keras metrics functions. +# pylint: disable=unused-import +"""Built-in metrics. """ from __future__ import absolute_import from __future__ import division @@ -21,7 +22,6 @@ from __future__ import print_function import six from tensorflow.python.keras._impl.keras import backend as K -# pylint: disable=unused-import from tensorflow.python.keras._impl.keras.losses import binary_crossentropy from tensorflow.python.keras._impl.keras.losses import categorical_crossentropy from tensorflow.python.keras._impl.keras.losses import cosine_proximity @@ -35,7 +35,6 @@ from tensorflow.python.keras._impl.keras.losses import mean_squared_logarithmic_ from tensorflow.python.keras._impl.keras.losses import poisson from tensorflow.python.keras._impl.keras.losses import sparse_categorical_crossentropy from tensorflow.python.keras._impl.keras.losses import squared_hinge -# pylint: disable=unused-import from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object @@ -60,8 +59,8 @@ def top_k_categorical_accuracy(y_true, y_pred, k=5): def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5): - return K.mean(K.in_top_k(y_pred, - K.cast(K.max(y_true, axis=-1), 'int32'), k), axis=-1) + return K.mean( + K.in_top_k(y_pred, K.cast(K.max(y_true, axis=-1), 'int32'), k), axis=-1) # Aliases diff --git a/tensorflow/python/keras/_impl/keras/models.py b/tensorflow/python/keras/_impl/keras/models.py index e262cc8c8e..9cd547200d 100644 --- a/tensorflow/python/keras/_impl/keras/models.py +++ b/tensorflow/python/keras/_impl/keras/models.py @@ -492,13 +492,13 @@ class Sequential(Model): # to the input layer we just created. layer(x) - if len(layer.inbound_nodes[-1].output_tensors) != 1: + if len(layer._inbound_nodes[-1].output_tensors) != 1: raise ValueError('All layers in a Sequential model ' 'should have a single output tensor. ' 'For multi-output layers, ' 'use the functional API.') - self.outputs = [layer.inbound_nodes[-1].output_tensors[0]] + self.outputs = [layer._inbound_nodes[-1].output_tensors[0]] self.inputs = topology.get_source_inputs(self.outputs[0]) # We create an input node, which we will keep updated diff --git a/tensorflow/python/keras/_impl/keras/models_test.py b/tensorflow/python/keras/_impl/keras/models_test.py index edfc0ce0eb..04017e4b28 100644 --- a/tensorflow/python/keras/_impl/keras/models_test.py +++ b/tensorflow/python/keras/_impl/keras/models_test.py @@ -340,6 +340,35 @@ class TestSequential(test.TestCase): inner_model.trainable = True self.assertEqual(len(model.trainable_weights), 4) + def test_sequential_update_disabling(self): + val_a = np.random.random((10, 4)) + val_out = np.random.random((10, 4)) + + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.BatchNormalization(input_shape=(4,))) + + model.trainable = False + assert not model.updates + + model.compile('sgd', 'mse') + assert not model.updates + assert not model.model.updates + + x1 = model.predict(val_a) + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + self.assertAllClose(x1, x2, atol=1e-7) + + model.trainable = True + model.compile('sgd', 'mse') + assert model.updates + assert model.model.updates + + model.train_on_batch(val_a, val_out) + x2 = model.predict(val_a) + assert np.abs(np.sum(x1 - x2)) > 1e-5 + class TestModelCloning(test.TestCase): diff --git a/tensorflow/python/keras/_impl/keras/optimizers.py b/tensorflow/python/keras/_impl/keras/optimizers.py index a08073fa86..e47987aadc 100644 --- a/tensorflow/python/keras/_impl/keras/optimizers.py +++ b/tensorflow/python/keras/_impl/keras/optimizers.py @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Keras optimizer classes (will eventually be replaced with core optimizers). +# pylint: disable=invalid-name +"""Built-in optimizer classes. """ from __future__ import absolute_import from __future__ import division @@ -121,9 +122,9 @@ class Optimizer(object): param_values = K.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: - raise ValueError('Optimizer weight shape ' + str(pv.shape) + - ' not compatible with ' - 'provided weight shape ' + str(w.shape)) + raise ValueError( + 'Optimizer weight shape ' + str(pv.shape) + ' not compatible with ' + 'provided weight shape ' + str(w.shape)) weight_value_tuples.append((p, w)) K.batch_set_value(weight_value_tuples) @@ -156,7 +157,8 @@ class SGD(Optimizer): Arguments: lr: float >= 0. Learning rate. - momentum: float >= 0. Parameter updates momentum. + momentum: float >= 0. Parameter that accelerates SGD + in the relevant direction and dampens oscillations. decay: float >= 0. Learning rate decay over each update. nesterov: boolean. Whether to apply Nesterov momentum. """ @@ -177,9 +179,8 @@ class SGD(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / (1. + self.decay * K.cast(self.iterations, - K.dtype(self.decay)))) - + lr *= (1. / + (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) # momentum shapes = [K.int_shape(p) for p in params] moments = [K.zeros(shape) for shape in shapes] @@ -224,32 +225,33 @@ class RMSprop(Optimizer): Arguments: lr: float >= 0. Learning rate. rho: float >= 0. - epsilon: float >= 0. Fuzz factor. + epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`. decay: float >= 0. Learning rate decay over each update. + """ - def __init__(self, lr=0.001, rho=0.9, epsilon=1e-8, decay=0., **kwargs): + def __init__(self, lr=0.001, rho=0.9, epsilon=None, decay=0., **kwargs): super(RMSprop, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.lr = K.variable(lr, name='lr') self.rho = K.variable(rho, name='rho') self.decay = K.variable(decay, name='decay') self.iterations = K.variable(0, dtype='int64', name='iterations') + if epsilon is None: + epsilon = K.epsilon() self.epsilon = epsilon self.initial_decay = decay def get_updates(self, loss, params): grads = self.get_gradients(loss, params) - accumulators = [ - K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params - ] + accumulators = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params] self.weights = accumulators self.updates = [K.update_add(self.iterations, 1)] lr = self.lr if self.initial_decay > 0: - lr *= (1. / (1. + self.decay * K.cast(self.iterations, - K.dtype(self.decay)))) + lr *= (1. / + (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) for p, g, a in zip(params, grads, accumulators): # update accumulator @@ -283,20 +285,19 @@ class Adagrad(Optimizer): Arguments: lr: float >= 0. Learning rate. - epsilon: float >= 0. + epsilon: float >= 0. If `None`, defaults to `K.epsilon()`. decay: float >= 0. Learning rate decay over each update. - References: - - [Adaptive Subgradient Methods for Online Learning and Stochastic - Optimization](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) """ - def __init__(self, lr=0.01, epsilon=1e-8, decay=0., **kwargs): + def __init__(self, lr=0.01, epsilon=None, decay=0., **kwargs): super(Adagrad, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.lr = K.variable(lr, name='lr') self.decay = K.variable(decay, name='decay') self.iterations = K.variable(0, dtype='int64', name='iterations') + if epsilon is None: + epsilon = K.epsilon() self.epsilon = epsilon self.initial_decay = decay @@ -309,8 +310,8 @@ class Adagrad(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / (1. + self.decay * K.cast(self.iterations, - K.dtype(self.decay)))) + lr *= (1. / + (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) for p, g, a in zip(params, grads, accumulators): new_a = a + K.square(g) # update accumulator @@ -344,20 +345,19 @@ class Adadelta(Optimizer): lr: float >= 0. Learning rate. It is recommended to leave it at the default value. rho: float >= 0. - epsilon: float >= 0. Fuzz factor. + epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`. decay: float >= 0. Learning rate decay over each update. - References: - - [Adadelta - an adaptive learning rate - method](http://arxiv.org/abs/1212.5701) """ - def __init__(self, lr=1.0, rho=0.95, epsilon=1e-8, decay=0., **kwargs): + def __init__(self, lr=1.0, rho=0.95, epsilon=None, decay=0., **kwargs): super(Adadelta, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.lr = K.variable(lr, name='lr') self.decay = K.variable(decay, name='decay') self.iterations = K.variable(0, dtype='int64', name='iterations') + if epsilon is None: + epsilon = K.epsilon() self.rho = rho self.epsilon = epsilon self.initial_decay = decay @@ -372,8 +372,8 @@ class Adadelta(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / (1. + self.decay * K.cast(self.iterations, - K.dtype(self.decay)))) + lr *= (1. / + (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) for p, g, a, d_a in zip(params, grads, accumulators, delta_accumulators): # update accumulator @@ -415,20 +415,21 @@ class Adam(Optimizer): lr: float >= 0. Learning rate. beta_1: float, 0 < beta < 1. Generally close to 1. beta_2: float, 0 < beta < 1. Generally close to 1. - epsilon: float >= 0. Fuzz factor. + epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`. decay: float >= 0. Learning rate decay over each update. + amsgrad: boolean. Whether to apply the AMSGrad variant of this + algorithm from the paper "On the Convergence of Adam and + Beyond". - References: - - [Adam - A Method for Stochastic - Optimization](http://arxiv.org/abs/1412.6980v8) """ def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, - epsilon=1e-8, + epsilon=None, decay=0., + amsgrad=False, **kwargs): super(Adam, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): @@ -437,8 +438,11 @@ class Adam(Optimizer): self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') + if epsilon is None: + epsilon = K.epsilon() self.epsilon = epsilon self.initial_decay = decay + self.amsgrad = amsgrad def get_updates(self, loss, params): grads = self.get_gradients(loss, params) @@ -446,21 +450,30 @@ class Adam(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / (1. + self.decay * K.cast(self.iterations, - K.dtype(self.decay)))) + lr *= (1. / + (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) t = K.cast(self.iterations, K.floatx()) + 1 - lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) / - (1. - K.pow(self.beta_1, t))) + lr_t = lr * ( + K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t))) ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params] vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params] - self.weights = [self.iterations] + ms + vs + if self.amsgrad: + vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params] + else: + vhats = [K.zeros(1) for _ in params] + self.weights = [self.iterations] + ms + vs + vhats - for p, g, m, v in zip(params, grads, ms, vs): + for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats): m_t = (self.beta_1 * m) + (1. - self.beta_1) * g v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g) - p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon) + if self.amsgrad: + vhat_t = K.maximum(vhat, v_t) + p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon) + self.updates.append(K.update(vhat, vhat_t)) + else: + p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon) self.updates.append(K.update(m, m_t)) self.updates.append(K.update(v, v_t)) @@ -479,7 +492,8 @@ class Adam(Optimizer): 'beta_1': float(K.get_value(self.beta_1)), 'beta_2': float(K.get_value(self.beta_2)), 'decay': float(K.get_value(self.decay)), - 'epsilon': self.epsilon + 'epsilon': self.epsilon, + 'amsgrad': self.amsgrad } base_config = super(Adam, self).get_config() return dict(list(base_config.items()) + list(config.items())) @@ -494,19 +508,16 @@ class Adamax(Optimizer): Arguments: lr: float >= 0. Learning rate. beta_1/beta_2: floats, 0 < beta < 1. Generally close to 1. - epsilon: float >= 0. Fuzz factor. + epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`. decay: float >= 0. Learning rate decay over each update. - References: - - [Adam - A Method for Stochastic - Optimization](http://arxiv.org/abs/1412.6980v8) """ def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, - epsilon=1e-8, + epsilon=None, decay=0., **kwargs): super(Adamax, self).__init__(**kwargs) @@ -516,6 +527,8 @@ class Adamax(Optimizer): self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') + if epsilon is None: + epsilon = K.epsilon() self.epsilon = epsilon self.initial_decay = decay @@ -525,8 +538,8 @@ class Adamax(Optimizer): lr = self.lr if self.initial_decay > 0: - lr *= (1. / (1. + self.decay * K.cast(self.iterations, - K.dtype(self.decay)))) + lr *= (1. / + (1. + self.decay * K.cast(self.iterations, K.dtype(self.decay)))) t = K.cast(self.iterations, K.floatx()) + 1 lr_t = lr / (1. - K.pow(self.beta_1, t)) @@ -580,19 +593,15 @@ class Nadam(Optimizer): Arguments: lr: float >= 0. Learning rate. beta_1/beta_2: floats, 0 < beta < 1. Generally close to 1. - epsilon: float >= 0. Fuzz factor. + epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`. - References: - - [Nadam report](http://cs229.stanford.edu/proj2015/054_report.pdf) - - [On the importance of initialization and momentum in deep - learning](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf) """ def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, - epsilon=1e-8, + epsilon=None, schedule_decay=0.004, **kwargs): super(Nadam, self).__init__(**kwargs) @@ -602,12 +611,15 @@ class Nadam(Optimizer): self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') + if epsilon is None: + epsilon = K.epsilon() self.epsilon = epsilon self.schedule_decay = schedule_decay def get_updates(self, loss, params): grads = self.get_gradients(loss, params) self.updates = [K.update_add(self.iterations, 1)] + t = K.cast(self.iterations, K.floatx()) + 1 # Due to the recommendations in [2], i.e. warming momentum schedule @@ -691,7 +703,6 @@ class TFOptimizer(Optimizer): # Aliases. -# pylint: disable=invalid-name sgd = SGD rmsprop = RMSprop adagrad = Adagrad @@ -700,8 +711,6 @@ adam = Adam adamax = Adamax nadam = Nadam -# pylint: enable=invalid-name - def serialize(optimizer): return serialize_keras_object(optimizer) diff --git a/tensorflow/python/keras/_impl/keras/optimizers_test.py b/tensorflow/python/keras/_impl/keras/optimizers_test.py index 6e9e4e6c99..57636afbf0 100644 --- a/tensorflow/python/keras/_impl/keras/optimizers_test.py +++ b/tensorflow/python/keras/_impl/keras/optimizers_test.py @@ -102,6 +102,7 @@ class KerasOptimizersTest(test.TestCase): with self.test_session(): _test_optimizer(keras.optimizers.Adam()) _test_optimizer(keras.optimizers.Adam(decay=1e-3)) + _test_optimizer(keras.optimizers.Adam(amsgrad=True)) def test_adamax(self): with self.test_session(): diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/image.py b/tensorflow/python/keras/_impl/keras/preprocessing/image.py index 82441de592..db1fdd4e6b 100644 --- a/tensorflow/python/keras/_impl/keras/preprocessing/image.py +++ b/tensorflow/python/keras/_impl/keras/preprocessing/image.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# pylint: disable=g-import-not-at-top """Fairly basic set of tools for real-time data augmentation on image data. Can easily be extended to include new transformations, @@ -28,25 +29,22 @@ import re import threading import numpy as np -from six.moves import range # pylint: disable=redefined-builtin - from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence from tensorflow.python.platform import tf_logging as logging - -# pylint: disable=g-import-not-at-top -try: - from PIL import Image as pil_image -except ImportError: - pil_image = None try: from scipy import linalg import scipy.ndimage as ndi except ImportError: linalg = None ndi = None -# pylint: enable=g-import-not-at-top + + +try: + from PIL import Image as pil_image +except ImportError: + pil_image = None if pil_image is not None: _PIL_INTERPOLATION_METHODS = { @@ -88,7 +86,7 @@ def random_rotation(x, Returns: Rotated Numpy image tensor. """ - theta = np.pi / 180 * np.random.uniform(-rg, rg) + theta = np.deg2rad(np.random.uniform(-rg, rg)) rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]) @@ -145,7 +143,7 @@ def random_shear(x, Arguments: x: Input tensor. Must be 3D. - intensity: Transformation intensity. + intensity: Transformation intensity in degrees. row_axis: Index of axis for rows in the input tensor. col_axis: Index of axis for columns in the input tensor. channel_axis: Index of axis for channels in the input tensor. @@ -158,7 +156,7 @@ def random_shear(x, Returns: Sheared Numpy image tensor. """ - shear = np.random.uniform(-intensity, intensity) + shear = np.deg2rad(np.random.uniform(-intensity, intensity)) shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], [0, 0, 1]]) @@ -188,8 +186,10 @@ def random_zoom(x, (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). cval: Value used for points outside the boundaries of the input if `mode='constant'`. + Returns: Zoomed Numpy image tensor. + Raises: ValueError: if `zoom_range` isn't a tuple. """ @@ -366,7 +366,7 @@ def load_img(path, grayscale=False, target_size=None, interpolation='nearest'): grayscale: Boolean, whether to load the image as grayscale. target_size: Either `None` (default to original size) or tuple of ints `(img_height, img_width)`. - interpolation: Interpolation method used to resample the image if the + interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. Supported methods are "nearest", "bilinear", and "bicubic". If PIL version 1.1.3 or newer is installed, "lanczos" is also @@ -394,11 +394,9 @@ def load_img(path, grayscale=False, target_size=None, interpolation='nearest'): width_height_tuple = (target_size[1], target_size[0]) if img.size != width_height_tuple: if interpolation not in _PIL_INTERPOLATION_METHODS: - raise ValueError( - 'Invalid interpolation method {} specified. Supported ' - 'methods are {}'.format( - interpolation, - ', '.join(_PIL_INTERPOLATION_METHODS.keys()))) + raise ValueError('Invalid interpolation method {} specified. Supported ' + 'methods are {}'.format(interpolation, ', '.join( + _PIL_INTERPOLATION_METHODS.keys()))) resample = _PIL_INTERPOLATION_METHODS[interpolation] img = img.resize(width_height_tuple, resample) return img @@ -407,7 +405,8 @@ def load_img(path, grayscale=False, target_size=None, interpolation='nearest'): def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'): return [ os.path.join(root, f) - for root, _, files in os.walk(directory) for f in files + for root, _, files in os.walk(directory) + for f in files if re.match(r'([\w]+\.(?:' + ext + '))', f) ] @@ -423,9 +422,9 @@ class ImageDataGenerator(object): zca_whitening: apply ZCA whitening. zca_epsilon: epsilon for ZCA whitening. Default is 1e-6. rotation_range: degrees (0 to 180). - width_shift_range: fraction of total width. - height_shift_range: fraction of total height. - shear_range: shear intensity (shear angle in radians). + width_shift_range: fraction of total width, if < 1, or pixels if >= 1. + height_shift_range: fraction of total height, if < 1, or pixels if >= 1. + shear_range: shear intensity (shear angle in degrees). zoom_range: amount of zoom. if scalar z, zoom will be randomly picked in the range [1-z, 1+z]. A sequence of two can be passed instead to select this range. @@ -433,6 +432,12 @@ class ImageDataGenerator(object): fill_mode: points outside the boundaries are filled according to the given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default is 'nearest'. + Points outside the boundaries of the input are filled according to the + given mode: + 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k) + 'nearest': aaaaaaaa|abcd|dddddddd + 'reflect': abcddcba|abcd|dcbaabcd + 'wrap': abcdabcd|abcd|abcdabcd cval: value used for points outside the boundaries when fill_mode is 'constant'. Default is 0. horizontal_flip: whether to randomly flip images horizontally. @@ -522,6 +527,32 @@ class ImageDataGenerator(object): raise ValueError('`zoom_range` should be a float or ' 'a tuple or list of two floats. ' 'Received arg: ', zoom_range) + if zca_whitening: + if not featurewise_center: + self.featurewise_center = True + logging.warning('This ImageDataGenerator specifies ' + '`zca_whitening`, which overrides ' + 'setting of `featurewise_center`.') + if featurewise_std_normalization: + self.featurewise_std_normalization = False + logging.warning('This ImageDataGenerator specifies ' + '`zca_whitening` ' + 'which overrides setting of' + '`featurewise_std_normalization`.') + if featurewise_std_normalization: + if not featurewise_center: + self.featurewise_center = True + logging.warning('This ImageDataGenerator specifies ' + '`featurewise_std_normalization`, ' + 'which overrides setting of ' + '`featurewise_center`.') + if samplewise_std_normalization: + if not samplewise_center: + self.samplewise_center = True + logging.warning('This ImageDataGenerator specifies ' + '`samplewise_std_normalization`, ' + 'which overrides setting of ' + '`samplewise_center`.') def flow(self, x, @@ -591,7 +622,7 @@ class ImageDataGenerator(object): if self.samplewise_center: x -= np.mean(x, keepdims=True) if self.samplewise_std_normalization: - x /= np.std(x, keepdims=True) + 1e-7 + x /= (np.std(x, keepdims=True) + K.epsilon()) if self.featurewise_center: if self.mean is not None: @@ -603,7 +634,7 @@ class ImageDataGenerator(object): 'first by calling `.fit(numpy_data)`.') if self.featurewise_std_normalization: if self.std is not None: - x /= (self.std + 1e-7) + x /= (self.std + K.epsilon()) else: logging.warning('This ImageDataGenerator specifies ' '`featurewise_std_normalization`, but it hasn\'t ' @@ -636,7 +667,6 @@ class ImageDataGenerator(object): """ if ndi is None: raise ImportError('Scipy is required for image transformations.') - # x is a single image, so it doesn't have image number at index 0 img_row_axis = self.row_axis - 1 img_col_axis = self.col_axis - 1 @@ -648,25 +678,27 @@ class ImageDataGenerator(object): # use composition of homographies # to generate final transform that needs to be applied if self.rotation_range: - theta = np.pi / 180 * np.random.uniform(-self.rotation_range, - self.rotation_range) + theta = np.deg2rad( + np.random.uniform(-self.rotation_range, self.rotation_range)) else: theta = 0 if self.height_shift_range: - tx = np.random.uniform(-self.height_shift_range, - self.height_shift_range) * x.shape[img_row_axis] + tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) + if self.height_shift_range < 1: + tx *= x.shape[img_row_axis] else: tx = 0 if self.width_shift_range: - ty = np.random.uniform(-self.width_shift_range, - self.width_shift_range) * x.shape[img_col_axis] + ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) + if self.width_shift_range < 1: + ty *= x.shape[img_col_axis] else: ty = 0 if self.shear_range: - shear = np.random.uniform(-self.shear_range, self.shear_range) + shear = np.deg2rad(np.random.uniform(-self.shear_range, self.shear_range)) else: shear = 0 @@ -744,7 +776,7 @@ class ImageDataGenerator(object): if x.ndim != 4: raise ValueError('Input to `.fit()` should have rank 4. ' 'Got array with shape: ' + str(x.shape)) - if x.shape[self.channel_axis] not in {3, 4}: + if x.shape[self.channel_axis] not in {1, 3, 4}: logging.warning( 'Expected input to be images (as Numpy array) ' 'following the data format convention "' + self.data_format + '" ' @@ -784,10 +816,12 @@ class ImageDataGenerator(object): raise ImportError('Scipy is required for zca_whitening.') flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3])) - sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0] - u, s, _ = linalg.svd(sigma) - self.principal_components = np.dot( - np.dot(u, np.diag(1. / np.sqrt(s + self.zca_epsilon))), u.T) + num_examples = flat_x.shape[0] + _, s, vt = linalg.svd(flat_x / np.sqrt(num_examples)) + s_expand = np.hstack( + (s, np.zeros(vt.shape[0] - num_examples, dtype=flat_x.dtype))) + self.principal_components = ( + vt.T / np.sqrt(s_expand**2 + self.zca_epsilon)).dot(vt) class Iterator(Sequence): @@ -797,10 +831,10 @@ class Iterator(Sequence): method. Arguments: - n: Integer, total number of samples in the dataset to loop over. - batch_size: Integer, size of a batch. - shuffle: Boolean, whether to shuffle the data between epochs. - seed: Random seeding for data shuffling. + n: Integer, total number of samples in the dataset to loop over. + batch_size: Integer, size of a batch. + shuffle: Boolean, whether to shuffle the data between epochs. + seed: Random seeding for data shuffling. """ def __init__(self, n, batch_size, shuffle, seed): @@ -823,15 +857,14 @@ class Iterator(Sequence): if idx >= len(self): raise ValueError('Asked to retrieve element {idx}, ' 'but the Sequence ' - 'has length {length}'.format(idx=idx, - length=len(self))) + 'has length {length}'.format(idx=idx, length=len(self))) if self.seed is not None: np.random.seed(self.seed + self.total_batches_seen) self.total_batches_seen += 1 if self.index_array is None: self._set_index_array() - index_array = self.index_array[self.batch_size * idx:self.batch_size * - (idx + 1)] + index_array = self.index_array[self.batch_size * idx:self.batch_size * ( + idx + 1)] return self._get_batches_of_transformed_samples(index_array) def __len__(self): @@ -873,6 +906,7 @@ class Iterator(Sequence): Arguments: index_array: array of sample indices to include in batch. + Returns: A batch of transformed samples. """ @@ -948,8 +982,8 @@ class NumpyArrayIterator(Iterator): seed) def _get_batches_of_transformed_samples(self, index_array): - batch_x = np.zeros(tuple([len(index_array)] + list(self.x.shape)[1:]), - dtype=K.floatx()) + batch_x = np.zeros( + tuple([len(index_array)] + list(self.x.shape)[1:]), dtype=K.floatx()) for i, j in enumerate(index_array): x = self.x[j] x = self.image_data_generator.random_transform(x.astype(K.floatx())) @@ -959,7 +993,9 @@ class NumpyArrayIterator(Iterator): for i, j in enumerate(index_array): img = array_to_img(batch_x[i], self.data_format, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format( - prefix=self.save_prefix, index=j, hash=np.random.randint(1e4), + prefix=self.save_prefix, + index=j, + hash=np.random.randint(1e4), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) if self.y is None: @@ -984,10 +1020,11 @@ class NumpyArrayIterator(Iterator): def _count_valid_files_in_directory(directory, white_list_formats, follow_links): - """Count files with extension in `white_list_formats` in a directory. + """Count files with extension in `white_list_formats` contained in directory. Arguments: - directory: absolute path to the directory containing files to be counted + directory: absolute path to the directory + containing files to be counted white_list_formats: set of strings containing allowed extensions for the files to be counted. follow_links: boolean. @@ -1003,7 +1040,7 @@ def _count_valid_files_in_directory(directory, white_list_formats, samples = 0 for _, _, files in _recursive_list(directory): - for fname in sorted(files): + for fname in files: is_valid = False for extension in white_list_formats: if fname.lower().endswith('.' + extension): @@ -1043,7 +1080,7 @@ def _list_valid_filenames_in_directory(directory, white_list_formats, subdir = os.path.basename(directory) basedir = os.path.dirname(directory) for root, _, files in _recursive_list(directory): - for fname in files: + for fname in sorted(files): is_valid = False for extension in white_list_formats: if fname.lower().endswith('.' + extension): @@ -1167,8 +1204,8 @@ class DirectoryIterator(Iterator): white_list_formats=white_list_formats, follow_links=follow_links) self.samples = sum( - pool.map(function_partial, (os.path.join(directory, subdir) - for subdir in classes))) + pool.map(function_partial, + (os.path.join(directory, subdir) for subdir in classes))) print('Found %d images belonging to %d classes.' % (self.samples, self.num_classes)) @@ -1181,8 +1218,9 @@ class DirectoryIterator(Iterator): i = 0 for dirpath in (os.path.join(directory, subdir) for subdir in classes): results.append( - pool.apply_async(_list_valid_filenames_in_directory, ( - dirpath, white_list_formats, self.class_indices, follow_links))) + pool.apply_async( + _list_valid_filenames_in_directory, + (dirpath, white_list_formats, self.class_indices, follow_links))) for res in results: classes, filenames = res.get() self.classes[i:i + len(classes)] = classes @@ -1199,10 +1237,11 @@ class DirectoryIterator(Iterator): # build batch of image data for i, j in enumerate(index_array): fname = self.filenames[j] - img = load_img(os.path.join(self.directory, fname), - grayscale=grayscale, - target_size=self.target_size, - interpolation=self.interpolation) + img = load_img( + os.path.join(self.directory, fname), + grayscale=grayscale, + target_size=self.target_size, + interpolation=self.interpolation) x = img_to_array(img, data_format=self.data_format) x = self.image_data_generator.random_transform(x) x = self.image_data_generator.standardize(x) @@ -1212,7 +1251,9 @@ class DirectoryIterator(Iterator): for i, j in enumerate(index_array): img = array_to_img(batch_x[i], self.data_format, scale=True) fname = '{prefix}_{index}_{hash}.{format}'.format( - prefix=self.save_prefix, index=j, hash=np.random.randint(1e7), + prefix=self.save_prefix, + index=j, + hash=np.random.randint(1e7), format=self.save_format) img.save(os.path.join(self.save_to_dir, fname)) # build batch of labels @@ -1241,4 +1282,3 @@ class DirectoryIterator(Iterator): # The transformation of images is not under thread lock # so it can be done in parallel return self._get_batches_of_transformed_samples(index_array) - diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py b/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py index 642f4f2fac..4d59250af0 100644 --- a/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py +++ b/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Preprocessing utilities for sequence data. +"""Utilities for preprocessing sequence data. """ from __future__ import absolute_import from __future__ import division @@ -129,7 +129,7 @@ def make_sampling_table(size, sampling_factor=1e-5): is the probability that a word of rank i should be sampled. """ gamma = 0.577 - rank = np.array(list(range(size))) + rank = np.arange(size) rank[0] = 1 inv_fq = rank * (np.log(rank) + gamma) + 0.5 - 1. / (12. * rank) f = sampling_factor * inv_fq @@ -170,7 +170,7 @@ def skipgrams(sequence, if True labels will be categorical eg. [[1,0],[0,1],[0,1] .. ] sampling_table: 1D array of size `vocabulary_size` where the entry i encodes the probability to sample a word of rank i. - seed: Random seed. + seed: random seed. Returns: couples, labels: where `couples` are int pairs and @@ -224,3 +224,22 @@ def skipgrams(sequence, random.shuffle(labels) return couples, labels + + +def _remove_long_seq(maxlen, seq, label): + """Removes sequences that exceed the maximum length. + + Arguments: + maxlen: int, maximum length + seq: list of lists where each sublist is a sequence + label: list where each element is an integer + + Returns: + new_seq, new_label: shortened lists for `seq` and `label`. + """ + new_seq, new_label = [], [] + for x, y in zip(seq, label): + if len(x) < maxlen: + new_seq.append(x) + new_label.append(y) + return new_seq, new_label diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/text.py b/tensorflow/python/keras/_impl/keras/preprocessing/text.py index 47e5aa064f..8f7f25dc0a 100644 --- a/tensorflow/python/keras/_impl/keras/preprocessing/text.py +++ b/tensorflow/python/keras/_impl/keras/preprocessing/text.py @@ -13,8 +13,6 @@ # limitations under the License. # ============================================================================== """Utilities for text input preprocessing. - -May benefit from a fast Cython rewrite. """ from __future__ import absolute_import from __future__ import division @@ -29,6 +27,9 @@ import numpy as np from six.moves import range # pylint: disable=redefined-builtin from six.moves import zip # pylint: disable=redefined-builtin +from tensorflow.python.platform import tf_logging as logging + + if sys.version_info < (3,): maketrans = string.maketrans else: @@ -68,6 +69,21 @@ def one_hot(text, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=' '): + """One-hot encodes a text into a list of word indexes of size n. + + This is a wrapper to the `hashing_trick` function using `hash` as the + hashing function; unicity of word to index mapping non-guaranteed. + + Arguments: + text: Input text (string). + n: Dimension of the hashing space. + filters: Sequence of characters to filter out. + lower: Whether to convert the input to lowercase. + split: Sentence split marker (string). + + Returns: + A list of integer word indices (unicity non-guaranteed). + """ return hashing_trick( text, n, hash_function=hash, filters=filters, lower=lower, split=split) @@ -99,6 +115,10 @@ def hashing_trick(text, Two or more words may be assigned to the same index, due to possible collisions by the hashing function. + The + probability + of a collision is in relation to the dimension of the hashing space and + the number of distinct objects. """ if hash_function is None: hash_function = hash @@ -127,6 +147,8 @@ class Tokenizer(object): lower: boolean. Whether to convert the texts to lowercase. split: character or string to use for token splitting. char_level: if True, every character will be treated as a token. + oov_token: if given, it will be added to word_index and used to + replace out-of-vocabulary words during text_to_sequence calls By default, all punctuation is removed, turning the texts into space-separated sequences of words @@ -141,7 +163,17 @@ class Tokenizer(object): filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=True, split=' ', - char_level=False): + char_level=False, + oov_token=None, + **kwargs): + # Legacy support + if 'nb_words' in kwargs: + logging.warning('The `nb_words` argument in `Tokenizer` ' + 'has been renamed `num_words`.') + num_words = kwargs.pop('nb_words') + if kwargs: + raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) + self.word_counts = OrderedDict() self.word_docs = {} self.filters = filters @@ -150,6 +182,7 @@ class Tokenizer(object): self.num_words = num_words self.document_count = 0 self.char_level = char_level + self.oov_token = oov_token def fit_on_texts(self, texts): """Updates internal vocabulary based on a list of texts. @@ -181,7 +214,13 @@ class Tokenizer(object): sorted_voc = [wc[0] for wc in wcounts] # note that index 0 is reserved, never assigned to an existing word self.word_index = dict( - list(zip(sorted_voc, list(range(1, len(sorted_voc) + 1))))) + list(zip(sorted_voc, list(range(1, + len(sorted_voc) + 1))))) + + if self.oov_token is not None: + i = self.word_index.get(self.oov_token) + if i is None: + self.word_index[self.oov_token] = len(self.word_index) + 1 self.index_docs = {} for w, c in list(self.word_docs.items()): @@ -248,6 +287,10 @@ class Tokenizer(object): continue else: vect.append(i) + elif self.oov_token is not None: + i = self.word_index.get(self.oov_token) + if i is not None: + vect.append(i) yield vect def texts_to_matrix(self, texts, mode='binary'): diff --git a/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py b/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py index 17ab48ba3f..a934e331c4 100644 --- a/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py +++ b/tensorflow/python/keras/_impl/keras/preprocessing/text_test.py @@ -76,6 +76,22 @@ class TestText(test.TestCase): self.assertLessEqual(np.max(encoded), 4) self.assertGreaterEqual(np.min(encoded), 1) + def test_tokenizer_oov_flag(self): + x_train = ['This text has only known words'] + x_test = ['This text has some unknown words'] # 2 OOVs: some, unknown + + # Defalut, without OOV flag + tokenizer = keras.preprocessing.text.Tokenizer() + tokenizer.fit_on_texts(x_train) + x_test_seq = tokenizer.texts_to_sequences(x_test) + assert len(x_test_seq[0]) == 4 # discards 2 OOVs + + # With OOV feature + tokenizer = keras.preprocessing.text.Tokenizer(oov_token='') + tokenizer.fit_on_texts(x_train) + x_test_seq = tokenizer.texts_to_sequences(x_test) + assert len(x_test_seq[0]) == 6 # OOVs marked in place + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/_impl/keras/regularizers.py b/tensorflow/python/keras/_impl/keras/regularizers.py index 161ff9bf5b..c53ee8a1ae 100644 --- a/tensorflow/python/keras/_impl/keras/regularizers.py +++ b/tensorflow/python/keras/_impl/keras/regularizers.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Keras built-in regularizers. +"""Built-in regularizers. """ from __future__ import absolute_import from __future__ import division diff --git a/tensorflow/python/keras/_impl/keras/utils/data_utils.py b/tensorflow/python/keras/_impl/keras/utils/data_utils.py index d9e8f37e36..fcee9fbcc3 100644 --- a/tensorflow/python/keras/_impl/keras/utils/data_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/data_utils.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,12 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# pylint: disable=g-import-not-at-top """Utilities for file download and caching.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from abc import abstractmethod +from contextlib import closing import hashlib import multiprocessing from multiprocessing.pool import ThreadPool @@ -38,12 +40,12 @@ from six.moves.urllib.error import URLError from six.moves.urllib.request import urlopen from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar -from tensorflow.python.util.tf_export import tf_export + try: - import queue # pylint:disable=g-import-not-at-top + import queue except ImportError: - import Queue as queue # pylint:disable=g-import-not-at-top + import Queue as queue if sys.version_info[0] == 2: @@ -87,7 +89,7 @@ if sys.version_info[0] == 2: for chunk in chunk_read(response, reporthook=reporthook): fd.write(chunk) else: - from six.moves.urllib.request import urlretrieve # pylint: disable=g-import-not-at-top + from six.moves.urllib.request import urlretrieve def _extract_archive(file_path, path='.', archive_format='auto'): @@ -136,7 +138,6 @@ def _extract_archive(file_path, path='.', archive_format='auto'): return False -@tf_export('keras.utils.get_file') def get_file(fname, origin, untar=False, @@ -188,7 +189,7 @@ def get_file(fname, Path to the downloaded file """ if cache_dir is None: - cache_dir = os.path.expanduser(os.path.join('~', '.keras')) + cache_dir = os.path.join(os.path.expanduser('~'), '.keras') if md5_hash is not None and file_hash is None: file_hash = md5_hash hash_algorithm = 'md5' @@ -317,37 +318,46 @@ def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535): return False -@tf_export('keras.utils.Sequence') class Sequence(object): """Base object for fitting to a sequence of data, such as a dataset. Every `Sequence` must implements the `__getitem__` and the `__len__` methods. If you want to modify your dataset between epochs you may implement - `on_epoch_end`. The method `__getitem__` should return a complete batch. + `on_epoch_end`. + The method `__getitem__` should return a complete batch. + + # Notes - Notes: `Sequence` are a safer way to do multiprocessing. This structure guarantees - that the network will only train once on each sample per epoch which is not - the case with generators. + that the network will only train once + on each sample per epoch which is not the case with generators. + Examples: + ```python from skimage.io import imread from skimage.transform import resize import numpy as np import math + # Here, `x_set` is list of path to the images # and `y_set` are the associated classes. + class CIFAR10Sequence(Sequence): + def __init__(self, x_set, y_set, batch_size): self.x, self.y = x_set, y_set self.batch_size = batch_size + def __len__(self): return math.ceil(len(self.x) / self.batch_size) + def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * - self.batch_size] + self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * - self.batch_size] + self.batch_size] + return np.array([ resize(imread(file_name), (200, 200)) for file_name in batch_x]), np.array(batch_y) @@ -375,7 +385,6 @@ class Sequence(object): """ raise NotImplementedError - @abstractmethod def on_epoch_end(self): """Method called at the end of every epoch. """ @@ -405,7 +414,6 @@ def get_index(uid, i): return _SHARED_SEQUENCES[uid][i] -@tf_export('keras.utils.SequenceEnqueuer') class SequenceEnqueuer(object): """Base class to enqueue inputs. @@ -474,35 +482,36 @@ class OrderedEnqueuer(SequenceEnqueuer): Arguments: sequence: A `keras.utils.data_utils.Sequence` object. - use_multiprocessing: Use multiprocessing if True, otherwise threading - shuffle: Whether to shuffle the data at the beginning of each epoch + use_multiprocessing: use multiprocessing if True, otherwise threading + shuffle: whether to shuffle the data at the beginning of each epoch """ def __init__(self, sequence, use_multiprocessing=False, shuffle=False): self.sequence = sequence self.use_multiprocessing = use_multiprocessing - # Doing Multiprocessing.Value += x is not process-safe. global _SEQUENCE_COUNTER if _SEQUENCE_COUNTER is None: - if self.use_multiprocessing: + try: _SEQUENCE_COUNTER = multiprocessing.Value('i', 0) - else: + except OSError: + # In this case the OS does not allow us to use + # multiprocessing. We resort to an int + # for enqueuer indexing. _SEQUENCE_COUNTER = 0 - if self.use_multiprocessing: + if isinstance(_SEQUENCE_COUNTER, int): + self.uid = _SEQUENCE_COUNTER + _SEQUENCE_COUNTER += 1 + else: + # Doing Multiprocessing.Value += x is not process-safe. with _SEQUENCE_COUNTER.get_lock(): self.uid = _SEQUENCE_COUNTER.value _SEQUENCE_COUNTER.value += 1 - else: - self.uid = _SEQUENCE_COUNTER - if isinstance(_SEQUENCE_COUNTER, int): - _SEQUENCE_COUNTER += 1 - else: - _SEQUENCE_COUNTER.value += 1 + self.shuffle = shuffle self.workers = 0 - self.executor = None + self.executor_fn = None self.queue = None self.run_thread = None self.stop_signal = None @@ -519,9 +528,9 @@ class OrderedEnqueuer(SequenceEnqueuer): (when full, workers could block on `put()`) """ if self.use_multiprocessing: - self.executor = multiprocessing.Pool(workers) + self.executor_fn = lambda: multiprocessing.Pool(workers) else: - self.executor = ThreadPool(workers) + self.executor_fn = lambda: ThreadPool(workers) self.workers = workers self.queue = queue.Queue(max_queue_size) self.stop_signal = threading.Event() @@ -537,24 +546,26 @@ class OrderedEnqueuer(SequenceEnqueuer): return def _run(self): - """Function to submit request to the executor & queue `Future` objects.""" + """Submits request to the executor and queue the `Future` objects.""" sequence = list(range(len(self.sequence))) self._send_sequence() # Share the initial sequence while True: if self.shuffle: random.shuffle(sequence) - for i in sequence: - if self.stop_signal.is_set(): - return - self.queue.put( - self.executor.apply_async(get_index, (self.uid, i)), block=True) - # Done with the current epoch, waiting for the final batches - self._wait_queue() + with closing(self.executor_fn()) as executor: + for i in sequence: + if self.stop_signal.is_set(): + return + self.queue.put( + executor.apply_async(get_index, (self.uid, i)), block=True) - if self.stop_signal.is_set(): - # We're done - return + # Done with the current epoch, waiting for the final batches + self._wait_queue() + + if self.stop_signal.is_set(): + # We're done + return # Call the internal on epoch end. self.sequence.on_epoch_end() @@ -566,8 +577,9 @@ class OrderedEnqueuer(SequenceEnqueuer): Skip the data if it is `None`. Yields: - Tuples (inputs, targets) - or (inputs, targets, sample_weights) + The next element in the queue, i.e. a tuple + `(inputs, targets)` or + `(inputs, targets, sample_weights)`. """ try: while self.is_running(): @@ -581,14 +593,8 @@ class OrderedEnqueuer(SequenceEnqueuer): def _send_sequence(self): """Send current Sequence to all workers.""" - _SHARED_SEQUENCES[ - self.uid] = self.sequence # For new processes that may spawn - - self._close_pool() - if self.use_multiprocessing: - self.executor = multiprocessing.Pool(self.workers) - else: - self.executor = ThreadPool(self.workers) + # For new processes that may spawn + _SHARED_SEQUENCES[self.uid] = self.sequence def stop(self, timeout=None): """Stops running threads and wait for them to exit, if necessary. @@ -603,16 +609,10 @@ class OrderedEnqueuer(SequenceEnqueuer): self.queue.queue.clear() self.queue.unfinished_tasks = 0 self.queue.not_full.notify() - self._close_pool() self.run_thread.join(timeout) _SHARED_SEQUENCES[self.uid] = None - def _close_pool(self): - self.executor.close() - self.executor.join() - -@tf_export('keras.utils.GeneratorEnqueuer') class GeneratorEnqueuer(SequenceEnqueuer): """Builds a queue out of a data generator. @@ -636,26 +636,53 @@ class GeneratorEnqueuer(SequenceEnqueuer): seed=None): self.wait_time = wait_time self._generator = generator - self._use_multiprocessing = use_multiprocessing + if os.name is 'nt' and use_multiprocessing is True: + # On Windows, avoid **SYSTEMATIC** error in `multiprocessing`: + # `TypeError: can't pickle generator objects` + # => Suggest multithreading instead of multiprocessing on Windows + raise ValueError('Using a generator with `use_multiprocessing=True`' + ' is not supported on Windows (no marshalling of' + ' generators across process boundaries). Instead,' + ' use single thread/process or multithreading.') + else: + self._use_multiprocessing = use_multiprocessing self._threads = [] self._stop_event = None self._manager = None self.queue = None self.seed = seed - def start(self, workers=1, max_queue_size=10): - """Kicks off threads which add data from the generator into the queue. - - Arguments: - workers: number of worker threads - max_queue_size: queue size - (when full, threads could block on `put()`) - """ - - def data_generator_task(): + def _data_generator_task(self): + if self._use_multiprocessing is False: + while not self._stop_event.is_set(): + with self.genlock: + try: + if (self.queue is not None and + self.queue.qsize() < self.max_queue_size): + # On all OSes, avoid **SYSTEMATIC** error + # in multithreading mode: + # `ValueError: generator already executing` + # => Serialize calls to + # infinite iterator/generator's next() function + generator_output = next(self._generator) + self.queue.put((True, generator_output)) + else: + time.sleep(self.wait_time) + except StopIteration: + break + except Exception as e: # pylint: disable=broad-except + # Can't pickle tracebacks. + # As a compromise, print the traceback and pickle None instead. + if not hasattr(e, '__traceback__'): + setattr(e, '__traceback__', sys.exc_info()[2]) + self.queue.put((False, e)) + self._stop_event.set() + break + else: while not self._stop_event.is_set(): try: - if self._use_multiprocessing or self.queue.qsize() < max_queue_size: + if (self.queue is not None and + self.queue.qsize() < self.max_queue_size): generator_output = next(self._generator) self.queue.put((True, generator_output)) else: @@ -663,24 +690,34 @@ class GeneratorEnqueuer(SequenceEnqueuer): except StopIteration: break except Exception as e: # pylint: disable=broad-except - # Can't pick tracebacks. + # Can't pickle tracebacks. # As a compromise, print the traceback and pickle None instead. - if self._use_multiprocessing: - traceback.print_exc() - setattr(e, '__traceback__', None) - elif not hasattr(e, '__traceback__'): - setattr(e, '__traceback__', sys.exc_info()[2]) + traceback.print_exc() + setattr(e, '__traceback__', None) self.queue.put((False, e)) self._stop_event.set() break + def start(self, workers=1, max_queue_size=10): + """Kicks off threads which add data from the generator into the queue. + + Arguments: + workers: number of worker threads + max_queue_size: queue size + (when full, threads could block on `put()`) + """ try: + self.max_queue_size = max_queue_size if self._use_multiprocessing: self._manager = multiprocessing.Manager() self.queue = self._manager.Queue(maxsize=max_queue_size) self._stop_event = multiprocessing.Event() else: - self.queue = queue.Queue() + # On all OSes, avoid **SYSTEMATIC** error in multithreading mode: + # `ValueError: generator already executing` + # => Serialize calls to infinite iterator/generator's next() function + self.genlock = threading.Lock() + self.queue = queue.Queue(maxsize=max_queue_size) self._stop_event = threading.Event() for _ in range(workers): @@ -688,12 +725,12 @@ class GeneratorEnqueuer(SequenceEnqueuer): # Reset random seed else all children processes # share the same seed np.random.seed(self.seed) - thread = multiprocessing.Process(target=data_generator_task) + thread = multiprocessing.Process(target=self._data_generator_task) thread.daemon = True if self.seed is not None: self.seed += 1 else: - thread = threading.Thread(target=data_generator_task) + thread = threading.Thread(target=self._data_generator_task) self._threads.append(thread) thread.start() except: @@ -715,11 +752,15 @@ class GeneratorEnqueuer(SequenceEnqueuer): self._stop_event.set() for thread in self._threads: - if thread.is_alive(): - if self._use_multiprocessing: + if self._use_multiprocessing: + if thread.is_alive(): thread.terminate() - else: - thread.join(timeout) + else: + # The thread.is_alive() test is subject to a race condition: + # the thread could terminate right after the test and before the + # join, rendering this test meaningless -> Call thread.join() + # always, which is ok no matter what the status of the thread. + thread.join(timeout) if self._manager: self._manager.shutdown() @@ -734,7 +775,9 @@ class GeneratorEnqueuer(SequenceEnqueuer): Skip the data if it is `None`. Yields: - Data arrays. + The next element in the queue, i.e. a tuple + `(inputs, targets)` or + `(inputs, targets, sample_weights)`. """ while self.is_running(): if not self.queue.empty(): @@ -752,7 +795,7 @@ class GeneratorEnqueuer(SequenceEnqueuer): else: time.sleep(self.wait_time) - # Make sure to rethrow the first exception in the queue, if any + # Make sure to rethrow the first exception in the queue, if any while not self.queue.empty(): success, value = self.queue.get() if not success: diff --git a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py index a805315c94..adbe6c3288 100644 --- a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import binascii import codecs import marshal import os @@ -255,7 +256,10 @@ def func_load(code, defaults=None, closure=None, globs=None): if closure is not None: closure = tuple(ensure_value_to_cell(_) for _ in closure) - raw_code = codecs.decode(code.encode('ascii'), 'base64') + try: + raw_code = codecs.decode(code.encode('ascii'), 'base64') + except (UnicodeEncodeError, binascii.Error): + raw_code = code.encode('raw_unicode_escape') code = marshal.loads(raw_code) if globs is None: globs = globals() diff --git a/tensorflow/python/keras/_impl/keras/utils/io_utils.py b/tensorflow/python/keras/_impl/keras/utils/io_utils.py index e123339f5a..b36c769843 100644 --- a/tensorflow/python/keras/_impl/keras/utils/io_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/io_utils.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# pylint: disable=g-import-not-at-top """Utilities related to disk I/O.""" from __future__ import absolute_import from __future__ import division @@ -21,16 +22,14 @@ from collections import defaultdict import sys import numpy as np -from tensorflow.python.util.tf_export import tf_export try: - import h5py # pylint:disable=g-import-not-at-top + import h5py except ImportError: h5py = None -@tf_export('keras.utils.HDF5Matrix') class HDF5Matrix(object): """Representation of HDF5 dataset to be used instead of a Numpy array. @@ -65,11 +64,11 @@ class HDF5Matrix(object): 'HDF5 and h5py installed.') if datapath not in list(self.refs.keys()): - self._f = h5py.File(datapath) - self.refs[datapath] = self._f + f = h5py.File(datapath) + self.refs[datapath] = f else: - self._f = self.refs[datapath] - self.data = self._f[dataset] + f = self.refs[datapath] + self.data = f[dataset] self.start = start if end is None: self.end = self.data.shape[0] @@ -80,9 +79,6 @@ class HDF5Matrix(object): def __len__(self): return self.end - self.start - def __del__(self): - self._f.close() - def __getitem__(self, key): if isinstance(key, slice): start, stop = key.start, key.stop diff --git a/tensorflow/python/keras/_impl/keras/utils/layer_utils.py b/tensorflow/python/keras/_impl/keras/utils/layer_utils.py index 30af285cbf..a2d32424b5 100644 --- a/tensorflow/python/keras/_impl/keras/utils/layer_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/layer_utils.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Utilities related to Keras layers. +# pylint: disable=protected-access +"""Utilities related to layer/model functionality. """ from __future__ import absolute_import from __future__ import division @@ -22,17 +23,16 @@ import numpy as np from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras.utils.conv_utils import convert_kernel -from tensorflow.python.util.tf_export import tf_export def count_params(weights): """Count the total number of scalars composing the weights. Arguments: - weights: An iterable containing the weights on which to compute params + weights: An iterable containing the weights on which to compute params Returns: - The total number of scalars composing the weights + The total number of scalars composing the weights """ return int(np.sum([K.count_params(p) for p in set(weights)])) @@ -47,10 +47,11 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): terminal window sizes). positions: Relative or absolute positions of log elements in each line. If not provided, defaults to `[.33, .55, .67, 1.]`. - print_fn: Print function to use (defaults to `print`). + print_fn: Print function to use. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary. + It defaults to `print` (prints to stdout). """ if print_fn is None: print_fn = print @@ -59,12 +60,13 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): sequential_like = True else: sequential_like = True - nodes_by_depth = model._nodes_by_depth.values() # pylint: disable=protected-access + nodes_by_depth = model._nodes_by_depth.values() nodes = [] for v in nodes_by_depth: if (len(v) > 1) or (len(v) == 1 and len(v[0].inbound_layers) > 1): - # If the model has multiple nodes or if the nodes have - # multiple inbound_layers, the model is no longer sequential. + # if the model has multiple nodes + # or if the nodes have multiple inbound_layers + # the model is no longer sequential sequential_like = False break nodes += v @@ -72,7 +74,7 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): # search for shared layers for layer in model.layers: flag = False - for node in layer.inbound_nodes: + for node in layer._inbound_nodes: if node in nodes: if flag: sequential_like = False @@ -97,7 +99,7 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): # header names for the different log elements to_display = ['Layer (type)', 'Output Shape', 'Param #', 'Connected to'] relevant_nodes = [] - for v in model._nodes_by_depth.values(): # pylint: disable=protected-access + for v in model._nodes_by_depth.values(): relevant_nodes += v def print_row(fields, positions): @@ -135,7 +137,7 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): except AttributeError: output_shape = 'multiple' connections = [] - for node in layer._inbound_nodes: # pylint: disable=protected-access + for node in layer._inbound_nodes: if relevant_nodes and node not in relevant_nodes: # node is not part of the current network continue @@ -143,8 +145,8 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): inbound_layer = node.inbound_layers[i].name inbound_node_index = node.node_indices[i] inbound_tensor_index = node.tensor_indices[i] - connections.append(inbound_layer + '[' + str(inbound_node_index) + '][' - + str(inbound_tensor_index) + ']') + connections.append(inbound_layer + '[' + str(inbound_node_index) + + '][' + str(inbound_tensor_index) + ']') name = layer.name cls_name = layer.__class__.__name__ @@ -173,9 +175,9 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): else: print_fn('_' * line_length) - model._check_trainable_weights_consistency() # pylint: disable=protected-access + model._check_trainable_weights_consistency() if hasattr(model, '_collected_trainable_weights'): - trainable_count = count_params(model._collected_trainable_weights) # pylint: disable=protected-access + trainable_count = count_params(model._collected_trainable_weights) else: trainable_count = count_params(model.trainable_weights) @@ -188,7 +190,6 @@ def print_summary(model, line_length=None, positions=None, print_fn=None): print_fn('_' * line_length) -@tf_export('keras.utils.convert_all_kernels_in_model') def convert_all_kernels_in_model(model): """Converts all convolution kernels in a model from Theano to TensorFlow. diff --git a/tensorflow/python/keras/_impl/keras/utils/np_utils.py b/tensorflow/python/keras/_impl/keras/utils/np_utils.py index 3dddb99191..231833e776 100644 --- a/tensorflow/python/keras/_impl/keras/utils/np_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/np_utils.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -18,10 +18,8 @@ from __future__ import division from __future__ import print_function import numpy as np -from tensorflow.python.util.tf_export import tf_export -@tf_export('keras.utils.to_categorical') def to_categorical(y, num_classes=None): """Converts a class vector (integers) to binary class matrix. @@ -50,7 +48,6 @@ def to_categorical(y, num_classes=None): return categorical -@tf_export('keras.utils.normalize') def normalize(x, axis=-1, order=2): """Normalizes a Numpy array. diff --git a/tensorflow/python/keras/_impl/keras/utils/vis_utils.py b/tensorflow/python/keras/_impl/keras/utils/vis_utils.py index 1ec8e3a2bf..0c5f2c19c7 100644 --- a/tensorflow/python/keras/_impl/keras/utils/vis_utils.py +++ b/tensorflow/python/keras/_impl/keras/utils/vis_utils.py @@ -1,4 +1,4 @@ -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,31 +12,29 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# pylint: disable=protected-access +# pylint: disable=g-import-not-at-top """Utilities related to model visualization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os -import sys -from tensorflow.python.util.tf_export import tf_export + try: # pydot-ng is a fork of pydot that is better maintained. - import pydot_ng as pydot # pylint: disable=g-import-not-at-top + import pydot_ng as pydot except ImportError: - # Fall back on pydot if necessary. - # Silence a `print` statement that occurs in case of import error, - # by temporarily replacing sys.stdout. - _stdout = sys.stdout - sys.stdout = sys.stderr + # pydotplus is an improved version of pydot try: - import pydot # pylint: disable=g-import-not-at-top + import pydotplus as pydot except ImportError: - pydot = None - finally: - # Restore sys.stdout. - sys.stdout = _stdout + # Fall back on pydot if necessary. + try: + import pydot + except ImportError: + pydot = None def _check_pydot(): @@ -66,8 +64,8 @@ def model_to_dot(model, show_shapes=False, show_layer_names=True, rankdir='TB'): Returns: A `pydot.Dot` instance representing the Keras model. """ - from tensorflow.python.keras._impl.keras.layers.wrappers import Wrapper # pylint: disable=g-import-not-at-top - from tensorflow.python.keras._impl.keras.models import Sequential # pylint: disable=g-import-not-at-top + from tensorflow.python.keras._impl.keras.layers.wrappers import Wrapper + from tensorflow.python.keras._impl.keras.models import Sequential _check_pydot() dot = pydot.Dot() @@ -119,9 +117,9 @@ def model_to_dot(model, show_shapes=False, show_layer_names=True, rankdir='TB'): # Connect nodes with edges. for layer in layers: layer_id = str(id(layer)) - for i, node in enumerate(layer._inbound_nodes): # pylint: disable=protected-access + for i, node in enumerate(layer._inbound_nodes): node_key = layer.name + '_ib-' + str(i) - if node_key in model._network_nodes: # pylint: disable=protected-access + if node_key in model._container_nodes: for inbound_layer in node.inbound_layers: inbound_layer_id = str(id(inbound_layer)) layer_id = str(id(layer)) @@ -129,7 +127,6 @@ def model_to_dot(model, show_shapes=False, show_layer_names=True, rankdir='TB'): return dot -@tf_export('keras.utils.plot_model') def plot_model(model, to_file='model.png', show_shapes=False, diff --git a/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py b/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py index bc788d874f..223ceac3de 100644 --- a/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py +++ b/tensorflow/python/keras/_impl/keras/wrappers/scikit_learn.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""API wrapper allowing to use certain Keras models with the Scikit-Learn API. +"""Wrapper for using the Scikit-Learn API with Keras models. """ from __future__ import absolute_import from __future__ import division @@ -24,8 +24,8 @@ import types import numpy as np from tensorflow.python.keras._impl.keras.models import Sequential +from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical -from tensorflow.python.util import tf_inspect class BaseWrapper(object): @@ -75,7 +75,7 @@ class BaseWrapper(object): self.check_params(sk_params) def check_params(self, params): - """Checks for user typos in "params". + """Checks for user typos in `params`. Arguments: params: dictionary; the parameters to be checked @@ -95,13 +95,11 @@ class BaseWrapper(object): else: legal_params_fns.append(self.build_fn) - legal_params = [] - for fn in legal_params_fns: - legal_params += tf_inspect.getargspec(fn)[0] - legal_params = set(legal_params) - for params_name in params: - if params_name not in legal_params: + for fn in legal_params_fns: + if has_arg(fn, params_name): + break + else: if params_name != 'nb_epoch': raise ValueError('{} is not a legal parameter'.format(params_name)) @@ -136,10 +134,10 @@ class BaseWrapper(object): Arguments: x : array-like, shape `(n_samples, n_features)` - Training samples where n_samples in the number of samples - and n_features is the number of features. + Training samples where `n_samples` is the number of samples + and `n_features` is the number of features. y : array-like, shape `(n_samples,)` or `(n_samples, n_outputs)` - True labels for X. + True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.fit` @@ -170,21 +168,20 @@ class BaseWrapper(object): return history def filter_sk_params(self, fn, override=None): - """Filters `sk_params` and return those in `fn`'s arguments. + """Filters `sk_params` and returns those in `fn`'s arguments. Arguments: fn : arbitrary function - override: dictionary, values to override sk_params + override: dictionary, values to override `sk_params` Returns: - res : dictionary dictionary containing variables - in both sk_params and fn's arguments. + res : dictionary containing variables + in both `sk_params` and `fn`'s arguments. """ override = override or {} res = {} - fn_args = tf_inspect.getargspec(fn)[0] for name, value in self.sk_params.items(): - if name in fn_args: + if has_arg(fn, name): res.update({name: value}) res.update(override) return res @@ -199,10 +196,10 @@ class KerasClassifier(BaseWrapper): Arguments: x : array-like, shape `(n_samples, n_features)` - Training samples where n_samples in the number of samples - and n_features is the number of features. + Training samples where `n_samples` is the number of samples + and `n_features` is the number of features. y : array-like, shape `(n_samples,)` or `(n_samples, n_outputs)` - True labels for X. + True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.fit` @@ -229,8 +226,8 @@ class KerasClassifier(BaseWrapper): Arguments: x: array-like, shape `(n_samples, n_features)` - Test samples where n_samples in the number of samples - and n_features is the number of features. + Test samples where `n_samples` is the number of samples + and `n_features` is the number of features. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.predict_classes`. @@ -248,8 +245,8 @@ class KerasClassifier(BaseWrapper): Arguments: x: array-like, shape `(n_samples, n_features)` - Test samples where n_samples in the number of samples - and n_features is the number of features. + Test samples where `n_samples` is the number of samples + and `n_features` is the number of features. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.predict_classes`. @@ -258,8 +255,8 @@ class KerasClassifier(BaseWrapper): proba: array-like, shape `(n_samples, n_outputs)` Class probability estimates. In the case of binary classification, - tp match the scikit-learn API, - will return an array of shape '(n_samples, 2)' + to match the scikit-learn API, + will return an array of shape `(n_samples, 2)` (instead of `(n_sample, 1)` as in Keras). """ kwargs = self.filter_sk_params(Sequential.predict_proba, kwargs) @@ -276,16 +273,16 @@ class KerasClassifier(BaseWrapper): Arguments: x: array-like, shape `(n_samples, n_features)` - Test samples where n_samples in the number of samples - and n_features is the number of features. + Test samples where `n_samples` is the number of samples + and `n_features` is the number of features. y: array-like, shape `(n_samples,)` or `(n_samples, n_outputs)` - True labels for x. + True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.evaluate`. Returns: score: float - Mean accuracy of predictions on X wrt. y. + Mean accuracy of predictions on `x` wrt. `y`. Raises: ValueError: If the underlying model isn't configured to @@ -321,8 +318,8 @@ class KerasRegressor(BaseWrapper): Arguments: x: array-like, shape `(n_samples, n_features)` - Test samples where n_samples in the number of samples - and n_features is the number of features. + Test samples where `n_samples` is the number of samples + and `n_features` is the number of features. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.predict`. @@ -338,16 +335,16 @@ class KerasRegressor(BaseWrapper): Arguments: x: array-like, shape `(n_samples, n_features)` - Test samples where n_samples in the number of samples - and n_features is the number of features. + Test samples where `n_samples` is the number of samples + and `n_features` is the number of features. y: array-like, shape `(n_samples,)` - True labels for X. + True labels for `x`. **kwargs: dictionary arguments Legal arguments are the arguments of `Sequential.evaluate`. Returns: score: float - Mean accuracy of predictions on X wrt. y. + Mean accuracy of predictions on `x` wrt. `y`. """ kwargs = self.filter_sk_params(Sequential.evaluate, kwargs) loss = self.model.evaluate(x, y, **kwargs) diff --git a/tensorflow/python/keras/applications/__init__.py b/tensorflow/python/keras/applications/__init__.py index 34f1435ffb..fccedf919a 100644 --- a/tensorflow/python/keras/applications/__init__.py +++ b/tensorflow/python/keras/applications/__init__.py @@ -18,16 +18,23 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.keras.applications import densenet from tensorflow.python.keras.applications import inception_resnet_v2 from tensorflow.python.keras.applications import inception_v3 from tensorflow.python.keras.applications import mobilenet +from tensorflow.python.keras.applications import nasnet from tensorflow.python.keras.applications import resnet50 from tensorflow.python.keras.applications import vgg16 from tensorflow.python.keras.applications import vgg19 from tensorflow.python.keras.applications import xception +from tensorflow.python.keras.applications.densenet import DenseNet121 +from tensorflow.python.keras.applications.densenet import DenseNet169 +from tensorflow.python.keras.applications.densenet import DenseNet201 from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2 from tensorflow.python.keras.applications.inception_v3 import InceptionV3 from tensorflow.python.keras.applications.mobilenet import MobileNet +from tensorflow.python.keras.applications.nasnet import NASNetLarge +from tensorflow.python.keras.applications.nasnet import NASNetMobile from tensorflow.python.keras.applications.resnet50 import ResNet50 from tensorflow.python.keras.applications.vgg16 import VGG16 from tensorflow.python.keras.applications.vgg19 import VGG19 diff --git a/tensorflow/python/keras/applications/densenet/__init__.py b/tensorflow/python/keras/applications/densenet/__init__.py new file mode 100644 index 0000000000..6b8ea83920 --- /dev/null +++ b/tensorflow/python/keras/applications/densenet/__init__.py @@ -0,0 +1,29 @@ +# 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. +# ============================================================================== +"""DenseNet Keras applications.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.keras._impl.keras.applications.densenet import decode_predictions +from tensorflow.python.keras._impl.keras.applications.densenet import DenseNet121 +from tensorflow.python.keras._impl.keras.applications.densenet import DenseNet169 +from tensorflow.python.keras._impl.keras.applications.densenet import DenseNet201 +from tensorflow.python.keras._impl.keras.applications.densenet import preprocess_input + +del absolute_import +del division +del print_function diff --git a/tensorflow/python/keras/applications/nasnet/__init__.py b/tensorflow/python/keras/applications/nasnet/__init__.py new file mode 100644 index 0000000000..94eb145b85 --- /dev/null +++ b/tensorflow/python/keras/applications/nasnet/__init__.py @@ -0,0 +1,28 @@ +# 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. +# ============================================================================== +"""NASNet Keras applications.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.keras._impl.keras.applications.nasnet import decode_predictions +from tensorflow.python.keras._impl.keras.applications.nasnet import NASNetLarge +from tensorflow.python.keras._impl.keras.applications.nasnet import NASNetMobile +from tensorflow.python.keras._impl.keras.applications.nasnet import preprocess_input + +del absolute_import +del division +del print_function diff --git a/tensorflow/python/keras/layers/__init__.py b/tensorflow/python/keras/layers/__init__.py index b94bf8f0f6..84ee5040dc 100644 --- a/tensorflow/python/keras/layers/__init__.py +++ b/tensorflow/python/keras/layers/__init__.py @@ -30,6 +30,7 @@ from tensorflow.python.keras._impl.keras.layers.advanced_activations import Leak from tensorflow.python.keras._impl.keras.layers.advanced_activations import PReLU from tensorflow.python.keras._impl.keras.layers.advanced_activations import ELU from tensorflow.python.keras._impl.keras.layers.advanced_activations import ThresholdedReLU +from tensorflow.python.keras._impl.keras.layers.advanced_activations import Softmax # Convolution layers. from tensorflow.python.keras._impl.keras.layers.convolutional import Conv1D @@ -37,6 +38,7 @@ from tensorflow.python.keras._impl.keras.layers.convolutional import Conv2D from tensorflow.python.keras._impl.keras.layers.convolutional import Conv3D from tensorflow.python.keras._impl.keras.layers.convolutional import Conv2DTranspose from tensorflow.python.keras._impl.keras.layers.convolutional import Conv3DTranspose +from tensorflow.python.keras._impl.keras.layers.convolutional import SeparableConv1D from tensorflow.python.keras._impl.keras.layers.convolutional import SeparableConv2D # Convolution layer aliases. @@ -45,6 +47,7 @@ from tensorflow.python.keras._impl.keras.layers.convolutional import Convolution from tensorflow.python.keras._impl.keras.layers.convolutional import Convolution3D from tensorflow.python.keras._impl.keras.layers.convolutional import Convolution2DTranspose from tensorflow.python.keras._impl.keras.layers.convolutional import Convolution3DTranspose +from tensorflow.python.keras._impl.keras.layers.convolutional import SeparableConvolution1D from tensorflow.python.keras._impl.keras.layers.convolutional import SeparableConvolution2D # Image processing layers. diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index d892654ebe..5d9feb07b4 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -99,8 +99,16 @@ class Layer(object): raise TypeError('Keyword argument not understood:', kwarg) # Mutable properties + # Indicates whether the layer's weights are updated during training + # and whether the layer's updates are run during training self.trainable = trainable + # A stateful layer is a layer whose updates are run during inference too, + # for instance stateful RNNs. + self.stateful = False + # Indicates whether `build` needs to be called upon layer call, to create + # the layer's weights. self.built = False + # Provides information about which inputs are compatible with the layer. self.input_spec = None if activity_regularizer and context.in_eager_mode(): @@ -223,6 +231,8 @@ class Layer(object): def updates(self): if context.in_eager_mode(): raise RuntimeError('Layer.updates not supported in Eager mode.') + if not self.trainable and not self.stateful: + return [] return self._updates def add_update(self, updates, inputs=None): @@ -284,6 +294,8 @@ class Layer(object): """ if context.in_eager_mode(): raise RuntimeError('Layer.get_updates_for not supported in Eager mode.') + if not self.trainable and not self.stateful: + return [] if inputs is not None: inputs = nest.flatten(inputs) if not inputs: @@ -1269,6 +1281,15 @@ class InputSpec(object): self.min_ndim = min_ndim self.axes = axes or {} + def __repr__(self): + spec = [('dtype=' + str(self.dtype)) if self.dtype else '', + ('shape=' + str(self.shape)) if self.shape else '', + ('ndim=' + str(self.ndim)) if self.ndim else '', + ('max_ndim=' + str(self.max_ndim)) if self.max_ndim else '', + ('min_ndim=' + str(self.min_ndim)) if self.min_ndim else '', + ('axes=' + str(self.axes)) if self.axes else ''] + return 'InputSpec(%s)' % ', '.join(x for x in spec if x) + class Node(object): """A `Node` describes the connectivity between two layers. diff --git a/tensorflow/python/layers/network.py b/tensorflow/python/layers/network.py index ade57da411..0a5dd57621 100644 --- a/tensorflow/python/layers/network.py +++ b/tensorflow/python/layers/network.py @@ -574,6 +574,11 @@ class GraphNetwork(base.Layer): return layer raise ValueError('No such layer: ' + name) + @property + def stateful(self): + return any([(hasattr(layer, 'stateful') and layer.stateful) + for layer in self.layers]) + @property def updates(self): """Retrieve the network's updates. @@ -586,6 +591,8 @@ class GraphNetwork(base.Layer): Returns: A list of update ops. """ + if not self.trainable and not self.stateful: + return [] updates = [] for layer in self.layers: if hasattr(layer, 'updates'): diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt index 7fe3e2db09..2bf584fa29 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt @@ -160,15 +160,15 @@ tf_class { } member_method { name: "evaluate_generator" - argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'10\', \'1\', \'False\'], " + argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\'], " } member_method { name: "fit" - argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\'], " } member_method { name: "fit_generator" - argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], " + argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], " } member_method { name: "from_config" @@ -228,7 +228,7 @@ tf_class { } member_method { name: "predict_generator" - argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], " + argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], " } member_method { name: "predict_on_batch" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.densenet.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.applications.densenet.pbtxt new file mode 100644 index 0000000000..42cb914450 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.applications.densenet.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.keras.applications.densenet" +tf_module { + member_method { + name: "DenseNet121" + argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "DenseNet169" + argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "DenseNet201" + argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "decode_predictions" + argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], " + } + member_method { + name: "preprocess_input" + argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.nasnet.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.applications.nasnet.pbtxt new file mode 100644 index 0000000000..cd75b87540 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.applications.nasnet.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.keras.applications.nasnet" +tf_module { + member_method { + name: "NASNetLarge" + argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "NASNetMobile" + argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "decode_predictions" + argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], " + } + member_method { + name: "preprocess_input" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.applications.pbtxt index daeb5aad41..9fc086eb8e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.applications.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.applications.pbtxt @@ -1,5 +1,9 @@ path: "tensorflow.keras.applications" tf_module { + member { + name: "densenet" + mtype: "" + } member { name: "inception_resnet_v2" mtype: "" @@ -12,6 +16,10 @@ tf_module { name: "mobilenet" mtype: "" } + member { + name: "nasnet" + mtype: "" + } member { name: "resnet50" mtype: "" @@ -28,6 +36,18 @@ tf_module { name: "xception" mtype: "" } + member_method { + name: "DenseNet121" + argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "DenseNet169" + argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "DenseNet201" + argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " + } member_method { name: "InceptionResNetV2" argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " @@ -40,6 +60,14 @@ tf_module { name: "MobileNet" argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " } + member_method { + name: "NASNetLarge" + argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " + } + member_method { + name: "NASNetMobile" + argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " + } member_method { name: "ResNet50" argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt index 44fbe0f7a0..ba2d083a75 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt @@ -398,7 +398,7 @@ tf_module { } member_method { name: "rnn" - argspec: "args=[\'step_function\', \'inputs\', \'initial_states\', \'go_backwards\', \'mask\', \'constants\', \'unroll\'], varargs=None, keywords=None, defaults=[\'False\', \'None\', \'None\', \'False\'], " + argspec: "args=[\'step_function\', \'inputs\', \'initial_states\', \'go_backwards\', \'mask\', \'constants\', \'unroll\', \'input_length\'], varargs=None, keywords=None, defaults=[\'False\', \'None\', \'None\', \'False\', \'None\'], " } member_method { name: "round" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt index 8719c07ca3..d4c85a4519 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'schedule\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'self\', \'schedule\', \'verbose\'], varargs=None, keywords=None, defaults=[\'0\'], " } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.datasets.boston_housing.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.datasets.boston_housing.pbtxt index ef08f9b20f..bda31751d4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.datasets.boston_housing.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.datasets.boston_housing.pbtxt @@ -2,6 +2,6 @@ path: "tensorflow.keras.datasets.boston_housing" tf_module { member_method { name: "load_data" - argspec: "args=[\'path\', \'seed\', \'test_split\'], varargs=None, keywords=None, defaults=[\'boston_housing.npz\', \'113\', \'0.2\'], " + argspec: "args=[\'path\', \'test_split\', \'seed\'], varargs=None, keywords=None, defaults=[\'boston_housing.npz\', \'0.2\', \'113\'], " } } diff --git a/tensorflow/tools/api/golden/tensorflow.keras.datasets.imdb.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.datasets.imdb.pbtxt index 8b1c17e9da..ff962876b6 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.datasets.imdb.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.datasets.imdb.pbtxt @@ -6,6 +6,6 @@ tf_module { } member_method { name: "load_data" - argspec: "args=[\'path\', \'num_words\', \'skip_top\', \'maxlen\', \'seed\', \'start_char\', \'oov_char\', \'index_from\'], varargs=None, keywords=None, defaults=[\'imdb.npz\', \'None\', \'0\', \'None\', \'113\', \'1\', \'2\', \'3\'], " + argspec: "args=[\'path\', \'num_words\', \'skip_top\', \'maxlen\', \'seed\', \'start_char\', \'oov_char\', \'index_from\'], varargs=None, keywords=kwargs, defaults=[\'imdb.npz\', \'None\', \'0\', \'None\', \'113\', \'1\', \'2\', \'3\'], " } } diff --git a/tensorflow/tools/api/golden/tensorflow.keras.datasets.reuters.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.datasets.reuters.pbtxt index 6b3ed1e9af..2da4a13067 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.datasets.reuters.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.datasets.reuters.pbtxt @@ -6,6 +6,6 @@ tf_module { } member_method { name: "load_data" - argspec: "args=[\'path\', \'num_words\', \'skip_top\', \'maxlen\', \'test_split\', \'seed\', \'start_char\', \'oov_char\', \'index_from\'], varargs=None, keywords=None, defaults=[\'reuters.npz\', \'None\', \'0\', \'None\', \'0.2\', \'113\', \'1\', \'2\', \'3\'], " + argspec: "args=[\'path\', \'num_words\', \'skip_top\', \'maxlen\', \'test_split\', \'seed\', \'start_char\', \'oov_char\', \'index_from\'], varargs=None, keywords=kwargs, defaults=[\'reuters.npz\', \'None\', \'0\', \'None\', \'0.2\', \'113\', \'1\', \'2\', \'3\'], " } } diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt index a32151e22f..770a107b66 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt @@ -115,7 +115,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -127,7 +127,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt index 46b1713196..0ce42b706e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt index 9bfaf27562..b371ad148c 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt @@ -115,7 +115,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -127,7 +127,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt index 2b8ac4f1f4..2f5e65a0c5 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt @@ -123,7 +123,7 @@ tf_class { } member_method { name: "call" - argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\', \'initial_state\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " } member_method { name: "compute_mask" @@ -131,7 +131,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt index c9a0b88725..ff08def0a0 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt @@ -115,7 +115,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -127,7 +127,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt index b847e224d6..6db22ca032 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt @@ -116,7 +116,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -128,7 +128,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt index 86578d958e..07d3f023e5 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt @@ -115,7 +115,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -127,7 +127,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt index 348012dcde..92b9760d53 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt index 0419251083..83c528b401 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt @@ -114,7 +114,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt index 337e85e812..b329f1c46b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt @@ -114,7 +114,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt index 1357dc0f0d..d0f6d2a14f 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt @@ -183,7 +183,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -195,7 +195,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt index b71a08f6c3..57596badf1 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt index a01a6067ef..3829353cc3 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt index 0dbbdf2838..3b171b137a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt @@ -114,7 +114,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt index 964ef89c2e..0036d6805b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt @@ -187,7 +187,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -199,7 +199,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt index 6a7b23c540..8134fb7386 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt index 324745e5a3..c5d4523009 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt @@ -114,7 +114,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt index e12ae05054..bcbed9241b 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt @@ -114,7 +114,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt index 9e889ca863..ff0db15f19 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt @@ -115,7 +115,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -127,7 +127,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt index 932680941d..1d3f33f045 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt @@ -115,7 +115,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -127,7 +127,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt index db644f958f..c86bc49b22 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt @@ -114,7 +114,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt index 74fa1db020..b29f65d79d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt @@ -94,7 +94,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'cell\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'unroll\', \'activity_regularizer\'], varargs=None, keywords=kwargs, defaults=[\'False\', \'False\', \'False\', \'False\', \'False\', \'None\'], " + argspec: "args=[\'self\', \'cell\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'unroll\'], varargs=None, keywords=kwargs, defaults=[\'False\', \'False\', \'False\', \'False\', \'False\'], " } member_method { name: "add_loss" @@ -118,7 +118,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -130,7 +130,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt new file mode 100644 index 0000000000..dd67b76523 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt @@ -0,0 +1,186 @@ +path: "tensorflow.keras.layers.SeparableConv1D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "scope_name" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'depth_multiplier\', \'activation\', \'use_bias\', \'depthwise_initializer\', \'pointwise_initializer\', \'bias_initializer\', \'depthwise_regularizer\', \'pointwise_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'depthwise_constraint\', \'pointwise_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'None\', \'1\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=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" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt new file mode 100644 index 0000000000..bf62c095e7 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt @@ -0,0 +1,186 @@ +path: "tensorflow.keras.layers.SeparableConvolution1D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "scope_name" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\', \'depth_multiplier\', \'activation\', \'use_bias\', \'depthwise_initializer\', \'pointwise_initializer\', \'bias_initializer\', \'depthwise_regularizer\', \'pointwise_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'depthwise_constraint\', \'pointwise_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'valid\', \'None\', \'1\', \'1\', \'None\', \'True\', \'glorot_uniform\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=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" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt index 3414810db4..6e3cde3e3e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt @@ -114,7 +114,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt index cf34034ef0..b875898a81 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt @@ -175,7 +175,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" @@ -187,7 +187,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt new file mode 100644 index 0000000000..ee4b2fa39e --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt @@ -0,0 +1,183 @@ +path: "tensorflow.keras.layers.Softmax" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "scope_name" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'axis\'], varargs=None, keywords=kwargs, defaults=[\'-1\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=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" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt index b76499658d..db9f90caef 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt @@ -118,7 +118,7 @@ tf_class { } member_method { name: "build" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "call" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt index 2376d815a6..ef31c5443e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt @@ -126,7 +126,7 @@ tf_class { } member_method { name: "compute_output_shape" - argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" } member_method { name: "count_params" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt index fe336c4be5..088c8e88e2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt @@ -292,10 +292,18 @@ tf_module { name: "Reshape" mtype: "" } + member { + name: "SeparableConv1D" + mtype: "" + } member { name: "SeparableConv2D" mtype: "" } + member { + name: "SeparableConvolution1D" + mtype: "" + } member { name: "SeparableConvolution2D" mtype: "" @@ -308,6 +316,10 @@ tf_module { name: "SimpleRNNCell" mtype: "" } + member { + name: "Softmax" + mtype: "" + } member { name: "SpatialDropout1D" mtype: "" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt index d239098b0b..0b816b5863 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt @@ -160,15 +160,15 @@ tf_class { } member_method { name: "evaluate_generator" - argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'10\', \'1\', \'False\'], " + argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\'], " } member_method { name: "fit" - argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\'], " } member_method { name: "fit_generator" - argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], " + argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], " } member_method { name: "from_config" @@ -228,7 +228,7 @@ tf_class { } member_method { name: "predict_generator" - argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], " + argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], " } member_method { name: "predict_on_batch" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adadelta.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adadelta.pbtxt index ed040c1586..32667cf31e 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adadelta.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adadelta.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'1.0\', \'0.95\', \'1e-08\', \'0.0\'], " + argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'1.0\', \'0.95\', \'None\', \'0.0\'], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adagrad.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adagrad.pbtxt index a24651429a..efca59e8e4 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adagrad.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adagrad.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'1e-08\', \'0.0\'], " + argspec: "args=[\'self\', \'lr\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'None\', \'0.0\'], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adam.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adam.pbtxt index a0d978fded..5546e2067a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adam.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adam.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-08\', \'0.0\'], " + argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\', \'amsgrad\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'None\', \'0.0\', \'False\'], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adamax.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adamax.pbtxt index 1b70c93ad5..aaa54a1060 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adamax.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adamax.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'1e-08\', \'0.0\'], " + argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.0\'], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-nadam.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-nadam.pbtxt index b49dbe5cf8..1fada7fd9c 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-nadam.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-nadam.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'schedule_decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'1e-08\', \'0.004\'], " + argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'schedule_decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.004\'], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-r-m-sprop.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-r-m-sprop.pbtxt index c8860d80d4..fd3f97f35d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-r-m-sprop.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-r-m-sprop.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'1e-08\', \'0.0\'], " + argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'None\', \'0.0\'], " } member_method { name: "from_config" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt index 5bc8c40120..ce91caa1af 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt @@ -4,7 +4,7 @@ tf_class { is_instance: "" member_method { name: "__init__" - argspec: "args=[\'self\', \'num_words\', \'filters\', \'lower\', \'split\', \'char_level\'], varargs=None, keywords=None, defaults=[\'None\', \'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \', \'False\'], " + argspec: "args=[\'self\', \'num_words\', \'filters\', \'lower\', \'split\', \'char_level\', \'oov_token\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \', \'False\', \'None\'], " } member_method { name: "fit_on_sequences" -- GitLab From 6e46b1abdb7f6077d2e3168008cd0ecbc6774b2a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 19:10:13 -0800 Subject: [PATCH 096/423] [tpu:profiler] Add infeed enqueue operation data to tf_op_stats.proto. PiperOrigin-RevId: 183328456 --- tensorflow/contrib/tpu/profiler/tf_op_stats.proto | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto index 5440bbbfdd..2094294baa 100644 --- a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto +++ b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto @@ -61,6 +61,11 @@ message OpMetricsResult { message OpMetricsDbResult { // A bunch of OpMetricsResults. repeated OpMetricsResult metrics_db = 1; + // The total host infeed-enqueue duration in picoseconds. + optional uint64 total_host_infeed_enq_duration_ps = 2; + // The total of the difference between the start times of two + // consecutive infeed-enqueues (per host) in picoseconds. + optional uint64 total_host_infeed_enq_start_timestamp_ps_diff = 3; } // Result proto for StepInfo. -- GitLab From 1fde827acd1ac75e1086fba0cd4e1f162c8d2cc0 Mon Sep 17 00:00:00 2001 From: Chris Leary Date: Thu, 25 Jan 2018 19:50:17 -0800 Subject: [PATCH 097/423] [XLA] Add source mapping utility translation unit, use it in the local client. PiperOrigin-RevId: 183331075 --- tensorflow/compiler/xla/client/BUILD | 1 + .../compiler/xla/client/local_client.cc | 10 ++- tensorflow/compiler/xla/python/xla_client.py | 25 ++++--- tensorflow/compiler/xla/service/BUILD | 13 ++++ .../xla/service/compile_only_service.cc | 2 +- .../compiler/xla/service/local_service.cc | 3 +- tensorflow/compiler/xla/service/service.cc | 33 ++++++---- tensorflow/compiler/xla/service/service.h | 6 +- .../compiler/xla/service/source_map_util.cc | 66 +++++++++++++++++++ .../compiler/xla/service/source_map_util.h | 46 +++++++++++++ .../xla/tests/check_execution_arity_test.cc | 9 ++- tensorflow/compiler/xla/util.cc | 15 +++-- tensorflow/compiler/xla/util.h | 10 +++ 13 files changed, 202 insertions(+), 37 deletions(-) create mode 100644 tensorflow/compiler/xla/service/source_map_util.cc create mode 100644 tensorflow/compiler/xla/service/source_map_util.h diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index d6b4ebfc39..952109dde2 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -98,6 +98,7 @@ cc_library( "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:shaped_buffer", + "//tensorflow/compiler/xla/service:source_map_util", "//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 523169fdd2..fbeedfcecd 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -21,10 +21,13 @@ limitations under the License. #include "tensorflow/compiler/xla/ptr_util.h" #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/status_macros.h" namespace se = ::perftools::gputools; +using xla::source_map_util::InvalidParameterArgument; + namespace xla { ExecutableBuildOptions& ExecutableBuildOptions::set_device_ordinal( @@ -79,9 +82,10 @@ tensorflow::Status LocalExecutable::ValidateExecutionOptions( for (int i = 0; i < arguments.size(); ++i) { if (!computation_layout.parameter_layout(i).MatchesLayoutInShape( arguments[i]->on_host_shape())) { - return InvalidArgument( - "argument does not match shape or layout of computation parameter " - "%d: expected %s, got %s", + return InvalidParameterArgument( + executable_.get(), i, + "Argument does not match shape or layout of computation parameter " + "%d: want %s, got %s", i, ShapeUtil::HumanString(computation_layout.parameter_layout(i).shape()) .c_str(), diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index 9cfe1249f5..66ace613a0 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -36,15 +36,22 @@ from tensorflow.compiler.xla.python import pywrap_xla as c_api # pylint: disable=invalid-name -OpMetadata = collections.namedtuple( - 'OpMetadata', - [ - 'op_type', - 'op_name', - 'source_file', - 'source_line', - ], -) +_OP_METADATA_FIELDS = [ + 'op_type', + 'op_name', + 'source_file', + 'source_line', +] +OpMetadata = collections.namedtuple('OpMetadata', _OP_METADATA_FIELDS) + + +def OpMetadataToProto(pyobj): + proto = xla_data_pb2.OpMetadata() + for field in _OP_METADATA_FIELDS: + attr = getattr(pyobj, field) + if attr is not None: + setattr(proto, field, attr) + return proto def CurrentSourceInfoMetadata(op_type=None, op_name=None, skip_frames=1): diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 2c5f3ea1dd..031d077c6a 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -462,6 +462,7 @@ cc_library( ":hlo_proto_util", ":platform_util", ":session_proto", + ":source_map_util", ":transfer_manager", ":user_computation", ":versioned_computation_handle", @@ -2350,6 +2351,18 @@ tf_cc_test( ], ) +cc_library( + name = "source_map_util", + srcs = ["source_map_util.cc"], + hdrs = ["source_map_util.h"], + deps = [ + ":executable", + "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + # ----------------------------------------------------------------------------- filegroup( diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc index b9306a8bb0..dab73596e1 100644 --- a/tensorflow/compiler/xla/service/compile_only_service.cc +++ b/tensorflow/compiler/xla/service/compile_only_service.cc @@ -101,7 +101,7 @@ CompileOnlyService::CompileAheadOfTime( TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, CreateModuleConfig(*program_shape, instance.argument_layouts, - &execution_options)); + &execution_options, *user_computation)); TF_ASSIGN_OR_RETURN(std::unique_ptr hlo_module, computation_tracker_.BuildHloModule( diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index 2194d24257..f30530db08 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -128,7 +128,8 @@ StatusOr> LocalService::CompileExecutable( } TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, argument_layouts, &execution_options)); + CreateModuleConfig(*program_shape, argument_layouts, &execution_options, + *user_computation)); TF_ASSIGN_OR_RETURN(se::StreamExecutor * executor, execute_backend_->stream_executor(device_ordinal)); diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 926ebbe314..849df1d8e6 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/session.pb.h" +#include "tensorflow/compiler/xla/service/source_map_util.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" #include "tensorflow/compiler/xla/shape_layout.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -56,6 +57,7 @@ namespace se = ::perftools::gputools; using ::tensorflow::strings::Printf; using ::tensorflow::strings::StrCat; +using ::xla::source_map_util::InvalidParameterArgument; namespace xla { @@ -261,7 +263,8 @@ StatusOr> Service::ResolveAndValidateArguments( StatusOr> Service::CreateModuleConfig( const ProgramShape& program_shape, tensorflow::gtl::ArraySlice argument_shapes, - const ExecutionOptions* execution_options) { + const ExecutionOptions* execution_options, + const UserComputation& user_computation) { auto config = MakeUnique(program_shape); auto* computation_layout = config->mutable_entry_computation_layout(); @@ -275,8 +278,10 @@ StatusOr> Service::CreateModuleConfig( // ProgramShape. if (!ShapeUtil::Compatible(*argument_shapes[i], program_shape.parameters(i))) { - return InvalidArgument( - "computation expects parameter %d to have shape %s, given shape %s", + return InvalidParameterArgument( + *user_computation.ParameterMetadata(i).value(), + "Argument does not match shape of computation parameter %d: want %s, " + "got %s", i, ShapeUtil::HumanString(program_shape.parameters(i)).c_str(), ShapeUtil::HumanString(*argument_shapes[i]).c_str()); } @@ -318,12 +323,14 @@ StatusOr> Service::CreateModuleConfig( StatusOr> Service::CreateModuleConfig( const ProgramShape& program_shape, tensorflow::gtl::ArraySlice arguments, - const ExecutionOptions& execution_options) { + const ExecutionOptions& execution_options, + const UserComputation& user_computation) { std::vector argument_shapes; for (const auto* arg : arguments) { argument_shapes.push_back(&arg->on_host_shape()); } - return CreateModuleConfig(program_shape, argument_shapes, &execution_options); + return CreateModuleConfig(program_shape, argument_shapes, &execution_options, + user_computation); } StatusOr>> Service::BuildExecutables( @@ -742,9 +749,10 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, // Create an HloModuleConfig object for the computation, given the shape of // the program and the argument allocations. - TF_ASSIGN_OR_RETURN(std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, - request.execution_options())); + TF_ASSIGN_OR_RETURN( + std::unique_ptr module_config, + CreateModuleConfig(*program_shape, arguments, + request.execution_options(), *user_computation)); VLOG(3) << "ExecuteParallel created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); @@ -852,7 +860,8 @@ tensorflow::Status Service::Execute(const ExecuteRequest* arg, TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, arg->execution_options())); + CreateModuleConfig(*program_shape, arguments, arg->execution_options(), + *user_computation)); VLOG(3) << "Execute created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); @@ -916,7 +925,8 @@ tensorflow::Status Service::ExecuteAsync(const ExecuteAsyncRequest* arg, TF_ASSIGN_OR_RETURN( std::unique_ptr module_config, - CreateModuleConfig(*program_shape, arguments, arg->execution_options())); + CreateModuleConfig(*program_shape, arguments, arg->execution_options(), + *user_computation)); VLOG(3) << "ExecuteAsync created HloModuleConfig computation layout: " << module_config->entry_computation_layout().ToString(); @@ -1236,7 +1246,8 @@ tensorflow::Status Service::ComputeConstant(const ComputeConstantRequest* arg, } TF_ASSIGN_OR_RETURN(std::unique_ptr module_config, - CreateModuleConfig(program_shape, {}, execution_options)); + CreateModuleConfig(program_shape, {}, execution_options, + *user_computation)); // Exclude dead parameter instructions for the purpose of computing constants. TF_ASSIGN_OR_RETURN( diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index 0a7d0b3a7d..ca77e8fe3a 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -251,7 +251,8 @@ class Service : public ServiceInterface { StatusOr> CreateModuleConfig( const ProgramShape& program_shape, tensorflow::gtl::ArraySlice arguments, - const ExecutionOptions& execution_options); + const ExecutionOptions& execution_options, + const UserComputation& user_computation); protected: friend class LocalExecutable; @@ -275,7 +276,8 @@ class Service : public ServiceInterface { StatusOr> CreateModuleConfig( const ProgramShape& program_shape, tensorflow::gtl::ArraySlice argument_shapes, - const ExecutionOptions* execution_options); + const ExecutionOptions* execution_options, + const UserComputation& user_computation); // Builds an Executable for the given parameters. StatusOr> BuildExecutable( diff --git a/tensorflow/compiler/xla/service/source_map_util.cc b/tensorflow/compiler/xla/service/source_map_util.cc new file mode 100644 index 0000000000..8cbaac7b37 --- /dev/null +++ b/tensorflow/compiler/xla/service/source_map_util.cc @@ -0,0 +1,66 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/source_map_util.h" + +#include "tensorflow/compiler/xla/util.h" + +namespace xla { +namespace source_map_util { +namespace { + +Status InvalidParameterArgumentV(const OpMetadata& op_metadata, + const char* format, va_list args) { + string message; + tensorflow::strings::Appendv(&message, format, args); + if (!op_metadata.source_file().empty()) { + tensorflow::strings::Appendf(&message, " (%s:%d)", + op_metadata.source_file().c_str(), + op_metadata.source_line()); + } + return InvalidArgument("%s", message.c_str()); +} + +} // namespace + +Status InvalidParameterArgument(const OpMetadata& op_metadata, + const char* format, ...) { + va_list args; + va_start(args, format); + Status result = InvalidParameterArgumentV(op_metadata, format, args); + va_end(args); + return result; +} + +Status InvalidParameterArgument(Executable* executable, int parameter_number, + const char* format, ...) { + va_list args; + va_start(args, format); + if (executable != nullptr && executable->has_module()) { + const HloModule& module = executable->module(); + const HloComputation& computation = *module.entry_computation(); + HloInstruction* param = computation.parameter_instruction(parameter_number); + const OpMetadata& metadata = param->metadata(); + Status result = InvalidParameterArgumentV(metadata, format, args); + va_end(args); + return result; + } + Status result = InvalidArgumentV(format, args); + va_end(args); + return result; +} + +} // namespace source_map_util +} // namespace xla diff --git a/tensorflow/compiler/xla/service/source_map_util.h b/tensorflow/compiler/xla/service/source_map_util.h new file mode 100644 index 0000000000..a776d745f4 --- /dev/null +++ b/tensorflow/compiler/xla/service/source_map_util.h @@ -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. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SOURCE_MAP_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SOURCE_MAP_UTIL_H_ + +#include "tensorflow/compiler/xla/service/executable.h" +#include "tensorflow/compiler/xla/status.h" +#include "tensorflow/core/platform/macros.h" + +namespace xla { +namespace source_map_util { + +// Creates an INVALID_ARUGMENT status with the given format string. +// +// Also, attempts to extract the OpMetadata for parameter_number on executable +// and append it to the status message for source mapping to user code. +// +// executable may be nullptr, but parameter_number should not be out of bounds +// or a CHECK-failure may occur. +Status InvalidParameterArgument(Executable* executable, int parameter_number, + const char* format, ...) + TF_PRINTF_ATTRIBUTE(3, 4); + +// As above, but takes the parameter metadata directly instead of extracting it +// from the executable. +Status InvalidParameterArgument(const OpMetadata& op_metadata, + const char* format, ...) + TF_PRINTF_ATTRIBUTE(2, 3); + +} // namespace source_map_util +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SOURCE_MAP_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 659660d91e..f594cc10ac 100644 --- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc +++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc @@ -104,7 +104,8 @@ XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) { ASSERT_FALSE(status.ok()); ASSERT_EQ(status.status().code(), tensorflow::error::INVALID_ARGUMENT); ASSERT_THAT(status.status().error_message(), - ContainsRegex("expects parameter 0")); + ContainsRegex( + "Argument does not match shape of computation parameter 0")); // Shape mismatch in parameter 1 (rank) status = client_->Execute(computation, {f32_data.get(), f32_data.get()}, @@ -112,7 +113,8 @@ XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) { ASSERT_FALSE(status.ok()); ASSERT_EQ(status.status().code(), tensorflow::error::INVALID_ARGUMENT); ASSERT_THAT(status.status().error_message(), - ContainsRegex("expects parameter 1")); + ContainsRegex( + "Argument does not match shape of computation parameter 1")); // Shape mismatch in parameter 1 (element type) status = client_->Execute(computation, {f32_data.get(), u8_4_data.get()}, @@ -120,7 +122,8 @@ XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) { ASSERT_FALSE(status.ok()); ASSERT_EQ(status.status().code(), tensorflow::error::INVALID_ARGUMENT); ASSERT_THAT(status.status().error_message(), - ContainsRegex("expects parameter 1")); + ContainsRegex( + "Argument does not match shape of computation parameter 1")); } } // namespace diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc index fe5d29a6b6..b020905035 100644 --- a/tensorflow/compiler/xla/util.cc +++ b/tensorflow/compiler/xla/util.cc @@ -30,9 +30,7 @@ limitations under the License. #include "tensorflow/core/platform/stacktrace.h" namespace xla { -namespace { -// Logs the provided status message with a backtrace. Status WithLogBacktrace(const Status& status) { CHECK(!status.ok()); VLOG(1) << status.ToString(); @@ -40,8 +38,6 @@ Status WithLogBacktrace(const Status& status) { return status; } -} // namespace - ScopedLoggingTimer::ScopedLoggingTimer(const string& label, bool enabled) : enabled(enabled), label(label) { if (enabled) { @@ -74,13 +70,18 @@ Status AppendStatus(Status prior, tensorflow::StringPiece context) { // Implementation note: we can't common these out (without using macros) because // they all need to va_start/va_end their varargs in their frame. -Status InvalidArgument(const char* format, ...) { +Status InvalidArgumentV(const char* format, va_list args) { string message; + tensorflow::strings::Appendv(&message, format, args); + return WithLogBacktrace(tensorflow::errors::InvalidArgument(message)); +} + +Status InvalidArgument(const char* format, ...) { va_list args; va_start(args, format); - tensorflow::strings::Appendv(&message, format, args); + Status result = InvalidArgumentV(format, args); va_end(args); - return WithLogBacktrace(tensorflow::errors::InvalidArgument(message)); + return result; } Status Unimplemented(const char* format, ...) { diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index 1d7dd34449..4bc2d632cd 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -40,6 +40,13 @@ limitations under the License. namespace xla { +// Logs the provided status message with a backtrace. +// +// For use by Status-factories, logs a backtrace at the point where the status +// is created, such that we can use --vmodule=util=1 to see all status +// creation backtraces. +Status WithLogBacktrace(const Status& status); + // Ranks greater than 8 are very rare, so use InlinedVector to store // the bounds and indices. And for the rare cases of ranks greater than 8, // the InlinedVector will just behave like an std::vector<> and allocate the @@ -207,6 +214,9 @@ Status ResourceExhausted(const char* format, ...) TF_PRINTF_ATTRIBUTE(1, 2); Status NotFound(const char* format, ...) TF_PRINTF_ATTRIBUTE(1, 2); Status Unavailable(const char* format, ...) TF_PRINTF_ATTRIBUTE(1, 2); +// Passed-varargs variant of the InvalidArgument factory above. +Status InvalidArgumentV(const char* format, va_list args); + // Splits the lines of the original, replaces leading whitespace with the prefix // given by "indentation", and returns the string joined by newlines again. As a // side effect, any additional trailing whitespace is removed. -- GitLab From d3d9dc68ec625ed853b6356757210b063302f396 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 20:26:35 -0800 Subject: [PATCH 098/423] internal change PiperOrigin-RevId: 183333411 --- .../contrib/tpu/profiler/capture_tpu_profile.cc | 11 +++++++++-- tensorflow/contrib/tpu/profiler/tpu_profiler.proto | 13 ++++++++++++- 2 files changed, 21 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc index 1cded9f8cf..7373d0e17c 100644 --- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc @@ -47,12 +47,14 @@ string GetCurrentTimeStampAsString() { return s; } -ProfileResponse Profile(const string& service_addr, int duration_ms) { +ProfileResponse Profile(const string& service_addr, int duration_ms, + const ProfileOptions& opts) { ProfileRequest request; request.set_duration_ms(duration_ms); request.set_max_events(kMaxEvents); request.add_tools("input_pipeline"); request.add_tools("overview_page"); + *request.mutable_opts() = opts; std::cout << "Limiting the number of trace events to " << kMaxEvents << std::endl; ::grpc::ClientContext context; @@ -76,6 +78,7 @@ int main(int argc, char** argv) { tensorflow::string FLAGS_service_addr; tensorflow::string FLAGS_logdir; int FLAGS_duration_ms = 2000; + bool FLAGS_include_dataset_ops = true; std::vector flag_list = { tensorflow::Flag("service_addr", &FLAGS_service_addr, "Address of TPU profiler service e.g. localhost:8466"), @@ -83,6 +86,8 @@ int main(int argc, char** argv) { "Path of TensorBoard log directory e.g. /tmp/tb_log"), tensorflow::Flag("duration_ms", &FLAGS_duration_ms, "Duration of tracing in ms. Default is 2000ms."), + tensorflow::Flag("include_dataset_ops", &FLAGS_include_dataset_ops, + "Set to false to profile longer TPU device traces."), }; std::cout << "Welcome to the Cloud TPU Profiler v" << TPU_PROFILER_VERSION @@ -97,8 +102,10 @@ int main(int argc, char** argv) { tensorflow::port::InitMain(argv[0], &argc, &argv); int duration_ms = FLAGS_duration_ms; + tensorflow::ProfileOptions opts; + opts.set_include_dataset_ops(FLAGS_include_dataset_ops); tensorflow::ProfileResponse response = - tensorflow::tpu::Profile(FLAGS_service_addr, duration_ms); + tensorflow::tpu::Profile(FLAGS_service_addr, duration_ms, opts); // Use the current timestamp as the run name. tensorflow::string run = tensorflow::tpu::GetCurrentTimeStampAsString(); TF_CHECK_OK(tensorflow::tpu::WriteTensorboardTPUProfile( diff --git a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto index bf30d2ce09..f3f3302ceb 100644 --- a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto +++ b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto @@ -13,6 +13,14 @@ service TPUProfiler { } } +message ProfileOptions { + // We don't collect the dataset ops by default for better trace-viewer + // scalability. The caller can mannually set this field to include the ops. + bool include_dataset_ops = 1; + + // next-field: 2 +} + message ProfileRequest { // In future, the caller will be able to customize when profiling starts and // stops. For now, it collects `duration_ms` milliseconds worth of data. @@ -25,10 +33,13 @@ message ProfileRequest { // required profiling tools name such as "input_pipeline_analyzer" etc repeated string tools = 3; + // Optional profiling options that control how a TF session will be profiled. + ProfileOptions opts = 4; + // In future, the caller will indicate which TF session is being profiled, and // only data relating to that program will be returned. For now, we assume // all activity during the profiling period is relevant. - // next-field: 4 + // next-field: 5 } message ProfileToolData { -- GitLab From e35d8a1752538197a48ad34eb256371260d97e1c Mon Sep 17 00:00:00 2001 From: Max Galkin Date: Thu, 25 Jan 2018 20:27:11 -0800 Subject: [PATCH 099/423] Log more info about the ill-formed node in ComputeTransitiveFanin. PiperOrigin-RevId: 183333452 --- tensorflow/core/grappler/grappler_item.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/core/grappler/grappler_item.cc b/tensorflow/core/grappler/grappler_item.cc index 149f6fc735..2f8549cf39 100644 --- a/tensorflow/core/grappler/grappler_item.cc +++ b/tensorflow/core/grappler/grappler_item.cc @@ -134,6 +134,7 @@ std::vector ComputeTransitiveFanin( const NodeDef* node = name_to_node[NodeName(root)]; if (!node) { *ill_formed = true; + VLOG(2) << "ComputeTransitiveFanin: problem with root node: " << root; return {}; } queue.push_back(node); @@ -153,6 +154,7 @@ std::vector ComputeTransitiveFanin( for (const string& input : node->input()) { const NodeDef* in = name_to_node[NodeName(input)]; if (!in) { + VLOG(2) << "ComputeTransitiveFanin: problem with node: " << input; *ill_formed = true; return {}; } -- GitLab From 4383f3d002ddb0712a7aac3303cde6e599de65eb Mon Sep 17 00:00:00 2001 From: Joel Shor Date: Thu, 25 Jan 2018 21:30:45 -0800 Subject: [PATCH 100/423] Increase tolerance on TFGAN losses test. fixes #16238 (#16435) --- tensorflow/contrib/gan/python/losses/python/losses_impl_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py index 7d2a7a254f..56ac45554d 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py @@ -620,7 +620,7 @@ class CombineAdversarialLossTest(test.TestCase): with self.test_session(use_gpu=True) as sess: for _ in range(10): # spot check closeness on more than one sample. gnorm_np, precond_gnorm_np = sess.run([gnorm, precond_gnorm]) - self.assertNear(gnorm_np, precond_gnorm_np, 1e-5) + self.assertNear(gnorm_np, precond_gnorm_np, 1e-4) class CycleConsistencyLossTest(test.TestCase): -- GitLab From 4e68568923332e14b45b5062391b30016ca3c607 Mon Sep 17 00:00:00 2001 From: Taehoon Lee Date: Fri, 26 Jan 2018 14:30:55 +0900 Subject: [PATCH 101/423] Fix docstrings in `scan` (#16432) --- tensorflow/python/ops/functional_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py index 7dbccf1caf..ac03d30fcd 100644 --- a/tensorflow/python/ops/functional_ops.py +++ b/tensorflow/python/ops/functional_ops.py @@ -458,7 +458,7 @@ def scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, For example, if `elems` is `(t1, [t2, t3])` and `initializer` is `[i1, i2]` then an appropriate signature for `fn` in `python2` is: - `fn = lambda (acc_p1, acc_p2), (t1 [t2, t3]):` and `fn` must return a list, + `fn = lambda (acc_p1, acc_p2), (t1, [t2, t3]):` and `fn` must return a list, `[acc_n1, acc_n2]`. An alternative correct signature for `fn`, and the one that works in `python3`, is: `fn = lambda a, t:`, where `a` and `t` correspond to the input tuples. -- GitLab From 0086c55a0faf41ff9e2a66947e4e94531b5d2cac Mon Sep 17 00:00:00 2001 From: Luke Schaefer Date: Fri, 26 Jan 2018 00:31:39 -0500 Subject: [PATCH 102/423] Updating error handling in normalize_tuple (#15822) On line 83 we test to see if single_value is an int (or able to be cast to an int). ValueError is fired if `int()` is called with an input like 'asdf' - this is caught and gives a helpful error, using the 'name' param to provide more context. However, when given an other than a string or int, this is *not* caught - making error messages much more esoteric than the helpful one written out here. For example, before, I was getting an error: > line 83, in normalize_tuple > int(single_value) > TypeError: int() argument must be a string or a number, not 'tuple' Now I get the more useful: > ValueError: The `kernel_size` argument must be a tuple of 2 integers. > Received: ((0, 3), 50) including element (0, 3) of type --- tensorflow/python/layers/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py index e8be347799..7407d9a7b3 100644 --- a/tensorflow/python/layers/utils.py +++ b/tensorflow/python/layers/utils.py @@ -81,7 +81,7 @@ def normalize_tuple(value, n, name): for single_value in value_tuple: try: int(single_value) - except ValueError: + except (ValueError, TypeError): raise ValueError('The `' + name + '` argument must be a tuple of ' + str(n) + ' integers. Received: ' + str(value) + ' ' 'including element ' + str(single_value) + ' of type' + -- GitLab From 73cf824a24e46766a1674c7879d8c48bd0728083 Mon Sep 17 00:00:00 2001 From: Armando Fandango Date: Fri, 26 Jan 2018 00:31:56 -0500 Subject: [PATCH 103/423] Hotfix/fix android example for focus mode continuous picture - #15487 (#15489) * fixed #15487 * fixed #15487 * simplify and tweak formatting to TF style --- .../tensorflow/demo/LegacyCameraConnectionFragment.java | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java index a317273acd..bc0c738e53 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/LegacyCameraConnectionFragment.java @@ -81,8 +81,11 @@ public class LegacyCameraConnectionFragment extends Fragment { try { Camera.Parameters parameters = camera.getParameters(); - parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); - + List focusModes = parameters.getSupportedFocusModes(); + if (focusModes != null + && focusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE)) { + parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); + } List cameraSizes = parameters.getSupportedPreviewSizes(); Size[] sizes = new Size[cameraSizes.size()]; int i = 0; -- GitLab From b6a4bc9d79cd4df561da3326ac921477e6fe301c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 21:57:50 -0800 Subject: [PATCH 104/423] Clarified documentation on resize_images.align_corners parameter. PiperOrigin-RevId: 183339087 --- .../api_def_FusedResizeAndPadConv2D.pbtxt | 5 +- .../api_def_QuantizedResizeBilinear.pbtxt | 5 +- .../api_def/base_api/api_def_ResizeArea.pbtxt | 5 +- .../base_api/api_def_ResizeBicubic.pbtxt | 5 +- .../base_api/api_def_ResizeBicubicGrad.pbtxt | 5 +- .../base_api/api_def_ResizeBilinear.pbtxt | 5 +- .../base_api/api_def_ResizeBilinearGrad.pbtxt | 5 +- .../api_def_ResizeNearestNeighbor.pbtxt | 5 +- .../api_def_ResizeNearestNeighborGrad.pbtxt | 5 +- tensorflow/go/op/wrappers.go | 69 +++++++++---------- tensorflow/python/ops/image_ops_impl.py | 5 +- 11 files changed, 54 insertions(+), 65 deletions(-) diff --git a/tensorflow/core/api_def/base_api/api_def_FusedResizeAndPadConv2D.pbtxt b/tensorflow/core/api_def/base_api/api_def_FusedResizeAndPadConv2D.pbtxt index a72f2bfe5f..118d0e2178 100644 --- a/tensorflow/core/api_def/base_api/api_def_FusedResizeAndPadConv2D.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_FusedResizeAndPadConv2D.pbtxt @@ -30,9 +30,8 @@ END attr { name: "resize_align_corners" description: < Date: Thu, 25 Jan 2018 22:32:15 -0800 Subject: [PATCH 105/423] For windows cmake build turn on CMAKE_SUPPRESS_REGENERATION to avoid flaky build failures. PiperOrigin-RevId: 183341561 --- tensorflow/contrib/cmake/CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index 817e96f5da..12bfd3c62b 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -134,6 +134,9 @@ if(WIN32) set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /D_ITERATOR_DEBUG_LEVEL=0") set(CMAKE_CXX_FLAGS_MINSIZEREL "${CMAKE_CXX_FLAGS_MINSIZEREL} /D_ITERATOR_DEBUG_LEVEL=0") set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /D_ITERATOR_DEBUG_LEVEL=0") + + # Try to avoid flaky failures due to failed generation of generate.stamp files. + set(CMAKE_SUPPRESS_REGENERATION ON) endif() if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") -- GitLab From e4912296bc67c13e3f53bf599b5ba5af4eea7b06 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 25 Jan 2018 22:45:59 -0800 Subject: [PATCH 106/423] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 183342483 --- tensorflow/go/op/wrappers.go | 69 +++++++++++++++++++----------------- 1 file changed, 36 insertions(+), 33 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 6d54429186..5b19c90238 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2589,10 +2589,10 @@ type ResizeBicubicAttr func(optionalAttr) // ResizeBicubicAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output images are -// aligned, preserving the values at the corner pixels. If false, rescale by -// new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale input by (new_height - 1) / (height - 1), which +// exactly aligns the 4 corners of images and resized images. If false, rescale +// by new_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr { return func(m optionalAttr) { m["align_corners"] = value @@ -5817,9 +5817,10 @@ type ResizeBilinearGradAttr func(optionalAttr) // ResizeBilinearGradAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the grads and the original_image are -// aligned. If false, rescale by new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale grads by (orig_height - 1) / (height - 1), which +// exactly aligns the 4 corners of grads and original_image. If false, rescale by +// orig_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func ResizeBilinearGradAlignCorners(value bool) ResizeBilinearGradAttr { return func(m optionalAttr) { m["align_corners"] = value @@ -6386,10 +6387,10 @@ type ResizeBilinearAttr func(optionalAttr) // ResizeBilinearAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output images are -// aligned, preserving the values at the corner pixels. If false, rescale by -// new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale input by (new_height - 1) / (height - 1), which +// exactly aligns the 4 corners of images and resized images. If false, rescale +// by new_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { return func(m optionalAttr) { m["align_corners"] = value @@ -7381,10 +7382,10 @@ type ResizeAreaAttr func(optionalAttr) // ResizeAreaAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output images are -// aligned, preserving the values at the corner pixels. If false, rescale by -// new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale input by (new_height - 1) / (height - 1), which +// exactly aligns the 4 corners of images and resized images. If false, rescale +// by new_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func ResizeAreaAlignCorners(value bool) ResizeAreaAttr { return func(m optionalAttr) { m["align_corners"] = value @@ -13687,10 +13688,10 @@ type FusedResizeAndPadConv2DAttr func(optionalAttr) // FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output images are -// aligned, preserving the values at the corner pixels. If false, rescale by -// new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale input by (new_height - 1) / (height - 1), +// which exactly aligns the 4 corners of images and resized images. If false, rescale +// by new_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr { return func(m optionalAttr) { m["resize_align_corners"] = value @@ -13832,10 +13833,10 @@ type QuantizedResizeBilinearAttr func(optionalAttr) // QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output images are -// aligned, preserving the values at the corner pixels. If false, rescale by -// new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale input by (new_height - 1) / (height - 1), which +// exactly aligns the 4 corners of images and resized images. If false, rescale +// by new_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr { return func(m optionalAttr) { m["align_corners"] = value @@ -18667,9 +18668,10 @@ type ResizeBicubicGradAttr func(optionalAttr) // ResizeBicubicGradAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the grads and the original_image are -// aligned. If false, rescale by new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale grads by (orig_height - 1) / (height - 1), which +// exactly aligns the 4 corners of grads and original_image. If false, rescale by +// orig_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func ResizeBicubicGradAlignCorners(value bool) ResizeBicubicGradAttr { return func(m optionalAttr) { m["align_corners"] = value @@ -18710,10 +18712,10 @@ type ResizeNearestNeighborAttr func(optionalAttr) // ResizeNearestNeighborAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output images are -// aligned, preserving the values at the corner pixels. If false, rescale by -// new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale input by (new_height - 1) / (height - 1), which +// exactly aligns the 4 corners of images and resized images. If false, rescale +// by new_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr { return func(m optionalAttr) { m["align_corners"] = value @@ -18753,9 +18755,10 @@ type ResizeNearestNeighborGradAttr func(optionalAttr) // ResizeNearestNeighborGradAlignCorners sets the optional align_corners attribute to value. // -// value: If true, the centers of the 4 corner pixels of the grads and the original_image are -// aligned. If false, rescale by new_height / height, new_width / width. -// If not specified, defaults to false. +// value: If true, rescale grads by (orig_height - 1) / (height - 1), which +// exactly aligns the 4 corners of grads and original_image. If false, rescale by +// orig_height / height. Treat similarly the width dimension. +// If not specified, defaults to false func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr { return func(m optionalAttr) { m["align_corners"] = value -- GitLab From 3cf36b440e7866895e5fc20f2c15ac56f0d96cbc Mon Sep 17 00:00:00 2001 From: Koan-Sin Tan Date: Fri, 26 Jan 2018 14:45:27 +0800 Subject: [PATCH 107/423] use bilinear op to resize input of label_image replace previous naive downsize() function with TF Lite RESIZE_BILINEAR operator --- .../contrib/lite/examples/label_image/BUILD | 5 +- .../examples/label_image/bitmap_helpers.h | 14 ++-- .../label_image/bitmap_helpers_impl.h | 80 ++++++++++++++----- .../lite/examples/label_image/label_image.cc | 12 +-- 4 files changed, 79 insertions(+), 32 deletions(-) diff --git a/tensorflow/contrib/lite/examples/label_image/BUILD b/tensorflow/contrib/lite/examples/label_image/BUILD index 476d85c031..d216cdf69b 100644 --- a/tensorflow/contrib/lite/examples/label_image/BUILD +++ b/tensorflow/contrib/lite/examples/label_image/BUILD @@ -42,7 +42,10 @@ cc_library( "bitmap_helpers_impl.h", "label_image.h", ], - deps = ["//tensorflow/contrib/lite:string"], + deps = [ + "//tensorflow/contrib/lite:string", + "//tensorflow/contrib/lite/kernels:builtin_ops", + ], ) # TODO(ahentz): Test disabled as it has a memory leek from read_bmp diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h index 860e27e5ba..471fda2ba4 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers.h @@ -26,15 +26,15 @@ uint8_t* read_bmp(const std::string& input_bmp_name, int* width, int* height, int* channels, Settings* s); template -void downsize(T* out, uint8_t* in, int image_height, int image_width, - int image_channels, int wanted_height, int wanted_width, - int wanted_channels, Settings* s); +void resize(T* out, uint8_t* in, int image_height, int image_width, + int image_channels, int wanted_height, int wanted_width, + int wanted_channels, Settings* s); // explicit instantiation -template void downsize(uint8_t*, unsigned char*, int, int, int, int, - int, int, Settings*); -template void downsize(float*, unsigned char*, int, int, int, int, int, - int, Settings*); +template void resize(uint8_t*, unsigned char*, int, int, int, int, + int, int, Settings*); +template void resize(float*, unsigned char*, int, int, int, int, int, + int, Settings*); } // namespace label_image } // namespace tflite diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h index 64a931082b..942906e269 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h @@ -16,30 +16,74 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H #define TENSORFLOW_CONTRIB_LITE_EXAMPLES_LABEL_IMAGE_BITMAP_HELPERS_IMPL_H +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/string_util.h" +#include "tensorflow/contrib/lite/version.h" + #include "tensorflow/contrib/lite/examples/label_image/label_image.h" namespace tflite { namespace label_image { template -void downsize(T* out, uint8_t* in, int image_height, int image_width, - int image_channels, int wanted_height, int wanted_width, - int wanted_channels, Settings* s) { - for (int y = 0; y < wanted_height; ++y) { - const int in_y = (y * image_height) / wanted_height; - uint8_t* in_row = in + (in_y * image_width * image_channels); - T* out_row = out + (y * wanted_width * wanted_channels); - for (int x = 0; x < wanted_width; ++x) { - const int in_x = (x * image_width) / wanted_width; - uint8_t* in_pixel = in_row + (in_x * image_channels); - T* out_pixel = out_row + (x * wanted_channels); - for (int c = 0; c < wanted_channels; ++c) { - if (s->input_floating) - out_pixel[c] = (in_pixel[c] - s->input_mean) / s->input_std; - else - out_pixel[c] = in_pixel[c]; - } - } +void resize(T* out, uint8_t* in, int image_height, int image_width, + int image_channels, int wanted_height, int wanted_width, + int wanted_channels, Settings* s) { + + int number_of_pixels = image_height * image_width * image_channels; + std::unique_ptr interpreter(new Interpreter); + + int base_index = 0; + + // two inputs: input and new_sizes + interpreter->AddTensors(2, &base_index); + // one output + interpreter->AddTensors(1, &base_index); + // set input and output tensors + interpreter->SetInputs({0, 1}); + interpreter->SetOutputs({2}); + + // set paramters of tensors + TfLiteQuantizationParams quant; + interpreter->SetTensorParametersReadWrite( + 0, kTfLiteFloat32, "input", + {1, image_height, image_width, image_channels}, quant); + interpreter->SetTensorParametersReadWrite(1, kTfLiteInt32, "new_size", {2}, + quant); + interpreter->SetTensorParametersReadWrite( + 2, kTfLiteFloat32, "output", + {1, wanted_height, wanted_width, wanted_channels}, quant); + + ops::builtin::BuiltinOpResolver resolver; + TfLiteRegistration* resize_op = + resolver.FindOp(BuiltinOperator_RESIZE_BILINEAR); + interpreter->AddNodeWithParameters({0, 1}, {2}, nullptr, 0, nullptr, + resize_op, nullptr); + + interpreter->AllocateTensors(); + + // fill input image + // in[] are integers, cannot do memcpy() directly + auto input = interpreter->typed_tensor(0); + for (int i = 0; i < number_of_pixels; i++) input[i] = in[i]; + + // fill new_sizes + interpreter->typed_tensor(1)[0] = wanted_height; + interpreter->typed_tensor(1)[1] = wanted_width; + + interpreter->Invoke(); + + auto output = interpreter->typed_tensor(2); + auto output_number_of_pixels = + wanted_height * wanted_height * wanted_channels; + + for (int i = 0; i < output_number_of_pixels; i++) { + if (s->input_floating) + out[i] = (output[i] - s->input_mean) / s->input_std; + else + out[i] = (uint8_t)output[i]; } } diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index d7f49ad875..a78900122e 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -151,14 +151,14 @@ void RunInference(Settings* s) { switch (interpreter->tensor(input)->type) { case kTfLiteFloat32: s->input_floating = true; - downsize(interpreter->typed_tensor(input), in, - image_height, image_width, image_channels, - wanted_height, wanted_width, wanted_channels, s); + resize(interpreter->typed_tensor(input), in, + image_height, image_width, image_channels, + wanted_height, wanted_width, wanted_channels, s); break; case kTfLiteUInt8: - downsize(interpreter->typed_tensor(input), in, - image_height, image_width, image_channels, - wanted_height, wanted_width, wanted_channels, s); + resize(interpreter->typed_tensor(input), in, + image_height, image_width, image_channels, + wanted_height, wanted_width, wanted_channels, s); break; default: LOG(FATAL) << "cannot handle input type " -- GitLab From ffda6079ed619df8fd3edb4db71ffc7d005c2430 Mon Sep 17 00:00:00 2001 From: Tayo Oguntebi Date: Thu, 25 Jan 2018 23:37:20 -0800 Subject: [PATCH 108/423] Adds R1 test for ReduceWindow. PiperOrigin-RevId: 183345779 --- .../compiler/xla/tests/reduce_window_test.cc | 76 ++++++++++++------- 1 file changed, 49 insertions(+), 27 deletions(-) diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index 73b37e201a..7f3c72671d 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -1016,37 +1016,39 @@ class R2ReduceWindowTest : public ReduceWindowTestBase, ::testing::tuple> { protected: R2ReduceWindowTest() { set_use_bfloat16(::testing::get<1>(GetParam())); } -}; -TEST_P(R2ReduceWindowTest, Add) { - ComputationBuilder b(client_, TestName()); - const auto& param = ::testing::get<0>(GetParam()); - CHECK(param.reducer == kAdd); - - const float kInitValue = 0.0f; - Array2D input(param.base_bounds[0], param.base_bounds[1], 1.0f); - std::unique_ptr input_literal = - Literal::CreateR2FromArray2DWithLayout( - input, LayoutUtil::MakeLayout(param.layout)); + void DoIt() { + ComputationBuilder b(client_, TestName()); + const auto& param = ::testing::get<0>(GetParam()); + CHECK(param.reducer == kAdd); - ComputationDataHandle parameter; - auto input_arg = CreateParameterAndTransferLiteral(0, *input_literal, "p0", - &b, ¶meter); - auto init_value = - CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); - b.ReduceWindow(/*operand=*/parameter, - /*init_value=*/init_value, - /*computation=*/CreateScalarAddComputation(FloatType(), &b), - /*window_dimensions=*/param.window_bounds, - /*window_strides=*/param.strides, /*padding=*/param.padding); + const float kInitValue = 0.0f; + Array2D input(param.base_bounds[0], param.base_bounds[1], 1.0f); + std::unique_ptr input_literal = + Literal::CreateR2FromArray2DWithLayout( + input, LayoutUtil::MakeLayout(param.layout)); - auto expected = ReferenceUtil::ReduceWindow2DAdd( - /*operand=*/input, /*init=*/kInitValue, /*window=*/param.window_bounds, - /*stride=*/param.strides, /*padding=*/param.padding); + ComputationDataHandle parameter; + auto input_arg = CreateParameterAndTransferLiteral(0, *input_literal, "p0", + &b, ¶meter); + auto init_value = + CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); + b.ReduceWindow(/*operand=*/parameter, + /*init_value=*/init_value, + /*computation=*/CreateScalarAddComputation(FloatType(), &b), + /*window_dimensions=*/param.window_bounds, + /*window_strides=*/param.strides, /*padding=*/param.padding); + + auto expected = ReferenceUtil::ReduceWindow2DAdd( + /*operand=*/input, /*init=*/kInitValue, /*window=*/param.window_bounds, + /*stride=*/param.strides, /*padding=*/param.padding); + + ComputeAndCompareLiteral(&b, *Literal::CreateFromArray(*expected), + {input_arg.get()}, DefaultErrorSpec()); + } +}; - ComputeAndCompareLiteral(&b, *Literal::CreateFromArray(*expected), - {input_arg.get()}, DefaultErrorSpec()); -} +TEST_P(R2ReduceWindowTest, DoIt) { DoIt(); } INSTANTIATE_TEST_CASE_P( R2ReduceWindowTestInstantiation, R2ReduceWindowTest, @@ -1054,6 +1056,26 @@ INSTANTIATE_TEST_CASE_P( ::testing::ValuesIn(use_bfloat16_params)), R2ReduceWindowTestDataToString); +class R2ReduceWindowFailingCpuGpuBf16Test : public R2ReduceWindowTest {}; + +// TODO(b/72234705): Fix the test cases failed on CPU and GPU. +XLA_TEST_P(R2ReduceWindowFailingCpuGpuBf16Test, + DISABLED_ON_CPU_PARALLEL(DISABLED_ON_CPU(DISABLED_ON_GPU(DoIt)))) { + DoIt(); +} + +const R2ReduceWindowTestData kR2FailingValuesCpuGpuBf16Test[] = { + {/*base_bounds=*/{8, 128}, /*window_bounds=*/{8, 128}, + /*strides=*/{1, 1}, /*layout=*/{1, 0}, + /*padding=*/Padding::kValid, /*reducer=*/Reducer::kAdd}, +}; + +INSTANTIATE_TEST_CASE_P( + R2ReduceWindowFailingInstantiation, R2ReduceWindowFailingCpuGpuBf16Test, + ::testing::Combine(::testing::ValuesIn(kR2FailingValuesCpuGpuBf16Test), + ::testing::ValuesIn(use_bfloat16_params)), + R2ReduceWindowTestDataToString); + struct R1ReduceWindowTestData { int64 base_bounds[1]; int64 window_bounds[1]; -- GitLab From 76f6938bafeb81a4ca41b8dac2b9c83e1286fa95 Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Thu, 25 Jan 2018 23:59:19 -0800 Subject: [PATCH 109/423] Set up TensorRT configurations for external use, and add a test. PiperOrigin-RevId: 183347199 --- configure.py | 119 ++++++++++ tensorflow/BUILD | 9 + tensorflow/contrib/tensorrt/BUILD | 45 ++++ tensorflow/contrib/tensorrt/tensorrt_test.cc | 159 +++++++++++++ tensorflow/tensorflow.bzl | 16 +- tensorflow/workspace.bzl | 2 + third_party/gpus/cuda_configure.bzl | 104 +++++---- third_party/tensorrt/BUILD | 0 third_party/tensorrt/BUILD.tpl | 67 ++++++ third_party/tensorrt/build_defs.bzl.tpl | 7 + third_party/tensorrt/tensorrt_configure.bzl | 224 +++++++++++++++++++ 11 files changed, 701 insertions(+), 51 deletions(-) create mode 100644 tensorflow/contrib/tensorrt/BUILD create mode 100644 tensorflow/contrib/tensorrt/tensorrt_test.cc create mode 100644 third_party/tensorrt/BUILD create mode 100644 third_party/tensorrt/BUILD.tpl create mode 100644 third_party/tensorrt/build_defs.bzl.tpl create mode 100644 third_party/tensorrt/tensorrt_configure.bzl diff --git a/configure.py b/configure.py index cf16ef4837..083fed1710 100644 --- a/configure.py +++ b/configure.py @@ -43,6 +43,7 @@ _DEFAULT_CUDA_PATH = '/usr/local/cuda' _DEFAULT_CUDA_PATH_LINUX = '/opt/cuda' _DEFAULT_CUDA_PATH_WIN = ('C:/Program Files/NVIDIA GPU Computing ' 'Toolkit/CUDA/v%s' % _DEFAULT_CUDA_VERSION) +_DEFAULT_TENSORRT_PATH_LINUX = '/usr/lib/x86_64-linux-gnu' _TF_OPENCL_VERSION = '1.2' _DEFAULT_COMPUTECPP_TOOLKIT_PATH = '/usr/local/computecpp' _DEFAULT_TRISYCL_INCLUDE_DIR = '/usr/local/triSYCL/include' @@ -959,6 +960,119 @@ def set_tf_cudnn_version(environ_cp): write_action_env_to_bazelrc('TF_CUDNN_VERSION', tf_cudnn_version) +def set_tf_tensorrt_install_path(environ_cp): + """Set TENSORRT_INSTALL_PATH and TF_TENSORRT_VERSION. + + Adapted from code contributed by Sami Kama (https://github.com/samikama). + + Args: + environ_cp: copy of the os.environ. + + Raises: + ValueError: if this method was called under non-Linux platform. + UserInputError: if user has provided invalid input multiple times. + """ + if not is_linux(): + raise ValueError('Currently TensorRT is only supported on Linux platform.') + + # Ask user whether to add TensorRT support. + if str(int(get_var( + environ_cp, 'TF_NEED_TENSORRT', 'TensorRT', False))) != '1': + return + + for _ in range(_DEFAULT_PROMPT_ASK_ATTEMPTS): + ask_tensorrt_path = (r'Please specify the location where TensorRT is ' + 'installed. [Default is %s]:') % ( + _DEFAULT_TENSORRT_PATH_LINUX) + trt_install_path = get_from_env_or_user_or_default( + environ_cp, 'TENSORRT_INSTALL_PATH', ask_tensorrt_path, + _DEFAULT_TENSORRT_PATH_LINUX) + + # Result returned from "read" will be used unexpanded. That make "~" + # unusable. Going through one more level of expansion to handle that. + trt_install_path = os.path.realpath( + os.path.expanduser(trt_install_path)) + + def find_libs(search_path): + """Search for libnvinfer.so in "search_path".""" + fl = set() + if os.path.exists(search_path) and os.path.isdir(search_path): + fl.update([os.path.realpath(os.path.join(search_path, x)) + for x in os.listdir(search_path) if 'libnvinfer.so' in x]) + return fl + + possible_files = find_libs(trt_install_path) + possible_files.update(find_libs(os.path.join(trt_install_path, 'lib'))) + possible_files.update(find_libs(os.path.join(trt_install_path, 'lib64'))) + + def is_compatible(tensorrt_lib, cuda_ver, cudnn_ver): + """Check the compatibility between tensorrt and cudnn/cudart libraries.""" + ldd_bin = which('ldd') or '/usr/bin/ldd' + ldd_out = run_shell([ldd_bin, tensorrt_lib]).split(os.linesep) + cudnn_pattern = re.compile('.*libcudnn.so\\.?(.*) =>.*$') + cuda_pattern = re.compile('.*libcudart.so\\.?(.*) =>.*$') + cudnn = None + cudart = None + for line in ldd_out: + if 'libcudnn.so' in line: + cudnn = cudnn_pattern.search(line) + elif 'libcudart.so' in line: + cudart = cuda_pattern.search(line) + if cudnn and len(cudnn.group(1)): + cudnn = convert_version_to_int(cudnn.group(1)) + if cudart and len(cudart.group(1)): + cudart = convert_version_to_int(cudart.group(1)) + return (cudnn == cudnn_ver) and (cudart == cuda_ver) + + cuda_ver = convert_version_to_int(environ_cp['TF_CUDA_VERSION']) + cudnn_ver = convert_version_to_int(environ_cp['TF_CUDNN_VERSION']) + nvinfer_pattern = re.compile('.*libnvinfer.so.?(.*)$') + highest_ver = [0, None, None] + + for lib_file in possible_files: + if is_compatible(lib_file, cuda_ver, cudnn_ver): + ver_str = nvinfer_pattern.search(lib_file).group(1) + ver = convert_version_to_int(ver_str) if len(ver_str) else 0 + if ver > highest_ver[0]: + highest_ver = [ver, ver_str, lib_file] + if highest_ver[1] is not None: + trt_install_path = os.path.dirname(highest_ver[2]) + tf_tensorrt_version = highest_ver[1] + break + + # Try another alternative from ldconfig. + ldconfig_bin = which('ldconfig') or '/sbin/ldconfig' + ldconfig_output = run_shell([ldconfig_bin, '-p']) + search_result = re.search( + '.*libnvinfer.so\\.?([0-9.]*).* => (.*)', ldconfig_output) + if search_result: + libnvinfer_path_from_ldconfig = search_result.group(2) + if os.path.exists(libnvinfer_path_from_ldconfig): + if is_compatible(libnvinfer_path_from_ldconfig, cuda_ver, cudnn_ver): + trt_install_path = os.path.dirname(libnvinfer_path_from_ldconfig) + tf_tensorrt_version = search_result.group(1) + break + + # Reset and Retry + print('Invalid path to TensorRT. None of the following files can be found:') + print(trt_install_path) + print(os.path.join(trt_install_path, 'lib')) + print(os.path.join(trt_install_path, 'lib64')) + if search_result: + print(libnvinfer_path_from_ldconfig) + + else: + raise UserInputError('Invalid TF_TENSORRT setting was provided %d ' + 'times in a row. Assuming to be a scripting mistake.' % + _DEFAULT_PROMPT_ASK_ATTEMPTS) + + # Set TENSORRT_INSTALL_PATH and TF_TENSORRT_VERSION + environ_cp['TENSORRT_INSTALL_PATH'] = trt_install_path + write_action_env_to_bazelrc('TENSORRT_INSTALL_PATH', trt_install_path) + environ_cp['TF_TENSORRT_VERSION'] = tf_tensorrt_version + write_action_env_to_bazelrc('TF_TENSORRT_VERSION', tf_tensorrt_version) + + def get_native_cuda_compute_capabilities(environ_cp): """Get native cuda compute capabilities. @@ -1244,9 +1358,11 @@ def main(): environ_cp['TF_NEED_COMPUTECPP'] = '0' environ_cp['TF_NEED_OPENCL'] = '0' environ_cp['TF_CUDA_CLANG'] = '0' + environ_cp['TF_NEED_TENSORRT'] = '0' if is_macos(): environ_cp['TF_NEED_JEMALLOC'] = '0' + environ_cp['TF_NEED_TENSORRT'] = '0' set_build_var(environ_cp, 'TF_NEED_JEMALLOC', 'jemalloc as malloc', 'with_jemalloc', True) @@ -1278,6 +1394,8 @@ def main(): 'TF_CUDA_CONFIG_REPO' not in environ_cp): set_tf_cuda_version(environ_cp) set_tf_cudnn_version(environ_cp) + if is_linux(): + set_tf_tensorrt_install_path(environ_cp) set_tf_cuda_compute_capabilities(environ_cp) set_tf_cuda_clang(environ_cp) @@ -1332,6 +1450,7 @@ def main(): 'more details.') config_info_line('mkl', 'Build with MKL support.') config_info_line('monolithic', 'Config for mostly static monolithic build.') + config_info_line('tensorrt', 'Build with TensorRT support.') if __name__ == '__main__': main() diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 9099463c4f..3d2411a266 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -370,6 +370,14 @@ 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 = [ @@ -558,6 +566,7 @@ filegroup( "//tensorflow/contrib/tensor_forest/proto:all_files", "//tensorflow/contrib/tensorboard:all_files", "//tensorflow/contrib/tensorboard/db:all_files", + "//tensorflow/contrib/tensorrt:all_files", "//tensorflow/contrib/testing:all_files", "//tensorflow/contrib/text:all_files", "//tensorflow/contrib/tfprof:all_files", diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD new file mode 100644 index 0000000000..28f571e1f0 --- /dev/null +++ b/tensorflow/contrib/tensorrt/BUILD @@ -0,0 +1,45 @@ +# Description: +# Wrap NVIDIA TensorRT (http://developer.nvidia.com/tensorrt) with tensorflow. +# APIs are meant to change over time. + +package(default_visibility = ["//tensorflow:__subpackages__"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "tf_cuda_cc_test") +load( + "@local_config_tensorrt//:build_defs.bzl", + "if_tensorrt", +) + +tf_cuda_cc_test( + name = "tensorrt_test_cc", + size = "small", + srcs = ["tensorrt_test.cc"], + tags = [ + "manual", + "notap", + ], + deps = [ + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ] + if_tensorrt([ + "@local_config_cuda//cuda:cuda_headers", + "@local_config_tensorrt//:nv_infer", + ]), +) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/contrib/tensorrt/tensorrt_test.cc b/tensorflow/contrib/tensorrt/tensorrt_test.cc new file mode 100644 index 0000000000..e11522ea5b --- /dev/null +++ b/tensorflow/contrib/tensorrt/tensorrt_test.cc @@ -0,0 +1,159 @@ +/* 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/platform/logging.h" +#include "tensorflow/core/platform/test.h" + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT +#include "cuda/include/cuda.h" +#include "cuda/include/cuda_runtime_api.h" +#include "tensorrt/include/NvInfer.h" + +namespace tensorflow { +namespace { + +class Logger : public nvinfer1::ILogger { + public: + void log(nvinfer1::ILogger::Severity severity, const char* msg) override { + switch (severity) { + case Severity::kINFO: + LOG(INFO) << msg; + break; + case Severity::kWARNING: + LOG(WARNING) << msg; + break; + case Severity::kINTERNAL_ERROR: + case Severity::kERROR: + LOG(ERROR) << msg; + break; + default: + break; + } + } +}; + +class ScopedWeights { + public: + ScopedWeights(float value) : value_(value) { + w.type = nvinfer1::DataType::kFLOAT; + w.values = &value_; + w.count = 1; + } + const nvinfer1::Weights& get() { return w; } + + private: + float value_; + nvinfer1::Weights w; +}; + +const char* kInputTensor = "input"; +const char* kOutputTensor = "output"; + +// Creates a network to compute y=2x+3. +nvinfer1::IHostMemory* CreateNetwork() { + Logger logger; + nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(logger); + ScopedWeights weights(2.0); + ScopedWeights bias(3.0); + + nvinfer1::INetworkDefinition* network = builder->createNetwork(); + // Add the input. + auto input = network->addInput(kInputTensor, nvinfer1::DataType::kFLOAT, + nvinfer1::DimsCHW{1, 1, 1}); + EXPECT_NE(input, nullptr); + // Add the hidden layer. + auto layer = network->addFullyConnected(*input, 1, weights.get(), bias.get()); + EXPECT_NE(layer, nullptr); + // Mark the output. + auto output = layer->getOutput(0); + output->setName(kOutputTensor); + network->markOutput(*output); + // Build the engine + builder->setMaxBatchSize(1); + builder->setMaxWorkspaceSize(1 << 10); + auto engine = builder->buildCudaEngine(*network); + EXPECT_NE(engine, nullptr); + // Serialize the engine to create a model, then close everything. + nvinfer1::IHostMemory* model = engine->serialize(); + network->destroy(); + engine->destroy(); + builder->destroy(); + return model; +} + +// Executes the network. +void Execute(nvinfer1::IExecutionContext& context, const float* input, + float* output) { + const nvinfer1::ICudaEngine& engine = context.getEngine(); + + // We have two bindings: input and output. + ASSERT_EQ(engine.getNbBindings(), 2); + const int input_index = engine.getBindingIndex(kInputTensor); + const int output_index = engine.getBindingIndex(kOutputTensor); + + // Create GPU buffers and a stream + void* buffers[2]; + ASSERT_EQ(0, cudaMalloc(&buffers[input_index], sizeof(float))); + ASSERT_EQ(0, cudaMalloc(&buffers[output_index], sizeof(float))); + cudaStream_t stream; + ASSERT_EQ(0, cudaStreamCreate(&stream)); + + // Copy the input to the GPU, execute the network, and copy the output back. + // + // Note that since the host buffer was not created as pinned memory, these + // async copies are turned into sync copies. So the following synchronization + // could be removed. + ASSERT_EQ(0, cudaMemcpyAsync(buffers[input_index], input, sizeof(float), + cudaMemcpyHostToDevice, stream)); + context.enqueue(1, buffers, stream, nullptr); + ASSERT_EQ(0, cudaMemcpyAsync(output, buffers[output_index], sizeof(float), + cudaMemcpyDeviceToHost, stream)); + cudaStreamSynchronize(stream); + + // Release the stream and the buffers + cudaStreamDestroy(stream); + ASSERT_EQ(0, cudaFree(buffers[input_index])); + ASSERT_EQ(0, cudaFree(buffers[output_index])); +} + +TEST(TensorrtTest, BasicFunctions) { + // Create the network model. + nvinfer1::IHostMemory* model = CreateNetwork(); + // Use the model to create an engine and then an execution context. + Logger logger; + nvinfer1::IRuntime* runtime = nvinfer1::createInferRuntime(logger); + nvinfer1::ICudaEngine* engine = + runtime->deserializeCudaEngine(model->data(), model->size(), nullptr); + model->destroy(); + nvinfer1::IExecutionContext* context = engine->createExecutionContext(); + + // Execute the network. + float input = 1234; + float output; + Execute(*context, &input, &output); + EXPECT_EQ(output, input * 2 + 3); + + // Destroy the engine. + context->destroy(); + engine->destroy(); + runtime->destroy(); +} + +} // namespace +} // namespace tensorflow + +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 383c97344a..7fe9c98726 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -10,6 +10,10 @@ load( "tf_additional_xla_deps_py", "if_static", ) +load( + "@local_config_tensorrt//:build_defs.bzl", + "if_tensorrt", +) load( "@local_config_cuda//cuda:build_defs.bzl", "if_cuda", @@ -197,6 +201,7 @@ def tf_copts(android_optimization_level_override="-O2", is_external=False): "-fno-exceptions", "-ftemplate-depth=900"]) + if_cuda(["-DGOOGLE_CUDA=1"]) + + if_tensorrt(["-DGOOGLE_TENSORRT=1"]) + if_mkl(["-DINTEL_MKL=1", "-DEIGEN_USE_VML", "-fopenmp",]) + if_android_arm(["-mfpu=neon"]) + if_linux_x86_64(["-msse3"]) @@ -861,9 +866,11 @@ def tf_cuda_library(deps=None, cuda_deps=None, copts=tf_copts(), **kwargs): When the library is built with --config=cuda: - - both deps and cuda_deps are used as dependencies - - the cuda runtime is added as a dependency (if necessary) - - The library additionally passes -DGOOGLE_CUDA=1 to the list of copts + - Both deps and cuda_deps are used as dependencies. + - The cuda runtime is added as a dependency (if necessary). + - The library additionally passes -DGOOGLE_CUDA=1 to the list of copts. + - In addition, when the library is also built with TensorRT enabled, it + additionally passes -DGOOGLE_TENSORRT=1 to the list of copts. Args: - cuda_deps: BUILD dependencies which will be linked if and only if: @@ -882,7 +889,8 @@ def tf_cuda_library(deps=None, cuda_deps=None, copts=tf_copts(), **kwargs): clean_dep("//tensorflow/core:cuda"), "@local_config_cuda//cuda:cuda_headers" ]), - copts=copts + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1"]), + copts=(copts + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1"]) + + if_tensorrt(["-DGOOGLE_TENSORRT=1"])), **kwargs) register_extension_info( diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 9145d9e58a..f7d9075032 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -1,6 +1,7 @@ # TensorFlow external dependencies that can be loaded in WORKSPACE files. load("//third_party/gpus:cuda_configure.bzl", "cuda_configure") +load("//third_party/tensorrt:tensorrt_configure.bzl", "tensorrt_configure") load("//third_party/mkl:build_defs.bzl", "mkl_repository") load("//third_party/git:git_configure.bzl", "git_configure") load("//third_party/py:python_configure.bzl", "python_configure") @@ -68,6 +69,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): check_bazel_version_at_least("0.5.4") clang6_configure(name="local_config_clang6") cuda_configure(name="local_config_cuda") + tensorrt_configure(name="local_config_tensorrt") git_configure(name="local_config_git") sycl_configure(name="local_config_sycl") python_configure(name="local_config_python") diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl index 2727fa5efe..8e1dd8a54f 100644 --- a/third_party/gpus/cuda_configure.bzl +++ b/third_party/gpus/cuda_configure.bzl @@ -236,7 +236,7 @@ def _cudnn_install_basedir(repository_ctx): return cudnn_install_path -def _matches_version(environ_version, detected_version): +def matches_version(environ_version, detected_version): """Checks whether the user-specified version matches the detected version. This function performs a weak matching so that if the user specifies only the @@ -317,7 +317,7 @@ def _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value): environ_version = "" if _TF_CUDA_VERSION in repository_ctx.os.environ: environ_version = repository_ctx.os.environ[_TF_CUDA_VERSION].strip() - if environ_version and not _matches_version(environ_version, full_version): + if environ_version and not matches_version(environ_version, full_version): auto_configure_fail( ("CUDA version detected from nvcc (%s) does not match " + "TF_CUDA_VERSION (%s)") % (full_version, environ_version)) @@ -338,35 +338,49 @@ _DEFINE_CUDNN_MINOR = "#define CUDNN_MINOR" _DEFINE_CUDNN_PATCHLEVEL = "#define CUDNN_PATCHLEVEL" -def _find_cuda_define(repository_ctx, cudnn_header_dir, define): - """Returns the value of a #define in cudnn.h +def find_cuda_define(repository_ctx, header_dir, header_file, define): + """Returns the value of a #define in a header file. - Greps through cudnn.h and returns the value of the specified #define. If the - #define is not found, then raise an error. + Greps through a header file and returns the value of the specified #define. + If the #define is not found, then raise an error. Args: repository_ctx: The repository context. - cudnn_header_dir: The directory containing the cuDNN header. + header_dir: The directory containing the header file. + header_file: The header file name. define: The #define to search for. Returns: - The value of the #define found in cudnn.h. + The value of the #define found in the header. """ - # Confirm location of cudnn.h and grep for the line defining CUDNN_MAJOR. - cudnn_h_path = repository_ctx.path("%s/cudnn.h" % cudnn_header_dir) - if not cudnn_h_path.exists: - auto_configure_fail("Cannot find cudnn.h at %s" % str(cudnn_h_path)) - result = repository_ctx.execute(["grep", "--color=never", "-E", define, str(cudnn_h_path)]) + # Confirm location of the header and grep for the line defining the macro. + h_path = repository_ctx.path("%s/%s" % (header_dir, header_file)) + if not h_path.exists: + auto_configure_fail("Cannot find %s at %s" % (header_file, str(h_path))) + result = repository_ctx.execute( + # Grep one more lines as some #defines are splitted into two lines. + ["grep", "--color=never", "-A1", "-E", define, str(h_path)]) if result.stderr: - auto_configure_fail("Error reading %s: %s" % - (result.stderr, str(cudnn_h_path))) + auto_configure_fail("Error reading %s: %s" % (str(h_path), result.stderr)) - # Parse the cuDNN major version from the line defining CUDNN_MAJOR - lines = result.stdout.splitlines() - if len(lines) == 0 or lines[0].find(define) == -1: + # Parse the version from the line defining the macro. + if result.stdout.find(define) == -1: auto_configure_fail("Cannot find line containing '%s' in %s" % - (define, str(cudnn_h_path))) - return lines[0].replace(define, "").strip() + (define, h_path)) + version = result.stdout + # Remove the new line and '\' character if any. + version = version.replace("\\", " ") + version = version.replace("\n", " ") + version = version.replace(define, "").lstrip() + # Remove the code after the version number. + version_end = version.find(" ") + if version_end != -1: + if version_end == 0: + auto_configure_fail( + "Cannot extract the version from line containing '%s' in %s" % + (define, str(h_path))) + version = version[:version_end].strip() + return version def _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value): @@ -382,12 +396,12 @@ def _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value): """ cudnn_header_dir = _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir) - major_version = _find_cuda_define(repository_ctx, cudnn_header_dir, - _DEFINE_CUDNN_MAJOR) - minor_version = _find_cuda_define(repository_ctx, cudnn_header_dir, - _DEFINE_CUDNN_MINOR) - patch_version = _find_cuda_define(repository_ctx, cudnn_header_dir, - _DEFINE_CUDNN_PATCHLEVEL) + major_version = find_cuda_define( + repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_MAJOR) + minor_version = find_cuda_define( + repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_MINOR) + patch_version = find_cuda_define( + repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_PATCHLEVEL) full_version = "%s.%s.%s" % (major_version, minor_version, patch_version) # Check whether TF_CUDNN_VERSION was set by the user and fail if it does not @@ -395,7 +409,7 @@ def _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value): environ_version = "" if _TF_CUDNN_VERSION in repository_ctx.os.environ: environ_version = repository_ctx.os.environ[_TF_CUDNN_VERSION].strip() - if environ_version and not _matches_version(environ_version, full_version): + if environ_version and not matches_version(environ_version, full_version): cudnn_h_path = repository_ctx.path("%s/include/cudnn.h" % cudnn_install_basedir) auto_configure_fail( @@ -427,7 +441,7 @@ def _compute_capabilities(repository_ctx): return capabilities -def _cpu_value(repository_ctx): +def get_cpu_value(repository_ctx): """Returns the name of the host operating system. Args: @@ -447,7 +461,7 @@ def _cpu_value(repository_ctx): def _is_windows(repository_ctx): """Returns true if the host operating system is windows.""" - return _cpu_value(repository_ctx) == "Windows" + return get_cpu_value(repository_ctx) == "Windows" def _lib_name(lib, cpu_value, version="", static=False): """Constructs the platform-specific name of a library. @@ -582,11 +596,8 @@ def _find_libs(repository_ctx, cuda_config): cuda_config: The CUDA config as returned by _get_cuda_config Returns: - Map of library names to structs of filename and path as returned by - _find_cuda_lib and _find_cupti_lib. + Map of library names to structs of filename and path. """ - cudnn_version = cuda_config.cudnn_version - cudnn_ext = ".%s" % cudnn_version if cudnn_version else "" cpu_value = cuda_config.cpu_value return { "cuda": _find_cuda_lib("cuda", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path), @@ -611,7 +622,7 @@ def _find_libs(repository_ctx, cuda_config): "cudnn": _find_cuda_lib( "cudnn", repository_ctx, cpu_value, cuda_config.cudnn_install_basedir, cuda_config.cudnn_version), - "cupti": _find_cupti_lib(repository_ctx, cuda_config), + "cupti": _find_cupti_lib(repository_ctx, cuda_config) } @@ -654,7 +665,7 @@ def _get_cuda_config(repository_ctx): compute_capabilities: A list of the system's CUDA compute capabilities. cpu_value: The name of the host operating system. """ - cpu_value = _cpu_value(repository_ctx) + cpu_value = get_cpu_value(repository_ctx) cuda_toolkit_path = _cuda_toolkit_path(repository_ctx) cuda_version = _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value) cudnn_install_basedir = _cudnn_install_basedir(repository_ctx) @@ -712,13 +723,13 @@ error_gpu_disabled() def _create_dummy_repository(repository_ctx): - cpu_value = _cpu_value(repository_ctx) + cpu_value = get_cpu_value(repository_ctx) # Set up BUILD file for cuda/. _tpl(repository_ctx, "cuda:build_defs.bzl", { "%{cuda_is_configured}": "False", - "%{cuda_extra_copts}": "[]" + "%{cuda_extra_copts}": "[]", }) _tpl(repository_ctx, "cuda:BUILD", { @@ -805,8 +816,8 @@ def _norm_path(path): return path -def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name, - src_files = [], dest_files = []): +def symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name, + src_files = [], dest_files = []): """Returns a genrule to symlink(or copy if on Windows) a set of files. If src_dir is passed, files will be read from the given directory; otherwise @@ -913,11 +924,11 @@ def _create_local_cuda_repository(repository_ctx): # cuda_toolkit_path cuda_toolkit_path = cuda_config.cuda_toolkit_path cuda_include_path = cuda_toolkit_path + "/include" - genrules = [_symlink_genrule_for_dir(repository_ctx, + genrules = [symlink_genrule_for_dir(repository_ctx, cuda_include_path, "cuda/include", "cuda-include")] - genrules.append(_symlink_genrule_for_dir(repository_ctx, + genrules.append(symlink_genrule_for_dir(repository_ctx, cuda_toolkit_path + "/nvvm", "cuda/nvvm", "cuda-nvvm")) - genrules.append(_symlink_genrule_for_dir(repository_ctx, + genrules.append(symlink_genrule_for_dir(repository_ctx, cuda_toolkit_path + "/extras/CUPTI/include", "cuda/extras/CUPTI/include", "cuda-extras")) @@ -927,15 +938,15 @@ def _create_local_cuda_repository(repository_ctx): for lib in cuda_libs.values(): cuda_lib_src.append(lib.path) cuda_lib_dest.append("cuda/lib/" + lib.file_name) - genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "", "cuda-lib", - cuda_lib_src, cuda_lib_dest)) + genrules.append(symlink_genrule_for_dir(repository_ctx, None, "", "cuda-lib", + cuda_lib_src, cuda_lib_dest)) - # Set up the symbolic links for cudnn if cudnn was was not installed to + # Set up the symbolic links for cudnn if cndnn was not installed to # CUDA_TOOLKIT_PATH. included_files = _read_dir(repository_ctx, cuda_include_path).replace( cuda_include_path, '').splitlines() if '/cudnn.h' not in included_files: - genrules.append(_symlink_genrule_for_dir(repository_ctx, None, + genrules.append(symlink_genrule_for_dir(repository_ctx, None, "cuda/include/", "cudnn-include", [cudnn_header_dir + "/cudnn.h"], ["cudnn.h"])) else: @@ -952,7 +963,6 @@ def _create_local_cuda_repository(repository_ctx): "%{cuda_is_configured}": "True", "%{cuda_extra_copts}": _compute_cuda_extra_copts( repository_ctx, cuda_config.compute_capabilities), - }) _tpl(repository_ctx, "cuda:BUILD", { diff --git a/third_party/tensorrt/BUILD b/third_party/tensorrt/BUILD new file mode 100644 index 0000000000..e69de29bb2 diff --git a/third_party/tensorrt/BUILD.tpl b/third_party/tensorrt/BUILD.tpl new file mode 100644 index 0000000000..feaeb0bea6 --- /dev/null +++ b/third_party/tensorrt/BUILD.tpl @@ -0,0 +1,67 @@ +# NVIDIA TensorRT +# A high-performance deep learning inference optimizer and runtime. + +licenses(["notice"]) + +load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts") + +package(default_visibility = ["//visibility:public"]) + +cc_library( + name = "tensorrt_headers", + hdrs = [%{tensorrt_headers}], + includes = [ + "include", + ], + visibility = ["//visibility:public"], +) + +cc_library( + name = "nv_infer", + srcs = [%{nv_infer}], + data = [%{nv_infer}], + includes = [ + "include", + ], + copts= cuda_default_copts(), + deps = [ + "@local_config_cuda//cuda:cuda", + ":tensorrt_headers", + ], + linkstatic = 1, + visibility = ["//visibility:public"], +) + +cc_library( + name = "nv_infer_plugin", + srcs = [%{nv_infer_plugin}], + data = [%{nv_infer_plugin}], + includes = [ + "include", + ], + copts= cuda_default_copts(), + deps = [ + "@local_config_cuda//cuda:cuda", + ":nv_infer", + ":tensorrt_headers", + ], + linkstatic = 1, + visibility = ["//visibility:public"], +) + +cc_library( + name = "nv_parsers", + srcs = [%{nv_parsers}], + data = [%{nv_parsers}], + includes = [ + "include", + ], + copts= cuda_default_copts(), + deps = [ + ":tensorrt_headers", + ], + linkstatic = 1, + visibility = ["//visibility:public"], +) + +%{tensorrt_genrules} diff --git a/third_party/tensorrt/build_defs.bzl.tpl b/third_party/tensorrt/build_defs.bzl.tpl new file mode 100644 index 0000000000..0dc3a7ba2d --- /dev/null +++ b/third_party/tensorrt/build_defs.bzl.tpl @@ -0,0 +1,7 @@ +# Build configurations for TensorRT. + +def if_tensorrt(if_true, if_false=[]): + """Tests whether TensorRT was enabled during the configure process.""" + if %{tensorrt_is_configured}: + return if_true + return if_false diff --git a/third_party/tensorrt/tensorrt_configure.bzl b/third_party/tensorrt/tensorrt_configure.bzl new file mode 100644 index 0000000000..8aa0f28f39 --- /dev/null +++ b/third_party/tensorrt/tensorrt_configure.bzl @@ -0,0 +1,224 @@ +# -*- Python -*- +"""Repository rule for TensorRT configuration. + +`tensorrt_configure` depends on the following environment variables: + + * `TF_TENSORRT_VERSION`: The TensorRT libnvinfer version. + * `TENSORRT_INSTALL_PATH`: The installation path of the TensorRT library. +""" + +load( + "//third_party/gpus:cuda_configure.bzl", + "auto_configure_fail", + "get_cpu_value", + "find_cuda_define", + "matches_version", + "symlink_genrule_for_dir", +) + +_TENSORRT_INSTALL_PATH = "TENSORRT_INSTALL_PATH" +_TF_TENSORRT_VERSION = "TF_TENSORRT_VERSION" + +_TF_TENSORRT_LIBS = ["nvinfer", "nvinfer_plugin", "nvparsers"] +_TF_TENSORRT_HEADERS = [ + "NvInfer.h", "NvInferPlugin.h", "NvCaffeParser.h", "NvUffParser.h", + "NvUtils.h" +] + +_DEFINE_TENSORRT_SONAME_MAJOR = "#define NV_TENSORRT_SONAME_MAJOR" +_DEFINE_TENSORRT_SONAME_MINOR = "#define NV_TENSORRT_SONAME_MINOR" +_DEFINE_TENSORRT_SONAME_PATCH = "#define NV_TENSORRT_SONAME_PATCH" + + +def _headers_exist(repository_ctx, path): + """Returns whether all TensorRT header files could be found in 'path'. + + Args: + repository_ctx: The repository context. + path: The TensorRT include path to check. + + Returns: + True if all TensorRT header files can be found in the path. + """ + for h in _TF_TENSORRT_HEADERS: + if not repository_ctx.path("%s/%s" % (path, h)).exists: + return False + return True + + +def _find_trt_header_dir(repository_ctx, trt_install_path): + """Returns the path to the directory containing headers of TensorRT. + + Args: + repository_ctx: The repository context. + trt_install_path: The TensorRT library install directory. + + Returns: + The path of the directory containing the TensorRT header. + """ + if trt_install_path == "/usr/lib/x86_64-linux-gnu": + path = "/usr/include/x86_64-linux-gnu" + if _headers_exist(repository_ctx, path): + return path + path = str(repository_ctx.path("%s/../include" % trt_install_path).realpath) + if _headers_exist(repository_ctx, path): + return path + auto_configure_fail( + "Cannot find NvInfer.h with TensorRT install path %s" % trt_install_path) + + +def _trt_lib_version(repository_ctx, trt_install_path): + """Detects the library (e.g. libnvinfer) version of TensorRT. + + Args: + repository_ctx: The repository context. + trt_install_path: The TensorRT library install directory. + + Returns: + A string containing the library version of TensorRT. + """ + trt_header_dir = _find_trt_header_dir(repository_ctx, trt_install_path) + major_version = find_cuda_define(repository_ctx, trt_header_dir, "NvInfer.h", + _DEFINE_TENSORRT_SONAME_MAJOR) + minor_version = find_cuda_define(repository_ctx, trt_header_dir, "NvInfer.h", + _DEFINE_TENSORRT_SONAME_MINOR) + patch_version = find_cuda_define(repository_ctx, trt_header_dir, "NvInfer.h", + _DEFINE_TENSORRT_SONAME_PATCH) + full_version = "%s.%s.%s" % (major_version, minor_version, patch_version) + environ_version = repository_ctx.os.environ[_TF_TENSORRT_VERSION].strip() + if not matches_version(environ_version, full_version): + auto_configure_fail( + ("TensorRT library version detected from %s/%s (%s) does not match " + + "TF_TENSORRT_VERSION (%s). To fix this rerun configure again.") % + (trt_header_dir, "NvInfer.h", full_version, environ_version)) + return environ_version + + +def _find_trt_libs(repository_ctx, trt_install_path, trt_lib_version): + """Finds the given TensorRT library on the system. + + Adapted from code contributed by Sami Kama (https://github.com/samikama). + + Args: + repository_ctx: The repository context. + trt_install_path: The TensorRT library installation directory. + trt_lib_version: The version of TensorRT library files as returned + by _trt_lib_version. + + Returns: + Map of library names to structs with the following fields: + src_file_path: The full path to the library found on the system. + dst_file_name: The basename of the target library. + """ + objdump = repository_ctx.which("objdump") + result = {} + for lib in _TF_TENSORRT_LIBS: + dst_file_name = "lib%s.so.%s" % (lib, trt_lib_version) + src_file_path = repository_ctx.path("%s/%s" % (trt_install_path, + dst_file_name)) + if not src_file_path.exists: + auto_configure_fail( + "Cannot find TensorRT library %s" % str(src_file_path)) + if objdump != None: + objdump_out = repository_ctx.execute([objdump, "-p", str(src_file_path)]) + for line in objdump_out.stdout.splitlines(): + if "SONAME" in line: + dst_file_name = line.strip().split(" ")[-1] + result.update({ + lib: + struct( + dst_file_name=dst_file_name, + src_file_path=str(src_file_path.realpath)) + }) + return result + + +def _tpl(repository_ctx, tpl, substitutions): + repository_ctx.template(tpl, Label("//third_party/tensorrt:%s.tpl" % tpl), + substitutions) + + +def _create_dummy_repository(repository_ctx): + """Create a dummy TensorRT repository.""" + _tpl(repository_ctx, "build_defs.bzl", {"%{tensorrt_is_configured}": "False"}) + substitutions = { + "%{tensorrt_genrules}": "", + "%{tensorrt_headers}": "", + } + for lib in _TF_TENSORRT_LIBS: + k = "%%{%s}" % lib.replace("nv", "nv_") + substitutions.update({k: ""}) + _tpl(repository_ctx, "BUILD", substitutions) + + +def _tensorrt_configure_impl(repository_ctx): + """Implementation of the tensorrt_configure repository rule.""" + if _TENSORRT_INSTALL_PATH not in repository_ctx.os.environ: + _create_dummy_repository(repository_ctx) + return + + if (get_cpu_value(repository_ctx) != "Linux"): + auto_configure_fail("TensorRT is supported only on Linux.") + if _TF_TENSORRT_VERSION not in repository_ctx.os.environ: + auto_configure_fail("TensorRT library (libnvinfer) version is not set.") + trt_install_path = repository_ctx.os.environ[_TENSORRT_INSTALL_PATH].strip() + if not repository_ctx.path(trt_install_path).exists: + auto_configure_fail( + "Cannot find TensorRT install path %s." % trt_install_path) + + # Set up the symbolic links for the library files. + trt_lib_version = _trt_lib_version(repository_ctx, trt_install_path) + trt_libs = _find_trt_libs(repository_ctx, trt_install_path, trt_lib_version) + trt_lib_src = [] + trt_lib_dest = [] + for lib in trt_libs.values(): + trt_lib_src.append(lib.src_file_path) + trt_lib_dest.append(lib.dst_file_name) + genrules = [ + symlink_genrule_for_dir(repository_ctx, None, "tensorrt/lib/", + "tensorrt_lib", trt_lib_src, trt_lib_dest) + ] + + # Set up the symbolic links for the header files. + trt_header_dir = _find_trt_header_dir(repository_ctx, trt_install_path) + src_files = [ + "%s/%s" % (trt_header_dir, header) for header in _TF_TENSORRT_HEADERS + ] + dest_files = _TF_TENSORRT_HEADERS + genrules.append( + symlink_genrule_for_dir(repository_ctx, None, "tensorrt/include/", + "tensorrt_include", src_files, dest_files)) + + # Set up config file. + _tpl(repository_ctx, "build_defs.bzl", {"%{tensorrt_is_configured}": "True"}) + + # Set up BUILD file. + substitutions = { + "%{tensorrt_genrules}": "\n".join(genrules), + "%{tensorrt_headers}": '":tensorrt_include"', + } + for lib in _TF_TENSORRT_LIBS: + k = "%%{%s}" % lib.replace("nv", "nv_") + v = '"tensorrt/lib/%s"' % trt_libs[lib].dst_file_name + substitutions.update({k: v}) + _tpl(repository_ctx, "BUILD", substitutions) + + +tensorrt_configure = repository_rule( + implementation=_tensorrt_configure_impl, + environ=[ + _TENSORRT_INSTALL_PATH, + _TF_TENSORRT_VERSION, + ], +) +"""Detects and configures the local CUDA toolchain. + +Add the following to your WORKSPACE FILE: + +```python +tensorrt_configure(name = "local_config_tensorrt") +``` + +Args: + name: A unique name for this workspace rule. +""" -- GitLab From 96cfcf190b900833b2d9a9c3f84c839e54cfb735 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 01:17:13 -0800 Subject: [PATCH 110/423] Fix checkpoint_utils.init_from_checkpoint() to be deterministic. PiperOrigin-RevId: 183354193 --- tensorflow/python/training/checkpoint_utils.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index 5054873bc1..b5d3e78797 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -176,7 +176,8 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): ckpt_file = _get_checkpoint_filename(ckpt_dir_or_file) reader = load_checkpoint(ckpt_dir_or_file) variable_map = reader.get_variable_to_shape_map() - for tensor_name_in_ckpt, current_var_or_name in six.iteritems(assignment_map): + for tensor_name_in_ckpt, current_var_or_name in sorted( + six.iteritems(assignment_map)): var = None # Check if this is Variable object or list of Variable objects (in case of # partitioned variables). @@ -233,7 +234,7 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): if "/part_" in var_name: var_name = var_name[:var_name.index("/part_")] scope_variables.add(var_name) - for var_name in scope_variables: + for var_name in sorted(scope_variables): # Lookup name with specified prefix and suffix from current variable. # If tensor_name given is '/' (root), don't use it for full name. full_tensor_name = var_name[len(scopes):] -- GitLab From 7578785dff668c63ba6b5423a6bf2a5984c7b409 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 03:50:58 -0800 Subject: [PATCH 111/423] Fix override annotations PiperOrigin-RevId: 183367326 --- tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h | 4 ++-- tensorflow/core/grappler/costs/virtual_scheduler.h | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h index f938d08c84..02c0fc687f 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h @@ -316,7 +316,7 @@ class DenseClassificationGrowStats : public ClassificationStats { void PackToProto(FertileSlot* slot) const override; void InitLeafClassStats(int best_split_index, LeafStat* left_stats, - LeafStat* right_stats) const; + LeafStat* right_stats) const override; protected: void ClassificationAddSplitStats() override { @@ -383,7 +383,7 @@ class SparseClassificationGrowStats : public ClassificationStats { void PackToProto(FertileSlot* slot) const override; void InitLeafClassStats(int best_split_index, LeafStat* left_stats, - LeafStat* right_stats) const; + LeafStat* right_stats) const override; protected: void ClassificationAddSplitStats() override { diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h index 8ccc51f545..9db6d46266 100644 --- a/tensorflow/core/grappler/costs/virtual_scheduler.h +++ b/tensorflow/core/grappler/costs/virtual_scheduler.h @@ -139,8 +139,8 @@ class FIFOManager : public ReadyNodeManager { public: FIFOManager() : ReadyNodeManager() {} ~FIFOManager() override {} - virtual void Init( - const std::unordered_map* node_state) {} + void Init(const std::unordered_map* node_state) + override {} void AddNode(const NodeDef* node) override { nodes_.push_back(node); } const NodeDef* GetCurrNode() override { CHECK(!nodes_.empty()) << "GetCurrNode(), but there's no ready node"; -- GitLab From c8c2e4932afccb594bfe05e22facea1aba9dd454 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 05:14:47 -0800 Subject: [PATCH 112/423] Remove dead code PiperOrigin-RevId: 183374040 --- .../cloud/kernels/bigquery_table_accessor.cc | 28 ------------- .../ops/fused_conv2d_bias_activation_op.cc | 7 ---- .../tensor_forest/kernels/v4/input_data.cc | 2 - tensorflow/core/framework/op_gen_lib.cc | 29 ------------- tensorflow/core/framework/op_kernel.cc | 7 ---- tensorflow/core/kernels/summary_kernels.cc | 2 - tensorflow/core/ops/data_flow_ops.cc | 19 --------- tensorflow/core/ops/image_ops.cc | 36 ---------------- tensorflow/core/ops/training_ops.cc | 42 ------------------- .../core/profiler/internal/tfprof_timeline.h | 1 - .../core/profiler/internal/tfprof_utils.cc | 3 -- 11 files changed, 176 deletions(-) diff --git a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc index deb324634b..1bfd27305d 100644 --- a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc +++ b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc @@ -18,7 +18,6 @@ limitations under the License. #include "tensorflow/core/lib/strings/numbers.h" namespace tensorflow { - namespace { constexpr size_t kBufferSize = 1024 * 1024; // In bytes. @@ -40,33 +39,6 @@ Status ParseJson(StringPiece json, Json::Value* result) { return Status::OK(); } -string ColumnTypeToString(BigQueryTableAccessor::ColumnType enum_type) { - switch (enum_type) { - case BigQueryTableAccessor::ColumnType::kRecord: - return "RECORD"; - case BigQueryTableAccessor::ColumnType::kString: - return "STRING"; - case BigQueryTableAccessor::ColumnType::kBytes: - return "BYTES"; - case BigQueryTableAccessor::ColumnType::kInteger: - return "INTEGER"; - case BigQueryTableAccessor::ColumnType::kFloat: - return "FLOAT"; - case BigQueryTableAccessor::ColumnType::kBoolean: - return "BOOLEAN"; - case BigQueryTableAccessor::ColumnType::kTimestamp: - return "TIMESTAMP"; - case BigQueryTableAccessor::ColumnType::kDate: - return "DATE"; - case BigQueryTableAccessor::ColumnType::kTime: - return "TIME"; - case BigQueryTableAccessor::ColumnType::kDatetime: - return "DATETIME"; - case BigQueryTableAccessor::ColumnType::kNone: - return "NONE"; - } -} - Status ParseColumnType(const string& type, BigQueryTableAccessor::ColumnType* enum_type) { if (type == "RECORD") { diff --git a/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc index 6a56237f67..bafd1d5941 100644 --- a/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc +++ b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc @@ -25,13 +25,6 @@ limitations under the License. namespace tensorflow { -namespace { -// Return the string containing the list of valid activation modes, that can be -// used as an Attr() in REGISTER_OP. -string GetAllActivationModeAttrString() { return "activation_mode: {'Relu'}"; } - -} // namespace - // -------------------------------------------------------------------------- // TODO(pauldonnelly): Add support for double inputs and scales to this Op, diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc index 14cb19d36f..bf0fb92450 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc @@ -21,8 +21,6 @@ namespace tensorflow { namespace tensorforest { namespace { -const int32 SPARSE_DEFAULT = 0; - bool DecideInequalityTest(const decision_trees::InequalityTest& test, float value) { float bias = test.threshold().float_value(); diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc index e78b6ab5d9..870bbb141b 100644 --- a/tensorflow/core/framework/op_gen_lib.cc +++ b/tensorflow/core/framework/op_gen_lib.cc @@ -266,35 +266,6 @@ static void StringReplace(const string& from, const string& to, string* s) { *s = str_util::Join(split, to.c_str()); } -static void RenameInDocs(const string& from, const string& to, OpDef* op_def) { - const string from_quoted = strings::StrCat("`", from, "`"); - const string to_quoted = strings::StrCat("`", to, "`"); - for (int i = 0; i < op_def->input_arg_size(); ++i) { - if (!op_def->input_arg(i).description().empty()) { - StringReplace(from_quoted, to_quoted, - op_def->mutable_input_arg(i)->mutable_description()); - } - } - for (int i = 0; i < op_def->output_arg_size(); ++i) { - if (!op_def->output_arg(i).description().empty()) { - StringReplace(from_quoted, to_quoted, - op_def->mutable_output_arg(i)->mutable_description()); - } - } - for (int i = 0; i < op_def->attr_size(); ++i) { - if (!op_def->attr(i).description().empty()) { - StringReplace(from_quoted, to_quoted, - op_def->mutable_attr(i)->mutable_description()); - } - } - if (!op_def->summary().empty()) { - StringReplace(from_quoted, to_quoted, op_def->mutable_summary()); - } - if (!op_def->description().empty()) { - StringReplace(from_quoted, to_quoted, op_def->mutable_description()); - } -} - static void RenameInDocs(const string& from, const string& to, ApiDef* api_def) { const string from_quoted = strings::StrCat("`", from, "`"); diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc index aee3a0afbc..16bf5c256f 100644 --- a/tensorflow/core/framework/op_kernel.cc +++ b/tensorflow/core/framework/op_kernel.cc @@ -943,13 +943,6 @@ Status FindKernelRegistration(const DeviceType& device_type, return Status::OK(); } -Status FindKernelRegistration(const DeviceType& device_type, const Node& node, - const KernelRegistration** reg, - bool* was_attr_mismatch) { - return FindKernelRegistration(device_type, node.def(), reg, - was_attr_mismatch); -} - } // namespace // TODO(irving): Change const NodeDef& to const Node& diff --git a/tensorflow/core/kernels/summary_kernels.cc b/tensorflow/core/kernels/summary_kernels.cc index da3644779d..d317a8d33d 100644 --- a/tensorflow/core/kernels/summary_kernels.cc +++ b/tensorflow/core/kernels/summary_kernels.cc @@ -278,8 +278,6 @@ class WriteAudioSummaryOp : public OpKernel { private: int max_outputs_; - bool has_sample_rate_attr_; - float sample_rate_attr_; }; REGISTER_KERNEL_BUILDER(Name("WriteAudioSummary").Device(DEVICE_CPU), WriteAudioSummaryOp); diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc index 12c27c7984..4f946fb3ca 100644 --- a/tensorflow/core/ops/data_flow_ops.cc +++ b/tensorflow/core/ops/data_flow_ops.cc @@ -171,29 +171,10 @@ Status TwoElementVectorInputsAndScalarOutputs(InferenceContext* c) { return Status::OK(); } -Status ScalarAndTwoElementVectorInputsAndScalarOutputs(InferenceContext* c) { - ShapeHandle handle; - DimensionHandle unused_handle; - TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle)); - for (int i = 1; i < c->num_inputs(); ++i) { - TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &handle)); - TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_handle)); - } - for (int i = 0; i < c->num_outputs(); ++i) { - c->set_output(i, c->Scalar()); - } - return Status::OK(); -} - Status TwoElementOutput(InferenceContext* c) { c->set_output(0, c->Vector(2)); return Status::OK(); } - -Status ScalarOutput(InferenceContext* c) { - c->set_output(0, c->Scalar()); - return Status::OK(); -} } // namespace REGISTER_OP("RandomShuffleQueue") diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index 7484ebb078..ef2ac267cc 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -25,42 +25,6 @@ using shape_inference::ShapeHandle; namespace { -const char kDecodeJpegCommonDocStr[] = R"doc( -The attr `channels` indicates the desired number of color channels for the -decoded image. - -Accepted values are: - -* 0: Use the number of channels in the JPEG-encoded image. -* 1: output a grayscale image. -* 3: output an RGB image. - -If needed, the JPEG-encoded image is transformed to match the requested number -of color channels. - -The attr `ratio` allows downscaling the image by an integer factor during -decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than -downscaling the image later. - -)doc"; - -const char kDecodeJpegCommonParamsDocStr[] = R"doc( -channels: Number of color channels for the decoded image. -ratio: Downscaling ratio. -fancy_upscaling: If true use a slower but nicer upscaling of the - chroma planes (yuv420/422 only). -try_recover_truncated: If true try to recover an image from truncated input. -acceptable_fraction: The minimum required fraction of lines before a truncated - input is accepted. -dct_method: string specifying a hint about the algorithm used for - decompression. Defaults to "" which maps to a system-specific - default. Currently valid values are ["INTEGER_FAST", - "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal - jpeg library changes to a version that does not have that specific - option.) -image: 3-D with shape `[height, width, channels]`.. -)doc"; - // Sets output[0] to shape [batch_dim,height,width,channel_dim], where // height and width come from the size_tensor. Status SetOutputToSizedImage(InferenceContext* c, DimensionHandle batch_dim, diff --git a/tensorflow/core/ops/training_ops.cc b/tensorflow/core/ops/training_ops.cc index e8d03877c9..6ce9595fb6 100644 --- a/tensorflow/core/ops/training_ops.cc +++ b/tensorflow/core/ops/training_ops.cc @@ -22,48 +22,6 @@ using shape_inference::DimensionHandle; using shape_inference::InferenceContext; using shape_inference::ShapeHandle; -const char kAddSignCommonDocStr[] = R"doc( -Update '*var' according to the AddSign update. - -m_t <- beta1 * m_{t-1} + (1 - beta1) * g -update <- (alpha + sign_decay * sign(g) *sign(m)) * g -variable <- variable - lr_t * update - -var: Should be from a Variable(). -m: Should be from a Variable(). -lr: Scaling factor. Must be a scalar. -sign_decay: Must be a scalar. -alpha: Must be a scalar. -beta: Must be a scalar. -grad: The gradient. -)doc"; - -const char kPowerSignCommonDocStr[] = R"doc( -Update '*var' according to the AddSign update. - -m_t <- beta1 * m_{t-1} + (1 - beta1) * g -update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g -variable <- variable - lr_t * update - -var: Should be from a Variable(). -m: Should be from a Variable(). -lr: Scaling factor. Must be a scalar. -logbase: Must be a scalar. -sign_decay: Must be a scalar. -beta: Must be a scalar. -grad: The gradient. -)doc"; - -const char kOutDocStr[] = R"doc( -out: Same as "var". -)doc"; - -const char kLockDocStr[] = R"doc( -use_locking: If `True`, updating of the var and m tensors is - protected by a lock; otherwise the behavior is undefined, but may exhibit less - contention. -)doc"; - static ShapeHandle ShapeOrHandleShape(InferenceContext* c, int input) { auto* handle_data = c->input_handle_shapes_and_types(input); if (handle_data != nullptr && !handle_data->empty() && diff --git a/tensorflow/core/profiler/internal/tfprof_timeline.h b/tensorflow/core/profiler/internal/tfprof_timeline.h index 4428ab571f..651ad3f0c1 100644 --- a/tensorflow/core/profiler/internal/tfprof_timeline.h +++ b/tensorflow/core/profiler/internal/tfprof_timeline.h @@ -178,7 +178,6 @@ class Timeline { int64 step_; const string outfile_; int64 next_pid_ = 0; - int64 allocator_pid_ = -1; MemoryTracker mem_tracker_; ChromeTraceFormatter chrome_formatter_; std::map device_pids_; diff --git a/tensorflow/core/profiler/internal/tfprof_utils.cc b/tensorflow/core/profiler/internal/tfprof_utils.cc index 2813bb46fa..7712ebd926 100644 --- a/tensorflow/core/profiler/internal/tfprof_utils.cc +++ b/tensorflow/core/profiler/internal/tfprof_utils.cc @@ -355,9 +355,6 @@ static const char* const kOpTypes = static const char* const kScope = "scope: The nodes in the model graph are organized by their names, which " "is hierarchical like filesystem."; -static const char* const kGraph = - "graph: The nodes in the model graph are organized by their operation " - "input and output."; static const char* const kCode = "code: When python trace is available, the nodes are python lines and " "their are organized by the python call stack."; -- GitLab From abdc62aee1eeba32be56d761a2f9988306356084 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 05:15:18 -0800 Subject: [PATCH 113/423] Roll CL 179861781 forward with fix: Wrappers for CUDA 9 warp-synchronous intrinsics. PiperOrigin-RevId: 183374082 --- .../kernels/reduce_slice_ops_gpu.cu.cc | 11 +- tensorflow/core/BUILD | 7 + tensorflow/core/kernels/bias_op_gpu.cu.cc | 18 +- .../core/kernels/depthwise_conv_op_gpu.cu.cc | 13 +- .../core/kernels/scatter_nd_op_gpu.cu.cc | 21 + tensorflow/core/kernels/svd_op_gpu.cu.cc | 4 +- tensorflow/core/util/cuda_device_functions.h | 499 ++++++++++ tensorflow/core/util/cuda_kernel_helper.h | 857 +++--------------- .../core/util/cuda_kernel_helper_test.cu.cc | 60 +- tensorflow/core/util/cuda_launch_config.h | 284 ++++++ 10 files changed, 988 insertions(+), 786 deletions(-) create mode 100644 tensorflow/core/util/cuda_device_functions.h create mode 100644 tensorflow/core/util/cuda_launch_config.h diff --git a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc index 8e6870fadd..501cddb8c8 100644 --- a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc +++ b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc @@ -34,9 +34,9 @@ namespace functor { __global__ void ReduceSliceDeviceKernel##reduceop( \ Cuda3DLaunchConfig config, Index indices_width, Index bound, \ const T begin, const Index *indices, const T *input, T *out) { \ - CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { \ - CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { \ - CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count, z) { \ + CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count.x, X) { \ + CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count.y, Y) { \ + CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count.z, Z) { \ Index outidx = x * config.virtual_thread_count.y * \ config.virtual_thread_count.z + \ y * config.virtual_thread_count.z + z; \ @@ -68,8 +68,9 @@ namespace functor { if (sizex * sizey * sizez == 0) { \ return; \ } \ - Cuda3DLaunchConfig config = GetCuda3DLaunchConfig(sizex, sizey, sizez, d,\ - ReduceSliceDeviceKernel##reduceop, 0, 0); \ + Cuda3DLaunchConfig config = GetCuda3DLaunchConfig( \ + sizex, sizey, sizez, d, ReduceSliceDeviceKernel##reduceop, \ + 0, 0); \ \ ReduceSliceDeviceKernel##reduceop \ <<>>( \ diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 94973a0e52..29c515121e 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1896,6 +1896,13 @@ cc_library( ], ) +tf_cuda_library( + name = "cuda_device_functions", + hdrs = ["util/cuda_device_functions.h"], + visibility = ["//visibility:public"], + deps = [":framework_lite"], +) + # TODO(josh11b): Is this needed, or can we just use ":protos_all_cc"? cc_library( name = "protos_cc", diff --git a/tensorflow/core/kernels/bias_op_gpu.cu.cc b/tensorflow/core/kernels/bias_op_gpu.cu.cc index 42f3db1d79..2ca194a77f 100644 --- a/tensorflow/core/kernels/bias_op_gpu.cu.cc +++ b/tensorflow/core/kernels/bias_op_gpu.cu.cc @@ -173,19 +173,13 @@ __global__ void BiasGradNCHW_SharedAtomics(const T* output_backprop, // Accumulate the results in the shared memory into the first element. // No syncthreads is needed since this is only in the same warp. int32 thread_index = threadIdx.x; - if (thread_index < 16) { - s_data[thread_index] += s_data[thread_index + 16]; - __syncwarp(0xFFFF); - if (thread_index < 8) s_data[thread_index] += s_data[thread_index + 8]; - __syncwarp(0xFF); - if (thread_index < 4) s_data[thread_index] += s_data[thread_index + 4]; - __syncwarp(0xF); - if (thread_index < 2) s_data[thread_index] += s_data[thread_index + 2]; - __syncwarp(0x3); + if (thread_index < 32) { + AccT data = s_data[thread_index]; + for (int32 delta = warpSize / 2; delta > 0; delta /= 2) { + data += CudaShuffleXorSync(kCudaWarpAll, data, delta); + } if (thread_index == 0) { - T val = T(s_data[0] + s_data[1]); - // The first thread writes out the accumulated result to global location. - CudaAtomicAdd(bias_backprop + bias_index, val); + CudaAtomicAdd(bias_backprop + bias_index, T(data)); } } } diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc index 903aac5d68..5493e33532 100644 --- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc +++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc @@ -34,6 +34,7 @@ limitations under the License. namespace tensorflow { +typedef Eigen::GpuDevice GPUDevice; using Eigen::GpuDevice; // Returns whether depthwise convolution forward or backward input pass can be @@ -1028,7 +1029,7 @@ __device__ __forceinline__ T WarpSumReduce(T val) { int zeros = sub_warp * kWidth; unsigned mask = ((1UL << kWidth) - 1) << zeros; for (int delta = kWidth / 2; delta > 0; delta /= 2) { - val += CudaShuffleXor(mask, val, delta); + val += CudaShuffleXorSync(mask, val, delta); } return val; } @@ -1145,7 +1146,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall( // Note: the condition to reach this is uniform across the entire block. __syncthreads(); - unsigned active_threads = CudaBallot(CUDA_WARP_ALL, depth_in_range); + unsigned active_threads = CudaBallotSync(kCudaWarpAll, depth_in_range); if (depth_in_range) { const T* const out_ptr = inout_offset + output; @@ -1159,7 +1160,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall( T val = out1 * tile_ptr[0] + out2 * tile_ptr[tile_offset]; // Warp-accumulate pixels of the same depth and write to accumulator. for (int delta = 16; delta >= kBlockSlices; delta /= 2) { - val += CudaShuffleDown(active_threads, val, delta); + val += CudaShuffleXorSync(active_threads, val, delta); } if (!(thread_idx & 32 - kBlockSlices) /* lane_idx < kBlockSlices */) { *accum_ptr = val; @@ -1399,7 +1400,7 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall( // Note: the condition to reach this is uniform across the entire block. __syncthreads(); - unsigned active_threads = CudaBallot(CUDA_WARP_ALL, slice_in_range); + unsigned active_threads = CudaBallotSync(kCudaWarpAll, slice_in_range); if (slice_in_range) { const T* const out_ptr = inout_offset + output; @@ -1413,10 +1414,10 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall( T val = out1 * tile_ptr[0] + out2 * tile_ptr[tile_offset]; // Warp-accumulate pixels of the same depth and write to accumulator. for (int delta = 16 / kBlockSlices; delta > 0; delta /= 2) { - val += CudaShuffleDown(active_threads, val, delta); + val += CudaShuffleXorSync(active_threads, val, delta); } if (!(thread_idx & 32 / kBlockSlices - 1)) { - *accum_ptr = val; + *accum_ptr = val; // kBlockSlices threads per warp. } ++shared_offset; accum_ptr += accum_increment; diff --git a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc index 31f74671ca..a3c21edc15 100644 --- a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc @@ -55,6 +55,27 @@ struct LeftUpdate { } }; +// Specializations for std::complex, updating real and imaginary part +// individually. Even though this is not an atomic op anymore, it is safe +// because there is only one type of op per kernel. +template +struct LeftUpdate, scatter_nd_op::UpdateOp::ADD> { + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void operator()( + std::complex* out, const std::complex& val) { + T* ptr = reinterpret_cast(out); + CudaAtomicAdd(ptr, val.real()); + CudaAtomicAdd(ptr, val.imag()); + } +}; + +template +struct LeftUpdate, scatter_nd_op::UpdateOp::SUB> { + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void operator()( + std::complex* out, const std::complex& val) { + LeftUpdate, scatter_nd_op::UpdateOp::ADD>()(out, -val); + } +}; + } // namespace template diff --git a/tensorflow/core/kernels/svd_op_gpu.cu.cc b/tensorflow/core/kernels/svd_op_gpu.cu.cc index dedc2da60b..8c3a58b108 100644 --- a/tensorflow/core/kernels/svd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/svd_op_gpu.cu.cc @@ -63,8 +63,8 @@ __global__ void ComputeValueOfVKernel(Cuda2DLaunchConfig config, int64 m, int64 ldu, const Scalar* M, const Scalar* U, const Scalar* S, Scalar* V) { - CUDA_AXIS_KERNEL_LOOP(batch, config.virtual_thread_count, x) { - CUDA_AXIS_KERNEL_LOOP(i, config.virtual_thread_count, y) { + CUDA_AXIS_KERNEL_LOOP(batch, config.virtual_thread_count.x, X) { + CUDA_AXIS_KERNEL_LOOP(i, config.virtual_thread_count.y, Y) { Scalar v = M[i + m * batch] * U[ldu * (i + m * batch)] * S[batch]; CudaAtomicAdd(V + batch, v); } diff --git a/tensorflow/core/util/cuda_device_functions.h b/tensorflow/core/util/cuda_device_functions.h new file mode 100644 index 0000000000..f787687f66 --- /dev/null +++ b/tensorflow/core/util/cuda_device_functions.h @@ -0,0 +1,499 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CORE_UTIL_CUDA_DEVICE_FUNCTIONS_H_ +#define TENSORFLOW_CORE_UTIL_CUDA_DEVICE_FUNCTIONS_H_ + +/** + * Wrappers and helpers for CUDA device code. + * + * Wraps the warp-cooperative intrinsics introduced in CUDA 9 to provide + * backwards compatibility, see go/volta-porting for details. + * Provides atomic operations on types that aren't natively supported. + */ + +#if GOOGLE_CUDA + +#include +#include +#include "cuda/include/cuda.h" +#include "cuda/include/device_functions.h" +#include "tensorflow/core/platform/types.h" + +#if CUDA_VERSION >= 7050 +#include "cuda/include/cuda_fp16.h" +#endif // CUDA_VERSION >= 7050 + +namespace tensorflow { + +namespace detail { + +// Helper for range-based for loop using 'delta' increments. +// Usage: see CudaGridRange?() functions below. +template +class CudaGridRange { + struct Iterator { + __device__ Iterator(T index, T delta) : index_(index), delta_(delta) {} + __device__ T operator*() const { return index_; } + __device__ Iterator& operator++() { + index_ += delta_; + return *this; + } + __device__ bool operator!=(const Iterator& other) const { + bool greater = index_ > other.index_; + bool less = index_ < other.index_; + // Anything past an end iterator (delta_ == 0) is equal. + // In range-based for loops, this optimizes to 'return less'. + if (!other.delta_) { + return less; + } + if (!delta_) { + return greater; + } + return less || greater; + } + + private: + T index_; + const T delta_; + }; + + public: + __device__ CudaGridRange(T begin, T delta, T end) + : begin_(begin), delta_(delta), end_(end) {} + + __device__ Iterator begin() const { return Iterator{begin_, delta_}; } + __device__ Iterator end() const { return Iterator{end_, 0}; } + + private: + T begin_; + T delta_; + T end_; +}; + +} // namespace detail + +// Helper to visit indices in the range 0 <= i < count, using the x-coordinate +// of the global thread index. That is, each index i is visited by all threads +// with the same x-coordinate. +// Usage: for(int i : CudaGridRangeX(count)) { visit(i); } +template +__device__ detail::CudaGridRange CudaGridRangeX(T count) { + return detail::CudaGridRange(blockIdx.x * blockDim.x + threadIdx.x, + gridDim.x * blockDim.x, count); +} + +// Helper to visit indices in the range 0 <= i < count using the y-coordinate. +// Usage: for(int i : CudaGridRangeY(count)) { visit(i); } +template +__device__ detail::CudaGridRange CudaGridRangeY(T count) { + return detail::CudaGridRange(blockIdx.y * blockDim.y + threadIdx.y, + gridDim.y * blockDim.y, count); +} + +// Helper to visit indices in the range 0 <= i < count using the z-coordinate. +// Usage: for(int i : CudaGridRangeZ(count)) { visit(i); } +template +__device__ detail::CudaGridRange CudaGridRangeZ(T count) { + return detail::CudaGridRange(blockIdx.z * blockDim.z + threadIdx.z, + gridDim.z * blockDim.z, count); +} + +// Mask for all 32 threads in a warp. +const unsigned kCudaWarpAll = 0xffffffff; + +// Returns the warp lane ID of the calling thread +__device__ inline unsigned CudaLaneId() { + unsigned int lane_id; + asm("mov.u32 %0, %%laneid;" : "=r"(lane_id)); + return lane_id; +} + +namespace detail { +// Returns true if mask is a valid parameter for __shfl*sync to return a well +// defined value, assuming the calling lane will read from src_lane as part of +// the shuffle operation. +// +// Specifically, returns true iff mask has the calling lane bit and the src_lane +// bit set, and the src_lane calls this function with the same mask value +// (required for the two threads to wait for each other). +// +// On Volta, for some invalid masks, this function hangs or returns false +// positives, because the implementation shuffles with the same mask that +// we are validating. Run on Pascal if you suspect that the mask is incorrect. +__device__ inline bool CudaValidateShuffleSyncMask(unsigned mask, + unsigned src_lane) { + unsigned src_dst_mask = 1u << CudaLaneId() | 1u << src_lane; +#if CUDA_VERSION >= 9000 + unsigned src_lane_mask = __shfl_sync(mask, mask, src_lane); +#else + unsigned src_lane_mask = __shfl(mask, src_lane); +#endif + return (src_dst_mask & ~mask) == 0 && src_lane_mask == mask; +} + +// Returns the actual source lane for shuffle. +__device__ inline unsigned CudaShuffleGetSrcLane(int src_lane, int width) { + int lane_id = CudaLaneId(); + int lane_base = lane_id & ~width + 1; + int lane_offset = src_lane & width - 1; + return lane_base + lane_offset; +} + +// Returns the source lane for shuffle up. +__device__ inline unsigned CudaShuffleUpGetSrcLane(unsigned delta, int width) { + unsigned lane_id = CudaLaneId(); + if ((lane_id & width - 1) < delta) { + return lane_id; + } + return lane_id - delta; +} + +// Returns the source lane for shuffle down. +__device__ inline unsigned CudaShuffleDownGetSrcLane(unsigned delta, + int width) { + unsigned lane_id = CudaLaneId(); + if ((lane_id & width - 1) + delta >= width) { + return lane_id; + } + return lane_id + delta; +} + +// Returns the source lane for shuffle xor. +__device__ inline unsigned CudaShuffleXorGetSrcLane(int lane_mask, int width) { + int lane_id = CudaLaneId(); + int src_lane = lane_id ^ lane_mask; + if (src_lane > (lane_id | width - 1)) { + return lane_id; + } + return src_lane; +} +} // namespace detail + +// For all *_sync wrappers below, it is illegal to synchronize threads from +// different program locations, because that is not supported before sm_70. +// In other words, all threads in 'mask' must call the functions in convergence. +// Code that requires sm_70 (and CUDA 9) may use the intrinsic directly. +// +// It is also illegal to shuffle with a mask that produces an undefined result +// for any of the threads. Specifically, all source threads of the shuffle +// must have their corresponding bit in 'mask' set. + +// Wrapper for __syncwarp. No-op for CUDA 8 and earlier. +__device__ inline void CudaSyncWarp(unsigned mask = kCudaWarpAll) { + assert(mask & 1u << CudaLaneId()); +#if CUDA_VERSION >= 9000 + __syncwarp(mask); +#endif +} + +// Wrapper for __ballot_sync. All threads in 'mask' must call this function in +// convergence, see comment above for details. +__device__ inline unsigned CudaBallotSync(unsigned mask, int pred) { + assert(mask & 1u << CudaLaneId()); +#if CUDA_VERSION >= 9000 + return __ballot_sync(mask, pred); +#else + return __ballot(pred) & mask; // Apply mask to match __ballot_sync's spec. +#endif +} + +// Wrapper for __any_sync. All threads in 'mask' must call this function in +// convergence, see comment above for details. +__device__ inline int CudaAnySync(unsigned mask, int pred) { + assert(mask & 1u << CudaLaneId()); +#if CUDA_VERSION >= 9000 + return __any_sync(mask, pred); +#else + return __any(pred); +#endif +} + +// Wrapper for __all_sync. All threads in 'mask' must call this function in +// convergence, see comment above for details. +__device__ inline int CudaAllSync(unsigned mask, int pred) { + assert(mask & 1u << CudaLaneId()); +#if CUDA_VERSION >= 9000 + return __all_sync(mask, pred); +#else + return __all(pred); +#endif +} + +// Wrapper for __shfl_sync. All threads in 'mask' must call this function in +// convergence, see comment above for details. +template +__device__ T CudaShuffleSync(unsigned mask, T value, int src_lane, + int width = warpSize) { + assert(!(width & width - 1)); + assert(detail::CudaValidateShuffleSyncMask( + mask, detail::CudaShuffleGetSrcLane(src_lane, width))); +#if CUDA_VERSION >= 9000 + return __shfl_sync(mask, value, src_lane, width); +#else + return __shfl(value, src_lane, width); +#endif +} + +// Variant of the (undocumented) version from the CUDA SDK, but using unsigned +// instead of float for lo and hi (which is incorrect with ftz, for example). +// See b/69446944. +__device__ inline double CudaShuffleSync(unsigned mask, double value, + int src_lane, int width = warpSize) { + unsigned lo, hi; + asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); + hi = CudaShuffleSync(mask, hi, src_lane, width); + lo = CudaShuffleSync(mask, lo, src_lane, width); + asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); + return value; +} + +// Wrapper for __shfl_up_sync. All threads in 'mask' must call this function in +// convergence, see comment above for details. +template +__device__ inline T CudaShuffleUpSync(unsigned mask, T value, unsigned delta, + int width = warpSize) { + assert(!(width & width - 1)); + assert(detail::CudaValidateShuffleSyncMask( + mask, detail::CudaShuffleUpGetSrcLane(delta, width))); +#if CUDA_VERSION >= 9000 + return __shfl_up_sync(mask, value, delta, width); +#else + return __shfl_up(value, delta, width); +#endif +} + +// Variant of the (undocumented) version from the CUDA SDK, but using unsigned +// instead of float for lo and hi (which is incorrect with ftz, for example). +// See b/69446944. +__device__ inline double CudaShuffleUpSync(unsigned mask, double value, + unsigned delta, + int width = warpSize) { + unsigned lo, hi; + asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); + hi = CudaShuffleUpSync(mask, hi, delta, width); + lo = CudaShuffleUpSync(mask, lo, delta, width); + asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); + return value; +} + +// Wrapper for __shfl_down_sync. All threads in 'mask' must call this function +// in convergence, see comment above for details. +template +__device__ inline T CudaShuffleDownSync(unsigned mask, T value, unsigned delta, + int width = warpSize) { + assert(!(width & width - 1)); + assert(detail::CudaValidateShuffleSyncMask( + mask, detail::CudaShuffleDownGetSrcLane(delta, width))); +#if CUDA_VERSION >= 9000 + return __shfl_down_sync(mask, value, delta, width); +#else + return __shfl_down(value, delta, width); +#endif +} + +// Variant of the (undocumented) version from the CUDA SDK, but using unsigned +// instead of float for lo and hi (which is incorrect with ftz, for example). +// See b/69446944. +__device__ inline double CudaShuffleDownSync(unsigned mask, double value, + unsigned delta, + int width = warpSize) { + unsigned lo, hi; + asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); + hi = CudaShuffleDownSync(mask, hi, delta, width); + lo = CudaShuffleDownSync(mask, lo, delta, width); + asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); + return value; +} + +// Wrapper for __shfl_xor_sync. All threads in 'mask' must call this function in +// convergence, see comment above for details. +template +__device__ T CudaShuffleXorSync(unsigned mask, T value, int lane_mask, + int width = warpSize) { + assert(!(width & width - 1)); + assert(detail::CudaValidateShuffleSyncMask( + mask, detail::CudaShuffleXorGetSrcLane(lane_mask, width))); +#if CUDA_VERSION >= 9000 + return __shfl_xor_sync(mask, value, lane_mask, width); +#else + return __shfl_xor(value, lane_mask, width); +#endif +} + +// Variant of the (undocumented) version from the CUDA SDK, but using unsigned +// instead of float for lo and hi (which is incorrect with ftz, for example). +// See b/69446944. +__device__ inline double CudaShuffleXorSync(unsigned mask, double value, + int lane_mask, + int width = warpSize) { + unsigned lo, hi; + asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); + hi = CudaShuffleXorSync(mask, hi, lane_mask, width); + lo = CudaShuffleXorSync(mask, lo, lane_mask, width); + asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); + return value; +} + +// Wrapper for __ldg. +template +__host__ __device__ T CudaLdg(const T* address) { +#if __CUDA_ARCH__ >= 350 + return __ldg(address); +#else + return *address; +#endif +} + +__host__ __device__ inline bool CudaLdg(const bool* address) { + return CudaLdg(reinterpret_cast(address)) != 0; +} + +__host__ __device__ inline std::complex CudaLdg( + const std::complex* address) { +#if __CUDA_ARCH__ >= 350 + float2 mem = __ldg(reinterpret_cast(address)); + return std::complex(mem.x, mem.y); +#else + return *address; +#endif +} + +__host__ __device__ inline std::complex CudaLdg( + const std::complex* address) { +#if __CUDA_ARCH__ >= 350 + double2 mem = __ldg(reinterpret_cast(address)); + return std::complex(mem.x, mem.y); +#else + return *address; +#endif +} + +// Zeroes count elements starting at ptr using all threads of a 1-D grid. +// Note: this function does not synchronize, and therefore the memory range is +// not guaranteed to be zero until the next kernel launch. +template +__global__ void SetZero(const int count, T* ptr) { + // Check that the grid is one dimensional and index doesn't overflow. + assert(blockDim.y == 1 && blockDim.z == 1); + assert(blockDim.x * gridDim.x / blockDim.x == gridDim.x); + for (int i : CudaGridRangeX(count)) { + ptr[i] = T(0); + } +} + +namespace detail { +// Helper function for atomic accumulation implemented as CAS. +template +__device__ T CudaAtomicCasHelper(T* ptr, F accumulate) { + T old = *ptr; + T assumed; + do { + assumed = old; + old = atomicCAS(ptr, assumed, accumulate(assumed)); + } while (assumed != old); + return old; +} + +// Overload for floating point (using integer comparison to handle NaN +// correctly). +template +__device__ float CudaAtomicCasHelper(float* ptr, F accumulate) { + return __float_as_int( + CudaAtomicCasHelper(reinterpret_cast(ptr), [accumulate](int32 a) { + return __float_as_int(accumulate(__int_as_float(a))); + })); +} +template +__device__ double CudaAtomicCasHelper(double* ptr, F accumulate) { + return __longlong_as_double(CudaAtomicCasHelper( + reinterpret_cast(ptr), + [accumulate](tensorflow::uint64 a) { + return __double_as_longlong(accumulate(__longlong_as_double(a))); + })); +} + +template +using ToTypeIfConvertible = + typename std::enable_if::value, To>::type; + +} // namespace detail + +// CUDA provides atomic ops, but not for all types. We provide wrappers +// for some ops and provide implementation for all reasonable types. + +template +__device__ detail::ToTypeIfConvertible CudaAtomicAdd(T* ptr, U value) { + return atomicAdd(ptr, value); +} +#if __CUDA_ARCH__ < 600 +__device__ inline double CudaAtomicAdd(double* ptr, double value) { + return detail::CudaAtomicCasHelper(ptr, + [value](double a) { return a + value; }); +} +#elif __clang__ +// Clang cannot compile __nvvm_atom_add_gen_d builtin yet, use inline PTX. +// see https://reviews.llvm.org/D39638 +__device__ inline double CudaAtomicAdd(double* ptr, double value) { + double result; + asm volatile("atom.add.f64 %0, [%1], %2;" + : "=d"(result) + : "l"(ptr), "d"(value) + : "memory"); + return result; +} +#endif + +template +__device__ detail::ToTypeIfConvertible CudaAtomicSub(T* ptr, U value) { + return atomicSub(ptr, value); +} +// Specializations of substraction which add the negative value. +__device__ inline float CudaAtomicSub(float* ptr, float value) { + return CudaAtomicAdd(ptr, -value); +} +__device__ inline double CudaAtomicSub(double* ptr, double value) { + return CudaAtomicAdd(ptr, -value); +} +__device__ inline tensorflow::uint64 CudaAtomicSub(tensorflow::uint64* ptr, + tensorflow::uint64 value) { + return CudaAtomicAdd(ptr, -value); +} + +template +__device__ detail::ToTypeIfConvertible CudaAtomicMax(T* ptr, U value) { + return atomicMax(ptr, value); +} +#if __CUDA_ARCH__ < 320 +__device__ inline tensorflow::uint64 CudaAtomicMax(tensorflow::uint64* ptr, + tensorflow::uint64 value) { + return detail::CudaAtomicCasHelper( + ptr, [value](tensorflow::uint64 a) { return max(a, value); }); +} +#endif + +template +__device__ detail::ToTypeIfConvertible CudaAtomicMul(T* ptr, U value) { + return detail::CudaAtomicCasHelper(ptr, [value](T a) { return a * value; }); +} +template +__device__ detail::ToTypeIfConvertible CudaAtomicDiv(T* ptr, U value) { + return detail::CudaAtomicCasHelper(ptr, [value](T a) { return a / value; }); +} + +} // namespace tensorflow + +#endif // GOOGLE_CUDA +#endif // TENSORFLOW_CORE_UTIL_CUDA_KERNEL_HELPER_H_ diff --git a/tensorflow/core/util/cuda_kernel_helper.h b/tensorflow/core/util/cuda_kernel_helper.h index 3e32ec7973..18a4c008f1 100644 --- a/tensorflow/core/util/cuda_kernel_helper.h +++ b/tensorflow/core/util/cuda_kernel_helper.h @@ -18,299 +18,133 @@ limitations under the License. #if GOOGLE_CUDA -#include +#include "tensorflow/core/util/cuda_device_functions.h" +#include "tensorflow/core/util/cuda_launch_config.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "cuda/include/cuda.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/platform/logging.h" -#include "tensorflow/core/platform/stream_executor.h" -#include "tensorflow/core/platform/types.h" +// Deprecated, use 'for(int i : CudaGridRangeX(n))' instead. +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i : ::tensorflow::CudaGridRangeX(n)) +// Deprecated, use 'for(int i : CudaGridRange?(n))' instead. +#define CUDA_AXIS_KERNEL_LOOP(i, n, axis) \ + for (int i : ::tensorflow::CudaGridRange##axis(n)) -// Mask for all 32 threads in a warp. -#define CUDA_WARP_ALL 0xFFFFFFFF - -#if defined(CUDA_VERSION) && CUDA_VERSION < 9000 -// CUDA 9.0 introduces a new, light-weight barrier synchronization primitive -// that operates at the warp-scope. This is required to ensure visibility of -// reads/writes among threads that can make indepenent progress on Volta. -// For previous CUDA versions these synchronizations not necessary, and we -// define an empty function as a convenience for backward compatibility. -__device__ inline void __syncwarp(unsigned mask = CUDA_WARP_ALL) {} - -// CUDA 9.0 deprecates the warp-intrinsic functions (shfl, ballot, etc.) in -// favor of synchronizing versions. These ensure that all warp lanes specified -// in mask execute the intrinsic in convergence. Here we provide legacy mappings -// to the less-verbose routines provided in previous versions of CUDA. -#define __ballot_sync(mask, predicate) __ballot(predicate) -#define __shfl_sync(mask, val, srcLane, width) __shfl(val, srcLane, width) -#define __shfl_down_sync(mask, val, delta, width) __shfl_down(val, delta, width) -#define __shfl_up_sync(mask, val, delta, width) __shfl_up(val, delta, width) -#define __shfl_xor_sync(mask, val, laneMask, width) \ - __shfl_xor(val, laneMask, width) -#endif - -// Usage of GetCudaLaunchConfig, GetCuda2DLaunchConfig, and -// GetCuda3DLaunchConfig: -// -// There are two versions of GetCudaLaunchConfig and GetCuda2DLaunchConfig, one -// version uses heuristics without any knowledge of the device kernel, the other -// version uses cudaOccupancyMaxPotentialBlockSize to determine the theoretical -// launch parameters that maximize occupancy. Currently, only the maximum -// occupancy version of GetCuda3DLaunchConfig is available. -// -// For large number of work elements, the convention is that each kernel would -// iterate through its assigned range. The return value of GetCudaLaunchConfig -// is struct CudaLaunchConfig, which contains all the information needed for the -// kernel launch, including: virtual number of threads, the number of threads -// per block and number of threads per block used inside <<< >>> of a kernel -// launch. GetCuda2DLaunchConfig and GetCuda3DLaunchConfig does the same thing -// as CudaLaunchConfig. The only difference is the dimension. The macros -// CUDA_1D_KERNEL_LOOP and CUDA_AXIS_KERNEL_LOOP might be used to do inner loop. -// -/* Sample code: - -__global__ void MyKernel1D(CudaLaunchConfig config, other_args...) { - CUDA_1D_KERNEL_LOOP(x, config.virtual_thread_count) { - do_your_job_here; - } +namespace tensorflow { +__host__ __device__ inline tensorflow::bfloat16 CudaLdg( + const tensorflow::bfloat16* address) { + tensorflow::bfloat16 return_value; + return_value.value = CudaLdg(reinterpret_cast(address)); + return return_value; } -__global__ void MyKernel2D(Cuda2DLaunchConfig config, other_args...) { - CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { - CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { - do_your_job_here; - } - } +template +__host__ __device__ inline T ldg(const T* ptr) { + return CudaLdg(ptr); } -__global__ void MyKernel3D(Cuda3DLaunchConfig config, other_args...) { - CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { - CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { - CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count, z) { - do_your_job_here; - } - } - } +template +__host__ __device__ inline const T& tf_min(const T& x, const T& y) { + return x < y ? x : y; } -void MyDriverFunc(const GPUDevice &d) { - // use heuristics - CudaLaunchConfig cfg1 = GetCudaLaunchConfig(10240, d); - MyKernel1D <<>> (cfg1, other_args...); - Cuda2DLaunchConfig cfg2 = GetCuda2DLaunchConfig(10240, 10240, d); - MyKernel2D <<>> (cfg2, other_args...); - Cuda3DLaunchConfig cfg3 = GetCuda3DLaunchConfig(4096, 4096, 100, d); - MyKernel3D <<>> (cfg3, other_args...); - - // maximize occupancy - CudaLaunchConfig cfg4 = GetCudaLaunchConfig(10240, d, MyKernel1D, 0, 0 ); - MyKernel1D <<>> (cfg4, other_args...); - Cuda2DLaunchConfig cfg5 = GetCuda2DLaunchConfig(10240, 10240, d, - MyKernel1D, 0, 0); - MyKernel2D <<>> (cfg5, other_args...); - Cuda3DLaunchConfig cfg6 = GetCuda3DLaunchConfig(4096, 4096, 100, d, - MyKernel1D, 0, 0); - MyKernel3D <<>> (cfg6, other_args...); +template +__host__ __device__ inline const T& tf_max(const T& x, const T& y) { + return x < y ? y : x; } -// See the test for this for more example: -// -https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/cuda_kernel_helper_test.cu.cc - -*/ - -#define CUDA_1D_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ - i += blockDim.x * gridDim.x) - -#define CUDA_AXIS_KERNEL_LOOP(i, n, axis) \ - for (int i = blockIdx.axis * blockDim.axis + threadIdx.axis; i < n.axis; \ - i += blockDim.axis * gridDim.axis) - -#define DIV_UP(a, b) (((a) + (b)-1) / (b)) - -namespace tensorflow { - -typedef Eigen::GpuDevice GPUDevice; - -struct CudaLaunchConfig { - // Logical number of thread that works on the elements. If each logical - // thread works on exactly a single element, this is the same as the working - // element count. - int virtual_thread_count = -1; - // Number of threads per block. - int thread_per_block = -1; - // Number of blocks for Cuda kernel launch. - int block_count = -1; -}; - -// Calculate the Cuda launch config we should use for a kernel launch. -// This is assuming the kernel is quite simple and will largely be -// memory-limited. -// REQUIRES: work_element_count > 0. -inline CudaLaunchConfig GetCudaLaunchConfig(int work_element_count, - const GPUDevice& d) { - CHECK_GT(work_element_count, 0); - CudaLaunchConfig config; - const int virtual_thread_count = work_element_count; - const int physical_thread_count = std::min( - d.getNumCudaMultiProcessors() * d.maxCudaThreadsPerMultiProcessor(), - virtual_thread_count); - const int thread_per_block = std::min(1024, d.maxCudaThreadsPerBlock()); - const int block_count = - std::min(DIV_UP(physical_thread_count, thread_per_block), - d.getNumCudaMultiProcessors()); - - config.virtual_thread_count = virtual_thread_count; - config.thread_per_block = thread_per_block; - config.block_count = block_count; - return config; +// Overloads of the above functions for float and double. +__host__ __device__ inline float tf_min(float x, float y) { + return fminf(x, y); } - -// Calculate the Cuda launch config we should use for a kernel launch. This -// variant takes the resource limits of func into account to maximize occupancy. -// REQUIRES: work_element_count > 0. -template -inline CudaLaunchConfig GetCudaLaunchConfig(int work_element_count, - const GPUDevice& d, DeviceFunc func, - size_t dynamic_shared_memory_size, - int block_size_limit) { - CHECK_GT(work_element_count, 0); - CudaLaunchConfig config; - int block_count = 0; - int thread_per_block = 0; - - cudaError_t err = cudaOccupancyMaxPotentialBlockSize( - &block_count, &thread_per_block, func, dynamic_shared_memory_size, - block_size_limit); - CHECK_EQ(err, cudaSuccess); - - block_count = - std::min(block_count, DIV_UP(work_element_count, thread_per_block)); - - config.virtual_thread_count = work_element_count; - config.thread_per_block = thread_per_block; - config.block_count = block_count; - return config; +__host__ __device__ inline double tf_min(double x, double y) { + return fmin(x, y); +} +__host__ __device__ inline float tf_max(float x, float y) { + return fmaxf(x, y); +} +__host__ __device__ inline double tf_max(double x, double y) { + return fmax(x, y); } -struct Cuda2DLaunchConfig { - dim3 virtual_thread_count = dim3(0, 0, 0); - dim3 thread_per_block = dim3(0, 0, 0); - dim3 block_count = dim3(0, 0, 0); -}; - -inline Cuda2DLaunchConfig GetCuda2DLaunchConfig(int xdim, int ydim, - const GPUDevice& d) { - Cuda2DLaunchConfig config; - - if (xdim <= 0 || ydim <= 0) { - return config; - } - - const int kThreadsPerBlock = 256; - int block_cols = std::min(xdim, kThreadsPerBlock); - // ok to round down here and just do more loops in the kernel - int block_rows = std::max(kThreadsPerBlock / block_cols, 1); - - const int physical_thread_count = - d.getNumCudaMultiProcessors() * d.maxCudaThreadsPerMultiProcessor(); - - const int max_blocks = std::max(physical_thread_count / kThreadsPerBlock, 1); - - config.virtual_thread_count = dim3(xdim, ydim, 1); - config.thread_per_block = dim3(block_cols, block_rows, 1); - - int grid_x = std::min(DIV_UP(xdim, block_cols), max_blocks); +__device__ inline Eigen::half CudaShuffleSync(unsigned mask, Eigen::half value, + int src_lane, + int width = warpSize) { + return Eigen::half( + CudaShuffleSync(mask, static_cast(value), src_lane, width)); +} - config.block_count = dim3( - grid_x, std::min(max_blocks / grid_x, std::max(ydim / block_rows, 1)), 1); - return config; +__device__ EIGEN_ALWAYS_INLINE Eigen::half CudaShuffleUpSync( + unsigned mask, Eigen::half value, int delta, int width = warpSize) { + return Eigen::half( + CudaShuffleUpSync(mask, static_cast(value), delta, width)); } -// Calculate the Cuda 2D and 3D launch config we should use for a kernel launch. -// This variant takes the resource limits of func into account to maximize -// occupancy. -using Cuda3DLaunchConfig = Cuda2DLaunchConfig; +__device__ EIGEN_ALWAYS_INLINE Eigen::half CudaShuffleDownSync( + unsigned mask, Eigen::half value, int delta, int width = warpSize) { + return Eigen::half( + CudaShuffleDownSync(mask, static_cast(value), delta, width)); +} -template -inline Cuda3DLaunchConfig GetCuda3DLaunchConfig( - int xdim, int ydim, int zdim, const GPUDevice& d, DeviceFunc func, - size_t dynamic_shared_memory_size, int block_size_limit) { - Cuda3DLaunchConfig config; +__device__ EIGEN_ALWAYS_INLINE Eigen::half CudaShuffleXorSync( + unsigned mask, Eigen::half value, int lane_mask, int width = warpSize) { + return Eigen::half( + CudaShuffleXorSync(mask, static_cast(value), lane_mask, width)); +} - if (xdim <= 0 || ydim <= 0 || zdim <= 0) { - return config; +namespace detail { +// Overload of above function for half. Note that we don't have +// atomicCAS() for anything less than 32 bits, so we need to include the +// other 16 bits in the operation. +// +// This version is going to be very slow +// under high concurrency, since most threads will be spinning on failing +// their compare-and-swap tests. (The fact that we get false sharing on the +// neighboring fp16 makes this even worse.) If you are doing a large reduction, +// you are much better off with doing the intermediate steps in fp32 and then +// switching to fp16 as late as you can in the calculations. +// +// Note: Assumes little endian. +template +__device__ Eigen::half CudaAtomicCasHelper(Eigen::half* ptr, F accumulate) { +#if defined(__BYTE_ORDER__) && defined(__ORDER_LITTLE_ENDIAN__) + static_assert(__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__, "Not little endian"); +#endif + namespace half_impl = Eigen::half_impl; + intptr_t intptr = reinterpret_cast(ptr); + assert(!(intptr & 0x1)); // should be 2-aligned. + if (intptr & 0x2) { + // The half is in the second part of the uint32 (upper 16 bits). + uint32* address = reinterpret_cast(intptr - 2); + uint32 result = CudaAtomicCasHelper(address, [accumulate](uint32 arg) { + unsigned short high = static_cast(arg >> 16); + Eigen::half acc = accumulate(half_impl::raw_uint16_to_half(high)); + return (static_cast(acc.x) << 16) | (arg & 0xffff); + }); + return half_impl::raw_uint16_to_half(static_cast(result >> 16)); + } else { + // The half is in the first part of the uint32 (lower 16 bits). + uint32* address = reinterpret_cast(intptr); + uint32 result = CudaAtomicCasHelper(address, [accumulate](uint32 arg) { + unsigned short low = static_cast(arg & 0xffff); + Eigen::half acc = accumulate(half_impl::raw_uint16_to_half(low)); + return (arg & 0xffff0000) | static_cast(acc.x); + }); + return half_impl::raw_uint16_to_half(static_cast(result & 0xffff)); } - - int dev; - cudaGetDevice(&dev); - cudaDeviceProp deviceProp; - cudaGetDeviceProperties(&deviceProp, dev); - int xthreadlimit = deviceProp.maxThreadsDim[0]; - int ythreadlimit = deviceProp.maxThreadsDim[1]; - int zthreadlimit = deviceProp.maxThreadsDim[2]; - int xgridlimit = deviceProp.maxGridSize[0]; - int ygridlimit = deviceProp.maxGridSize[1]; - int zgridlimit = deviceProp.maxGridSize[2]; - - int block_count = 0; - int thread_per_block = 0; - cudaError_t err = cudaOccupancyMaxPotentialBlockSize( - &block_count, &thread_per_block, func, dynamic_shared_memory_size, - block_size_limit); - CHECK_EQ(err, cudaSuccess); - -#define MIN3(a, b, c) std::min((a), std::min((b), (c))) - int threadsx = MIN3(xdim, thread_per_block, xthreadlimit); - int threadsy = - MIN3(ydim, std::max(thread_per_block / threadsx, 1), ythreadlimit); - int threadsz = - MIN3(zdim, std::max(thread_per_block / (threadsx * threadsy), 1), - zthreadlimit); - - int blocksx = MIN3(block_count, DIV_UP(xdim, threadsx), xgridlimit); - int blocksy = - MIN3(DIV_UP(block_count, blocksx), DIV_UP(ydim, threadsy), ygridlimit); - int blocksz = MIN3(DIV_UP(block_count, (blocksx * blocksy)), - DIV_UP(zdim, threadsz), zgridlimit); -#undef MIN3 - - config.virtual_thread_count = dim3(xdim, ydim, zdim); - config.thread_per_block = dim3(threadsx, threadsy, threadsz); - config.block_count = dim3(blocksx, blocksy, blocksz); - return config; } +} // namespace detail -template -inline Cuda2DLaunchConfig GetCuda2DLaunchConfig( - int xdim, int ydim, const GPUDevice& d, DeviceFunc func, - size_t dynamic_shared_memory_size, int block_size_limit) { - return GetCuda3DLaunchConfig(xdim, ydim, 1, d, func, - dynamic_shared_memory_size, block_size_limit); +__device__ inline Eigen::half CudaAtomicAdd(Eigen::half* ptr, + Eigen::half value) { + return detail::CudaAtomicCasHelper( + ptr, [value](Eigen::half a) { return a + value; }); } - -// Returns a raw reference to the current cuda stream. Required by a -// number of kernel calls (for which StreamInterface* does not work), i.e. -// CUB and certain cublas primitives. -inline const cudaStream_t& GetCudaStream(OpKernelContext* context) { - const cudaStream_t* ptr = CHECK_NOTNULL( - reinterpret_cast(context->op_device_context() - ->stream() - ->implementation() - ->CudaStreamMemberHack())); - return *ptr; +__device__ inline Eigen::half CudaAtomicSub(Eigen::half* ptr, + Eigen::half value) { + return detail::CudaAtomicCasHelper( + ptr, [value](Eigen::half a) { return a - value; }); } namespace cuda_helper { - template __device__ IntType upper_bound(IntType* first, IntType count, IntType val) { IntType* orig = first; @@ -330,495 +164,8 @@ __device__ IntType upper_bound(IntType* first, IntType count, IntType val) { return first - orig; } - } // namespace cuda_helper - -template -__device__ __host__ inline T ldg(const T* address) { -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 - return __ldg(address); -#else - return *address; -#endif -} - -template <> -__device__ __host__ inline std::complex ldg( - const std::complex* address) { -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 - float2 mem = __ldg(reinterpret_cast(address)); - return std::complex(mem.x, mem.y); -#else - return *address; -#endif -} - -template <> -__device__ __host__ inline std::complex ldg( - const std::complex* address) { -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 - double2 mem = __ldg(reinterpret_cast(address)); - return std::complex(mem.x, mem.y); -#else - return *address; -#endif -} - -template <> -__device__ __host__ inline Eigen::half ldg(const Eigen::half* address) { -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 - return Eigen::half_impl::raw_uint16_to_half( - __ldg(reinterpret_cast(address))); -#else - return *address; -#endif -} - -template <> -__device__ __host__ inline tensorflow::bfloat16 ldg( - const tensorflow::bfloat16* address) { -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 - tensorflow::bfloat16 return_value; - asm volatile("ld.global.nc.u16 %0, [%1];" - : "=h"(return_value.value) - : "l"(address)); - return return_value; -#else - return *address; -#endif -} - -template <> -__device__ __host__ inline bool ldg(const bool* address) { -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 - return *reinterpret_cast( - __ldg(reinterpret_cast(address))); -#else - return *address; -#endif -} - -// CUDA provides atomic ops, but not for all types. We provide wrappers -// for some ops and provide implementation for all reasonable types. -#define CUDA_ATOMIC_WRAPPER(op, T) \ - __device__ __forceinline__ T CudaAtomic##op(T* address, T val) - -#define USE_CUDA_ATOMIC(op, T) \ - CUDA_ATOMIC_WRAPPER(op, T) { return atomic##op(address, val); } - -// For atomicAdd. -USE_CUDA_ATOMIC(Add, int32); -USE_CUDA_ATOMIC(Add, uint32); -USE_CUDA_ATOMIC(Add, uint64); -USE_CUDA_ATOMIC(Add, float); - -// For atomicMax. -USE_CUDA_ATOMIC(Max, int32); -USE_CUDA_ATOMIC(Max, uint32); -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 -USE_CUDA_ATOMIC(Max, uint64); -#else -// The uint64 overload of atomicMax() is only available for __CUDA_ARCH__ >= -// 350. If not satisfied, we provide a custom implementation using atomicCAS(). -CUDA_ATOMIC_WRAPPER(Max, uint64) { - uint64* address_as_ull = reinterpret_cast(address); - uint64 old = *address_as_ull, assumed; - - do { - assumed = old; - old = atomicCAS(address_as_ull, assumed, max(val, assumed)); - } while (assumed != old); - - return old; -} -#endif - -// Custom implementation of atomicAdd for double. -// This implementation is copied from CUDA manual. -CUDA_ATOMIC_WRAPPER(Add, double) { - uint64* address_as_ull = reinterpret_cast(address); - uint64 old = *address_as_ull, assumed; - - do { - assumed = old; - old = atomicCAS(address_as_ull, assumed, - __double_as_longlong(val + __longlong_as_double(assumed))); - - // Note: uses integer comparison to avoid hang in case of NaN - } while (assumed != old); - - return __longlong_as_double(old); -} - -// Custom implementation of atomicAdd for std::complex. -// This implementation performs to atomic additions on the components. -CUDA_ATOMIC_WRAPPER(Add, std::complex) { -#if defined(__CUDA_ARCH__) -#if __CUDA_ARCH__ >= 350 - float2* addr_as_float2 = reinterpret_cast(address); - float2* val_as_float2 = reinterpret_cast(&val); - CudaAtomicAdd(&(addr_as_float2->x), val_as_float2->x); - CudaAtomicAdd(&(addr_as_float2->y), val_as_float2->y); -#else - static_assert(sizeof(std::complex) == 2 * sizeof(float), - "Unable to compile CudaAtomicAdd for complex64 because " - "sizeof(complex64) != 2*sizeof(float32)"); - float* addr_as_float = reinterpret_cast(address); - float* val_as_float = reinterpret_cast(&val); - CudaAtomicAdd(addr_as_float, *val_as_float); - CudaAtomicAdd(addr_as_float + 1, *(val_as_float + 1)); -#endif -#endif - return *address; -} - -// Custom implementation of atomicAdd for std::complex. -// This implementation performs to atomic additions on the components -// using the double atomic wrapper above. -CUDA_ATOMIC_WRAPPER(Add, complex128) { -#if defined(__CUDA_ARCH__) -#if __CUDA_ARCH__ >= 350 - double2* addr_as_double2 = reinterpret_cast(address); - double2* val_as_double2 = reinterpret_cast(&val); - CudaAtomicAdd(&(addr_as_double2->x), val_as_double2->x); - CudaAtomicAdd(&(addr_as_double2->y), val_as_double2->y); -#else - static_assert(sizeof(std::complex) == 2 * sizeof(double), - "Unable to compile CudaAtomicAdd for complex128 because " - "sizeof(complex128) != 2*sizeof(float64)"); - double* addr_as_double = reinterpret_cast(address); - double* val_as_double = reinterpret_cast(&val); - CudaAtomicAdd(addr_as_double, *val_as_double); - CudaAtomicAdd(addr_as_double + 1, *(val_as_double + 1)); -#endif -#endif - return *address; -} - -// Helper functions for CudaAtomicAdd(half*, half), below. -// -// Note that if __CUDA_ARCH__ >= 530, we could probably use __hadd2() -// for a more efficient implementation, assuming that adding -0.0 -// will never harm the neighboring value. In this version, we take special -// care to guarantee the bits of the untouched value are unchanged. -inline __device__ uint32 add_to_low_half(uint32 val, float x) { - Eigen::half low_half; - low_half.x = static_cast(val & 0xffffu); - low_half = static_cast(static_cast(low_half) + x); - return (val & 0xffff0000u) | low_half.x; -} - -inline __device__ uint32 add_to_high_half(uint32 val, float x) { - Eigen::half high_half; - high_half.x = static_cast(val >> 16); - high_half = static_cast(static_cast(high_half) + x); - return (val & 0xffffu) | (high_half.x << 16); -} - -// Custom implementation of atomicAdd for half. Note that we don't have -// atomicCAS() for anything less than 32 bits, so we need to include the -// other 16 bits in the operation. -// -// Unlike the other atomic adds, this version is going to be very slow -// under high concurrency, since most threads will be spinning on failing -// their compare-and-swap tests. (The fact that we get false sharing on the -// neighboring fp16 makes this even worse.) If you are doing a large reduction, -// you are much better off with doing the intermediate steps in fp32 and then -// switching to fp16 as late as you can in the calculations. -// -// Note: Assumes little endian. -CUDA_ATOMIC_WRAPPER(Add, Eigen::half) { - float val_as_float(val); - intptr_t address_int = reinterpret_cast(address); - if ((address_int & 0x2) == 0) { - // The half is in the first part of the uint32 (lower 16 bits). - uint32* address_as_uint32 = reinterpret_cast(address); - assert(((intptr_t)address_as_uint32 & 0x3) == 0); - uint32 old = *address_as_uint32, assumed; - - do { - assumed = old; - old = atomicCAS(address_as_uint32, assumed, - add_to_low_half(assumed, val_as_float)); - - // Note: uses integer comparison to avoid hang in case of NaN - } while (assumed != old); - - Eigen::half ret; - ret.x = old & 0xffffu; - return ret; - } else { - // The half is in the second part of the uint32 (upper 16 bits). - uint32* address_as_uint32 = reinterpret_cast(address_int - 2); - assert(((intptr_t)address_as_uint32 & 0x3) == 0); - uint32 old = *address_as_uint32, assumed; - - do { - assumed = old; - old = atomicCAS(address_as_uint32, assumed, - add_to_high_half(assumed, val_as_float)); - - // Note: uses integer comparison to avoid hang in case of NaN - } while (assumed != old); - - Eigen::half ret; - ret.x = old >> 16; - return ret; - } -} - -template -__global__ void SetZero(const int nthreads, T* bottom_diff) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { *(bottom_diff + index) = T(0); } -} - -// For atomicSub. - -// Custom implementation for sub by just negating the value. -#define WRAPPED_ATOMIC_SUB(T) \ - CUDA_ATOMIC_WRAPPER(Sub, T) { return CudaAtomicAdd(address, -val); } - -WRAPPED_ATOMIC_SUB(uint64); -WRAPPED_ATOMIC_SUB(int32); -WRAPPED_ATOMIC_SUB(uint32); -WRAPPED_ATOMIC_SUB(Eigen::half); -WRAPPED_ATOMIC_SUB(float); -WRAPPED_ATOMIC_SUB(double); - -CUDA_ATOMIC_WRAPPER(Sub, complex64) { - const std::complex Tneg(-val.real(), -val.imag()); - return CudaAtomicAdd(address, Tneg); -} - -CUDA_ATOMIC_WRAPPER(Sub, complex128) { - const std::complex Tneg(-val.real(), -val.imag()); - return CudaAtomicAdd(address, Tneg); -} - -#undef WRAPPED_ATOMIC_SUB - -// For atomicMul. -CUDA_ATOMIC_WRAPPER(Mul, int32) { - int32 old = *address, assumed; - do { - assumed = old; - old = atomicCAS(address, assumed, val * assumed); - } while (assumed != old); - return old; -} - -CUDA_ATOMIC_WRAPPER(Mul, uint32) { - uint32 old = *address, assumed; - do { - assumed = old; - old = atomicCAS(address, assumed, val * assumed); - } while (assumed != old); - return old; -} - -CUDA_ATOMIC_WRAPPER(Mul, uint64) { - uint64 old = *address, assumed; - do { - assumed = old; - old = atomicCAS(address, assumed, val * assumed); - } while (assumed != old); - return old; -} - -CUDA_ATOMIC_WRAPPER(Mul, float) { - int32* address_as_int = reinterpret_cast(address); - int32 old = *address_as_int, assumed; - do { - assumed = old; - old = atomicCAS(address_as_int, assumed, - __float_as_int(val * __int_as_float(assumed))); - } while (assumed != old); - return __int_as_float(old); -} - -CUDA_ATOMIC_WRAPPER(Mul, double) { - uint64* address_as_ull = reinterpret_cast(address); - uint64 old = *address_as_ull, assumed; - do { - assumed = old; - old = atomicCAS(address_as_ull, assumed, - __double_as_longlong(val * __longlong_as_double(assumed))); - } while (assumed != old); - return __longlong_as_double(old); -} - -// For atomicDiv. -CUDA_ATOMIC_WRAPPER(Div, int32) { - int32 old = *address, assumed; - do { - assumed = old; - old = atomicCAS(address, assumed, assumed / val); - } while (assumed != old); - return old; -} - -CUDA_ATOMIC_WRAPPER(Div, uint32) { - uint32 old = *address, assumed; - do { - assumed = old; - old = atomicCAS(address, assumed, assumed / val); - } while (assumed != old); - return old; -} - -CUDA_ATOMIC_WRAPPER(Div, uint64) { - uint64 old = *address, assumed; - do { - assumed = old; - old = atomicCAS(address, assumed, assumed / val); - } while (assumed != old); - return old; -} - -CUDA_ATOMIC_WRAPPER(Div, float) { - int32* address_as_int = reinterpret_cast(address); - int32 old = *address_as_int, assumed; - do { - assumed = old; - old = atomicCAS(address_as_int, assumed, - __float_as_int(__int_as_float(assumed) / val)); - } while (assumed != old); - return __int_as_float(old); -} - -CUDA_ATOMIC_WRAPPER(Div, double) { - uint64* address_as_ull = reinterpret_cast(address); - uint64 old = *address_as_ull, assumed; - do { - assumed = old; - old = atomicCAS(address_as_ull, assumed, - __double_as_longlong(__longlong_as_double(assumed) / val)); - } while (assumed != old); - return __longlong_as_double(old); -} - -#undef USE_CUDA_ATOMIC -#undef CUDA_ATOMIC_WRAPPER - -template -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T tf_min(const T& x, const T& y) { - return x > y ? y : x; -} - -template -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T tf_max(const T& x, const T& y) { - return x < y ? y : x; -} - -__device__ EIGEN_ALWAYS_INLINE unsigned CudaBallot(unsigned mask, - int predicate) { - return __ballot_sync(mask, predicate); -} - -template -__device__ EIGEN_ALWAYS_INLINE T CudaShuffle(unsigned mask, T value, - int srcLane, - int width = warpSize) { - return __shfl_sync(mask, value, srcLane, width); -} - -// Variant of the (undocumented) version from the CUDA SDK, but using unsigned -// instead of float for lo and hi (which is incorrect with ftz, for example). -// A bug has been filed with NVIDIA and will be fixed in the next CUDA release. -// TODO(csigg): remove when the bug is fixed in the next CUDA release. -__device__ EIGEN_ALWAYS_INLINE double CudaShuffle(unsigned mask, double value, - int srcLane, - int width = warpSize) { - unsigned lo, hi; - asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); - hi = __shfl_sync(mask, hi, srcLane, width); - lo = __shfl_sync(mask, lo, srcLane, width); - asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); - return value; -} - -template -__device__ EIGEN_ALWAYS_INLINE T CudaShuffleUp(unsigned mask, T value, - int delta, - int width = warpSize) { - return __shfl_up_sync(mask, value, delta, width); -} - -// Variant of the (undocumented) version from the CUDA SDK, but using unsigned -// instead of float for lo and hi (which is incorrect with ftz, for example). -// A bug has been filed with NVIDIA and will be fixed in the next CUDA release. -// TODO(csigg): remove when the bug is fixed in the next CUDA release. -__device__ EIGEN_ALWAYS_INLINE double CudaShuffleUp(unsigned mask, double value, - int delta, - int width = warpSize) { - unsigned lo, hi; - asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); - hi = __shfl_up_sync(mask, hi, delta, width); - lo = __shfl_up_sync(mask, lo, delta, width); - asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); - return value; -} - -template -__device__ EIGEN_ALWAYS_INLINE T CudaShuffleDown(unsigned mask, T value, - int delta, - int width = warpSize) { - return __shfl_down_sync(mask, value, delta, width); -} - -__device__ EIGEN_ALWAYS_INLINE Eigen::half CudaShuffleDown( - unsigned mask, Eigen::half value, int delta, int width = warpSize) { - return Eigen::half( - __shfl_down_sync(mask, static_cast(value), delta, width)); -} - -// Variant of the (undocumented) version from the CUDA SDK, but using unsigned -// instead of float for lo and hi (which is incorrect with ftz, for example). -// A bug has been filed with NVIDIA and will be fixed in the next CUDA release. -// TODO(csigg): remove when the bug is fixed in the next CUDA release. -__device__ EIGEN_ALWAYS_INLINE double CudaShuffleDown(unsigned mask, - double value, int delta, - int width = warpSize) { - unsigned lo, hi; - asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); - hi = __shfl_down_sync(mask, hi, delta, width); - lo = __shfl_down_sync(mask, lo, delta, width); - asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); - return value; -} - -template -__device__ EIGEN_ALWAYS_INLINE T CudaShuffleXor(unsigned mask, T value, - int laneMask, - int width = warpSize) { - return __shfl_xor_sync(mask, value, laneMask, width); -} - -__device__ EIGEN_ALWAYS_INLINE Eigen::half CudaShuffleXor( - unsigned mask, Eigen::half value, int laneMask, int width = warpSize) { - return Eigen::half( - __shfl_xor_sync(mask, static_cast(value), laneMask, width)); -} - -// Variant of the (undocumented) version from the CUDA SDK, but using unsigned -// instead of float for lo and hi (which is incorrect with ftz, for example). -// A bug has been filed with NVIDIA and will be fixed in the next CUDA release. -// TODO(csigg): remove when the bug is fixed in the next CUDA release. -__device__ EIGEN_ALWAYS_INLINE double CudaShuffleXor(unsigned mask, - double value, int laneMask, - int width = warpSize) { - unsigned lo, hi; - asm volatile("mov.b64 {%0,%1}, %2;" : "=r"(lo), "=r"(hi) : "d"(value)); - hi = __shfl_xor_sync(mask, hi, laneMask, width); - lo = __shfl_xor_sync(mask, lo, laneMask, width); - asm volatile("mov.b64 %0, {%1,%2};" : "=d"(value) : "r"(lo), "r"(hi)); - return value; -} - } // namespace tensorflow -#undef DIV_UP - #endif // GOOGLE_CUDA - #endif // TENSORFLOW_CORE_UTIL_CUDA_KERNEL_HELPER_H_ diff --git a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc index 6991554eff..bd4c356ea0 100644 --- a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc +++ b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc @@ -52,11 +52,11 @@ __global__ void Count1D(CudaLaunchConfig config, int bufsize, int* outbuf) { } } __global__ void Count2D(Cuda2DLaunchConfig config, int bufsize, int* outbuf) { - CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { + CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count.x, X) { if (x < 0) { // x might overflow when testing extreme case break; } - CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { + CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count.y, Y) { if (y < 0) { // y might overflow when testing extreme case break; } @@ -66,15 +66,15 @@ __global__ void Count2D(Cuda2DLaunchConfig config, int bufsize, int* outbuf) { } } __global__ void Count3D(Cuda3DLaunchConfig config, int bufsize, int* outbuf) { - CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { + CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count.x, X) { if (x < 0) { // x might overflow when testing extreme case break; } - CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { + CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count.y, Y) { if (y < 0) { // y might overflow when testing extreme case break; } - CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count, z) { + CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count.z, Z) { if (z < 0) { // z might overflow when testing extreme case break; } @@ -87,6 +87,44 @@ __global__ void Count3D(Cuda3DLaunchConfig config, int bufsize, int* outbuf) { } } +__global__ void CudaShuffleGetSrcLaneTest(unsigned* failure_count) { + unsigned lane_id = CudaLaneId(); + for (int width = warpSize; width > 1; width /= 2) { + auto check_result = [&](const char* op_name, int param, unsigned actual, + unsigned expected) { + if (actual != expected) { + printf("Cuda%sGetSrcLane(%d, %d) for lane %d returned %d, not %d\n", + op_name, param, width, lane_id, actual, expected); + CudaAtomicAdd(failure_count, 1); + } + }; + for (int src_lane = -warpSize; src_lane <= warpSize; ++src_lane) { + unsigned actual_lane = detail::CudaShuffleGetSrcLane(src_lane, width); + unsigned expect_lane = + CudaShuffleSync(kCudaWarpAll, lane_id, src_lane, width); + check_result("Shuffle", src_lane, actual_lane, expect_lane); + } + for (unsigned delta = 0; delta <= warpSize; ++delta) { + unsigned actual_lane = detail::CudaShuffleUpGetSrcLane(delta, width); + unsigned expect_lane = + CudaShuffleUpSync(kCudaWarpAll, lane_id, delta, width); + check_result("ShuffleUp", delta, actual_lane, expect_lane); + } + for (unsigned delta = 0; delta <= warpSize; ++delta) { + unsigned actual_lane = detail::CudaShuffleDownGetSrcLane(delta, width); + unsigned expect_lane = + CudaShuffleDownSync(kCudaWarpAll, lane_id, delta, width); + check_result("ShuffleDown", delta, actual_lane, expect_lane); + } + for (int lane_lane = warpSize; lane_lane > 0; lane_lane /= 2) { + unsigned actual_lane = detail::CudaShuffleXorGetSrcLane(lane_lane, width); + unsigned expect_lane = + CudaShuffleXorSync(kCudaWarpAll, lane_id, lane_lane, width); + check_result("ShuffleXor", lane_lane, actual_lane, expect_lane); + } + } +} + } // namespace class CudaLaunchConfigTest : public ::testing::Test { @@ -94,7 +132,7 @@ class CudaLaunchConfigTest : public ::testing::Test { const int bufsize = 1024; int* outbuf = nullptr; Eigen::CudaStreamDevice stream; - GPUDevice d = GPUDevice(&stream); + Eigen::GpuDevice d = Eigen::GpuDevice(&stream); virtual void SetUp() { cudaError_t err = cudaMallocManaged(&outbuf, sizeof(int) * bufsize); @@ -229,6 +267,16 @@ TEST_F(CudaLaunchConfigTest, GetCuda3DLaunchConfig) { #undef TEST_LAUNCH_PARAMETER } +TEST(CudaDeviceFunctionsTest, ShuffleGetSrcLane) { + unsigned* failure_count; + ASSERT_EQ(cudaMallocManaged(&failure_count, sizeof(unsigned)), cudaSuccess); + *failure_count = 0; + CudaShuffleGetSrcLaneTest<<<1, 32>>>(failure_count); + ASSERT_EQ(cudaDeviceSynchronize(), cudaSuccess); + ASSERT_EQ(*failure_count, 0); + cudaFree(failure_count); +} + } // namespace tensorflow #endif // GOOGLE_CUDA diff --git a/tensorflow/core/util/cuda_launch_config.h b/tensorflow/core/util/cuda_launch_config.h new file mode 100644 index 0000000000..3ea33ee6cf --- /dev/null +++ b/tensorflow/core/util/cuda_launch_config.h @@ -0,0 +1,284 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CORE_UTIL_CUDA_LAUNCH_CONFIG_H_ +#define TENSORFLOW_CORE_UTIL_CUDA_LAUNCH_CONFIG_H_ + +#if GOOGLE_CUDA + +#include + +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "cuda/include/cuda.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/stream_executor.h" +#include "tensorflow/core/platform/types.h" + +// Usage of GetCudaLaunchConfig, GetCuda2DLaunchConfig, and +// GetCuda3DLaunchConfig: +// +// There are two versions of GetCudaLaunchConfig and GetCuda2DLaunchConfig, one +// version uses heuristics without any knowledge of the device kernel, the other +// version uses cudaOccupancyMaxPotentialBlockSize to determine the theoretical +// launch parameters that maximize occupancy. Currently, only the maximum +// occupancy version of GetCuda3DLaunchConfig is available. +// +// For large number of work elements, the convention is that each kernel would +// iterate through its assigned range. The return value of GetCudaLaunchConfig +// is struct CudaLaunchConfig, which contains all the information needed for the +// kernel launch, including: virtual number of threads, the number of threads +// per block and number of threads per block used inside <<< >>> of a kernel +// launch. GetCuda2DLaunchConfig and GetCuda3DLaunchConfig does the same thing +// as CudaLaunchConfig. The only difference is the dimension. The macros +// CUDA_1D_KERNEL_LOOP and CUDA_AXIS_KERNEL_LOOP might be used to do inner loop. +// +/* Sample code: + +__global__ void MyKernel1D(CudaLaunchConfig config, other_args...) { + CUDA_1D_KERNEL_LOOP(x, config.virtual_thread_count) { + do_your_job_here; + } +} + +__global__ void MyKernel2D(Cuda2DLaunchConfig config, other_args...) { + CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { + CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { + do_your_job_here; + } + } +} + +__global__ void MyKernel3D(Cuda3DLaunchConfig config, other_args...) { + CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { + CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { + CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count, z) { + do_your_job_here; + } + } + } +} + +void MyDriverFunc(const Eigen::GpuDevice &d) { + // use heuristics + CudaLaunchConfig cfg1 = GetCudaLaunchConfig(10240, d); + MyKernel1D <<>> (cfg1, other_args...); + Cuda2DLaunchConfig cfg2 = GetCuda2DLaunchConfig(10240, 10240, d); + MyKernel2D <<>> (cfg2, other_args...); + Cuda3DLaunchConfig cfg3 = GetCuda3DLaunchConfig(4096, 4096, 100, d); + MyKernel3D <<>> (cfg3, other_args...); + + // maximize occupancy + CudaLaunchConfig cfg4 = GetCudaLaunchConfig(10240, d, MyKernel1D, 0, 0 ); + MyKernel1D <<>> (cfg4, other_args...); + Cuda2DLaunchConfig cfg5 = GetCuda2DLaunchConfig(10240, 10240, d, + MyKernel1D, 0, 0); + MyKernel2D <<>> (cfg5, other_args...); + Cuda3DLaunchConfig cfg6 = GetCuda3DLaunchConfig(4096, 4096, 100, d, + MyKernel1D, 0, 0); + MyKernel3D <<>> (cfg6, other_args...); +} + +// See the test for this for more example: +// +https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/cuda_kernel_helper_test.cu.cc + +*/ + +namespace tensorflow { + +inline int DivUp(int a, int b) { return (a + b - 1) / b; } + +struct CudaLaunchConfig { + // Logical number of thread that works on the elements. If each logical + // thread works on exactly a single element, this is the same as the working + // element count. + int virtual_thread_count = -1; + // Number of threads per block. + int thread_per_block = -1; + // Number of blocks for Cuda kernel launch. + int block_count = -1; +}; + +// Calculate the Cuda launch config we should use for a kernel launch. +// This is assuming the kernel is quite simple and will largely be +// memory-limited. +// REQUIRES: work_element_count > 0. +inline CudaLaunchConfig GetCudaLaunchConfig(int work_element_count, + const Eigen::GpuDevice& d) { + CHECK_GT(work_element_count, 0); + CudaLaunchConfig config; + const int virtual_thread_count = work_element_count; + const int physical_thread_count = std::min( + d.getNumCudaMultiProcessors() * d.maxCudaThreadsPerMultiProcessor(), + virtual_thread_count); + const int thread_per_block = std::min(1024, d.maxCudaThreadsPerBlock()); + const int block_count = + std::min(DivUp(physical_thread_count, thread_per_block), + d.getNumCudaMultiProcessors()); + + config.virtual_thread_count = virtual_thread_count; + config.thread_per_block = thread_per_block; + config.block_count = block_count; + return config; +} + +// Calculate the Cuda launch config we should use for a kernel launch. This +// variant takes the resource limits of func into account to maximize occupancy. +// REQUIRES: work_element_count > 0. +template +inline CudaLaunchConfig GetCudaLaunchConfig(int work_element_count, + const Eigen::GpuDevice& d, + DeviceFunc func, + size_t dynamic_shared_memory_size, + int block_size_limit) { + CHECK_GT(work_element_count, 0); + CudaLaunchConfig config; + int block_count = 0; + int thread_per_block = 0; + + cudaError_t err = cudaOccupancyMaxPotentialBlockSize( + &block_count, &thread_per_block, func, dynamic_shared_memory_size, + block_size_limit); + CHECK_EQ(err, cudaSuccess); + + block_count = + std::min(block_count, DivUp(work_element_count, thread_per_block)); + + config.virtual_thread_count = work_element_count; + config.thread_per_block = thread_per_block; + config.block_count = block_count; + return config; +} + +struct Cuda2DLaunchConfig { + dim3 virtual_thread_count = dim3(0, 0, 0); + dim3 thread_per_block = dim3(0, 0, 0); + dim3 block_count = dim3(0, 0, 0); +}; + +inline Cuda2DLaunchConfig GetCuda2DLaunchConfig(int xdim, int ydim, + const Eigen::GpuDevice& d) { + Cuda2DLaunchConfig config; + + if (xdim <= 0 || ydim <= 0) { + return config; + } + + const int kThreadsPerBlock = 256; + int block_cols = std::min(xdim, kThreadsPerBlock); + // ok to round down here and just do more loops in the kernel + int block_rows = std::max(kThreadsPerBlock / block_cols, 1); + + const int physical_thread_count = + d.getNumCudaMultiProcessors() * d.maxCudaThreadsPerMultiProcessor(); + + const int max_blocks = std::max(physical_thread_count / kThreadsPerBlock, 1); + + config.virtual_thread_count = dim3(xdim, ydim, 1); + config.thread_per_block = dim3(block_cols, block_rows, 1); + + int grid_x = std::min(DivUp(xdim, block_cols), max_blocks); + + config.block_count = dim3( + grid_x, std::min(max_blocks / grid_x, std::max(ydim / block_rows, 1)), 1); + return config; +} + +// Calculate the Cuda 2D and 3D launch config we should use for a kernel launch. +// This variant takes the resource limits of func into account to maximize +// occupancy. +using Cuda3DLaunchConfig = Cuda2DLaunchConfig; + +template +inline Cuda3DLaunchConfig GetCuda3DLaunchConfig( + int xdim, int ydim, int zdim, const Eigen::GpuDevice& d, DeviceFunc func, + size_t dynamic_shared_memory_size, int block_size_limit) { + Cuda3DLaunchConfig config; + + if (xdim <= 0 || ydim <= 0 || zdim <= 0) { + return config; + } + + int dev; + cudaGetDevice(&dev); + cudaDeviceProp deviceProp; + cudaGetDeviceProperties(&deviceProp, dev); + int xthreadlimit = deviceProp.maxThreadsDim[0]; + int ythreadlimit = deviceProp.maxThreadsDim[1]; + int zthreadlimit = deviceProp.maxThreadsDim[2]; + int xgridlimit = deviceProp.maxGridSize[0]; + int ygridlimit = deviceProp.maxGridSize[1]; + int zgridlimit = deviceProp.maxGridSize[2]; + + int block_count = 0; + int thread_per_block = 0; + cudaError_t err = cudaOccupancyMaxPotentialBlockSize( + &block_count, &thread_per_block, func, dynamic_shared_memory_size, + block_size_limit); + CHECK_EQ(err, cudaSuccess); + + auto min3 = [](int a, int b, int c) { return std::min(a, std::min(b, c)); }; + + int threadsx = min3(xdim, thread_per_block, xthreadlimit); + int threadsy = + min3(ydim, std::max(thread_per_block / threadsx, 1), ythreadlimit); + int threadsz = + min3(zdim, std::max(thread_per_block / (threadsx * threadsy), 1), + zthreadlimit); + + int blocksx = min3(block_count, DivUp(xdim, threadsx), xgridlimit); + int blocksy = + min3(DivUp(block_count, blocksx), DivUp(ydim, threadsy), ygridlimit); + int blocksz = min3(DivUp(block_count, (blocksx * blocksy)), + DivUp(zdim, threadsz), zgridlimit); + + config.virtual_thread_count = dim3(xdim, ydim, zdim); + config.thread_per_block = dim3(threadsx, threadsy, threadsz); + config.block_count = dim3(blocksx, blocksy, blocksz); + return config; +} + +template +inline Cuda2DLaunchConfig GetCuda2DLaunchConfig( + int xdim, int ydim, const Eigen::GpuDevice& d, DeviceFunc func, + size_t dynamic_shared_memory_size, int block_size_limit) { + return GetCuda3DLaunchConfig(xdim, ydim, 1, d, func, + dynamic_shared_memory_size, block_size_limit); +} + +// Returns a raw reference to the current cuda stream. Required by a +// number of kernel calls (for which StreamInterface* does not work), i.e. +// CUB and certain cublas primitives. +inline const cudaStream_t& GetCudaStream(OpKernelContext* context) { + const cudaStream_t* ptr = CHECK_NOTNULL( + reinterpret_cast(context->op_device_context() + ->stream() + ->implementation() + ->CudaStreamMemberHack())); + return *ptr; +} + +} // namespace tensorflow + +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CORE_UTIL_CUDA_KERNEL_HELPER_H_ -- GitLab From c5c2eab10bdeb48c864ccb364cf08b4820a8c031 Mon Sep 17 00:00:00 2001 From: Keiji Ariyama Date: Sat, 27 Jan 2018 01:33:29 +0900 Subject: [PATCH 114/423] Fixing the url for the pip3. (#16447) --- tensorflow/docs_src/install/install_mac.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index e13ddadab7..555a6837d8 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -115,7 +115,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -238,7 +238,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0rc1-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). -- GitLab From 76f70f5d62f35b5cc95121e6dfffa63a8214b626 Mon Sep 17 00:00:00 2001 From: Santiago Castro Date: Fri, 26 Jan 2018 13:39:07 -0300 Subject: [PATCH 115/423] Update contrib/HVX readme (#16131) * Update contrib/HVX readme * Fix PR comments --- tensorflow/contrib/hvx/README.md | 137 ++++++++++-------- .../contrib/makefile/build_all_android.sh | 4 +- .../build_and_run_inception_hexagon.sh | 4 +- 3 files changed, 84 insertions(+), 61 deletions(-) diff --git a/tensorflow/contrib/hvx/README.md b/tensorflow/contrib/hvx/README.md index 5a6f2f3086..cb3a1087de 100644 --- a/tensorflow/contrib/hvx/README.md +++ b/tensorflow/contrib/hvx/README.md @@ -1,60 +1,67 @@ # TensorFlow Runtime with HVX Acceleration -## Description +This README explain how to build and use the TensorFlow runtime with HVX Acceleration. HVX is an extension of Hexagon, a DSP provided by Qualcomm, which can compute vector calculations faster using less energy than ARM processors. -This README explain how to build and use the TensorFlow Runtime with HVX Acceleration. HVX is an extension of Hexagon which is a DSP provided by qualcomm which can compute vector calculations faster using lower energy than ARM processors. +## Dependencies + +* [Android SDK](https://developer.android.com/studio/index.html). +* [Android NDK](https://developer.android.com/ndk/index.html). Save the path in `${NDK_ROOT}`. +* A rooted Qualcomm-based Android device connected to the computer (preferably, a [Snapdragon Development Board](https://developer.qualcomm.com/hardware/additional-snapdragon), but it could be a rooted phone with a Qualcomm SoC, albeit this guide may not work with it). The device needs to be rooted for development and testing purposes, and shouldn't be needed in production. See [Behold, The Snapdragon MDP](https://developer.qualcomm.com/blog/behold-snapdragon-mdp) for more information. +* [Hexagon SDK v3.0](https://developer.qualcomm.com/software/hexagon-dsp-sdk/tools). Save the path in `${QUALCOMM_SDK}`. +* The current directory should be TensorFlow source code (`git clone https://github.com/tensorflow/tensorflow.git && cd tensorflow`), and saved into `${TF_ROOT_DIR}`. + +You may also need to add a test signature in the device to run HVX-based binaries. Follow the instructions in `${QUALCOMM_SDK}/docs/Tools_Signing.html`, using Python 2. + +Note that if the device is not rooted, you may not be able to get the serial number, push the test signature and/or run binary files that call HVX libraries. ## Quick Start Guide -We provides several tools to build and run inference with this runtime quickly. +We provide several tools to build and run inference with this runtime quickly. -#### All-in-one script to run inception model with prebuild hexagon library -If you don’t need to build your own implementation of hexagon HVX, we provide a shortcut to execute graphs by using pre-compiled binaries. +### Run inception model with a prebuilt Hexagon library +If you don’t need to build your own implementation of Hexagon HVX, we provide a shortcut to execute graphs by using pre-compiled binaries. + +```shell +./tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh -p ``` -git clone https://github.com/tensorflow/tensorflow.git -cd tensorflow -NDK_ROOT="/path/to/ndk" ./tensorflow/contrib/makefile/build_all_android.sh -X -``` -(-X downloads dependencies to hexagon HVX and graphs, and copy all dependencies to android and execute a test) -#### All-in-one script to run inception model by building entire libraries from source code - If you want to build your own implementation of hexagon HVX, we provide a sample all-in-one script to execute graphs which downloads source and build everything for hexagon. +The `-p` option makes the script download dependencies (i.e., Hexagon HVX binaries and graphs models), copy them to the Android device and execute a test. -``` -git clone https://github.com/tensorflow/tensorflow.git -cd tensorflow -QUALCOMM_SDK="/path/to/qualcomm/sdk" NDK_ROOT="/path/to/ndk" ./tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh +### Run inception model by building all from the source code + +If you want to build your own implementation of Hexagon HVX, we provide a sample all-in-one script to execute graphs which downloads the source and builds everything that's necessary. + +```shell +./tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh ``` ## Building libraries If you've finished walking through the quick start guide, you may want to try building each binary manually. -#### Build libhexagon_nn_skel.so -Download hexagon nn library from codeaurora.org and build it. +### Build libhexagon\_nn\_skel.so -``` +Download Hexagon NN library from codeaurora.org and build it. + +```shell git clone https://source.codeaurora.org/quic/hexagon_nn/nnlib cd nnlib ``` -(Just follow instructions in README.HOW_TO_BUILD. You can find libhexagon_nn_skel.so in hexagon_Release_dynamic_toolv72_v60/ship) -Then copy the generated binary to GEN_LIBS_DIR +Just follow the instructions in `README.HOW_TO_BUILD`. You can find the file `libhexagon_nn_skel.so` in `hexagon_Release_dynamic_toolv72_v60/ship`. +Then copy the generated binary to `${GEN_LIBS_DIR}`. -``` +```shell GEN_LIBS_DIR="/path/to/a/dir/to/store/hexagon/libraries" cp -v "hexagon_Release_dynamic_toolv72_v60/ship/libhexagon_nn_skel.so" "${GEN_LIBS_DIR}" ``` -#### Build libhexagon_controller.so +### Build libhexagon\_controller.so + Download tensorflow and build hexagon controller. -``` -git clone https://github.com/tensorflow/tensorflow.git -cd tensorflow -TF_ROOT_DIR="$(pwd)" -QUALCOMM_SDK="/path/to/qualcomm/sdk" +```shell GENERATED_NNLIB_DIRECTORY="/path/to/nnlib" GENERATED_HEXAGON_CONTROLLER_DIRECTORY="${QUALCOMM_SDK}/examples/common/generated_hexagon_controller" rm -rf "${GENERATED_HEXAGON_CONTROLLER_DIRECTORY}" @@ -70,12 +77,12 @@ make tree VERBOSE=1 V=android_Release cp -v "${GENERATED_HEXAGON_CONTROLLER_DIRECTORY}/android_Release/ship/libhexagon_controller.so" "${GEN_LIBS_DIR}" ``` -#### Build tensorflow linking hexagon library -Build tensorflow with the build_all_android.sh with specifying -x option. +### Build TensorFlow linking Hexagon library -``` +Build TensorFlow with `build_all_android.sh` specifying the `-x` option. + +```shell BUILD_ALL_ANDROID_PATH="${TF_ROOT_DIR}/tensorflow/contrib/makefile/build_all_android.sh" -NDK_ROOT="/path/to/ndk/root" CC_PREFIX=${CC_PREFIX} NDK_ROOT=${NDK_ROOT} "${BUILD_ALL_ANDROID_PATH}" \ -x "${GEN_LIBS_DIR}" \ @@ -83,11 +90,11 @@ CC_PREFIX=${CC_PREFIX} NDK_ROOT=${NDK_ROOT} "${BUILD_ALL_ANDROID_PATH}" \ -t hexagon_graph_execution ``` -#### Push binaries to your Android device +### Push binaries to your Android device Before running tests on your Android device, you need to push several binaries to it. -``` +```shell adb push "${GEN_LIBS_DIR}/libhexagon_controller.so" "/data/local/tmp" adb push "${GEN_LIBS_DIR}/libhexagon_nn_skel.so" "/vendor/lib/rfsa/adsp" adb push -p \ @@ -100,40 +107,54 @@ adb shell chmod "${ANDROID_EXEC_FILE_MODE}" \ adb wait-for-device ``` -#### Run tests on the device +### Run tests on the device Finally, you can run the inference tests on your device. -``` +```shell adb shell 'LD_LIBRARY_PATH=/data/local/tmp:$LD_LIBRARY_PATH' \ "/data/local/tmp/hexagon_graph_execution" ``` -#### Troubleshooting -If you're using the Open-Q 820 Snapdragon development kit, you may run into an issue with running the executable due to a missing testsig library. From the Hexagon SDK documentation: *Dynamic shared objects are required to be digitally signed and then authenticated at runtime before they are allowed to be loaded and executed.* Generating a testsig library is necessary to run the unsigned sample library built from this project. +### Troubleshooting + +#### Testsig issue + +If you're using the Open-Q 820 Snapdragon Development Kit, you may run into an issue with running the executable due to a missing `testsig` library. From the Hexagon SDK documentation: *Dynamic shared objects are required to be digitally signed and then authenticated at runtime before they are allowed to be loaded and executed.* Generating a testsig library is necessary to run the unsigned sample library built from this project. -If the lack of a testsig library is your problem, you will see errors of the type: +If the lack of a `testsig` library is your problem, you will see errors of the type: `vendor/qcom/proprietary/adsprpc/src/fastrpc_apps_user.c:169::error: -1: 0 == (nErr = remotectl_open(name, (int*)ph, dlerrstr, sizeof(dlerrstr), &dlerr))` -appearing in adb logcat. - -There are several ways to create the testsig library, the only prerequisite is Python and the correct version of the Hexagon-SDK. The following steps is one way to create this library: -1. Run adb as root: `adb root` -2. Run the command `adb shell cat /sys/devices/soc0/serial_number` -3. Convert the decimal number you get as output to hex -4. Run the python script: `python ${QUALCOMM_SDK}/tools/elfsigner/elfsigner.py -t $(SERIAL_NUMBER_HEX_VALUE)` -5. The output of the python script is a shared library stored in ${QUALCOMM_SDK}/tools/elfsigner/output/testsig-$(SERIAL_NUMBER_HEX_VALUE).so -6. Push the shared library to your device: +appearing in `adb logcat` or ["Expected: (version) >= (1), actual: 0 vs 1" while running a binary from adb](https://github.com/tensorflow/tensorflow/issues/11210). + +You need to add a test signature, as described at the beginning of this README. After rebooting your device, you should be able to run the sample application. + +#### Qualcomm SDK Linux installation fails with "Malformed \uxxxx encoding" + +The installation file is based on LaunchAnywhere, which fails in Linux if the `PS1` env variable contains non-common Unicode chars: + ``` -adb root -adb wait-for-device -adb remount -adb wait-for-device -adb shell mkdir /system/lib/rfsa -adb shell mkdir /system/lib/rfsa/adsp -adb push ${QUALCOMM_SDK}/tools/elfsigner/output/testsig-$(SERIAL_NUMBER_HEX_VALUE).so /system/lib/rfsa/adsp/ +Preparing to install... +Extracting the JRE from the installer archive... +Unpacking the JRE... +Extracting the installation resources from the installer archive... +Configuring the installer for this system's environment... + +Launching installer... + +An internal LaunchAnywhere application error has occured and this application cannot proceed. (LAX) + +Stack Trace: +java.lang.IllegalArgumentException: Malformed \uxxxx encoding. + at java.util.Properties.loadConvert(Properties.java:574) + at java.util.Properties.load0(Properties.java:391) + at java.util.Properties.load(Properties.java:317) + at com.zerog.common.java.util.PropertiesUtil.loadProperties(Unknown Source) + at com.zerog.lax.LAX.(Unknown Source) + at com.zerog.lax.LAX.main(Unknown Source) ``` -After rebooting your device, you should be able to run the sample application. +It can be solved by temporarily assigning the `PS1` environment variable to something simple, such as '$'. + +## Maintainers -Maintainers: -- Satoshi Kataoka (satok@google.com, github.com/satok16) +* Satoshi Kataoka (satok@google.com, github.com/satok16) diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh index 980a44a595..281c4653c6 100755 --- a/tensorflow/contrib/makefile/build_all_android.sh +++ b/tensorflow/contrib/makefile/build_all_android.sh @@ -18,7 +18,7 @@ set -e usage() { - echo "Usage: NDK_ROOT= $(basename "$0") [-Es:t:Tx:a:X]" + echo "Usage: NDK_ROOT= $(basename "$0") [-Es:t:Tx:a]" echo "-E enable experimental hexnn ops" echo "-s [sub_makefiles] sub makefiles separated by white space" echo "-t [build_target] build target for Android makefile [default=all]" @@ -37,7 +37,7 @@ fi ARCH=armeabi-v7a -while getopts "Es:t:Tx:a:" opt_name; do +while getopts "Es:t:Tx:a" opt_name; do case "$opt_name" in E) ENABLE_EXPERIMENTAL_HEXNN_OPS="true";; s) SUB_MAKEFILES="${OPTARG}";; diff --git a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh index 861bb885c7..203ff4f890 100755 --- a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh +++ b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh @@ -76,6 +76,8 @@ GEN_LIBS_DIR="${GEN_DIR}/libs" GEN_DOWNLOAD_DIR="${GEN_DIR}/downloads" URL_BASE="https://storage.googleapis.com/download.tensorflow.org" +ARCH="armeabi-v7a" + source "${SCRIPT_DIR}/../build_helper.subr" rm -rf "${GEN_DIR}" @@ -219,7 +221,7 @@ if [[ "${BUILD_ONLY}" != "true" ]]; then adb push "${GEN_LIBS_DIR}/libhexagon_nn_skel.so" "/vendor/lib/rfsa/adsp" adb push -p \ - "${TF_ROOT_DIR}/tensorflow/contrib/makefile/gen/bin/hexagon_graph_execution" \ + "${TF_ROOT_DIR}/tensorflow/contrib/makefile/gen/bin/android_${ARCH}/hexagon_graph_execution" \ "/data/local/tmp/" adb wait-for-device adb shell chmod "${ANDROID_EXEC_FILE_MODE}" \ -- GitLab From 78021a9a70923f1fdaa65b41271ad0ea70cd7e67 Mon Sep 17 00:00:00 2001 From: namrata-ibm Date: Fri, 26 Jan 2018 22:10:04 +0530 Subject: [PATCH 116/423] Decoding contents of BMP file on big endian (#16145) * Decoding contents of BMP file on big endian * Updated as per review comments * Update decode_bmp_op.cc Corrected function name --- tensorflow/core/kernels/decode_bmp_op.cc | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/kernels/decode_bmp_op.cc b/tensorflow/core/kernels/decode_bmp_op.cc index c778278e8f..b7d120a617 100644 --- a/tensorflow/core/kernels/decode_bmp_op.cc +++ b/tensorflow/core/kernels/decode_bmp_op.cc @@ -39,6 +39,13 @@ class DecodeBmpOp : public OpKernel { errors::InvalidArgument("channels must be 0, 1, 3 or 4, got ", channels_)); } + inline int32 ByteSwapInt32ForBigEndian(int32 x) { +#if (__BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) + return le32toh(x); +#else + return x; +#endif + } void Compute(OpKernelContext* context) override { const Tensor& contents = context->input(0); @@ -56,14 +63,18 @@ class DecodeBmpOp : public OpKernel { input.size(), " bytes")); const uint8* img_bytes = reinterpret_cast(input.data()); - const int32 header_size = internal::SubtleMustCopy( + int32 header_size_ = internal::SubtleMustCopy( *(reinterpret_cast(img_bytes + 10))); - const int32 width = internal::SubtleMustCopy( + const int32 header_size = ByteSwapInt32ForBigEndian(header_size_); + int32 width_ = internal::SubtleMustCopy( *(reinterpret_cast(img_bytes + 18))); - const int32 height = internal::SubtleMustCopy( + const int32 width = ByteSwapInt32ForBigEndian(width_); + int32 height_ = internal::SubtleMustCopy( *(reinterpret_cast(img_bytes + 22))); - const int32 bpp = internal::SubtleMustCopy( + const int32 height = ByteSwapInt32ForBigEndian(height_); + int32 bpp_ = internal::SubtleMustCopy( *(reinterpret_cast(img_bytes + 28))); + const int32 bpp = ByteSwapInt32ForBigEndian(bpp_); if (channels_) { OP_REQUIRES(context, (channels_ == bpp / 8), -- GitLab From 9efe775925ab61e938414e91cd3cf48f0c0efae2 Mon Sep 17 00:00:00 2001 From: namrata-ibm Date: Fri, 26 Jan 2018 22:18:26 +0530 Subject: [PATCH 117/423] Compare_and_bitpack function for bool for big endian (#16398) --- .../core/kernels/compare_and_bitpack_op.cc | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/compare_and_bitpack_op.cc b/tensorflow/core/kernels/compare_and_bitpack_op.cc index 9f626a274a..39e4f24ed5 100644 --- a/tensorflow/core/kernels/compare_and_bitpack_op.cc +++ b/tensorflow/core/kernels/compare_and_bitpack_op.cc @@ -110,7 +110,20 @@ struct ComputeShard::ConstMatrix input, typename TTypes::Matrix output, bool /*thresh*/, int64 start, int64 limit) { - // NOTE(ebrevdo): This assumes memory is little-endian. +#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ + for (int64 i = start; i < limit; ++i) { + uint8* out = output.data() + i; + const int64 block = *reinterpret_cast(input.data() + 8 * i); + *out = + ((((block & (1LL << (7 * 8))) >> (7 * 8 - 7))) | + (((block & (1LL << (6 * 8))) >> (6 * 8 - 6))) | + (((block & (1LL << (5 * 8))) >> (5 * 8 - 5))) | + (((block & (1LL << (4 * 8))) >> (4 * 8 - 4))) | + (((block & (1LL << (3 * 8))) >> (3 * 8 - 3))) | + (((block & (1LL << (2 * 8))) >> (2 * 8 - 2))) | + (((block & (1LL << 8)) >> (1 * 8 - 1))) | (((block & (1LL))))); + } +#else for (int64 i = start; i < limit; ++i) { uint8* out = output.data() + i; const int64 block = *reinterpret_cast(input.data() + 8 * i); @@ -123,6 +136,7 @@ struct ComputeShard> (2 * 8 - 5))) | (((block & (1LL << 8)) >> (1 * 8 - 6))) | (((block & (1LL)) << 7))); } +#endif } }; -- GitLab From 7fc61bfb50aac4e2d0ff9dab9d99a6001aa5cccf Mon Sep 17 00:00:00 2001 From: "Joshua V. Dillon" Date: Fri, 26 Jan 2018 08:57:10 -0800 Subject: [PATCH 118/423] Change `reduce_logsumexp` to internally use `reshape` rather than `squeeze` since the latter requires the `axis` arg to be a Python `list`. PiperOrigin-RevId: 183396533 --- tensorflow/python/ops/math_ops.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 9ad1031354..827e3caa36 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -1841,12 +1841,11 @@ def reduce_logsumexp(input_tensor, reduce_sum( gen_math_ops.exp(input_tensor - my_max), axis, - keepdims=True, - reduction_indices=reduction_indices)) + my_max + keepdims=keepdims, + reduction_indices=reduction_indices)) if not keepdims: - if isinstance(axis, int): - axis = [axis] - result = array_ops.squeeze(result, axis) + my_max = array_ops.reshape(my_max, array_ops.shape(result)) + result += my_max return _may_reduce_to_scalar(keepdims, axis, reduction_indices, result) -- GitLab From c4ace4e2abf6f19f34357e53ba4aebce5113af01 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 09:03:49 -0800 Subject: [PATCH 119/423] Kernel utils to support broadcast add and mul. PiperOrigin-RevId: 183397494 --- tensorflow/contrib/lite/kernels/BUILD | 29 +++- .../contrib/lite/kernels/kernel_util.cc | 26 +++ tensorflow/contrib/lite/kernels/kernel_util.h | 17 ++ .../contrib/lite/kernels/kernel_util_test.cc | 150 ++++++++++++++++++ 4 files changed, 220 insertions(+), 2 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/kernel_util_test.cc diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 4195e7553c..b5428d3246 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -71,6 +71,32 @@ cc_library( ], ) +cc_library( + name = "kernel_util", + srcs = [ + "kernel_util.cc", + ], + hdrs = [ + "kernel_util.h", + ], + deps = [ + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:context", + "//tensorflow/contrib/lite/kernels/internal:round", + ], +) + +tf_cc_test( + name = "kernel_util_test", + size = "small", + srcs = ["kernel_util_test.cc"], + deps = [ + ":kernel_util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + cc_library( name = "builtin_ops", srcs = [ @@ -87,7 +113,6 @@ cc_library( "fully_connected.cc", "gather.cc", "hashtable_lookup.cc", - "kernel_util.cc", "l2norm.cc", "local_response_norm.cc", "lsh_projection.cc", @@ -111,7 +136,6 @@ cc_library( "unidirectional_sequence_rnn.cc", ], hdrs = [ - "kernel_util.h", "padding.h", "register.h", ], @@ -125,6 +149,7 @@ cc_library( }), deps = [ ":activation_functor", + ":kernel_util", ":op_macros", "//tensorflow/contrib/lite:builtin_op_data", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/kernels/kernel_util.cc b/tensorflow/contrib/lite/kernels/kernel_util.cc index b0546c00cf..955e8c5764 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.cc +++ b/tensorflow/contrib/lite/kernels/kernel_util.cc @@ -13,8 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/kernels/kernel_util.h" + #include #include +#include + #include "tensorflow/contrib/lite/kernels/internal/round.h" namespace tflite { @@ -84,4 +87,27 @@ void CalculateActivationRangeFloat(TfLiteFusedActivation activation, } } +bool HaveSameShapes(TfLiteTensor* input1, TfLiteTensor* input2) { + return TfLiteIntArrayEqual(input1->dims, input2->dims); +} + +TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, + TfLiteTensor* input1, + TfLiteTensor* input2, + TfLiteIntArray** output_shape) { + int64_t dims1 = NumDimensions(input1); + int64_t dims2 = NumDimensions(input2); + int64_t out_dims = std::max(dims1, dims2); + std::unique_ptr shape( + TfLiteIntArrayCreate(out_dims), TfLiteIntArrayFree); + for (int i = 0; i < out_dims; ++i) { + int64_t d1 = i >= dims1 ? 1 : SizeOfDimension(input1, dims1 - i - 1); + int64_t d2 = i >= dims2 ? 1 : SizeOfDimension(input2, dims2 - i - 1); + TF_LITE_ENSURE(context, d1 == d2 || d1 == 1 || d2 == 1); + shape->data[out_dims - i - 1] = std::max(d1, d2); + } + *output_shape = shape.release(); + return kTfLiteOk; +} + } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h index bfdfba00f5..3cfa72615a 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.h +++ b/tensorflow/contrib/lite/kernels/kernel_util.h @@ -35,6 +35,14 @@ inline TfLiteTensor* GetOutput(TfLiteContext* context, TfLiteNode* node, inline int NumInputs(const TfLiteNode* node) { return node->inputs->size; } inline int NumOutputs(const TfLiteNode* node) { return node->outputs->size; } +inline int64_t NumElements(const TfLiteTensor* t) { + int64_t count = 1; + for (int i = 0; i < NumDimensions(t); ++i) { + count *= SizeOfDimension(t, i); + } + return count; +} + inline TfLiteTensor* GetOptionalInputTensor(TfLiteContext* context, const TfLiteNode* node, int index) { const bool use_tensor = node->inputs->data[index] != kOptionalTensor; @@ -76,6 +84,15 @@ void CalculateActivationRangeFloat(TfLiteFusedActivation activation, float* activation_min, float* activation_max); +// Return true if the given tensors have the same shape. +bool HaveSameShapes(TfLiteTensor* input1, TfLiteTensor* input2); + +// Calculate the output_shape that is necessary for element-wise operations +// with broadcasting involving the two input tensors. +TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, + TfLiteTensor* input1, + TfLiteTensor* input2, + TfLiteIntArray** output_shape); } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_KERNEL_UTIL_H_ diff --git a/tensorflow/contrib/lite/kernels/kernel_util_test.cc b/tensorflow/contrib/lite/kernels/kernel_util_test.cc new file mode 100644 index 0000000000..63a317f338 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/kernel_util_test.cc @@ -0,0 +1,150 @@ +/* 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/kernels/kernel_util.h" + +#include +#include +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { +namespace { + +void ReportError(TfLiteContext* context, const char* format, ...) {} + +class KernelUtilTest : public ::testing::Test { + public: + KernelUtilTest() { + context_.ReportError = ReportError; + + tensor1_.dims = nullptr; + tensor2_.dims = nullptr; + } + ~KernelUtilTest() { + TfLiteTensorFree(&tensor1_); + TfLiteTensorFree(&tensor2_); + } + + void SetShape(TfLiteTensor* tensor, std::initializer_list dims) { + TfLiteTensorFree(tensor); + tensor->dims = TfLiteIntArrayCreate(dims.size()); + int i = 0; + for (int d : dims) { + tensor->dims->data[i] = d; + ++i; + } + } + + std::vector GetShape(TfLiteIntArray* dims) { + std::vector result; + for (int i = 0; i < dims->size; ++i) { + result.push_back(dims->data[i]); + } + return result; + } + + protected: + TfLiteContext context_; + TfLiteTensor tensor1_; + TfLiteTensor tensor2_; +}; + +TEST_F(KernelUtilTest, SameShapeEmpty) { + EXPECT_TRUE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor1_, {1, 2, 3}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {1, 2}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {1, 2, 3, 4}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {1, 2, 3}); + EXPECT_TRUE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor1_, {}); + EXPECT_TRUE(HaveSameShapes(&tensor1_, &tensor2_)); +} + +TEST_F(KernelUtilTest, BroadcastShapeIncompatibleDim) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {1, 3}); + EXPECT_NE(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_EQ(output, nullptr); +} + +TEST_F(KernelUtilTest, BroadcastShapeOnes) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 1}); + SetShape(&tensor2_, {1, 3}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + TfLiteIntArrayFree(output); + + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {1, 1}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + TfLiteIntArrayFree(output); +} + +TEST_F(KernelUtilTest, BroadcastShapeScalars) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(1, 2)); + TfLiteIntArrayFree(output); + + SetShape(&tensor1_, {}); + SetShape(&tensor2_, {2}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(2)); + TfLiteIntArrayFree(output); +} + +TEST_F(KernelUtilTest, BroadcastShapeDifferentSizes) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {3, 1, 1}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(3, 1, 2)); + TfLiteIntArrayFree(output); + + SetShape(&tensor1_, {1, 2, 3, 4}); + SetShape(&tensor2_, {1, 3, 1}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(1, 2, 3, 4)); + TfLiteIntArrayFree(output); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} -- GitLab From b773acad69605a2afdc0d7a08923484eb5e7f17a Mon Sep 17 00:00:00 2001 From: Sergii Khomenko Date: Fri, 26 Jan 2018 18:25:36 +0100 Subject: [PATCH 120/423] Fix a couple minor typos (#16459) --- tensorflow/python/data/util/nest.py | 4 ++-- tensorflow/python/data/util/sparse.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/data/util/nest.py b/tensorflow/python/data/util/nest.py index 2455395635..df5498be5f 100644 --- a/tensorflow/python/data/util/nest.py +++ b/tensorflow/python/data/util/nest.py @@ -266,7 +266,7 @@ def map_structure(func, *structure, **check_types_dict): and the return value will contain the results in the same structure. Args: - func: A callable that acceps as many arguments are there are structures. + func: A callable that accepts as many arguments are there are structures. *structure: scalar, or tuple or list of constructed scalars and/or other tuples/lists, or scalars. Note: numpy arrays are considered scalars. **check_types_dict: only valid keyword argument is `check_types`. If set to @@ -479,7 +479,7 @@ def map_structure_up_to(shallow_tree, func, *inputs): The `inputs`, can be thought of as having the same structure as `shallow_tree`, but with leaf nodes that are themselves tree structures. - This function therefore will return something with the same base structure as + This function, therefore, will return something with the same base structure as `shallow_tree`. Examples: diff --git a/tensorflow/python/data/util/sparse.py b/tensorflow/python/data/util/sparse.py index 5ebcb4ea81..5e6d224709 100644 --- a/tensorflow/python/data/util/sparse.py +++ b/tensorflow/python/data/util/sparse.py @@ -141,7 +141,7 @@ def serialize_sparse_tensors(tensors): tensors: a tensor structure to serialize. Returns: - `tensors` with any sparse tensors replaced by the their serialized version. + `tensors` with any sparse tensors replaced by their serialized version. """ ret = nest.pack_sequence_as(tensors, [ -- GitLab From c1338b14149b6313280bea455ec1dec2a336bd31 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 09:47:58 -0800 Subject: [PATCH 121/423] Updating sparsify_gather. PiperOrigin-RevId: 183402917 --- .../tools/graph_transforms/sparsify_gather.cc | 109 ++++++++++++------ .../graph_transforms/sparsify_gather_test.cc | 40 ++++++- 2 files changed, 110 insertions(+), 39 deletions(-) diff --git a/tensorflow/tools/graph_transforms/sparsify_gather.cc b/tensorflow/tools/graph_transforms/sparsify_gather.cc index 96324d0dea..593c654f9f 100644 --- a/tensorflow/tools/graph_transforms/sparsify_gather.cc +++ b/tensorflow/tools/graph_transforms/sparsify_gather.cc @@ -15,6 +15,7 @@ limitations under the License. #include #include +#include #include "tensorflow/c/checkpoint_reader.h" #include "tensorflow/core/framework/tensor.h" @@ -28,9 +29,10 @@ limitations under the License. #include "tensorflow/tools/graph_transforms/transform_utils.h" namespace tensorflow { -using strings::StrCat; using str_util::Join; using str_util::Split; +using str_util::StringReplace; +using strings::StrCat; namespace graph_transforms { @@ -89,7 +91,7 @@ Status ObtainTensorSlice(const GraphDef& input_graph_def, string* shape_slice_string) { string restore_node_name; for (const auto& node : input_graph_def.node()) { - std::vector node_name_parts = str_util::Split(node.name(), "/"); + std::vector node_name_parts = Split(node.name(), "/"); if (node_name_parts.size() == 2 && StringPiece(node_name_parts[0]).starts_with("save") && StringPiece(node_name_parts[1]).starts_with("Assign") && @@ -119,13 +121,13 @@ Status ObtainTensorSlice(const GraphDef& input_graph_def, } string GetMonolithicTensorKey(const string& tensor_slice_name) { - std::vector names = str_util::Split(tensor_slice_name, "/"); + std::vector names = Split(tensor_slice_name, "/"); CHECK_GE(names.size(), 2); CHECK(StringPiece(names[names.size() - 1]).starts_with("part_")); // Remove the "part_x" suffix names.pop_back(); - return str_util::Join(names, "/"); + return Join(names, "/"); } Status ReadTensorFromCheckpoint( @@ -193,6 +195,15 @@ Status SparsifyGatherInternal( GraphDef current_graph_def = input_graph_def; bool any_match_found = false; + // Populate references. + std::unordered_map refs; + for (const auto& node : current_graph_def.node()) { + for (const auto& input : node.input()) { + auto parsed_input = StringReplace(input, "^", "", true); + refs[parsed_input] += 1; + } + } + // The subgraphs may have overlapping components, therefore GraphMatcher // doesn't return all subgraphs in one round -- this has to be multi-round // update. @@ -200,15 +211,15 @@ Status SparsifyGatherInternal( any_match_found = false; GraphDef replaced_graph_def = current_graph_def; std::vector init_table_node_names; - std::vector removed_variable_names; + std::vector removed_node_names; TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes( current_graph_def, pattern, [&ckpt_reader, &any_match_found, &init_table_node_names, - &shapes_and_slices, &removed_variable_names]( - const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, - std::vector* new_nodes) { + &shapes_and_slices, &removed_node_names, + &refs](const NodeMatch& match, const std::set& input_nodes, + const std::set& output_nodes, + std::vector* new_nodes) { any_match_found = true; // The captured subgraph should be of the following pattern: @@ -291,8 +302,12 @@ Status SparsifyGatherInternal( weights_node.name(), ckpt_reader, (*shapes_and_slices)[weights_node.name()], &weight)); // Add both both weight and identity node names. - removed_variable_names.push_back(weights_node.name()); - removed_variable_names.push_back(match.inputs[0].node.name()); + removed_node_names.push_back(weights_node.name()); + removed_node_names.push_back(match.inputs[0].node.name()); + for (auto input_node : match.inputs[0].node.input()) { + auto parsed_input = StringReplace(input_node, "^", "", true); + refs[parsed_input]--; + } } Tensor indices_tensor; Tensor values_tensor; @@ -362,15 +377,23 @@ Status SparsifyGatherInternal( // Connect nodes AddNodeInput(hashtable_node.name(), &init_table_node); + refs[hashtable_node.name()]++; AddNodeInput(indices_node.name(), &init_table_node); + refs[indices_node.name()]++; AddNodeInput(values_node.name(), &init_table_node); + refs[values_node.name()]++; AddNodeInput(hashtable_node.name(), &lookup_node); + refs[hashtable_node.name()]++; AddNodeInput(gather_node.input(1), &lookup_node); + refs[gather_node.input(1)]++; AddNodeInput(default_value_node.name(), &lookup_node); + refs[default_value_node.name()]++; AddNodeInput(lookup_node.name(), &expand_dims_node); + refs[lookup_node.name()]++; AddNodeInput(dim_idx_node.name(), &expand_dims_node); + refs[dim_idx_node.name()]++; // Copy 'ids' input of original 'Gather' new_nodes->push_back(match.inputs[1].node); @@ -404,22 +427,44 @@ Status SparsifyGatherInternal( for (const string& name : init_table_node_names) { // Add control dependence from init_table_node to group_deps_node AddNodeInput(StrCat("^", name), init_op); + refs[name]++; + } + + // Erase inputs and outputs as they are not considered for deletion. + for (const auto& output : context.output_names) { + refs.erase(output); + } + + for (const auto& input : context.input_names) { + refs.erase(input); } - // Remove all dependencies associated with removed variables. - while (!removed_variable_names.empty()) { - auto name = removed_variable_names.back(); - removed_variable_names.pop_back(); + // Add nodes with a reference count of 0 for deletion. + for (auto entry : refs) { + if (entry.second == 0) { + removed_node_names.push_back(entry.first); + } + } + + while (!removed_node_names.empty()) { + auto name = removed_node_names.back(); + removed_node_names.pop_back(); + int i = 0; while (i < replaced_graph_def.node_size()) { - if (!replaced_graph_def.node(i).input_size()) { - if (replaced_graph_def.node(i).name() == name) { - replaced_graph_def.mutable_node()->SwapElements( - i, replaced_graph_def.node_size() - 1); - replaced_graph_def.mutable_node()->RemoveLast(); - continue; + // Revisit this to see if we can safely remove RestoreV2 nodes. + if ((replaced_graph_def.node(i).name() == name) && + (replaced_graph_def.node(i).op() != "RestoreV2")) { + for (const auto& input : replaced_graph_def.node(i).input()) { + auto parsed_input = StringReplace(input, "^", "", true); + refs[parsed_input] -= 1; + if (refs[parsed_input] == 0) { + removed_node_names.push_back(parsed_input); + } } - i++; + replaced_graph_def.mutable_node()->SwapElements( + i, replaced_graph_def.node_size() - 1); + replaced_graph_def.mutable_node()->RemoveLast(); continue; } int j = 0; @@ -433,18 +478,16 @@ Status SparsifyGatherInternal( } j++; } - if ((replaced_graph_def.node(i).input_size() == 0) || - (replaced_graph_def.node(i).op() == "Assign" && - replaced_graph_def.node(i).input_size() == 1)) { - removed_variable_names.push_back(replaced_graph_def.node(i).name()); - if (replaced_graph_def.node(i).input_size() == 1) { - removed_variable_names.push_back( - replaced_graph_def.node(i).input(0)); + if (!replaced_graph_def.node(i).input_size()) { + if ((refs.find(replaced_graph_def.node(i).name()) != refs.end()) && + (refs[replaced_graph_def.node(i).name()] == 0)) { + removed_node_names.push_back(replaced_graph_def.node(i).name()); } - replaced_graph_def.mutable_node()->SwapElements( - i, replaced_graph_def.node_size() - 1); - replaced_graph_def.mutable_node()->RemoveLast(); - continue; + } + + if (replaced_graph_def.node(i).op() == "Assign" && + replaced_graph_def.node(i).input_size() == 1) { + removed_node_names.push_back(replaced_graph_def.node(i).name()); } i++; } diff --git a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc index 000568a0cc..6627df1331 100644 --- a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc +++ b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc @@ -80,6 +80,8 @@ class SparsifyGatherTest : public ::testing::Test { // Build the graph. NodeDef* input_node = CreateNode("ids", "Const", {}, &graph_def); NodeDef* w_node; + NodeDef* zeros_const; + NodeDef* zeros_shape; NodeDef* zeros_node; NodeDef* assign_node; @@ -92,8 +94,12 @@ class SparsifyGatherTest : public ::testing::Test { } else { w_node = CreateNode("w/part_1", "VariableV2", {}, &graph_def); - zeros_node = - CreateNode("w/part_1/Initializer/zeros", "Const", {}, &graph_def); + zeros_shape = CreateNode("w/part_1/Initializer/zeros/shape_as_tensor", + "Const", {}, &graph_def); + zeros_const = CreateNode("w/part_1/Initializer/zeros/Const", "Const", {}, + &graph_def); + zeros_node = CreateNode("w/part_1/Initializer/zeros", "Fill", + {zeros_shape, zeros_const}, &graph_def); assign_node = CreateNode("w/part_1/Assign", "Assign", {w_node, zeros_node}, &graph_def); @@ -151,6 +157,9 @@ class SparsifyGatherTest : public ::testing::Test { MapNamesToNodes(result, &node_lookup); // Check nodes. + EXPECT_EQ(0, + node_lookup.count("w/part_1/Initializer/zeros/shape_as_tensor")); + EXPECT_EQ(0, node_lookup.count("w/part_1/Initializer/zeros/Const")); EXPECT_EQ(0, node_lookup.count("w/part_1/Initializer/zeros")); EXPECT_EQ(0, node_lookup.count("w/part_1/Assign")); @@ -247,7 +256,11 @@ class SparsifyGatherTest : public ::testing::Test { // Two partitions NodeDef* w_node1; NodeDef* w_node2; + NodeDef* zeros_const1; + NodeDef* zeros_shape1; NodeDef* zeros_node1; + NodeDef* zeros_const2; + NodeDef* zeros_shape2; NodeDef* zeros_node2; NodeDef* assign_node1; NodeDef* assign_node2; @@ -261,8 +274,13 @@ class SparsifyGatherTest : public ::testing::Test { SetNodeTensorAttr("value", weights, w_node2); } else { w_node1 = CreateNode("w1/part_1", "VariableV2", {}, &graph_def); - zeros_node1 = - CreateNode("w1/part_1/Initializer/zeros", "Const", {}, &graph_def); + + zeros_shape1 = CreateNode("w1/part_1/Initializer/zeros/shape_as_tensor", + "Const", {}, &graph_def); + zeros_const1 = CreateNode("w1/part_1/Initializer/zeros/Const", "Const", + {}, &graph_def); + zeros_node1 = CreateNode("w1/part_1/Initializer/zeros", "Fill", + {zeros_shape1, zeros_const1}, &graph_def); assign_node1 = CreateNode("w1/part_1/Assign", "Assign", {w_node1, zeros_node1}, &graph_def); @@ -285,8 +303,12 @@ class SparsifyGatherTest : public ::testing::Test { CreateNode("save/Assign", "Assign", {w_node1, restore_node1}, &graph_def); w_node2 = CreateNode("w2/part_1", "VariableV2", {}, &graph_def); - zeros_node2 = - CreateNode("w2/part_1/Initializer/zeros", "Const", {}, &graph_def); + zeros_shape2 = CreateNode("w2/part_1/Initializer/zeros/shape_as_tensor", + "Const", {}, &graph_def); + zeros_const2 = CreateNode("w2/part_1/Initializer/zeros/Const", "Const", + {}, &graph_def); + zeros_node2 = CreateNode("w2/part_1/Initializer/zeros", "Fill", + {zeros_shape2, zeros_const2}, &graph_def); assign_node2 = CreateNode("w2/part_1/Assign", "Assign", {w_node2, zeros_node2}, &graph_def); @@ -350,8 +372,14 @@ class SparsifyGatherTest : public ::testing::Test { MapNamesToNodes(result, &node_lookup); // Check nodes. + EXPECT_EQ(0, + node_lookup.count("w1/part_1/Initializer/zeros/shape_as_tensor")); + EXPECT_EQ(0, node_lookup.count("w1/part_1/Initializer/zeros/Const")); EXPECT_EQ(0, node_lookup.count("w1/part_1/Initializer/zeros")); EXPECT_EQ(0, node_lookup.count("w1/part_1/Assign")); + EXPECT_EQ(0, + node_lookup.count("w2/part_1/Initializer/zeros/shape_as_tensor")); + EXPECT_EQ(0, node_lookup.count("w2/part_1/Initializer/zeros/Const")); EXPECT_EQ(0, node_lookup.count("w2/part_1/Initializer/zeros")); EXPECT_EQ(0, node_lookup.count("w2/part_1/Assign")); EXPECT_EQ(1, node_lookup.count("ids")); -- GitLab From 72e0eaa9c2e408ae720bff55d6f31a75d6accf29 Mon Sep 17 00:00:00 2001 From: cclauss Date: Fri, 26 Jan 2018 19:10:55 +0100 Subject: [PATCH 122/423] from six.moves import xrange (#16438) --- .../cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py | 1 + .../contrib/eager/python/examples/resnet50/resnet50_test.py | 1 + tensorflow/contrib/eager/python/examples/spinn/spinn_test.py | 1 + tensorflow/python/client/session_benchmark.py | 1 + tensorflow/python/kernel_tests/conv_ops_test.py | 1 + tensorflow/python/kernel_tests/decode_jpeg_op_test.py | 1 + tensorflow/python/kernel_tests/rnn_test.py | 1 + 7 files changed, 7 insertions(+) diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py index 4fc5ff1bd1..56c562a3ba 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_ops_benchmark.py @@ -20,6 +20,7 @@ from __future__ import print_function import time +from six.moves import xrange from tensorflow.contrib import rnn as contrib_rnn from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops from tensorflow.contrib.rnn.python.ops import lstm_ops diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py index 76e06269b6..1f7beee685 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py @@ -22,6 +22,7 @@ import gc import tempfile import time +from six.moves import xrange import tensorflow as tf import tensorflow.contrib.eager as tfe diff --git a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py index 84e25cf81a..19b0104c80 100644 --- a/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py +++ b/tensorflow/contrib/eager/python/examples/spinn/spinn_test.py @@ -26,6 +26,7 @@ import tempfile import time import numpy as np +from six.moves import xrange import tensorflow as tf # pylint: disable=g-bad-import-order diff --git a/tensorflow/python/client/session_benchmark.py b/tensorflow/python/client/session_benchmark.py index 721bca91b7..06e9a09926 100644 --- a/tensorflow/python/client/session_benchmark.py +++ b/tensorflow/python/client/session_benchmark.py @@ -22,6 +22,7 @@ import time import numpy as np +from six.moves import xrange from tensorflow.python.client import session from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 3e9bd3dade..c5446326ba 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -24,6 +24,7 @@ import time import numpy as np +from six.moves import xrange from tensorflow.contrib import layers from tensorflow.python.client import session as session_lib from tensorflow.python.framework import constant_op diff --git a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py index ead55cd03b..89fd26c544 100644 --- a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py +++ b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import os import time +from six.moves import xrange from tensorflow.python.client import session from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index 0c77d1db92..a86b65affe 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -23,6 +23,7 @@ import timeit import numpy as np +from six.moves import xrange from tensorflow.contrib import rnn as contrib_rnn from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session -- GitLab From 8bab27948aa53db37cb2bbbd89d0009ed2b90014 Mon Sep 17 00:00:00 2001 From: Jerome Date: Sat, 27 Jan 2018 02:11:11 +0800 Subject: [PATCH 123/423] Imported lstm1d and lstm2d in ndlstm __init__.py. (#16434) * Added ctc_loss_dense_labels. This does the conversion of dense labels into sparse ones to be passed into the core ctc_loss function. * Removed constant_op from the import. * Matched ctc_loss_dense_labels with the other layers ops. * Added ctc_loss_dense_labels to contrib.layers __init__.py file * Added missing comma to list of ops. * Reordred arguments for ctc_loss_dense_labels Labels should be first then inputs for ctc_loss. * Removed ctc_loss_dense_labels. Replaced it with dense_to_sparse instead so that there'll be only one ctc_loss function. * Replaced ctc_loss_dense_labels with dense_to_sparse * Fixed dense_to_sparse. Some of the names of the variables did not match with that of the parameters. * Updated documentation for dense_to_sparse since it can accept a tensor of any shape. * Added test case for dense_to_sparse. * Updated documentation. Dense to sparse accepts int tensors. * Fixed testDenseFromConstantToSparse. The sparse_to_dense order of arguments in the test are wrong and the expected constant should be of int64. * Modified implementation of ndlstm_base_dynamic. It now uses a BasicLSTMCell that has state_is_tuple=True to address deprecation. Right now it is still unknown why it was set to false in the first place. * Imported lstm1d and lstm2d in ndlstm __init__.py. Makes importing ndlstm modules easier. * Added testGetBlocks in lstm2d_test. * Removed testGetBlocks.py --- tensorflow/contrib/ndlstm/__init__.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/contrib/ndlstm/__init__.py b/tensorflow/contrib/ndlstm/__init__.py index 52e83069cb..da89bb4ab6 100644 --- a/tensorflow/contrib/ndlstm/__init__.py +++ b/tensorflow/contrib/ndlstm/__init__.py @@ -16,3 +16,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function + +from tensorflow.contrib.ndlstm.python import lstm2d +from tensorflow.contrib.ndlstm.python import lstm1d -- GitLab From d623b8240687944483ab2b7df870fd2f1435cec9 Mon Sep 17 00:00:00 2001 From: Shengpeng Liu Date: Sat, 27 Jan 2018 02:11:30 +0800 Subject: [PATCH 124/423] Make raw_rnn accept scalar or TensorArray values for state. (#16441) --- tensorflow/python/ops/rnn.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index a10e1963d1..e0052b8869 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -1125,6 +1125,12 @@ def raw_rnn(cell, loop_fn, def _copy_some_through(current, candidate): """Copy some tensors through via array_ops.where.""" def copy_fn(cur_i, cand_i): + # TensorArray and scalar get passed through. + if isinstance(cur_i, tensor_array_ops.TensorArray): + return cand_i + if cur_i.shape.ndims == 0: + return cand_i + # Otherwise propagate the old or the new value. with ops.colocate_with(cand_i): return array_ops.where(elements_finished, cur_i, cand_i) return nest.map_structure(copy_fn, current, candidate) -- GitLab From 4078ecd4e107cb61da8e4a3ce0293bbb8d9dbb62 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 26 Jan 2018 10:11:50 -0800 Subject: [PATCH 125/423] Improve profiler error message when graph_path is not available. (#16463) This fix tries to address the issue raised in 16451 to provide a better error message when graph_path is not available for profiler. Previously if graph_path is not available, the process will crash with not very imformative message and a core dump: ``` 2018-01-26 01:43:29.458032: F tensorflow/core/profiler/profiler.cc:206] Non-OK-status: ReadProtoFile(Env::Default(), FLAGS_graph_path, graph.get(), false) status: Not found: ; No such file or directory Aborted (core dumped) ``` With this fix, the error message is improved to: ``` Failed to read graph_path: Invalid argument: Cannot parse proto file. ``` and the process exit with 1. This fix fixes 16451. Signed-off-by: Yong Tang --- tensorflow/core/profiler/profiler.cc | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/profiler/profiler.cc b/tensorflow/core/profiler/profiler.cc index 2cc212d589..808e3c853b 100644 --- a/tensorflow/core/profiler/profiler.cc +++ b/tensorflow/core/profiler/profiler.cc @@ -206,8 +206,12 @@ int Run(int argc, char** argv) { "graph_path,op_log_path,run_meta_path\n"); std::unique_ptr graph(new GraphDef()); if (!FLAGS_graph_path.empty()) { - TF_CHECK_OK( - ReadProtoFile(Env::Default(), FLAGS_graph_path, graph.get(), false)); + s = ReadProtoFile(Env::Default(), FLAGS_graph_path, graph.get(), false); + if (!s.ok()) { + fprintf(stderr, "Failed to read graph_path: %s\n", + s.ToString().c_str()); + return 1; + } } std::unique_ptr op_log(new OpLogProto()); -- GitLab From a7d4f82660e1e0bfb8b4bd3a4378e389795b7f9e Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Fri, 26 Jan 2018 10:09:52 -0800 Subject: [PATCH 126/423] [tf.data] Move slow-path-related code into the slow path in IteratorHandleOp::Compute(). This slightly reduces the amount of work performed when an iterator is accessed (after the first access), and potentially reduces contention if concurrent steps are accessing the same iterator. PiperOrigin-RevId: 183406221 --- tensorflow/core/kernels/data/iterator_ops.cc | 95 +++++++++----------- 1 file changed, 43 insertions(+), 52 deletions(-) diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index 56044a3d41..ca22f10a85 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -430,13 +430,10 @@ class IteratorStateVariant { REGISTER_UNARY_VARIANT_DECODE_FUNCTION(IteratorStateVariant, kIteratorVariantTypeName); -// TODO(mrry): Can we simply use the template kernel here? class IteratorHandleOp : public OpKernel { public: explicit IteratorHandleOp(OpKernelConstruction* ctx) : OpKernel(ctx), graph_def_version_(ctx->graph_def_version()) { - OP_REQUIRES_OK(ctx, ctx->allocate_persistent(DT_STRING, TensorShape({2}), - &handle_, nullptr)); OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_dtypes_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("shared_name", &name_)); @@ -460,56 +457,51 @@ class IteratorHandleOp : public OpKernel { } void Compute(OpKernelContext* context) override LOCKS_EXCLUDED(mu_) { - mutex_lock l(mu_); - FunctionLibraryRuntime* lib = context->function_library(); - std::unique_ptr device_mgr(nullptr); - std::unique_ptr flib_def(nullptr); - std::unique_ptr pflr(nullptr); - // If the iterator is shared then we construct a new FLR, and pass that in. - // NOTE(mrry,rohanj): In this case it is not possible to call remote - // functions from the iterator. We may add this functionality if there - // is sufficient demand, but it will require a significant refactoring. - if (!name_.empty()) { - lib = CreateFLR(context, &device_mgr, &flib_def, &pflr); - } + { + mutex_lock l(mu_); + if (resource_ == nullptr) { + FunctionLibraryRuntime* lib = context->function_library(); + std::unique_ptr device_mgr(nullptr); + std::unique_ptr flib_def(nullptr); + std::unique_ptr pflr(nullptr); + // If the iterator is shared then we construct a new FLR, and pass that + // in. NOTE(mrry,rohanj): In this case it is not possible to call remote + // functions from the iterator. We may add this functionality if there + // is sufficient demand, but it will require a significant refactoring. + if (!name_.empty()) { + lib = CreatePrivateFLR(context, &device_mgr, &flib_def, &pflr); + } - if (resource_ == nullptr) { - ResourceMgr* mgr = context->resource_manager(); - OP_REQUIRES_OK(context, cinfo_.Init(mgr, def())); + ResourceMgr* mgr = context->resource_manager(); + OP_REQUIRES_OK(context, cinfo_.Init(mgr, def())); + + IteratorResource* resource; + OP_REQUIRES_OK( + context, + mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [lib, &device_mgr, &flib_def, &pflr, + this](IteratorResource** ret) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + *ret = new IteratorResource( + output_dtypes_, output_shapes_, graph_def_version_, + std::move(device_mgr), 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; + } - IteratorResource* resource; - OP_REQUIRES_OK( - context, - mgr->LookupOrCreate( - cinfo_.container(), cinfo_.name(), &resource, - [lib, &device_mgr, &flib_def, &pflr, this](IteratorResource** ret) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - *ret = new IteratorResource( - output_dtypes_, output_shapes_, graph_def_version_, - std::move(device_mgr), 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; } - - auto h = handle_.AccessTensor(context)->template flat(); - h(0) = cinfo_.container(); - h(1) = cinfo_.name(); - resource_ = resource; - } - if (context->expected_output_dtype(0) == DT_RESOURCE) { - OP_REQUIRES_OK(context, MakeResourceHandleToOutput( - context, 0, cinfo_.container(), cinfo_.name(), - MakeTypeIndex())); - } else { - context->set_output_ref(0, &mu_, handle_.AccessTensor(context)); } + OP_REQUIRES_OK(context, MakeResourceHandleToOutput( + context, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); } private: @@ -526,7 +518,7 @@ class IteratorHandleOp : public OpKernel { return Status::OK(); } - FunctionLibraryRuntime* CreateFLR( + FunctionLibraryRuntime* CreatePrivateFLR( OpKernelContext* ctx, std::unique_ptr* device_mgr, std::unique_ptr* flib_def, std::unique_ptr* pflr) { @@ -546,9 +538,8 @@ class IteratorHandleOp : public OpKernel { } mutex mu_; - ContainerInfo cinfo_ GUARDED_BY(mu_); + ContainerInfo cinfo_; // Written once under mu_ then constant afterwards. IteratorResource* resource_ GUARDED_BY(mu_) = nullptr; - PersistentTensor handle_ GUARDED_BY(mu_); DataTypeVector output_dtypes_; std::vector output_shapes_; const int graph_def_version_; -- GitLab From 3222a8bccc20bd43e657d700c6f9d95a2caf8c1e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 10:11:09 -0800 Subject: [PATCH 127/423] Cleanup: Ran clang-format on all *.{cc,h} in under grappler. PiperOrigin-RevId: 183406440 --- tensorflow/core/grappler/clusters/cluster.cc | 3 +-- tensorflow/core/grappler/costs/virtual_scheduler.h | 2 +- tensorflow/core/grappler/optimizers/auto_parallel.h | 2 +- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/grappler/clusters/cluster.cc b/tensorflow/core/grappler/clusters/cluster.cc index 01a618ed77..39bfca244e 100644 --- a/tensorflow/core/grappler/clusters/cluster.cc +++ b/tensorflow/core/grappler/clusters/cluster.cc @@ -23,8 +23,7 @@ Cluster::Cluster(int timeout_s) : timeout_s_(timeout_s) { DisableDetailedStats(false); } -Cluster::~Cluster() { -} +Cluster::~Cluster() {} void Cluster::AllowSoftPlacement(bool soft_placement_state) { options_.config.set_allow_soft_placement(soft_placement_state); diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h index 9db6d46266..5116c8183c 100644 --- a/tensorflow/core/grappler/costs/virtual_scheduler.h +++ b/tensorflow/core/grappler/costs/virtual_scheduler.h @@ -325,7 +325,7 @@ class VirtualScheduler { // Boolean field for whether the cost is accurate. std::map> op_costs_; - Costs graph_costs_; // Graph cost. + Costs graph_costs_; // Graph cost. std::map op_to_cost_; // Per-op cost. // Auxilliary data structures for constructing NodeState and DeviceState. diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.h b/tensorflow/core/grappler/optimizers/auto_parallel.h index c5d2d47782..8d1098d877 100644 --- a/tensorflow/core/grappler/optimizers/auto_parallel.h +++ b/tensorflow/core/grappler/optimizers/auto_parallel.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ #define TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ -#include "tensorflow/core/grappler/optimizers/graph_optimizer.h" #include "tensorflow/core/framework/variable.pb.h" +#include "tensorflow/core/grappler/optimizers/graph_optimizer.h" #include "tensorflow/core/lib/core/status.h" namespace tensorflow { -- GitLab From a12bb2e5719e3cd17916690cc8caef0ec7bcdf4d Mon Sep 17 00:00:00 2001 From: cclauss Date: Fri, 26 Jan 2018 19:45:30 +0100 Subject: [PATCH 128/423] long was removed in Python 3 (#16439) --- tensorflow/python/tools/saved_model_cli.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py index 21e8e803fc..5b0a584c10 100644 --- a/tensorflow/python/tools/saved_model_cli.py +++ b/tensorflow/python/tools/saved_model_cli.py @@ -31,6 +31,7 @@ import warnings import numpy as np +from six import integer_types from tensorflow.contrib.saved_model.python.saved_model import reader from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils from tensorflow.core.example import example_pb2 @@ -440,7 +441,7 @@ def _create_example_string(example_dict): elif isinstance(feature_list[0], str): example.features.feature[feature_name].bytes_list.value.extend( feature_list) - elif isinstance(feature_list[0], (int, long)): + elif isinstance(feature_list[0], integer_types): example.features.feature[feature_name].int64_list.value.extend( feature_list) else: -- GitLab From c33286926af522f6fe16a0b0d8e76d83d120bdfb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 11:00:37 -0800 Subject: [PATCH 129/423] Increase shard count of //third_party/tensorflow/python:nn_batchnorm_test to avoid timeouts When run under asan, the test runs for about 5 minutes, and sometimes longer, causing frequent timeouts. This change increases the shard count of the test to 4, which brings the run time of the longest running shard under asan to about 2 minutes. PiperOrigin-RevId: 183414888 --- tensorflow/python/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 01b3e92d2d..a323d5bc39 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2668,6 +2668,7 @@ cuda_py_test( ":nn_ops_gen", "//third_party/py/numpy", ], + shard_count = 4, tags = ["no_windows"], ) -- GitLab From a0a524e2b6e0e34ab79595a1cba80f5357ee8724 Mon Sep 17 00:00:00 2001 From: Suharsh Sivakumar Date: Fri, 26 Jan 2018 11:04:47 -0800 Subject: [PATCH 130/423] Add available choices to toco flags and fix minor formatting issues. PiperOrigin-RevId: 183415713 --- .../contrib/lite/toco/toco_cmdline_flags.cc | 36 +++++++++---------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc index f8281f3a57..c5a62fdb62 100644 --- a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc @@ -44,9 +44,11 @@ bool ParseTocoFlagsFromCommandLineFlags( "For Protobuf formats, the binary format will be used."), Flag("input_format", parsed_flags.input_format.bind(), parsed_flags.input_format.default_value(), - "Input file format. One of: tensorflow_graphdef, "), + "Input file format. One of: TENSORFLOW_GRAPHDEF, TFLITE."), Flag("output_format", parsed_flags.output_format.bind(), - parsed_flags.output_format.default_value(), "Output file format."), + parsed_flags.output_format.default_value(), + "Output file format. " + "One of TENSORFLOW_GRAPHDEF, TFLITE, GRAPHVIZ_DOT."), Flag("default_ranges_min", parsed_flags.default_ranges_min.bind(), parsed_flags.default_ranges_min.default_value(), "If defined, will be used as the default value for the min bound " @@ -58,11 +60,13 @@ bool ParseTocoFlagsFromCommandLineFlags( Flag("inference_type", parsed_flags.inference_type.bind(), parsed_flags.inference_type.default_value(), "Target data type of arrays in the output file (for input_arrays, " - "this may be overridden by inference_input_type)."), + "this may be overridden by inference_input_type). " + "One of FLOAT, QUANTIZED_UINT8."), Flag("inference_input_type", parsed_flags.inference_input_type.bind(), parsed_flags.inference_input_type.default_value(), - "Target data type of input arrays. If not specified, inference_type " - "is used."), + "Target data type of input arrays. " + "If not specified, inference_type is used. " + "One of FLOAT, QUANTIZED_UINT8."), Flag("input_type", parsed_flags.input_type.bind(), parsed_flags.input_type.default_value(), "Deprecated ambiguous flag that set both --input_data_types and " @@ -76,35 +80,31 @@ bool ParseTocoFlagsFromCommandLineFlags( Flag("drop_fake_quant", parsed_flags.drop_fake_quant.bind(), parsed_flags.drop_fake_quant.default_value(), - "Ignore and discard FakeQuant nodes. For instance, that can be used " - "to " + "Ignore and discard FakeQuant nodes. For instance, to " "generate plain float code without fake-quantization from a " - "quantized " - "graph."), + "quantized graph."), Flag( "reorder_across_fake_quant", parsed_flags.reorder_across_fake_quant.bind(), parsed_flags.reorder_across_fake_quant.default_value(), "Normally, FakeQuant nodes must be strict boundaries for graph " "transformations, in order to ensure that quantized inference has " - "the " - "exact same arithmetic behavior as quantized training --- which is " - "the " - "whole point of quantized training and of FakeQuant nodes in the " - "first " - "place. However, that entails subtle requirements on where exactly " + "the exact same arithmetic behavior as quantized training --- which " + "is the whole point of quantized training and of FakeQuant nodes in " + "the first place. " + "However, that entails subtle requirements on where exactly " "FakeQuant nodes must be placed in the graph. Some quantized graphs " "have FakeQuant nodes at unexpected locations, that prevent graph " "transformations that are necessary in order to generate inference " "code for these graphs. Such graphs should be fixed, but as a " "temporary work-around, setting this reorder_across_fake_quant flag " - "allows toco to perform necessary graph transformaitons on them, " + "allows TOCO to perform necessary graph transformaitons on them, " "at the cost of no longer faithfully matching inference and training " "arithmetic."), Flag("allow_custom_ops", parsed_flags.allow_custom_ops.bind(), parsed_flags.allow_custom_ops.default_value(), - "If true, allow TOCO to create TF Lite Custom operators for all the" - "unsupported Tensorflow ops."), + "If true, allow TOCO to create TF Lite Custom operators for all the " + "unsupported TensorFlow ops."), Flag( "drop_control_dependency", parsed_flags.drop_control_dependency.bind(), -- GitLab From c99daaf104105a4e711d91dca8c73c1badecbe5f Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Fri, 26 Jan 2018 11:23:02 -0800 Subject: [PATCH 131/423] Performance improvements to some GPU code to use shared locks instead of unique locks for some hotspot cases. PiperOrigin-RevId: 183418559 --- .../core/common_runtime/gpu/process_state.cc | 18 ++++++++++++++- tensorflow/stream_executor/executor_cache.cc | 23 +++++++++++++++---- .../stream_executor/multi_platform_manager.cc | 4 ++-- 3 files changed, 37 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/common_runtime/gpu/process_state.cc b/tensorflow/core/common_runtime/gpu/process_state.cc index 995fd1253f..2f13cf8bd7 100644 --- a/tensorflow/core/common_runtime/gpu/process_state.cc +++ b/tensorflow/core/common_runtime/gpu/process_state.cc @@ -230,8 +230,24 @@ Allocator* ProcessState::GetCUDAHostAllocator(int numa_node) { // TODO(tucker): actually maintain separate CPUAllocators for // different numa_nodes. For now, just one. numa_node = 0; - mutex_lock lock(mu_); + { + // Here we optimize the most common use case where cuda_host_allocators_ + // and cuda_al_ have already been populated and since we're only reading + // these vectors, we can get by with a shared lock. In the slower case, + // we take a unique lock and populate these vectors. + tf_shared_lock lock(mu_); + + if (FLAGS_brain_gpu_record_mem_types && + static_cast(cuda_al_.size()) > 0) { + return cuda_al_[0]; + } + if (static_cast(cuda_host_allocators_.size()) > numa_node) { + return cuda_host_allocators_[0]; + } + } + + mutex_lock lock(mu_); // Find the first valid StreamExecutor to request CUDA host memory // through, since any will work. // diff --git a/tensorflow/stream_executor/executor_cache.cc b/tensorflow/stream_executor/executor_cache.cc index a23d6a70ba..d1a8aae167 100644 --- a/tensorflow/stream_executor/executor_cache.cc +++ b/tensorflow/stream_executor/executor_cache.cc @@ -23,6 +23,14 @@ namespace gputools { port::StatusOr ExecutorCache::GetOrCreate( const StreamExecutorConfig& config, const std::function& factory) { + // In the fast path case, the cache already has an entry and we can just + // return after Get() which only takes a shared lock and not a unique lock. + // If we need to create, we take a unique lock on cache_. + auto fast_result = Get(config); + if (fast_result.ok()) { + return fast_result; + } + Entry* entry = nullptr; { mutex_lock lock{mutex_}; @@ -59,12 +67,17 @@ port::StatusOr ExecutorCache::Get( const StreamExecutorConfig& config) { Entry* entry = nullptr; { - mutex_lock lock{mutex_}; - entry = &cache_[config.ordinal]; - // Release the map lock; the address of 'entry' is stable because - // std::map guarantees reference stability. + tf_shared_lock lock{mutex_}; + auto it = cache_.find(config.ordinal); + if (it != cache_.end()) { + entry = &it->second; + } else { + return port::Status(port::error::NOT_FOUND, + port::Printf("No executors registered for ordinal %d", + config.ordinal)); + } } - mutex_lock lock{entry->configurations_mutex}; + tf_shared_lock lock{entry->configurations_mutex}; if (entry->configurations.empty()) { return port::Status( port::error::NOT_FOUND, diff --git a/tensorflow/stream_executor/multi_platform_manager.cc b/tensorflow/stream_executor/multi_platform_manager.cc index cc32a6beaa..f23224ae77 100644 --- a/tensorflow/stream_executor/multi_platform_manager.cc +++ b/tensorflow/stream_executor/multi_platform_manager.cc @@ -45,7 +45,7 @@ namespace gputools { /* static */ port::StatusOr MultiPlatformManager::PlatformWithName( const string& target) { - mutex_lock lock(GetPlatformsMutex()); + tf_shared_lock lock(GetPlatformsMutex()); auto it = GetPlatformMap()->find(port::Lowercase(target)); if (it == GetPlatformMap()->end()) { @@ -59,7 +59,7 @@ namespace gputools { /* static */ port::StatusOr MultiPlatformManager::PlatformWithId( const Platform::Id& id) { - mutex_lock lock(GetPlatformsMutex()); + tf_shared_lock lock(GetPlatformsMutex()); auto it = GetPlatformByIdMap()->find(id); if (it == GetPlatformByIdMap()->end()) { return port::Status( -- GitLab From a169db5d066efd696d80fd989091a54cc4fe5baf Mon Sep 17 00:00:00 2001 From: Mikalai Drabovich Date: Fri, 26 Jan 2018 11:27:28 -0800 Subject: [PATCH 132/423] Update README.md (#16437) * Update README.md -png, --sample_index options are not available in google-perftools (2.4-0ubuntu5.16.04.1). Also, since Ubuntu 16.04 wrongly recommends to install pprof from 'tau' package " The program 'pprof' is currently not installed. You can install it by typing: sudo apt install tau " the typical user command should probably be google-pprof --pdf --nodecount=100 * Add `google-perftools` installation Note. --- tensorflow/core/profiler/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/profiler/README.md b/tensorflow/core/profiler/README.md index 9e628b1065..7997bdfa05 100644 --- a/tensorflow/core/profiler/README.md +++ b/tensorflow/core/profiler/README.md @@ -240,8 +240,9 @@ Open a Chrome browser, enter URL chrome://tracing and load the timeline file. # can also generate memory profile using `-select bytes` tfprof> code -select accelerator_micros -max_depth 100000 -output pprof:outfile= -trim_name_regexes .*apply_op.* -# Use pprof to visualize the generated file. -pprof -png --nodecount=100 --sample_index=1 +# Use google-pprof, from the google-perftools package to visualize the generated file. +# On Ubuntu you can install it with `apt-get install it google-perftools`. +google-pprof --pdf --nodecount=100 ``` ![PprofGraph](g3doc/pprof.jpg) -- GitLab From 62b54bfc4bf112eab2a60034fe0ff7b6cfb2ddd7 Mon Sep 17 00:00:00 2001 From: Shrinidhi KL Date: Fri, 26 Jan 2018 14:28:34 -0500 Subject: [PATCH 133/423] Fix build errors in contrib/mpi introduced by commit 6042b5d267f (#16427) * Add missing header. * Fix typo. Should refer to incoming argument. --- tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc | 2 +- tensorflow/contrib/mpi/mpi_rendezvous_mgr.h | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc index 8d14a3ef04..0252bc7992 100644 --- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc +++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc @@ -152,7 +152,7 @@ MPIRemoteRendezvous::~MPIRemoteRendezvous() {} void MPIRendezvousMgr::AddRequest(RecvTensorRequest request, const int mpi_dst) { TF_CHECK_OK(recv_tensor_recent_request_ids_.TrackUnique( - req.request_id(), "RecvTensor (MPIRendezvousMgr)", req)); + request.request_id(), "RecvTensor (MPIRendezvousMgr)", request)); const int64 step_id = request.step_id(); const std::string& key = request.rendezvous_key(); Rendezvous::ParsedKey parsed; diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h index ca42ee2f6d..d35e65363f 100644 --- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h +++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/contrib/mpi/mpi_msg.pb.h" #include "tensorflow/contrib/mpi/mpi_utils.h" #include "tensorflow/core/distributed_runtime/base_rendezvous_mgr.h" +#include "tensorflow/core/distributed_runtime/recent_request_ids.h" #include "tensorflow/core/distributed_runtime/request_id.h" #include "tensorflow/core/distributed_runtime/worker_env.h" #include "tensorflow/core/protobuf/worker.pb.h" -- GitLab From de946383a20ca5aada87dd4ca956371ec4a743b2 Mon Sep 17 00:00:00 2001 From: Chris Leary Date: Fri, 26 Jan 2018 11:32:38 -0800 Subject: [PATCH 134/423] [XLA] Improve error message for bad slices. PiperOrigin-RevId: 183420038 --- .../compiler/xla/service/shape_inference.cc | 55 +++++++++++-------- .../xla/service/shape_inference_test.cc | 15 +++++ 2 files changed, 47 insertions(+), 23 deletions(-) diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index a6d6c8b27f..4ba6da6ccc 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -37,6 +37,9 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/protobuf.h" +using tensorflow::str_util::Join; +using tensorflow::strings::Printf; + namespace xla { namespace { @@ -934,7 +937,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "inferring shape for <%s>(%s, %s) with broadcast_dimensions={%s}", BinaryOperation_Name(operation).c_str(), ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str(), - tensorflow::str_util::Join(broadcast_dimensions, ", ").c_str()); + Join(broadcast_dimensions, ", ").c_str()); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(rhs)); @@ -1097,7 +1100,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Map operation requires all operands to have the same shape; got: " "%s", - tensorflow::str_util::Join(pieces, ", ").c_str()); + Join(pieces, ", ").c_str()); } // Check that dimensions.size == arg_shape.dimensions_size() (we currently @@ -1114,7 +1117,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (dimensions[i] != i) { return InvalidArgument( "Map requires monotonically increasing dimension numbers, found: %s ", - tensorflow::str_util::Join(dimensions, ", ").c_str()); + Join(dimensions, ", ").c_str()); } } @@ -1914,21 +1917,28 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& arg, tensorflow::gtl::ArraySlice starts, tensorflow::gtl::ArraySlice limits, tensorflow::gtl::ArraySlice strides) { + auto error = [&](const string& message) { + return InvalidArgument( + "%s in slice operation; argument shape: %s; starts: {%s}; limits: " + "{%s}; strides: {%s}", + message.c_str(), ShapeUtil::HumanString(arg).c_str(), + Join(starts, ",").c_str(), Join(limits, ",").c_str(), + Join(strides, ",").c_str()); + }; TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of slice")); VLOG(2) << tensorflow::strings::Printf( "slicing shape %s starts={%s} limits={%s}", - ShapeUtil::HumanString(arg).c_str(), - tensorflow::str_util::Join(starts, ", ").c_str(), - tensorflow::str_util::Join(limits, ", ").c_str()); + ShapeUtil::HumanString(arg).c_str(), Join(starts, ", ").c_str(), + Join(limits, ", ").c_str()); if (starts.size() != limits.size()) { - return InvalidArgument("slice start and limit sizes differ: %zu vs %zu", - starts.size(), limits.size()); + return error(Printf("slice start and limit sizes differ: %zu vs %zu", + starts.size(), limits.size())); } if (starts.size() != strides.size()) { - return InvalidArgument("slice start and strides sizes differ: %zu vs %zu", - starts.size(), strides.size()); + return error(Printf("slice start and strides sizes differ: %zu vs %zu", + starts.size(), strides.size())); } if (starts.size() != ShapeUtil::Rank(arg)) { @@ -1947,20 +1957,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( start_index); } if (limit_index > arg.dimensions(dimension)) { - return InvalidArgument( - "limit index (%lld) must be less than or equal to dimension " - "size (%lld)", - limit_index, arg.dimensions(dimension)); + return error( + Printf("limit index (%lld) must be less than or equal to dimension " + "size (%lld)", + limit_index, arg.dimensions(dimension))); } VLOG(2) << tensorflow::strings::Printf("starts[%lld] = %lld", dimension, start_index); VLOG(2) << tensorflow::strings::Printf("limits[%lld] = %lld", dimension, limit_index); if (start_index > limit_index) { - return InvalidArgument( - "limit index (%lld) must be greater or equal to " - "start index (%lld) in slice with positive stride", - limit_index, start_index); + return error( + Printf("limit index (%lld) must be greater or equal to " + "start index (%lld) in slice with positive stride", + limit_index, start_index)); } if (stride <= 0) { return InvalidArgument("stride (%lld) must be positive", stride); @@ -1983,7 +1993,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "slicing shape %s at dynamic start_indices %s with slice_sizes={%s}", ShapeUtil::HumanString(operand_shape).c_str(), ShapeUtil::HumanString(start_indices_shape).c_str(), - tensorflow::str_util::Join(slice_sizes, ", ").c_str()); + Join(slice_sizes, ", ").c_str()); if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( @@ -2280,8 +2290,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Reshape dimensions [%s] are not a permutation of the operand " "dimensions (operand shape is %s).", - tensorflow::str_util::Join(dimensions, ",").c_str(), - ShapeUtil::HumanString(operand).c_str()); + Join(dimensions, ",").c_str(), ShapeUtil::HumanString(operand).c_str()); } return inferred_shape; @@ -2373,8 +2382,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // The applied function's arity equals the number of arguments. if (arg_shapes.size() != to_apply.parameters_size()) { string computation_signature = ShapeUtil::HumanString(to_apply); - string argument_shapes = tensorflow::str_util::Join( - arg_shapes, ", ", [](string* out, const Shape* shape) { + string argument_shapes = + Join(arg_shapes, ", ", [](string* out, const Shape* shape) { tensorflow::strings::StrAppend(out, ShapeUtil::HumanString(*shape)); }); return InvalidArgument( diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index 99d87f3b55..026c021165 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -1512,5 +1512,20 @@ TEST_F(ShapeInferenceTest, Conditional) { "must have the same shape")); } +TEST_F(ShapeInferenceTest, BadSlice) { + auto arg = ShapeUtil::MakeShape(F32, {4}); + StatusOr statusor = + ShapeInference::InferSliceShape(arg, {0}, {5}, {1}); + ASSERT_FALSE(statusor.ok()); + + LOG(INFO) << statusor.status(); + + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("less than or equal to dimension size")) + << statusor.status(); + EXPECT_THAT(statusor.status().error_message(), HasSubstr("argument shape")) + << statusor.status(); +} + } // namespace } // namespace xla -- GitLab From 8d5019f7e11d51234a45b3e77541c9f3879bfd93 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Fri, 26 Jan 2018 11:46:21 -0800 Subject: [PATCH 135/423] Fix py3 build rules for all py tests under py2tf. PiperOrigin-RevId: 183422144 --- tensorflow/contrib/py2tf/BUILD | 3 +++ tensorflow/contrib/py2tf/converters/BUILD | 8 ++++++++ tensorflow/contrib/py2tf/pyct/BUILD | 5 +++++ tensorflow/contrib/py2tf/pyct/static_analysis/BUILD | 3 +++ 4 files changed, 19 insertions(+) diff --git a/tensorflow/contrib/py2tf/BUILD b/tensorflow/contrib/py2tf/BUILD index d395de986d..3e846aefeb 100644 --- a/tensorflow/contrib/py2tf/BUILD +++ b/tensorflow/contrib/py2tf/BUILD @@ -57,6 +57,7 @@ py_library( py_test( name = "api_test", srcs = ["api_test.py"], + srcs_version = "PY2AND3", deps = [ ":py2tf_internal", "//tensorflow/python:client_testlib", @@ -66,6 +67,7 @@ py_test( py_test( name = "conversion_test", srcs = ["conversion_test.py"], + srcs_version = "PY2AND3", deps = [ ":py2tf_internal", "//tensorflow/python:client_testlib", @@ -76,6 +78,7 @@ py_test( py_test( name = "naming_test", srcs = ["naming_test.py"], + srcs_version = "PY2AND3", deps = [ ":py2tf_internal", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD index 2b0a1234e6..4f90f94e09 100644 --- a/tensorflow/contrib/py2tf/converters/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -52,6 +52,7 @@ py_library( py_test( name = "break_canonicalization_test", srcs = ["break_canonicalization_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -62,6 +63,7 @@ py_test( py_test( name = "call_trees_test", srcs = ["call_trees_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -72,6 +74,7 @@ py_test( py_test( name = "continue_canonicalization_test", srcs = ["continue_canonicalization_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -82,6 +85,7 @@ py_test( py_test( name = "control_flow_test", srcs = ["control_flow_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -92,6 +96,7 @@ py_test( py_test( name = "builtin_functions_test", srcs = ["builtin_functions_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -112,6 +117,7 @@ py_test( py_test( name = "logical_expressions_test", srcs = ["logical_expressions_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -122,6 +128,7 @@ py_test( py_test( name = "print_functions_test", srcs = ["print_functions_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -133,6 +140,7 @@ py_test( py_test( name = "side_effect_guards_test", srcs = ["side_effect_guards_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", diff --git a/tensorflow/contrib/py2tf/pyct/BUILD b/tensorflow/contrib/py2tf/pyct/BUILD index e0331dbc97..88902dea84 100644 --- a/tensorflow/contrib/py2tf/pyct/BUILD +++ b/tensorflow/contrib/py2tf/pyct/BUILD @@ -38,6 +38,7 @@ py_library( py_test( name = "anno_test", srcs = ["anno_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -47,6 +48,7 @@ py_test( py_test( name = "compiler_test", srcs = ["compiler_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -57,6 +59,7 @@ py_test( py_test( name = "parser_test", srcs = ["parser_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -66,6 +69,7 @@ py_test( py_test( name = "pretty_printer_test", srcs = ["pretty_printer_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -75,6 +79,7 @@ py_test( py_test( name = "templates_test", srcs = ["templates_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD index abaf953678..32e2954fff 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD @@ -32,6 +32,7 @@ py_library( py_test( name = "access_test", srcs = ["access_test.py"], + srcs_version = "PY2AND3", deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", @@ -43,6 +44,7 @@ py_test( py_test( name = "live_values_test", srcs = ["live_values_test.py"], + srcs_version = "PY2AND3", deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", @@ -53,6 +55,7 @@ py_test( py_test( name = "type_info_test", srcs = ["type_info_test.py"], + srcs_version = "PY2AND3", deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", -- GitLab From 05eb6df4c5c28bd24606b6ddf971a7c4b3fa3e78 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Fri, 26 Jan 2018 11:46:44 -0800 Subject: [PATCH 136/423] Fix bug with Operation._control_inputs setter. PiperOrigin-RevId: 183422192 --- tensorflow/python/framework/ops.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index b107670275..e3a52141a0 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -2103,6 +2103,10 @@ class Operation(object): logging.warning("Operation._control_inputs is private, use " "Operation.control_inputs instead. " "Operation._control_inputs will eventually be removed.") + # Copy value because it may be self._control_inputs_val (in particular if + # this is called from self._control_inputs += ...), and we don't want to + # clear value below. + value = copy.copy(value) self._remove_all_control_inputs() self._add_control_inputs(value) -- GitLab From ff6463f4277f412b98d6e6bb1283841ff66902de Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Fri, 26 Jan 2018 11:51:03 -0800 Subject: [PATCH 137/423] Make softmax_op_test.py work with C API enabled. PiperOrigin-RevId: 183422829 --- tensorflow/python/kernel_tests/softmax_op_test.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/softmax_op_test.py b/tensorflow/python/kernel_tests/softmax_op_test.py index be72c19407..bb3f6970e4 100644 --- a/tensorflow/python/kernel_tests/softmax_op_test.py +++ b/tensorflow/python/kernel_tests/softmax_op_test.py @@ -25,11 +25,13 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn_ops from tensorflow.python.platform import test +@test_util.with_c_api class SoftmaxTest(test.TestCase): def _npSoftmax(self, features, dim=-1, log=False): @@ -174,8 +176,11 @@ class SoftmaxTest(test.TestCase): def testDimTooLarge(self): with self.test_session(): + # Use placeholder to make sure we get runtime error instead of shape + # inference error. + dim = array_ops.placeholder_with_default(100, shape=[]) with self.assertRaises(errors_impl.InvalidArgumentError): - nn_ops.softmax([1., 2., 3., 4.], dim=100).eval() + nn_ops.softmax([1., 2., 3., 4.], dim=dim).eval() def testLargeDims(self): # Make sure that we properly handle large inputs. See -- GitLab From 2c505193696b106e5e705c2e7e7bcc6e9bb2a566 Mon Sep 17 00:00:00 2001 From: Yoni Tsafir Date: Fri, 26 Jan 2018 22:01:56 +0200 Subject: [PATCH 138/423] Add a way to provide target nodes in Android (#14722) * Add a way to provide target nodes in Android This is required when running some models as a step for initializing the graph etc. * Fix enableStats mistake on overload Falsely passed enableStats as false instead of the parameter for the non overloaded version. --- .../android/TensorFlowInferenceInterface.java | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java index dc5b9fb887..e51e3f747b 100644 --- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java +++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java @@ -194,6 +194,13 @@ public class TensorFlowInferenceInterface { * @param outputNames A list of output nodes which should be filled by the inference pass. */ public void run(String[] outputNames, boolean enableStats) { + run(outputNames, enableStats, new String[] {}); + } + + /** + * An overloaded version of runInference that allows supplying targetNodeNames as well + */ + public void run(String[] outputNames, boolean enableStats, String[] targetNodeNames) { // Release any Tensors from the previous run calls. closeFetches(); @@ -204,6 +211,11 @@ public class TensorFlowInferenceInterface { runner.fetch(tid.name, tid.outputIndex); } + // Add targets. + for (String t : targetNodeNames) { + runner.addTarget(t); + } + // Run the session. try { if (enableStats) { -- GitLab From 0f65c8f572201f8838189f3e3c3e455759112c14 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 11:59:56 -0800 Subject: [PATCH 139/423] Cleanup: Ran clang-format on all *.{cc,h} files in tensorflow/core/kernels. PiperOrigin-RevId: 183423961 --- tensorflow/core/kernels/adjust_contrast_op.cc | 4 +- .../core/kernels/adjust_contrast_op_test.cc | 3 +- .../core/kernels/adjust_saturation_op.cc | 5 +- tensorflow/core/kernels/aggregate_ops_cpu.h | 12 +- tensorflow/core/kernels/attention_ops.cc | 5 +- tensorflow/core/kernels/avgpooling_op.h | 5 +- .../core/kernels/avgpooling_op_gpu.cu.cc | 14 +- tensorflow/core/kernels/barrier_ops.cc | 34 +- tensorflow/core/kernels/batch_kernels.cc | 4 +- .../core/kernels/batch_matmul_op_impl.h | 27 +- .../core/kernels/batch_matmul_op_real.cc | 2 +- .../core/kernels/batch_matmul_op_test.cc | 7 +- tensorflow/core/kernels/batch_norm_op.cc | 2 +- tensorflow/core/kernels/batch_norm_op_test.cc | 2 +- tensorflow/core/kernels/batchtospace_op.cc | 7 +- tensorflow/core/kernels/bcast_ops.cc | 2 +- tensorflow/core/kernels/bias_op_gpu.cu.cc | 43 +- tensorflow/core/kernels/bounds_check.h | 2 +- .../core/kernels/candidate_sampler_ops.cc | 17 +- tensorflow/core/kernels/cast_op.cc | 17 +- tensorflow/core/kernels/cast_op.h | 3 +- tensorflow/core/kernels/cast_op_impl.h | 28 +- tensorflow/core/kernels/cast_op_test.cc | 12 +- tensorflow/core/kernels/colorspace_op.cc | 65 +- tensorflow/core/kernels/colorspace_op.h | 7 +- .../core/kernels/colorspace_op_gpu.cu.cc | 4 +- tensorflow/core/kernels/colorspace_op_test.cc | 56 +- tensorflow/core/kernels/concat_lib.h | 20 +- tensorflow/core/kernels/concat_lib_cpu.cc | 32 +- tensorflow/core/kernels/concat_lib_cpu.h | 6 +- tensorflow/core/kernels/concat_op.cc | 21 +- tensorflow/core/kernels/concat_op_test.cc | 3 +- .../kernels/conditional_accumulator_base.h | 2 +- .../kernels/conditional_accumulator_op.cc | 8 +- tensorflow/core/kernels/constant_op.cc | 1 - tensorflow/core/kernels/control_flow_ops.cc | 96 +-- .../core/kernels/control_flow_ops_test.cc | 1 + tensorflow/core/kernels/conv_ops.cc | 2 +- tensorflow/core/kernels/conv_ops_fused.cc | 78 ++- tensorflow/core/kernels/conv_ops_gpu.h | 1 - tensorflow/core/kernels/conv_ops_gpu_3.cu.cc | 31 +- .../core/kernels/conv_ops_using_gemm.cc | 29 +- tensorflow/core/kernels/cross_op_gpu.cu.cc | 2 +- tensorflow/core/kernels/ctc_decoder_ops.cc | 11 +- tensorflow/core/kernels/ctc_loss_op.cc | 4 +- tensorflow/core/kernels/cwise_op_abs.cc | 2 +- tensorflow/core/kernels/cwise_op_acos.cc | 2 +- tensorflow/core/kernels/cwise_op_acosh.cc | 6 +- tensorflow/core/kernels/cwise_op_add_1.cc | 3 +- tensorflow/core/kernels/cwise_op_add_2.cc | 4 +- tensorflow/core/kernels/cwise_op_asin.cc | 2 +- tensorflow/core/kernels/cwise_op_asinh.cc | 8 +- tensorflow/core/kernels/cwise_op_atan.cc | 2 +- tensorflow/core/kernels/cwise_op_atanh.cc | 4 +- tensorflow/core/kernels/cwise_op_ceil.cc | 2 +- tensorflow/core/kernels/cwise_op_cos.cc | 2 +- tensorflow/core/kernels/cwise_op_cosh.cc | 16 +- tensorflow/core/kernels/cwise_op_div.cc | 2 +- tensorflow/core/kernels/cwise_op_exp.cc | 2 +- tensorflow/core/kernels/cwise_op_expm1.cc | 2 +- tensorflow/core/kernels/cwise_op_floor.cc | 2 +- tensorflow/core/kernels/cwise_op_floor_div.cc | 2 +- tensorflow/core/kernels/cwise_op_floor_mod.cc | 2 +- .../core/kernels/cwise_op_gpu_conj.cu.cc | 4 +- .../core/kernels/cwise_op_gpu_equal_to.cu.cc | 2 +- .../core/kernels/cwise_op_gpu_select.cu.cc | 24 +- tensorflow/core/kernels/cwise_op_greater.cc | 2 +- .../core/kernels/cwise_op_greater_equal.cc | 5 +- tensorflow/core/kernels/cwise_op_invert.cc | 2 +- tensorflow/core/kernels/cwise_op_isfinite.cc | 2 +- tensorflow/core/kernels/cwise_op_isinf.cc | 2 +- tensorflow/core/kernels/cwise_op_isnan.cc | 2 +- tensorflow/core/kernels/cwise_op_less.cc | 2 +- .../core/kernels/cwise_op_less_equal.cc | 2 +- tensorflow/core/kernels/cwise_op_log.cc | 2 +- tensorflow/core/kernels/cwise_op_log1p.cc | 2 +- tensorflow/core/kernels/cwise_op_maximum.cc | 2 +- tensorflow/core/kernels/cwise_op_minimum.cc | 2 +- tensorflow/core/kernels/cwise_op_mul_1.cc | 8 +- tensorflow/core/kernels/cwise_op_mul_2.cc | 6 +- tensorflow/core/kernels/cwise_op_neg.cc | 2 +- .../core/kernels/cwise_op_not_equal_to_1.cc | 2 +- .../core/kernels/cwise_op_not_equal_to_2.cc | 2 +- .../core/kernels/cwise_op_reciprocal.cc | 4 +- tensorflow/core/kernels/cwise_op_select.cc | 30 +- tensorflow/core/kernels/cwise_op_sigmoid.cc | 4 +- tensorflow/core/kernels/cwise_op_sign.cc | 2 +- tensorflow/core/kernels/cwise_op_sin.cc | 2 +- tensorflow/core/kernels/cwise_op_sinh.cc | 16 +- tensorflow/core/kernels/cwise_op_sqrt.cc | 4 +- tensorflow/core/kernels/cwise_op_square.cc | 2 +- tensorflow/core/kernels/cwise_op_sub.cc | 2 +- tensorflow/core/kernels/cwise_op_tan.cc | 2 +- tensorflow/core/kernels/cwise_op_tanh.cc | 2 +- tensorflow/core/kernels/cwise_ops_common.cc | 6 +- .../core/kernels/cwise_ops_gpu_gradients.cu.h | 14 +- tensorflow/core/kernels/cwise_ops_gradients.h | 3 +- .../core/kernels/cwise_ops_sycl_common.h | 10 +- tensorflow/core/kernels/cwise_ops_test.cc | 42 +- tensorflow/core/kernels/debug_ops.cc | 6 +- tensorflow/core/kernels/debug_ops.h | 4 +- tensorflow/core/kernels/decode_csv_op.cc | 40 +- tensorflow/core/kernels/decode_image_op.cc | 32 +- tensorflow/core/kernels/deep_conv2d.cc | 6 +- tensorflow/core/kernels/dense_update_ops.cc | 16 +- .../core/kernels/depthwise_conv_grad_op.cc | 4 +- tensorflow/core/kernels/depthwise_conv_op.cc | 15 +- .../core/kernels/depthwise_conv_op_gpu.cu.cc | 4 +- tensorflow/core/kernels/diag_op.cc | 56 +- tensorflow/core/kernels/diag_op.h | 8 +- tensorflow/core/kernels/diag_op_gpu.cu.cc | 52 +- tensorflow/core/kernels/diag_op_test.cc | 5 +- tensorflow/core/kernels/dilation_ops.cc | 26 +- .../core/kernels/dilation_ops_gpu.cu.cc | 15 +- .../core/kernels/draw_bounding_box_op.cc | 28 +- .../core/kernels/dynamic_partition_op.cc | 6 +- .../kernels/dynamic_partition_op_gpu.cu.cc | 57 +- tensorflow/core/kernels/eigen_activations.h | 38 +- .../core/kernels/eigen_activations_test.cc | 2 +- tensorflow/core/kernels/eigen_attention.h | 83 ++- .../core/kernels/eigen_attention_test.cc | 2 +- .../eigen_backward_spatial_convolutions.h | 60 +- ...igen_backward_spatial_convolutions_test.cc | 2 +- tensorflow/core/kernels/eigen_pooling.h | 8 +- tensorflow/core/kernels/eigen_pooling_test.cc | 2 +- tensorflow/core/kernels/eigen_softmax.h | 61 +- tensorflow/core/kernels/eigen_softmax_test.cc | 2 +- .../core/kernels/eigen_spatial_convolutions.h | 32 +- tensorflow/core/kernels/encode_jpeg_op.cc | 16 +- .../core/kernels/example_parsing_ops.cc | 29 +- tensorflow/core/kernels/fact_op.cc | 12 +- .../core/kernels/fake_quant_ops_test.cc | 44 +- tensorflow/core/kernels/fifo_queue.cc | 169 +++-- tensorflow/core/kernels/fill_functor.cc | 10 +- .../core/kernels/fractional_avg_pool_op.cc | 5 +- tensorflow/core/kernels/function_ops.cc | 31 +- .../core/kernels/fused_batch_norm_op.cu.cc | 3 +- tensorflow/core/kernels/gather_functor.cc | 10 +- tensorflow/core/kernels/gather_functor.h | 52 +- tensorflow/core/kernels/gather_op.cc | 3 +- tensorflow/core/kernels/hinge-loss.h | 5 +- .../core/kernels/histogram_op_gpu.cu.cc | 8 +- tensorflow/core/kernels/image_resizer_state.h | 10 +- tensorflow/core/kernels/in_topk_op.cc | 64 +- tensorflow/core/kernels/inplace_ops.cc | 10 +- tensorflow/core/kernels/l2loss_op.cc | 2 +- tensorflow/core/kernels/linalg_ops_common.cc | 1 - tensorflow/core/kernels/lmdb_reader_op.cc | 15 +- tensorflow/core/kernels/logistic-loss.h | 7 +- tensorflow/core/kernels/loss_test.cc | 201 +++--- tensorflow/core/kernels/lrn_op.cc | 25 +- tensorflow/core/kernels/matching_files_op.cc | 11 +- tensorflow/core/kernels/matmul_op.cc | 4 +- tensorflow/core/kernels/matmul_op.h | 3 +- .../core/kernels/matrix_exponential_op.cc | 12 +- .../core/kernels/matrix_logarithm_op.cc | 12 +- tensorflow/core/kernels/matrix_set_diag_op.cc | 4 +- tensorflow/core/kernels/maxpooling_op.cc | 6 +- .../core/kernels/maxpooling_op_gpu.cu.cc | 4 +- tensorflow/core/kernels/meta_support.cc | 6 +- tensorflow/core/kernels/mfcc.cc | 26 +- tensorflow/core/kernels/mfcc.h | 5 +- .../core/kernels/mfcc_mel_filterbank.cc | 46 +- tensorflow/core/kernels/mfcc_mel_filterbank.h | 8 +- .../core/kernels/mfcc_mel_filterbank_test.cc | 15 +- tensorflow/core/kernels/mfcc_test.cc | 9 +- tensorflow/core/kernels/mirror_pad_op.cc | 8 +- tensorflow/core/kernels/mkl_avgpooling_op.cc | 279 ++++---- .../core/kernels/mkl_batch_matmul_op.cc | 1 - tensorflow/core/kernels/mkl_concat_op.cc | 132 ++-- .../core/kernels/mkl_conv_grad_bias_ops.cc | 2 +- .../core/kernels/mkl_conv_grad_filter_ops.cc | 182 ++--- .../core/kernels/mkl_conv_grad_input_ops.cc | 107 ++- tensorflow/core/kernels/mkl_conv_ops.cc | 252 +++---- tensorflow/core/kernels/mkl_conv_ops.h | 230 +++---- .../core/kernels/mkl_fused_batch_norm_op.cc | 506 ++++++-------- .../core/kernels/mkl_input_conversion_op.cc | 10 +- tensorflow/core/kernels/mkl_lrn_op.cc | 647 +++++++++--------- tensorflow/core/kernels/mkl_maxpooling_op.cc | 497 +++++++------- .../core/kernels/mkl_pooling_ops_common.cc | 14 +- .../core/kernels/mkl_pooling_ops_common.h | 328 +++++---- tensorflow/core/kernels/mkl_relu_op.cc | 212 +++--- tensorflow/core/kernels/mkl_reshape_op.cc | 105 ++- tensorflow/core/kernels/mkl_tfconv_op.cc | 2 +- .../core/kernels/non_max_suppression_op.cc | 24 +- .../kernels/non_max_suppression_op_test.cc | 30 +- tensorflow/core/kernels/nth_element_op.cc | 39 +- tensorflow/core/kernels/nth_element_op.h | 6 +- tensorflow/core/kernels/one_hot_op_gpu.cu.cc | 6 +- tensorflow/core/kernels/ops_util_test.cc | 13 +- tensorflow/core/kernels/pack_op.cc | 4 +- .../parameterized_truncated_normal_op.cc | 40 +- ...arameterized_truncated_normal_op_gpu.cu.cc | 13 +- tensorflow/core/kernels/parse_tensor_op.cc | 1 - tensorflow/core/kernels/pooling_ops_3d.cc | 6 +- tensorflow/core/kernels/pooling_ops_3d_sycl.h | 17 +- tensorflow/core/kernels/pooling_ops_common.h | 2 - .../core/kernels/quantization_utils_test.cc | 8 +- .../core/kernels/quantize_and_dequantize_op.h | 3 +- tensorflow/core/kernels/quantize_op_test.cc | 3 +- .../core/kernels/quantized_batch_norm_op.cc | 2 +- .../core/kernels/quantized_concat_op.cc | 8 +- tensorflow/core/kernels/quantized_conv_ops.cc | 7 +- .../core/kernels/quantized_instance_norm.cc | 8 +- .../core/kernels/quantized_matmul_op.cc | 6 +- .../core/kernels/quantized_matmul_op_test.cc | 39 +- tensorflow/core/kernels/quantized_mul_op.cc | 5 +- .../core/kernels/quantized_mul_op_test.cc | 11 +- tensorflow/core/kernels/queue_base.cc | 4 +- tensorflow/core/kernels/queue_ops.cc | 11 +- tensorflow/core/kernels/random_crop_op.cc | 8 +- tensorflow/core/kernels/random_op.cc | 253 ++++--- tensorflow/core/kernels/random_op_gpu.cu.cc | 5 +- tensorflow/core/kernels/random_poisson_op.cc | 2 +- .../core/kernels/random_shuffle_queue_op.cc | 169 +++-- .../core/kernels/reduction_gpu_kernels.cu.h | 6 +- tensorflow/core/kernels/relu_op.cc | 13 +- tensorflow/core/kernels/relu_op_functor.h | 11 +- tensorflow/core/kernels/resize_bicubic_op.cc | 2 +- .../core/kernels/resize_bicubic_op_test.cc | 15 +- .../core/kernels/resize_bilinear_op_gpu.cu.cc | 20 +- tensorflow/core/kernels/reverse_op.cc | 8 +- tensorflow/core/kernels/reverse_op_gpu.cu.cc | 16 +- .../core/kernels/reverse_sequence_op.cc | 68 +- .../kernels/reverse_sequence_op_gpu.cu.cc | 12 +- .../core/kernels/save_restore_tensor.cc | 10 +- tensorflow/core/kernels/scatter_functor.h | 16 +- .../core/kernels/scatter_functor_gpu.cu.h | 11 +- .../core/kernels/scatter_nd_op_cpu_impl.h | 4 +- tensorflow/core/kernels/scatter_op.cc | 30 +- tensorflow/core/kernels/sdca_internal.cc | 45 +- tensorflow/core/kernels/sdca_internal.h | 3 +- tensorflow/core/kernels/sdca_ops.cc | 14 +- .../core/kernels/segment_reduction_ops.cc | 30 +- .../core/kernels/segment_reduction_ops.h | 19 +- .../kernels/segment_reduction_ops_gpu.cu.cc | 12 +- .../core/kernels/self_adjoint_eig_op.cc | 1 - tensorflow/core/kernels/sendrecv_ops.cc | 6 +- tensorflow/core/kernels/sequence_ops.cc | 8 +- tensorflow/core/kernels/session_ops.cc | 2 +- tensorflow/core/kernels/shape_ops.h | 8 +- tensorflow/core/kernels/slice_op.cc | 168 +++-- tensorflow/core/kernels/slice_op.h | 1 - tensorflow/core/kernels/slice_op_cpu_impl.h | 2 +- tensorflow/core/kernels/softmax_op.cc | 6 +- .../kernels/spacetobatch_benchmark_test.cc | 2 +- .../core/kernels/spacetobatch_functor.cc | 2 +- .../core/kernels/spacetobatch_functor.h | 2 +- .../kernels/spacetobatch_functor_gpu.cu.cc | 10 +- tensorflow/core/kernels/spacetobatch_op.cc | 7 +- tensorflow/core/kernels/sparse_add_grad_op.cc | 12 +- tensorflow/core/kernels/sparse_add_op.cc | 15 +- tensorflow/core/kernels/sparse_add_op_test.cc | 4 +- .../sparse_conditional_accumulator_op.cc | 5 +- tensorflow/core/kernels/sparse_cross_op.cc | 16 +- .../kernels/sparse_dense_binary_op_shared.cc | 10 +- .../sparse_dense_binary_op_shared_test.cc | 16 +- tensorflow/core/kernels/sparse_matmul_op.cc | 24 +- tensorflow/core/kernels/sparse_matmul_op.h | 8 +- .../core/kernels/sparse_matmul_op_test.cc | 8 +- .../core/kernels/sparse_reduce_sum_op_test.cc | 8 +- tensorflow/core/kernels/sparse_softmax_op.cc | 5 +- .../kernels/sparse_sparse_binary_op_shared.cc | 15 +- tensorflow/core/kernels/sparse_split_op.cc | 26 +- tensorflow/core/kernels/sparse_to_dense_op.cc | 5 +- .../core/kernels/sparse_to_dense_op_test.cc | 1 - tensorflow/core/kernels/sparse_xent_op.cc | 8 +- .../core/kernels/sparse_xent_op_test.cc | 6 +- tensorflow/core/kernels/split_lib.h | 2 +- tensorflow/core/kernels/split_lib_cpu.cc | 4 +- tensorflow/core/kernels/split_op.cc | 37 +- tensorflow/core/kernels/split_v_op.cc | 14 +- tensorflow/core/kernels/stack_ops.cc | 18 +- tensorflow/core/kernels/stage_op.cc | 74 +- tensorflow/core/kernels/strided_slice_op.cc | 2 +- .../core/kernels/strided_slice_op_impl.h | 2 +- tensorflow/core/kernels/string_join_op.cc | 6 +- tensorflow/core/kernels/substr_op.cc | 6 +- tensorflow/core/kernels/summary_image_op.cc | 16 +- tensorflow/core/kernels/summary_op.cc | 11 +- tensorflow/core/kernels/tile_functor_cpu.cc | 2 +- tensorflow/core/kernels/tile_ops_cpu_impl.h | 2 +- tensorflow/core/kernels/training_ops.cc | 42 +- .../core/kernels/training_ops_gpu.cu.cc | 24 +- tensorflow/core/kernels/training_ops_test.cc | 5 +- tensorflow/core/kernels/transpose_op.cc | 7 +- tensorflow/core/kernels/typed_queue.h | 6 +- tensorflow/core/kernels/unpack_op.cc | 7 +- tensorflow/core/kernels/word2vec_kernels.cc | 6 +- tensorflow/core/kernels/xent_op.cc | 14 +- tensorflow/core/kernels/xsmm_conv2d_test.cc | 344 +++++----- 291 files changed, 4072 insertions(+), 4227 deletions(-) diff --git a/tensorflow/core/kernels/adjust_contrast_op.cc b/tensorflow/core/kernels/adjust_contrast_op.cc index 37976f7183..72155fd037 100644 --- a/tensorflow/core/kernels/adjust_contrast_op.cc +++ b/tensorflow/core/kernels/adjust_contrast_op.cc @@ -40,8 +40,8 @@ typedef Eigen::SyclDevice SYCLDevice; template class AdjustContrastOp : public OpKernel { public: - explicit AdjustContrastOp(OpKernelConstruction* context) : OpKernel(context) { - } + explicit AdjustContrastOp(OpKernelConstruction* context) + : OpKernel(context) {} void Compute(OpKernelContext* context) override { const Tensor& input = context->input(0); diff --git a/tensorflow/core/kernels/adjust_contrast_op_test.cc b/tensorflow/core/kernels/adjust_contrast_op_test.cc index 0fc03b5a23..7522b32040 100644 --- a/tensorflow/core/kernels/adjust_contrast_op_test.cc +++ b/tensorflow/core/kernels/adjust_contrast_op_test.cc @@ -29,8 +29,7 @@ limitations under the License. namespace tensorflow { -class AdjustContrastOpTest : public OpsTestBase { -}; +class AdjustContrastOpTest : public OpsTestBase {}; TEST_F(AdjustContrastOpTest, Simple_1113) { TF_EXPECT_OK(NodeDefBuilder("adjust_contrast_op", "AdjustContrastv2") diff --git a/tensorflow/core/kernels/adjust_saturation_op.cc b/tensorflow/core/kernels/adjust_saturation_op.cc index 4643d4e6ef..f0c6ae499d 100644 --- a/tensorflow/core/kernels/adjust_saturation_op.cc +++ b/tensorflow/core/kernels/adjust_saturation_op.cc @@ -192,8 +192,9 @@ class AdjustSaturationOp : public AdjustSaturationOpBase { const DeviceBase::CpuWorkerThreads& worker_threads = *context->device()->tensorflow_cpu_worker_threads(); Shard(worker_threads.num_threads, worker_threads.workers, channel_count, - kCostPerChannel, [channel_count, &input_data, &output_data, scale_h]( - int64 start_channel, int64 end_channel) { + kCostPerChannel, + [channel_count, &input_data, &output_data, scale_h]( + int64 start_channel, int64 end_channel) { const float* p = input_data.data() + start_channel * kChannelSize; float* q = output_data.data() + start_channel * kChannelSize; for (int i = start_channel; i < end_channel; i++) { diff --git a/tensorflow/core/kernels/aggregate_ops_cpu.h b/tensorflow/core/kernels/aggregate_ops_cpu.h index dfa3fe585e..aa1cead928 100644 --- a/tensorflow/core/kernels/aggregate_ops_cpu.h +++ b/tensorflow/core/kernels/aggregate_ops_cpu.h @@ -25,7 +25,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace tensorflow { @@ -201,7 +201,7 @@ struct Add7Functor { typename TTypes::ConstFlat in6, typename TTypes::ConstFlat in7) { Add7EigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7); + in7); } }; @@ -214,7 +214,7 @@ struct Add8Functor { typename TTypes::ConstFlat in5, typename TTypes::ConstFlat in6, typename TTypes::ConstFlat in7, typename TTypes::ConstFlat in8) { Add8EigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7, in8); + in7, in8); } }; @@ -227,7 +227,7 @@ struct Add8pFunctor { typename TTypes::ConstFlat in5, typename TTypes::ConstFlat in6, typename TTypes::ConstFlat in7, typename TTypes::ConstFlat in8) { Add8pEigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7, in8); + in7, in8); } }; @@ -241,10 +241,10 @@ struct Add9Functor { typename TTypes::ConstFlat in7, typename TTypes::ConstFlat in8, typename TTypes::ConstFlat in9) { Add9EigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7, in8, in9); + in7, in8, in9); } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor diff --git a/tensorflow/core/kernels/attention_ops.cc b/tensorflow/core/kernels/attention_ops.cc index cc8f122cab..ce2fce92e4 100644 --- a/tensorflow/core/kernels/attention_ops.cc +++ b/tensorflow/core/kernels/attention_ops.cc @@ -52,8 +52,9 @@ class ExtractGlimpseOp : public OpKernel { const int64 batch_size = input_shape.dim_size(0); const Tensor& window_size = context->input(1); - OP_REQUIRES(context, (window_size.shape().dims() == 1) && - window_size.shape().dim_size(0) == 2, + OP_REQUIRES(context, + (window_size.shape().dims() == 1) && + window_size.shape().dim_size(0) == 2, errors::InvalidArgument( "input must be a vector of size 2 (height, width)", window_size.shape().DebugString())); diff --git a/tensorflow/core/kernels/avgpooling_op.h b/tensorflow/core/kernels/avgpooling_op.h index dea2683184..f5e81dbc09 100644 --- a/tensorflow/core/kernels/avgpooling_op.h +++ b/tensorflow/core/kernels/avgpooling_op.h @@ -48,9 +48,8 @@ struct SpatialAvgPooling { typedef Eigen::GpuDevice GPUDevice; -// Launch a custom GPU kernels from Yanqing for the avgpooling backward operation -// that works NHWC data formats. -// Arguments: +// Launch a custom GPU kernels from Yanqing for the avgpooling backward +// operation that works NHWC data formats. Arguments: // top_diff: backprop to the output of the pooling layer // num: number of input batches // height: input height diff --git a/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc b/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc index 2be330d142..6537b42f1e 100644 --- a/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc +++ b/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc @@ -71,8 +71,8 @@ __global__ void AvePoolBackwardNHWC(const int nthreads, hstart = max(hstart, 0); wstart = max(wstart, 0); int pool_size = (hend - hstart) * (wend - wstart); - gradient += - top_diff_slice[(ph * pooled_width + pw) * channels] / dtype(pool_size); + gradient += top_diff_slice[(ph * pooled_width + pw) * channels] / + dtype(pool_size); } } bottom_diff[index] = gradient; @@ -90,11 +90,11 @@ bool RunAvePoolBackwardNHWC(const T* const top_diff, const int num, const GPUDevice& d) { int x_size = num * height * width * channels; CudaLaunchConfig config = GetCudaLaunchConfig(x_size, d); - AvePoolBackwardNHWC< - T><<>>( - config.virtual_thread_count, top_diff, num, height, width, channels, - pooled_height, pooled_width, kernel_h, kernel_w, stride_h, stride_w, - pad_t, pad_t, bottom_diff); + AvePoolBackwardNHWC + <<>>( + config.virtual_thread_count, top_diff, num, height, width, channels, + pooled_height, pooled_width, kernel_h, kernel_w, stride_h, stride_w, + pad_t, pad_t, bottom_diff); return d.ok(); } diff --git a/tensorflow/core/kernels/barrier_ops.cc b/tensorflow/core/kernels/barrier_ops.cc index d0bbea9fe2..944564dfba 100644 --- a/tensorflow/core/kernels/barrier_ops.cc +++ b/tensorflow/core/kernels/barrier_ops.cc @@ -111,13 +111,14 @@ class Barrier : public ResourceBase { mutex_lock lock(mu_); if (closed_) { OP_REQUIRES_ASYNC( - ctx, !cancel_pending_enqueues_ && - (num_inserted == 0 || !incomplete_.empty()), + ctx, + !cancel_pending_enqueues_ && + (num_inserted == 0 || !incomplete_.empty()), errors::Cancelled( "Barrier ", name_, " is closed. Pending enqueues cancelled: ", - cancel_pending_enqueues_, ". Number of new insertions: ", - num_inserted, ". Number of incomplete keys: ", - incomplete_.size(), "."), + cancel_pending_enqueues_, + ". Number of new insertions: ", num_inserted, + ". Number of incomplete keys: ", incomplete_.size(), "."), callback); } @@ -128,9 +129,10 @@ class Barrier : public ResourceBase { for (int i = 0; i < num_inserted; ++i) { OP_REQUIRES_OK_ASYNC( - ctx, InsertOneLocked(ctx, keys, values, element_shape, - component_index, i, &ready_tuples, - &new_elements), + ctx, + InsertOneLocked(ctx, keys, values, element_shape, + component_index, i, &ready_tuples, + &new_elements), callback); } @@ -317,8 +319,9 @@ class Barrier : public ResourceBase { return errors::Cancelled( "Barrier ", name_, " is closed, but attempted to insert a brand new key: ", - keys_vec(i), ". Pending enqueues cancelled: ", - cancel_pending_enqueues_, ". Insertion index: ", i, + keys_vec(i), + ". Pending enqueues cancelled: ", cancel_pending_enqueues_, + ". Insertion index: ", i, ". Number of incomplete keys: ", incomplete_.size(), "."); } } else { @@ -532,13 +535,14 @@ class InsertManyOp : public BarrierOpKernel { OP_REQUIRES_ASYNC( ctx, component_index_ < barrier->num_components(), errors::InvalidArgument("The component ID is out of range ", - component_index_, " > num_components", " (= ", - barrier->num_components(), ")"), + component_index_, " > num_components", + " (= ", barrier->num_components(), ")"), callback); OP_REQUIRES_OK_ASYNC( - ctx, ctx->MatchSignature({DT_STRING_REF, DT_STRING, - barrier->component_type(component_index_)}, - {}), + ctx, + ctx->MatchSignature({DT_STRING_REF, DT_STRING, + barrier->component_type(component_index_)}, + {}), callback); const Tensor* keys; diff --git a/tensorflow/core/kernels/batch_kernels.cc b/tensorflow/core/kernels/batch_kernels.cc index 5b4e1a809f..c447db842d 100644 --- a/tensorflow/core/kernels/batch_kernels.cc +++ b/tensorflow/core/kernels/batch_kernels.cc @@ -13,22 +13,20 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ - #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_util.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/batching_util/shared_batch_scheduler.h" #include "tensorflow/core/kernels/batching_util/periodic_function.h" +#include "tensorflow/core/kernels/batching_util/shared_batch_scheduler.h" #include "tensorflow/core/kernels/concat_lib.h" #include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/kernels/split_lib.h" #include "tensorflow/core/lib/random/random.h" #include "tensorflow/core/platform/macros.h" - namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; diff --git a/tensorflow/core/kernels/batch_matmul_op_impl.h b/tensorflow/core/kernels/batch_matmul_op_impl.h index 93c3918319..43e716c542 100644 --- a/tensorflow/core/kernels/batch_matmul_op_impl.h +++ b/tensorflow/core/kernels/batch_matmul_op_impl.h @@ -41,7 +41,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace { @@ -429,14 +429,13 @@ template struct LaunchBatchMatMul { static void Launch(OpKernelContext* context, const Tensor& in_x, const Tensor& in_y, bool adj_x, bool adj_y, Tensor* out) { - - // Number of matrix multiplies i.e. size of the batch. - const int64 batch_size = in_x.dim_size(0); - ParallelMatMulKernelSYCL::Run(context, in_x, in_y, adj_x, adj_y, out, - 0, batch_size); + // Number of matrix multiplies i.e. size of the batch. + const int64 batch_size = in_x.dim_size(0); + ParallelMatMulKernelSYCL::Run(context, in_x, in_y, adj_x, adj_y, + out, 0, batch_size); } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class BatchMatMul : public OpKernel { @@ -462,10 +461,10 @@ class BatchMatMul : public OpKernel { TensorShape out_shape; for (int i = 0; i < ndims - 2; ++i) { OP_REQUIRES(ctx, in0.dim_size(i) == in1.dim_size(i), - errors::InvalidArgument("In[0].dim(", i, ") and In[1].dim(", - i, ") must be the same: ", - in0.shape().DebugString(), " vs ", - in1.shape().DebugString())); + errors::InvalidArgument( + "In[0].dim(", i, ") and In[1].dim(", i, + ") must be the same: ", in0.shape().DebugString(), " vs ", + in1.shape().DebugString())); out_shape.AddDim(in0.dim_size(i)); } auto n = (ndims == 2) ? 1 : out_shape.num_elements(); @@ -507,12 +506,12 @@ class BatchMatMul : public OpKernel { bool adj_y_; }; -#define REGISTER_BATCH_MATMUL_CPU(TYPE) \ +#define REGISTER_BATCH_MATMUL_CPU(TYPE) \ REGISTER_KERNEL_BUILDER( \ Name("BatchMatMul").Device(DEVICE_CPU).TypeConstraint("T"), \ BatchMatMul) -#define REGISTER_BATCH_MATMUL_GPU(TYPE) \ +#define REGISTER_BATCH_MATMUL_GPU(TYPE) \ REGISTER_KERNEL_BUILDER( \ Name("BatchMatMul").Device(DEVICE_GPU).TypeConstraint("T"), \ BatchMatMul) @@ -522,5 +521,5 @@ class BatchMatMul : public OpKernel { REGISTER_KERNEL_BUILDER( \ Name("BatchMatMul").Device(DEVICE_SYCL).TypeConstraint("T"), \ BatchMatMul) -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow diff --git a/tensorflow/core/kernels/batch_matmul_op_real.cc b/tensorflow/core/kernels/batch_matmul_op_real.cc index 8d155ca62b..7e1e2aa4ec 100644 --- a/tensorflow/core/kernels/batch_matmul_op_real.cc +++ b/tensorflow/core/kernels/batch_matmul_op_real.cc @@ -35,5 +35,5 @@ TF_CALL_half(REGISTER_BATCH_MATMUL_GPU); #ifdef TENSORFLOW_USE_SYCL TF_CALL_float(REGISTER_BATCH_MATMUL_SYCL); TF_CALL_double(REGISTER_BATCH_MATMUL_SYCL); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/batch_matmul_op_test.cc b/tensorflow/core/kernels/batch_matmul_op_test.cc index 7923f34155..c3932cd7b9 100644 --- a/tensorflow/core/kernels/batch_matmul_op_test.cc +++ b/tensorflow/core/kernels/batch_matmul_op_test.cc @@ -53,9 +53,10 @@ static Graph* BatchMatmul(int b, int m, int k, int n, bool adjoint_a, /* Uncomment to enable benchmarks for double & complex types: */ // BM_BatchMatmulDev(B, M, K, N, TA, TB, std::complex, DT_COMPLEX64, // gpu); -// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, cpu); \ -// BM_BatchMatmulDev(M, K, N, TA, TB, std::complex, DT_COMPLEX128, cpu); \ -// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, gpu); \ +// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, cpu); \ +// BM_BatchMatmulDev(M, K, N, TA, TB, std::complex, DT_COMPLEX128, cpu); +// \ +// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, gpu); \ // BM_BatchMatmulDev(M, K, N, TA, TB, std::complex, DT_COMPLEX128, gpu); // Typical fully connected layers diff --git a/tensorflow/core/kernels/batch_norm_op.cc b/tensorflow/core/kernels/batch_norm_op.cc index d3ed617f71..c34ea14bf6 100644 --- a/tensorflow/core/kernels/batch_norm_op.cc +++ b/tensorflow/core/kernels/batch_norm_op.cc @@ -30,7 +30,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class BatchNormOp : public OpKernel { diff --git a/tensorflow/core/kernels/batch_norm_op_test.cc b/tensorflow/core/kernels/batch_norm_op_test.cc index 5e3fcd2114..45ddc85329 100644 --- a/tensorflow/core/kernels/batch_norm_op_test.cc +++ b/tensorflow/core/kernels/batch_norm_op_test.cc @@ -54,7 +54,7 @@ TEST_F(BatchNormOpTest, Simple) { Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 6, 2})); test::FillValues( &expected, {-17.86f, -22.00f, -15.87f, -20.59f, -13.87f, -19.18f, -21.86f, - -33.31f, -23.85f, -34.72f, -25.85f, -36.13f }); + -33.31f, -23.85f, -34.72f, -25.85f, -36.13f}); test::ExpectTensorNear(expected, *GetOutput(0), 0.01); } diff --git a/tensorflow/core/kernels/batchtospace_op.cc b/tensorflow/core/kernels/batchtospace_op.cc index c1c0d6d329..b07c5fd718 100644 --- a/tensorflow/core/kernels/batchtospace_op.cc +++ b/tensorflow/core/kernels/batchtospace_op.cc @@ -56,9 +56,10 @@ static void BatchToSpaceOpCompute(OpKernelContext* context, errors::InvalidArgument("input rank should be >= ", 1 + block_dims, " instead of ", orig_input_tensor.dims())); - OP_REQUIRES(context, TensorShapeUtils::IsMatrix(orig_crops.shape()) && - block_dims == orig_crops.dim_size(0) && - 2 == orig_crops.dim_size(1), + OP_REQUIRES(context, + TensorShapeUtils::IsMatrix(orig_crops.shape()) && + block_dims == orig_crops.dim_size(0) && + 2 == orig_crops.dim_size(1), errors::InvalidArgument("crops should have shape [", block_dims, ", 2] instead of ", orig_crops.shape().DebugString())); diff --git a/tensorflow/core/kernels/bcast_ops.cc b/tensorflow/core/kernels/bcast_ops.cc index 7fc4b1762d..8e4f08e473 100644 --- a/tensorflow/core/kernels/bcast_ops.cc +++ b/tensorflow/core/kernels/bcast_ops.cc @@ -13,11 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/util/bcast.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/util/bcast.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/bias_op_gpu.cu.cc b/tensorflow/core/kernels/bias_op_gpu.cu.cc index 2ca194a77f..754b93b073 100644 --- a/tensorflow/core/kernels/bias_op_gpu.cu.cc +++ b/tensorflow/core/kernels/bias_op_gpu.cu.cc @@ -77,14 +77,14 @@ void BiasGPU::compute(const GPUDevice& d, const T* input, const T* bias, } CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d); if (data_format == FORMAT_NHWC) { - BiasNHWCKernel< - T><<>>( - config.virtual_thread_count, input, bias, output, bias_size); + BiasNHWCKernel + <<>>( + config.virtual_thread_count, input, bias, output, bias_size); } else { - BiasNCHWKernel< - T><<>>( - config.virtual_thread_count, input, bias, output, bias_size, - image_size); + BiasNCHWKernel + <<>>( + config.virtual_thread_count, input, bias, output, bias_size, + image_size); } } @@ -206,10 +206,10 @@ void BiasGradGPU::compute(const GPUDevice& d, const T* output_backprop, // Check if we have enough shared memory. if (shared_memory_size <= max_shared_memory_size) { if (data_format == FORMAT_NHWC) { - BiasGradNHWC_SharedAtomics< - T><<>>(total_count, output_backprop, bias_backprop, - bias_size); + BiasGradNHWC_SharedAtomics + <<>>(total_count, output_backprop, bias_backprop, + bias_size); } else { // Round up the block count to multiple of bias_size. int group_size = (config.block_count + bias_size - 1) / bias_size; @@ -217,23 +217,24 @@ void BiasGradGPU::compute(const GPUDevice& d, const T* output_backprop, if (config.thread_per_block < kWarpSize) { config.thread_per_block = kWarpSize; } - BiasGradNCHW_SharedAtomics< - T><<>>( - output_backprop, bias_backprop, batch, bias_size, image_size, - group_size); + BiasGradNCHW_SharedAtomics + <<>>( + output_backprop, bias_backprop, batch, bias_size, image_size, + group_size); } } else { // Note that even if we don't have enough shared memory to fit the entire // output block, it is possible to process one group of elements at a time. // But for now, we simply fall back to the naive implementation. if (data_format == FORMAT_NHWC) { - BiasGradNHWC_Naive< - T><<>>( - total_count, output_backprop, bias_backprop, bias_size); + BiasGradNHWC_Naive + <<>>( + total_count, output_backprop, bias_backprop, bias_size); } else { - BiasGradNCHW_Naive< - T><<>>( - total_count, output_backprop, bias_backprop, bias_size, image_size); + BiasGradNCHW_Naive + <<>>( + total_count, output_backprop, bias_backprop, bias_size, + image_size); } } } diff --git a/tensorflow/core/kernels/bounds_check.h b/tensorflow/core/kernels/bounds_check.h index e35f42ad41..c8c60c5524 100644 --- a/tensorflow/core/kernels/bounds_check.h +++ b/tensorflow/core/kernels/bounds_check.h @@ -48,7 +48,7 @@ EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC const T SubtleMustCopy(const T &x) { auto *to_x = reinterpret_cast(&x); return *to_x; } -} // namespace tensorflow::internal +} // namespace internal } // namespace tensorflow #endif // TENSORFLOW_UTIL_BOUNDS_CHECK_H_ diff --git a/tensorflow/core/kernels/candidate_sampler_ops.cc b/tensorflow/core/kernels/candidate_sampler_ops.cc index e937c4f11b..654d99301a 100644 --- a/tensorflow/core/kernels/candidate_sampler_ops.cc +++ b/tensorflow/core/kernels/candidate_sampler_ops.cc @@ -126,13 +126,13 @@ REGISTER_KERNEL_BUILDER(Name("UniformCandidateSampler").Device(DEVICE_CPU), REGISTER_KERNEL_BUILDER(Name("LogUniformCandidateSampler").Device(DEVICE_CPU), SimpleCandidateSamplerOp); -REGISTER_KERNEL_BUILDER(Name("LearnedUnigramCandidateSampler") - .Device(DEVICE_CPU), - SimpleCandidateSamplerOp); +REGISTER_KERNEL_BUILDER( + Name("LearnedUnigramCandidateSampler").Device(DEVICE_CPU), + SimpleCandidateSamplerOp); -REGISTER_KERNEL_BUILDER(Name("ThreadUnsafeUnigramCandidateSampler") - .Device(DEVICE_CPU), - SimpleCandidateSamplerOp); +REGISTER_KERNEL_BUILDER( + Name("ThreadUnsafeUnigramCandidateSampler").Device(DEVICE_CPU), + SimpleCandidateSamplerOp); class AllCandidateSamplerOp : public BaseCandidateSamplerOp { public: @@ -197,8 +197,9 @@ class ComputeAccidentalHitsOp : public OpKernel { void Compute(OpKernelContext* context) override { const Tensor& in_true_candidates = context->input(0); const TensorShape& in_true_candidates_shape = in_true_candidates.shape(); - OP_REQUIRES(context, TensorShapeUtils::IsMatrix(in_true_candidates_shape) && - in_true_candidates_shape.dim_size(1) == num_true_, + OP_REQUIRES(context, + TensorShapeUtils::IsMatrix(in_true_candidates_shape) && + in_true_candidates_shape.dim_size(1) == num_true_, errors::InvalidArgument( "true_candidates must be a batch_size * num_true matrix")); diff --git a/tensorflow/core/kernels/cast_op.cc b/tensorflow/core/kernels/cast_op.cc index f16abb2b79..626db9131a 100644 --- a/tensorflow/core/kernels/cast_op.cc +++ b/tensorflow/core/kernels/cast_op.cc @@ -36,7 +36,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define CURRY_TYPES2(FN, arg0) \ FN(arg0, bool); \ @@ -223,11 +223,11 @@ class SyclCastOp : public CastOpBase { } }; -#define REGISTER_CAST_SYCL(srctype, dsttype) \ - REGISTER_KERNEL_BUILDER(Name("Cast") \ - .TypeConstraint("SrcT") \ - .TypeConstraint("DstT") \ - .Device(DEVICE_SYCL), \ +#define REGISTER_CAST_SYCL(srctype, dsttype) \ + REGISTER_KERNEL_BUILDER(Name("Cast") \ + .TypeConstraint("SrcT") \ + .TypeConstraint("DstT") \ + .Device(DEVICE_SYCL), \ SyclCastOp) CURRY_TYPES2(REGISTER_CAST_SYCL, bool); CURRY_TYPES2(REGISTER_CAST_SYCL, int32); @@ -237,7 +237,7 @@ CURRY_TYPES2(REGISTER_CAST_SYCL, double); #undef REGISTER_CAST_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef CURRY_TYPES2 @@ -250,6 +250,5 @@ REGISTER_KERNEL_BUILDER( REGISTER_KERNEL_BUILDER( Name("_HostCast").Device(DEVICE_SYCL).HostMemory("x").HostMemory("y"), CpuCastOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow - diff --git a/tensorflow/core/kernels/cast_op.h b/tensorflow/core/kernels/cast_op.h index 8fedf2c271..fd4e75d26f 100644 --- a/tensorflow/core/kernels/cast_op.h +++ b/tensorflow/core/kernels/cast_op.h @@ -131,7 +131,8 @@ struct scalar_cast_op<::tensorflow::bfloat16, float> { p[0] = a.value; p[1] = 0; #else - static_assert(::tensorflow::port::kLittleEndian, "Not a little endian system!"); + static_assert(::tensorflow::port::kLittleEndian, + "Not a little endian system!"); p[0] = 0; p[1] = a.value; #endif diff --git a/tensorflow/core/kernels/cast_op_impl.h b/tensorflow/core/kernels/cast_op_impl.h index 470e9e0804..3ae9f2ab4d 100644 --- a/tensorflow/core/kernels/cast_op_impl.h +++ b/tensorflow/core/kernels/cast_op_impl.h @@ -41,25 +41,25 @@ struct CastFunctor { o.device(d) = i.template cast(); } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor -#define CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ - FN(arg0, arg1, bool); \ - FN(arg0, arg1, uint8); \ - FN(arg0, arg1, int8); \ - FN(arg0, arg1, uint16); \ - FN(arg0, arg1, int16); \ - FN(arg0, arg1, int32); \ - FN(arg0, arg1, int64); \ - FN(arg0, arg1, float); \ - FN(arg0, arg1, double); \ - FN(arg0, arg1, std::complex); \ +#define CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ + FN(arg0, arg1, bool); \ + FN(arg0, arg1, uint8); \ + FN(arg0, arg1, int8); \ + FN(arg0, arg1, uint16); \ + FN(arg0, arg1, int16); \ + FN(arg0, arg1, int32); \ + FN(arg0, arg1, int64); \ + FN(arg0, arg1, float); \ + FN(arg0, arg1, double); \ + FN(arg0, arg1, std::complex); \ FN(arg0, arg1, std::complex) -#define CURRY_TYPES3(FN, arg0, arg1) \ - CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ +#define CURRY_TYPES3(FN, arg0, arg1) \ + CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ FN(arg0, arg1, Eigen::half); #define CAST_CASE(DEVICE, IN, OUT) \ diff --git a/tensorflow/core/kernels/cast_op_test.cc b/tensorflow/core/kernels/cast_op_test.cc index a106f287c1..057e209a71 100644 --- a/tensorflow/core/kernels/cast_op_test.cc +++ b/tensorflow/core/kernels/cast_op_test.cc @@ -107,10 +107,10 @@ static void BM_gpu_float_int64(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL test::Benchmark("sycl", Cast(num)).Run(iters); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } BENCHMARK(BM_gpu_float_int64)->Arg(64 << 10)->Arg(32 << 20); @@ -130,10 +130,10 @@ static void BM_gpu_bool_float(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL test::Benchmark("sycl", Cast(num)).Run(iters); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } BENCHMARK(BM_gpu_bool_float)->Arg(64 << 10)->Arg(32 << 20); @@ -180,7 +180,7 @@ static void BM_gpu_float_half(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA } BENCHMARK(BM_gpu_float_half)->Arg(64 << 10)->Arg(32 << 20); @@ -191,7 +191,7 @@ static void BM_gpu_half_float(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA } BENCHMARK(BM_gpu_half_float)->Arg(64 << 10)->Arg(32 << 20); diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc index ba100b32e7..9cc2e67bbe 100644 --- a/tensorflow/core/kernels/colorspace_op.cc +++ b/tensorflow/core/kernels/colorspace_op.cc @@ -107,14 +107,14 @@ class HSVToRGBOp : public OpKernel { } }; -#define REGISTER_CPU(T) \ - REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - RGBToHSVOp); \ - template class RGBToHSVOp; \ - REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - HSVToRGBOp); \ +#define REGISTER_CPU(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("RGBToHSV").Device(DEVICE_CPU).TypeConstraint("T"), \ + RGBToHSVOp); \ + template class RGBToHSVOp; \ + REGISTER_KERNEL_BUILDER( \ + Name("HSVToRGB").Device(DEVICE_CPU).TypeConstraint("T"), \ + HSVToRGBOp); \ template class HSVToRGBOp; TF_CALL_float(REGISTER_CPU); TF_CALL_double(REGISTER_CPU); @@ -123,40 +123,39 @@ TF_CALL_double(REGISTER_CPU); // Forward declarations of the function specializations for GPU (to prevent // building the GPU versions here, they will be built compiling _gpu.cu.cc). namespace functor { -#define DECLARE_GPU(T) \ - template <> \ - void RGBToHSV::operator()(const GPUDevice& d, \ - TTypes::ConstTensor input_data, \ - TTypes::Tensor range, \ - TTypes::Tensor output_data); \ - extern template struct RGBToHSV; \ - template <> \ - void HSVToRGB::operator()(const GPUDevice& d, \ - TTypes::ConstTensor input_data, \ - TTypes::Tensor output_data); \ +#define DECLARE_GPU(T) \ + template <> \ + void RGBToHSV::operator()( \ + const GPUDevice& d, TTypes::ConstTensor input_data, \ + TTypes::Tensor range, TTypes::Tensor output_data); \ + extern template struct RGBToHSV; \ + template <> \ + void HSVToRGB::operator()( \ + const GPUDevice& d, TTypes::ConstTensor input_data, \ + TTypes::Tensor output_data); \ extern template struct HSVToRGB; TF_CALL_float(DECLARE_GPU); TF_CALL_double(DECLARE_GPU); } // namespace functor -#define REGISTER_GPU(T) \ - REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_GPU) \ - .TypeConstraint("T"), \ - RGBToHSVOp); \ - REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_GPU) \ - .TypeConstraint("T"), \ - HSVToRGBOp); +#define REGISTER_GPU(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("RGBToHSV").Device(DEVICE_GPU).TypeConstraint("T"), \ + RGBToHSVOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("HSVToRGB").Device(DEVICE_GPU).TypeConstraint("T"), \ + HSVToRGBOp); TF_CALL_float(REGISTER_GPU); TF_CALL_double(REGISTER_GPU); #endif #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL(T) \ - REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - RGBToHSVOp); \ - REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - HSVToRGBOp); +#define REGISTER_SYCL(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("RGBToHSV").Device(DEVICE_SYCL).TypeConstraint("T"), \ + RGBToHSVOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("HSVToRGB").Device(DEVICE_SYCL).TypeConstraint("T"), \ + HSVToRGBOp); TF_CALL_float(REGISTER_SYCL); TF_CALL_double(REGISTER_SYCL); #endif diff --git a/tensorflow/core/kernels/colorspace_op.h b/tensorflow/core/kernels/colorspace_op.h index c5721ef6dd..90bfce1419 100644 --- a/tensorflow/core/kernels/colorspace_op.h +++ b/tensorflow/core/kernels/colorspace_op.h @@ -54,10 +54,9 @@ struct RGBToHSV { // TODO(wicke): all these assignments are only necessary because a combined // expression is larger than kernel parameter space. A custom kernel is // probably in order. - H.device(d) = (R == V).select(norm * (G - B), - (G == V).select( - norm * (B - R) + T(2) / T(6), - norm * (R - G) + T(4) / T(6))); + H.device(d) = (R == V).select( + norm * (G - B), (G == V).select(norm * (B - R) + T(2) / T(6), + norm * (R - G) + T(4) / T(6))); H.device(d) = (range > T(0)).select(H, H.constant(T(0))); H.device(d) = (H < T(0)).select(H + T(1), H); } diff --git a/tensorflow/core/kernels/colorspace_op_gpu.cu.cc b/tensorflow/core/kernels/colorspace_op_gpu.cu.cc index e19d0b14d5..61f9ba44c4 100644 --- a/tensorflow/core/kernels/colorspace_op_gpu.cu.cc +++ b/tensorflow/core/kernels/colorspace_op_gpu.cu.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/colorspace_op.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/colorspace_op.h" namespace tensorflow { @@ -29,6 +29,6 @@ typedef Eigen::GpuDevice GPUDevice; template class functor::HSVToRGB; TF_CALL_float(INSTANTIATE_GPU); TF_CALL_double(INSTANTIATE_GPU); -} +} // namespace tensorflow #endif // GOOGLE_CUDA diff --git a/tensorflow/core/kernels/colorspace_op_test.cc b/tensorflow/core/kernels/colorspace_op_test.cc index 8c6fb732ab..bd82826770 100644 --- a/tensorflow/core/kernels/colorspace_op_test.cc +++ b/tensorflow/core/kernels/colorspace_op_test.cc @@ -224,34 +224,34 @@ class HSVToRGBOpTest : public OpsTestBase { } }; -#define TEST_COLORSPACE(test, dt) \ - TEST_F(test, CheckBlack) { \ - MakeOp(dt); \ - CheckBlack(dt); \ - } \ - TEST_F(test, CheckGray) { \ - MakeOp(dt); \ - CheckGray(dt); \ - } \ - TEST_F(test, CheckWhite) { \ - MakeOp(dt); \ - CheckWhite(dt); \ - } \ - TEST_F(test, CheckRedMax) { \ - MakeOp(dt); \ - CheckRedMax(dt); \ - } \ - TEST_F(test, CheckGreenMax) { \ - MakeOp(dt); \ - CheckGreenMax(dt); \ - } \ - TEST_F(test, CheckBlueMax) { \ - MakeOp(dt); \ - CheckBlueMax(dt); \ - } \ - TEST_F(test, CheckNegativeDifference) { \ - MakeOp(dt); \ - CheckNegativeDifference(dt); \ +#define TEST_COLORSPACE(test, dt) \ + TEST_F(test, CheckBlack) { \ + MakeOp(dt); \ + CheckBlack(dt); \ + } \ + TEST_F(test, CheckGray) { \ + MakeOp(dt); \ + CheckGray(dt); \ + } \ + TEST_F(test, CheckWhite) { \ + MakeOp(dt); \ + CheckWhite(dt); \ + } \ + TEST_F(test, CheckRedMax) { \ + MakeOp(dt); \ + CheckRedMax(dt); \ + } \ + TEST_F(test, CheckGreenMax) { \ + MakeOp(dt); \ + CheckGreenMax(dt); \ + } \ + TEST_F(test, CheckBlueMax) { \ + MakeOp(dt); \ + CheckBlueMax(dt); \ + } \ + TEST_F(test, CheckNegativeDifference) { \ + MakeOp(dt); \ + CheckNegativeDifference(dt); \ } typedef RGBToHSVOpTest rgb_to_hsv_float; diff --git a/tensorflow/core/kernels/concat_lib.h b/tensorflow/core/kernels/concat_lib.h index 526f9420d7..16784c4770 100644 --- a/tensorflow/core/kernels/concat_lib.h +++ b/tensorflow/core/kernels/concat_lib.h @@ -41,10 +41,11 @@ namespace tensorflow { // Assumes all inputs are nonempty template -void ConcatCPU(DeviceBase* d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output); +void ConcatCPU( + DeviceBase* d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output); #if GOOGLE_CUDA template void ConcatGPU( @@ -57,11 +58,12 @@ void ConcatGPU( #ifdef TENSORFLOW_USE_SYCL template -void ConcatSYCL(const Eigen::SyclDevice& d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output); -#endif // TENSORFLOW_USE_SYCL +void ConcatSYCL( + const Eigen::SyclDevice& d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output); +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow #endif // TENSORFLOW_KERNELS_CONCAT_LIB_H_ diff --git a/tensorflow/core/kernels/concat_lib_cpu.cc b/tensorflow/core/kernels/concat_lib_cpu.cc index 43731114c0..fc5a3e6288 100644 --- a/tensorflow/core/kernels/concat_lib_cpu.cc +++ b/tensorflow/core/kernels/concat_lib_cpu.cc @@ -48,10 +48,11 @@ struct MemCpyCopier { } // namespace template -void ConcatCPU(DeviceBase* d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output) { +void ConcatCPU( + DeviceBase* d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output) { if (std::is_same::value) { // use a large cost here to force strings to be handled by separate threads ConcatCPUImpl(d, inputs, 100000, MemCpyCopier(), output); @@ -86,21 +87,22 @@ TF_CALL_variant(REGISTER) #ifdef TENSORFLOW_USE_SYCL template -void ConcatSYCL(const Eigen::SyclDevice& d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output) { +void ConcatSYCL( + const Eigen::SyclDevice& d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output) { ConcatSYCLImpl(d, inputs, sizeof(T) /* cost_per_unit */, MemCpyCopier(), - output); + output); } -#define REGISTER_SYCL(T) \ - template void ConcatSYCL( \ - const Eigen::SyclDevice&, \ - const std::vector::ConstMatrix>>&, \ - typename TTypes::Matrix* output); +#define REGISTER_SYCL(T) \ + template void ConcatSYCL( \ + const Eigen::SyclDevice&, \ + const std::vector::ConstMatrix>>&, \ + typename TTypes::Matrix* output); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL) #undef REGISTER_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/concat_lib_cpu.h b/tensorflow/core/kernels/concat_lib_cpu.h index 6a933efde4..720b506537 100644 --- a/tensorflow/core/kernels/concat_lib_cpu.h +++ b/tensorflow/core/kernels/concat_lib_cpu.h @@ -15,9 +15,9 @@ limitations under the License. #define EIGEN_USE_THREADS -#include "tensorflow/core/kernels/concat_lib.h" #include #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/concat_lib.h" #include "tensorflow/core/util/work_sharder.h" namespace tensorflow { @@ -73,7 +73,7 @@ void ConcatCPUImpl( // Sharded mode. auto work = [&row_size, &sizes, &inputs, &output, &copier, &num_inputs]( - int64 start, int64 end) { + int64 start, int64 end) { int64 skipped_rows = start / row_size; T* out = output->data() + skipped_rows * row_size; T* out_start = output->data() + start; @@ -160,5 +160,5 @@ void ConcatSYCLImpl( } } } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/concat_op.cc b/tensorflow/core/kernels/concat_op.cc index ae1b5da32e..7011550f7e 100644 --- a/tensorflow/core/kernels/concat_op.cc +++ b/tensorflow/core/kernels/concat_op.cc @@ -37,7 +37,7 @@ typedef Eigen::GpuDevice GPUDevice; #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL enum AxisArgumentName { NAME_IS_AXIS, NAME_IS_CONCAT_DIM }; @@ -71,8 +71,9 @@ class ConcatBaseOp : public OpKernel { const TensorShape& input_shape = values[0].shape(); int32 axis = concat_dim < 0 ? concat_dim + input_dims : concat_dim; - OP_REQUIRES(c, (0 <= axis && axis < input_dims) || - (allow_legacy_scalars() && concat_dim == 0), + OP_REQUIRES(c, + (0 <= axis && axis < input_dims) || + (allow_legacy_scalars() && concat_dim == 0), errors::InvalidArgument( "ConcatOp : Expected concatenating dimensions in the range " "[", @@ -97,8 +98,8 @@ class ConcatBaseOp : public OpKernel { c, in.dims() == input_dims || (input_is_scalar && in_is_scalar), errors::InvalidArgument( "ConcatOp : Ranks of all input tensors should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); for (int j = 0; j < input_dims; ++j) { if (j == axis) { continue; @@ -107,8 +108,8 @@ class ConcatBaseOp : public OpKernel { c, in.dim_size(j) == input_shape.dim_size(j), errors::InvalidArgument( "ConcatOp : Dimensions of inputs should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); } if (in.NumElements() > 0) { int64 inputs_flat_dim1 = in.NumElements() / inputs_flat_dim0; @@ -142,7 +143,7 @@ class ConcatBaseOp : public OpKernel { ConcatSYCL(c->eigen_sycl_device(), inputs_flat, &output_flat); return; } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL ConcatCPU(c->device(), inputs_flat, &output_flat); } } @@ -252,7 +253,7 @@ REGISTER_KERNEL_BUILDER(Name("ConcatV2") ConcatV2Op); #undef REGISTER_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class ConcatOffsetOp : public OpKernel { public: @@ -347,5 +348,5 @@ REGISTER_KERNEL_BUILDER(Name("ConcatOffset") .HostMemory("shape") .HostMemory("offset"), ConcatOffsetOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/concat_op_test.cc b/tensorflow/core/kernels/concat_op_test.cc index c5bded9daf..e3ba8ae9f6 100644 --- a/tensorflow/core/kernels/concat_op_test.cc +++ b/tensorflow/core/kernels/concat_op_test.cc @@ -157,7 +157,8 @@ BENCHMARK(BM_MemcpyAlternativeDim0)->Arg(1000)->Arg(100000)->Arg(1000000); BENCHMARK(BM_MemcpyAlternativeDim1)->Arg(1000)->Arg(100000)->Arg(1000000); typedef Eigen::TensorMap, - Eigen::Unaligned> EigenMap; + Eigen::Unaligned> + EigenMap; static void MemcpyManyAlternative1(int iters, int dim2) { testing::StopTiming(); diff --git a/tensorflow/core/kernels/conditional_accumulator_base.h b/tensorflow/core/kernels/conditional_accumulator_base.h index 794ac6fa6d..c7c7c98369 100644 --- a/tensorflow/core/kernels/conditional_accumulator_base.h +++ b/tensorflow/core/kernels/conditional_accumulator_base.h @@ -160,7 +160,7 @@ class ConditionalAccumulatorBase : public ResourceBase { * Modifications to convenience macros defined in core/framework/op_kernel.h. * The below macros return a boolean if the test fails, so that the calling * function can get an indication that a failure has occurred. -*/ + */ #define OP_REQUIRES_BOOLEAN(CTX, EXP, STATUS) \ do { \ if (!TF_PREDICT_TRUE(EXP)) { \ diff --git a/tensorflow/core/kernels/conditional_accumulator_op.cc b/tensorflow/core/kernels/conditional_accumulator_op.cc index fa37916eab..e13bf8a4c6 100644 --- a/tensorflow/core/kernels/conditional_accumulator_op.cc +++ b/tensorflow/core/kernels/conditional_accumulator_op.cc @@ -99,9 +99,10 @@ class AccumulatorTakeGradientOp ConditionalAccumulatorBase* accumulator, DoneCallback callback) override { // Check signature - OP_REQUIRES_OK_ASYNC(ctx, ctx->MatchSignature({DT_STRING_REF, DT_INT32}, - {accumulator->dtype()}), - callback); + OP_REQUIRES_OK_ASYNC( + ctx, + ctx->MatchSignature({DT_STRING_REF, DT_INT32}, {accumulator->dtype()}), + callback); } private: @@ -111,5 +112,4 @@ class AccumulatorTakeGradientOp REGISTER_KERNEL_BUILDER(Name("AccumulatorTakeGradient").Device(DEVICE_CPU), AccumulatorTakeGradientOp); - } // namespace tensorflow diff --git a/tensorflow/core/kernels/constant_op.cc b/tensorflow/core/kernels/constant_op.cc index 59f9f69315..920cd87858 100644 --- a/tensorflow/core/kernels/constant_op.cc +++ b/tensorflow/core/kernels/constant_op.cc @@ -146,7 +146,6 @@ typedef Eigen::GpuDevice GPUDevice; typedef Eigen::SyclDevice SYCLDevice; #endif // TENSORFLOW_USE_SYCL - template class FillOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/control_flow_ops.cc b/tensorflow/core/kernels/control_flow_ops.cc index 8fe82d118a..7d5d54e5be 100644 --- a/tensorflow/core/kernels/control_flow_ops.cc +++ b/tensorflow/core/kernels/control_flow_ops.cc @@ -113,47 +113,47 @@ REGISTER_GPU_HOST_REF_KERNEL(string); #undef REGISTER_GPU_HOST_REF_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_SWITCH(type) \ - REGISTER_KERNEL_BUILDER(Name("Switch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("pred") \ - .TypeConstraint("T"),\ +#define REGISTER_SYCL_SWITCH(type) \ + REGISTER_KERNEL_BUILDER(Name("Switch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("pred") \ + .TypeConstraint("T"), \ SwitchOp) TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_SWITCH); -#define REGISTER_SYCL_REF_SWITCH(type) \ - REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("pred") \ - .TypeConstraint("T"), \ +#define REGISTER_SYCL_REF_SWITCH(type) \ + REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("pred") \ + .TypeConstraint("T"), \ SwitchOp) TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH); #undef REGISTER_SYCL_SWITCH #undef REGISTER_SYCL_REF_SWITCH -#define REGISTER_SYCL_HOST_KERNEL(type) \ - REGISTER_KERNEL_BUILDER(Name("Switch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("data") \ - .HostMemory("pred") \ - .HostMemory("output_false")\ - .HostMemory("output_true") \ - .TypeConstraint("T"),\ +#define REGISTER_SYCL_HOST_KERNEL(type) \ + REGISTER_KERNEL_BUILDER(Name("Switch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("data") \ + .HostMemory("pred") \ + .HostMemory("output_false") \ + .HostMemory("output_true") \ + .TypeConstraint("T"), \ SwitchOp) REGISTER_SYCL_HOST_KERNEL(bool); REGISTER_SYCL_HOST_KERNEL(string); REGISTER_SYCL_HOST_KERNEL(int32); -#define REGISTER_SYCL_HOST_REF_KERNEL(type) \ - REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("data") \ - .HostMemory("pred") \ - .HostMemory("output_false") \ - .HostMemory("output_true") \ - .TypeConstraint("T"), \ +#define REGISTER_SYCL_HOST_REF_KERNEL(type) \ + REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("data") \ + .HostMemory("pred") \ + .HostMemory("output_false") \ + .HostMemory("output_true") \ + .TypeConstraint("T"), \ SwitchOp) REGISTER_SYCL_HOST_REF_KERNEL(int32); @@ -162,7 +162,7 @@ REGISTER_SYCL_HOST_REF_KERNEL(string); #undef REGISTER_SYCL_HOST_KERNEL #undef REGISTER_SYCL_HOST_REF_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class RefSelectOp : public OpKernel { public: @@ -282,7 +282,7 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL); #undef REGISTER_SYCL_KERNEL #undef REGISTER_SYCL_REF_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Special GPU kernels for int32 and string. // TODO(b/25387198): Also enable int32 in device memory. This kernel @@ -331,7 +331,7 @@ REGISTER_SYCL_HOST_KERNEL(string); REGISTER_SYCL_HOST_KERNEL(ResourceHandle); #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL void EnterOp::Compute(OpKernelContext* context) { if (IsRefType(context->input_dtype(0))) { @@ -360,14 +360,14 @@ REGISTER_GPU_REF_KERNEL(bool); #undef REGISTER_GPU_REF_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ +#define REGISTER_SYCL_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ Name("Enter").Device(DEVICE_SYCL).TypeConstraint("T"), EnterOp) REGISTER_SYCL_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); -#define REGISTER_SYCL_REF_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ +#define REGISTER_SYCL_REF_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ Name("RefEnter").Device(DEVICE_SYCL).TypeConstraint("T"), EnterOp) REGISTER_SYCL_REF_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL); @@ -398,7 +398,7 @@ REGISTER_SYCL_HOST_KERNEL(ResourceHandle); #undef REGISTER_SYCL_HOST_KERNEL #undef REGISTER_SYCL_HOST_REF_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Special GPU kernels for int32 and string. // TODO(b/25387198): Also enable int32 in device memory. This kernel @@ -455,10 +455,10 @@ REGISTER_GPU_REF_KERNEL(bool); #undef REGISTER_GPU_REF_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ - Name("Exit").Device(DEVICE_SYCL).TypeConstraint("T"), ExitOp); \ - REGISTER_KERNEL_BUILDER( \ +#define REGISTER_SYCL_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("Exit").Device(DEVICE_SYCL).TypeConstraint("T"), ExitOp); \ + REGISTER_KERNEL_BUILDER( \ Name("RefExit").Device(DEVICE_SYCL).TypeConstraint("T"), ExitOp); REGISTER_SYCL_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); @@ -483,7 +483,7 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); REGISTER_SYCL_HOST_KERNEL(int32); REGISTER_SYCL_HOST_KERNEL(string); #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Special GPU kernels for int32 and string. // TODO(b/25387198): Also enable int32 in device memory. This kernel @@ -556,12 +556,12 @@ REGISTER_GPU_HOST_KERNEL(string); #undef REGISTER_GPU_HOST_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ - Name("NextIteration").Device(DEVICE_SYCL).TypeConstraint("T"), \ - NextIterationOp); \ - REGISTER_KERNEL_BUILDER( \ - Name("RefNextIteration").Device(DEVICE_SYCL).TypeConstraint("T"),\ +#define REGISTER_SYCL_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("NextIteration").Device(DEVICE_SYCL).TypeConstraint("T"), \ + NextIterationOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("RefNextIteration").Device(DEVICE_SYCL).TypeConstraint("T"), \ NextIterationOp) REGISTER_SYCL_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); @@ -585,7 +585,7 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); REGISTER_SYCL_HOST_KERNEL(int32); REGISTER_SYCL_HOST_KERNEL(string); #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // A LoopCond op has one input and one output. The input is a boolean // scalar representing the taken branches of the "pivot" Switch that @@ -619,7 +619,7 @@ REGISTER_KERNEL_BUILDER(Name("LoopCond") .HostMemory("input") .HostMemory("output"), LoopCondOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // ControlTrigger kernels REGISTER_KERNEL_BUILDER(Name("ControlTrigger").Device(DEVICE_CPU), @@ -631,7 +631,7 @@ REGISTER_KERNEL_BUILDER(Name("ControlTrigger").Device(DEVICE_GPU), #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("ControlTrigger").Device(DEVICE_SYCL), ControlTriggerOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // When called, abort op will abort the current process. This can be used to // abort remote PSs when needed. diff --git a/tensorflow/core/kernels/control_flow_ops_test.cc b/tensorflow/core/kernels/control_flow_ops_test.cc index affa0e8ca6..a2f7bd4069 100644 --- a/tensorflow/core/kernels/control_flow_ops_test.cc +++ b/tensorflow/core/kernels/control_flow_ops_test.cc @@ -91,6 +91,7 @@ class KilledBySignal { public: explicit KilledBySignal(int signum) : signum_(signum) {} bool operator()(int exit_status) const { return exit_status == signum_; } + private: const int signum_; }; diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc index 985586d626..dbddaf3dc6 100644 --- a/tensorflow/core/kernels/conv_ops.cc +++ b/tensorflow/core/kernels/conv_ops.cc @@ -688,7 +688,7 @@ void LaunchConv2DOp::operator()( static int64 ConvolveScratchSize = GetCudnnWorkspaceLimit( // default value is in bytes despite the name of the environment variable "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB - ); + ); int device_id = stream->parent()->device_ordinal(); DataType dtype = input.dtype(); diff --git a/tensorflow/core/kernels/conv_ops_fused.cc b/tensorflow/core/kernels/conv_ops_fused.cc index 291ebf2298..1b40ad81f4 100644 --- a/tensorflow/core/kernels/conv_ops_fused.cc +++ b/tensorflow/core/kernels/conv_ops_fused.cc @@ -679,8 +679,9 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { const int dims = resized_shape.dims(); OP_REQUIRES( - context, TensorShapeUtils::IsMatrix(paddings.shape()) && - paddings.dim_size(1) == 2, + context, + TensorShapeUtils::IsMatrix(paddings.shape()) && + paddings.dim_size(1) == 2, errors::InvalidArgument("paddings must be a matrix with 2 columns: ", paddings.shape().DebugString())); const int fixed_dims = @@ -715,20 +716,22 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { const int32 after = paddings_matrix(d, 1); // Pad after existing elements. OP_REQUIRES(context, before >= 0 && after >= 0, - errors::InvalidArgument("paddings must be non-negative: ", - before, " ", after)); + errors::InvalidArgument( + "paddings must be non-negative: ", before, " ", after)); if (offset_ == 0) { // SYMMETRIC mode. OP_REQUIRES( - context, before <= resized_shape.dim_size(d) && - after <= resized_shape.dim_size(d), + context, + before <= resized_shape.dim_size(d) && + after <= resized_shape.dim_size(d), errors::InvalidArgument("paddings must be no greater " "than the dimension size: ", before, ", ", after, " greater than ", resized_shape.dim_size(d))); } else if (offset_ == 1) { // REFLECT mode. OP_REQUIRES( - context, before < resized_shape.dim_size(d) && - after < resized_shape.dim_size(d), + context, + before < resized_shape.dim_size(d) && + after < resized_shape.dim_size(d), errors::InvalidArgument("paddings must be less than" " the dimension size: ", before, ", ", after, " not less than ", @@ -767,18 +770,19 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { // We only check the first three dims, since the depth is accessed as an // int64 below. for (int i = 0; i < 3; i++) { - OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES( + context, + FastBoundsCheck(filter.dim_size(i), std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } // The last dimension for input is in_depth. It must be the same as the // filter's in_depth. const int64 in_depth = padded_shape.dim_size(3); - OP_REQUIRES( - context, in_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - in_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, in_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", in_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -786,9 +790,10 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { // The second dimension for input is rows/height. // The first dimension for filter is rows/height. const int64 padded_rows_raw = padded_shape.dim_size(1); - OP_REQUIRES(context, FastBoundsCheck(padded_rows_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input rows too large")); + OP_REQUIRES( + context, + FastBoundsCheck(padded_rows_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input rows too large")); const int padded_rows = static_cast(padded_rows_raw); const int filter_rows = static_cast(filter.dim_size(0)); const int resized_rows = static_cast(resized_shape.dim_size(1)); @@ -796,9 +801,10 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { // The third dimension for input is columns/width. // The second dimension for filter is columns/width. const int64 padded_cols_raw = padded_shape.dim_size(2); - OP_REQUIRES(context, FastBoundsCheck(padded_cols_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input cols too large")); + OP_REQUIRES( + context, + FastBoundsCheck(padded_cols_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input cols too large")); const int padded_cols = static_cast(padded_cols_raw); const int filter_cols = static_cast(filter.dim_size(1)); const int resized_cols = static_cast(resized_shape.dim_size(2)); @@ -864,24 +870,26 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { TF_DISALLOW_COPY_AND_ASSIGN(FusedResizeConv2DUsingGemmOp); }; -#define REGISTER_FUSED(T) \ - REGISTER_KERNEL_BUILDER( \ - Name("FusedResizeAndPadConv2D") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - FusedResizeConv2DUsingGemmOp< \ - T, FusedResizeAndPadConvFunctor, \ - BILINEAR>, \ +#define REGISTER_FUSED(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("FusedResizeAndPadConv2D") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T"), \ + FusedResizeConv2DUsingGemmOp< \ + T, \ + FusedResizeAndPadConvFunctor, \ + BILINEAR>, \ true>); TF_CALL_float(REGISTER_FUSED); -#define REGISTER_PAD_ONLY_FUSED(T) \ - REGISTER_KERNEL_BUILDER( \ - Name("FusedPadConv2D").Device(DEVICE_CPU).TypeConstraint("T"), \ - FusedResizeConv2DUsingGemmOp< \ - T, FusedResizeAndPadConvFunctor, \ - NEAREST>, \ +#define REGISTER_PAD_ONLY_FUSED(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("FusedPadConv2D").Device(DEVICE_CPU).TypeConstraint("T"), \ + FusedResizeConv2DUsingGemmOp< \ + T, \ + FusedResizeAndPadConvFunctor, \ + NEAREST>, \ false>); TF_CALL_float(REGISTER_PAD_ONLY_FUSED); diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h index 57e196c67c..f0085be3a5 100644 --- a/tensorflow/core/kernels/conv_ops_gpu.h +++ b/tensorflow/core/kernels/conv_ops_gpu.h @@ -27,7 +27,6 @@ limitations under the License. namespace tensorflow { - // Get the Cudnn workspace limit from the environment variable, which is in MB. // Return the workspace memory limit in bytes. If no value is set, return the // default value. diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc index af6013c974..e58f5f61f3 100644 --- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc +++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc @@ -25,9 +25,9 @@ limitations under the License. #include "cuda/include/cuda.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/kernels/conv_2d.h" +#include "tensorflow/core/lib/math/math_util.h" #include "tensorflow/core/util/cuda_kernel_helper.h" #include "tensorflow/core/util/tensor_format.h" -#include "tensorflow/core/lib/math/math_util.h" namespace tensorflow { @@ -252,11 +252,14 @@ __global__ void SwapDimension1And2InTensor3UsingTiles( int x = threadIdx.x; Dimension<3> output_dims = { - input_dims[0], input_dims[2], input_dims[1], + input_dims[0], + input_dims[2], + input_dims[1], }; Dimension<3> input_dims_in_tiles = { - input_dims[0], (input_dims[1] + TileSizeI - 1) / TileSizeI, + input_dims[0], + (input_dims[1] + TileSizeI - 1) / TileSizeI, (input_dims[2] + TileSizeJ - 1) / TileSizeJ, }; @@ -264,7 +267,8 @@ __global__ void SwapDimension1And2InTensor3UsingTiles( FlatToTensorIndex(blockIdx.x, input_dims_in_tiles); Index<3> input_tile_origin = { - input_tile_index[0], input_tile_index[1] * TileSizeI, + input_tile_index[0], + input_tile_index[1] * TileSizeI, input_tile_index[2] * TileSizeJ, }; @@ -322,11 +326,14 @@ __global__ void SwapDimension1And2InTensor3UsingTiles( __syncthreads(); Index<3> output_tile_index = { - input_tile_index[0], input_tile_index[2], input_tile_index[1], + input_tile_index[0], + input_tile_index[2], + input_tile_index[1], }; Index<3> output_tile_origin = { - output_tile_index[0], output_tile_index[1] * TileSizeJ, + output_tile_index[0], + output_tile_index[1] * TileSizeJ, output_tile_index[2] * TileSizeI, }; @@ -799,7 +806,7 @@ struct TransposeElemType<16> { // A helper function to make RunSwapDimension1And2InTensor3 concise. This // helper function looks at the data type and input matrix sizes and decides // the thread numbers and tile sizes to use. -template +template void SwapDimension1And2InTensor3WithNarrowMatrices( const GPUDevice& d, const T* input, const Dimension<3>& input_dims, T* output, const int kMinDimensionToUseTiles) { @@ -902,19 +909,21 @@ void RunSwapDimension1And2InTensor3(const GPUDevice& d, const T* input, constexpr int kNumThreads = 256; Dimension<3> input_dims_in_tiles = { - input_dims[0], MathUtil::CeilOfRatio(input_dims[1], kTileSize), + input_dims[0], + MathUtil::CeilOfRatio(input_dims[1], kTileSize), MathUtil::CeilOfRatio(input_dims[2], kTileSize), }; int total_tiles_count = input_dims_in_tiles[0] * input_dims_in_tiles[1] * input_dims_in_tiles[2]; - SwapDimension1And2InTensor3UsingTiles + SwapDimension1And2InTensor3UsingTiles <<>>(input, input_dims, output); } else if (narrow_matrix) { - SwapDimension1And2InTensor3WithNarrowMatrices(d, input, input_dims, output, - kMinDimensionToUseTiles); + SwapDimension1And2InTensor3WithNarrowMatrices( + d, input, input_dims, output, kMinDimensionToUseTiles); } else { int total_element_count = input_dims[0] * input_dims[1] * input_dims[2]; CudaLaunchConfig config = GetCudaLaunchConfig(total_element_count, d); diff --git a/tensorflow/core/kernels/conv_ops_using_gemm.cc b/tensorflow/core/kernels/conv_ops_using_gemm.cc index 20da77c36f..af0a9fa82e 100644 --- a/tensorflow/core/kernels/conv_ops_using_gemm.cc +++ b/tensorflow/core/kernels/conv_ops_using_gemm.cc @@ -468,18 +468,19 @@ class Conv2DUsingGemmOp : public BinaryOp { filter.shape().DebugString())); for (int i = 0; i < 3; i++) { - OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES( + context, + FastBoundsCheck(filter.dim_size(i), std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } // The last dimension for input is in_depth. It must be the same as the // filter's in_depth. const int64 in_depth = GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES( - context, in_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - in_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, in_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", in_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -487,18 +488,20 @@ class Conv2DUsingGemmOp : public BinaryOp { // The second dimension for input is rows/height. // The first dimension for filter is rows/height. const int64 input_rows_raw = GetTensorDim(input, data_format_, 'H'); - OP_REQUIRES(context, FastBoundsCheck(input_rows_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input rows too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_rows_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input rows too large")); const int input_rows = static_cast(input_rows_raw); const int filter_rows = static_cast(filter.dim_size(0)); // The third dimension for input is columns/width. // The second dimension for filter is columns/width. const int64 input_cols_raw = GetTensorDim(input, data_format_, 'W'); - OP_REQUIRES(context, FastBoundsCheck(input_cols_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input cols too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_cols_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input cols too large")); const int input_cols = static_cast(input_cols_raw); const int filter_cols = static_cast(filter.dim_size(1)); diff --git a/tensorflow/core/kernels/cross_op_gpu.cu.cc b/tensorflow/core/kernels/cross_op_gpu.cu.cc index 7ea0b3be0c..4a37f6cfbb 100644 --- a/tensorflow/core/kernels/cross_op_gpu.cu.cc +++ b/tensorflow/core/kernels/cross_op_gpu.cu.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/cross_op.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/cross_op.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/ctc_decoder_ops.cc b/tensorflow/core/kernels/ctc_decoder_ops.cc index 73ee310604..96bdb6a241 100644 --- a/tensorflow/core/kernels/ctc_decoder_ops.cc +++ b/tensorflow/core/kernels/ctc_decoder_ops.cc @@ -19,13 +19,13 @@ limitations under the License. #include -#include "tensorflow/core/util/ctc/ctc_beam_search.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/util/ctc/ctc_beam_search.h" #include "tensorflow/core/util/sparse/sparse_tensor.h" namespace tensorflow { @@ -80,16 +80,17 @@ class CTCDecodeHelper { if (!(batch_size == (*seq_len)->dim_size(0))) { return errors::FailedPrecondition( - "len(sequence_length) != batch_size. ", "len(sequence_length): ", - (*seq_len)->dim_size(0), " batch_size: ", batch_size); + "len(sequence_length) != batch_size. ", + "len(sequence_length): ", (*seq_len)->dim_size(0), + " batch_size: ", batch_size); } auto seq_len_t = (*seq_len)->vec(); for (int b = 0; b < batch_size; ++b) { if (!(seq_len_t(b) <= max_time)) { - return errors::FailedPrecondition("sequence_length(", b, ") <= ", - max_time); + return errors::FailedPrecondition("sequence_length(", b, + ") <= ", max_time); } } diff --git a/tensorflow/core/kernels/ctc_loss_op.cc b/tensorflow/core/kernels/ctc_loss_op.cc index fb03adb7a5..b38d838bf1 100644 --- a/tensorflow/core/kernels/ctc_loss_op.cc +++ b/tensorflow/core/kernels/ctc_loss_op.cc @@ -113,8 +113,8 @@ class CTCLossOp : public OpKernel { const int64 batch_indices = g.group()[0]; OP_REQUIRES(ctx, FastBoundsCheck(batch_indices, batch_size), errors::InvalidArgument("labels batch index must be between ", - 0, " and ", batch_size, " but saw: ", - batch_indices)); + 0, " and ", batch_size, + " but saw: ", batch_indices)); auto values = g.values(); std::vector* b_values = &labels_t[batch_indices]; diff --git a/tensorflow/core/kernels/cwise_op_abs.cc b/tensorflow/core/kernels/cwise_op_abs.cc index 5fd38d9dc2..1466f24202 100644 --- a/tensorflow/core/kernels/cwise_op_abs.cc +++ b/tensorflow/core/kernels/cwise_op_abs.cc @@ -45,5 +45,5 @@ REGISTER_KERNEL_BUILDER(Name("Abs") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_acos.cc b/tensorflow/core/kernels/cwise_op_acos.cc index 12cc6c8bdd..4919122607 100644 --- a/tensorflow/core/kernels/cwise_op_acos.cc +++ b/tensorflow/core/kernels/cwise_op_acos.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Acos", functor::acos, float, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Acos", functor::acos, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_acosh.cc b/tensorflow/core/kernels/cwise_op_acosh.cc index 39c8814073..c2b355ab7f 100644 --- a/tensorflow/core/kernels/cwise_op_acosh.cc +++ b/tensorflow/core/kernels/cwise_op_acosh.cc @@ -17,12 +17,12 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_gradients.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Acosh", functor::acosh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Acosh", functor::acosh, float, double, complex64, + complex128); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Acosh", functor::acosh, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA REGISTER2(UnaryOp, GPU, "Acosh", functor::acosh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_add_1.cc b/tensorflow/core/kernels/cwise_op_add_1.cc index 608a6dce3d..bf32c8a54b 100644 --- a/tensorflow/core/kernels/cwise_op_add_1.cc +++ b/tensorflow/core/kernels/cwise_op_add_1.cc @@ -44,7 +44,6 @@ REGISTER_KERNEL_BUILDER(Name("AddV2") BinaryOp>); #endif - #if TENSORFLOW_USE_SYCL #define REGISTER_KERNEL(type) \ REGISTER(BinaryOp, SYCL, "Add", functor::add, type); \ @@ -66,5 +65,5 @@ REGISTER_KERNEL_BUILDER(Name("AddV2") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_add_2.cc b/tensorflow/core/kernels/cwise_op_add_2.cc index ac21ca06c9..e8acbac285 100644 --- a/tensorflow/core/kernels/cwise_op_add_2.cc +++ b/tensorflow/core/kernels/cwise_op_add_2.cc @@ -22,8 +22,8 @@ namespace tensorflow { // sharded files, only make its register calls when not __ANDROID_TYPES_SLIM__. #if !defined(__ANDROID_TYPES_SLIM__) -REGISTER6(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64, - uint8, complex128, string); +REGISTER6(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64, uint8, + complex128, string); // Notice: String is excluded to allow marking AddV2 is_commutative and // is_aggregate. REGISTER5(BinaryOp, CPU, "AddV2", functor::add, int8, int16, complex64, uint8, diff --git a/tensorflow/core/kernels/cwise_op_asin.cc b/tensorflow/core/kernels/cwise_op_asin.cc index c28e27d95a..fe8dfea117 100644 --- a/tensorflow/core/kernels/cwise_op_asin.cc +++ b/tensorflow/core/kernels/cwise_op_asin.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Asin", functor::asin, float, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Asin", functor::asin, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_asinh.cc b/tensorflow/core/kernels/cwise_op_asinh.cc index 0aec6aac34..7cf0405f52 100644 --- a/tensorflow/core/kernels/cwise_op_asinh.cc +++ b/tensorflow/core/kernels/cwise_op_asinh.cc @@ -1,10 +1,10 @@ - /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. +/* 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 +http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, @@ -17,8 +17,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_gradients.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Asinh", functor::asinh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Asinh", functor::asinh, float, double, complex64, + complex128); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Asinh", functor::asinh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_atan.cc b/tensorflow/core/kernels/cwise_op_atan.cc index 7d73de4810..09f0448874 100644 --- a/tensorflow/core/kernels/cwise_op_atan.cc +++ b/tensorflow/core/kernels/cwise_op_atan.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Atan", functor::atan, float, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Atan", functor::atan, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_atanh.cc b/tensorflow/core/kernels/cwise_op_atanh.cc index 7b688db4c5..6170683fa6 100644 --- a/tensorflow/core/kernels/cwise_op_atanh.cc +++ b/tensorflow/core/kernels/cwise_op_atanh.cc @@ -17,8 +17,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_gradients.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Atanh", functor::atanh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Atanh", functor::atanh, float, double, complex64, + complex128); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Atanh", functor::atanh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_ceil.cc b/tensorflow/core/kernels/cwise_op_ceil.cc index 0111e9d5fd..816eadc80e 100644 --- a/tensorflow/core/kernels/cwise_op_ceil.cc +++ b/tensorflow/core/kernels/cwise_op_ceil.cc @@ -24,5 +24,5 @@ REGISTER3(UnaryOp, GPU, "Ceil", functor::ceil, float, Eigen::half, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Ceil", functor::ceil, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_cos.cc b/tensorflow/core/kernels/cwise_op_cos.cc index d4b3b0e393..71ad0ff0dc 100644 --- a/tensorflow/core/kernels/cwise_op_cos.cc +++ b/tensorflow/core/kernels/cwise_op_cos.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Cos", functor::cos, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Cos", functor::cos, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_cosh.cc b/tensorflow/core/kernels/cwise_op_cosh.cc index bca99a4f89..31b4bb3cad 100644 --- a/tensorflow/core/kernels/cwise_op_cosh.cc +++ b/tensorflow/core/kernels/cwise_op_cosh.cc @@ -16,20 +16,18 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Cosh", functor::cosh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Cosh", functor::cosh, float, double, complex64, + complex128); #if TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("Cosh") \ - .Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - UnaryOp>); +#define REGISTER_SYCL_KERNEL(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("Cosh").Device(DEVICE_SYCL).TypeConstraint("T"), \ + UnaryOp>); REGISTER_SYCL_KERNEL(float); REGISTER_SYCL_KERNEL(double); #undef REGISTER_SYCL_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA REGISTER2(UnaryOp, GPU, "Cosh", functor::cosh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_div.cc b/tensorflow/core/kernels/cwise_op_div.cc index d44c1bf473..c71c756e44 100644 --- a/tensorflow/core/kernels/cwise_op_div.cc +++ b/tensorflow/core/kernels/cwise_op_div.cc @@ -54,5 +54,5 @@ REGISTER_KERNEL_BUILDER(Name("Div") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_exp.cc b/tensorflow/core/kernels/cwise_op_exp.cc index 66d7b7d22e..8f4ac98016 100644 --- a/tensorflow/core/kernels/cwise_op_exp.cc +++ b/tensorflow/core/kernels/cwise_op_exp.cc @@ -26,5 +26,5 @@ REGISTER5(UnaryOp, GPU, "Exp", functor::exp, float, Eigen::half, double, #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Exp", functor::exp, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_expm1.cc b/tensorflow/core/kernels/cwise_op_expm1.cc index 4f72308006..ce03ad5de6 100644 --- a/tensorflow/core/kernels/cwise_op_expm1.cc +++ b/tensorflow/core/kernels/cwise_op_expm1.cc @@ -23,5 +23,5 @@ REGISTER3(UnaryOp, GPU, "Expm1", functor::expm1, float, Eigen::half, double); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Expm1", functor::expm1, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_floor.cc b/tensorflow/core/kernels/cwise_op_floor.cc index 5a142b9ce9..d554d41c41 100644 --- a/tensorflow/core/kernels/cwise_op_floor.cc +++ b/tensorflow/core/kernels/cwise_op_floor.cc @@ -23,5 +23,5 @@ REGISTER3(UnaryOp, GPU, "Floor", functor::floor, float, Eigen::half, double); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Floor", functor::floor, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_floor_div.cc b/tensorflow/core/kernels/cwise_op_floor_div.cc index fa81ef0872..fecbf85989 100644 --- a/tensorflow/core/kernels/cwise_op_floor_div.cc +++ b/tensorflow/core/kernels/cwise_op_floor_div.cc @@ -49,5 +49,5 @@ REGISTER_KERNEL_BUILDER(Name("FloorDiv") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_floor_mod.cc b/tensorflow/core/kernels/cwise_op_floor_mod.cc index 55f8a30461..29340b8850 100644 --- a/tensorflow/core/kernels/cwise_op_floor_mod.cc +++ b/tensorflow/core/kernels/cwise_op_floor_mod.cc @@ -40,5 +40,5 @@ REGISTER_KERNEL_BUILDER(Name("FloorMod") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc index e7dff5d0ac..77723b3169 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc @@ -19,8 +19,8 @@ limitations under the License. namespace tensorflow { namespace functor { - DEFINE_UNARY1(conj, complex64); - DEFINE_UNARY1(conj, complex128); +DEFINE_UNARY1(conj, complex64); +DEFINE_UNARY1(conj, complex128); } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc index 3675398126..26748ef0e7 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc @@ -20,7 +20,7 @@ limitations under the License. namespace tensorflow { namespace functor { DEFINE_BINARY10(equal_to, float, Eigen::half, double, uint8, int8, int16, int64, - complex64, complex128, bool); + complex64, complex128, bool); DEFINE_APPROXIMATE_EQUAL2(float, double); } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc index a54dbdfc24..627ecc8c80 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc @@ -15,8 +15,10 @@ limitations under the License. #if GOOGLE_CUDA -#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" +#define EIGEN_USE_GPU + #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" namespace tensorflow { namespace functor { @@ -38,19 +40,17 @@ struct SelectScalarFunctor { typename TTypes::ConstScalar cond, typename TTypes::ConstFlat then_flat, typename TTypes::ConstFlat else_flat) { - #if !defined(EIGEN_HAS_INDEX_LIST) - Eigen::array rank1{1}; + Eigen::array rank1{1}; #else - Eigen::IndexList> rank1; + Eigen::IndexList > rank1; #endif - const int size = then_flat.dimension(0); - Eigen::array broadcast_dims{size}; - - To32Bit(out).device(d) = cond.reshape(rank1) - .broadcast(broadcast_dims) - .select(then_flat, else_flat); + const int size = then_flat.dimension(0); + Eigen::array broadcast_dims{size}; + To32Bit(out).device(d) = cond.reshape(rank1) + .broadcast(broadcast_dims) + .select(then_flat, else_flat); } }; @@ -89,8 +89,8 @@ struct BatchSelectFunctor { } }; -#define SELECT_FUNCTOR(T) \ - template struct SelectFunctor; \ +#define SELECT_FUNCTOR(T) \ + template struct SelectFunctor; \ template struct SelectScalarFunctor; \ template struct BatchSelectFunctor; diff --git a/tensorflow/core/kernels/cwise_op_greater.cc b/tensorflow/core/kernels/cwise_op_greater.cc index ba89899fb3..a4ea408836 100644 --- a/tensorflow/core/kernels/cwise_op_greater.cc +++ b/tensorflow/core/kernels/cwise_op_greater.cc @@ -43,5 +43,5 @@ REGISTER_KERNEL_BUILDER(Name("Greater") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_greater_equal.cc b/tensorflow/core/kernels/cwise_op_greater_equal.cc index 8f0c483aec..3f34d6269e 100644 --- a/tensorflow/core/kernels/cwise_op_greater_equal.cc +++ b/tensorflow/core/kernels/cwise_op_greater_equal.cc @@ -35,7 +35,8 @@ REGISTER_KERNEL_BUILDER(Name("GreaterEqual") #endif #ifdef TENSORFLOW_USE_SYCL -REGISTER2(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float, double); +REGISTER2(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float, + double); REGISTER_KERNEL_BUILDER(Name("GreaterEqual") .Device(DEVICE_SYCL) @@ -44,5 +45,5 @@ REGISTER_KERNEL_BUILDER(Name("GreaterEqual") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc index df2c02e42e..f5cafcc780 100644 --- a/tensorflow/core/kernels/cwise_op_invert.cc +++ b/tensorflow/core/kernels/cwise_op_invert.cc @@ -21,7 +21,7 @@ REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64, #ifdef TENSORFLOW_USE_SYCL REGISTER6(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64, - uint8, uint16); + uint8, uint16); #endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA diff --git a/tensorflow/core/kernels/cwise_op_isfinite.cc b/tensorflow/core/kernels/cwise_op_isfinite.cc index 53ec1c1c63..ae1e590d24 100644 --- a/tensorflow/core/kernels/cwise_op_isfinite.cc +++ b/tensorflow/core/kernels/cwise_op_isfinite.cc @@ -26,5 +26,5 @@ REGISTER3(UnaryOp, GPU, "IsFinite", functor::isfinite, float, Eigen::half, #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "IsFinite", functor::isfinite, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_isinf.cc b/tensorflow/core/kernels/cwise_op_isinf.cc index 4b34744304..f22ca21e1c 100644 --- a/tensorflow/core/kernels/cwise_op_isinf.cc +++ b/tensorflow/core/kernels/cwise_op_isinf.cc @@ -24,5 +24,5 @@ REGISTER3(UnaryOp, GPU, "IsInf", functor::isinf, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "IsInf", functor::isinf, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_isnan.cc b/tensorflow/core/kernels/cwise_op_isnan.cc index ad2dd3f722..aa180c247e 100644 --- a/tensorflow/core/kernels/cwise_op_isnan.cc +++ b/tensorflow/core/kernels/cwise_op_isnan.cc @@ -24,5 +24,5 @@ REGISTER3(UnaryOp, GPU, "IsNan", functor::isnan, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "IsNan", functor::isnan, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_less.cc b/tensorflow/core/kernels/cwise_op_less.cc index 136c3666df..00cdecdbd1 100644 --- a/tensorflow/core/kernels/cwise_op_less.cc +++ b/tensorflow/core/kernels/cwise_op_less.cc @@ -42,5 +42,5 @@ REGISTER_KERNEL_BUILDER(Name("Less") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_less_equal.cc b/tensorflow/core/kernels/cwise_op_less_equal.cc index 97a2508d12..11806c5fc7 100644 --- a/tensorflow/core/kernels/cwise_op_less_equal.cc +++ b/tensorflow/core/kernels/cwise_op_less_equal.cc @@ -44,5 +44,5 @@ REGISTER_KERNEL_BUILDER(Name("LessEqual") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_log.cc b/tensorflow/core/kernels/cwise_op_log.cc index 7fdfdff0e3..98936e0f96 100644 --- a/tensorflow/core/kernels/cwise_op_log.cc +++ b/tensorflow/core/kernels/cwise_op_log.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Log", functor::log, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Log", functor::log, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_log1p.cc b/tensorflow/core/kernels/cwise_op_log1p.cc index 25ad7b24bb..162ca9e07c 100644 --- a/tensorflow/core/kernels/cwise_op_log1p.cc +++ b/tensorflow/core/kernels/cwise_op_log1p.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Log1p", functor::log1p, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Log1p", functor::log1p, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_maximum.cc b/tensorflow/core/kernels/cwise_op_maximum.cc index 87d54e380b..8c54f22f10 100644 --- a/tensorflow/core/kernels/cwise_op_maximum.cc +++ b/tensorflow/core/kernels/cwise_op_maximum.cc @@ -43,5 +43,5 @@ REGISTER_KERNEL_BUILDER(Name("Maximum") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_minimum.cc b/tensorflow/core/kernels/cwise_op_minimum.cc index 442171193b..dff83df828 100644 --- a/tensorflow/core/kernels/cwise_op_minimum.cc +++ b/tensorflow/core/kernels/cwise_op_minimum.cc @@ -43,6 +43,6 @@ REGISTER_KERNEL_BUILDER(Name("Minimum") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_mul_1.cc b/tensorflow/core/kernels/cwise_op_mul_1.cc index 023eb07ca3..0e8d2e3735 100644 --- a/tensorflow/core/kernels/cwise_op_mul_1.cc +++ b/tensorflow/core/kernels/cwise_op_mul_1.cc @@ -17,8 +17,8 @@ limitations under the License. namespace tensorflow { -REGISTER5(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double, - uint8, int32); +REGISTER5(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double, uint8, + int32); #if defined(__ANDROID_TYPES_SLIM__) // We only register the first type when we have multi-argument calls in the // case where we're trying to reduce executable size, but it turns out that the @@ -28,7 +28,7 @@ REGISTER(BinaryOp, CPU, "Mul", functor::mul, int32); #if GOOGLE_CUDA REGISTER4(BinaryOp, GPU, "Mul", functor::mul, float, Eigen::half, double, - uint8); + uint8); // A special GPU kernel for int32. // TODO(b/25387198): Also enable int32 in device memory. This kernel // registration requires all int32 inputs and outputs to be in host memory. @@ -50,5 +50,5 @@ REGISTER_KERNEL_BUILDER(Name("Mul") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_mul_2.cc b/tensorflow/core/kernels/cwise_op_mul_2.cc index 7be5857cc0..6aa8f88364 100644 --- a/tensorflow/core/kernels/cwise_op_mul_2.cc +++ b/tensorflow/core/kernels/cwise_op_mul_2.cc @@ -22,11 +22,11 @@ namespace tensorflow { // sharded files, only make its register calls when not __ANDROID_TYPES_SLIM__. #if !defined(__ANDROID_TYPES_SLIM__) -REGISTER6(BinaryOp, CPU, "Mul", functor::mul, - int8, uint16, int16, int64, complex64, complex128); +REGISTER6(BinaryOp, CPU, "Mul", functor::mul, int8, uint16, int16, int64, + complex64, complex128); #if GOOGLE_CUDA REGISTER6(BinaryOp, GPU, "Mul", functor::mul, int8, uint16, int16, int64, - complex64, complex128); + complex64, complex128); #endif // GOOGLE_CUDA diff --git a/tensorflow/core/kernels/cwise_op_neg.cc b/tensorflow/core/kernels/cwise_op_neg.cc index 536891b548..a136769b91 100644 --- a/tensorflow/core/kernels/cwise_op_neg.cc +++ b/tensorflow/core/kernels/cwise_op_neg.cc @@ -27,7 +27,7 @@ REGISTER_KERNEL_BUILDER(Name("Neg") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA REGISTER6(UnaryOp, GPU, "Neg", functor::neg, float, Eigen::half, double, int64, diff --git a/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc b/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc index 7bd81ee127..02cd298745 100644 --- a/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc +++ b/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc @@ -17,7 +17,7 @@ limitations under the License. namespace tensorflow { REGISTER6(BinaryOp, CPU, "NotEqual", functor::not_equal_to, float, Eigen::half, - double, uint8, int8, int16); + double, uint8, int8, int16); #if GOOGLE_CUDA REGISTER4(BinaryOp, GPU, "NotEqual", functor::not_equal_to, float, Eigen::half, double, uint8); diff --git a/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc b/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc index 7d4ecec59f..05bdea6636 100644 --- a/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc +++ b/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc @@ -30,5 +30,5 @@ REGISTER6(BinaryOp, GPU, "NotEqual", functor::not_equal_to, int8, int16, int64, #endif // GOOGLE_CUDA -#endif // !defined(__ANDROID_TYPES_SLIM__) +#endif // !defined(__ANDROID_TYPES_SLIM__) } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_reciprocal.cc b/tensorflow/core/kernels/cwise_op_reciprocal.cc index 8c0e21f9cf..aee25747b8 100644 --- a/tensorflow/core/kernels/cwise_op_reciprocal.cc +++ b/tensorflow/core/kernels/cwise_op_reciprocal.cc @@ -38,7 +38,7 @@ REGISTER4(UnaryOp, GPU, "Reciprocal", functor::inverse, float, Eigen::half, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(UnaryOp, SYCL, "Reciprocal", functor::inverse, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "ReciprocalGrad", functor::inverse_grad, float, Eigen::half, double, complex64, complex128); @@ -48,5 +48,5 @@ REGISTER3(SimpleBinaryOp, GPU, "ReciprocalGrad", functor::inverse_grad, float, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(SimpleBinaryOp, SYCL, "ReciprocalGrad", functor::inverse_grad, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_select.cc b/tensorflow/core/kernels/cwise_op_select.cc index 3dd9de8d89..e259daaba4 100644 --- a/tensorflow/core/kernels/cwise_op_select.cc +++ b/tensorflow/core/kernels/cwise_op_select.cc @@ -30,7 +30,7 @@ typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class SelectOp : public OpKernel { @@ -185,7 +185,7 @@ REGISTER_SELECT_SYCL(double); REGISTER_SELECT_SYCL(int32); REGISTER_SELECT_SYCL(int64); #undef REGISTER_SELECT_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace functor { @@ -201,13 +201,11 @@ struct SelectFunctorBase { }; template -struct SelectFunctor - : SelectFunctorBase {}; +struct SelectFunctor : SelectFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template -struct SelectFunctor - : SelectFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL +struct SelectFunctor : SelectFunctorBase {}; +#endif // TENSORFLOW_USE_SYCL template struct SelectScalarFunctorBase { @@ -222,12 +220,12 @@ struct SelectScalarFunctorBase { // CPU Specializations of Select functors with scalar template struct SelectScalarFunctor - : SelectScalarFunctorBase {}; + : SelectScalarFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template struct SelectScalarFunctor - : SelectScalarFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL + : SelectScalarFunctorBase {}; +#endif // TENSORFLOW_USE_SYCL template struct BatchSelectFunctorBase { @@ -240,8 +238,8 @@ struct BatchSelectFunctorBase { const Eigen::DenseIndex all_but_batch = then_flat_outer_dims.dimension(1); #if !defined(EIGEN_HAS_INDEX_LIST) - Eigen::array broadcast_dims{{ 1, all_but_batch }}; - Eigen::Tensor::Dimensions reshape_dims{{ batch, 1 }}; + Eigen::array broadcast_dims{{1, all_but_batch}}; + Eigen::Tensor::Dimensions reshape_dims{{batch, 1}}; #else Eigen::IndexList, Eigen::DenseIndex> broadcast_dims; broadcast_dims.set(1, all_but_batch); @@ -257,13 +255,13 @@ struct BatchSelectFunctorBase { }; template -struct BatchSelectFunctor - : BatchSelectFunctorBase {}; +struct BatchSelectFunctor : BatchSelectFunctorBase { +}; #ifdef TENSORFLOW_USE_SYCL template struct BatchSelectFunctor - : BatchSelectFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL + : BatchSelectFunctorBase {}; +#endif // TENSORFLOW_USE_SYCL } // namespace functor diff --git a/tensorflow/core/kernels/cwise_op_sigmoid.cc b/tensorflow/core/kernels/cwise_op_sigmoid.cc index a76a088ac8..c132fdb63f 100644 --- a/tensorflow/core/kernels/cwise_op_sigmoid.cc +++ b/tensorflow/core/kernels/cwise_op_sigmoid.cc @@ -25,7 +25,7 @@ REGISTER3(UnaryOp, GPU, "Sigmoid", functor::sigmoid, float, Eigen::half, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(UnaryOp, SYCL, "Sigmoid", functor::sigmoid, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "SigmoidGrad", functor::sigmoid_grad, float, Eigen::half, double, complex64, complex128); @@ -35,6 +35,6 @@ REGISTER3(SimpleBinaryOp, GPU, "SigmoidGrad", functor::sigmoid_grad, float, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(SimpleBinaryOp, SYCL, "SigmoidGrad", functor::sigmoid_grad, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sign.cc b/tensorflow/core/kernels/cwise_op_sign.cc index a4084d5ad1..02915ff4ce 100644 --- a/tensorflow/core/kernels/cwise_op_sign.cc +++ b/tensorflow/core/kernels/cwise_op_sign.cc @@ -41,6 +41,6 @@ REGISTER_KERNEL_BUILDER(Name("Sign") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sin.cc b/tensorflow/core/kernels/cwise_op_sin.cc index b91ff1ac30..16c6057864 100644 --- a/tensorflow/core/kernels/cwise_op_sin.cc +++ b/tensorflow/core/kernels/cwise_op_sin.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Sin", functor::sin, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Sin", functor::sin, float, double); -#endif // TENSORFLOW_USE_SYC +#endif // TENSORFLOW_USE_SYC } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sinh.cc b/tensorflow/core/kernels/cwise_op_sinh.cc index 055f0b12e1..26b7a940aa 100644 --- a/tensorflow/core/kernels/cwise_op_sinh.cc +++ b/tensorflow/core/kernels/cwise_op_sinh.cc @@ -16,20 +16,18 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Sinh", functor::sinh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Sinh", functor::sinh, float, double, complex64, + complex128); #if TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("Sinh") \ - .Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - UnaryOp>); +#define REGISTER_SYCL_KERNEL(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("Sinh").Device(DEVICE_SYCL).TypeConstraint("T"), \ + UnaryOp>); REGISTER_SYCL_KERNEL(float); REGISTER_SYCL_KERNEL(double); #undef REGISTER_SYCL_KERNEL -#endif // TENSORFLOW_USE_SYC +#endif // TENSORFLOW_USE_SYC #if GOOGLE_CUDA REGISTER2(UnaryOp, GPU, "Sinh", functor::sinh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_sqrt.cc b/tensorflow/core/kernels/cwise_op_sqrt.cc index 00efbb00f1..497756133d 100644 --- a/tensorflow/core/kernels/cwise_op_sqrt.cc +++ b/tensorflow/core/kernels/cwise_op_sqrt.cc @@ -25,7 +25,7 @@ REGISTER3(UnaryOp, GPU, "Sqrt", functor::sqrt, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Sqrt", functor::sqrt, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "SqrtGrad", functor::sqrt_grad, float, Eigen::half, double, complex64, complex128); @@ -36,5 +36,5 @@ REGISTER3(SimpleBinaryOp, GPU, "SqrtGrad", functor::sqrt_grad, float, #ifdef TENSORFLOW_USE_SYCL REGISTER2(SimpleBinaryOp, SYCL, "SqrtGrad", functor::sqrt_grad, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_square.cc b/tensorflow/core/kernels/cwise_op_square.cc index 07a4b0b084..7fc2f6bf08 100644 --- a/tensorflow/core/kernels/cwise_op_square.cc +++ b/tensorflow/core/kernels/cwise_op_square.cc @@ -42,5 +42,5 @@ REGISTER_KERNEL_BUILDER(Name("Square") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYC +#endif // TENSORFLOW_USE_SYC } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sub.cc b/tensorflow/core/kernels/cwise_op_sub.cc index 6adaecba04..025041946a 100644 --- a/tensorflow/core/kernels/cwise_op_sub.cc +++ b/tensorflow/core/kernels/cwise_op_sub.cc @@ -53,5 +53,5 @@ REGISTER_KERNEL_BUILDER(Name("Sub") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_tan.cc b/tensorflow/core/kernels/cwise_op_tan.cc index 7891b1183d..c1a25767d3 100644 --- a/tensorflow/core/kernels/cwise_op_tan.cc +++ b/tensorflow/core/kernels/cwise_op_tan.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Tan", functor::tan, float, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Tan", functor::tan, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_tanh.cc b/tensorflow/core/kernels/cwise_op_tanh.cc index 8b3900892c..c5005f5ea8 100644 --- a/tensorflow/core/kernels/cwise_op_tanh.cc +++ b/tensorflow/core/kernels/cwise_op_tanh.cc @@ -26,7 +26,7 @@ REGISTER3(UnaryOp, GPU, "Tanh", functor::tanh, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Tanh", functor::tanh, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "TanhGrad", functor::tanh_grad, float, Eigen::half, double, complex64, complex128); diff --git a/tensorflow/core/kernels/cwise_ops_common.cc b/tensorflow/core/kernels/cwise_ops_common.cc index e561e59cf5..980edffceb 100644 --- a/tensorflow/core/kernels/cwise_ops_common.cc +++ b/tensorflow/core/kernels/cwise_ops_common.cc @@ -57,9 +57,9 @@ BinaryOpShared::BinaryOpState::BinaryOpState(OpKernelContext* ctx) in1(ctx->input(1)), bcast(BCast::FromShape(in0.shape()), BCast::FromShape(in1.shape())) { if (!bcast.IsValid()) { - ctx->SetStatus(errors::InvalidArgument("Incompatible shapes: ", - in0.shape().DebugString(), " vs. ", - in1.shape().DebugString())); + ctx->SetStatus(errors::InvalidArgument( + "Incompatible shapes: ", in0.shape().DebugString(), " vs. ", + in1.shape().DebugString())); return; } const TensorShape output_shape = BCast::ToShape(bcast.output_shape()); diff --git a/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h b/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h index 4394770708..e81b840a50 100644 --- a/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h +++ b/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h @@ -50,16 +50,16 @@ struct SimpleBinaryFunctor { // Macros to explicitly instantiate kernels on GPU for multiple types // (T0, T1, etc.) for SimpleBiaryFunctor (e.g., functor::tanh_grad). -#define DEFINE_SIMPLE_BINARY1(F, T) \ +#define DEFINE_SIMPLE_BINARY1(F, T) \ template struct SimpleBinaryFunctor > -#define DEFINE_SIMPLE_BINARY2(F, T0, T1) \ - DEFINE_SIMPLE_BINARY1(F, T0); \ +#define DEFINE_SIMPLE_BINARY2(F, T0, T1) \ + DEFINE_SIMPLE_BINARY1(F, T0); \ DEFINE_SIMPLE_BINARY1(F, T1) -#define DEFINE_SIMPLE_BINARY3(F, T0, T1, T2) \ - DEFINE_SIMPLE_BINARY2(F, T0, T1); \ +#define DEFINE_SIMPLE_BINARY3(F, T0, T1, T2) \ + DEFINE_SIMPLE_BINARY2(F, T0, T1); \ DEFINE_SIMPLE_BINARY1(F, T2) -#define DEFINE_SIMPLE_BINARY4(F, T0, T1, T2, T3) \ - DEFINE_SIMPLE_BINARY2(F, T0, T1); \ +#define DEFINE_SIMPLE_BINARY4(F, T0, T1, T2, T3) \ + DEFINE_SIMPLE_BINARY2(F, T0, T1); \ DEFINE_SIMPLE_BINARY2(F, T2, T3) #define DEFINE_SIMPLE_BINARY5(F, T0, T1, T2, T3, T4) \ DEFINE_SIMPLE_BINARY2(F, T0, T1); \ diff --git a/tensorflow/core/kernels/cwise_ops_gradients.h b/tensorflow/core/kernels/cwise_ops_gradients.h index 77b330f589..82cdae9a34 100644 --- a/tensorflow/core/kernels/cwise_ops_gradients.h +++ b/tensorflow/core/kernels/cwise_ops_gradients.h @@ -171,7 +171,6 @@ struct SimpleBinaryFunctor { } }; - #ifdef TENSORFLOW_USE_SYCL // Partial specialization of BinaryFunctor for SYCL devices typedef Eigen::SyclDevice SYCLDevice; @@ -184,7 +183,7 @@ struct SimpleBinaryFunctor { } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template struct tanh_grad : base> {}; diff --git a/tensorflow/core/kernels/cwise_ops_sycl_common.h b/tensorflow/core/kernels/cwise_ops_sycl_common.h index 3f6ff7303d..3e107cee04 100644 --- a/tensorflow/core/kernels/cwise_ops_sycl_common.h +++ b/tensorflow/core/kernels/cwise_ops_sycl_common.h @@ -51,7 +51,8 @@ struct BinaryFunctor { void operator()(const SYCLDevice& d, typename Functor::tout_type out, typename Functor::tin_type in0, typename Functor::tin_type in1, bool* error) { - To32Bit(out).device(d) = To32Bit(in0).binaryExpr(To32Bit(in1), typename Functor::func()); + To32Bit(out).device(d) = + To32Bit(in0).binaryExpr(To32Bit(in1), typename Functor::func()); } void Left(const SYCLDevice& d, typename Functor::tout_type out, @@ -61,7 +62,9 @@ struct BinaryFunctor { constexpr int NumDims = Functor::tin_type::NumDimensions; static_assert(NumDims == 1, "Unexpected size"); Eigen::Sizes<1> scalar_dim; - out.device(d) = scalar.reshape(scalar_dim).broadcast(in.dimensions()).binaryExpr(in, Binary()); + out.device(d) = scalar.reshape(scalar_dim) + .broadcast(in.dimensions()) + .binaryExpr(in, Binary()); } void Right(const SYCLDevice& d, typename Functor::tout_type out, @@ -71,7 +74,8 @@ struct BinaryFunctor { constexpr int NumDims = Functor::tin_type::NumDimensions; static_assert(NumDims == 1, "Unexpected size"); Eigen::Sizes<1> scalar_dim; - out.device(d) = in.binaryExpr(scalar.reshape(scalar_dim).broadcast(in.dimensions()), Binary()); + out.device(d) = in.binaryExpr( + scalar.reshape(scalar_dim).broadcast(in.dimensions()), Binary()); } void BCast(const SYCLDevice& d, diff --git a/tensorflow/core/kernels/cwise_ops_test.cc b/tensorflow/core/kernels/cwise_ops_test.cc index bca0f1004d..39f497e716 100644 --- a/tensorflow/core/kernels/cwise_ops_test.cc +++ b/tensorflow/core/kernels/cwise_ops_test.cc @@ -54,36 +54,36 @@ int ColsFromArg(int arg) { return (arg % kRows); } BM_UNARY(cpu, Floor, float, DT_FLOAT); #if GOOGLE_CUDA BM_UNARY(gpu, Floor, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_UNARY(sycl, Floor, float, DT_FLOAT); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL BM_UNARY(cpu, Floor, double, DT_DOUBLE); #if GOOGLE_CUDA BM_UNARY(gpu, Floor, double, DT_DOUBLE); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_UNARY(sycl, Floor, double, DT_DOUBLE); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL BM_UNARY(cpu, Conj, std::complex, DT_COMPLEX64); #if GOOGLE_CUDA BM_UNARY(gpu, Conj, std::complex, DT_COMPLEX64); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_UNARY(cpu, Conj, std::complex, DT_COMPLEX128); #if GOOGLE_CUDA BM_UNARY(gpu, Conj, std::complex, DT_COMPLEX128); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_UNARY(cpu, Rint, double, DT_DOUBLE); #if GOOGLE_CUDA BM_UNARY(gpu, Rint, double, DT_DOUBLE); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_UNARY(cpu, Rint, float, DT_FLOAT); #if GOOGLE_CUDA BM_UNARY(gpu, Rint, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA // data func scalar. Graph* BinaryScalar(int num, const string& func) { @@ -113,18 +113,18 @@ Graph* BinaryScalar(int num, const string& func) { BM_BINARY_SCALAR(cpu, Less); #if GOOGLE_CUDA BM_BINARY_SCALAR(gpu, Less); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BINARY_SCALAR(sycl, Less); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL BM_BINARY_SCALAR(cpu, Add); #if GOOGLE_CUDA BM_BINARY_SCALAR(gpu, Add); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BINARY_SCALAR(sycl, Add); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef BM_BINARY_SCALAR template @@ -163,11 +163,11 @@ using Eigen::half; BM_BIAS_ADD_ALL(cpu, float, DT_FLOAT); #if GOOGLE_CUDA BM_BIAS_ADD_ALL(gpu, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_BIAS_ADD_ALL(cpu, half, DT_HALF); #if GOOGLE_CUDA BM_BIAS_ADD_ALL(gpu, half, DT_HALF); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #undef BM_BIAS_ADD_ALL #undef BM_BIAS_ADD @@ -217,15 +217,15 @@ using Eigen::half; #if GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(gpu, NCHW, float, DT_FLOAT); BM_BIAS_ADD_GRAD_ALL(gpu, NCHW, half, DT_HALF); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(cpu, NHWC, float, DT_FLOAT); #if GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(gpu, NHWC, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(cpu, NHWC, half, DT_HALF); #if GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(gpu, NHWC, half, DT_HALF); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #undef BM_BIAS_ADD_GRAD_ALL #undef BM_BIAS_ADD_GRAD @@ -265,10 +265,10 @@ Graph* BcastAdd(int rows, int cols, int dim) { BM_BCAST_ADD_ROW_ALL(cpu); #if GOOGLE_CUDA BM_BCAST_ADD_ROW_ALL(gpu); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BCAST_ADD_ROW_ALL(sycl); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef BM_BCAST_ADD_ROW_ALL #undef BM_BCAST_ADD_ROW @@ -291,10 +291,10 @@ BM_BCAST_ADD_ROW_ALL(sycl); BM_BCAST_ADD_COL_ALL(cpu); #if GOOGLE_CUDA BM_BCAST_ADD_COL_ALL(gpu); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BCAST_ADD_COL_ALL(sycl); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef BM_BCAST_ADD_COL_ALL #undef BM_BCAST_ADD_COL diff --git a/tensorflow/core/kernels/debug_ops.cc b/tensorflow/core/kernels/debug_ops.cc index 965a60c7e0..1b94ea0544 100644 --- a/tensorflow/core/kernels/debug_ops.cc +++ b/tensorflow/core/kernels/debug_ops.cc @@ -46,7 +46,7 @@ REGISTER_KERNEL_BUILDER(Name("CopyHost") .HostMemory("input") .HostMemory("output"), CopyOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register debug identity (non-ref and ref) ops. REGISTER_KERNEL_BUILDER(Name("DebugIdentity").Device(DEVICE_CPU), @@ -66,7 +66,7 @@ REGISTER_KERNEL_BUILDER(Name("DebugIdentity") .HostMemory("input") .HostMemory("output"), DebugIdentityOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register debug NaN-counter (non-ref and ref) ops. #define REGISTER_DEBUG_NAN_COUNT(type) \ @@ -98,7 +98,7 @@ REGISTER_GPU_DEBUG_NAN_COUNT(double); DebugNanCountOp); REGISTER_GPU_DEBUG_NAN_COUNT(float); REGISTER_GPU_DEBUG_NAN_COUNT(double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register debug numeric summary ops. #define REGISTER_DEBUG_NUMERIC_SUMMARY_COUNT(type) \ diff --git a/tensorflow/core/kernels/debug_ops.h b/tensorflow/core/kernels/debug_ops.h index 381add3fb3..53a23b1306 100644 --- a/tensorflow/core/kernels/debug_ops.h +++ b/tensorflow/core/kernels/debug_ops.h @@ -21,7 +21,7 @@ limitations under the License. #endif #ifdef TENSORFLOW_USE_SYCL #include "tensorflow/core/common_runtime/sycl/sycl_util.h" -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #include "tensorflow/core/debug/debug_io_utils.h" #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/framework/op_kernel.h" @@ -91,7 +91,7 @@ class CopyOp : public OpKernel { Device* device = static_cast(context->device()); // Determine if the input tensor is not on CPU (e.g., on GPU). const bool off_host_input = device->device_type() == DEVICE_SYCL && - !context->input_alloc_attr(0).on_host(); + !context->input_alloc_attr(0).on_host(); if (off_host_input) { SYCLmemcpy(context->eigen_sycl_device(), src_tensor, copied_tensor); diff --git a/tensorflow/core/kernels/decode_csv_op.cc b/tensorflow/core/kernels/decode_csv_op.cc index c4555db453..0c42f63252 100644 --- a/tensorflow/core/kernels/decode_csv_op.cc +++ b/tensorflow/core/kernels/decode_csv_op.cc @@ -91,9 +91,9 @@ class DecodeCSVOp : public OpKernel { } else { int32 value; OP_REQUIRES(ctx, strings::safe_strto32(fields[f], &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid int32: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid int32: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -111,9 +111,9 @@ class DecodeCSVOp : public OpKernel { } else { int64 value; OP_REQUIRES(ctx, strings::safe_strto64(fields[f], &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid int64: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid int64: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -130,9 +130,9 @@ class DecodeCSVOp : public OpKernel { } else { float value; OP_REQUIRES(ctx, strings::safe_strtof(fields[f].c_str(), &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid float: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid float: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -150,9 +150,9 @@ class DecodeCSVOp : public OpKernel { } else { double value; OP_REQUIRES(ctx, strings::safe_strtod(fields[f].c_str(), &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid double: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid double: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -208,9 +208,10 @@ class DecodeCSVOp : public OpKernel { if (!quoted) { while (static_cast(current_idx) < input.size() && input[current_idx] != delim_) { - OP_REQUIRES(ctx, (!use_quote_delim_ || input[current_idx] != '"') && - input[current_idx] != '\n' && - input[current_idx] != '\r', + OP_REQUIRES(ctx, + (!use_quote_delim_ || input[current_idx] != '"') && + input[current_idx] != '\n' && + input[current_idx] != '\r', errors::InvalidArgument( "Unquoted fields cannot have quotes/CRLFs inside")); field += input[current_idx]; @@ -238,10 +239,11 @@ class DecodeCSVOp : public OpKernel { } OP_REQUIRES( - ctx, (static_cast(current_idx) < input.size() && - input[current_idx] == '"' && - (static_cast(current_idx) == input.size() - 1 || - input[current_idx + 1] == delim_)), + ctx, + (static_cast(current_idx) < input.size() && + input[current_idx] == '"' && + (static_cast(current_idx) == input.size() - 1 || + input[current_idx + 1] == delim_)), errors::InvalidArgument("Quoted field has to end with quote " "followed by delim or end")); diff --git a/tensorflow/core/kernels/decode_image_op.cc b/tensorflow/core/kernels/decode_image_op.cc index 44dcbf834c..912d04c153 100644 --- a/tensorflow/core/kernels/decode_image_op.cc +++ b/tensorflow/core/kernels/decode_image_op.cc @@ -87,10 +87,11 @@ class DecodeImageOp : public OpKernel { channels_ = 3; } else { OP_REQUIRES_OK(context, context->GetAttr("channels", &channels_)); - OP_REQUIRES(context, channels_ == 0 || channels_ == 1 || channels_ == 3 || - channels_ == 4, - errors::InvalidArgument( - "channels must be 0, 1, 3, or 4, got ", channels_)); + OP_REQUIRES( + context, + channels_ == 0 || channels_ == 1 || channels_ == 3 || channels_ == 4, + errors::InvalidArgument("channels must be 0, 1, 3, or 4, got ", + channels_)); } flags_.components = channels_; @@ -114,8 +115,9 @@ class DecodeImageOp : public OpKernel { if (format_ == kJpgFormat) { OP_REQUIRES_OK(context, context->GetAttr("ratio", &flags_.ratio)); - OP_REQUIRES(context, flags_.ratio == 1 || flags_.ratio == 2 || - flags_.ratio == 4 || flags_.ratio == 8, + OP_REQUIRES(context, + flags_.ratio == 1 || flags_.ratio == 2 || flags_.ratio == 4 || + flags_.ratio == 8, errors::InvalidArgument("ratio must be 1, 2, 4, or 8, got ", flags_.ratio)); OP_REQUIRES_OK(context, context->GetAttr("fancy_upscaling", @@ -130,8 +132,9 @@ class DecodeImageOp : public OpKernel { string dct_method; OP_REQUIRES_OK(context, context->GetAttr("dct_method", &dct_method)); OP_REQUIRES( - context, (dct_method.empty() || dct_method == "INTEGER_FAST" || - dct_method == "INTEGER_ACCURATE"), + context, + (dct_method.empty() || dct_method == "INTEGER_FAST" || + dct_method == "INTEGER_ACCURATE"), errors::InvalidArgument("dct_method must be one of " "{'', 'INTEGER_FAST', 'INTEGER_ACCURATE'}")); if (dct_method == "INTEGER_FAST") { @@ -157,9 +160,9 @@ class DecodeImageOp : public OpKernel { errors::InvalidArgument("Expected image (JPEG, PNG, or GIF), got ", FileFormatString(magic, input))); OP_REQUIRES(context, input.size() <= std::numeric_limits::max(), - errors::InvalidArgument(FileFormatString(magic, input), - " contents are too large for int: ", - input.size())); + errors::InvalidArgument( + FileFormatString(magic, input), + " contents are too large for int: ", input.size())); OP_REQUIRES(context, magic == kPngFormat || channel_bits_ == 8, errors::InvalidArgument(FileFormatString(magic, input), " does not support uint16 output")); @@ -212,9 +215,10 @@ class DecodeImageOp : public OpKernel { input.data(), input.size(), flags, nullptr /* nwarn */, [=, &output](int width, int height, int channels) -> uint8* { Status status(context->allocate_output( - 0, format_ == kGifFormat - ? TensorShape({1, height, width, channels}) - : TensorShape({height, width, channels}), + 0, + format_ == kGifFormat + ? TensorShape({1, height, width, channels}) + : TensorShape({height, width, channels}), &output)); if (!status.ok()) { VLOG(1) << status; diff --git a/tensorflow/core/kernels/deep_conv2d.cc b/tensorflow/core/kernels/deep_conv2d.cc index 8e9b8a7e2e..829155fb31 100644 --- a/tensorflow/core/kernels/deep_conv2d.cc +++ b/tensorflow/core/kernels/deep_conv2d.cc @@ -120,9 +120,9 @@ bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows, VLOG(2) << "CanUseDeepConv2D" << " deep_conv_cost: " << deep_conv_cost - << " direct_conv_cost: " << direct_conv_cost - << " deep_direct_ratio: " << (static_cast(deep_conv_cost) / - static_cast(direct_conv_cost)) + << " direct_conv_cost: " << direct_conv_cost << " deep_direct_ratio: " + << (static_cast(deep_conv_cost) / + static_cast(direct_conv_cost)) << " use_deep_conv: " << (deep_conv_cost < direct_conv_cost); return deep_conv_cost < direct_conv_cost; } diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc index 6d44a92fa3..6497c8f371 100644 --- a/tensorflow/core/kernels/dense_update_ops.cc +++ b/tensorflow/core/kernels/dense_update_ops.cc @@ -89,7 +89,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ @@ -113,14 +113,14 @@ TF_CALL_GPU_ALL_TYPES(REGISTER_GPU_KERNELS); #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNELS(type) \ -REGISTER_KERNEL_BUILDER( \ - Name("Assign").Device(DEVICE_SYCL).TypeConstraint("T"), \ - AssignOpT); +#define REGISTER_SYCL_KERNELS(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("Assign").Device(DEVICE_SYCL).TypeConstraint("T"), \ + AssignOpT); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS); #undef REGISTER_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ @@ -146,7 +146,7 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); #endif // end GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNELS(type) \ +#define REGISTER_SYCL_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ Name("AssignAdd").Device(DEVICE_SYCL).TypeConstraint("T"), \ DenseUpdateOp); \ @@ -156,5 +156,5 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS); #undef REGISTER_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/depthwise_conv_grad_op.cc b/tensorflow/core/kernels/depthwise_conv_grad_op.cc index 9347978d51..91a9587174 100644 --- a/tensorflow/core/kernels/depthwise_conv_grad_op.cc +++ b/tensorflow/core/kernels/depthwise_conv_grad_op.cc @@ -400,7 +400,7 @@ struct LaunchDepthwiseConvBackpropInputOp { // Computes one shard of depthwise conv2d backprop input. auto shard = [&ctx, &args, &out_backprop, &filter_data, &in_backprop]( - int64 start, int64 limit) { + int64 start, int64 limit) { static const int64 kPacketSize = (sizeof(Packet) / sizeof(T)); const int64 input_image_size = @@ -750,7 +750,7 @@ struct LaunchDepthwiseConvBackpropFilterOp { // Computes one shard of depthwise conv2d backprop filter. auto shard = [&ctx, &args, &out_backprop, &input, &output_buffer_data]( - int64 start, int64 limit) { + int64 start, int64 limit) { static const int64 kPacketSize = (sizeof(Packet) / sizeof(T)); const int64 filter_spatial_size = args.filter_rows * args.filter_cols; const int64 padded_out_depth_size = diff --git a/tensorflow/core/kernels/depthwise_conv_op.cc b/tensorflow/core/kernels/depthwise_conv_op.cc index a5fd07fbe1..c060b2e14d 100644 --- a/tensorflow/core/kernels/depthwise_conv_op.cc +++ b/tensorflow/core/kernels/depthwise_conv_op.cc @@ -308,10 +308,10 @@ class DepthwiseConv2dNativeOp : public BinaryOp { // in_depth for input and filter must match. const int64 in_depth = GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES( - context, in_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - in_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, in_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", in_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is depth multiplier. const int32 depth_multiplier = filter.dim_size(3); @@ -430,9 +430,10 @@ TF_CALL_double(REGISTER_CPU_KERNEL); #endif #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER( - Name("DepthwiseConv2dNative").Device(DEVICE_GPU).TypeConstraint("T"), - DepthwiseConv2dNativeOp); +REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNative") + .Device(DEVICE_GPU) + .TypeConstraint("T"), + DepthwiseConv2dNativeOp); REGISTER_KERNEL_BUILDER( Name("DepthwiseConv2dNative").Device(DEVICE_GPU).TypeConstraint("T"), diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc index 5493e33532..126b64f73d 100644 --- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc +++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc @@ -17,12 +17,12 @@ limitations under the License. #define EIGEN_USE_GPU #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "external/cub_archive/cub/util_ptx.cuh" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/kernels/depthwise_conv_op.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/cuda_kernel_helper.h" #include "tensorflow/core/util/tensor_format.h" -#include "external/cub_archive/cub/util_ptx.cuh" #if !defined(_MSC_VER) #define UNROLL _Pragma("unroll") @@ -1021,7 +1021,7 @@ __global__ void __launch_bounds__(640, 2) // Device function to compute sub-warp sum reduction for a power-of-two group of // neighboring threads. -template +template __device__ __forceinline__ T WarpSumReduce(T val) { // support only power-of-two widths. assert(__popc(kWidth) == 1); diff --git a/tensorflow/core/kernels/diag_op.cc b/tensorflow/core/kernels/diag_op.cc index 86fa7dce36..d228153d4c 100644 --- a/tensorflow/core/kernels/diag_op.cc +++ b/tensorflow/core/kernels/diag_op.cc @@ -29,8 +29,8 @@ limitations under the License. #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_types.h" -#include "tensorflow/core/platform/types.h" #include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/work_sharder.h" namespace tensorflow { @@ -47,8 +47,9 @@ class DiagOp : public OpKernel { void Compute(OpKernelContext* context) override { const Tensor& diagonal = context->input(0); const int num_dims = diagonal.dims(); - OP_REQUIRES(context, 0 != num_dims, errors::InvalidArgument( - "Input must be at least rank 1, got 0")); + OP_REQUIRES( + context, 0 != num_dims, + errors::InvalidArgument("Input must be at least rank 1, got 0")); TensorShape out_shape; for (int i = 0; i < num_dims; ++i) { out_shape.AddDim(diagonal.dim_size(i)); @@ -60,10 +61,9 @@ class DiagOp : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output_tensor)); functor::DiagFunctor diagFunc; - Status s = diagFunc(context, - diagonal.NumElements(), - diagonal.flat().data(), - output_tensor->flat().data()); + Status s = + diagFunc(context, diagonal.NumElements(), diagonal.flat().data(), + output_tensor->flat().data()); OP_REQUIRES_OK(context, s); } }; @@ -82,12 +82,12 @@ class DiagPartOp : public OpKernel { errors::InvalidArgument("The rank of the tensor should be \ even and positive, got shape ", tensor.shape().DebugString())); - for (int i = 0; i < out_dims; i++){ - OP_REQUIRES(context, tensor.dim_size(i) == tensor.dim_size(i + out_dims), - errors::InvalidArgument( - "Invalid shape ", tensor.shape().DebugString(), - ": dimensions ", i, " and ", i + out_dims, " do not match.") - ); + for (int i = 0; i < out_dims; i++) { + OP_REQUIRES( + context, tensor.dim_size(i) == tensor.dim_size(i + out_dims), + errors::InvalidArgument("Invalid shape ", + tensor.shape().DebugString(), ": dimensions ", + i, " and ", i + out_dims, " do not match.")); } TensorShape out_shape; @@ -96,13 +96,10 @@ class DiagPartOp : public OpKernel { } Tensor* output = nullptr; - OP_REQUIRES_OK(context, - context->allocate_output(0, out_shape, &output)); + OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output)); functor::DiagPartFunctor diagPartFunc; - Status s = diagPartFunc(context, - out_shape.num_elements(), - tensor.flat().data(), - output->flat().data()); + Status s = diagPartFunc(context, out_shape.num_elements(), + tensor.flat().data(), output->flat().data()); OP_REQUIRES_OK(context, s); } }; @@ -129,9 +126,8 @@ class DiagPartOp : public OpKernel { namespace functor { template struct DiagFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // This subprocess is responsible for writing values in index range // [start*size, limit*size) auto subDiag = [in, out, size](int64 start, int64 limit) { @@ -143,17 +139,16 @@ struct DiagFunctor { // Here, 5 is a empirical factor of cost_per_unit. auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - Shard(worker_threads.num_threads, worker_threads.workers, size, - 5 * size, subDiag); + Shard(worker_threads.num_threads, worker_threads.workers, size, 5 * size, + subDiag); return Status::OK(); } }; template struct DiagPartFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // This subprocess is responsible for extracting values in index range // [start, limit) auto subDiagPart = [in, out, size](int64 start, int64 limit) { @@ -164,14 +159,13 @@ struct DiagPartFunctor { // Here, 5 is a empirical factor of cost_per_unit. auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - Shard(worker_threads.num_threads, worker_threads.workers, size, - 5, subDiagPart); + Shard(worker_threads.num_threads, worker_threads.workers, size, 5, + subDiagPart); return Status::OK(); } }; } // namespace functor - // Register the CPU kernels. #define REGISTER_DIAGOP(T) \ REGISTER_KERNEL_BUILDER( \ @@ -250,6 +244,4 @@ TF_CALL_complex128(REGISTER_DIAGPARTOP_GPU); #endif // GOOGLE_CUDA - } // namespace tensorflow - diff --git a/tensorflow/core/kernels/diag_op.h b/tensorflow/core/kernels/diag_op.h index c6ca6a2047..baf16ddb4b 100644 --- a/tensorflow/core/kernels/diag_op.h +++ b/tensorflow/core/kernels/diag_op.h @@ -26,14 +26,14 @@ namespace functor { template struct DiagFunctor { - Status operator() (OpKernelContext* context, const int64 size, - const T* in, T* out); + Status operator()(OpKernelContext* context, const int64 size, const T* in, + T* out); }; template struct DiagPartFunctor { - Status operator() (OpKernelContext* context, const int64 size, - const T* in, T* out); + Status operator()(OpKernelContext* context, const int64 size, const T* in, + T* out); }; } // namespace functor diff --git a/tensorflow/core/kernels/diag_op_gpu.cu.cc b/tensorflow/core/kernels/diag_op_gpu.cu.cc index d3c529d784..910f3093b2 100644 --- a/tensorflow/core/kernels/diag_op_gpu.cu.cc +++ b/tensorflow/core/kernels/diag_op_gpu.cu.cc @@ -19,8 +19,8 @@ limitations under the License. #include #include "tensorflow/core/framework/register_types.h" -#include "tensorflow/core/util/cuda_kernel_helper.h" #include "tensorflow/core/kernels/diag_op.h" +#include "tensorflow/core/util/cuda_kernel_helper.h" namespace tensorflow { namespace functor { @@ -28,10 +28,8 @@ namespace functor { typedef Eigen::GpuDevice GPUDevice; template -__global__ void DiagCudaKernel(const int num_threads, - const int64 size, - const T* in, - T* out) { +__global__ void DiagCudaKernel(const int num_threads, const int64 size, + const T* in, T* out) { CUDA_1D_KERNEL_LOOP(index, num_threads) { // Fill the diagonal elements or set to zero in other place. if (index % (1 + size) == 0) { @@ -44,9 +42,8 @@ __global__ void DiagCudaKernel(const int num_threads, template struct DiagFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // Empty tensor couldn't launch the kernel. if (size == 0) { return Status::OK(); @@ -56,25 +53,22 @@ struct DiagFunctor { // so this may overflow for `size*size` in extreme cases, // here is checking the multiplication overflow for integer. if (size && (int(size * size) / size) != size) { - return errors::Internal( - "DiagOp got input size too large."); + return errors::Internal("DiagOp got input size too large."); } int virtual_thread_count = int(size * size); // Launch the GPU kernel. const GPUDevice& device = context->eigen_device(); - CudaLaunchConfig diag_config = GetCudaLaunchConfig( - virtual_thread_count, device); - DiagCudaKernel<<>>( - diag_config.virtual_thread_count, size, in, out); + CudaLaunchConfig diag_config = + GetCudaLaunchConfig(virtual_thread_count, device); + DiagCudaKernel<<>>(diag_config.virtual_thread_count, size, + in, out); auto err = cudaGetLastError(); if (err != cudaSuccess) { return errors::Internal( - "Could not launch DiagOp kernel: ", - cudaGetErrorString(err), "."); + "Could not launch DiagOp kernel: ", cudaGetErrorString(err), "."); } return Status::OK(); } @@ -87,12 +81,9 @@ template struct DiagFunctor; template struct DiagFunctor; template struct DiagFunctor; - template -__global__ void DiagPartCudaKernel(const int num_threads, - const int64 size, - const T* in, - T* out) { +__global__ void DiagPartCudaKernel(const int num_threads, const int64 size, + const T* in, T* out) { CUDA_1D_KERNEL_LOOP(index, num_threads) { out[index] = in[(1 + size) * index]; } @@ -100,9 +91,8 @@ __global__ void DiagPartCudaKernel(const int num_threads, template struct DiagPartFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // Empty tensor couldn't launch the kernel. if (size == 0) { return Status::OK(); @@ -111,16 +101,14 @@ struct DiagPartFunctor { // Extract the diagonal elements. CudaLaunchConfig diag_config = GetCudaLaunchConfig(size, device); - DiagPartCudaKernel<<>>( - diag_config.virtual_thread_count, size, in, out); + DiagPartCudaKernel<<>>(diag_config.virtual_thread_count, + size, in, out); auto err = cudaGetLastError(); if (err != cudaSuccess) { return errors::Internal( - "Could not launch DiagPartOp kernel: ", - cudaGetErrorString(err), "."); + "Could not launch DiagPartOp kernel: ", cudaGetErrorString(err), "."); } return Status::OK(); } diff --git a/tensorflow/core/kernels/diag_op_test.cc b/tensorflow/core/kernels/diag_op_test.cc index 2d1417854c..a708e53dd0 100644 --- a/tensorflow/core/kernels/diag_op_test.cc +++ b/tensorflow/core/kernels/diag_op_test.cc @@ -30,8 +30,8 @@ static Graph* Diag(int n, DataType type) { return g; } -#define BM_DiagDev(N, T, TFTYPE, DEVICE) \ - static void BM_Diag##_##N##_##TFTYPE##_##DEVICE(int iters) { \ +#define BM_DiagDev(N, T, TFTYPE, DEVICE) \ + static void BM_Diag##_##N##_##TFTYPE##_##DEVICE(int iters) { \ testing::UseRealTime(); \ testing::ItemsProcessed(static_cast(iters) * N * N); \ test::Benchmark(#DEVICE, Diag(N, TFTYPE)).Run(iters); \ @@ -51,4 +51,3 @@ BM_Diag(128); BM_Diag(512); } // end namespace tensorflow - diff --git a/tensorflow/core/kernels/dilation_ops.cc b/tensorflow/core/kernels/dilation_ops.cc index 6f5c0e9156..441a63465c 100644 --- a/tensorflow/core/kernels/dilation_ops.cc +++ b/tensorflow/core/kernels/dilation_ops.cc @@ -91,10 +91,10 @@ void ParseSizes(OpKernelContext* context, const std::vector& strides, filter.shape().DebugString())); const int filter_rows = filter.dim_size(0); const int filter_cols = filter.dim_size(1); - OP_REQUIRES( - context, depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", depth, " vs ", + filter.dim_size(2))); // Effective filter size, after introducing rate - 1 zeros between each // non-zero filter element. @@ -234,10 +234,11 @@ class DilationBackpropInputOp : public OpKernel { // [ batch, out_rows, out_cols, depth ] const int batch = input.dim_size(0); const int depth = input.dim_size(3); - OP_REQUIRES(context, batch == out_backprop.dim_size(0) && - out_rows == out_backprop.dim_size(1) && - out_cols == out_backprop.dim_size(2) && - depth == out_backprop.dim_size(3), + OP_REQUIRES(context, + batch == out_backprop.dim_size(0) && + out_rows == out_backprop.dim_size(1) && + out_cols == out_backprop.dim_size(2) && + depth == out_backprop.dim_size(3), errors::InvalidArgument("out_backprop has incompatible size.")); // The computed in_backprop has the same dimensions as the input: @@ -353,10 +354,11 @@ class DilationBackpropFilterOp : public OpKernel { // [ batch, out_rows, out_cols, depth ] const int batch = input.dim_size(0); const int depth = input.dim_size(3); - OP_REQUIRES(context, batch == out_backprop.dim_size(0) && - out_rows == out_backprop.dim_size(1) && - out_cols == out_backprop.dim_size(2) && - depth == out_backprop.dim_size(3), + OP_REQUIRES(context, + batch == out_backprop.dim_size(0) && + out_rows == out_backprop.dim_size(1) && + out_cols == out_backprop.dim_size(2) && + depth == out_backprop.dim_size(3), errors::InvalidArgument("out_backprop has incompatible size.")); // The computed filter_backprop has the same dimensions as the filter: diff --git a/tensorflow/core/kernels/dilation_ops_gpu.cu.cc b/tensorflow/core/kernels/dilation_ops_gpu.cu.cc index ac0775fbef..c63806a7f6 100644 --- a/tensorflow/core/kernels/dilation_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/dilation_ops_gpu.cu.cc @@ -61,9 +61,8 @@ __global__ void DilationKernel(const int32 nthreads, const T* input_ptr, const int w_in = w_beg + w * rate_cols; if (w_in >= 0 && w_in < input_cols) { const T val = - input_ptr[d + - depth * - (w_in + input_cols * (h_in + input_rows * b))] + + input_ptr[d + depth * (w_in + + input_cols * (h_in + input_rows * b))] + filter_ptr[d + depth * (w + filter_cols * h)]; if (val > cur_val) { cur_val = val; @@ -106,9 +105,8 @@ __global__ void DilationBackpropInputKernel( const int w_in = w_beg + w * rate_cols; if (w_in >= 0 && w_in < input_cols) { const T val = - input_ptr[d + - depth * - (w_in + input_cols * (h_in + input_rows * b))] + + input_ptr[d + depth * (w_in + + input_cols * (h_in + input_rows * b))] + filter_ptr[d + depth * (w + filter_cols * h)]; if (val > cur_val) { cur_val = val; @@ -156,9 +154,8 @@ __global__ void DilationBackpropFilterKernel( const int w_in = w_beg + w * rate_cols; if (w_in >= 0 && w_in < input_cols) { const T val = - input_ptr[d + - depth * - (w_in + input_cols * (h_in + input_rows * b))] + + input_ptr[d + depth * (w_in + + input_cols * (h_in + input_rows * b))] + filter_ptr[d + depth * (w + filter_cols * h)]; if (val > cur_val) { cur_val = val; diff --git a/tensorflow/core/kernels/draw_bounding_box_op.cc b/tensorflow/core/kernels/draw_bounding_box_op.cc index a8818b7385..b5d5b880bb 100644 --- a/tensorflow/core/kernels/draw_bounding_box_op.cc +++ b/tensorflow/core/kernels/draw_bounding_box_op.cc @@ -29,8 +29,7 @@ template class DrawBoundingBoxesOp : public OpKernel { public: explicit DrawBoundingBoxesOp(OpKernelConstruction* context) - : OpKernel(context) { - } + : OpKernel(context) {} void Compute(OpKernelContext* context) override { const Tensor& images = context->input(0); @@ -94,35 +93,28 @@ class DrawBoundingBoxesOp : public OpKernel { int64 color_index = bb % color_table_length; const int64 min_box_row = static_cast(tboxes(b, bb, 0)) * (height - 1); - const int64 min_box_row_clamp = - std::max(min_box_row, 0); + const int64 min_box_row_clamp = std::max(min_box_row, 0); const int64 max_box_row = static_cast(tboxes(b, bb, 2)) * (height - 1); const int64 max_box_row_clamp = std::min(max_box_row, height - 1); const int64 min_box_col = static_cast(tboxes(b, bb, 1)) * (width - 1); - const int64 min_box_col_clamp = - std::max(min_box_col, 0); + const int64 min_box_col_clamp = std::max(min_box_col, 0); const int64 max_box_col = static_cast(tboxes(b, bb, 3)) * (width - 1); - const int64 max_box_col_clamp = - std::min(max_box_col, width - 1); + const int64 max_box_col_clamp = std::min(max_box_col, width - 1); if (min_box_row > max_box_row || min_box_col > max_box_col) { - LOG(WARNING) << "Bounding box (" << min_box_row - << "," << min_box_col - << "," << max_box_row - << "," << max_box_col + LOG(WARNING) << "Bounding box (" << min_box_row << "," << min_box_col + << "," << max_box_row << "," << max_box_col << ") is inverted and will not be drawn."; continue; } - if (min_box_row >= height || max_box_row < 0 || - min_box_col >= width || max_box_col < 0) { - LOG(WARNING) << "Bounding box (" << min_box_row - << "," << min_box_col - << "," << max_box_row - << "," << max_box_col + if (min_box_row >= height || max_box_row < 0 || min_box_col >= width || + max_box_col < 0) { + LOG(WARNING) << "Bounding box (" << min_box_row << "," << min_box_col + << "," << max_box_row << "," << max_box_col << ") is completely outside the image" << " and will not be drawn."; continue; diff --git a/tensorflow/core/kernels/dynamic_partition_op.cc b/tensorflow/core/kernels/dynamic_partition_op.cc index 861e16b2fd..3c988db5e6 100644 --- a/tensorflow/core/kernels/dynamic_partition_op.cc +++ b/tensorflow/core/kernels/dynamic_partition_op.cc @@ -103,7 +103,8 @@ class DynamicPartitionOp : public DynamicPartitionOp_Shared { // Walk through data and copy the data to the appropriate output tensor const auto data_flat = data->flat(); std::vector, - Eigen::Aligned> > out_vec; + Eigen::Aligned> > + out_vec; out_vec.reserve(num_partitions_); for (int p = 0; p < num_partitions_; p++) { out_vec.push_back(outputs[p]->vec()); @@ -124,7 +125,8 @@ class DynamicPartitionOp : public DynamicPartitionOp_Shared { } else { // If data has extra dimensions, use Eigen slices std::vector, - Eigen::Aligned> > out_flat; + Eigen::Aligned> > + out_flat; out_flat.reserve(num_partitions_); for (int p = 0; p < num_partitions_; p++) { out_flat.push_back(outputs[p]->flat_outer_dims()); diff --git a/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc b/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc index 9bb58b13f3..9dfeccff0e 100644 --- a/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc +++ b/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc @@ -79,9 +79,9 @@ template void RangeInit(const GPUDevice& d, const T start, const T delta, const int32 size, typename TTypes::Flat out) { CudaLaunchConfig config = GetCudaLaunchConfig(size, d); - RangeInitKernel< - T><<>>( - start, delta, size, out.data()); + RangeInitKernel + <<>>( + start, delta, size, out.data()); } // Given *num_runs pairs (key, value), this function moves the value @@ -103,11 +103,10 @@ void CallGatherKernel(const GPUDevice& d, const T* params, const int32* indices, T* out, int64 gather_dim_size, int64 indices_size, int64 slice_size, int64 out_size) { CudaLaunchConfig config = GetCudaLaunchConfig(out_size, d); - GatherOpKernel< - T, int32, - true><<>>( - params, indices, out, gather_dim_size, indices_size, slice_size, - out_size); + GatherOpKernel + <<>>( + params, indices, out, gather_dim_size, indices_size, slice_size, + out_size); } struct IdentityOp { @@ -231,10 +230,10 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { OP_REQUIRES_ASYNC( c, TensorShapeUtils::StartsWith(data.shape(), partitions.shape()), - errors::InvalidArgument("data.shape must start with partitions.shape, ", - "got data.shape = ", data.shape().DebugString(), - ", partitions.shape = ", - partitions.shape().DebugString()), + errors::InvalidArgument( + "data.shape must start with partitions.shape, ", + "got data.shape = ", data.shape().DebugString(), + ", partitions.shape = ", partitions.shape().DebugString()), done); Tensor partition_count; @@ -245,8 +244,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { AllocatorAttributes alloc_attr; alloc_attr.set_on_host(true); OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), - &partition_count, alloc_attr), + c, + c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), + &partition_count, alloc_attr), done); auto e_part_count = partition_count.flat(); for (int i = 0; i < num_partitions_; i++) e_part_count(i) = 0; @@ -259,8 +259,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { // Prepare for counting. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), - &partition_count), + c, + c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), + &partition_count), done); Tensor indices_out; // Count how many times each partition index occurs. @@ -280,8 +281,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { alloc_attr.set_on_host(true); alloc_attr.set_gpu_compatible(true); OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(partition_count.dtype(), partition_count.shape(), - &cpu_tensor, alloc_attr), + c, + c->allocate_temp(partition_count.dtype(), partition_count.shape(), + &cpu_tensor, alloc_attr), done); perftools::gputools::DeviceMemoryBase wrapped( partition_count.flat().data(), num_partitions_ * sizeof(int32)); @@ -340,9 +342,10 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { indices_in_ptr, indices_out_ptr, N, 0, sizeof(int32) * 8, cu_stream); // Allocate temporary storage. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &cub_temp_storage), + c, + c->allocate_temp(DT_INT8, + TensorShape({static_cast(temp_storage_bytes)}), + &cub_temp_storage), done); // Radix-sort the partition information. cub::DeviceRadixSort::SortPairs( @@ -376,8 +379,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { zero_functor(device, partition_count->flat()); // Allocate memory for aggregates_out. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), - &aggregates_out), + c, + c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), + &aggregates_out), done); // Obtain the pointers to inner buffers. int32* keys_in_ptr = partitions_out.flat().data(); @@ -408,9 +412,10 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { num_runs_ptr, reduction_op, N, cu_stream); // Allocate temporary storage. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &cub_temp_storage), + c, + c->allocate_temp(DT_INT8, + TensorShape({static_cast(temp_storage_bytes)}), + &cub_temp_storage), done); // Run reduce-by-key. The effect is that we count how many times // each index appears in partitions. The distinct indices are stored diff --git a/tensorflow/core/kernels/eigen_activations.h b/tensorflow/core/kernels/eigen_activations.h index 99b4b2abe6..302033e47c 100644 --- a/tensorflow/core/kernels/eigen_activations.h +++ b/tensorflow/core/kernels/eigen_activations.h @@ -21,13 +21,13 @@ limitations under the License. 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() - */ + * \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) @@ -55,13 +55,13 @@ struct functor_traits > { } // 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() - */ + * \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) @@ -89,11 +89,11 @@ struct functor_traits > { } // namespace internal /** - * \ingroup CXX11_NeuralNetworks_Module - * \brief Template functor to clip the magnitude of the first scalar. - * - * \sa class CwiseBinaryOp, MatrixBase::Clip - */ + * \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) diff --git a/tensorflow/core/kernels/eigen_activations_test.cc b/tensorflow/core/kernels/eigen_activations_test.cc index 907233103d..34952f5abb 100644 --- a/tensorflow/core/kernels/eigen_activations_test.cc +++ b/tensorflow/core/kernels/eigen_activations_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenBackwardSpatialConvolutionsTest, SigmoidFastDerivative) { const ptrdiff_t depth = 3; diff --git a/tensorflow/core/kernels/eigen_attention.h b/tensorflow/core/kernels/eigen_attention.h index 3a94b8c993..4d86f9deb9 100644 --- a/tensorflow/core/kernels/eigen_attention.h +++ b/tensorflow/core/kernels/eigen_attention.h @@ -21,35 +21,47 @@ limitations under the License. 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. - */ + * \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) { } + 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; + internal::traits::Layout, IndexType> > + Ref; Ref in(input); DSizes dims = in.dimensions(); @@ -62,12 +74,12 @@ struct GlimpseExtractionOp { } template - EIGEN_DEVICE_FUNC - void eval(const Input& input, Output& output, const Device& device) const - { + 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; + internal::traits::Layout, IndexType> > + Ref; Ref in(input); const Index num_channels = in.dimension(0); const Index input_width = in.dimension(1); @@ -97,8 +109,8 @@ struct GlimpseExtractionOp { x -= width_ / 2.0f; y -= height_ / 2.0f; - const Index offset_x = (Index) x; - const Index offset_y = (Index) y; + const Index offset_x = (Index)x; + const Index offset_y = (Index)y; Index glimpse_width = width_; Index glimpse_height = height_; bool partial_overlap = false; @@ -135,7 +147,7 @@ struct GlimpseExtractionOp { if (uniform_noise_) { // Initialize the glimpse with uniform noise. typedef typename internal::remove_const< - typename internal::traits::Scalar>::type Scalar; + typename internal::traits::Scalar>::type Scalar; TensorFixedSize > mini; mini.device(device) = input.template chip<3>(i).minimum(); TensorFixedSize > range; @@ -215,21 +227,22 @@ struct GlimpseExtractionOp { const bool centered_; const bool uniform_noise_; }; -} - +} // namespace template -EIGEN_ALWAYS_INLINE -static const TensorCustomUnaryOp::Index>, const Input> +EIGEN_ALWAYS_INLINE static const TensorCustomUnaryOp< + const GlimpseExtractionOp::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); + 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, @@ -237,6 +250,6 @@ ExtractGlimpses(const Input& input, return input.customOp(op); } -} // end namespace Eigen +} // end namespace Eigen #endif // TENSORFLOW_CORE_KERNELS_EIGEN_ATTENTION_H_ diff --git a/tensorflow/core/kernels/eigen_attention_test.cc b/tensorflow/core/kernels/eigen_attention_test.cc index 3a2eeb0595..08f6187718 100644 --- a/tensorflow/core/kernels/eigen_attention_test.cc +++ b/tensorflow/core/kernels/eigen_attention_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenAttentionTest, Simple) { const ptrdiff_t depth = 3; diff --git a/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h b/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h index aec7697810..099696105b 100644 --- a/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h +++ b/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h @@ -21,29 +21,29 @@ limitations under the License. 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 + * \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, + * 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 + * The output_backward and the kernel must both be in col-major layout. The * result will also be in col-major layout. - * - * If row_in_stride, col_in_stride > 1, then applies convolution with holes + * + * If row_in_stride, col_in_stride > 1, then applies convolution with holes * (aka atrous convolution), sampling every row_in_stride, col_in_stride input * pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the + * + * 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 + * + * 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. - * - */ + * + */ #ifdef EIGEN_HAS_INDEX_LIST typedef IndexList, type2index<0>, type2index<1>, type2index<1> > ReverseColMajor; @@ -293,29 +293,29 @@ SpatialConvolutionBackwardInput( } /** 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 + * \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, + * 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 + * The output_backward and the kernel must both be in col-major layout. The * result will also be in col-major layout. - * - * If row_in_stride, col_stride > 1, then applies convolution with holes (aka + * + * If row_in_stride, col_stride > 1, then applies convolution with holes (aka * atrous convolution), sampling every row_in_stride, col_in_stride input * pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the + * + * 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 + * + * 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< diff --git a/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc b/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc index 1758067829..2229ec9659 100644 --- a/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc +++ b/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc @@ -25,7 +25,7 @@ void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } static int ceil_div(int a, int b) { return (a + b - 1) / b; } -} +} // namespace TEST(EigenBackwardSpatialConvolutionsTest, test_simple_spatial_convolution_backward_input_valid) { diff --git a/tensorflow/core/kernels/eigen_pooling.h b/tensorflow/core/kernels/eigen_pooling.h index 972036833f..896c995761 100644 --- a/tensorflow/core/kernels/eigen_pooling.h +++ b/tensorflow/core/kernels/eigen_pooling.h @@ -309,10 +309,10 @@ struct AvgPoolMeanReducer { _mm512_castsi512_ps( \ _mm512_maskz_set1_epi32(_mm512_cmp_ps_mask(a, b, _CMP_EQ_UQ), -1)) -// The ternarylogic function immediate determines the values in the result -// In the case below, 0xd8 implies (false_mask) ? (b) : (a) -// For details, refer to the vpternlogd instruction table at -// http://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-software-developer-vol-2c-manual.pdf + // The ternarylogic function immediate determines the values in the result + // In the case below, 0xd8 implies (false_mask) ? (b) : (a) + // For details, refer to the vpternlogd instruction table at + // http://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-software-developer-vol-2c-manual.pdf #define psel(a, b, false_mask) \ _mm512_castsi512_ps(_mm512_ternarylogic_epi32( \ diff --git a/tensorflow/core/kernels/eigen_pooling_test.cc b/tensorflow/core/kernels/eigen_pooling_test.cc index 9383972b9f..47b6665e68 100644 --- a/tensorflow/core/kernels/eigen_pooling_test.cc +++ b/tensorflow/core/kernels/eigen_pooling_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenPoolingTest, Simple) { const int depth = 10; diff --git a/tensorflow/core/kernels/eigen_softmax.h b/tensorflow/core/kernels/eigen_softmax.h index a2930a726f..12148c54b3 100644 --- a/tensorflow/core/kernels/eigen_softmax.h +++ b/tensorflow/core/kernels/eigen_softmax.h @@ -21,19 +21,21 @@ limitations under the License. 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. - * -*/ + * \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 { struct SoftmaxOp { - SoftmaxOp(const float beta) : beta_(beta) { } + SoftmaxOp(const float beta) : beta_(beta) {} template typename Input::Dimensions dimensions(const Input& input) const { @@ -41,8 +43,7 @@ struct SoftmaxOp { } template - void eval(const Input& input, Output& output, const Device& device) const - { + 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; @@ -56,35 +57,43 @@ struct SoftmaxOp { #else // Take advantage of cxx11 to give the compiler information it can use to // optimize the code. - Eigen::IndexList> depth_dim; - Eigen::IndexList> bcast; + Eigen::IndexList > depth_dim; + Eigen::IndexList > bcast; bcast.set(0, dimensions(input)[0]); - Eigen::IndexList, typename internal::traits::Index> dims2d; + 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)); + 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_; }; -} - +} // namespace 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); +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 +} // end namespace Eigen #endif // TENSORFLOW_CORE_KERNELS_EIGEN_SOFTMAX_H_ diff --git a/tensorflow/core/kernels/eigen_softmax_test.cc b/tensorflow/core/kernels/eigen_softmax_test.cc index ba681d68ab..7f985d7136 100644 --- a/tensorflow/core/kernels/eigen_softmax_test.cc +++ b/tensorflow/core/kernels/eigen_softmax_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenSoftmaxTest, Simple) { const int depth = 1024; diff --git a/tensorflow/core/kernels/eigen_spatial_convolutions.h b/tensorflow/core/kernels/eigen_spatial_convolutions.h index 2fe64cd72a..1acbe3a658 100644 --- a/tensorflow/core/kernels/eigen_spatial_convolutions.h +++ b/tensorflow/core/kernels/eigen_spatial_convolutions.h @@ -877,29 +877,29 @@ struct gemm_pack_rhs< } // end namespace internal /** SpatialConvolution - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies a 2D convolution over a multichannel input image. - * - * The input parameter is expected to be a tensor with a rank of 3 or more + * \ingroup CXX11_NeuralNetworks_Module + * + * \brief Applies a 2D convolution over a multichannel input image. + * + * The input 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, + * The kernel parameter is expected to be a 4D tensor (filters, channels, * kernel_height, kernel_width) - * The input and the kernel must both be in col-major layout. The result will + * The input and the kernel must both be in col-major layout. The result will * also be in col-major layout. - * - * If col_in_stride, row_in_stride > 1, then applies convolution with holes + * + * If col_in_stride, row_in_stride > 1, then applies convolution with holes * (aka atrous convolution), sampling every col_in_stride, row_in_stride input * pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the + * + * 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 + * + * 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_DEVICE_FUNC EIGEN_ALWAYS_INLINE static const typename internal::conditional< @@ -993,7 +993,7 @@ EIGEN_DEVICE_FUNC default: // Initialize unused variables to avoid a compiler warning out_height = 0; - out_width = 0; + out_width = 0; eigen_assert(false && "unexpected padding"); } diff --git a/tensorflow/core/kernels/encode_jpeg_op.cc b/tensorflow/core/kernels/encode_jpeg_op.cc index 4fcae25aa6..1a5b0f2b67 100644 --- a/tensorflow/core/kernels/encode_jpeg_op.cc +++ b/tensorflow/core/kernels/encode_jpeg_op.cc @@ -80,10 +80,11 @@ class EncodeJpegOp : public OpKernel { errors::InvalidArgument("image must be 3-dimensional", image.shape().DebugString())); - OP_REQUIRES(context, FastBoundsCheck(image.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument( - "Cannot encode images with >= max int32 elements")); + OP_REQUIRES( + context, + FastBoundsCheck(image.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument( + "Cannot encode images with >= max int32 elements")); const int32 dim_size0 = static_cast(image.dim_size(0)); const int32 dim_size1 = static_cast(image.dim_size(1)); @@ -100,9 +101,10 @@ class EncodeJpegOp : public OpKernel { } else if (channels == 3) { adjusted_flags.format = jpeg::FORMAT_RGB; } else { - OP_REQUIRES(context, false, errors::InvalidArgument( - "image must have 1 or 3 channels, got ", - image.shape().DebugString())); + OP_REQUIRES( + context, false, + errors::InvalidArgument("image must have 1 or 3 channels, got ", + image.shape().DebugString())); } } else { if (flags_.format == jpeg::FORMAT_GRAYSCALE) { diff --git a/tensorflow/core/kernels/example_parsing_ops.cc b/tensorflow/core/kernels/example_parsing_ops.cc index 268a059275..83cd0e9b47 100644 --- a/tensorflow/core/kernels/example_parsing_ops.cc +++ b/tensorflow/core/kernels/example_parsing_ops.cc @@ -346,8 +346,9 @@ class SingleSequenceExampleParserOp : public OpKernel { feature_list_sparse_keys[di].scalar()(); } OP_REQUIRES( - ctx, TensorShapeUtils::IsVector( - feature_list_dense_missing_assumed_empty->shape()), + ctx, + TensorShapeUtils::IsVector( + feature_list_dense_missing_assumed_empty->shape()), errors::InvalidArgument( "Expected feature_list_dense_missing_assumed_empty ", "to be a vector, got shape: ", @@ -386,12 +387,12 @@ class SingleSequenceExampleParserOp : public OpKernel { required[d] = (def_value.NumElements() == 0); // No default provided. if (def_value.NumElements() > 0) { - OP_REQUIRES( - ctx, def_value.shape() == attrs_.context_dense_shapes[d], - errors::InvalidArgument( - "def_value[", d, "].shape() == ", - def_value.shape().DebugString(), " != context_dense_shapes_[", - d, "] == ", attrs_.context_dense_shapes[d].DebugString())); + OP_REQUIRES(ctx, def_value.shape() == attrs_.context_dense_shapes[d], + errors::InvalidArgument( + "def_value[", d, + "].shape() == ", def_value.shape().DebugString(), + " != context_dense_shapes_[", d, + "] == ", attrs_.context_dense_shapes[d].DebugString())); OP_REQUIRES( ctx, def_value.dtype() == attrs_.context_dense_types[d], errors::InvalidArgument( @@ -576,12 +577,12 @@ class SingleSequenceExampleParserOp : public OpKernel { const Feature& f = fl.feature(t); bool types_match; OP_REQUIRES_OK(ctx, CheckTypesMatch(f, dtype, &types_match)); - OP_REQUIRES( - ctx, types_match, - errors::InvalidArgument( - "Name: ", name, ", Feature list: ", key, ", Index: ", t, - ". Data types don't match. ", "Expected type: ", - DataTypeString(dtype), " Feature is: ", ProtoDebugString(f))); + OP_REQUIRES(ctx, types_match, + errors::InvalidArgument( + "Name: ", name, ", Feature list: ", key, ", Index: ", t, + ". Data types don't match. ", + "Expected type: ", DataTypeString(dtype), + " Feature is: ", ProtoDebugString(f))); OP_REQUIRES_OK(ctx, FeatureDenseCopy(t, name, key, dtype, shape, f, feature_list_dense_values[d])); } diff --git a/tensorflow/core/kernels/fact_op.cc b/tensorflow/core/kernels/fact_op.cc index 4fbf76d2d0..4a1aa433bc 100644 --- a/tensorflow/core/kernels/fact_op.cc +++ b/tensorflow/core/kernels/fact_op.cc @@ -122,13 +122,9 @@ static string D(const char* s) { return ret; } -REGISTER_KERNEL_BUILDER(Name("Fact") - .Device(DEVICE_CPU) - .Label(D("Yoxmos").c_str()), - FactOpKernel2); -REGISTER_KERNEL_BUILDER(Name("Fact") - .Device(DEVICE_CPU) - .Label(D("yoxmos").c_str()), - FactOpKernel2); +REGISTER_KERNEL_BUILDER( + Name("Fact").Device(DEVICE_CPU).Label(D("Yoxmos").c_str()), FactOpKernel2); +REGISTER_KERNEL_BUILDER( + Name("Fact").Device(DEVICE_CPU).Label(D("yoxmos").c_str()), FactOpKernel2); } // namespace tensorflow diff --git a/tensorflow/core/kernels/fake_quant_ops_test.cc b/tensorflow/core/kernels/fake_quant_ops_test.cc index 5953db1476..af3a42135d 100644 --- a/tensorflow/core/kernels/fake_quant_ops_test.cc +++ b/tensorflow/core/kernels/fake_quant_ops_test.cc @@ -378,9 +378,8 @@ TEST_F(QuantOpsTest, WithArgsGradient_RegularRange) { Tensor* output = GetOutput(0); auto input_flat = GetInput(0).flat(); Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 3})); - FillValues(&expected, - {0.0f, input_flat(1), input_flat(2), - input_flat(3), input_flat(4), 0.0f}); + FillValues(&expected, {0.0f, input_flat(1), input_flat(2), + input_flat(3), input_flat(4), 0.0f}); ExpectClose(expected, *output); } @@ -2167,21 +2166,19 @@ TEST_F(QuantOpsTest, Tensor* output_bprop_wrt_input = GetOutput(0); Tensor expected_bprop_wrt_input(allocator(), DT_FLOAT, TensorShape({2, 3})); auto grad_flat = GetInput(0).flat(); - FillValues(&expected_bprop_wrt_input, - {0.0f, grad_flat(1), grad_flat(2), - grad_flat(3), grad_flat(4), 0.0f}); + FillValues( + &expected_bprop_wrt_input, + {0.0f, grad_flat(1), grad_flat(2), grad_flat(3), grad_flat(4), 0.0f}); ExpectClose(expected_bprop_wrt_input, *output_bprop_wrt_input); Tensor* output_bprop_wrt_min = GetOutput(1); Tensor expected_bprop_wrt_min(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_min, - {grad_flat(0), 0.0f, 0.0f}); + FillValues(&expected_bprop_wrt_min, {grad_flat(0), 0.0f, 0.0f}); ExpectClose(expected_bprop_wrt_min, *output_bprop_wrt_min); Tensor* output_bprop_wrt_max = GetOutput(2); Tensor expected_bprop_wrt_max(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_max, - {0.0f, 0.0f, grad_flat(5)}); + FillValues(&expected_bprop_wrt_max, {0.0f, 0.0f, grad_flat(5)}); ExpectClose(expected_bprop_wrt_max, *output_bprop_wrt_max); } @@ -2215,21 +2212,19 @@ TEST_F(QuantOpsTest, WithVarsPerChannelDim2GradientNudgedUp_4Bits_NarrowRange) { Tensor* output_bprop_wrt_input = GetOutput(0); Tensor expected_bprop_wrt_input(allocator(), DT_FLOAT, TensorShape({2, 3})); auto grad_flat = GetInput(0).flat(); - FillValues(&expected_bprop_wrt_input, - {0.0f, grad_flat(1), grad_flat(2), - grad_flat(3), grad_flat(4), 0.0f}); + FillValues( + &expected_bprop_wrt_input, + {0.0f, grad_flat(1), grad_flat(2), grad_flat(3), grad_flat(4), 0.0f}); ExpectClose(expected_bprop_wrt_input, *output_bprop_wrt_input); Tensor* output_bprop_wrt_min = GetOutput(1); Tensor expected_bprop_wrt_min(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_min, - {grad_flat(0), 0.0f, 0.0f}); + FillValues(&expected_bprop_wrt_min, {grad_flat(0), 0.0f, 0.0f}); ExpectClose(expected_bprop_wrt_min, *output_bprop_wrt_min); Tensor* output_bprop_wrt_max = GetOutput(2); Tensor expected_bprop_wrt_max(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_max, - {0.0f, 0.0f, grad_flat(5)}); + FillValues(&expected_bprop_wrt_max, {0.0f, 0.0f, grad_flat(5)}); ExpectClose(expected_bprop_wrt_max, *output_bprop_wrt_max); } @@ -2270,14 +2265,13 @@ TEST_F(QuantOpsTest, Tensor expected_bprop_wrt_input(allocator(), DT_FLOAT, TensorShape({1, 2, 3, 4})); auto grad_flat = GetInput(0).flat(); - FillValues( - &expected_bprop_wrt_input, - {0.0f, grad_flat(1), grad_flat(2), 0.0f, - 0.0f, grad_flat(5), grad_flat(6), 0.0f, - 0.0f, grad_flat(9), grad_flat(10), 0.0f, - 0.0f, grad_flat(13), grad_flat(14), 0.0f, - 0.0f, grad_flat(17), grad_flat(18), 0.0f, - 0.0f, grad_flat(21), grad_flat(22), 0.0f}); + FillValues(&expected_bprop_wrt_input, + {0.0f, grad_flat(1), grad_flat(2), 0.0f, + 0.0f, grad_flat(5), grad_flat(6), 0.0f, + 0.0f, grad_flat(9), grad_flat(10), 0.0f, + 0.0f, grad_flat(13), grad_flat(14), 0.0f, + 0.0f, grad_flat(17), grad_flat(18), 0.0f, + 0.0f, grad_flat(21), grad_flat(22), 0.0f}); ExpectClose(expected_bprop_wrt_input, *output_bprop_wrt_input); Tensor* output_bprop_wrt_min = GetOutput(1); diff --git a/tensorflow/core/kernels/fifo_queue.cc b/tensorflow/core/kernels/fifo_queue.cc index 82ec879119..479f7be4b5 100644 --- a/tensorflow/core/kernels/fifo_queue.cc +++ b/tensorflow/core/kernels/fifo_queue.cc @@ -255,97 +255,96 @@ void FIFOQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx, // TODO(josh11b): This makes two copies of callback, avoid this if possible. dequeue_attempts_.emplace_back( num_elements, [callback]() { callback(Tuple()); }, ctx, cm, token, - [callback, allow_small_batch, this](Attempt* attempt) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - int64 queue_size = queues_[0].size(); + [callback, allow_small_batch, + this](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + int64 queue_size = queues_[0].size(); - if (closed_ && queue_size < attempt->elements_requested) { - // If we don't have enough for a full dequeue, we have - // to reset the attempt tuple. - if (!attempt->tuple.empty()) { - // Restore already-dequeued elements to the front of the - // queue. - for (int64 i = attempt->tuple[0].dim_size(0) - - attempt->elements_requested - 1; - i >= 0; --i) { - for (int j = 0; j < num_components(); ++j) { - PersistentTensor element; - Status s = GetElementComponentFromBatch( - attempt->tuple, i, j, attempt->context, &element); - if (!s.ok()) { - attempt->context->SetStatus( - errors::DataLoss("Failed to restore element from " - "partially-dequeued batch " - "to FIFOQueue: ", - s.error_message())); - } - queues_[j].push_front(element); - } - } - } - if (allow_small_batch && !queues_[0].empty()) { - // Request all remaining elements in the queue. - queue_size = queues_[0].size(); - attempt->tuple.clear(); - attempt->elements_requested = queue_size; - } else { - if (allow_small_batch) { - // There may be some other attempts containing - // values. If so, we'll yield and wait for them - // to add elements to the queue. - if (!enqueue_attempts_.empty()) return kProgress; - } - if (attempt->context->status().ok()) { - attempt->context->SetStatus(errors::OutOfRange( - "FIFOQueue '", name_, "' is closed and has ", - "insufficient elements (requested ", - attempt->elements_requested, ", current size ", - queue_size, ")")); + if (closed_ && queue_size < attempt->elements_requested) { + // If we don't have enough for a full dequeue, we have + // to reset the attempt tuple. + if (!attempt->tuple.empty()) { + // Restore already-dequeued elements to the front of the + // queue. + for (int64 i = attempt->tuple[0].dim_size(0) - + attempt->elements_requested - 1; + i >= 0; --i) { + for (int j = 0; j < num_components(); ++j) { + PersistentTensor element; + Status s = GetElementComponentFromBatch( + attempt->tuple, i, j, attempt->context, &element); + if (!s.ok()) { + attempt->context->SetStatus( + errors::DataLoss("Failed to restore element from " + "partially-dequeued batch " + "to FIFOQueue: ", + s.error_message())); } - return kComplete; + queues_[j].push_front(element); } } + } + if (allow_small_batch && !queues_[0].empty()) { + // Request all remaining elements in the queue. + queue_size = queues_[0].size(); + attempt->tuple.clear(); + attempt->elements_requested = queue_size; + } else { + if (allow_small_batch) { + // There may be some other attempts containing + // values. If so, we'll yield and wait for them + // to add elements to the queue. + if (!enqueue_attempts_.empty()) return kProgress; + } + if (attempt->context->status().ok()) { + attempt->context->SetStatus(errors::OutOfRange( + "FIFOQueue '", name_, "' is closed and has ", + "insufficient elements (requested ", + attempt->elements_requested, ", current size ", + queue_size, ")")); + } + return kComplete; + } + } - RunResult result = kNoProgress; - for (; queue_size > 0; --queue_size) { - if (attempt->tuple.empty()) { - // Only allocate tuple when we have something to dequeue - // so we don't use excessive memory when there are many - // blocked dequeue attempts waiting. - attempt->tuple.reserve(num_components()); - for (int i = 0; i < num_components(); ++i) { - const TensorShape shape = - ManyOutShape(i, attempt->elements_requested); - Tensor element; - attempt->context->SetStatus( - attempt->context->allocate_temp(component_dtypes_[i], - shape, &element)); - if (!attempt->context->status().ok()) return kComplete; - attempt->tuple.emplace_back(element); - } - } - result = kProgress; - Tuple tuple; - DequeueLocked(attempt->context, &tuple); - const int64 index = attempt->tuple[0].dim_size(0) - - attempt->elements_requested; - for (int i = 0; i < num_components(); ++i) { - attempt->context->SetStatus(batch_util::CopyElementToSlice( - std::move(tuple[i]), &attempt->tuple[i], index)); - if (!attempt->context->status().ok()) return kComplete; - } - tuple.clear(); - --attempt->elements_requested; - if (attempt->elements_requested == 0) { - tuple = attempt->tuple; - attempt->done_callback = [callback, tuple]() { - callback(tuple); - }; - return kComplete; - } + RunResult result = kNoProgress; + for (; queue_size > 0; --queue_size) { + if (attempt->tuple.empty()) { + // Only allocate tuple when we have something to dequeue + // so we don't use excessive memory when there are many + // blocked dequeue attempts waiting. + attempt->tuple.reserve(num_components()); + for (int i = 0; i < num_components(); ++i) { + const TensorShape shape = + ManyOutShape(i, attempt->elements_requested); + Tensor element; + attempt->context->SetStatus(attempt->context->allocate_temp( + component_dtypes_[i], shape, &element)); + if (!attempt->context->status().ok()) return kComplete; + attempt->tuple.emplace_back(element); } - return result; - }); + } + result = kProgress; + Tuple tuple; + DequeueLocked(attempt->context, &tuple); + const int64 index = + attempt->tuple[0].dim_size(0) - attempt->elements_requested; + for (int i = 0; i < num_components(); ++i) { + attempt->context->SetStatus(batch_util::CopyElementToSlice( + std::move(tuple[i]), &attempt->tuple[i], index)); + if (!attempt->context->status().ok()) return kComplete; + } + tuple.clear(); + --attempt->elements_requested; + if (attempt->elements_requested == 0) { + tuple = attempt->tuple; + attempt->done_callback = [callback, tuple]() { + callback(tuple); + }; + return kComplete; + } + } + return result; + }); } } if (!already_cancelled) { diff --git a/tensorflow/core/kernels/fill_functor.cc b/tensorflow/core/kernels/fill_functor.cc index bde39770de..7090417dfd 100644 --- a/tensorflow/core/kernels/fill_functor.cc +++ b/tensorflow/core/kernels/fill_functor.cc @@ -18,8 +18,8 @@ limitations under the License. #define EIGEN_USE_THREADS #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/framework/variant_encode_decode.h" @@ -60,7 +60,7 @@ DEFINE_SETZERO_CPU(Variant); template void SetZeroFunctor::operator()( const Eigen::SyclDevice& d, typename TTypes::Flat out) { - To32Bit(out).device(d) = To32Bit(out).constant(T(0)); + To32Bit(out).device(d) = To32Bit(out).constant(T(0)); } #define DEFINE_SETZERO_SYCL(T) \ @@ -118,7 +118,8 @@ DEFINE_SETONE_SYCL(double); template struct FillFunctor { - void operator()(const Eigen::ThreadPoolDevice& d, typename TTypes::Flat out, + void operator()(const Eigen::ThreadPoolDevice& d, + typename TTypes::Flat out, typename TTypes::ConstScalar in) { out.device(d) = out.constant(in()); } @@ -150,8 +151,7 @@ struct FillFunctor { } }; -#define DEFINE_FILL_SYCL(T) \ - template struct FillFunctor; +#define DEFINE_FILL_SYCL(T) template struct FillFunctor; DEFINE_FILL_SYCL(float); DEFINE_FILL_SYCL(double); TF_CALL_INTEGRAL_TYPES(DEFINE_FILL_SYCL) diff --git a/tensorflow/core/kernels/fractional_avg_pool_op.cc b/tensorflow/core/kernels/fractional_avg_pool_op.cc index 47f4189c30..135d002345 100644 --- a/tensorflow/core/kernels/fractional_avg_pool_op.cc +++ b/tensorflow/core/kernels/fractional_avg_pool_op.cc @@ -232,8 +232,9 @@ class FractionalAvgPoolGradOp : public OpKernel { // Grab the inputs. const Tensor& orig_input_tensor_shape = context->input(0); - OP_REQUIRES(context, orig_input_tensor_shape.dims() == 1 && - orig_input_tensor_shape.NumElements() == 4, + OP_REQUIRES(context, + orig_input_tensor_shape.dims() == 1 && + orig_input_tensor_shape.NumElements() == 4, errors::InvalidArgument("original input tensor shape must be" "1-dimensional and 4 elements")); const Tensor& out_backprop = context->input(1); diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc index ef9e848413..9d4bc35ba8 100644 --- a/tensorflow/core/kernels/function_ops.cc +++ b/tensorflow/core/kernels/function_ops.cc @@ -253,22 +253,21 @@ class SymbolicGradientOp : public AsyncOpKernel { args.push_back(ctx->input(i)); } std::vector* rets = new std::vector; - lib->Run( - opts, handle, args, rets, [ctx, done, rets](const Status& status) { - if (!status.ok()) { - ctx->SetStatus(status); - } else if (rets->size() != ctx->num_outputs()) { - ctx->SetStatus(errors::InvalidArgument( - "SymGrad expects to return ", ctx->num_outputs(), - " tensor(s), but get ", rets->size(), " tensor(s) instead.")); - } else { - for (size_t i = 0; i < rets->size(); ++i) { - ctx->set_output(i, (*rets)[i]); - } - } - delete rets; - done(); - }); + lib->Run(opts, handle, args, rets, [ctx, done, rets](const Status& status) { + if (!status.ok()) { + ctx->SetStatus(status); + } else if (rets->size() != ctx->num_outputs()) { + ctx->SetStatus(errors::InvalidArgument( + "SymGrad expects to return ", ctx->num_outputs(), + " tensor(s), but get ", rets->size(), " tensor(s) instead.")); + } else { + for (size_t i = 0; i < rets->size(); ++i) { + ctx->set_output(i, (*rets)[i]); + } + } + delete rets; + done(); + }); } private: diff --git a/tensorflow/core/kernels/fused_batch_norm_op.cu.cc b/tensorflow/core/kernels/fused_batch_norm_op.cu.cc index a8484390b9..4a67b2b3a3 100644 --- a/tensorflow/core/kernels/fused_batch_norm_op.cu.cc +++ b/tensorflow/core/kernels/fused_batch_norm_op.cu.cc @@ -68,7 +68,8 @@ void InvVarianceToVariance::operator()(const Eigen::GpuDevice& d, template void SetNanFunctor::operator()(const Eigen::GpuDevice& d, typename TTypes::Flat out) { - To32Bit(out).device(d) = To32Bit(out).constant(Eigen::NumTraits::quiet_NaN()); + To32Bit(out).device(d) = + To32Bit(out).constant(Eigen::NumTraits::quiet_NaN()); } template class VarianceToInvVariance; diff --git a/tensorflow/core/kernels/gather_functor.cc b/tensorflow/core/kernels/gather_functor.cc index dde08b37ea..e6fefe643b 100644 --- a/tensorflow/core/kernels/gather_functor.cc +++ b/tensorflow/core/kernels/gather_functor.cc @@ -25,12 +25,12 @@ typedef Eigen::GpuDevice GPUDevice; namespace functor { // Forward declarations of the functor specializations for GPU. -#define DECLARE_GPU_SPECS_INDEX(T, Index) \ - template <> \ - int64 GatherFunctor::operator()( \ +#define DECLARE_GPU_SPECS_INDEX(T, Index) \ + template <> \ + int64 GatherFunctor::operator()( \ OpKernelContext* ctx, typename TTypes::ConstTensor Tparams, \ - typename TTypes::ConstFlat Tindices, \ - typename TTypes::Tensor Tout); \ + typename TTypes::ConstFlat Tindices, \ + typename TTypes::Tensor Tout); \ extern template struct GatherFunctor; #define DECLARE_GPU_SPECS(T) \ diff --git a/tensorflow/core/kernels/gather_functor.h b/tensorflow/core/kernels/gather_functor.h index 1e429a037e..16ccb03b85 100644 --- a/tensorflow/core/kernels/gather_functor.h +++ b/tensorflow/core/kernels/gather_functor.h @@ -18,12 +18,12 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/type_traits.h" #include "tensorflow/core/kernels/bounds_check.h" #include "tensorflow/core/platform/prefetch.h" #include "tensorflow/core/platform/types.h" -#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/work_sharder.h" namespace tensorflow { @@ -52,21 +52,23 @@ SliceIndex HandleCopies(OpKernelContext* ctx, const size_t slice_bytes = slice_elems * sizeof(T); auto worker_threads = ctx->device()->tensorflow_cpu_worker_threads(); mutex mu; - // Store the value of invalidate index for printing error information, it's a shared variable. + // Store the value of invalidate index for printing error information, it's a + // shared variable. SliceIndex result = -1; - auto work = [&] (int64 start, int64 end) { + auto work = [&](int64 start, int64 end) { SliceIndex batch_idx = static_cast(start / indices_size); SliceIndex indices_idx = static_cast(start % indices_size); SliceIndex batch_idx_end = static_cast(end / indices_size); SliceIndex indices_idx_end = static_cast(end % indices_size); while ((batch_idx < batch_idx_end) || - (batch_idx == batch_idx_end && indices_idx < indices_idx_end)) { + (batch_idx == batch_idx_end && indices_idx < indices_idx_end)) { SliceIndex i_next = indices_idx + 1; SliceIndex b_next = batch_idx + 1; if ((batch_idx == batch_idx_end && i_next < indices_idx_end) || - (i_next < indices_size)) { - port::prefetch(¶ms(batch_idx, indices(i_next), 0)); + (i_next < indices_size)) { + port::prefetch( + ¶ms(batch_idx, indices(i_next), 0)); port::prefetch(&out(batch_idx, i_next, 0)); b_next = batch_idx; } else if (b_next <= batch_idx_end) { @@ -85,11 +87,12 @@ SliceIndex HandleCopies(OpKernelContext* ctx, // ahead-of-time compilation binary size). if (is_simple_type::value) { // Avoid auto-promotion to Index from SliceIndex by casting. - memcpy(out_base + (batch_idx * indices_size + indices_idx) * slice_elems, - params_base + (batch_idx * static_cast(limit) + - static_cast(index)) * - slice_elems, - slice_bytes); + memcpy( + out_base + (batch_idx * indices_size + indices_idx) * slice_elems, + params_base + (batch_idx * static_cast(limit) + + static_cast(index)) * + slice_elems, + slice_bytes); } else { // For non-"simple" types (e.g. strings). out.template chip<1>(indices_idx) = params.template chip<1>(index); @@ -99,8 +102,8 @@ SliceIndex HandleCopies(OpKernelContext* ctx, } }; - Shard(worker_threads->num_threads, worker_threads->workers, batch_size*indices_size, - slice_elems * sizeof(T), work); + Shard(worker_threads->num_threads, worker_threads->workers, + batch_size * indices_size, slice_elems * sizeof(T), work); return result; } @@ -117,16 +120,16 @@ struct GatherFunctorCPU { bool use_large = (slice_size > std::numeric_limits::max() || params.size() > std::numeric_limits::max() || N > std::numeric_limits::max()); -#define CALL(elems) \ - do { \ - if (use_large) { \ - bad_i = HandleCopies(ctx, params, indices, \ - slice_size, out); \ - } else { \ - const int32 small_slice = static_cast(slice_size); \ - bad_i = HandleCopies(ctx, params, indices, \ - small_slice, out); \ - } \ +#define CALL(elems) \ + do { \ + if (use_large) { \ + bad_i = HandleCopies(ctx, params, indices, \ + slice_size, out); \ + } else { \ + const int32 small_slice = static_cast(slice_size); \ + bad_i = HandleCopies(ctx, params, indices, \ + small_slice, out); \ + } \ } while (0) if (slice_size == 10) @@ -143,7 +146,8 @@ struct GatherFunctorCPU { template struct GatherFunctor { - int64 operator()(OpKernelContext* ctx, typename TTypes::ConstTensor params, + int64 operator()(OpKernelContext* ctx, + typename TTypes::ConstTensor params, typename TTypes::ConstFlat indices, typename TTypes::Tensor out); }; diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc index 239d5d2e99..d6cbcf1d93 100644 --- a/tensorflow/core/kernels/gather_op.cc +++ b/tensorflow/core/kernels/gather_op.cc @@ -106,8 +106,7 @@ class GatherOp : public OpKernel { auto out_flat = out->shaped({outer_size, N, inner_size}); functor::GatherFunctor functor; - int64 bad_i = functor(c, params_flat, - indices_flat, out_flat); + int64 bad_i = functor(c, params_flat, indices_flat, out_flat); OP_REQUIRES( c, bad_i < 0, diff --git a/tensorflow/core/kernels/hinge-loss.h b/tensorflow/core/kernels/hinge-loss.h index 789a7ce7a3..d303e9c877 100644 --- a/tensorflow/core/kernels/hinge-loss.h +++ b/tensorflow/core/kernels/hinge-loss.h @@ -50,9 +50,8 @@ class HingeLossUpdater : public DualLossUpdater { // valid value for new dual = 0 // c. new optimal value > 1.0. Then new optimal value should be set to 1.0. const double candidate_optimal_dual = - current_dual + - (label - wx) / - (num_loss_partitions * example_weight * weighted_example_norm); + current_dual + (label - wx) / (num_loss_partitions * example_weight * + weighted_example_norm); if (label * candidate_optimal_dual < 0) { return 0.0; } diff --git a/tensorflow/core/kernels/histogram_op_gpu.cu.cc b/tensorflow/core/kernels/histogram_op_gpu.cu.cc index c2bb958be8..a88e9b0ddc 100644 --- a/tensorflow/core/kernels/histogram_op_gpu.cu.cc +++ b/tensorflow/core/kernels/histogram_op_gpu.cu.cc @@ -17,16 +17,16 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/histogram_op.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "external/cub_archive/cub/device/device_histogram.cuh" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/kernels/histogram_op.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/cuda_kernel_helper.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { @@ -104,8 +104,8 @@ struct HistogramFixedWidthFunctor { /* num_samples */ num_samples, /* stream */ stream); if (err != cudaSuccess) { - return errors::Internal("Could not launch HistogramRange: ", - cudaGetErrorString(err), "."); + return errors::Internal( + "Could not launch HistogramRange: ", cudaGetErrorString(err), "."); } return Status::OK(); diff --git a/tensorflow/core/kernels/image_resizer_state.h b/tensorflow/core/kernels/image_resizer_state.h index f088315ff5..faf997be05 100644 --- a/tensorflow/core/kernels/image_resizer_state.h +++ b/tensorflow/core/kernels/image_resizer_state.h @@ -109,8 +109,9 @@ struct ImageResizerState { ValidateAndCalculateOutputSize(context, input); if (!context->status().ok()) return; OP_REQUIRES_OK(context, context->allocate_output( - 0, TensorShape({input.dim_size(0), out_height, - out_width, input.dim_size(3)}), + 0, + TensorShape({input.dim_size(0), out_height, + out_width, input.dim_size(3)}), &output)); } @@ -168,8 +169,9 @@ struct ImageResizerGradientState { CalculateResizeScale(original_width, resized_width, align_corners_); output = nullptr; OP_REQUIRES_OK(context, context->allocate_output( - 0, TensorShape({batch_size, original_height, - original_width, channels}), + 0, + TensorShape({batch_size, original_height, + original_width, channels}), &output)); } diff --git a/tensorflow/core/kernels/in_topk_op.cc b/tensorflow/core/kernels/in_topk_op.cc index e2861ae090..c37055239c 100644 --- a/tensorflow/core/kernels/in_topk_op.cc +++ b/tensorflow/core/kernels/in_topk_op.cc @@ -17,11 +17,11 @@ limitations under the License. #define EIGEN_USE_THREADS +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/kernels/bounds_check.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { @@ -98,36 +98,36 @@ class InTopK : public OpKernel { int k_; }; -REGISTER_KERNEL_BUILDER( - Name("InTopK").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); -REGISTER_KERNEL_BUILDER( - Name("InTopK").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); - -REGISTER_KERNEL_BUILDER( - Name("InTopKV2").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("k") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); -REGISTER_KERNEL_BUILDER( - Name("InTopKV2").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("k") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); +REGISTER_KERNEL_BUILDER(Name("InTopK") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); +REGISTER_KERNEL_BUILDER(Name("InTopK") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); + +REGISTER_KERNEL_BUILDER(Name("InTopKV2") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("k") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); +REGISTER_KERNEL_BUILDER(Name("InTopKV2") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("k") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); } // namespace tensorflow diff --git a/tensorflow/core/kernels/inplace_ops.cc b/tensorflow/core/kernels/inplace_ops.cc index 7728ba850c..a71d047ed1 100644 --- a/tensorflow/core/kernels/inplace_ops.cc +++ b/tensorflow/core/kernels/inplace_ops.cc @@ -27,13 +27,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SyclDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace functor { template -Status DoParallelConcatUpdate(const Device& d, const Tensor& value, - int32 loc, Tensor* output) { +Status DoParallelConcatUpdate(const Device& d, const Tensor& value, int32 loc, + Tensor* output) { auto Tvalue = value.shaped({1, value.NumElements()}); auto Toutput = output->flat_outer_dims(); auto nrows = Toutput.dimension(0); @@ -74,7 +74,7 @@ Status DoParallelConcat(const SyclDevice& d, const Tensor& value, int32 loc, return errors::InvalidArgument("Unsupported data type: ", value.dtype()); } } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace functor @@ -207,7 +207,7 @@ REGISTER_KERNEL_BUILDER(Name("_ParallelConcatUpdate") .HostMemory("output") .TypeConstraint("T"), ParallelConcatUpdate); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA diff --git a/tensorflow/core/kernels/l2loss_op.cc b/tensorflow/core/kernels/l2loss_op.cc index f8ed935157..f561287f7a 100644 --- a/tensorflow/core/kernels/l2loss_op.cc +++ b/tensorflow/core/kernels/l2loss_op.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_THREADS -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/kernels/l2loss_op.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/core/kernels/linalg_ops_common.cc b/tensorflow/core/kernels/linalg_ops_common.cc index 36907fb571..b58bcf5834 100644 --- a/tensorflow/core/kernels/linalg_ops_common.cc +++ b/tensorflow/core/kernels/linalg_ops_common.cc @@ -108,7 +108,6 @@ void LinearAlgebraOp::Compute(OpKernelContext* context) { auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); Shard(worker_threads.num_threads, worker_threads.workers, batch_shape.num_elements(), GetCostPerUnit(input_matrix_shapes), shard); - } template diff --git a/tensorflow/core/kernels/lmdb_reader_op.cc b/tensorflow/core/kernels/lmdb_reader_op.cc index 31a427f2c9..1335a95dce 100755 --- a/tensorflow/core/kernels/lmdb_reader_op.cc +++ b/tensorflow/core/kernels/lmdb_reader_op.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/framework/reader_op_kernel.h" #include "tensorflow/core/framework/reader_base.h" +#include "tensorflow/core/framework/reader_op_kernel.h" #include "tensorflow/core/lib/core/errors.h" #include @@ -77,15 +77,13 @@ class LMDBReader : public ReaderBase { *at_end = true; return Status::OK(); } - } - else { + } else { if (Seek(MDB_NEXT) == false) { *at_end = true; return Status::OK(); } } - *key = string(static_cast(mdb_key_.mv_data), - mdb_key_.mv_size); + *key = string(static_cast(mdb_key_.mv_data), mdb_key_.mv_size); *value = string(static_cast(mdb_value_.mv_data), mdb_value_.mv_size); *produced = true; @@ -123,13 +121,10 @@ class LMDBReaderOp : public ReaderOpKernel { explicit LMDBReaderOp(OpKernelConstruction* context) : ReaderOpKernel(context) { Env* env = context->env(); - SetReaderFactory([this, env]() { - return new LMDBReader(name(), env); - }); + SetReaderFactory([this, env]() { return new LMDBReader(name(), env); }); } }; -REGISTER_KERNEL_BUILDER(Name("LMDBReader").Device(DEVICE_CPU), - LMDBReaderOp); +REGISTER_KERNEL_BUILDER(Name("LMDBReader").Device(DEVICE_CPU), LMDBReaderOp); } // namespace tensorflow diff --git a/tensorflow/core/kernels/logistic-loss.h b/tensorflow/core/kernels/logistic-loss.h index 2765f42bbd..6479e6f5dc 100644 --- a/tensorflow/core/kernels/logistic-loss.h +++ b/tensorflow/core/kernels/logistic-loss.h @@ -122,10 +122,9 @@ class LogisticLossUpdater : public DualLossUpdater { num_loss_partitions * weighted_example_norm * example_weight * (0.5 * (1 + tanhx) / label - current_dual); - const double denominator = -2 * label - - num_loss_partitions * weighted_example_norm * - example_weight * (1 - tanhx * tanhx) * 0.5 / - label; + const double denominator = + -2 * label - num_loss_partitions * weighted_example_norm * + example_weight * (1 - tanhx * tanhx) * 0.5 / label; return x - numerator / denominator; } }; diff --git a/tensorflow/core/kernels/loss_test.cc b/tensorflow/core/kernels/loss_test.cc index 89f0677e1f..460d65c5c2 100644 --- a/tensorflow/core/kernels/loss_test.cc +++ b/tensorflow/core/kernels/loss_test.cc @@ -32,14 +32,17 @@ namespace { TEST(LogisticLoss, ComputePrimalLoss) { LogisticLossUpdater loss_updater; - EXPECT_NEAR(0.693147, loss_updater.ComputePrimalLoss( - 0 /* wx */, 1 /* label */, 1 /* example weight */), + EXPECT_NEAR(0.693147, + loss_updater.ComputePrimalLoss(0 /* wx */, 1 /* label */, + 1 /* example weight */), 1e-3); - EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(70 /* wx */, 1 /* label */, - 1 /* example weight */), + EXPECT_NEAR(0.0, + loss_updater.ComputePrimalLoss(70 /* wx */, 1 /* label */, + 1 /* example weight */), 1e-3); - EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(-70 /* wx */, -1 /* label */, - 1 /* example weight */), + EXPECT_NEAR(0.0, + loss_updater.ComputePrimalLoss(-70 /* wx */, -1 /* label */, + 1 /* example weight */), 1e-3); } @@ -53,31 +56,35 @@ TEST(LogisticLoss, ComputeDualLoss) { loss_updater.ComputeDualLoss(1 /* current dual */, 1 /* label */, 1 /* example weight */), 1e-3); - EXPECT_NEAR(-0.693147, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1 /* label */, - 1 /* example weight */), - 1e-3); + EXPECT_NEAR( + -0.693147, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1 /* label */, + 1 /* example weight */), + 1e-3); } TEST(LogisticLoss, ComputeUpdatedDual) { LogisticLossUpdater loss_updater; - EXPECT_NEAR(0.479, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.5 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.479, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.5 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); - EXPECT_NEAR(-0.031, loss_updater.ComputeUpdatedDual( - 2 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, 0.1 /* current_dual */, - -0.8 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.031, + loss_updater.ComputeUpdatedDual( + 2 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, 0.1 /* current_dual */, + -0.8 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } TEST(SquaredLoss, ComputePrimalLoss) { SquaredLossUpdater loss_updater; - EXPECT_NEAR(0.5, loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, - 1.0 /* example weight */), + EXPECT_NEAR(0.5, + loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, + 1.0 /* example weight */), 1e-3); EXPECT_NEAR(40.5, loss_updater.ComputePrimalLoss(10.0 /* wx */, 1.0 /* label */, @@ -95,43 +102,50 @@ TEST(SquaredLoss, ComputePrimalLoss) { TEST(SquaredLoss, ComputeDualLoss) { SquaredLossUpdater loss_updater; - EXPECT_NEAR(0.0, loss_updater.ComputeDualLoss(0.0 /* current dual */, - -1.0 /* label */, - 1.0 /* example weight */), - 1e-3); - EXPECT_NEAR(0.66, loss_updater.ComputeDualLoss(0.2 /* current dual */, - -1.0 /* label */, - 3.0 /* example weight */), - 1e-3); - EXPECT_NEAR(-0.375, loss_updater.ComputeDualLoss(1.5 /* current dual */, - 1.0 /* label */, - 1.0 /* example weight */), - 1e-3); - EXPECT_NEAR(-1.125, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1.0 /* label */, - 3.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + 0.0, + loss_updater.ComputeDualLoss(0.0 /* current dual */, -1.0 /* label */, + 1.0 /* example weight */), + 1e-3); + EXPECT_NEAR( + 0.66, + loss_updater.ComputeDualLoss(0.2 /* current dual */, -1.0 /* label */, + 3.0 /* example weight */), + 1e-3); + EXPECT_NEAR( + -0.375, + loss_updater.ComputeDualLoss(1.5 /* current dual */, 1.0 /* label */, + 1.0 /* example weight */), + 1e-3); + EXPECT_NEAR( + -1.125, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1.0 /* label */, + 3.0 /* example weight */), + 1e-3); } TEST(SquaredLoss, ComputeUpdatedDual) { SquaredLossUpdater loss_updater; - EXPECT_NEAR(0.336, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.3 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.336, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.3 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); - EXPECT_NEAR(-0.427, loss_updater.ComputeUpdatedDual( - 5 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, -0.4 /* current_dual */, - 0.8 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.427, + loss_updater.ComputeUpdatedDual( + 5 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, -0.4 /* current_dual */, + 0.8 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } TEST(HingeLoss, ComputePrimalLoss) { HingeLossUpdater loss_updater; - EXPECT_NEAR(1.0, loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, - 1.0 /* example weight */), + EXPECT_NEAR(1.0, + loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, + 1.0 /* example weight */), 1e-3); EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(10.0 /* wx */, 1.0 /* label */, @@ -149,10 +163,11 @@ TEST(HingeLoss, ComputePrimalLoss) { TEST(HingeLoss, ComputeDualLoss) { HingeLossUpdater loss_updater; - EXPECT_NEAR(0.0, loss_updater.ComputeDualLoss(0.0 /* current dual */, - -1.0 /* label */, - 1.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + 0.0, + loss_updater.ComputeDualLoss(0.0 /* current dual */, -1.0 /* label */, + 1.0 /* example weight */), + 1e-3); EXPECT_NEAR( std::numeric_limits::max(), loss_updater.ComputeDualLoss(0.2 /* current dual */, -1.0 /* label */, @@ -163,10 +178,11 @@ TEST(HingeLoss, ComputeDualLoss) { loss_updater.ComputeDualLoss(1.5 /* current dual */, 1.0 /* label */, 1.0 /* example weight */), 1e-3); - EXPECT_NEAR(-1.5, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1.0 /* label */, - 3.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + -1.5, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1.0 /* label */, + 3.0 /* example weight */), + 1e-3); } TEST(HingeLoss, ConvertLabel) { @@ -195,28 +211,31 @@ TEST(HingeLoss, ComputeUpdatedDual) { // weighted_example_norm=100.0, it turns out that the optimal value to update // the dual to is 0.507 which is within the permitted range and thus should be // the value returned. - EXPECT_NEAR(0.507, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.5 /* current_dual */, - 0.3 /* wx */, 100.0 /* weighted_example_norm */), + EXPECT_NEAR(0.507, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.5 /* current_dual */, + 0.3 /* wx */, 100.0 /* weighted_example_norm */), 1e-3); // When label=-1.0, example_weight=1.0, current_dual=0.4, wx=0.6, // weighted_example_norm=10.0 and num_loss_partitions=10, it turns out that // the optimal value to update the dual to is 0.384 which is within the // permitted range and thus should be the value returned. - EXPECT_NEAR(-0.416, loss_updater.ComputeUpdatedDual( - 10 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, -0.4 /* current_dual */, - 0.6 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.416, + loss_updater.ComputeUpdatedDual( + 10 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, -0.4 /* current_dual */, + 0.6 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); // When label=1.0, example_weight=1.0, current_dual=-0.5, wx=0.3 and // weighted_example_norm=10.0, it turns out that the optimal value to update // the dual to is -0.43. However, this is outside the allowed [0.0, 1.0] range // and hence the closest permitted value (0.0) should be returned instead. - EXPECT_NEAR(0.0, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, -0.5 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.0, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, -0.5 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); // When label=-1.0, example_weight=2.0, current_dual=-1.0, wx=0.3 and @@ -224,17 +243,19 @@ TEST(HingeLoss, ComputeUpdatedDual) { // the dual to is -1.065. However, this is outside the allowed [-1.0, 0.0] // range and hence the closest permitted value (-1.0) should be returned // instead. - EXPECT_NEAR(-1.0, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, -1.0 /* label */, - 2.0 /* example weight */, -1.0 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-1.0, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, -1.0 /* label */, + 2.0 /* example weight */, -1.0 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } TEST(SmoothHingeLoss, ComputePrimalLoss) { SmoothHingeLossUpdater loss_updater; - EXPECT_NEAR(0.5, loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, - 1.0 /* example weight */), + EXPECT_NEAR(0.5, + loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, + 1.0 /* example weight */), 1e-3); EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(10.0 /* wx */, 1.0 /* label */, @@ -252,10 +273,11 @@ TEST(SmoothHingeLoss, ComputePrimalLoss) { TEST(SmoothHingeLoss, ComputeDualLoss) { SmoothHingeLossUpdater loss_updater; - EXPECT_NEAR(0.0, loss_updater.ComputeDualLoss(0.0 /* current dual */, - -1.0 /* label */, - 1.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + 0.0, + loss_updater.ComputeDualLoss(0.0 /* current dual */, -1.0 /* label */, + 1.0 /* example weight */), + 1e-3); EXPECT_NEAR( std::numeric_limits::max(), loss_updater.ComputeDualLoss(0.2 /* current dual */, -1.0 /* label */, @@ -266,24 +288,27 @@ TEST(SmoothHingeLoss, ComputeDualLoss) { loss_updater.ComputeDualLoss(1.5 /* current dual */, 1.0 /* label */, 1.0 /* example weight */), 1e-3); - EXPECT_NEAR(-1.125, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1.0 /* label */, - 3.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + -1.125, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1.0 /* label */, + 3.0 /* example weight */), + 1e-3); } TEST(SmoothHingeLoss, ComputeUpdatedDual) { SmoothHingeLossUpdater loss_updater; - EXPECT_NEAR(0.336, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.3 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.336, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.3 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); - EXPECT_NEAR(-0.427, loss_updater.ComputeUpdatedDual( - 5 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, -0.4 /* current_dual */, - 0.8 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.427, + loss_updater.ComputeUpdatedDual( + 5 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, -0.4 /* current_dual */, + 0.8 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } diff --git a/tensorflow/core/kernels/lrn_op.cc b/tensorflow/core/kernels/lrn_op.cc index c905ebc84a..c3a59c9576 100644 --- a/tensorflow/core/kernels/lrn_op.cc +++ b/tensorflow/core/kernels/lrn_op.cc @@ -229,10 +229,11 @@ class LRNOp : public OpKernel { explicit LRNOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); float tmp; OP_REQUIRES_OK(context, context->GetAttr("bias", &tmp)); @@ -247,9 +248,10 @@ class LRNOp : public OpKernel { const Tensor& in = context->input(0); OP_REQUIRES(context, in.dims() == 4, errors::InvalidArgument("in must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(in.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES( + context, + FastBoundsCheck(in.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); // Cast to platform-specific int to avoid conversion warnings. const int batch = static_cast(in.dim_size(0)); const int rows = static_cast(in.dim_size(1)); @@ -448,10 +450,11 @@ class LRNGradOp : public OpKernel { explicit LRNGradOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); float tmp; OP_REQUIRES_OK(context, context->GetAttr("bias", &tmp)); diff --git a/tensorflow/core/kernels/matching_files_op.cc b/tensorflow/core/kernels/matching_files_op.cc index 5eb060f664..cdff7bad5f 100644 --- a/tensorflow/core/kernels/matching_files_op.cc +++ b/tensorflow/core/kernels/matching_files_op.cc @@ -45,15 +45,14 @@ class MatchingFilesOp : public OpKernel { int num_files = 0; std::vector> all_fnames(num_patterns); for (int i = 0; i < num_patterns; i++) { - OP_REQUIRES_OK( - context, - context->env()->GetMatchingPaths(patterns(i), &all_fnames[i])); + OP_REQUIRES_OK(context, context->env()->GetMatchingPaths(patterns(i), + &all_fnames[i])); num_files += all_fnames[i].size(); } Tensor* output_t = nullptr; - OP_REQUIRES_OK(context, - context->allocate_output( - "filenames", TensorShape({num_files}), &output_t)); + OP_REQUIRES_OK( + context, context->allocate_output("filenames", TensorShape({num_files}), + &output_t)); auto output = output_t->vec(); int index = 0; for (int i = 0; i < num_patterns; ++i) { diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc index cb68690f28..f499ce6519 100644 --- a/tensorflow/core/kernels/matmul_op.cc +++ b/tensorflow/core/kernels/matmul_op.cc @@ -261,12 +261,12 @@ struct LaunchMatMul { std::vector* algorithms, bool use_autotune, Tensor* out) { using perftools::gputools::blas::AlgorithmConfig; using perftools::gputools::blas::ComputationType; - using perftools::gputools::blas::ProfileResult; - using perftools::gputools::blas::Transpose; using perftools::gputools::blas::kDefaultAlgorithm; using perftools::gputools::blas::kDefaultBlasGemm; using perftools::gputools::blas::kDefaultBlasGemv; using perftools::gputools::blas::kNoAlgorithm; + using perftools::gputools::blas::ProfileResult; + using perftools::gputools::blas::Transpose; Transpose trans[] = {Transpose::kNoTranspose, Transpose::kTranspose}; const uint64 m = a.dim_size(1 - dim_pair[0].first); const uint64 k = a.dim_size(dim_pair[0].first); diff --git a/tensorflow/core/kernels/matmul_op.h b/tensorflow/core/kernels/matmul_op.h index 6398da2fb9..628895ca86 100644 --- a/tensorflow/core/kernels/matmul_op.h +++ b/tensorflow/core/kernels/matmul_op.h @@ -30,7 +30,8 @@ struct MatMulTypes { typedef Eigen::TensorMap, Eigen::Aligned> out_type; typedef Eigen::TensorMap, - Eigen::Aligned> in_type; + Eigen::Aligned> + in_type; }; template @@ -40,7 +39,8 @@ class MatrixExponentialOp : public LinearAlgebraOp { MatrixMaps* outputs) final { const ConstMatrixMap& input = inputs[0]; if (input.rows() == 0) return; - using Matrix = Eigen::Matrix; + using Matrix = + Eigen::Matrix; Matrix tmp = input; outputs->at(0) = tmp.exp(); } @@ -51,9 +51,9 @@ class MatrixExponentialOp : public LinearAlgebraOp { REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), float); REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), double); -REGISTER_LINALG_OP("MatrixExponential", - (MatrixExponentialOp), complex64); -REGISTER_LINALG_OP("MatrixExponential", - (MatrixExponentialOp), complex128); +REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), + complex64); +REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), + complex128); } // namespace tensorflow diff --git a/tensorflow/core/kernels/matrix_logarithm_op.cc b/tensorflow/core/kernels/matrix_logarithm_op.cc index cf0007b5b6..22ca094e24 100644 --- a/tensorflow/core/kernels/matrix_logarithm_op.cc +++ b/tensorflow/core/kernels/matrix_logarithm_op.cc @@ -26,7 +26,6 @@ limitations under the License. #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/types.h" - namespace tensorflow { template @@ -40,7 +39,8 @@ class MatrixLogarithmOp : public LinearAlgebraOp { MatrixMaps* outputs) final { const ConstMatrixMap& input = inputs[0]; if (input.rows() == 0) return; - using Matrix = Eigen::Matrix; + using Matrix = + Eigen::Matrix; Matrix tmp = input; outputs->at(0) = tmp.log(); } @@ -53,9 +53,9 @@ class MatrixLogarithmOp : public LinearAlgebraOp { // logarithm. If all eigenvalues are positive, then this returns the correct // logarithm, however checking for positive definiteness adds significant // overhead. Therefore at present we only register this Op for complex types. -REGISTER_LINALG_OP("MatrixLogarithm", - (MatrixLogarithmOp), complex64); -REGISTER_LINALG_OP("MatrixLogarithm", - (MatrixLogarithmOp), complex128); +REGISTER_LINALG_OP("MatrixLogarithm", (MatrixLogarithmOp), + complex64); +REGISTER_LINALG_OP("MatrixLogarithm", (MatrixLogarithmOp), + complex128); } // namespace tensorflow diff --git a/tensorflow/core/kernels/matrix_set_diag_op.cc b/tensorflow/core/kernels/matrix_set_diag_op.cc index 9dd665392b..502d593474 100644 --- a/tensorflow/core/kernels/matrix_set_diag_op.cc +++ b/tensorflow/core/kernels/matrix_set_diag_op.cc @@ -69,8 +69,8 @@ class MatrixSetDiagOp : public OpKernel { errors::InvalidArgument( "must have diagonal.shape == input.shape[:-2] + " "min(input.shape[-2:]), but received input shape: ", - input_shape.DebugString(), " and diagonal shape: ", - diag_shape.DebugString())); + input_shape.DebugString(), + " and diagonal shape: ", diag_shape.DebugString())); if (input.NumElements() == 0) { // This is a no-op. diff --git a/tensorflow/core/kernels/maxpooling_op.cc b/tensorflow/core/kernels/maxpooling_op.cc index 2eefadad49..9be7408012 100644 --- a/tensorflow/core/kernels/maxpooling_op.cc +++ b/tensorflow/core/kernels/maxpooling_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/core/kernels/maxpooling_op.h" #include +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -37,7 +38,6 @@ limitations under the License. #include "tensorflow/core/util/padding.h" #include "tensorflow/core/util/tensor_format.h" #include "tensorflow/core/util/use_cudnn.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #if GOOGLE_CUDA #include "tensorflow/core/kernels/maxpooling_op_gpu.h" @@ -89,7 +89,6 @@ static void SpatialMaxPoolWithArgMaxHelper( // max value. auto shard = [¶ms, &in_mat, &out_mat, &out_arg_max_mat, &input_backprop, &output_arg_max, &out_backprop](int64 start, int64 limit) { - const int32 depth = params.depth; const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; @@ -180,7 +179,6 @@ static void SpatialMaxPoolWithArgMaxHelper( input_backprop_flat(input_backprop_index) += out_backprop_flat(index); } } - }; const int64 shard_cost = params.tensor_in_rows * params.tensor_in_cols * @@ -567,7 +565,7 @@ class MaxPoolingGradGradOp : public OpKernel { // tensor_out_as_matrix with the corresponding values in // top_diff_as_matrix. auto shard = [¶ms, &in_mat, &out_mat, &top_diff_mat, &bottom_diff_mat]( - int64 start, int64 limit) { + int64 start, int64 limit) { const int32 depth = params.depth; const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; diff --git a/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc b/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc index f8daaca4c9..0c7a236b2f 100644 --- a/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc +++ b/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc @@ -450,10 +450,10 @@ bool MaxPoolBackwardWithArgmax::operator()( T* bottom_diff, const Eigen::GpuDevice& d) { const int kThreadsPerBlock = 1024; SetZero<<<(input_size + kThreadsPerBlock - 1) / kThreadsPerBlock, - kThreadsPerBlock, 0, d.stream()>>>(input_size, bottom_diff); + kThreadsPerBlock, 0, d.stream()>>>(input_size, bottom_diff); MaxPoolBackward<<<(output_size + kThreadsPerBlock - 1) / kThreadsPerBlock, kThreadsPerBlock, 0, d.stream()>>>( - output_size, top_diff, mask, top_offset, bottom_offset, bottom_diff); + output_size, top_diff, mask, top_offset, bottom_offset, bottom_diff); return d.ok(); } diff --git a/tensorflow/core/kernels/meta_support.cc b/tensorflow/core/kernels/meta_support.cc index 9fed01189f..39e60c9fce 100644 --- a/tensorflow/core/kernels/meta_support.cc +++ b/tensorflow/core/kernels/meta_support.cc @@ -98,9 +98,9 @@ typedef gemmlowp::meta::SimpleContext LocalContext; template void MultiThreadGemm(Context* context, const Params& params) { if (params.m <= 4) { - gemmlowp::meta::MultiThreadGemm< - Context, gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>, Params, - 1, 8, 8>(context, params); + gemmlowp::meta::MultiThreadGemm< + Context, gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>, Params, 1, + 8, 8>(context, params); } else { if (params.m >= params.n) { gemmlowp::meta::MultiThreadGemm< diff --git a/tensorflow/core/kernels/mfcc.cc b/tensorflow/core/kernels/mfcc.cc index 2793005aa2..8c755e0df8 100644 --- a/tensorflow/core/kernels/mfcc.cc +++ b/tensorflow/core/kernels/mfcc.cc @@ -27,21 +27,19 @@ const double kFilterbankFloor = 1e-12; const int kDefaultFilterbankChannelCount = 40; const int kDefaultDCTCoefficientCount = 13; -Mfcc::Mfcc() : initialized_(false), - lower_frequency_limit_(kDefaultLowerFrequencyLimit), - upper_frequency_limit_(kDefaultUpperFrequencyLimit), - filterbank_channel_count_(kDefaultFilterbankChannelCount), - dct_coefficient_count_(kDefaultDCTCoefficientCount) { } +Mfcc::Mfcc() + : initialized_(false), + lower_frequency_limit_(kDefaultLowerFrequencyLimit), + upper_frequency_limit_(kDefaultUpperFrequencyLimit), + filterbank_channel_count_(kDefaultFilterbankChannelCount), + dct_coefficient_count_(kDefaultDCTCoefficientCount) {} -bool Mfcc::Initialize(int input_length, - double input_sample_rate) { - bool initialized = mel_filterbank_.Initialize(input_length, - input_sample_rate, - filterbank_channel_count_, - lower_frequency_limit_, - upper_frequency_limit_); - initialized &= dct_.Initialize(filterbank_channel_count_, - dct_coefficient_count_); +bool Mfcc::Initialize(int input_length, double input_sample_rate) { + bool initialized = mel_filterbank_.Initialize( + input_length, input_sample_rate, filterbank_channel_count_, + lower_frequency_limit_, upper_frequency_limit_); + initialized &= + dct_.Initialize(filterbank_channel_count_, dct_coefficient_count_); initialized_ = initialized; return initialized; } diff --git a/tensorflow/core/kernels/mfcc.h b/tensorflow/core/kernels/mfcc.h index 8268f47203..8eee76f7f0 100644 --- a/tensorflow/core/kernels/mfcc.h +++ b/tensorflow/core/kernels/mfcc.h @@ -20,18 +20,17 @@ limitations under the License. #include +#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/kernels/mfcc_dct.h" #include "tensorflow/core/kernels/mfcc_mel_filterbank.h" #include "tensorflow/core/platform/logging.h" -#include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { class Mfcc { public: Mfcc(); - bool Initialize(int input_length, - double input_sample_rate); + bool Initialize(int input_length, double input_sample_rate); // Input is a single squared-magnitude spectrogram frame. The input spectrum // is converted to linear magnitude and weighted into bands using a diff --git a/tensorflow/core/kernels/mfcc_mel_filterbank.cc b/tensorflow/core/kernels/mfcc_mel_filterbank.cc index 630de8a5a3..3db3b51e8b 100644 --- a/tensorflow/core/kernels/mfcc_mel_filterbank.cc +++ b/tensorflow/core/kernels/mfcc_mel_filterbank.cc @@ -38,13 +38,12 @@ namespace tensorflow { MfccMelFilterbank::MfccMelFilterbank() : initialized_(false) {} -bool MfccMelFilterbank::Initialize(int input_length, - double input_sample_rate, - int output_channel_count, - double lower_frequency_limit, - double upper_frequency_limit) { +bool MfccMelFilterbank::Initialize(int input_length, double input_sample_rate, + int output_channel_count, + double lower_frequency_limit, + double upper_frequency_limit) { num_channels_ = output_channel_count; - sample_rate_ = input_sample_rate; + sample_rate_ = input_sample_rate; input_length_ = input_length; if (num_channels_ < 1) { @@ -85,10 +84,9 @@ bool MfccMelFilterbank::Initialize(int input_length, } // Always exclude DC; emulate HTK. - const double hz_per_sbin = 0.5 * sample_rate_ / - static_cast(input_length_ - 1); - start_index_ = static_cast(1.5 + (lower_frequency_limit / - hz_per_sbin)); + const double hz_per_sbin = + 0.5 * sample_rate_ / static_cast(input_length_ - 1); + start_index_ = static_cast(1.5 + (lower_frequency_limit / hz_per_sbin)); end_index_ = static_cast(upper_frequency_limit / hz_per_sbin); // Maps the input spectrum bin indices to filter bank channels/indices. For @@ -121,12 +119,12 @@ bool MfccMelFilterbank::Initialize(int input_length, weights_[i] = 0.0; } else { if (channel >= 0) { - weights_[i] = (center_frequencies_[channel + 1] - - FreqToMel(i * hz_per_sbin)) / + weights_[i] = + (center_frequencies_[channel + 1] - FreqToMel(i * hz_per_sbin)) / (center_frequencies_[channel + 1] - center_frequencies_[channel]); } else { weights_[i] = (center_frequencies_[0] - FreqToMel(i * hz_per_sbin)) / - (center_frequencies_[0] - mel_low); + (center_frequencies_[0] - mel_low); } } } @@ -152,16 +150,16 @@ bool MfccMelFilterbank::Initialize(int input_length, } } if (!bad_channels.empty()) { - LOG(ERROR) << "Missing " << bad_channels.size() << " bands " << - " starting at " << bad_channels[0] << - " in mel-frequency design. " << - "Perhaps too many channels or " << - "not enough frequency resolution in spectrum. (" << - "input_length: " << input_length << - " input_sample_rate: " << input_sample_rate << - " output_channel_count: " << output_channel_count << - " lower_frequency_limit: " << lower_frequency_limit << - " upper_frequency_limit: " << upper_frequency_limit; + LOG(ERROR) << "Missing " << bad_channels.size() << " bands " + << " starting at " << bad_channels[0] + << " in mel-frequency design. " + << "Perhaps too many channels or " + << "not enough frequency resolution in spectrum. (" + << "input_length: " << input_length + << " input_sample_rate: " << input_sample_rate + << " output_channel_count: " << output_channel_count + << " lower_frequency_limit: " << lower_frequency_limit + << " upper_frequency_limit: " << upper_frequency_limit; } initialized_ = true; return true; @@ -171,7 +169,7 @@ bool MfccMelFilterbank::Initialize(int input_length, // square root, then summing FFT magnitudes under triangular integration windows // whose widths increase with frequency. void MfccMelFilterbank::Compute(const std::vector &input, - std::vector *output) const { + std::vector *output) const { if (!initialized_) { LOG(ERROR) << "Mel Filterbank not initialized."; return; diff --git a/tensorflow/core/kernels/mfcc_mel_filterbank.h b/tensorflow/core/kernels/mfcc_mel_filterbank.h index 1bdc2dc93b..37c3936e80 100644 --- a/tensorflow/core/kernels/mfcc_mel_filterbank.h +++ b/tensorflow/core/kernels/mfcc_mel_filterbank.h @@ -27,10 +27,8 @@ class MfccMelFilterbank { public: MfccMelFilterbank(); bool Initialize(int input_length, // Number of unique FFT bins fftsize/2+1. - double input_sample_rate, - int output_channel_count, - double lower_frequency_limit, - double upper_frequency_limit); + double input_sample_rate, int output_channel_count, + double lower_frequency_limit, double upper_frequency_limit); // Takes a squared-magnitude spectrogram slice as input, computes a // triangular-mel-weighted linear-magnitude filterbank, and places the result @@ -56,7 +54,7 @@ class MfccMelFilterbank { // FFT bin i contributes to the upper side of mel channel band_mapper_[i] std::vector band_mapper_; int start_index_; // Lowest FFT bin used to calculate mel spectrum. - int end_index_; // Highest FFT bin used to calculate mel spectrum. + int end_index_; // Highest FFT bin used to calculate mel spectrum. TF_DISALLOW_COPY_AND_ASSIGN(MfccMelFilterbank); }; diff --git a/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc b/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc index 602dfeb4e5..54f31e1699 100644 --- a/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc +++ b/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc @@ -34,11 +34,9 @@ TEST(MfccMelFilterbankTest, AgreesWithPythonGoldenValues) { input.push_back(i + 1); } const int kChannelCount = 20; - filterbank.Initialize(input.size(), - 22050 /* sample rate */, - kChannelCount /* channels */, - 20.0 /* lower frequency limit */, - 4000.0 /* upper frequency limit */); + filterbank.Initialize( + input.size(), 22050 /* sample rate */, kChannelCount /* channels */, + 20.0 /* lower frequency limit */, 4000.0 /* upper frequency limit */); std::vector output; filterbank.Compute(input, &output); @@ -65,13 +63,10 @@ TEST(MfccMelFilterbankTest, IgnoresExistingContentOfOutputVector) { std::vector input; std::vector output; - filterbank.Initialize(kSampleCount, - 22050 /* sample rate */, - 20 /* channels */, - 20.0 /* lower frequency limit */, + filterbank.Initialize(kSampleCount, 22050 /* sample rate */, + 20 /* channels */, 20.0 /* lower frequency limit */, 4000.0 /* upper frequency limit */); - // First call with nonzero input value, and an empty output vector, // will resize the output and fill it with the correct, nonzero outputs. input.assign(kSampleCount, 1.0); diff --git a/tensorflow/core/kernels/mfcc_test.cc b/tensorflow/core/kernels/mfcc_test.cc index cb32df8811..72c1d331d6 100644 --- a/tensorflow/core/kernels/mfcc_test.cc +++ b/tensorflow/core/kernels/mfcc_test.cc @@ -36,11 +36,10 @@ TEST(MfccTest, AgreesWithPythonGoldenValues) { std::vector output; mfcc.Compute(input, &output); - std::vector expected = {29.13970072, -6.41568601, -0.61903012, - -0.96778652, -0.26819878, -0.40907028, - -0.15614748, -0.23203119, -0.10481487, - -0.1543029, -0.0769791, -0.10806114, - -0.06047613}; + std::vector expected = { + 29.13970072, -6.41568601, -0.61903012, -0.96778652, -0.26819878, + -0.40907028, -0.15614748, -0.23203119, -0.10481487, -0.1543029, + -0.0769791, -0.10806114, -0.06047613}; ASSERT_EQ(expected.size(), output.size()); for (int i = 0; i < output.size(); ++i) { diff --git a/tensorflow/core/kernels/mirror_pad_op.cc b/tensorflow/core/kernels/mirror_pad_op.cc index fbdeaf43eb..26e1082989 100644 --- a/tensorflow/core/kernels/mirror_pad_op.cc +++ b/tensorflow/core/kernels/mirror_pad_op.cc @@ -87,8 +87,8 @@ class MirrorPadOp : public OpKernel { const Tpaddings before = paddings(d, 0); // Pad before existing elements. const Tpaddings after = paddings(d, 1); // Pad after existing elements. OP_REQUIRES(context, before >= 0 && after >= 0, - errors::InvalidArgument("paddings must be non-negative: ", - before, " ", after)); + errors::InvalidArgument( + "paddings must be non-negative: ", before, " ", after)); if (offset_ == 0) { // SYMMETRIC mode. OP_REQUIRES(context, before <= in0.dim_size(d) && after <= in0.dim_size(d), @@ -296,8 +296,8 @@ class MirrorPadGradOp : public OpKernel { const Tpaddings before = paddings(d, 0); // Pad before existing elements. const Tpaddings after = paddings(d, 1); // Pad after existing elements. OP_REQUIRES(context, before >= 0 && after >= 0, - errors::InvalidArgument("Paddings must be non-negative: ", - before, ", ", after)); + errors::InvalidArgument( + "Paddings must be non-negative: ", before, ", ", after)); const int64 out_size = in0.dim_size(d) - (before + after); if (offset_ == 0) { // SYMMETRIC mode. diff --git a/tensorflow/core/kernels/mkl_avgpooling_op.cc b/tensorflow/core/kernels/mkl_avgpooling_op.cc index d751a70fc8..a7c569ee05 100644 --- a/tensorflow/core/kernels/mkl_avgpooling_op.cc +++ b/tensorflow/core/kernels/mkl_avgpooling_op.cc @@ -26,14 +26,14 @@ #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::memory; +using mkldnn::algorithm; +using mkldnn::engine; using mkldnn::error; -using mkldnn::pooling_forward; -using mkldnn::pooling_backward; +using mkldnn::memory; using mkldnn::padding_kind; -using mkldnn::engine; +using mkldnn::pooling_backward; +using mkldnn::pooling_forward; using mkldnn::prop_kind; -using mkldnn::algorithm; #endif namespace tensorflow { @@ -358,10 +358,11 @@ class MklAvgPoolingGradOp : public OpKernel { if (!outbackprop_in_mkl_format) { // For avgpooling, tensor_in_shape should have 1 dimension, and 4 // elements. - OP_REQUIRES(context, tensor_in_shape.dims() == 1 && - tensor_in_shape.NumElements() == 4, - errors::InvalidArgument("original input shape must be " - "1-dimensional and 4 elements")); + OP_REQUIRES( + context, + tensor_in_shape.dims() == 1 && tensor_in_shape.NumElements() == 4, + errors::InvalidArgument("original input shape must be " + "1-dimensional and 4 elements")); // For avgpooling, out_backprop should have 4 dimensions. OP_REQUIRES(context, out_backprop.dims() == 4, @@ -428,14 +429,13 @@ class MklAvgPoolingGradOp : public OpKernel { TensorFormat data_format_; }; // MklAvgPoolingGradOp - #else // INTEL_MKL_DNN is defined template class MklAvgPoolingOp : public MklPoolingForwardOpBase { public: explicit MklAvgPoolingOp(OpKernelConstruction* context) - : MklPoolingForwardOpBase(context) { + : MklPoolingForwardOpBase(context) { // Workspace is an MKLDNN construct that is only used in Max Pooling. // So set workspace_enabled_ to false. this->workspace_enabled_ = false; @@ -444,8 +444,8 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase { void Compute(OpKernelContext* context) override { try { auto cpu_engine = engine(engine::cpu, 0); - const Tensor& input_tensor = MklGetInput(context, - this->kInputTensorIndexInput); + const Tensor& input_tensor = + MklGetInput(context, this->kInputTensorIndexInput); MklDnnShape dnn_shape_input; GetMklShape(context, this->kInputTensorIndexInput, &dnn_shape_input); this->SanityCheckInput(context, input_tensor, dnn_shape_input); @@ -457,9 +457,8 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase { // initialize variables for the pooling op MklPoolParameters pool_params; // Get the input tensor and initialize the pooling parameters - this->ConfigureInput(context, dnn_shape_input, - input_tensor, &pool_params, - &dnn_data_input); + this->ConfigureInput(context, dnn_shape_input, input_tensor, &pool_params, + &dnn_data_input); OP_REQUIRES_OK(context, context->status()); // Declare output tensor @@ -470,56 +469,52 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase { // If input is in Mkl layout, then just get the memory format from it // directly, instead of using input data_format to AvgPool. if (dnn_shape_input.IsMklTensor()) { - dnn_data_output.SetUsrMem(output_dims_mkl_order, - static_cast(dnn_data_input.GetUsrMemDesc() - .data.format)); + dnn_data_output.SetUsrMem( + output_dims_mkl_order, + static_cast( + dnn_data_input.GetUsrMemDesc().data.format)); } else { - dnn_data_output.SetUsrMem(output_dims_mkl_order, - this->data_format_mkldnn_); + dnn_data_output.SetUsrMem(output_dims_mkl_order, + this->data_format_mkldnn_); } - // describe the memory layout + // describe the memory layout dnn_data_output.SetOpMemDesc(output_dims_mkl_order, memory::format::any); // 3. create a pooling primitive descriptor - auto pool_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_avg_exclude_padding, - dnn_data_input.GetUsrMemDesc(), - dnn_data_output.GetUsrMemDesc(), - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, - cpu_engine); + auto pool_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_avg_exclude_padding, + dnn_data_input.GetUsrMemDesc(), dnn_data_output.GetUsrMemDesc(), + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_prim_desc = + pooling_forward::primitive_desc(pool_desc, cpu_engine); this->AllocateOutputTensor(context, pool_prim_desc, output_dims_mkl_order, - this->data_format_mkldnn_, &output_tensor); + this->data_format_mkldnn_, &output_tensor); CHECK_NOTNULL(output_tensor); OP_REQUIRES_OK(context, context->status()); dnn_data_output.SetUsrMemDataHandle(output_tensor); - this->PrepareAndExecuteNet(pool_prim_desc, - &dnn_data_input, - &dnn_data_output); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + this->PrepareAndExecuteNet(pool_prim_desc, &dnn_data_input, + &dnn_data_output); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } // Compute -}; // MklAvgPoolingOp +}; // MklAvgPoolingOp //----------------------------------------------------------------------------- @@ -527,27 +522,23 @@ template class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { public: explicit MklAvgPoolingGradOp(OpKernelConstruction* context) - : MklPoolingBackwardOpBase(context) { - } + : MklPoolingBackwardOpBase(context) {} void Compute(OpKernelContext* context) override { try { auto cpu_engine = engine(engine::cpu, 0); MklDnnShape original_input_mkl_shape, input_gradient_mkl_shape; - const Tensor& tensor_in_shape = MklGetInput(context, - kInputTensorIndexInputShape); - const Tensor& input_gradient_tensor = MklGetInput(context, - kInputTensorIndexInputGradient); + const Tensor& tensor_in_shape = + MklGetInput(context, kInputTensorIndexInputShape); + const Tensor& input_gradient_tensor = + MklGetInput(context, kInputTensorIndexInputGradient); GetMklShape(context, kInputTensorIndexInputShape, - &original_input_mkl_shape); + &original_input_mkl_shape); GetMklShape(context, kInputTensorIndexInputGradient, - &input_gradient_mkl_shape); - + &input_gradient_mkl_shape); - SanityCheckInputs(context, tensor_in_shape, - input_gradient_tensor, - original_input_mkl_shape, - input_gradient_mkl_shape); + SanityCheckInputs(context, tensor_in_shape, input_gradient_tensor, + original_input_mkl_shape, input_gradient_mkl_shape); if (!context->status().ok()) return; // Used to allocate output_diff_src/diff_src @@ -562,90 +553,70 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { MklPoolParameters pool_params; memory::dims output_dims_mkl_order, original_input_dims_nchw; // Configure the original input memory descriptor - memory::desc original_input_md = ConfigureOriginalInput(context, - tensor_in_shape, - original_input_mkl_shape, - &original_input_dims_nchw, - &pool_params, - &original_input_shape); + memory::desc original_input_md = ConfigureOriginalInput( + context, tensor_in_shape, original_input_mkl_shape, + &original_input_dims_nchw, &pool_params, &original_input_shape); // configure the original output memory descriptor // by definition, the shape of the original output is the same // as the shape of the gradient diff_dst memory::desc original_output_md = this->ConfigureOriginalOutput( - pool_params, input_gradient_mkl_shape, output_dims_mkl_order); + pool_params, input_gradient_mkl_shape, output_dims_mkl_order); memory::desc target_diff_dst_md = this->ConfigureInputGradient( - input_gradient_mkl_shape, - input_gradient_tensor, - &input_gradient_diff_dst, - original_output_md); + input_gradient_mkl_shape, input_gradient_tensor, + &input_gradient_diff_dst, original_output_md); // The shape of the output diff src needs to be the same shape as the // original input. But we will set its format to be same as the format of // input gradient. We won't use format of original input since it will // always be in Tensorflow layout (given that AvgPoolGrad gets shape of // the input rather than actual input). - output_diff_src.SetUsrMem(original_input_dims_nchw, - static_cast( - target_diff_dst_md.data.format)); + output_diff_src.SetUsrMem( + original_input_dims_nchw, + static_cast(target_diff_dst_md.data.format)); // Create the forward pooling primitive descriptor so we can reference it // in the backward pooling primitive descriptor - auto pool_fwd_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_avg_exclude_padding, - original_input_md, - original_output_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_fwd_prim_desc - = pooling_forward::primitive_desc(pool_fwd_desc, - cpu_engine); + auto pool_fwd_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_avg_exclude_padding, + original_input_md, original_output_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_fwd_prim_desc = + pooling_forward::primitive_desc(pool_fwd_desc, cpu_engine); auto pool_bkwd_desc = pooling_backward::desc( - algorithm::pooling_avg_exclude_padding, - output_diff_src.GetUsrMemDesc(), - target_diff_dst_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_bkwd_prim_desc - = pooling_backward::primitive_desc(pool_bkwd_desc, - cpu_engine, - pool_fwd_prim_desc); - this->AllocateOutputTensor(context, pool_bkwd_prim_desc, - original_input_dims_nchw, - this->data_format_mkldnn_, - &output_tensor_diff_src); + algorithm::pooling_avg_exclude_padding, + output_diff_src.GetUsrMemDesc(), target_diff_dst_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_bkwd_prim_desc = pooling_backward::primitive_desc( + pool_bkwd_desc, cpu_engine, pool_fwd_prim_desc); + this->AllocateOutputTensor( + context, pool_bkwd_prim_desc, original_input_dims_nchw, + this->data_format_mkldnn_, &output_tensor_diff_src); output_diff_src.SetUsrMemDataHandle(output_tensor_diff_src); - this->PrepareAndExecuteNet(pool_bkwd_prim_desc, - &input_gradient_diff_dst, - &output_diff_src, - memory::primitive_desc( - target_diff_dst_md, - cpu_engine)); - } catch (mkldnn::error &e) { + this->PrepareAndExecuteNet( + pool_bkwd_prim_desc, &input_gradient_diff_dst, &output_diff_src, + memory::primitive_desc(target_diff_dst_md, cpu_engine)); + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Compute received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK(context, errors::Aborted("Compute received an exception:", + error_msg)); } } // Compute @@ -655,12 +626,11 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { const int kInputTensorIndexInputShape = 0; const int kInputTensorIndexInputGradient = 1; - memory::desc ConfigureOriginalInput(OpKernelContext* context, - const Tensor& tensor_original_input_shape, - const MklDnnShape& original_input_mkl_shape, - memory::dims* original_input_dims_mkl_order, - MklPoolParameters* pool_params, - TensorShape* input_tensor_shape) { + memory::desc ConfigureOriginalInput( + OpKernelContext* context, const Tensor& tensor_original_input_shape, + const MklDnnShape& original_input_mkl_shape, + memory::dims* original_input_dims_mkl_order, + MklPoolParameters* pool_params, TensorShape* input_tensor_shape) { CHECK_NOTNULL(original_input_dims_mkl_order); CHECK_NOTNULL(pool_params); CHECK_NOTNULL(input_tensor_shape); @@ -672,46 +642,42 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { } return MklPoolingBackwardOpBase::ConfigureOriginalInput( - context, - tensor_original_input_shape, - original_input_mkl_shape, - original_input_dims_mkl_order, - pool_params, - *input_tensor_shape); -} + context, tensor_original_input_shape, original_input_mkl_shape, + original_input_dims_mkl_order, pool_params, *input_tensor_shape); + } void SanityCheckInputs(OpKernelContext* context, - const Tensor& tensor_in_shape, - const Tensor& input_gradient_tensor, - const MklDnnShape& original_input_mkl_shape, - const MklDnnShape& input_gradient_mkl_shape) { + const Tensor& tensor_in_shape, + const Tensor& input_gradient_tensor, + const MklDnnShape& original_input_mkl_shape, + const MklDnnShape& input_gradient_mkl_shape) { if (!original_input_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, tensor_in_shape.dims() == 1 && - tensor_in_shape.NumElements() == 4, + OP_REQUIRES( + context, + tensor_in_shape.dims() == 1 && tensor_in_shape.NumElements() == 4, errors::InvalidArgument("original input shape must be " - "1-dimensional and 4 elements")); + "1-dimensional and 4 elements")); } else { - OP_REQUIRES(context, original_input_mkl_shape.GetDimension() == 1 && - original_input_mkl_shape.DimSize(0) == 4, - errors::InvalidArgument("original input shape must be " - "1-dimensional and 4 elements")); + OP_REQUIRES(context, + original_input_mkl_shape.GetDimension() == 1 && + original_input_mkl_shape.DimSize(0) == 4, + errors::InvalidArgument("original input shape must be " + "1-dimensional and 4 elements")); } if (!input_gradient_mkl_shape.IsMklTensor()) { // For avgpooling, input_gradient_diff_dst should have 4 dimensions. OP_REQUIRES(context, input_gradient_tensor.dims() == 4, - errors::InvalidArgument("Gradient shape must be " - "4-dimensional")); + errors::InvalidArgument("Gradient shape must be " + "4-dimensional")); } else { OP_REQUIRES(context, input_gradient_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Gradient shape must be " - "4-dimensional")); + errors::InvalidArgument("Gradient shape must be " + "4-dimensional")); } } }; // MklAvgPoolingGradOp - - #endif // INTEL_MKL_DNN REGISTER_KERNEL_BUILDER(Name("_MklAvgPool") @@ -728,4 +694,3 @@ REGISTER_KERNEL_BUILDER(Name("_MklAvgPoolGrad") } // namespace tensorflow #endif // INTEL_MKL - diff --git a/tensorflow/core/kernels/mkl_batch_matmul_op.cc b/tensorflow/core/kernels/mkl_batch_matmul_op.cc index 9fee94f946..d9713075be 100644 --- a/tensorflow/core/kernels/mkl_batch_matmul_op.cc +++ b/tensorflow/core/kernels/mkl_batch_matmul_op.cc @@ -40,7 +40,6 @@ limitations under the License. #include "tensorflow/core/kernels/fill_functor.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #define MKL_Complex8 tensorflow::complex64 #define MKL_Complex16 tensorflow::complex128 diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index d109bb6bcf..7da63604d2 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -33,8 +33,8 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; using mkldnn::concat; +using mkldnn::stream; #endif namespace tensorflow { @@ -45,7 +45,6 @@ typedef std::vector TensorShapeList; enum AxisArgumentName { NAME_IS_AXIS, NAME_IS_CONCAT_DIM }; - // TODO(intelft) Check if we can reuse existing EigenConcatOp using Mutable // reference inputs. // -------------------------------------------------------------------------- @@ -152,8 +151,8 @@ class EigenConcatBaseOp : public OpKernel { #else // MKL_DNN -void Compute(OpKernelContext* c, const std::vector& values, - const TensorShapeList& input_shapes) { + void Compute(OpKernelContext* c, const std::vector& values, + const TensorShapeList& input_shapes) { const Tensor* concat_dim_tensor; const char* axis_attribute_name = AxisArgName == NAME_IS_AXIS @@ -197,7 +196,8 @@ void Compute(OpKernelContext* c, const std::vector& values, const auto in = values[i]; const bool in_is_scalar = IsLegacyScalar(input_shapes[i]); OP_REQUIRES( - c, (input_shapes[i].dims() == input_dims) || + c, + (input_shapes[i].dims() == input_dims) || (input_is_scalar && in_is_scalar), errors::InvalidArgument( "ConcatOp : Ranks of all input tensors should match: shape[0] = ", @@ -208,8 +208,8 @@ void Compute(OpKernelContext* c, const std::vector& values, inputs_flat.emplace_back(new typename TTypes::ConstMatrix( in.shaped({inputs_flat_dim0, inputs_flat_dim1}))); } - output_concat_dim += input_shapes[i].dims() > 0 ? - input_shapes[i].dim_size(axis) : 1; + output_concat_dim += + input_shapes[i].dims() > 0 ? input_shapes[i].dim_size(axis) : 1; } TensorShape output_shape(input_shape); @@ -418,7 +418,6 @@ class MklConcatOp : public OpKernel { OP_REQUIRES_OK(context, context->status()); } - private: typedef struct { TensorFormat data_format; @@ -590,39 +589,45 @@ class MklConcatOp : public OpKernel { GetMklShapeList(context, "values", &input_shapes); const Tensor& concat_dim_tensor = (AxisArgName == NAME_IS_CONCAT_DIM) - ? MklGetInput(context, 0) : MklGetInput(context, N); + ? MklGetInput(context, 0) + : MklGetInput(context, N); // Sanity checks - OP_REQUIRES(context, IsLegacyScalar(concat_dim_tensor.shape()), - errors::InvalidArgument( - "Concat dim tensor should be a scalar integer, but got shape ", - concat_dim_tensor.shape().DebugString())); - int32 concat_dim = internal::SubtleMustCopy( - concat_dim_tensor.scalar()()); + OP_REQUIRES( + context, IsLegacyScalar(concat_dim_tensor.shape()), + errors::InvalidArgument( + "Concat dim tensor should be a scalar integer, but got shape ", + concat_dim_tensor.shape().DebugString())); + int32 concat_dim = + internal::SubtleMustCopy(concat_dim_tensor.scalar()()); // check that ranks of all tensors match // and that their shapes match except for concat_dim. int i = 0; bool invoke_eigen = false; bool are_all_mkl_inputs = true, are_all_tf_inputs = true; - const TensorShape expected_shape = input_shapes[0].IsMklTensor() ? - input_shapes[0].GetTfShape() : - input_tensors[0].shape(); + const TensorShape expected_shape = input_shapes[0].IsMklTensor() + ? input_shapes[0].GetTfShape() + : input_tensors[0].shape(); size_t expected_dims = expected_shape.dims(); if (concat_dim < 0) concat_dim = expected_dims + concat_dim; for (auto& s : input_shapes) { - if (s == expected_shape) {++i; continue;} + if (s == expected_shape) { + ++i; + continue; + } - TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : - input_tensors[i].shape(); + TensorShape s_shape = + s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); size_t s_dims = s_shape.dims(); - OP_REQUIRES(context, s_dims == expected_dims, - errors::InvalidArgument( - "_MklConcatOp : Ranks of all input tensors should match:" - " input dimensions = ", - s_dims, " vs. expected rank = ", expected_dims)); + OP_REQUIRES( + context, s_dims == expected_dims, + errors::InvalidArgument( + "_MklConcatOp : Ranks of all input tensors should match:" + " input dimensions = ", + s_dims, " vs. expected rank = ", expected_dims)); for (int d = 0; d < expected_dims; ++d) { if (d == concat_dim) continue; @@ -630,10 +635,11 @@ class MklConcatOp : public OpKernel { size_t expected_size = expected_shape.dim_size(d); size_t s_size = s_shape.dim_size(d); OP_REQUIRES( - context, expected_size == s_size, - errors::InvalidArgument("_MklConcatOp : Dimensions of inputs " - "should match: shape[0][", d, "]= ", expected_size, - " vs. shape[", i, "][", d, "] = ", s_size)); + context, expected_size == s_size, + errors::InvalidArgument("_MklConcatOp : Dimensions of inputs " + "should match: shape[0][", + d, "]= ", expected_size, " vs. shape[", i, + "][", d, "] = ", s_size)); } if (s.IsMklTensor()) @@ -657,8 +663,8 @@ class MklConcatOp : public OpKernel { TensorShapeList tf_input_shapes; i = 0; for (auto& s : input_shapes) { - TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : - input_tensors[i].shape(); + TensorShape s_shape = + s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); tf_input_shapes.push_back(s_shape); ++i; } @@ -678,21 +684,22 @@ class MklConcatOp : public OpKernel { std::vector srcs_pd; std::vector> srcs(N, MklDnnData(&cpu_engine)); int64 dst_concat_dim_size = 0; - for (int k =0; k < N; k++) { + for (int k = 0; k < N; k++) { bool is_mkl_tensor = input_shapes[k].IsMklTensor(); memory::dims src_dims; // Same comment as dst_dims for src_dims. - src_dims = (is_mkl_tensor) ? - TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) : - TFShapeToMklDnnDims(input_tensors[k].shape()); + src_dims = (is_mkl_tensor) + ? TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) + : TFShapeToMklDnnDims(input_tensors[k].shape()); dst_concat_dim_size += src_dims[concat_dim]; - auto src_md = is_mkl_tensor ? input_shapes[k].GetMklLayout() : - // It does not matter what data format we use here (NHWC or NCHW). - // We just need to ensure that output of Concat uses same data format - // as input. - memory::desc(src_dims, MklDnnType(), memory::format::nchw); + auto src_md = + is_mkl_tensor ? input_shapes[k].GetMklLayout() : + // It does not matter what data format we use here + // (NHWC or NCHW). We just need to ensure that output + // of Concat uses same data format as input. + memory::desc(src_dims, MklDnnType(), memory::format::nchw); srcs[k].SetUsrMem(src_md, &input_tensors[k]); auto src_mpd = srcs[k].GetUsrMemPrimDesc(); @@ -707,14 +714,15 @@ class MklConcatOp : public OpKernel { // Since we are passing a specific format for destination, // we need to have dst_dims in MklDnn order (NCHW). auto orig_tf_format = input_shapes[0].GetTfDataFormat(); - dst_dims_in_nchw = MklDnnDimsInNCHW(dst_dims, - MklDnnDataFormatToTFDataFormat(orig_tf_format)); + dst_dims_in_nchw = MklDnnDimsInNCHW( + dst_dims, MklDnnDataFormatToTFDataFormat(orig_tf_format)); // We will set the output in the same format as input to avoid layout // conversions. // Currently we are setting dst format same as input format. // See if we can make this choice in a better way. - dst_md = memory::desc(dst_dims_in_nchw, MklDnnType(), - (memory::format) input_shapes[0].GetMklLayout().data.format); + dst_md = memory::desc( + dst_dims_in_nchw, MklDnnType(), + (memory::format)input_shapes[0].GetMklLayout().data.format); } else { // Again, format does not matter here. We just need to make it same as // input format. @@ -722,7 +730,7 @@ class MklConcatOp : public OpKernel { } std::vector inputs; - for (int k=0; k < input_tensors.size(); k++) + for (int k = 0; k < input_tensors.size(); k++) inputs.push_back(srcs[k].GetOpMem()); // If all inputs are in MKL format, then meaning of concat_dim needs to @@ -732,8 +740,7 @@ class MklConcatOp : public OpKernel { // But ifinput tensors are in NHWC order, then semantics need to change. // E.g., if we are concatinating over Channel (dimension 3 for NHWC), // then since MklDnn order is NCHW, concat_dim needs to be 1. - if (are_all_mkl_inputs) - concat_dim = input_shapes[0].TfDimIdx(concat_dim); + if (are_all_mkl_inputs) concat_dim = input_shapes[0].TfDimIdx(concat_dim); auto concat_pd = concat::primitive_desc(dst_md, concat_dim, srcs_pd); @@ -752,24 +759,25 @@ class MklConcatOp : public OpKernel { dnn_shape_dst.SetMklTensor(false); tf_shape_dst = MklDnnDimsToTFShape(dst_dims); } - AllocateOutputSetMklShape(context, 0, &dst_tensor, - tf_shape_dst, dnn_shape_dst); + AllocateOutputSetMklShape(context, 0, &dst_tensor, tf_shape_dst, + dnn_shape_dst); CHECK_NOTNULL(dst_tensor); - dst_md = dnn_shape_dst.IsMklTensor() ? - dnn_shape_dst.GetMklLayout() : dst_md; + dst_md = + dnn_shape_dst.IsMklTensor() ? dnn_shape_dst.GetMklLayout() : dst_md; dst.SetUsrMem(dst_md, dst_tensor); auto concat_op = concat(concat_pd, inputs, dst.GetOpMem()); std::vector net; net.push_back(concat_op); stream(stream::kind::eager).submit(net).wait(); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + ", in file " + - string(__FILE__) + ":" + std::to_string(__LINE__); - OP_REQUIRES_OK(context, errors::Aborted( - "Operation received an exception:", error_msg)); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -790,11 +798,9 @@ class MklConcatOp : public OpKernel { dnn_shape_output.SetDimensions(4); Tensor* output_tensor = nullptr; TensorShape tf_shape_output; - tf_shape_output.AddDim( - dnn_shape_output.GetSerializeBufferSize()); - context->allocate_output( - GetTensorMetaDataIndex(0, context->num_outputs()), - tf_shape_output, &output_tensor); + tf_shape_output.AddDim(dnn_shape_output.GetSerializeBufferSize()); + context->allocate_output(GetTensorMetaDataIndex(0, context->num_outputs()), + tf_shape_output, &output_tensor); dnn_shape_output.SerializeMklDnnShape( output_tensor->flat().data(), output_tensor->flat().size() * sizeof(uint8)); diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc index 0f1a218fe6..25c2573741 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc @@ -38,9 +38,9 @@ limitations under the License. #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#include "tensorflow/core/util/mkl_util.h" #include "mkl_dnn.h" #include "mkl_dnn_types.h" +#include "tensorflow/core/util/mkl_util.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc index 54d4916d49..ef3f8cfec1 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc @@ -38,17 +38,17 @@ limitations under the License. #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#include "tensorflow/core/util/mkl_util.h" #include "mkl_dnn.h" #include "mkl_dnn_types.h" +#include "tensorflow/core/util/mkl_util.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; -using mkldnn::prop_kind; using mkldnn::convolution_backward_weights; using mkldnn::memory; +using mkldnn::prop_kind; +using mkldnn::stream; #endif namespace tensorflow { @@ -360,8 +360,8 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel { (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input; const Tensor& out_backprop = MklGetInput(context, 2); - void* mkl_buf_out_backprop = const_cast(static_cast( - out_backprop.flat().data())); + void* mkl_buf_out_backprop = const_cast( + static_cast(out_backprop.flat().data())); CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_out_backprop, prim_conv_bwdfilter, @@ -371,10 +371,11 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel { !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop, lt_out_backprop); if (mkl_convert_out_backprop) { CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_out_backprop, - lt_out_backprop, mkl_lt_internal_out_backprop), + lt_out_backprop, + mkl_lt_internal_out_backprop), E_SUCCESS); AllocTmpBuffer(context, mkl_tmp_out_backprop_buf_tensor, - lt_out_backprop, &mkl_buf_convert_out_backprop); + lt_out_backprop, &mkl_buf_convert_out_backprop); CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_out_backprop, mkl_buf_out_backprop, mkl_buf_convert_out_backprop), @@ -428,18 +429,18 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel { .Device(DEVICE_CPU) \ .TypeConstraint("T") \ .Label(mkl_op_registry::kMklOpLabel), \ - MklConv2DCustomBackpropFilterOp); + MklConv2DCustomBackpropFilterOp); TF_CALL_float(REGISTER_MKL_FILTER_KERNELS); #undef REGISTER_MKL_FILTER_KERNELS #else template -class MklConv2DCustomBackpropFilterOp : - public MklConv2DBackpropCommonOp { +class MklConv2DCustomBackpropFilterOp + : public MklConv2DBackpropCommonOp { public: explicit MklConv2DCustomBackpropFilterOp(OpKernelConstruction* context) - : MklConv2DBackpropCommonOp(context) { } + : MklConv2DBackpropCommonOp(context) {} ~MklConv2DCustomBackpropFilterOp() {} private: @@ -447,7 +448,7 @@ class MklConv2DCustomBackpropFilterOp : const MklDnnShape& filter_mkl_shape, const MklDnnShape& obp_mkl_shape) { CHECK(!filter_mkl_shape.IsMklTensor()) - << "Conv2DBackpropFilter: filter should not be in MKL Layout"; + << "Conv2DBackpropFilter: filter should not be in MKL Layout"; } size_t GetInputTensorIndexWithSizes() { return 1; /* filter index */ } @@ -462,8 +463,10 @@ class MklConv2DCustomBackpropFilterOp : const Tensor& filter_tensor) { TensorShape filter_tf_shape; CHECK_EQ(TensorShapeUtils::IsVector(filter_tensor.shape()), true); - CHECK_EQ(TensorShapeUtils::MakeShape( - filter_tensor.vec(), &filter_tf_shape).ok(), true); + CHECK_EQ(TensorShapeUtils::MakeShape(filter_tensor.vec(), + &filter_tf_shape) + .ok(), + true); return filter_tf_shape; } @@ -485,16 +488,13 @@ class MklConv2DCustomBackpropFilterOp : return memory::format::hwio; } - void CreatePrimitive(OpKernelContext* context, - const engine& cpu_engine, + void CreatePrimitive(OpKernelContext* context, const engine& cpu_engine, const convolution_forward::primitive_desc& conv_fwd_pd, MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, MklDnnData* output, - Tensor** output_tensor, - const memory::dims& strides, + Tensor** output_tensor, const memory::dims& strides, const memory::dims& padding_l, - const memory::dims& padding_r, - padding_kind padding, + const memory::dims& padding_r, padding_kind padding, const memory::dims& bwd_output_dims, memory::format bwd_output_format) { CHECK_NOTNULL(context); @@ -508,34 +508,35 @@ class MklConv2DCustomBackpropFilterOp : int depth = 0; if (biasEnabled) { // Data structure for bias_grad - bias_grad = new MklDnnData (&cpu_engine); + bias_grad = new MklDnnData(&cpu_engine); TensorShape obp_tf_shape = GetTfShape(context, 2); - depth = (MklConv2DBackpropCommonOp::GetTFDataFormat() - == FORMAT_NCHW) ? - obp_tf_shape.dim_size(1) : obp_tf_shape.dim_size(3); + depth = (MklConv2DBackpropCommonOp::GetTFDataFormat() == + FORMAT_NCHW) + ? obp_tf_shape.dim_size(1) + : obp_tf_shape.dim_size(3); memory::dims bias_grad_dims = {depth}; bias_grad->SetOpMemDesc(bias_grad_dims, memory::format::x); } // Create convolution backward weights primitive. - auto bwd_desc = (biasEnabled && (bias_grad != nullptr))? - convolution_backward_weights::desc(convolution_direct, - input->GetOpMemDesc(), output->GetOpMemDesc(), - bias_grad->GetOpMemDesc(), - outbackprop->GetOpMemDesc(), strides, padding_l, - padding_r, padding) : - convolution_backward_weights::desc(convolution_direct, - input->GetOpMemDesc(), output->GetOpMemDesc(), - outbackprop->GetOpMemDesc(), strides, padding_l, - padding_r, padding); - - auto bwd_pd = convolution_backward_weights::primitive_desc(bwd_desc, - cpu_engine, - conv_fwd_pd); + auto bwd_desc = + (biasEnabled && (bias_grad != nullptr)) + ? convolution_backward_weights::desc( + convolution_direct, input->GetOpMemDesc(), + output->GetOpMemDesc(), bias_grad->GetOpMemDesc(), + outbackprop->GetOpMemDesc(), strides, padding_l, padding_r, + padding) + : convolution_backward_weights::desc( + convolution_direct, input->GetOpMemDesc(), + output->GetOpMemDesc(), outbackprop->GetOpMemDesc(), strides, + padding_l, padding_r, padding); + + auto bwd_pd = convolution_backward_weights::primitive_desc( + bwd_desc, cpu_engine, conv_fwd_pd); // Allocate output tensor. - AllocateOutputTensor(context, bwd_pd, bwd_output_dims, - bwd_output_format, output_tensor); + AllocateOutputTensor(context, bwd_pd, bwd_output_dims, bwd_output_format, + output_tensor); CHECK_NOTNULL(*output_tensor); // Set buffer handle using allocated output tensor. @@ -548,8 +549,8 @@ class MklConv2DCustomBackpropFilterOp : AllocateBiasGradTensor(context, bias_grad_shape, &bias_grad_tensor); memory::dims bias_grad_dims = {depth}; // Since Bias is 1D, we use format::x from MKLDNN to represent it. - auto bias_grad_md = memory::desc({bias_grad_dims}, MklDnnType(), - memory::format::x); + auto bias_grad_md = + memory::desc({bias_grad_dims}, MklDnnType(), memory::format::x); bias_grad->SetUsrMem(bias_grad_md, bias_grad_tensor); bias_grad->SetUsrMemDataHandle(bias_grad_tensor); } @@ -562,28 +563,29 @@ class MklConv2DCustomBackpropFilterOp : } // Allocate output tensor. - void AllocateOutputTensor(OpKernelContext* context, - const convolution_backward_weights::primitive_desc& conv_pd, - const memory::dims& output_dims_mkl_order, - memory::format output_tf_format, Tensor** output_tensor) { - CHECK_NOTNULL(output_tensor); - - // For BackpropFilter, we convert the output tensor back in Tensorflow - // layout. Because typically, BackpropFilter is the last operator in the - // graph that emit filter gradient that is provided to ApplyGradient - // method to update the filter. But it may be possible to eliminate this - // by forwarding filter in MKL layout if we support ApplyGradient method - // for MKL layout propagation. - MklDnnShape output_mkl_shape; - output_mkl_shape.SetMklTensor(false); - // output_dims_mkl_order is in OIHW format. - // Allocate shape of TF tensor in HWIO format. - TensorShape output_tf_shape({output_dims_mkl_order[MklDnnDims::Dim_H], - output_dims_mkl_order[MklDnnDims::Dim_W], - output_dims_mkl_order[MklDnnDims::Dim_I], - output_dims_mkl_order[MklDnnDims::Dim_O]}); - AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, - output_mkl_shape); + void AllocateOutputTensor( + OpKernelContext* context, + const convolution_backward_weights::primitive_desc& conv_pd, + const memory::dims& output_dims_mkl_order, + memory::format output_tf_format, Tensor** output_tensor) { + CHECK_NOTNULL(output_tensor); + + // For BackpropFilter, we convert the output tensor back in Tensorflow + // layout. Because typically, BackpropFilter is the last operator in the + // graph that emit filter gradient that is provided to ApplyGradient + // method to update the filter. But it may be possible to eliminate this + // by forwarding filter in MKL layout if we support ApplyGradient method + // for MKL layout propagation. + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(false); + // output_dims_mkl_order is in OIHW format. + // Allocate shape of TF tensor in HWIO format. + TensorShape output_tf_shape({output_dims_mkl_order[MklDnnDims::Dim_H], + output_dims_mkl_order[MklDnnDims::Dim_W], + output_dims_mkl_order[MklDnnDims::Dim_I], + output_dims_mkl_order[MklDnnDims::Dim_O]}); + AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, + output_mkl_shape); } // Allocate tensor for bias grad @@ -600,9 +602,9 @@ class MklConv2DCustomBackpropFilterOp : // Prepare and execute net - checks for input and output reorders. void PrepareAndExecutePrimitive( - const convolution_backward_weights::primitive_desc& conv_pd, - MklDnnData* input, MklDnnData* obp, - MklDnnData* output, MklDnnData* bias_grad = nullptr) { + const convolution_backward_weights::primitive_desc& conv_pd, + MklDnnData* input, MklDnnData* obp, MklDnnData* output, + MklDnnData* bias_grad = nullptr) { // Create reorders between user layout and MKL layout if it is needed and // add it to the net before convolution. std::vector net; @@ -612,15 +614,15 @@ class MklConv2DCustomBackpropFilterOp : // For BackpropFilter, we convert the output tensor back in Tensorflow // layout. bool output_reorder_required = output->PrepareReorderToUserMemIfReq( - conv_pd.diff_weights_primitive_desc()); + conv_pd.diff_weights_primitive_desc()); if (biasEnabled && (bias_grad != nullptr)) { - net.push_back(convolution_backward_weights(conv_pd, input->GetOpMem(), - obp->GetOpMem(), output->GetOpMem(), - bias_grad->GetOpMem())); + net.push_back(convolution_backward_weights( + conv_pd, input->GetOpMem(), obp->GetOpMem(), output->GetOpMem(), + bias_grad->GetOpMem())); } else { - net.push_back(convolution_backward_weights(conv_pd, input->GetOpMem(), - obp->GetOpMem(), output->GetOpMem())); + net.push_back(convolution_backward_weights( + conv_pd, input->GetOpMem(), obp->GetOpMem(), output->GetOpMem())); } if (output_reorder_required) { @@ -631,22 +633,24 @@ class MklConv2DCustomBackpropFilterOp : } }; -#define REGISTER_MKL_FILTER_KERNELS(T) \ - REGISTER_KERNEL_BUILDER(Name("_MklConv2DBackpropFilter") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ - MklConv2DCustomBackpropFilterOp);\ - REGISTER_KERNEL_BUILDER(Name("_MklConv2DBackpropFilterWithBias") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ - MklConv2DCustomBackpropFilterOp); \ - REGISTER_KERNEL_BUILDER(Name("__MklDummyConv2DBackpropFilterWithBias") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ - MklDummyOp); +#define REGISTER_MKL_FILTER_KERNELS(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("_MklConv2DBackpropFilter") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklConv2DCustomBackpropFilterOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("_MklConv2DBackpropFilterWithBias") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklConv2DCustomBackpropFilterOp); \ + REGISTER_KERNEL_BUILDER(Name("__MklDummyConv2DBackpropFilterWithBias") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklDummyOp); TF_CALL_float(REGISTER_MKL_FILTER_KERNELS); #undef REGISTER_MKL_FILTER_KERNELS diff --git a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc index ef6db58d31..a6745489f4 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc @@ -23,6 +23,8 @@ limitations under the License. #define EIGEN_USE_THREADS #include #include +#include "mkl_dnn.h" +#include "mkl_dnn_types.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -41,15 +43,13 @@ limitations under the License. #include "tensorflow/core/util/tensor_format.h" #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#include "mkl_dnn.h" -#include "mkl_dnn_types.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; -using mkldnn::prop_kind; using mkldnn::convolution_backward_data; +using mkldnn::prop_kind; +using mkldnn::stream; #endif namespace tensorflow { @@ -359,16 +359,15 @@ class MklConv2DCustomBackpropInputOp : public OpKernel { #else template -class MklConv2DCustomBackpropInputOp : - public MklConv2DBackpropCommonOp { +class MklConv2DCustomBackpropInputOp + : public MklConv2DBackpropCommonOp { public: explicit MklConv2DCustomBackpropInputOp(OpKernelConstruction* context) - : MklConv2DBackpropCommonOp(context) { } + : MklConv2DBackpropCommonOp(context) {} ~MklConv2DCustomBackpropInputOp() {} private: - const int kInputIndex_Filter = 1, - kInputIndex_InputSizes = 0, + const int kInputIndex_Filter = 1, kInputIndex_InputSizes = 0, kInputIndex_OutBackProp = 2; void ValidateMklShapes(const MklDnnShape& input_mkl_shape, const MklDnnShape& filter_mkl_shape, @@ -377,7 +376,7 @@ class MklConv2DCustomBackpropInputOp : // of the Tensor and never an actual tensor. So it will never be in MKL // layout. CHECK(!input_mkl_shape.IsMklTensor()) - << "Conv2DBackpropInput: input should not be in MKL Layout"; + << "Conv2DBackpropInput: input should not be in MKL Layout"; } size_t GetInputTensorIndexWithSizes() { return kInputIndex_InputSizes; } @@ -386,8 +385,10 @@ class MklConv2DCustomBackpropInputOp : const Tensor& input_tensor) { TensorShape input_tf_shape; CHECK_EQ(TensorShapeUtils::IsVector(input_tensor.shape()), true); - CHECK_EQ(TensorShapeUtils::MakeShape(input_tensor.vec(), - &input_tf_shape).ok(), true); + CHECK_EQ( + TensorShapeUtils::MakeShape(input_tensor.vec(), &input_tf_shape) + .ok(), + true); return input_tf_shape; } @@ -414,16 +415,13 @@ class MklConv2DCustomBackpropInputOp : return data_format; } - void CreatePrimitive(OpKernelContext* context, - const engine& cpu_engine, + void CreatePrimitive(OpKernelContext* context, const engine& cpu_engine, const convolution_forward::primitive_desc& conv_fwd_pd, MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, MklDnnData* output, - Tensor** output_tensor, - const memory::dims& strides, + Tensor** output_tensor, const memory::dims& strides, const memory::dims& padding_l, - const memory::dims& padding_r, - padding_kind padding, + const memory::dims& padding_r, padding_kind padding, const memory::dims& bwd_output_dims, memory::format bwd_output_format) { CHECK_NOTNULL(context); @@ -434,19 +432,16 @@ class MklConv2DCustomBackpropInputOp : CHECK_NOTNULL(output_tensor); // Create convolution backward data primitive. - auto bwd_desc = convolution_backward_data::desc(convolution_direct, - output->GetOpMemDesc(), filter->GetOpMemDesc(), - outbackprop->GetOpMemDesc(), strides, padding_l, - padding_r, padding); - - auto bwd_pd = convolution_backward_data::primitive_desc(bwd_desc, - cpu_engine, - conv_fwd_pd); + auto bwd_desc = convolution_backward_data::desc( + convolution_direct, output->GetOpMemDesc(), filter->GetOpMemDesc(), + outbackprop->GetOpMemDesc(), strides, padding_l, padding_r, padding); + auto bwd_pd = convolution_backward_data::primitive_desc( + bwd_desc, cpu_engine, conv_fwd_pd); // Allocate output tensor in TensorFlow and MKL layout. - AllocateOutputTensor(context, bwd_pd, bwd_output_dims, - bwd_output_format, output_tensor); + AllocateOutputTensor(context, bwd_pd, bwd_output_dims, bwd_output_format, + output_tensor); CHECK_NOTNULL(*output_tensor); // Set buffer handle using allocated output tensor. output->SetUsrMemDataHandle(*output_tensor); @@ -455,44 +450,44 @@ class MklConv2DCustomBackpropInputOp : } // Allocate output tensor. - void AllocateOutputTensor(OpKernelContext* context, - const convolution_backward_data::primitive_desc& conv_pd, - const memory::dims& output_dims_mkl_order, - memory::format output_tf_format, Tensor** output_tensor) { - CHECK_NOTNULL(output_tensor); - - // Output primitive descriptor for backward data is diff_src. - auto dst_pd = conv_pd.diff_src_primitive_desc(); - - // Allocate shape of Mkl tensor. - MklDnnShape output_mkl_shape; - output_mkl_shape.SetMklTensor(true); - output_mkl_shape.SetMklLayout(&dst_pd); - output_mkl_shape.SetElemType(MklDnnType()); - output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, output_tf_format); - - // Allocate shape of TF tensor. - TensorShape output_tf_shape; - output_tf_shape.AddDim(dst_pd.get_size() / sizeof(T)); - - AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, - output_mkl_shape); + void AllocateOutputTensor( + OpKernelContext* context, + const convolution_backward_data::primitive_desc& conv_pd, + const memory::dims& output_dims_mkl_order, + memory::format output_tf_format, Tensor** output_tensor) { + CHECK_NOTNULL(output_tensor); + + // Output primitive descriptor for backward data is diff_src. + auto dst_pd = conv_pd.diff_src_primitive_desc(); + + // Allocate shape of Mkl tensor. + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(true); + output_mkl_shape.SetMklLayout(&dst_pd); + output_mkl_shape.SetElemType(MklDnnType()); + output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), + output_dims_mkl_order, output_tf_format); + + // Allocate shape of TF tensor. + TensorShape output_tf_shape; + output_tf_shape.AddDim(dst_pd.get_size() / sizeof(T)); + + AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, + output_mkl_shape); } // Prepare and execute net - checks for input and output reorders. void PrepareAndExecutePrimitive( - const convolution_backward_data::primitive_desc& conv_pd, - MklDnnData* filter, MklDnnData* obp, - MklDnnData* output) { + const convolution_backward_data::primitive_desc& conv_pd, + MklDnnData* filter, MklDnnData* obp, MklDnnData* output) { // Create reorders between user layout and MKL layout if it is needed and // add it to the net before convolution. std::vector net; filter->CheckReorderToOpMem(conv_pd.weights_primitive_desc(), &net); obp->CheckReorderToOpMem(conv_pd.diff_dst_primitive_desc(), &net); - net.push_back(convolution_backward_data(conv_pd, obp->GetOpMem(), - filter->GetOpMem(), output->GetOpMem())); + net.push_back(convolution_backward_data( + conv_pd, obp->GetOpMem(), filter->GetOpMem(), output->GetOpMem())); stream(stream::kind::eager).submit(net).wait(); } diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index 0e77b45993..e44fba754b 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -18,8 +18,8 @@ limitations under the License. #include #include -#include #include +#include #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -41,15 +41,14 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" - #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; using mkldnn::prop_kind; +using mkldnn::stream; -using mkldnn::convolution_forward; using mkldnn::convolution_direct; +using mkldnn::convolution_forward; #else #include "mkl_dnn.h" #include "mkl_dnn_types.h" @@ -116,18 +115,19 @@ class MklConv2DOp : public OpKernel { filter.shape().DebugString())); for (int i = 0; i < 3; i++) { - OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES( + context, + FastBoundsCheck(filter.dim_size(i), std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } const int64 input_depth = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'C') : GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES( - context, input_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - input_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, input_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", input_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -136,9 +136,10 @@ class MklConv2DOp : public OpKernel { const int64 input_rows_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'H') : GetTensorDim(input, data_format_, 'H'); - OP_REQUIRES(context, FastBoundsCheck(input_rows_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input rows too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_rows_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input rows too large")); const int input_rows = static_cast(input_rows_raw); const int filter_rows = static_cast(filter.dim_size(0)); @@ -147,9 +148,10 @@ class MklConv2DOp : public OpKernel { const int64 input_cols_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'W') : GetTensorDim(input, data_format_, 'W'); - OP_REQUIRES(context, FastBoundsCheck(input_cols_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input cols too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_cols_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input cols too large")); const int input_cols = static_cast(input_cols_raw); const int filter_cols = static_cast(filter.dim_size(1)); @@ -157,9 +159,10 @@ class MklConv2DOp : public OpKernel { const int64 input_batch_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'N') : GetTensorDim(input, data_format_, 'N'); - OP_REQUIRES(context, FastBoundsCheck(input_batch_raw, - std::numeric_limits::max()), - errors::InvalidArgument("batch is too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_batch_raw, std::numeric_limits::max()), + errors::InvalidArgument("batch is too large")); const int batch = static_cast(input_batch_raw); // For now we take the stride from the second and third dimensions only (we @@ -313,8 +316,7 @@ class MklConv2DOp : public OpKernel { // Temp tensor used to allocate tmp buffers Tensor mkl_tmp_input_buf_tensor, mkl_tmp_filter_buf_tensor, mkl_tmp_bias_buf_tensor; - mkl_context.MklPrepareConvolutionInputs(context, - &mkl_tmp_input_buf_tensor, + mkl_context.MklPrepareConvolutionInputs(context, &mkl_tmp_input_buf_tensor, &mkl_tmp_filter_buf_tensor, &mkl_tmp_bias_buf_tensor); @@ -398,8 +400,9 @@ class MklConv2DOp : public OpKernel { mkl_convert_input = !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input); if (mkl_convert_input) { - CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, - lt_input, mkl_lt_internal_input), E_SUCCESS); + CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input, + mkl_lt_internal_input), + E_SUCCESS); AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input, &mkl_buf_convert_input); CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input, @@ -517,8 +520,8 @@ class MklConv2DOp : public OpKernel { GetMklShape(context, kInputIndex_Src, &src_mkl_shape); GetMklShape(context, kInputIndex_Filter, &filter_mkl_shape); OP_REQUIRES(context, filter_mkl_shape.IsMklTensor() == false, - errors::InvalidArgument("Filter should not be in " - "Mkl Layout")); + errors::InvalidArgument("Filter should not be in " + "Mkl Layout")); MklDnnData src(&cpu_engine); MklDnnData filter(&cpu_engine); @@ -531,11 +534,10 @@ class MklConv2DOp : public OpKernel { MklDnnConvUtil conv_utl(context, strides_, padding_, data_format_); auto src_tf_shape = GetTfShape(context, kInputIndex_Src); auto filter_tf_shape = GetTfShape(context, kInputIndex_Filter); - conv_utl.GetConvFwdSizesInMklOrder(src_tf_shape, filter_tf_shape, - &src_dims, &filter_dims, &strides, - &output_dims_tf_order, - &output_dims_mkl_order, &padding_l, - &padding_r); + conv_utl.GetConvFwdSizesInMklOrder( + src_tf_shape, filter_tf_shape, &src_dims, &filter_dims, &strides, + &output_dims_tf_order, &output_dims_mkl_order, &padding_l, + &padding_r); if (!context->status().ok()) return; // Check for corner case - if there is nothing to compute, return. @@ -543,21 +545,20 @@ class MklConv2DOp : public OpKernel { // Corner cases: output with 0 elements and 0 batch size. Tensor* output_tensor = nullptr; - if (output_tf_shape.num_elements() == 0 || - output_dims_tf_order[0] == 0) { + if (output_tf_shape.num_elements() == 0 || output_dims_tf_order[0] == 0) { // TODO(jbobba): Verify correctness here // Need semantics for Null MKL tensor MklDnnShape output_mkl_shape; output_mkl_shape.SetMklTensor(false); AllocateOutputSetMklShape(context, kOutputIndex_Dst, &output_tensor, - src_tf_shape, output_mkl_shape); + src_tf_shape, output_mkl_shape); // MklConv2D also outputs converted filter as 2nd output of Conv2D. filter_mkl_shape.SetMklTensor(false); Tensor* output_filter_tensor = nullptr; AllocateOutputSetMklShape(context, kOutputIndex_Filter, - &output_filter_tensor, - filter_tf_shape, filter_mkl_shape); + &output_filter_tensor, filter_tf_shape, + filter_mkl_shape); return; } @@ -570,14 +571,15 @@ class MklConv2DOp : public OpKernel { // (src_dims) required is in MKL-DNN order, the layout is Tensorflow's // layout (NHWC or NCHW depending on data format). auto src_md = src_mkl_shape.IsMklTensor() - ? src_mkl_shape.GetMklLayout() - : memory::desc(src_dims, MklDnnType(), tf_fmt); + ? src_mkl_shape.GetMklLayout() + : memory::desc(src_dims, MklDnnType(), tf_fmt); src.SetUsrMem(src_md, &src_tensor); // Although filter shape (filter_dims) required is in MKL-DNN order, // the layout is Tensorflow's layout (HWIO). auto filter_md = filter_mkl_shape.IsMklTensor() // Should NEVER be true - ? filter_mkl_shape.GetMklLayout() - : memory::desc(filter_dims, MklDnnType(), memory::format::hwio); + ? filter_mkl_shape.GetMklLayout() + : memory::desc(filter_dims, MklDnnType(), + memory::format::hwio); filter.SetUsrMem(filter_md, &filter_tensor); // Set output shape (output_dims) required in MKL-DNN order. @@ -601,34 +603,34 @@ class MklConv2DOp : public OpKernel { bias.SetOpMemDesc(bias_size, memory::format::any); // Create convolution primitive with Bias. - auto conv_desc = convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), filter.GetOpMemDesc(), - bias.GetOpMemDesc(), output.GetOpMemDesc(), strides, - padding_l, padding_r, TFPaddingToMklDnnPadding(padding_)); - - auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, - cpu_engine); - AllocateOutputTensor(context, conv_prim_desc, - output_dims_mkl_order, tf_fmt, &output_tensor); + auto conv_desc = convolution_forward::desc( + prop_kind::forward, convolution_direct, src.GetOpMemDesc(), + filter.GetOpMemDesc(), bias.GetOpMemDesc(), output.GetOpMemDesc(), + strides, padding_l, padding_r, TFPaddingToMklDnnPadding(padding_)); + + auto conv_prim_desc = + convolution_forward::primitive_desc(conv_desc, cpu_engine); + AllocateOutputTensor(context, conv_prim_desc, output_dims_mkl_order, + tf_fmt, &output_tensor); // Set data handle for output. output.SetUsrMemDataHandle(output_tensor); Tensor* filter_out_tensor = nullptr; AllocateFilterOutputTensor(context, conv_prim_desc, - TFShapeToMklDnnDims(filter_tf_shape), - &filter_out_tensor); + TFShapeToMklDnnDims(filter_tf_shape), + &filter_out_tensor); - PrepareAndExecuteNet(conv_prim_desc, &src, &filter, - &bias, &output, filter_out_tensor); + PrepareAndExecuteNet(conv_prim_desc, &src, &filter, &bias, &output, + filter_out_tensor); } else { // Create convolution primitive without Bias. - auto conv_desc = convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), filter.GetOpMemDesc(), - output.GetOpMemDesc(), strides, padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_)); + auto conv_desc = convolution_forward::desc( + prop_kind::forward, convolution_direct, src.GetOpMemDesc(), + filter.GetOpMemDesc(), output.GetOpMemDesc(), strides, padding_l, + padding_r, TFPaddingToMklDnnPadding(padding_)); - auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, - cpu_engine); + auto conv_prim_desc = + convolution_forward::primitive_desc(conv_desc, cpu_engine); AllocateOutputTensor(context, conv_prim_desc, output_dims_mkl_order, tf_fmt, &output_tensor); // Set data handle for output. @@ -636,18 +638,18 @@ class MklConv2DOp : public OpKernel { Tensor* filter_out_tensor = nullptr; AllocateFilterOutputTensor(context, conv_prim_desc, - TFShapeToMklDnnDims(filter_tf_shape), - &filter_out_tensor); - PrepareAndExecuteNet(conv_prim_desc, &src, &filter, - nullptr, &output, filter_out_tensor); + TFShapeToMklDnnDims(filter_tf_shape), + &filter_out_tensor); + PrepareAndExecuteNet(conv_prim_desc, &src, &filter, nullptr, &output, + filter_out_tensor); } - } catch (mkldnn::error &e) { + } 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__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", error_msg)); + ", message: " + std::string(e.message) + ", in file " + + std::string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -655,71 +657,67 @@ class MklConv2DOp : public OpKernel { std::vector strides_; Padding padding_; TensorFormat data_format_; - const int kInputIndex_Src = 0, - kInputIndex_Filter = 1, - kInputIndex_Bias = 2; + const int kInputIndex_Src = 0, kInputIndex_Filter = 1, kInputIndex_Bias = 2; const int kOutputIndex_Dst = 0, kOutputIndex_Filter = 1; // Allocate output tensor. void AllocateOutputTensor( - OpKernelContext* context, - const convolution_forward::primitive_desc& conv_prim_desc, - const memory::dims& output_dims_mkl_order, - memory::format output_tf_format, Tensor** output_tensor) { - CHECK_NOTNULL(output_tensor); - auto dst_pd = conv_prim_desc.dst_primitive_desc(); - - // Allocate shape of Mkl tensor. - MklDnnShape output_mkl_shape; - output_mkl_shape.SetMklTensor(true); - output_mkl_shape.SetMklLayout(&dst_pd); - output_mkl_shape.SetElemType(MklDnnType()); - output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, output_tf_format); - - // Allocate shape of TF tensor. - TensorShape output_tf_shape; - output_tf_shape.AddDim((dst_pd.get_size() / sizeof(T))); - - AllocateOutputSetMklShape(context, kOutputIndex_Dst, output_tensor, - output_tf_shape, output_mkl_shape); + OpKernelContext* context, + const convolution_forward::primitive_desc& conv_prim_desc, + const memory::dims& output_dims_mkl_order, + memory::format output_tf_format, Tensor** output_tensor) { + CHECK_NOTNULL(output_tensor); + auto dst_pd = conv_prim_desc.dst_primitive_desc(); + + // Allocate shape of Mkl tensor. + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(true); + output_mkl_shape.SetMklLayout(&dst_pd); + output_mkl_shape.SetElemType(MklDnnType()); + output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), + output_dims_mkl_order, output_tf_format); + + // Allocate shape of TF tensor. + TensorShape output_tf_shape; + output_tf_shape.AddDim((dst_pd.get_size() / sizeof(T))); + + AllocateOutputSetMklShape(context, kOutputIndex_Dst, output_tensor, + output_tf_shape, output_mkl_shape); } // Allocate output tensor. void AllocateFilterOutputTensor( - OpKernelContext* context, - const convolution_forward::primitive_desc& conv_prim_desc, - const memory::dims& filter_dims_tf_order, - Tensor** filter_tensor) { - CHECK_NOTNULL(filter_tensor); - auto filter_pd = conv_prim_desc.weights_primitive_desc(); - - // Allocate shape of Mkl tensor. - MklDnnShape filter_mkl_shape; - filter_mkl_shape.SetMklTensor(true); - filter_mkl_shape.SetMklLayout(&filter_pd); - filter_mkl_shape.SetElemType(MklDnnType()); - - // The format of the filter is actually OIhw8i8o, but TF doesn't support - // this format. Just use format::blocked for now because the layout - // is stored in the MKL data. - filter_mkl_shape.SetTfLayout(filter_dims_tf_order.size(), - filter_dims_tf_order, memory::format::blocked); - - // Allocate the data space for the filter to propagate as TF tensor. - TensorShape filter_tf_shape; - filter_tf_shape.AddDim((filter_pd.get_size() / sizeof(T))); - - AllocateOutputSetMklShape(context, kOutputIndex_Filter, filter_tensor, - filter_tf_shape, filter_mkl_shape); + OpKernelContext* context, + const convolution_forward::primitive_desc& conv_prim_desc, + const memory::dims& filter_dims_tf_order, Tensor** filter_tensor) { + CHECK_NOTNULL(filter_tensor); + auto filter_pd = conv_prim_desc.weights_primitive_desc(); + + // Allocate shape of Mkl tensor. + MklDnnShape filter_mkl_shape; + filter_mkl_shape.SetMklTensor(true); + filter_mkl_shape.SetMklLayout(&filter_pd); + filter_mkl_shape.SetElemType(MklDnnType()); + + // The format of the filter is actually OIhw8i8o, but TF doesn't support + // this format. Just use format::blocked for now because the layout + // is stored in the MKL data. + filter_mkl_shape.SetTfLayout(filter_dims_tf_order.size(), + filter_dims_tf_order, memory::format::blocked); + + // Allocate the data space for the filter to propagate as TF tensor. + TensorShape filter_tf_shape; + filter_tf_shape.AddDim((filter_pd.get_size() / sizeof(T))); + + AllocateOutputSetMklShape(context, kOutputIndex_Filter, filter_tensor, + filter_tf_shape, filter_mkl_shape); } // Prepare and execute net - checks for input and output reorders. void PrepareAndExecuteNet( - const convolution_forward::primitive_desc& conv_prim_desc, - MklDnnData* src, MklDnnData* filter, - MklDnnData* bias, MklDnnData* output, - Tensor* filter_out_tensor) { + const convolution_forward::primitive_desc& conv_prim_desc, + MklDnnData* src, MklDnnData* filter, MklDnnData* bias, + MklDnnData* output, Tensor* filter_out_tensor) { CHECK_NOTNULL(filter_out_tensor); // Create reorders between user layout and MKL layout if it is needed and @@ -731,18 +729,20 @@ class MklConv2DOp : public OpKernel { // rather than re-order to a temp buffer, reorder directly to the // filter output tensor filter->CheckReorderToOpMem(conv_prim_desc.weights_primitive_desc(), - filter->GetTensorBuffer(filter_out_tensor), &net); + filter->GetTensorBuffer(filter_out_tensor), + &net); // Create convolution primitive and add it to net. if (bias) { CHECK_EQ(biasEnabled, true); net.push_back(convolution_forward(conv_prim_desc, src->GetOpMem(), - filter->GetOpMem(), bias->GetOpMem(), - output->GetOpMem())); + filter->GetOpMem(), bias->GetOpMem(), + output->GetOpMem())); } else { CHECK_EQ(biasEnabled, false); net.push_back(convolution_forward(conv_prim_desc, src->GetOpMem(), - filter->GetOpMem(), output->GetOpMem())); + filter->GetOpMem(), + output->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); diff --git a/tensorflow/core/kernels/mkl_conv_ops.h b/tensorflow/core/kernels/mkl_conv_ops.h index c6456bd5c3..8b65eaea0d 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.h +++ b/tensorflow/core/kernels/mkl_conv_ops.h @@ -16,9 +16,9 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_MKL_CONV_OPS_H_ #define TENSORFLOW_CORE_KERNELS_MKL_CONV_OPS_H_ -#include #include #include +#include #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -27,8 +27,8 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/tensor_slice.h" #include "tensorflow/core/kernels/bounds_check.h" -#include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/kernels/conv_grad_ops.h" +#include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/strings/numbers.h" @@ -43,11 +43,11 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; using mkldnn::prop_kind; +using mkldnn::stream; -using mkldnn::convolution_forward; using mkldnn::convolution_direct; +using mkldnn::convolution_forward; #endif namespace tensorflow { @@ -63,13 +63,13 @@ class MklDnnConvUtil { public: MklDnnConvUtil(OpKernelContext* context, const std::vector& strides, - Padding pad, TensorFormat fm) : context_(context), - strides_(strides), padding_(pad), data_format_(fm) {} + Padding pad, TensorFormat fm) + : context_(context), strides_(strides), padding_(pad), data_format_(fm) {} virtual ~MklDnnConvUtil() { context_ = nullptr; } // Calculate Convolution strides - virtual inline void GetStridesInMklOrder(memory::dims *strides) { + virtual inline void GetStridesInMklOrder(memory::dims* strides) { // For now we take the stride from the second and third dimensions only // (we do not support striding on the batch or depth dimension). CHECK_NOTNULL(strides); @@ -82,14 +82,14 @@ class MklDnnConvUtil { // requires input in NCHW format. Function does not return anything. // But errors arising from sanity checks are returned in context's // status. - virtual inline void - GetInputSizeInMklOrder(const TensorShape& input_shape, - memory::dims *input_dims) { - #define CHECK_BOUNDS(val, err_msg) do { \ - OP_REQUIRES(context_, FastBoundsCheck(val, \ - std::numeric_limits::max()), \ - errors::InvalidArgument(err_msg)); \ - }while(0) + virtual inline void GetInputSizeInMklOrder(const TensorShape& input_shape, + memory::dims* input_dims) { +#define CHECK_BOUNDS(val, err_msg) \ + do { \ + OP_REQUIRES(context_, \ + FastBoundsCheck(val, std::numeric_limits::max()), \ + errors::InvalidArgument(err_msg)); \ + } while (0) CHECK_NOTNULL(input_dims); @@ -112,7 +112,7 @@ class MklDnnConvUtil { CHECK_BOUNDS(input_batch_raw, "Input batch too large"); int input_batch = static_cast(input_batch_raw); - #undef CHECK_BOUNDS +#undef CHECK_BOUNDS // MKL-DNN always requires input in NCHW format. std::vector mkldnn_sizes(4, -1); @@ -138,10 +138,9 @@ class MklDnnConvUtil { // forward gets actual tensor as input). // // TODO(nhasabni): Add similar function for input and filter in MklShape. - virtual inline void - GetFilterSizeInMklOrder(const TensorShape& input_shape, - const TensorShape& filter_shape, - memory::dims *filter_dims) { + virtual inline void GetFilterSizeInMklOrder(const TensorShape& input_shape, + const TensorShape& filter_shape, + memory::dims* filter_dims) { CHECK_NOTNULL(filter_dims); OP_REQUIRES(context_, filter_shape.dims() == 4, @@ -149,17 +148,18 @@ class MklDnnConvUtil { filter_shape.DebugString())); for (int i = 0; i < 3; i++) { - OP_REQUIRES(context_, FastBoundsCheck(filter_shape.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES(context_, + FastBoundsCheck(filter_shape.dim_size(i), + std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } int input_depth = GetTensorDim(input_shape, data_format_, 'C'); - OP_REQUIRES( - context_, input_depth == filter_shape.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - input_depth, " vs ", filter_shape.dim_size(2))); + OP_REQUIRES(context_, input_depth == filter_shape.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", input_depth, + " vs ", filter_shape.dim_size(2))); // TF filter is always in (rows, cols, in_depth, out_depth) order. int filter_rows = static_cast(filter_shape.dim_size(0)); @@ -182,25 +182,24 @@ class MklDnnConvUtil { // requires filter in OIHW format. Function does not return anything. // But errors arising from sanity checks are returned in context's // status. - virtual inline void - GetFilterSizeInMklOrder(size_t src_index, size_t filter_index, - memory::dims *filter_dims) { + virtual inline void GetFilterSizeInMklOrder(size_t src_index, + size_t filter_index, + memory::dims* filter_dims) { CHECK_NOTNULL(filter_dims); GetFilterSizeInMklOrder(GetTfShape(context_, src_index), - GetTfShape(context_, filter_index), - filter_dims); + GetTfShape(context_, filter_index), filter_dims); } // Calculate Bias size for 2D Convolution. Function does not return // anything, but sets error in context status. - virtual inline void - GetBiasSizeInMklOrder(size_t bias_index, memory::dims *bias_dims) { + virtual inline void GetBiasSizeInMklOrder(size_t bias_index, + memory::dims* bias_dims) { const Tensor& bias = MklGetInput(context_, bias_index); OP_REQUIRES(context_, bias.dims() == 1, errors::InvalidArgument("bias must be 1-dimensional: ", bias.shape().DebugString())); - *bias_dims = { static_cast(bias.dim_size(0)) }; + *bias_dims = {static_cast(bias.dim_size(0))}; } // Function to calculate output and padding size for 2D convolution. @@ -212,13 +211,11 @@ class MklDnnConvUtil { // status is returned via context status. // // TODO(nhasabni): Add similar function for input and filter in MklShape. - virtual inline void - GetOutputAndPadSizeInMklOrder(const TensorShape& input_shape, - const TensorShape& filter_shape, - const memory::dims& strides, - memory::dims *output_dims_tf_order, - memory::dims *output_dims_mkl_order, - memory::dims *pad_l, memory::dims *pad_r) { + virtual inline void GetOutputAndPadSizeInMklOrder( + const TensorShape& input_shape, const TensorShape& filter_shape, + const memory::dims& strides, memory::dims* output_dims_tf_order, + memory::dims* output_dims_mkl_order, memory::dims* pad_l, + memory::dims* pad_r) { CHECK_NOTNULL(output_dims_tf_order); CHECK_NOTNULL(output_dims_mkl_order); CHECK_NOTNULL(pad_l); @@ -244,16 +241,16 @@ class MklDnnConvUtil { int64 out_rows = 0, out_cols = 0; int64 pad_top = 0, pad_bottom = 0, pad_left, pad_right; - OP_REQUIRES_OK(context_, - GetWindowedOutputSizeVerbose(input_rows, filter_rows, stride_rows, - padding_, &out_rows, &pad_top, &pad_bottom)); - OP_REQUIRES_OK(context_, - GetWindowedOutputSizeVerbose(input_cols, filter_cols, stride_cols, - padding_, &out_cols, &pad_left, &pad_right)); + OP_REQUIRES_OK(context_, GetWindowedOutputSizeVerbose( + input_rows, filter_rows, stride_rows, padding_, + &out_rows, &pad_top, &pad_bottom)); + OP_REQUIRES_OK(context_, GetWindowedOutputSizeVerbose( + input_cols, filter_cols, stride_cols, padding_, + &out_cols, &pad_left, &pad_right)); // Tensorflow output is in data_format order. (NHWC or NCHW) - TensorShape out_shape = ShapeFromFormat(data_format_, out_batch, - out_rows, out_cols, out_depth); + TensorShape out_shape = + ShapeFromFormat(data_format_, out_batch, out_rows, out_cols, out_depth); *output_dims_tf_order = TFShapeToMklDnnDims(out_shape); // MKL-DNN always needs output in NCHW format. @@ -273,12 +270,10 @@ class MklDnnConvUtil { // See comment on GetConvOutputAndPadSizeInMklOrder for parameters. // // Function does not return anything, but sets error in context status. - inline void - GetOutputAndPadSizeInMklOrder(size_t src_index, size_t filter_index, - const memory::dims& strides, - memory::dims *output_dims_tf_order, - memory::dims *output_dims_mkl_order, - memory::dims *pad_l, memory::dims *pad_r) { + inline void GetOutputAndPadSizeInMklOrder( + size_t src_index, size_t filter_index, const memory::dims& strides, + memory::dims* output_dims_tf_order, memory::dims* output_dims_mkl_order, + memory::dims* pad_l, memory::dims* pad_r) { CHECK_NOTNULL(output_dims_tf_order); CHECK_NOTNULL(output_dims_mkl_order); CHECK_NOTNULL(pad_l); @@ -289,11 +284,11 @@ class MklDnnConvUtil { OP_REQUIRES(context_, input_tf_shape.dims() == 4, errors::InvalidArgument("input must be 4-dimensional", - input_tf_shape.DebugString())); + input_tf_shape.DebugString())); - GetOutputAndPadSizeInMklOrder(input_tf_shape, filter_tf_shape, - strides, output_dims_tf_order, - output_dims_mkl_order, pad_l, pad_r); + GetOutputAndPadSizeInMklOrder(input_tf_shape, filter_tf_shape, strides, + output_dims_tf_order, output_dims_mkl_order, + pad_l, pad_r); } // Wrapper function to calculate input, filter, and output sizes of @@ -302,15 +297,12 @@ class MklDnnConvUtil { // also calculates strides and paddings for 2D Convolution. // // Function does not return anything, but sets error in context status. - inline void GetConvFwdSizesInMklOrder(const TensorShape& input_shape, - const TensorShape& filter_shape, - memory::dims *input_dims, - memory::dims *filter_dims, - memory::dims *strides, - memory::dims *output_dims_tf_order, - memory::dims *output_dims_mkl_order, - memory::dims *pad_l, - memory::dims *pad_r) { + inline void GetConvFwdSizesInMklOrder( + const TensorShape& input_shape, const TensorShape& filter_shape, + memory::dims* input_dims, memory::dims* filter_dims, + memory::dims* strides, memory::dims* output_dims_tf_order, + memory::dims* output_dims_mkl_order, memory::dims* pad_l, + memory::dims* pad_r) { CHECK_NOTNULL(input_dims); CHECK_NOTNULL(filter_dims); CHECK_NOTNULL(strides); @@ -325,8 +317,7 @@ class MklDnnConvUtil { if (!context_->status().ok()) return; GetStridesInMklOrder(strides); GetOutputAndPadSizeInMklOrder(input_shape, filter_shape, *strides, - output_dims_tf_order, - output_dims_mkl_order, + output_dims_tf_order, output_dims_mkl_order, pad_l, pad_r); if (!context_->status().ok()) return; } @@ -337,7 +328,7 @@ class MklDnnConvUtil { ///////////////////////////////////////////////////////////////////// template -class MklConv2DBackpropCommonOp : public OpKernel { +class MklConv2DBackpropCommonOp : public OpKernel { public: ~MklConv2DBackpropCommonOp() {} explicit MklConv2DBackpropCommonOp(OpKernelConstruction* context) @@ -397,12 +388,11 @@ class MklConv2DBackpropCommonOp : public OpKernel { outbprop_tf_shape.num_elements() == 0) { MklDnnShape output_mkl_shape; output_mkl_shape.SetMklTensor(false); - TensorShape output_tf_shape = GetOutputTfShape(input_tf_shape, - filter_tf_shape, - outbprop_tf_shape); + TensorShape output_tf_shape = GetOutputTfShape( + input_tf_shape, filter_tf_shape, outbprop_tf_shape); const int kOutputIdx = 0; AllocateOutputSetMklShape(context, kOutputIdx, &output_tensor, - output_tf_shape, output_mkl_shape); + output_tf_shape, output_mkl_shape); CHECK_NOTNULL(output_tensor); // if output tensor has more than 0 elements, we need to 0 them out. @@ -421,12 +411,10 @@ class MklConv2DBackpropCommonOp : public OpKernel { // Get forward convolution parameters. MklDnnConvUtil conv_utl(context, strides_, padding_, data_format_); - conv_utl.GetConvFwdSizesInMklOrder(input_tf_shape, filter_tf_shape, - &fwd_input_dims, &fwd_filter_dims, - &strides, - &fwd_output_dims_tf_order, - &fwd_output_dims, - &padding_l, &padding_r); + conv_utl.GetConvFwdSizesInMklOrder( + input_tf_shape, filter_tf_shape, &fwd_input_dims, &fwd_filter_dims, + &strides, &fwd_output_dims_tf_order, &fwd_output_dims, &padding_l, + &padding_r); if (!context->status().ok()) return; // Create Convolution forward descriptor since Convolution backward @@ -437,20 +425,22 @@ class MklConv2DBackpropCommonOp : public OpKernel { // construct input TF layout. For TF layout, although input shape // required is in MKL-DNN order, the layout is Tensorflow's layout // (NHWC or NCHW depending on data format). - auto fwd_input_md = input_mkl_shape.IsMklTensor() ? - input_mkl_shape.GetMklLayout() : - memory::desc(fwd_input_dims, MklDnnType(), tf_fmt); + auto fwd_input_md = + input_mkl_shape.IsMklTensor() + ? input_mkl_shape.GetMklLayout() + : memory::desc(fwd_input_dims, MklDnnType(), tf_fmt); // If filter is in MKL layout, then simply grab filter layout; otherwise // construct filter in TF layout. For TF layout, filter is in HWIO format. - auto fwd_filter_md = filter_mkl_shape.IsMklTensor() ? - filter_mkl_shape.GetMklLayout() : - memory::desc(fwd_filter_dims, MklDnnType(), - memory::format::hwio); + auto fwd_filter_md = filter_mkl_shape.IsMklTensor() + ? filter_mkl_shape.GetMklLayout() + : memory::desc(fwd_filter_dims, MklDnnType(), + memory::format::hwio); // Tensorflow Output of Conv2D is in data_format order. auto fwd_out_md = memory::desc(fwd_output_dims, MklDnnType(), tf_fmt); - auto fwd_desc = convolution_forward::desc(prop_kind::forward, - convolution_direct, fwd_input_md, fwd_filter_md, fwd_out_md, - strides, padding_l, padding_r, TFPaddingToMklDnnPadding(padding_)); + auto fwd_desc = convolution_forward::desc( + prop_kind::forward, convolution_direct, fwd_input_md, fwd_filter_md, + fwd_out_md, strides, padding_l, padding_r, + TFPaddingToMklDnnPadding(padding_)); auto fwd_pd = convolution_forward::primitive_desc(fwd_desc, cpu_engine); // Create memory for user data. Describe how the inputs and outputs of @@ -495,17 +485,16 @@ class MklConv2DBackpropCommonOp : public OpKernel { // Operator-specific call to create and execute primitive. CreatePrimitive(context, cpu_engine, fwd_pd, &input, &filter, - &outbackprop, &output, &output_tensor, - strides, padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_), + &outbackprop, &output, &output_tensor, strides, padding_l, + padding_r, TFPaddingToMklDnnPadding(padding_), bwd_output_dims, bwd_output_format); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, errors::Aborted("Operation received an exception:", - error_msg)); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -523,11 +512,11 @@ class MklConv2DBackpropCommonOp : public OpKernel { /// Get TensorFlow shape of input tensor. virtual TensorShape MakeInputTfShape(OpKernelContext* context, - const Tensor& input_tensor) = 0; + const Tensor& input_tensor) = 0; /// Get TensorFlow shape of filter tensor. virtual TensorShape MakeFilterTfShape(OpKernelContext* context, - const Tensor& filter_tensor) = 0; + const Tensor& filter_tensor) = 0; /// Get the TensorFlow shape of output tensor. virtual TensorShape GetOutputTfShape(const TensorShape& input_shape, @@ -536,9 +525,9 @@ class MklConv2DBackpropCommonOp : public OpKernel { /// Get shape of output in MKL-DNN order. Computes shape of output from /// input shape (fwd_input_dims) and filter shape (fwd_filter_dims). - virtual - const memory::dims& GetOutputDims(const memory::dims& fwd_input_dims, - const memory::dims& fwd_filter_dims) = 0; + virtual const memory::dims& GetOutputDims( + const memory::dims& fwd_input_dims, + const memory::dims& fwd_filter_dims) = 0; /// Get data_format of output in MKL-DNN order. If output data format is /// same as input data format, then it simply returns value of data_format @@ -546,17 +535,18 @@ class MklConv2DBackpropCommonOp : public OpKernel { virtual memory::format GetOutputFormat(const memory::format data_format) = 0; /// Create and execute the primitive storing output in the output_tensor. - virtual void CreatePrimitive(OpKernelContext* context, - const engine& cpu_engine, - const convolution_forward::primitive_desc& conv_fwd_pd, - MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, - MklDnnData* output, Tensor** output_tensor, const memory::dims& strides, - const memory::dims& padding_l, const memory::dims& padding_r, - padding_kind padding, const memory::dims& bwd_output_dims, - memory::format bwd_output_format) = 0; + virtual void CreatePrimitive( + OpKernelContext* context, const engine& cpu_engine, + const convolution_forward::primitive_desc& conv_fwd_pd, + MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, + MklDnnData* output, Tensor** output_tensor, + const memory::dims& strides, const memory::dims& padding_l, + const memory::dims& padding_r, padding_kind padding, + const memory::dims& bwd_output_dims, + memory::format bwd_output_format) = 0; // Get the data_format {NCHW, NHWC} - TensorFormat GetTFDataFormat () { return data_format_; } + TensorFormat GetTFDataFormat() { return data_format_; } private: std::vector strides_; @@ -575,12 +565,12 @@ class MklDummyOp : public OpKernel { public: ~MklDummyOp() {} - explicit MklDummyOp(OpKernelConstruction* context) : - OpKernel(context) {} + explicit MklDummyOp(OpKernelConstruction* context) : OpKernel(context) {} void Compute(OpKernelContext* context) override { - TF_CHECK_OK(errors::Unimplemented("This is a dummy op." - "It should not have been invoked.")); + TF_CHECK_OK( + errors::Unimplemented("This is a dummy op." + "It should not have been invoked.")); } }; diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc index 8340a91d05..0b6d838e09 100644 --- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc +++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc @@ -28,12 +28,12 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; +using mkldnn::batch_normalization_backward; +using mkldnn::batch_normalization_forward; using mkldnn::prop_kind; -using mkldnn::use_scale_shift; +using mkldnn::stream; using mkldnn::use_global_stats; -using mkldnn::batch_normalization_forward; -using mkldnn::batch_normalization_backward; +using mkldnn::use_scale_shift; #endif // TODO(inteltf) Address comments from PR 8968. @@ -601,7 +601,7 @@ class MklFusedBatchNormGradOp : public OpKernel { mkl_res_batchnorm_bwd[dnnResourceSrc] = (mkl_convert_input) ? mkl_buf_converted_input : mkl_buf_input; - bool mkl_convert_out_backprop; + bool mkl_convert_out_backprop; dnnPrimitive_t mkl_prim_convert_out_backprop = nullptr; dnnLayout_t mkl_lt_internal_out_backprop = nullptr; void* mkl_buf_converted_out_backprop = nullptr; @@ -709,12 +709,11 @@ class MklFusedBatchNormOp : public OpKernel { const size_t kMeanIndex = 3; // index of est_mean tensor const size_t kVarianceIndex = 4; // index of est_variance tensor - const Tensor& src_tensor = MklGetInput(context, kSrcIndex); - const Tensor& scale_tensor = MklGetInput(context, kScaleIndex); - const Tensor& shift_tensor = MklGetInput(context, kShiftIndex); - const Tensor& est_mean_tensor = MklGetInput(context, kMeanIndex); - const Tensor& est_variance_tensor = MklGetInput(context, - kVarianceIndex); + const Tensor& src_tensor = MklGetInput(context, kSrcIndex); + const Tensor& scale_tensor = MklGetInput(context, kScaleIndex); + const Tensor& shift_tensor = MklGetInput(context, kShiftIndex); + const Tensor& est_mean_tensor = MklGetInput(context, kMeanIndex); + const Tensor& est_variance_tensor = MklGetInput(context, kVarianceIndex); TensorShape tf_shape_src; MklDnnShape dnn_shape_src; @@ -723,37 +722,34 @@ class MklFusedBatchNormOp : public OpKernel { if (dnn_shape_src.IsMklTensor()) { tf_shape_src = dnn_shape_src.GetTfShape(); OP_REQUIRES(context, dnn_shape_src.GetDimension() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } else { tf_shape_src = src_tensor.shape(); OP_REQUIRES(context, src_tensor.dims() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } OP_REQUIRES(context, scale_tensor.dims() == 1, - errors::InvalidArgument( - "scale must be 1-dimensional", - scale_tensor.shape().DebugString())); + errors::InvalidArgument("scale must be 1-dimensional", + scale_tensor.shape().DebugString())); OP_REQUIRES(context, shift_tensor.dims() == 1, errors::InvalidArgument("offset must be 1-dimensional", - shift_tensor.shape().DebugString())); - OP_REQUIRES(context, est_mean_tensor.dims() == 1, - errors::InvalidArgument( - "estimated_mean must be 1-dimensional", - est_mean_tensor.shape().DebugString())); - OP_REQUIRES(context, est_variance_tensor.dims() == 1, - errors::InvalidArgument( - "estimated_variance must be 1-dimensional", - est_variance_tensor.shape().DebugString())); + shift_tensor.shape().DebugString())); + OP_REQUIRES( + context, est_mean_tensor.dims() == 1, + errors::InvalidArgument("estimated_mean must be 1-dimensional", + est_mean_tensor.shape().DebugString())); + OP_REQUIRES( + context, est_variance_tensor.dims() == 1, + errors::InvalidArgument("estimated_variance must be 1-dimensional", + est_variance_tensor.shape().DebugString())); if (is_training_) { - OP_REQUIRES(context, est_mean_tensor.dim_size(0) == 0, - errors::InvalidArgument( - "estimated_mean must be empty for training", - est_mean_tensor.shape().DebugString())); + OP_REQUIRES( + context, est_mean_tensor.dim_size(0) == 0, + errors::InvalidArgument("estimated_mean must be empty for training", + est_mean_tensor.shape().DebugString())); OP_REQUIRES(context, est_variance_tensor.dim_size(0) == 0, errors::InvalidArgument( "estimated_variance must be empty for training", @@ -763,11 +759,9 @@ class MklFusedBatchNormOp : public OpKernel { // special case: input with 0 element and 0 batch size Tensor* dst_tensor = nullptr; if (tf_shape_src.num_elements() == 0) { - HandleEmptyInput(context, - tf_shape_src, - scale_tensor.shape(), - &dst_tensor); - return; + HandleEmptyInput(context, tf_shape_src, scale_tensor.shape(), + &dst_tensor); + return; } if (dnn_shape_src.IsMklTensor()) @@ -783,11 +777,8 @@ class MklFusedBatchNormOp : public OpKernel { Tensor* batch_variance_tensor = nullptr; Tensor* saved_mean_tensor = nullptr; Tensor* saved_variance_tensor = nullptr; - AllocateTFOutputs(context, - scale_tensor.shape(), - &batch_mean_tensor, - &batch_variance_tensor, - &saved_mean_tensor, + AllocateTFOutputs(context, scale_tensor.shape(), &batch_mean_tensor, + &batch_variance_tensor, &saved_mean_tensor, &saved_variance_tensor); if (is_training_) @@ -815,69 +806,63 @@ class MklFusedBatchNormOp : public OpKernel { src_dims = TFShapeToMklDnnDimsInNCHW(dnn_shape_src.GetTfShape(), tensor_format_); } else { - src_dims = TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), - tensor_format_); + src_dims = + TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), tensor_format_); } auto src_md = dnn_shape_src.IsMklTensor() - ? dnn_shape_src.GetMklLayout() - : memory::desc(src_dims, MklDnnType(), format_m); + ? dnn_shape_src.GetMklLayout() + : memory::desc(src_dims, MklDnnType(), format_m); src.SetUsrMem(src_md, &src_tensor); // set weights primitive // MKL-DNN packs scale & shift as "weights": // ...... - auto weights_desc = memory::desc({2, depth_}, - MklDnnType(), - memory::format::nc); + auto weights_desc = + memory::desc({2, depth_}, MklDnnType(), memory::format::nc); auto weights_pd = memory::primitive_desc(weights_desc, cpu_engine); auto weights_m = memory(weights_pd); - T* weights_data = reinterpret_cast( - weights_m.get_data_handle()); - T* scale_tf = reinterpret_cast( - const_cast(scale_tensor.flat().data())); - T* shift_tf = reinterpret_cast( - const_cast(shift_tensor.flat().data())); - - for (int k=0; k < depth_; k++) { + T* weights_data = reinterpret_cast(weights_m.get_data_handle()); + T* scale_tf = + reinterpret_cast(const_cast(scale_tensor.flat().data())); + T* shift_tf = + reinterpret_cast(const_cast(shift_tensor.flat().data())); + + for (int k = 0; k < depth_; k++) { weights_data[k] = scale_tf[k]; weights_data[k + depth_] = shift_tf[k]; } // set mean primitive - auto mean_desc = memory::desc({1, depth_}, - MklDnnType(), - memory::format::nc); + auto mean_desc = + memory::desc({1, depth_}, MklDnnType(), memory::format::nc); auto mean_pd = memory::primitive_desc(mean_desc, cpu_engine); - char* saved_mean_data_tf = reinterpret_cast - (saved_mean_tensor->flat().data()); - std::memcpy(saved_mean_data_tf, - reinterpret_cast(mean_values_), - depth_*sizeof(T)); - auto mean_m = memory(mean_pd, - reinterpret_cast(saved_mean_data_tf)); + char* saved_mean_data_tf = + reinterpret_cast(saved_mean_tensor->flat().data()); + std::memcpy(saved_mean_data_tf, reinterpret_cast(mean_values_), + depth_ * sizeof(T)); + auto mean_m = + memory(mean_pd, reinterpret_cast(saved_mean_data_tf)); // set variance primitive - auto variance_desc = memory::desc({1, depth_}, - MklDnnType(), - memory::format::nc); + auto variance_desc = + memory::desc({1, depth_}, MklDnnType(), memory::format::nc); auto variance_pd = memory::primitive_desc(variance_desc, cpu_engine); - char* saved_variance_data_tf = reinterpret_cast - (saved_variance_tensor->flat().data()); + char* saved_variance_data_tf = + reinterpret_cast(saved_variance_tensor->flat().data()); std::memcpy(saved_variance_data_tf, reinterpret_cast(variance_values_), - depth_*sizeof(T)); + depth_ * sizeof(T)); auto variance_m = memory(variance_pd, saved_variance_data_tf); - prop_kind pk = (is_training_) ? - prop_kind::forward_training : - prop_kind::forward_scoring; + prop_kind pk = (is_training_) ? prop_kind::forward_training + : prop_kind::forward_scoring; auto bnrm_fwd_desc = batch_normalization_forward::desc( - pk, src.GetUsrMemDesc(), epsilon_, - is_training_ ? use_scale_shift : - (use_scale_shift | use_global_stats)); + pk, src.GetUsrMemDesc(), epsilon_, + is_training_ ? use_scale_shift + : (use_scale_shift | use_global_stats)); auto bnrm_fwd_pd = batch_normalization_forward::primitive_desc( - bnrm_fwd_desc, cpu_engine); + bnrm_fwd_desc, cpu_engine); // allocate dst tensor MklDnnShape dnn_shape_dst; @@ -887,47 +872,39 @@ class MklFusedBatchNormOp : public OpKernel { auto dst_pd = bnrm_fwd_pd.dst_primitive_desc(); dnn_shape_dst.SetMklLayout(&dst_pd); dnn_shape_dst.SetElemType(MklDnnType()); - dnn_shape_dst.SetTfLayout(dnn_shape_src.GetDimension(), - src_dims, format_m); - tf_shape_dst.AddDim(dst_pd.get_size()/sizeof(T)); + dnn_shape_dst.SetTfLayout(dnn_shape_src.GetDimension(), src_dims, + format_m); + tf_shape_dst.AddDim(dst_pd.get_size() / sizeof(T)); } else { dnn_shape_dst.SetMklTensor(false); tf_shape_dst = src_tensor.shape(); } - AllocateOutputSetMklShape(context, kDstIndex, &dst_tensor, - tf_shape_dst, dnn_shape_dst); + AllocateOutputSetMklShape(context, kDstIndex, &dst_tensor, tf_shape_dst, + dnn_shape_dst); // Output of batchnorm has same shape as input. dst.SetUsrMem(src_md, dst_tensor); primitive bnrm_fwd_op; if (is_training_) { - bnrm_fwd_op = batch_normalization_forward( - bnrm_fwd_pd, - src.GetOpMem(), - weights_m, - dst.GetOpMem(), - mean_m, - variance_m); + bnrm_fwd_op = + batch_normalization_forward(bnrm_fwd_pd, src.GetOpMem(), weights_m, + dst.GetOpMem(), mean_m, variance_m); } else { bnrm_fwd_op = batch_normalization_forward( - bnrm_fwd_pd, - src.GetOpMem(), - mean_m, - variance_m, - (const primitive::at) weights_m, - dst.GetOpMem()); + bnrm_fwd_pd, src.GetOpMem(), mean_m, variance_m, + (const primitive::at)weights_m, dst.GetOpMem()); } std::vector net; net.push_back(bnrm_fwd_op); stream(stream::kind::eager).submit(net).wait(); // copy batch_mean data - T* batch_mean_data_tf = reinterpret_cast( - batch_mean_tensor->flat().data()); + T* batch_mean_data_tf = + reinterpret_cast(batch_mean_tensor->flat().data()); std::memcpy(reinterpret_cast(batch_mean_data_tf), reinterpret_cast(mean_m.get_data_handle()), - depth_*sizeof(T)); + depth_ * sizeof(T)); // copy batch_variance data with Bessel's correction // if training mode is on @@ -937,18 +914,17 @@ class MklFusedBatchNormOp : public OpKernel { size_t adjust_size = orig_size - 1; adjust_factor = (static_cast(orig_size)) / adjust_size; } - for (int k=0; k < depth_; k++) + for (int k = 0; k < depth_; k++) batch_variance_tensor->flat().data()[k] = - (reinterpret_cast(variance_m.get_data_handle()))[k] - * adjust_factor; - } catch (mkldnn::error &e) { + (reinterpret_cast(variance_m.get_data_handle()))[k] * + adjust_factor; + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -958,7 +934,7 @@ class MklFusedBatchNormOp : public OpKernel { bool is_training_; T* mean_values_; T* variance_values_; - size_t depth_; // batch normalization is done for per channel. + size_t depth_; // batch normalization is done for per channel. void ExtractParams(OpKernelContext* context) { const Tensor& input = MklGetInput(context, 0); @@ -966,23 +942,20 @@ class MklFusedBatchNormOp : public OpKernel { } void SetMeanVariance(const Tensor& mean, const Tensor& variance) { - mean_values_ = reinterpret_cast( - const_cast(mean.flat().data())); - variance_values_ = reinterpret_cast( - const_cast(variance.flat().data())); + mean_values_ = reinterpret_cast(const_cast(mean.flat().data())); + variance_values_ = + reinterpret_cast(const_cast(variance.flat().data())); } - void HandleEmptyInput(OpKernelContext* context, - TensorShape tf_shape_src, - TensorShape tf_shape_scale, - Tensor** dst_tensor) { + void HandleEmptyInput(OpKernelContext* context, TensorShape tf_shape_src, + TensorShape tf_shape_scale, Tensor** dst_tensor) { CHECK_NOTNULL(dst_tensor); const size_t kDstIndex = 0; MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); - AllocateOutputSetMklShape(context, kDstIndex, dst_tensor, - tf_shape_src, dnn_shape_dst); + AllocateOutputSetMklShape(context, kDstIndex, dst_tensor, tf_shape_src, + dnn_shape_dst); CHECK_NOTNULL(*dst_tensor); memset(const_cast((*dst_tensor)->tensor_data().data()), 0, (*dst_tensor)->tensor_data().size()); @@ -991,15 +964,12 @@ class MklFusedBatchNormOp : public OpKernel { Tensor* batch_variance_tensor = nullptr; Tensor* saved_mean_tensor = nullptr; Tensor* saved_variance_tensor = nullptr; - AllocateTFOutputs(context, tf_shape_scale, - &batch_mean_tensor, - &batch_variance_tensor, - &saved_mean_tensor, + AllocateTFOutputs(context, tf_shape_scale, &batch_mean_tensor, + &batch_variance_tensor, &saved_mean_tensor, &saved_variance_tensor); } - void AllocateTFOutputs(OpKernelContext* context, - TensorShape tf_shape_scale, + void AllocateTFOutputs(OpKernelContext* context, TensorShape tf_shape_scale, Tensor** batch_mean_tensor, Tensor** batch_variance_tensor, Tensor** saved_mean_tensor, @@ -1017,51 +987,43 @@ class MklFusedBatchNormOp : public OpKernel { // allocate batch mean output tensor MklDnnShape mkl_shape_batch_mean; mkl_shape_batch_mean.SetMklTensor(false); - AllocateOutputSetMklShape(context, - kBatchMeanIndex, - batch_mean_tensor, - tf_shape_scale, - mkl_shape_batch_mean); + AllocateOutputSetMklShape(context, kBatchMeanIndex, batch_mean_tensor, + tf_shape_scale, mkl_shape_batch_mean); CHECK_NOTNULL(*batch_mean_tensor); // set NAN mean value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*batch_mean_tensor)->flat().data()[k] = NAN; // allocate batch variance output tensor MklDnnShape mkl_shape_batch_variance; mkl_shape_batch_variance.SetMklTensor(false); - AllocateOutputSetMklShape(context, - kBatchVarianceIndex, - batch_variance_tensor, - tf_shape_scale, + AllocateOutputSetMklShape(context, kBatchVarianceIndex, + batch_variance_tensor, tf_shape_scale, mkl_shape_batch_variance); CHECK_NOTNULL(*batch_variance_tensor); // set NAN variance value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*batch_variance_tensor)->flat().data()[k] = NAN; // Mean and variance (without Bessel's correction) saved for backward // computation to serve as pre-computed mean and variance. MklDnnShape mkl_shape_saved_mean; mkl_shape_saved_mean.SetMklTensor(false); - AllocateOutputSetMklShape(context, kSavedMeanIndex, - saved_mean_tensor, - tf_shape_scale, - mkl_shape_saved_mean); + AllocateOutputSetMklShape(context, kSavedMeanIndex, saved_mean_tensor, + tf_shape_scale, mkl_shape_saved_mean); CHECK_NOTNULL(*saved_mean_tensor); // set NAN mean value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*saved_mean_tensor)->flat().data()[k] = NAN; MklDnnShape mkl_shape_saved_variance; mkl_shape_saved_variance.SetMklTensor(false); AllocateOutputSetMklShape(context, kSavedVarianceIndex, - saved_variance_tensor, - tf_shape_scale, + saved_variance_tensor, tf_shape_scale, mkl_shape_saved_variance); CHECK_NOTNULL(*saved_variance_tensor); // set NAN variance value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*saved_variance_tensor)->flat().data()[k] = NAN; } }; @@ -1093,8 +1055,8 @@ class MklFusedBatchNormGradOp : public OpKernel { const Tensor& src_tensor = MklGetInput(context, kSrcIndex); const Tensor& scale_tensor = MklGetInput(context, kScaleIndex); const Tensor& saved_mean_tensor = MklGetInput(context, kMeanIndex); - const Tensor& saved_variance_tensor = MklGetInput(context, - kVarianceIndex); + const Tensor& saved_variance_tensor = + MklGetInput(context, kVarianceIndex); MklDnnShape dnn_shape_src, dnn_shape_diff_dst; GetMklShape(context, kSrcIndex, &dnn_shape_src); @@ -1103,53 +1065,49 @@ class MklFusedBatchNormGradOp : public OpKernel { if (dnn_shape_diff_dst.IsMklTensor()) { tf_shape_diff_dst = dnn_shape_diff_dst.GetTfShape(); - OP_REQUIRES(context, dnn_shape_diff_dst.GetDimension() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - diff_dst_tensor.shape().DebugString())); + OP_REQUIRES( + context, dnn_shape_diff_dst.GetDimension() == 4, + errors::InvalidArgument("input must be 4-dimensional", + diff_dst_tensor.shape().DebugString())); } else { tf_shape_diff_dst = diff_dst_tensor.shape(); - OP_REQUIRES(context, diff_dst_tensor.dims() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - diff_dst_tensor.shape().DebugString())); + OP_REQUIRES( + context, diff_dst_tensor.dims() == 4, + errors::InvalidArgument("input must be 4-dimensional", + diff_dst_tensor.shape().DebugString())); } if (dnn_shape_src.IsMklTensor()) { tf_shape_src = dnn_shape_src.GetTfShape(); OP_REQUIRES(context, dnn_shape_src.GetDimension() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } else { tf_shape_src = src_tensor.shape(); OP_REQUIRES(context, src_tensor.dims() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } OP_REQUIRES(context, scale_tensor.dims() == 1, - errors::InvalidArgument( - "scale must be 1-dimensional", - scale_tensor.shape().DebugString())); - OP_REQUIRES(context, saved_mean_tensor.dims() == 1, - errors::InvalidArgument( - "saved mean must be 1-dimensional", - saved_mean_tensor.shape().DebugString())); - - OP_REQUIRES(context, saved_variance_tensor.dims() == 1, - errors::InvalidArgument( - "saved variance must be 1-dimensional", - saved_variance_tensor.shape().DebugString())); + errors::InvalidArgument("scale must be 1-dimensional", + scale_tensor.shape().DebugString())); + OP_REQUIRES( + context, saved_mean_tensor.dims() == 1, + errors::InvalidArgument("saved mean must be 1-dimensional", + saved_mean_tensor.shape().DebugString())); + + OP_REQUIRES( + context, saved_variance_tensor.dims() == 1, + errors::InvalidArgument("saved variance must be 1-dimensional", + saved_variance_tensor.shape().DebugString())); Tensor* diff_src_tensor = nullptr; if (tf_shape_src.num_elements() == 0 || tf_shape_diff_dst.num_elements() == 0) { - HandleEmptyInput(context, tf_shape_src, - scale_tensor.shape(), - &diff_src_tensor); - return; + HandleEmptyInput(context, tf_shape_src, scale_tensor.shape(), + &diff_src_tensor); + return; } if (dnn_shape_src.IsMklTensor()) @@ -1175,20 +1133,18 @@ class MklFusedBatchNormGradOp : public OpKernel { memory::dims src_dims, diff_dst_dims; if (dnn_shape_src.IsMklTensor()) - src_dims = TFShapeToMklDnnDimsInNCHW( - dnn_shape_src.GetTfShape(), tensor_format_); + src_dims = TFShapeToMklDnnDimsInNCHW(dnn_shape_src.GetTfShape(), + tensor_format_); else - src_dims = TFShapeToMklDnnDimsInNCHW( - src_tensor.shape(), tensor_format_); + src_dims = + TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), tensor_format_); if (dnn_shape_diff_dst.IsMklTensor()) diff_dst_dims = TFShapeToMklDnnDimsInNCHW( - dnn_shape_diff_dst.GetTfShape(), - tensor_format_); + dnn_shape_diff_dst.GetTfShape(), tensor_format_); else - diff_dst_dims = TFShapeToMklDnnDimsInNCHW( - diff_dst_tensor.shape(), - tensor_format_); + diff_dst_dims = + TFShapeToMklDnnDimsInNCHW(diff_dst_tensor.shape(), tensor_format_); // set src and diff_dst primitives memory::desc src_md({}, memory::data_undef, memory::format_undef); @@ -1202,7 +1158,7 @@ class MklFusedBatchNormGradOp : public OpKernel { src_md = diff_dst_md; } } else { - src_md = memory::desc(src_dims, MklDnnType(), format_m); + src_md = memory::desc(src_dims, MklDnnType(), format_m); diff_dst_md = src_md; } src.SetUsrMem(src_md, &src_tensor); @@ -1210,55 +1166,47 @@ class MklFusedBatchNormGradOp : public OpKernel { // weights -- DNN packs scales/shifts as weights in order of // scale, ..., scale, shift, ..., shift - auto weights_desc = memory::desc({2, depth_}, - MklDnnType(), - memory::format::nc); + auto weights_desc = + memory::desc({2, depth_}, MklDnnType(), memory::format::nc); auto weights_pd = memory::primitive_desc(weights_desc, cpu_engine); auto weights_m = memory(weights_pd); T* weights_data = reinterpret_cast(weights_m.get_data_handle()); - T* scale_tf = reinterpret_cast(const_cast - (scale_tensor.flat().data())); - for (int k=0; k < depth_; k++) { + T* scale_tf = + reinterpret_cast(const_cast(scale_tensor.flat().data())); + for (int k = 0; k < depth_; k++) { weights_data[k] = scale_tf[k]; weights_data[k + depth_] = 0; } // set mean primitive memory::dims mv_dims = GetMeanVarianceDims(); - mean.SetUsrMem(mv_dims, - memory::format::nc, - const_cast(static_cast - (saved_mean_tensor.flat().data()))); + mean.SetUsrMem(mv_dims, memory::format::nc, + const_cast(static_cast( + saved_mean_tensor.flat().data()))); mean.SetOpMemDesc(mv_dims, memory::format::nc); // set variance primitive - variance.SetUsrMem(mv_dims, memory::format::nc, - const_cast(static_cast - (saved_variance_tensor.flat().data()))); + variance.SetUsrMem(mv_dims, memory::format::nc, + const_cast(static_cast( + saved_variance_tensor.flat().data()))); variance.SetOpMemDesc(mv_dims, memory::format::nc); // set diff_weight primitive - auto diff_weights_desc = memory::desc( - {2, depth_}, - MklDnnType(), - memory::format::nc); - auto diff_weights_pd = memory::primitive_desc( - diff_weights_desc, - cpu_engine); + auto diff_weights_desc = + memory::desc({2, depth_}, MklDnnType(), memory::format::nc); + auto diff_weights_pd = + memory::primitive_desc(diff_weights_desc, cpu_engine); auto diff_weights_m = memory(diff_weights_pd); auto bnrm_fwd_desc = batch_normalization_forward::desc( - prop_kind::forward_training, - src.GetUsrMemDesc(), - epsilon_, - is_training_ ? use_scale_shift : - (use_scale_shift | use_global_stats)); + prop_kind::forward_training, src.GetUsrMemDesc(), epsilon_, + is_training_ ? use_scale_shift + : (use_scale_shift | use_global_stats)); auto bnrm_fwd_pd = batch_normalization_forward::primitive_desc( - bnrm_fwd_desc, - cpu_engine); + bnrm_fwd_desc, cpu_engine); // Indices of output tensors - const size_t kDiffSrcIndex = 0; // index of diff_src tensor + const size_t kDiffSrcIndex = 0; // index of diff_src tensor // allocate diff_src tensor MklDnnShape dnn_shape_diff_src; @@ -1268,14 +1216,11 @@ class MklFusedBatchNormGradOp : public OpKernel { auto diff_src_pd = bnrm_fwd_pd.dst_primitive_desc(); dnn_shape_diff_src.SetMklLayout(&diff_src_pd); dnn_shape_diff_src.SetElemType(MklDnnType()); - dnn_shape_diff_src.SetTfLayout( - dnn_shape_src.GetDimension(), - src_dims, - format_m); - dnn_shape_diff_src.SetTfDimOrder( - dnn_shape_src.GetDimension(), - tensor_format_); - tf_shape_diff_src.AddDim(diff_src_pd.get_size()/sizeof(T)); + dnn_shape_diff_src.SetTfLayout(dnn_shape_src.GetDimension(), src_dims, + format_m); + dnn_shape_diff_src.SetTfDimOrder(dnn_shape_src.GetDimension(), + tensor_format_); + tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T)); } else { dnn_shape_diff_src.SetMklTensor(false); tf_shape_diff_src = src_tensor.shape(); @@ -1287,33 +1232,22 @@ class MklFusedBatchNormGradOp : public OpKernel { prop_kind pk = prop_kind::backward; auto bnrm_bwd_desc = batch_normalization_backward::desc( - pk, - diff_src.GetUsrMemDesc(), - src.GetUsrMemDesc(), - epsilon_, - /* for inference, specify use_global_stats - 1. on fwd prop, use mean and variance - provided as inputs - 2. on bwd prop, mean and variance are - considered as constants. Thus, - reduce the amout of MKL computations - */ - is_training_ ? use_scale_shift : - (use_scale_shift | use_global_stats)); + pk, diff_src.GetUsrMemDesc(), src.GetUsrMemDesc(), epsilon_, + /* for inference, specify use_global_stats + 1. on fwd prop, use mean and variance + provided as inputs + 2. on bwd prop, mean and variance are + considered as constants. Thus, + reduce the amout of MKL computations + */ + is_training_ ? use_scale_shift + : (use_scale_shift | use_global_stats)); auto bnrm_bwd_pd = batch_normalization_backward::primitive_desc( - bnrm_bwd_desc, - cpu_engine, - bnrm_fwd_pd); + bnrm_bwd_desc, cpu_engine, bnrm_fwd_pd); auto bnrm_bwd_op = batch_normalization_backward( - bnrm_bwd_pd, - src.GetOpMem(), - mean.GetOpMem(), - variance.GetOpMem(), - diff_dst.GetOpMem(), - weights_m, - diff_src.GetOpMem(), - diff_weights_m); + bnrm_bwd_pd, src.GetOpMem(), mean.GetOpMem(), variance.GetOpMem(), + diff_dst.GetOpMem(), weights_m, diff_src.GetOpMem(), diff_weights_m); std::vector net; net.push_back(bnrm_bwd_op); @@ -1322,43 +1256,39 @@ class MklFusedBatchNormGradOp : public OpKernel { // allocate 4 output TF tensors Tensor* diff_scale_tensor = nullptr; Tensor* diff_shift_tensor = nullptr; - AllocateTFOutputs(context, scale_tensor.shape(), - &diff_scale_tensor, + AllocateTFOutputs(context, scale_tensor.shape(), &diff_scale_tensor, &diff_shift_tensor); // copy data: diff_scale and diff_shift - T* diff_weights_data_dnn = reinterpret_cast - (diff_weights_m.get_data_handle()); + T* diff_weights_data_dnn = + reinterpret_cast(diff_weights_m.get_data_handle()); for (int i = 0; i < depth_; i++) { - diff_scale_tensor->flat().data()[i] = - diff_weights_data_dnn[i]; + diff_scale_tensor->flat().data()[i] = diff_weights_data_dnn[i]; diff_shift_tensor->flat().data()[i] = - diff_weights_data_dnn[i + depth_]; + diff_weights_data_dnn[i + depth_]; } - } catch (mkldnn::error &e) { + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } private: T epsilon_; TensorFormat tensor_format_; - int depth_; // batch normalization is done for per channel. + int depth_; // batch normalization is done for per channel. bool is_training_; void ExtractParams(OpKernelContext* context) { - const Tensor& input = MklGetInput(context, 0); - depth_ = static_cast(GetTensorDim(input, tensor_format_, 'C')); + const Tensor& input = MklGetInput(context, 0); + depth_ = static_cast(GetTensorDim(input, tensor_format_, 'C')); } - void HandleEmptyInput(OpKernelContext* context, - TensorShape tf_shape_src, + void HandleEmptyInput(OpKernelContext* context, TensorShape tf_shape_src, TensorShape tf_shape_scale_shift, Tensor** diff_src_tensor) { const size_t kDiffSrcIndex = 0; @@ -1366,22 +1296,20 @@ class MklFusedBatchNormGradOp : public OpKernel { MklDnnShape dnn_shape_diff_src; dnn_shape_diff_src.SetMklTensor(false); AllocateOutputSetMklShape(context, kDiffSrcIndex, diff_src_tensor, - tf_shape_src, dnn_shape_diff_src); - for (size_t i=0; i < (*diff_src_tensor)->shape().num_elements(); i++) - (*diff_src_tensor)->flat().data()[i] = 0; + tf_shape_src, dnn_shape_diff_src); + for (size_t i = 0; i < (*diff_src_tensor)->shape().num_elements(); i++) + (*diff_src_tensor)->flat().data()[i] = 0; Tensor* diff_scale_tensor = nullptr; Tensor* diff_shift_tensor = nullptr; - AllocateTFOutputs(context, - tf_shape_scale_shift, - &diff_scale_tensor, + AllocateTFOutputs(context, tf_shape_scale_shift, &diff_scale_tensor, &diff_shift_tensor); } void AllocateTFOutputs(OpKernelContext* context, - TensorShape tf_shape_scale_shift, - Tensor** diff_scale_tensor, - Tensor** diff_shift_tensor) { + TensorShape tf_shape_scale_shift, + Tensor** diff_scale_tensor, + Tensor** diff_shift_tensor) { CHECK_NOTNULL(diff_scale_tensor); CHECK_NOTNULL(diff_shift_tensor); @@ -1396,31 +1324,29 @@ class MklFusedBatchNormGradOp : public OpKernel { AllocateOutputSetMklShape(context, kDiffScaleIndex, diff_scale_tensor, tf_shape_scale_shift, mkl_shape_diff_scale); CHECK_NOTNULL(*diff_scale_tensor); - for (size_t i=0; i < (*diff_scale_tensor)->shape().num_elements(); i++) - (*diff_scale_tensor)->flat().data()[i] = 0; + for (size_t i = 0; i < (*diff_scale_tensor)->shape().num_elements(); i++) + (*diff_scale_tensor)->flat().data()[i] = 0; MklDnnShape mkl_shape_diff_shift; mkl_shape_diff_shift.SetMklTensor(false); AllocateOutputSetMklShape(context, kDiffShiftIndex, diff_shift_tensor, tf_shape_scale_shift, mkl_shape_diff_shift); CHECK_NOTNULL(*diff_shift_tensor); - for (size_t i=0; i < (*diff_shift_tensor)->shape().num_elements(); i++) - (*diff_shift_tensor)->flat().data()[i] = 0; + for (size_t i = 0; i < (*diff_shift_tensor)->shape().num_elements(); i++) + (*diff_shift_tensor)->flat().data()[i] = 0; // Placeholders for estimated_mean and estimated_variance, which are // used for inference and thus not needed here for gradient computation. - Tensor* p1_tensor = nullptr, *p2_tensor = nullptr; + Tensor *p1_tensor = nullptr, *p2_tensor = nullptr; MklDnnShape mkl_shape_p; mkl_shape_p.SetMklTensor(false); - AllocateOutputSetMklShape(context, kP1Index, &p1_tensor, - TensorShape({}), mkl_shape_p); - AllocateOutputSetMklShape(context, kP2Index, &p2_tensor, - TensorShape({}), mkl_shape_p); + AllocateOutputSetMklShape(context, kP1Index, &p1_tensor, TensorShape({}), + mkl_shape_p); + AllocateOutputSetMklShape(context, kP2Index, &p2_tensor, TensorShape({}), + mkl_shape_p); } - memory::dims GetMeanVarianceDims() { - return memory::dims({1, depth_}); - } + memory::dims GetMeanVarianceDims() { return memory::dims({1, depth_}); } }; #endif diff --git a/tensorflow/core/kernels/mkl_input_conversion_op.cc b/tensorflow/core/kernels/mkl_input_conversion_op.cc index 4b5f7b8310..73d41efce1 100644 --- a/tensorflow/core/kernels/mkl_input_conversion_op.cc +++ b/tensorflow/core/kernels/mkl_input_conversion_op.cc @@ -271,8 +271,8 @@ class MklInputConversionOp : public OpKernel { MklDnnShape input_shape_1; GetMklShape(context, 1, &input_shape_1); - bool tf_shapes_are_same = context->input(0).shape() == - context->input(1).shape(); + bool tf_shapes_are_same = + context->input(0).shape() == context->input(1).shape(); VLOG(1) << "MklInputConversionOp: Input shapes are " << (tf_shapes_are_same ? "*same*" : "*different*") << ": " @@ -400,9 +400,9 @@ class MklInputConversionOp : public OpKernel { // Create reorder between tensorflow layout and Mkl layout. std::vector net; - CHECK_EQ(tf_input.CheckReorderToOpMem(memory::primitive_desc( - output_mkl_md, cpu_engine), - tensor_out, &net), + CHECK_EQ(tf_input.CheckReorderToOpMem( + memory::primitive_desc(output_mkl_md, cpu_engine), + tensor_out, &net), true); stream(stream::kind::eager).submit(net).wait(); diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc index 95e0404ba8..a8b45004b7 100644 --- a/tensorflow/core/kernels/mkl_lrn_op.cc +++ b/tensorflow/core/kernels/mkl_lrn_op.cc @@ -22,6 +22,9 @@ limitations under the License. #define EIGEN_USE_THREADS #include +#include "mkl_dnn.h" +#include "mkl_dnn_types.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" @@ -30,9 +33,6 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/tensor_format.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "mkl_dnn.h" -#include "mkl_dnn_types.h" #if !defined(IS_MOBILE_PLATFORM) #include "tensorflow/core/util/work_sharder.h" @@ -40,10 +40,10 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::lrn_forward; +using mkldnn::lrn_across_channels; using mkldnn::lrn_backward; +using mkldnn::lrn_forward; using mkldnn::prop_kind; -using mkldnn::lrn_across_channels; using mkldnn::stream; #endif @@ -77,10 +77,11 @@ class MklLRNOp : public OpKernel { explicit MklLRNOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); @@ -103,9 +104,10 @@ class MklLRNOp : public OpKernel { : input.dims(); OP_REQUIRES(context, mkl_context.in_dims == 4, errors::InvalidArgument("input must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); if (!input_in_mkl_format) { mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_, @@ -339,17 +341,17 @@ class MklLRNOp : public OpKernel { float beta_; }; - template class MklLRNGradOp : public OpKernel { public: explicit MklLRNGradOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_)); @@ -740,10 +742,11 @@ class MklLRNOp : public OpKernel { explicit MklLRNOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); @@ -773,10 +776,10 @@ class MklLRNOp : public OpKernel { if (!src_dnn_shape.IsMklTensor()) { MklDefaultToEigen(context, src_tensor); return; - } else if (!src_dnn_shape.IsMklChannelDim( - src_dnn_shape.GetDimension() - 1) ) { + } else if (!src_dnn_shape.IsMklChannelDim(src_dnn_shape.GetDimension() - + 1)) { Tensor converted_tensor = - ConvertMklToTF(context, src_tensor, src_dnn_shape); + ConvertMklToTF(context, src_tensor, src_dnn_shape); MklDefaultToEigen(context, converted_tensor); return; } @@ -807,18 +810,16 @@ class MklLRNOp : public OpKernel { // Create LRN primitive descriptor. // Tensorflow's normalization semantics is across channels. // MKL-DNN also supports normalization within channel. - auto lrn_desc = lrn_forward::desc(prop_kind::forward, - lrn_across_channels, + auto lrn_desc = lrn_forward::desc(prop_kind::forward, lrn_across_channels, src_dnn_data.GetUsrMemDesc(), - kernel_size, - new_alpha, beta_, bias_); + kernel_size, new_alpha, beta_, bias_); auto lrn_prim_desc = lrn_forward::primitive_desc(lrn_desc, cpu_engine); // Allocate output_dnn_data tensor. Tensor* output_tensor = nullptr; memory::format input_format = src_dnn_shape.GetTfDataFormat(); - AllocateOutputTensor(context, lrn_prim_desc, input_dims, - input_format, &output_tensor); + AllocateOutputTensor(context, lrn_prim_desc, input_dims, input_format, + &output_tensor); OP_REQUIRES_OK(context, context->status()); CHECK_NOTNULL(output_tensor); dst_dnn_data.SetUsrMemDataHandle(output_tensor); @@ -827,25 +828,23 @@ class MklLRNOp : public OpKernel { AllocateWorkspaceTensor(context, lrn_prim_desc, &workspace_dnn_data); OP_REQUIRES_OK(context, context->status()); - PrepareAndExecuteNet(lrn_prim_desc, &src_dnn_data, - &dst_dnn_data, &workspace_dnn_data); - } catch (mkldnn::error &e) { + PrepareAndExecuteNet(lrn_prim_desc, &src_dnn_data, &dst_dnn_data, + &workspace_dnn_data); + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } private: - void PrepareAndExecuteNet( - const lrn_forward::primitive_desc& lrn_fwd_desc, - MklDnnData* src_dnn_data, - MklDnnData* dst_dnn_data, - MklDnnData* wksp_dnn_data = nullptr) { + void PrepareAndExecuteNet(const lrn_forward::primitive_desc& lrn_fwd_desc, + MklDnnData* src_dnn_data, + MklDnnData* dst_dnn_data, + MklDnnData* wksp_dnn_data = nullptr) { std::vector net; // Check for input reorder @@ -853,23 +852,21 @@ class MklLRNOp : public OpKernel { // Create pooling primitive and add it to net if (wksp_dnn_data != nullptr) { - net.push_back(lrn_forward(lrn_fwd_desc, - src_dnn_data->GetOpMem(), - wksp_dnn_data->GetOpMem(), - dst_dnn_data->GetOpMem())); + net.push_back(lrn_forward(lrn_fwd_desc, src_dnn_data->GetOpMem(), + wksp_dnn_data->GetOpMem(), + dst_dnn_data->GetOpMem())); } else { - net.push_back(lrn_forward(lrn_fwd_desc, - src_dnn_data->GetOpMem(), - dst_dnn_data->GetOpMem())); + net.push_back(lrn_forward(lrn_fwd_desc, src_dnn_data->GetOpMem(), + dst_dnn_data->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } - void AllocateOutputTensor(OpKernelContext* context, - const lrn_forward::primitive_desc& lrn_fwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const lrn_forward::primitive_desc& lrn_fwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); memory::primitive_desc dst_pd = lrn_fwd_prim_desc.dst_primitive_desc(); @@ -880,111 +877,106 @@ class MklLRNOp : public OpKernel { output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; // only allocate enough space for the elements we need. size_t num_bytes = dst_pd.get_size(); CHECK_EQ(num_bytes % sizeof(T), 0); output_tf_shape.AddDim(num_bytes / sizeof(T)); - AllocateOutputSetMklShape(context, kIdxOutput, - output_tensor, - output_tf_shape, output_mkl_shape); - } - - // Fallback implementation - Taken from lrn_op.cc - // TODO(inteltf) Check if we can use EigenLRNOp directly instead of making a - // copy. - void MklDefaultToEigen(OpKernelContext* context, - const Tensor& input) { - const int batch = static_cast(input.dim_size(0)); - const int rows = static_cast(input.dim_size(1)); - const int cols = static_cast(input.dim_size(2)); - const int depth = static_cast(input.dim_size(3)); - const int nodes = cols * rows; - - auto in_shaped = input.shaped({nodes * batch, depth}); - // Multiplying the input with the band matrix has the effect of reducing - // the - // correct patch along the depth. - Eigen::Tensor multiplier(depth, depth); - GetBandMatrix(depth, depth_radius_, &multiplier); + AllocateOutputSetMklShape(context, kIdxOutput, output_tensor, + output_tf_shape, output_mkl_shape); + } - Tensor *output_dnn_data = nullptr; - MklDnnShape mkl_output_mkl_shape; - mkl_output_mkl_shape.SetMklTensor(false); - mkl_output_mkl_shape.SetDimensions(4); - AllocateOutputSetMklShape(context, kIdxOutput, &output_dnn_data, - input.shape(), mkl_output_mkl_shape); - CHECK_NOTNULL(output_dnn_data); - - Tensor* workspace_tensor = nullptr; - MklDnnShape workspace_mkl_shape; - workspace_mkl_shape.SetMklTensor(false); - TensorShape workspace_tf_shape; - workspace_tf_shape.AddDim(0); - AllocateOutputSetMklShape(context, kIdxWorkspace, - &workspace_tensor, + // Fallback implementation - Taken from lrn_op.cc + // TODO(inteltf) Check if we can use EigenLRNOp directly instead of making a + // copy. + void MklDefaultToEigen(OpKernelContext* context, const Tensor& input) { + const int batch = static_cast(input.dim_size(0)); + const int rows = static_cast(input.dim_size(1)); + const int cols = static_cast(input.dim_size(2)); + const int depth = static_cast(input.dim_size(3)); + const int nodes = cols * rows; + + auto in_shaped = input.shaped({nodes * batch, depth}); + // Multiplying the input with the band matrix has the effect of reducing + // the + // correct patch along the depth. + Eigen::Tensor multiplier(depth, depth); + GetBandMatrix(depth, depth_radius_, &multiplier); + + Tensor* output_dnn_data = nullptr; + MklDnnShape mkl_output_mkl_shape; + mkl_output_mkl_shape.SetMklTensor(false); + mkl_output_mkl_shape.SetDimensions(4); + AllocateOutputSetMklShape(context, kIdxOutput, &output_dnn_data, + input.shape(), mkl_output_mkl_shape); + CHECK_NOTNULL(output_dnn_data); + + Tensor* workspace_tensor = nullptr; + MklDnnShape workspace_mkl_shape; + workspace_mkl_shape.SetMklTensor(false); + TensorShape workspace_tf_shape; + workspace_tf_shape.AddDim(0); + AllocateOutputSetMklShape(context, kIdxWorkspace, &workspace_tensor, workspace_tf_shape, workspace_mkl_shape); - CHECK_NOTNULL(workspace_tensor); - - auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); - Eigen::array dims = {{DimPair(1, 0)}}; - auto tmp = in_shaped.square().contract(multiplier, dims) * alpha_ + bias_; - if (beta_ == T(1)) { - out_shaped.device(context->eigen_cpu_device()) = - in_shaped * tmp.inverse(); - } else if (beta_ == T(0.5)) { - out_shaped.device(context->eigen_cpu_device()) = - in_shaped * tmp.rsqrt(); - } else { - out_shaped.device(context->eigen_cpu_device()) = - in_shaped * (tmp.log() * -beta_).exp(); - } + CHECK_NOTNULL(workspace_tensor); + + auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); + Eigen::array dims = {{DimPair(1, 0)}}; + auto tmp = in_shaped.square().contract(multiplier, dims) * alpha_ + bias_; + if (beta_ == T(1)) { + out_shaped.device(context->eigen_cpu_device()) = + in_shaped * tmp.inverse(); + } else if (beta_ == T(0.5)) { + out_shaped.device(context->eigen_cpu_device()) = in_shaped * tmp.rsqrt(); + } else { + out_shaped.device(context->eigen_cpu_device()) = + in_shaped * (tmp.log() * -beta_).exp(); } + } - void AllocateWorkspaceTensor(OpKernelContext* context, - const lrn_forward::primitive_desc& lrn_fwd_prim_desc, - MklDnnData* dnn_data_wksp) { - CHECK_NOTNULL(dnn_data_wksp); - Tensor* workspace_tensor = nullptr; - memory::primitive_desc workspace_pd - = lrn_fwd_prim_desc.workspace_primitive_desc(); - size_t workspace_bytes = workspace_pd.get_size(); - MklDnnShape workspace_mkl_shape; - // the workspace tensor is a uint8 tensor that has - // exactly the number of bytes necessary - workspace_mkl_shape.SetMklTensor(false); - TensorShape workspace_tf_shape; - workspace_tf_shape.AddDim(workspace_bytes); - AllocateOutputSetMklShape(context, kIdxWorkspace, - &workspace_tensor, + void AllocateWorkspaceTensor( + OpKernelContext* context, + const lrn_forward::primitive_desc& lrn_fwd_prim_desc, + MklDnnData* dnn_data_wksp) { + CHECK_NOTNULL(dnn_data_wksp); + Tensor* workspace_tensor = nullptr; + memory::primitive_desc workspace_pd = + lrn_fwd_prim_desc.workspace_primitive_desc(); + size_t workspace_bytes = workspace_pd.get_size(); + MklDnnShape workspace_mkl_shape; + // the workspace tensor is a uint8 tensor that has + // exactly the number of bytes necessary + workspace_mkl_shape.SetMklTensor(false); + TensorShape workspace_tf_shape; + workspace_tf_shape.AddDim(workspace_bytes); + AllocateOutputSetMklShape(context, kIdxWorkspace, &workspace_tensor, workspace_tf_shape, workspace_mkl_shape); - CHECK_NOTNULL(workspace_tensor); - dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); - } + CHECK_NOTNULL(workspace_tensor); + dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); + } void SanityCheckInputs(OpKernelContext* context) { const Tensor& src_tensor = MklGetInput(context, kIdxInput); MklDnnShape src_dnn_shape; GetMklShape(context, kIdxInput, &src_dnn_shape); if (src_dnn_shape.IsMklTensor()) { - OP_REQUIRES(context, src_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("input must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(src_tensor.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES(context, src_dnn_shape.GetDimension() == 4, + errors::InvalidArgument("input must be 4-dimensional")); + OP_REQUIRES(context, + FastBoundsCheck(src_tensor.NumElements(), + std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); } else { - OP_REQUIRES(context, src_tensor.dims() == 4, - errors::InvalidArgument("input must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(src_tensor.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES(context, src_tensor.dims() == 4, + errors::InvalidArgument("input must be 4-dimensional")); + OP_REQUIRES(context, + FastBoundsCheck(src_tensor.NumElements(), + std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); } } - const int kIdxInput = 0, - kIdxOutput = 0, - kIdxWorkspace = 1; + const int kIdxInput = 0, kIdxOutput = 0, kIdxWorkspace = 1; typedef typename Eigen::Tensor::DimensionPair DimPair; bool workspace_enabled_; @@ -994,17 +986,17 @@ class MklLRNOp : public OpKernel { float beta_; }; - template class MklLRNGradOp : public OpKernel { public: explicit MklLRNGradOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_)); @@ -1025,7 +1017,7 @@ class MklLRNGradOp : public OpKernel { MklDnnData output_dnn_data(&cpu_engine); MklDnnShape input_grad_dnn_shape, orig_input_dnn_shape, - orig_output_dnn_shape; + orig_output_dnn_shape; GetMklShape(context, kIdxGradient, &input_grad_dnn_shape); GetMklShape(context, kIdxOrigInput, &orig_input_dnn_shape); GetMklShape(context, kIdxOrigOutput, &orig_output_dnn_shape); @@ -1037,16 +1029,16 @@ class MklLRNGradOp : public OpKernel { orig_input_dnn_shape.IsMklTensor() && orig_output_dnn_shape.IsMklTensor() && input_grad_dnn_shape.IsMklChannelDim( - input_grad_dnn_shape.GetDimension() - 1) && + input_grad_dnn_shape.GetDimension() - 1) && orig_input_dnn_shape.IsMklChannelDim( - orig_input_dnn_shape.GetDimension() - 1) && + orig_input_dnn_shape.GetDimension() - 1) && orig_output_dnn_shape.IsMklChannelDim( - orig_output_dnn_shape.GetDimension() - 1); + orig_output_dnn_shape.GetDimension() - 1); if (!can_use_mkldnn) { - // Fallback to eigen - MklDefaultToEigen(context); - return; + // Fallback to eigen + MklDefaultToEigen(context); + return; } // At this point, we have the all clear to use MklDnn constructs // Naming: diff_dst is input_gradient_tensor; src is orig_input_tensor. @@ -1059,13 +1051,11 @@ class MklLRNGradOp : public OpKernel { // NHWC format. memory::desc original_output_md = orig_output_dnn_shape.GetCurLayout(); memory::desc target_diff_dst_md = ConfigureInputGradient( - input_grad_tensor, - input_grad_dnn_shape, - &input_grad_dnn_data); + input_grad_tensor, input_grad_dnn_shape, &input_grad_dnn_data); memory::desc orig_input_md = orig_input_dnn_shape.GetCurLayout(); memory::dims orig_input_dims = - orig_input_dnn_shape.GetSizesAsMklDnnDims(); + orig_input_dnn_shape.GetSizesAsMklDnnDims(); orig_input_dnn_data.SetUsrMem(orig_input_md, &orig_input_tensor); orig_input_dnn_data.SetOpMemDesc(orig_input_dims, memory::format::nhwc); @@ -1079,27 +1069,21 @@ class MklLRNGradOp : public OpKernel { // Create LRN backward primitive descriptor. It requires LRN forward // primitive descriptor also. - auto lrn_fwd_desc = lrn_forward::desc(prop_kind::forward, - lrn_across_channels, - orig_input_md, - kernel_size, - new_alpha, beta_, bias_); - auto lrn_fwd_prim_desc = lrn_forward::primitive_desc(lrn_fwd_desc, - cpu_engine); - auto lrn_bwd_desc = lrn_backward::desc(lrn_across_channels, - original_output_md, - target_diff_dst_md, - kernel_size, - new_alpha, beta_, bias_); - auto lrn_bwd_prim_desc = lrn_backward::primitive_desc(lrn_bwd_desc, - cpu_engine, - lrn_fwd_prim_desc); + auto lrn_fwd_desc = lrn_forward::desc( + prop_kind::forward, lrn_across_channels, orig_input_md, kernel_size, + new_alpha, beta_, bias_); + auto lrn_fwd_prim_desc = + lrn_forward::primitive_desc(lrn_fwd_desc, cpu_engine); + auto lrn_bwd_desc = lrn_backward::desc( + lrn_across_channels, original_output_md, target_diff_dst_md, + kernel_size, new_alpha, beta_, bias_); + auto lrn_bwd_prim_desc = lrn_backward::primitive_desc( + lrn_bwd_desc, cpu_engine, lrn_fwd_prim_desc); Tensor* output_tensor = nullptr; - memory::format orig_input_format - = orig_input_dnn_shape.GetTfDataFormat(); - AllocateOutputTensor(context, lrn_bwd_prim_desc, - orig_input_dims, orig_input_format, &output_tensor); + memory::format orig_input_format = orig_input_dnn_shape.GetTfDataFormat(); + AllocateOutputTensor(context, lrn_bwd_prim_desc, orig_input_dims, + orig_input_format, &output_tensor); OP_REQUIRES_OK(context, context->status()); CHECK_NOTNULL(output_tensor); output_dnn_data.SetUsrMemDataHandle(output_tensor); @@ -1110,35 +1094,32 @@ class MklLRNGradOp : public OpKernel { const Tensor& workspace_tensor = MklGetInput(context, kIdxWorkspace); MklDnnData workspace_dnn_data(&cpu_engine); ConfigureWorkspace(workspace_tensor, - lrn_fwd_prim_desc.workspace_primitive_desc(), - &workspace_dnn_data); - - PrepareAndExecuteNet(lrn_bwd_prim_desc, - lrn_fwd_prim_desc, - &orig_input_dnn_data, - &input_grad_dnn_data, - &output_dnn_data, - memory::primitive_desc(target_diff_dst_md, cpu_engine), - &workspace_dnn_data); - } catch (mkldnn::error &e) { + lrn_fwd_prim_desc.workspace_primitive_desc(), + &workspace_dnn_data); + + PrepareAndExecuteNet( + lrn_bwd_prim_desc, lrn_fwd_prim_desc, &orig_input_dnn_data, + &input_grad_dnn_data, &output_dnn_data, + memory::primitive_desc(target_diff_dst_md, cpu_engine), + &workspace_dnn_data); + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } - void AllocateOutputTensor(OpKernelContext* context, - const lrn_backward::primitive_desc& lrn_bkwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const lrn_backward::primitive_desc& lrn_bkwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); - memory::primitive_desc dst_pd - = lrn_bkwd_prim_desc.diff_src_primitive_desc(); + memory::primitive_desc dst_pd = + lrn_bkwd_prim_desc.diff_src_primitive_desc(); MklDnnShape output_mkl_shape; // We assume that all outputs at this point are MKL Tensors @@ -1146,170 +1127,153 @@ class MklLRNGradOp : public OpKernel { output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; size_t num_bytes = dst_pd.get_size(); CHECK_EQ(num_bytes % sizeof(T), 0); output_tf_shape.AddDim(num_bytes / sizeof(T)); - AllocateOutputSetMklShape(context, kIdxOutput, - output_tensor, - output_tf_shape, output_mkl_shape); + AllocateOutputSetMklShape(context, kIdxOutput, output_tensor, + output_tf_shape, output_mkl_shape); } memory::desc ConfigureInputGradient(const Tensor& input_grad_tensor, - const MklDnnShape& input_grad_dnn_shape, - MklDnnData *input_grad_dnn_data) { + const MklDnnShape& input_grad_dnn_shape, + MklDnnData* input_grad_dnn_data) { CHECK_NOTNULL(input_grad_dnn_data); // This shouldn't be necessary at this point, but just in case CHECK_EQ(input_grad_dnn_shape.IsMklTensor(), true); memory::desc input_grad_md = input_grad_dnn_shape.GetCurLayout(); - memory::dims orig_input_dims = - input_grad_dnn_shape.GetSizesAsMklDnnDims(); + memory::dims orig_input_dims = input_grad_dnn_shape.GetSizesAsMklDnnDims(); input_grad_dnn_data->SetUsrMem(input_grad_md, &input_grad_tensor); input_grad_dnn_data->SetOpMemDesc(orig_input_dims, memory::format::nhwc); return input_grad_md; } void PrepareAndExecuteNet( - const lrn_backward::primitive_desc& lrn_bkwd_desc, - const lrn_forward::primitive_desc& lrn_fwd_desc, - MklDnnData* src_dnn_data, - MklDnnData* input_gradient_diff_dst, - MklDnnData* output_diff_src, - const memory::primitive_desc& target_diff_dst_pd, - const MklDnnData* workspace_dnn_data = nullptr) { + const lrn_backward::primitive_desc& lrn_bkwd_desc, + const lrn_forward::primitive_desc& lrn_fwd_desc, + MklDnnData* src_dnn_data, MklDnnData* input_gradient_diff_dst, + MklDnnData* output_diff_src, + const memory::primitive_desc& target_diff_dst_pd, + const MklDnnData* workspace_dnn_data = nullptr) { std::vector net; // Check for input reordering on the diff dst input input_gradient_diff_dst->CheckReorderToOpMem( - lrn_bkwd_desc.diff_dst_primitive_desc(), &net); + lrn_bkwd_desc.diff_dst_primitive_desc(), &net); // Check for input reordering on the original input - src_dnn_data->CheckReorderToOpMem(lrn_fwd_desc.src_primitive_desc(), - &net); + src_dnn_data->CheckReorderToOpMem(lrn_fwd_desc.src_primitive_desc(), &net); // Create pooling primitive and add it to net if (nullptr == workspace_dnn_data) { - net.push_back(lrn_backward(lrn_bkwd_desc, - src_dnn_data->GetOpMem(), - input_gradient_diff_dst->GetOpMem(), - output_diff_src->GetOpMem())); + net.push_back(lrn_backward(lrn_bkwd_desc, src_dnn_data->GetOpMem(), + input_gradient_diff_dst->GetOpMem(), + output_diff_src->GetOpMem())); } else { - net.push_back(lrn_backward(lrn_bkwd_desc, - src_dnn_data->GetOpMem(), - input_gradient_diff_dst->GetOpMem(), - workspace_dnn_data->GetOpMem(), - output_diff_src->GetOpMem())); + net.push_back(lrn_backward(lrn_bkwd_desc, src_dnn_data->GetOpMem(), + input_gradient_diff_dst->GetOpMem(), + workspace_dnn_data->GetOpMem(), + output_diff_src->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } void ConfigureWorkspace(const Tensor& workspace_tensor, - memory::primitive_desc workspace_pd, - MklDnnData *workspace_dnn_data) { + memory::primitive_desc workspace_pd, + MklDnnData* workspace_dnn_data) { CHECK_NOTNULL(workspace_dnn_data); workspace_dnn_data->SetUsrMem(workspace_pd, &workspace_tensor); } - // Fallback implementation - Taken from lrn_op.cc - // TODO(intelft) Check if we can use EigenLRNOp directly instead of making a - // copy. - void MklDefaultToEigen(OpKernelContext* context) { - Tensor input_gradient_tensor; - Tensor orig_input_tensor; - Tensor orig_output_tensor; - - MklDnnShape input_grad_dnn_shape, orig_input_dnn_shape, - orig_output_dnn_shape; - GetMklShape(context, kIdxGradient, &input_grad_dnn_shape); - GetMklShape(context, kIdxOrigInput, &orig_input_dnn_shape); - GetMklShape(context, kIdxOrigOutput, &orig_output_dnn_shape); - - if (input_grad_dnn_shape.IsMklTensor()) { - input_gradient_tensor = - ConvertMklToTF(context, - MklGetInput(context, kIdxGradient), - input_grad_dnn_shape); - } else { - input_gradient_tensor = MklGetInput(context, kIdxGradient); - } - - if (orig_input_dnn_shape.IsMklTensor()) { - orig_input_tensor = - ConvertMklToTF(context, - MklGetInput(context, kIdxOrigInput), - orig_input_dnn_shape); - } else { - orig_input_tensor = MklGetInput(context, kIdxOrigInput); - } + // Fallback implementation - Taken from lrn_op.cc + // TODO(intelft) Check if we can use EigenLRNOp directly instead of making a + // copy. + void MklDefaultToEigen(OpKernelContext* context) { + Tensor input_gradient_tensor; + Tensor orig_input_tensor; + Tensor orig_output_tensor; + + MklDnnShape input_grad_dnn_shape, orig_input_dnn_shape, + orig_output_dnn_shape; + GetMklShape(context, kIdxGradient, &input_grad_dnn_shape); + GetMklShape(context, kIdxOrigInput, &orig_input_dnn_shape); + GetMklShape(context, kIdxOrigOutput, &orig_output_dnn_shape); + + if (input_grad_dnn_shape.IsMklTensor()) { + input_gradient_tensor = ConvertMklToTF( + context, MklGetInput(context, kIdxGradient), input_grad_dnn_shape); + } else { + input_gradient_tensor = MklGetInput(context, kIdxGradient); + } - if (orig_output_dnn_shape.IsMklTensor()) { - orig_output_tensor = - ConvertMklToTF(context, - MklGetInput(context, kIdxOrigOutput), - orig_output_dnn_shape); - } else { - orig_output_tensor = MklGetInput(context, kIdxOrigOutput); - } + if (orig_input_dnn_shape.IsMklTensor()) { + orig_input_tensor = ConvertMklToTF( + context, MklGetInput(context, kIdxOrigInput), orig_input_dnn_shape); + } else { + orig_input_tensor = MklGetInput(context, kIdxOrigInput); + } - const int64 batch = static_cast(input_gradient_tensor.dim_size(0)); - const int64 rows = static_cast(input_gradient_tensor.dim_size(1)); - const int64 cols = static_cast(input_gradient_tensor.dim_size(2)); - const int64 depth = static_cast(input_gradient_tensor.dim_size(3)); - const auto nodes = cols * rows; + if (orig_output_dnn_shape.IsMklTensor()) { + orig_output_tensor = ConvertMklToTF( + context, MklGetInput(context, kIdxOrigOutput), orig_output_dnn_shape); + } else { + orig_output_tensor = MklGetInput(context, kIdxOrigOutput); + } - auto grads_shaped = - input_gradient_tensor.shaped({nodes * batch, depth}); + const int64 batch = static_cast(input_gradient_tensor.dim_size(0)); + const int64 rows = static_cast(input_gradient_tensor.dim_size(1)); + const int64 cols = static_cast(input_gradient_tensor.dim_size(2)); + const int64 depth = static_cast(input_gradient_tensor.dim_size(3)); + const auto nodes = cols * rows; - auto in_shaped = orig_input_tensor.shaped({nodes * batch, depth}); - auto activations = - orig_output_tensor.shaped({nodes * batch, depth}); + auto grads_shaped = + input_gradient_tensor.shaped({nodes * batch, depth}); - Tensor* output_dnn_data; - MklShape mkl_output_mkl_shape; - mkl_output_mkl_shape.SetMklTensor(false); - mkl_output_mkl_shape.SetDimensions(4); - AllocateOutputSetMklShape(context, kIdxOutput, - &output_dnn_data, - input_gradient_tensor.shape(), - mkl_output_mkl_shape); + auto in_shaped = orig_input_tensor.shaped({nodes * batch, depth}); + auto activations = orig_output_tensor.shaped({nodes * batch, depth}); - auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); - out_shaped.setZero(); - auto shard = [this, activations, in_shaped, grads_shaped, out_shaped, - depth](int64 begin, int64 end) { - for (int64 i = begin; i < end; ++i) { - for (int64 j = 0; j < depth; ++j) { - int64 depth_begin = std::max(0, j - depth_radius_); - int64 depth_end = std::min(depth, j + depth_radius_ + 1); + Tensor* output_dnn_data; + MklShape mkl_output_mkl_shape; + mkl_output_mkl_shape.SetMklTensor(false); + mkl_output_mkl_shape.SetDimensions(4); + AllocateOutputSetMklShape(context, kIdxOutput, &output_dnn_data, + input_gradient_tensor.shape(), + mkl_output_mkl_shape); - T norm(0); - for (int64 k = depth_begin; k < depth_end; ++k) { - norm += in_shaped(i, k) * in_shaped(i, k); - } - norm = alpha_ * norm + bias_; - DCHECK_GT(norm, T(1e-6)); - for (int64 k = depth_begin; k < depth_end; ++k) { - T dyi = T(-2) * alpha_ * beta_ * in_shaped(i, k) * - activations(i, j) / norm; - if (k == j) { - dyi += Eigen::numext::pow(norm, -beta_); - } - dyi *= grads_shaped(i, j); - const_cast::Tensor&>(out_shaped)(i, k) += - dyi; + auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); + out_shaped.setZero(); + auto shard = [this, activations, in_shaped, grads_shaped, out_shaped, + depth](int64 begin, int64 end) { + for (int64 i = begin; i < end; ++i) { + for (int64 j = 0; j < depth; ++j) { + int64 depth_begin = std::max(0, j - depth_radius_); + int64 depth_end = std::min(depth, j + depth_radius_ + 1); + + T norm(0); + for (int64 k = depth_begin; k < depth_end; ++k) { + norm += in_shaped(i, k) * in_shaped(i, k); + } + norm = alpha_ * norm + bias_; + DCHECK_GT(norm, T(1e-6)); + for (int64 k = depth_begin; k < depth_end; ++k) { + T dyi = T(-2) * alpha_ * beta_ * in_shaped(i, k) * + activations(i, j) / norm; + if (k == j) { + dyi += Eigen::numext::pow(norm, -beta_); } + dyi *= grads_shaped(i, j); + const_cast::Tensor&>(out_shaped)(i, k) += dyi; } } - }; - auto worker_threads = - *(context->device()->tensorflow_cpu_worker_threads()); - Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch, - depth * depth, shard); - } + } + }; + auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); + Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch, + depth * depth, shard); + } void SanityCheckInputs(OpKernelContext* context) { const Tensor& input_gradient_tensor = MklGetInput(context, kIdxGradient); @@ -1317,59 +1281,59 @@ class MklLRNGradOp : public OpKernel { const Tensor& orig_output_tensor = MklGetInput(context, kIdxOrigOutput); const Tensor& workspace_tensor = MklGetInput(context, kIdxWorkspace); MklDnnShape in_grads_dnn_shape, in_image_dnn_shape, out_image_dnn_shape, - workspace_dnn_shape; + workspace_dnn_shape; GetMklShape(context, kIdxGradient, &in_grads_dnn_shape); GetMklShape(context, kIdxOrigInput, &in_image_dnn_shape); GetMklShape(context, kIdxOrigOutput, &out_image_dnn_shape); GetMklShape(context, kIdxWorkspace, &workspace_dnn_shape); if (in_grads_dnn_shape.IsMklTensor()) { OP_REQUIRES(context, in_grads_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("Input gradient must be " - "4-dimensional")); + errors::InvalidArgument("Input gradient must be " + "4-dimensional")); } else { - OP_REQUIRES(context, input_gradient_tensor.dims() == 4, - errors::InvalidArgument("input gradient must be 4-dimensional")); + OP_REQUIRES( + context, input_gradient_tensor.dims() == 4, + errors::InvalidArgument("input gradient must be 4-dimensional")); } if (in_image_dnn_shape.IsMklTensor()) { OP_REQUIRES(context, in_image_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("input images must be " - "4-dimensional")); + errors::InvalidArgument("input images must be " + "4-dimensional")); } else { OP_REQUIRES(context, orig_input_tensor.dims() == 4, errors::InvalidArgument("input images must be " - "4-dimensional")); + "4-dimensional")); } if (out_image_dnn_shape.IsMklTensor()) { OP_REQUIRES(context, out_image_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("Output image must be " - "4-dimensional")); + errors::InvalidArgument("Output image must be " + "4-dimensional")); } else { - OP_REQUIRES(context, orig_output_tensor.dims() == 4, - errors::InvalidArgument("Output image must be 4-dimensional")); + OP_REQUIRES( + context, orig_output_tensor.dims() == 4, + errors::InvalidArgument("Output image must be 4-dimensional")); } if (workspace_enabled_) { if (workspace_dnn_shape.IsMklTensor()) { - OP_REQUIRES(context, workspace_dnn_shape.IsMklTensor() == false, - errors::InvalidArgument("Workspace should not be MKL Tensor.")); + OP_REQUIRES( + context, workspace_dnn_shape.IsMklTensor() == false, + errors::InvalidArgument("Workspace should not be MKL Tensor.")); } else { OP_REQUIRES(context, workspace_tensor.dims() == 1, - errors::InvalidArgument("Workspace must be 1-dimensional")); + errors::InvalidArgument("Workspace must be 1-dimensional")); } } } -// Input("input_grads: T") -// Input("input_image: T") -// Input("output_image: T") -// Input("workspace: uint8") - const int kIdxGradient = 0, - kIdxOrigInput = 1, - kIdxOrigOutput = 2, - kIdxWorkspace = 3, - kIdxOutput = 0; + // Input("input_grads: T") + // Input("input_image: T") + // Input("output_image: T") + // Input("workspace: uint8") + const int kIdxGradient = 0, kIdxOrigInput = 1, kIdxOrigOutput = 2, + kIdxWorkspace = 3, kIdxOutput = 0; typedef typename Eigen::Tensor::DimensionPair DimPair; bool workspace_enabled_; @@ -1393,7 +1357,6 @@ class MklLRNGradOp : public OpKernel { .Label(mkl_op_registry::kMklOpLabel), \ MklLRNGradOp); - TF_CALL_float(REGISTER_MKL_LRN_CPU); } // namespace tensorflow diff --git a/tensorflow/core/kernels/mkl_maxpooling_op.cc b/tensorflow/core/kernels/mkl_maxpooling_op.cc index 82c5229bab..0de27ccd60 100644 --- a/tensorflow/core/kernels/mkl_maxpooling_op.cc +++ b/tensorflow/core/kernels/mkl_maxpooling_op.cc @@ -25,14 +25,14 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include #include "mkldnn.hpp" -using mkldnn::memory; +using mkldnn::algorithm; +using mkldnn::engine; using mkldnn::error; -using mkldnn::pooling_forward; -using mkldnn::pooling_backward; +using mkldnn::memory; using mkldnn::padding_kind; -using mkldnn::engine; +using mkldnn::pooling_backward; +using mkldnn::pooling_forward; using mkldnn::prop_kind; -using mkldnn::algorithm; #endif namespace tensorflow { @@ -397,18 +397,19 @@ class MklMaxPoolingGradOp : public OpKernel { if (workspace_enabled == false) { if (convert_input != nullptr) { if (input_in_mkl_format == false) { - CHECK_EQ( - dnnConversionExecute_F32( - convert_input, const_cast(static_cast( - tensor_in.flat().data())), - input_buf), - E_SUCCESS); + CHECK_EQ(dnnConversionExecute_F32( + convert_input, + const_cast(static_cast( + tensor_in.flat().data())), + input_buf), + E_SUCCESS); CHECK_EQ(dnnDelete_F32(convert_input), E_SUCCESS); convert_input = nullptr; } else { input_shape.GetConvertedFlatData( - lt_input_prim, const_cast(static_cast( - tensor_in.flat().data())), + lt_input_prim, + const_cast( + static_cast(tensor_in.flat().data())), input_buf); } pooling_resfwd[dnnResourceSrc] = input_buf; @@ -453,8 +454,9 @@ class MklMaxPoolingGradOp : public OpKernel { CHECK_EQ(dnnDelete_F32(convert_outbackprop), E_SUCCESS); } else { output_backprop_shape.GetConvertedFlatData( - lt_outbackprop_prim, const_cast(static_cast( - out_backprop.flat().data())), + lt_outbackprop_prim, + const_cast( + static_cast(out_backprop.flat().data())), outbackprop_buf); } pooling_res[dnnResourceDiffDst] = outbackprop_buf; @@ -499,7 +501,7 @@ template class MklMaxPoolingOp : public MklPoolingForwardOpBase { public: explicit MklMaxPoolingOp(OpKernelConstruction* context) - : MklPoolingForwardOpBase(context) { + : MklPoolingForwardOpBase(context) { // In Max Pooling, MKLDNN does not allow passing workspace as NULL. // So we set workspace_enabled_ to true. this->workspace_enabled_ = true; @@ -508,8 +510,8 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { void Compute(OpKernelContext* context) override { try { auto cpu_engine = engine(engine::cpu, 0); - const Tensor& input_tensor = MklGetInput(context, - this->kInputTensorIndexInput); + const Tensor& input_tensor = + MklGetInput(context, this->kInputTensorIndexInput); MklDnnShape dnn_shape_input; GetMklShape(context, this->kInputTensorIndexInput, &dnn_shape_input); this->SanityCheckInput(context, input_tensor, dnn_shape_input); @@ -522,9 +524,8 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { // initialize variables for the pooling op MklPoolParameters pool_params; // Get the input tensor and initialize the pooling parameters - this->ConfigureInput(context, dnn_shape_input, - input_tensor, &pool_params, - &dnn_data_input); + this->ConfigureInput(context, dnn_shape_input, input_tensor, &pool_params, + &dnn_data_input); OP_REQUIRES_OK(context, context->status()); // Declare output tensor @@ -535,9 +536,10 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { // If input is in Mkl layout, then just get the memory format from it // directly, instead of using input data_format to MaxPool. if (dnn_shape_input.IsMklTensor()) { - dnn_data_output.SetUsrMem(output_dims_mkl_order, - static_cast( - dnn_data_input.GetUsrMemDesc().data.format)); + dnn_data_output.SetUsrMem( + output_dims_mkl_order, + static_cast( + dnn_data_input.GetUsrMemDesc().data.format)); } else { dnn_data_output.SetUsrMem(output_dims_mkl_order, this->data_format_mkldnn_); @@ -546,24 +548,21 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { // describe the memory layout; let mkl-dnn choose the best for the op dnn_data_output.SetOpMemDesc(output_dims_mkl_order, memory::format::any); - auto pool_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_max, - dnn_data_input.GetUsrMemDesc(), - dnn_data_output.GetUsrMemDesc(), - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_fwd_desc = pooling_forward::primitive_desc(pool_desc, - cpu_engine); + auto pool_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_max, + dnn_data_input.GetUsrMemDesc(), dnn_data_output.GetUsrMemDesc(), + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_fwd_desc = + pooling_forward::primitive_desc(pool_desc, cpu_engine); this->AllocateOutputTensor(context, pool_fwd_desc, output_dims_mkl_order, - this->data_format_mkldnn_, &output_tensor); + this->data_format_mkldnn_, &output_tensor); OP_REQUIRES_OK(context, context->status()); dnn_data_output.SetUsrMemDataHandle(output_tensor); @@ -571,39 +570,38 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { OP_REQUIRES_OK(context, context->status()); this->PrepareAndExecuteNet(pool_fwd_desc, &dnn_data_input, - &dnn_data_output, &dnn_data_wksp); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Compute received an exception:", - error_msg)); + &dnn_data_output, &dnn_data_wksp); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK(context, errors::Aborted("Compute received an exception:", + error_msg)); } } // Compute private: - const int kOutputTensorIndexWorkspace = 1; - - void AllocateWorkspaceTensor(OpKernelContext* context, - const pooling_forward::primitive_desc& pool_fwd_prim_desc, - MklDnnData* dnn_data_wksp) { - CHECK_NOTNULL(dnn_data_wksp); - Tensor* workspace_tensor = nullptr; - memory::primitive_desc workspace_pd - = pool_fwd_prim_desc.workspace_primitive_desc(); - size_t workspace_bytes = workspace_pd.get_size(); - MklDnnShape workspace_mkl_shape; - workspace_mkl_shape.SetMklTensor(false); - TensorShape workspace_tf_shape; - workspace_tf_shape.AddDim(workspace_bytes); - AllocateOutputSetMklShape(context, kOutputTensorIndexWorkspace, - &workspace_tensor, - workspace_tf_shape, workspace_mkl_shape); - CHECK_NOTNULL(workspace_tensor); - dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); - } + const int kOutputTensorIndexWorkspace = 1; + + void AllocateWorkspaceTensor( + OpKernelContext* context, + const pooling_forward::primitive_desc& pool_fwd_prim_desc, + MklDnnData* dnn_data_wksp) { + CHECK_NOTNULL(dnn_data_wksp); + Tensor* workspace_tensor = nullptr; + memory::primitive_desc workspace_pd = + pool_fwd_prim_desc.workspace_primitive_desc(); + size_t workspace_bytes = workspace_pd.get_size(); + MklDnnShape workspace_mkl_shape; + workspace_mkl_shape.SetMklTensor(false); + TensorShape workspace_tf_shape; + workspace_tf_shape.AddDim(workspace_bytes); + AllocateOutputSetMklShape(context, kOutputTensorIndexWorkspace, + &workspace_tensor, workspace_tf_shape, + workspace_mkl_shape); + CHECK_NOTNULL(workspace_tensor); + dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); + } }; // The operation to compute MaxPool gradients. @@ -616,218 +614,183 @@ template class MklMaxPoolingGradOp : public MklPoolingBackwardOpBase { public: explicit MklMaxPoolingGradOp(OpKernelConstruction* context) - : MklPoolingBackwardOpBase(context) { - } + : MklPoolingBackwardOpBase(context) {} void Compute(OpKernelContext* context) override { try { - auto cpu_engine = engine(engine::cpu, 0); - const Tensor& orig_input_tensor = MklGetInput(context, - kInputTensorIndexOrigInput); - const Tensor& orig_output_tensor = MklGetInput(context, - kInputTensorIndexOrigOutput); - const Tensor& grad_tensor = MklGetInput(context, - kInputTensorIndexGradient); - const Tensor& workspace_tensor = MklGetInput(context, - kInputTensorIndexWorkspace); - MklDnnShape orig_input_mkl_shape, - orig_output_mkl_shape, - grad_mkl_shape, - workspace_mkl_shape; - GetMklShape(context, kInputTensorIndexOrigInput, - &orig_input_mkl_shape); - GetMklShape(context, kInputTensorIndexOrigOutput, - &orig_output_mkl_shape); - GetMklShape(context, kInputTensorIndexGradient, - &grad_mkl_shape); - GetMklShape(context, kInputTensorIndexWorkspace, - &workspace_mkl_shape); - - SanityCheckInputs(context, - orig_input_tensor, orig_output_tensor, - grad_tensor, workspace_tensor, - orig_input_mkl_shape, orig_output_mkl_shape, - grad_mkl_shape, workspace_mkl_shape); - if (!context->status().ok()) return; - - MklDnnData grad_dnn_data(&cpu_engine); - MklDnnData workspace_dnn_data(&cpu_engine); - MklDnnData output_dnn_data(&cpu_engine); - Tensor* output_tensor = nullptr; - MklPoolParameters pool_params; - TensorShape orig_input_shape; - memory::dims output_dims_mkl_order, orig_input_dims_mkl_order; - memory::desc original_input_md = ConfigureOriginalInput(context, - orig_input_tensor, - orig_input_mkl_shape, - &orig_input_dims_mkl_order, - &pool_params, - &orig_input_shape); - - memory::desc original_output_md = this->ConfigureOriginalOutput( - pool_params, - orig_output_mkl_shape, - output_dims_mkl_order); - - memory::desc target_diff_dst_md = this->ConfigureInputGradient( - grad_mkl_shape, - grad_tensor, - &grad_dnn_data, - original_output_md); - - output_dnn_data.SetUsrMem(original_input_md); - - // Create the forward pooling primitive descriptor so we can - // pass it as a hint to the backward pooling primitive descriptor - auto pool_fwd_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_max, - original_input_md, - original_output_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_fwd_prim_desc - = pooling_forward::primitive_desc(pool_fwd_desc, - cpu_engine); - - auto pool_bkwd_desc = pooling_backward::desc( - algorithm::pooling_max, - output_dnn_data.GetUsrMemDesc(), - target_diff_dst_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_bkwd_prim_desc - = pooling_backward::primitive_desc(pool_bkwd_desc, - cpu_engine, - pool_fwd_prim_desc); - - this->AllocateOutputTensor(context, pool_bkwd_prim_desc, - orig_input_dims_mkl_order, - this->data_format_mkldnn_, - &output_tensor); - output_dnn_data.SetUsrMemDataHandle(output_tensor); - - ConfigureWorkspace(workspace_tensor, - pool_fwd_prim_desc.workspace_primitive_desc(), - &workspace_dnn_data); - this->PrepareAndExecuteNet(pool_bkwd_prim_desc, - &grad_dnn_data, - &output_dnn_data, - memory::primitive_desc( - target_diff_dst_md, - cpu_engine), - &workspace_dnn_data); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Compute received an exception:", - error_msg)); + auto cpu_engine = engine(engine::cpu, 0); + const Tensor& orig_input_tensor = + MklGetInput(context, kInputTensorIndexOrigInput); + const Tensor& orig_output_tensor = + MklGetInput(context, kInputTensorIndexOrigOutput); + const Tensor& grad_tensor = + MklGetInput(context, kInputTensorIndexGradient); + const Tensor& workspace_tensor = + MklGetInput(context, kInputTensorIndexWorkspace); + MklDnnShape orig_input_mkl_shape, orig_output_mkl_shape, grad_mkl_shape, + workspace_mkl_shape; + GetMklShape(context, kInputTensorIndexOrigInput, &orig_input_mkl_shape); + GetMklShape(context, kInputTensorIndexOrigOutput, &orig_output_mkl_shape); + GetMklShape(context, kInputTensorIndexGradient, &grad_mkl_shape); + GetMklShape(context, kInputTensorIndexWorkspace, &workspace_mkl_shape); + + SanityCheckInputs(context, orig_input_tensor, orig_output_tensor, + grad_tensor, workspace_tensor, orig_input_mkl_shape, + orig_output_mkl_shape, grad_mkl_shape, + workspace_mkl_shape); + if (!context->status().ok()) return; + + MklDnnData grad_dnn_data(&cpu_engine); + MklDnnData workspace_dnn_data(&cpu_engine); + MklDnnData output_dnn_data(&cpu_engine); + Tensor* output_tensor = nullptr; + MklPoolParameters pool_params; + TensorShape orig_input_shape; + memory::dims output_dims_mkl_order, orig_input_dims_mkl_order; + memory::desc original_input_md = ConfigureOriginalInput( + context, orig_input_tensor, orig_input_mkl_shape, + &orig_input_dims_mkl_order, &pool_params, &orig_input_shape); + + memory::desc original_output_md = this->ConfigureOriginalOutput( + pool_params, orig_output_mkl_shape, output_dims_mkl_order); + + memory::desc target_diff_dst_md = this->ConfigureInputGradient( + grad_mkl_shape, grad_tensor, &grad_dnn_data, original_output_md); + + output_dnn_data.SetUsrMem(original_input_md); + + // Create the forward pooling primitive descriptor so we can + // pass it as a hint to the backward pooling primitive descriptor + auto pool_fwd_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_max, original_input_md, + original_output_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_fwd_prim_desc = + pooling_forward::primitive_desc(pool_fwd_desc, cpu_engine); + + auto pool_bkwd_desc = pooling_backward::desc( + algorithm::pooling_max, output_dnn_data.GetUsrMemDesc(), + target_diff_dst_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_bkwd_prim_desc = pooling_backward::primitive_desc( + pool_bkwd_desc, cpu_engine, pool_fwd_prim_desc); + + this->AllocateOutputTensor(context, pool_bkwd_prim_desc, + orig_input_dims_mkl_order, + this->data_format_mkldnn_, &output_tensor); + output_dnn_data.SetUsrMemDataHandle(output_tensor); + + ConfigureWorkspace(workspace_tensor, + pool_fwd_prim_desc.workspace_primitive_desc(), + &workspace_dnn_data); + this->PrepareAndExecuteNet( + pool_bkwd_prim_desc, &grad_dnn_data, &output_dnn_data, + memory::primitive_desc(target_diff_dst_md, cpu_engine), + &workspace_dnn_data); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK(context, errors::Aborted("Compute received an exception:", + error_msg)); } } // Compute private: - // .Input("orig_input: T") - // .Input("orig_output: T") - // .Input("grad: T") - // .Input("workspace: T") - const int kInputTensorIndexOrigInput = 0; - const int kInputTensorIndexOrigOutput = 1; - const int kInputTensorIndexGradient = 2; - const int kInputTensorIndexWorkspace = 3; - // Output("output: T") in Base Class - - memory::desc ConfigureOriginalInput(OpKernelContext* context, - const Tensor& tensor_original_input, - const MklDnnShape& original_input_mkl_shape, - memory::dims* original_input_dims_mkl_order, - MklPoolParameters* pool_params, - TensorShape* input_tensor_shape) { - *input_tensor_shape = tensor_original_input.shape(); - return MklPoolingBackwardOpBase::ConfigureOriginalInput( - context, - tensor_original_input, - original_input_mkl_shape, - original_input_dims_mkl_order, - pool_params, - *input_tensor_shape); - } + // .Input("orig_input: T") + // .Input("orig_output: T") + // .Input("grad: T") + // .Input("workspace: T") + const int kInputTensorIndexOrigInput = 0; + const int kInputTensorIndexOrigOutput = 1; + const int kInputTensorIndexGradient = 2; + const int kInputTensorIndexWorkspace = 3; + // Output("output: T") in Base Class + + memory::desc ConfigureOriginalInput( + OpKernelContext* context, const Tensor& tensor_original_input, + const MklDnnShape& original_input_mkl_shape, + memory::dims* original_input_dims_mkl_order, + MklPoolParameters* pool_params, TensorShape* input_tensor_shape) { + *input_tensor_shape = tensor_original_input.shape(); + return MklPoolingBackwardOpBase::ConfigureOriginalInput( + context, tensor_original_input, original_input_mkl_shape, + original_input_dims_mkl_order, pool_params, *input_tensor_shape); + } - void ConfigureWorkspace(const Tensor& workspace_tensor, - memory::primitive_desc workspace_pd, - MklDnnData *workspace_dnn_data) { - CHECK_NOTNULL(workspace_dnn_data); + void ConfigureWorkspace(const Tensor& workspace_tensor, + memory::primitive_desc workspace_pd, + MklDnnData* workspace_dnn_data) { + CHECK_NOTNULL(workspace_dnn_data); - workspace_dnn_data->SetUsrMem(workspace_pd, &workspace_tensor); - } + workspace_dnn_data->SetUsrMem(workspace_pd, &workspace_tensor); + } - void SanityCheckInputs(OpKernelContext* context, - const Tensor& orig_input_tensor, - const Tensor& orig_output_tensor, - const Tensor& grad_tensor, - const Tensor& workspace_tensor, - const MklDnnShape& orig_input_mkl_shape, - const MklDnnShape& orig_output_mkl_shape, - const MklDnnShape& grad_mkl_shape, - const MklDnnShape& workspace_mkl_shape) { - if (!orig_input_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, orig_input_tensor.dims() == 4, - errors::InvalidArgument("Original input shape must be " - "4-dimensional")); - } else { - OP_REQUIRES(context, orig_input_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Original input shape must be " - "4-dimensional")); - } - if (!orig_output_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, orig_output_tensor.dims() == 4, - errors::InvalidArgument("Original output must be " - "4-dimensional")); - } else { - OP_REQUIRES(context, orig_output_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Original output must be " - "4-dimensional")); - } - if (!grad_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, grad_tensor.dims() == 4, - errors::InvalidArgument("Gradient must be 4-dimensional")); - } else { - OP_REQUIRES(context, grad_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Gradient must be " - "4-dimensional")); - } - if (this->workspace_enabled_) { - // The workspace should not be an MKL tensor - OP_REQUIRES(context, workspace_mkl_shape.IsMklTensor() == false, - errors::InvalidArgument("Workspace tensor should not" - " be an MKL Tensor.")); - // It should only have one dimension - OP_REQUIRES(context, workspace_tensor.dims() == 1, - errors::InvalidArgument("Workspace tensor must be " - "1-dimensional")); - } else { - OP_REQUIRES(context, this->workspace_enabled_, - errors::Unimplemented("MKL-DNN Max Pooling does not " + void SanityCheckInputs(OpKernelContext* context, + const Tensor& orig_input_tensor, + const Tensor& orig_output_tensor, + const Tensor& grad_tensor, + const Tensor& workspace_tensor, + const MklDnnShape& orig_input_mkl_shape, + const MklDnnShape& orig_output_mkl_shape, + const MklDnnShape& grad_mkl_shape, + const MklDnnShape& workspace_mkl_shape) { + if (!orig_input_mkl_shape.IsMklTensor()) { + OP_REQUIRES(context, orig_input_tensor.dims() == 4, + errors::InvalidArgument("Original input shape must be " + "4-dimensional")); + } else { + OP_REQUIRES(context, orig_input_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Original input shape must be " + "4-dimensional")); + } + if (!orig_output_mkl_shape.IsMklTensor()) { + OP_REQUIRES(context, orig_output_tensor.dims() == 4, + errors::InvalidArgument("Original output must be " + "4-dimensional")); + } else { + OP_REQUIRES(context, orig_output_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Original output must be " + "4-dimensional")); + } + if (!grad_mkl_shape.IsMklTensor()) { + OP_REQUIRES(context, grad_tensor.dims() == 4, + errors::InvalidArgument("Gradient must be 4-dimensional")); + } else { + OP_REQUIRES(context, grad_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Gradient must be " + "4-dimensional")); + } + if (this->workspace_enabled_) { + // The workspace should not be an MKL tensor + OP_REQUIRES(context, workspace_mkl_shape.IsMklTensor() == false, + errors::InvalidArgument("Workspace tensor should not" + " be an MKL Tensor.")); + // It should only have one dimension + OP_REQUIRES(context, workspace_tensor.dims() == 1, + errors::InvalidArgument("Workspace tensor must be " + "1-dimensional")); + } else { + OP_REQUIRES( + context, this->workspace_enabled_, + errors::Unimplemented("MKL-DNN Max Pooling does not " "yet support the use case " "where MaxPoolGrad is called without first" " calling MaxPool.")); - } } + } }; // MklMaxPoolingGradOp #endif // INTEL_MKL_DNN diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.cc b/tensorflow/core/kernels/mkl_pooling_ops_common.cc index f7cadffd39..ef8597b057 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.cc +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.cc @@ -15,9 +15,9 @@ limitations under the License. #ifdef INTEL_MKL -#include -#include #include "tensorflow/core/kernels/mkl_pooling_ops_common.h" +#include +#include #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/framework/common_shape_fns.h" #include "tensorflow/core/kernels/bounds_check.h" @@ -111,17 +111,17 @@ void MklPoolParameters::Init(OpKernelContext* context, // TF can work with int64, but mkldnn only supports int32 // Fail if the height or width are greater than MAX_INT - OP_REQUIRES(context, FastBoundsCheck(out_height, - std::numeric_limits::max()), + OP_REQUIRES(context, + FastBoundsCheck(out_height, std::numeric_limits::max()), errors::InvalidArgument("output height is too large")); - OP_REQUIRES(context, FastBoundsCheck(out_width, - std::numeric_limits::max()), + OP_REQUIRES(context, + FastBoundsCheck(out_width, std::numeric_limits::max()), errors::InvalidArgument("output width is too large")); #endif out_depth = depth; // output will have the same depth as the input - } else { // we are pooling in the depth dimension + } else { // we are pooling in the depth dimension // Our current version of depthwise max pooling does not support // any padding, and expects the depth_window to equal the depth // stride (no overlapping). diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index b974b2c59a..880e45ab1e 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -17,16 +17,16 @@ limitations under the License. #define TENSORFLOW_CORE_KERNELS_MKL_POOLING_OPS_COMMON_H_ #ifdef INTEL_MKL -#include #include +#include #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/padding.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" using mkldnn::memory; -using mkldnn::pooling_forward; using mkldnn::pooling_backward; +using mkldnn::pooling_forward; using mkldnn::stream; #endif @@ -61,13 +61,25 @@ struct MklPoolParameters { TensorFormat data_format; MklPoolParameters() - : depth(0) - , tensor_in_cols(0), tensor_in_rows(0), tensor_in_batch(0) - , window_rows(0), window_cols(0), depth_window(0) - , row_stride(0), col_stride(0), depth_stride(0) - , out_height(0), out_width(0), out_depth(0) - , pad_left(0), pad_right(0), pad_top(0), pad_bottom(0), pad_depth(0) - , data_format(TensorFormat::FORMAT_NCHW) {} + : depth(0), + tensor_in_cols(0), + tensor_in_rows(0), + tensor_in_batch(0), + window_rows(0), + window_cols(0), + depth_window(0), + row_stride(0), + col_stride(0), + depth_stride(0), + out_height(0), + out_width(0), + out_depth(0), + pad_left(0), + pad_right(0), + pad_top(0), + pad_bottom(0), + pad_depth(0), + data_format(TensorFormat::FORMAT_NCHW) {} // Updates context->status if there is an invalid input. void Init(OpKernelContext* context, const std::vector& ksize, @@ -96,33 +108,31 @@ template class MklPoolingOpBase : public OpKernel { public: explicit MklPoolingOpBase(OpKernelConstruction* context) - : OpKernel(context) - , workspace_enabled_(false) { - string data_format; - OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format)); - OP_REQUIRES(context, - FormatFromString(data_format, &this->data_format_tf_), - errors::InvalidArgument("Invalid data format")); - this->data_format_mkldnn_ - = TFDataFormatToMklDnnDataFormat(this->data_format_tf_); - OP_REQUIRES_OK(context, context->GetAttr("ksize", &this->ksize_)); - OP_REQUIRES(context, this->ksize_.size() == 4, - errors::InvalidArgument("Sliding window ksize field must " - "specify 4 dimensions")); - OP_REQUIRES_OK(context, context->GetAttr("strides", &this->stride_)); - OP_REQUIRES(context, this->stride_.size() == 4, - errors::InvalidArgument("Sliding window strides field must " - "specify 4 dimensions")); - OP_REQUIRES_OK(context, context->GetAttr("padding", &this->padding_)); - OP_REQUIRES(context, this->ksize_[0] == 1 && this->stride_[0] == 1, - errors::Unimplemented("Pooling is not yet supported on the " - "batch dimension.")); - - // We may not get this attribute for this node if it does not go through - // graph rewrite pass. So we do not check for error while retrieving this - // attribute value. - context->GetAttr("workspace_enabled", &this->workspace_enabled_); - } + : OpKernel(context), workspace_enabled_(false) { + string data_format; + OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format)); + OP_REQUIRES(context, FormatFromString(data_format, &this->data_format_tf_), + errors::InvalidArgument("Invalid data format")); + this->data_format_mkldnn_ = + TFDataFormatToMklDnnDataFormat(this->data_format_tf_); + OP_REQUIRES_OK(context, context->GetAttr("ksize", &this->ksize_)); + OP_REQUIRES(context, this->ksize_.size() == 4, + errors::InvalidArgument("Sliding window ksize field must " + "specify 4 dimensions")); + OP_REQUIRES_OK(context, context->GetAttr("strides", &this->stride_)); + OP_REQUIRES(context, this->stride_.size() == 4, + errors::InvalidArgument("Sliding window strides field must " + "specify 4 dimensions")); + OP_REQUIRES_OK(context, context->GetAttr("padding", &this->padding_)); + OP_REQUIRES(context, this->ksize_[0] == 1 && this->stride_[0] == 1, + errors::Unimplemented("Pooling is not yet supported on the " + "batch dimension.")); + + // We may not get this attribute for this node if it does not go through + // graph rewrite pass. So we do not check for error while retrieving this + // attribute value. + context->GetAttr("workspace_enabled", &this->workspace_enabled_); + } void Compute(OpKernelContext* context) override = 0; protected: @@ -132,24 +142,24 @@ class MklPoolingOpBase : public OpKernel { // output height and output width to have already been int32 // bounds-checked void GetOutputDims(const MklPoolParameters& mkl_pool_params, - memory::dims* output_dims_mkl_order) { + memory::dims* output_dims_mkl_order) { // MKL-DNN always needs output in NCHW format. - *output_dims_mkl_order = { mkl_pool_params.tensor_in_batch, + *output_dims_mkl_order = {mkl_pool_params.tensor_in_batch, mkl_pool_params.out_depth, static_cast(mkl_pool_params.out_height), static_cast(mkl_pool_params.out_width)}; } void InitMklPoolParameters(OpKernelContext* context, - MklPoolParameters* pool_params, - const MklDnnShape& original_input_mkl_shape, - const TensorShape& input_tensor_shape) { + MklPoolParameters* pool_params, + const MklDnnShape& original_input_mkl_shape, + const TensorShape& input_tensor_shape) { if (!original_input_mkl_shape.IsMklTensor()) { pool_params->Init(context, this->ksize_, this->stride_, this->padding_, - this->data_format_tf_, input_tensor_shape); + this->data_format_tf_, input_tensor_shape); } else { pool_params->Init(context, this->ksize_, this->stride_, this->padding_, - this->data_format_tf_, &original_input_mkl_shape); + this->data_format_tf_, &original_input_mkl_shape); } } @@ -159,13 +169,12 @@ class MklPoolingOpBase : public OpKernel { size_t GetNumTElements(const memory::primitive_desc& pd) { size_t num_bytes = pd.get_size(); size_t ret_val = num_bytes / sizeof(T); - if ( num_bytes % sizeof(T) != 0 ) { - ret_val++; + if (num_bytes % sizeof(T) != 0) { + ret_val++; } return ret_val; } - std::vector ksize_; std::vector stride_; Padding padding_; @@ -183,30 +192,29 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { protected: void ConfigureInput(OpKernelContext* context, - const MklDnnShape& input_mkl_shape, - const Tensor& input_tensor, - MklPoolParameters* pool_params, - MklDnnData* dnn_data_input) { + const MklDnnShape& input_mkl_shape, + const Tensor& input_tensor, + MklPoolParameters* pool_params, + MklDnnData* dnn_data_input) { CHECK_NOTNULL(pool_params); CHECK_NOTNULL(dnn_data_input); TensorShape input_tensor_shape = input_tensor.shape(); - memory::desc input_md = input_mkl_shape.IsMklTensor() - ? input_mkl_shape.GetMklLayout() - : memory::desc( - TFShapeToMklDnnDimsInNCHW( - input_tensor_shape, this->data_format_tf_), - MklDnnType(), - this->data_format_mkldnn_); + memory::desc input_md = + input_mkl_shape.IsMklTensor() + ? input_mkl_shape.GetMklLayout() + : memory::desc(TFShapeToMklDnnDimsInNCHW(input_tensor_shape, + this->data_format_tf_), + MklDnnType(), this->data_format_mkldnn_); dnn_data_input->SetUsrMem(input_md, &input_tensor); - this->InitMklPoolParameters(context, pool_params, - input_mkl_shape, input_tensor_shape); + this->InitMklPoolParameters(context, pool_params, input_mkl_shape, + input_tensor_shape); } - void AllocateOutputTensor(OpKernelContext* context, - const pooling_forward::primitive_desc& pool_fwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const pooling_forward::primitive_desc& pool_fwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); memory::primitive_desc dst_pd = pool_fwd_prim_desc.dst_primitive_desc(); @@ -215,50 +223,42 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; // only allocate enough space for the elements we need. output_tf_shape.AddDim(this->GetNumTElements(dst_pd)); - AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, - output_tensor, - output_tf_shape, output_mkl_shape); + AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, output_tensor, + output_tf_shape, output_mkl_shape); CHECK_NOTNULL(*output_tensor); } void PrepareAndExecuteNet( - const pooling_forward::primitive_desc& pool_fwd_desc, - const MklDnnData* src, - MklDnnData* dst, - MklDnnData* wksp = nullptr) { + const pooling_forward::primitive_desc& pool_fwd_desc, + const MklDnnData* src, MklDnnData* dst, + MklDnnData* wksp = nullptr) { std::vector net; // Create pooling primitive and add it to net if (wksp != nullptr) { - net.push_back(pooling_forward(pool_fwd_desc, - src->GetOpMem(), - dst->GetOpMem(), - wksp->GetOpMem())); + net.push_back(pooling_forward(pool_fwd_desc, src->GetOpMem(), + dst->GetOpMem(), wksp->GetOpMem())); } else { - net.push_back(pooling_forward(pool_fwd_desc, - src->GetOpMem(), - dst->GetOpMem())); + net.push_back( + pooling_forward(pool_fwd_desc, src->GetOpMem(), dst->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } - - void SanityCheckInput(OpKernelContext* context, - const Tensor& input_tensor, - const MklDnnShape& input_mkl_shape) { + void SanityCheckInput(OpKernelContext* context, const Tensor& input_tensor, + const MklDnnShape& input_mkl_shape) { if (!input_mkl_shape.IsMklTensor()) { OP_REQUIRES(context, input_tensor.dims() == 4, - errors::InvalidArgument("Input must be 4-dimensional")); + errors::InvalidArgument("Input must be 4-dimensional")); } else { - OP_REQUIRES(context, input_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Input shape must be " - "4-dimensional")); + OP_REQUIRES(context, input_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Input shape must be " + "4-dimensional")); } } // .Input("value: T") @@ -267,66 +267,58 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { const int kOutputTensorIndexOutput = 0; }; // MklPoolingForwardBaseOp - template class MklPoolingBackwardOpBase : public MklPoolingOpBase { public: explicit MklPoolingBackwardOpBase(OpKernelConstruction* context) - : MklPoolingOpBase(context) { } + : MklPoolingOpBase(context) {} void Compute(OpKernelContext* context) override = 0; protected: const int kOutputTensorIndexOutput = 0; - void AllocateOutputTensor(OpKernelContext* context, - const pooling_backward::primitive_desc& pool_bkwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const pooling_backward::primitive_desc& pool_bkwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); - memory::primitive_desc dst_pd - = pool_bkwd_prim_desc.diff_src_primitive_desc(); + memory::primitive_desc dst_pd = + pool_bkwd_prim_desc.diff_src_primitive_desc(); MklDnnShape output_mkl_shape; output_mkl_shape.SetMklTensor(true); output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; output_tf_shape.AddDim(this->GetNumTElements(dst_pd)); - AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, - output_tensor, - output_tf_shape, output_mkl_shape); + AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, output_tensor, + output_tf_shape, output_mkl_shape); CHECK_NOTNULL(*output_tensor); } void PrepareAndExecuteNet( - const pooling_backward::primitive_desc& pool_bkwd_desc, - MklDnnData* input_gradient_diff_dst, - MklDnnData* output_diff_src, - const memory::primitive_desc& target_diff_dst_pd, - const MklDnnData* workspace = nullptr) { - + const pooling_backward::primitive_desc& pool_bkwd_desc, + MklDnnData* input_gradient_diff_dst, MklDnnData* output_diff_src, + const memory::primitive_desc& target_diff_dst_pd, + const MklDnnData* workspace = nullptr) { std::vector net; // If the input gradient isn't in the same format as the output // reorder it to the same format as the output - input_gradient_diff_dst->CheckReorderToOpMem( - target_diff_dst_pd, - &net); + input_gradient_diff_dst->CheckReorderToOpMem(target_diff_dst_pd, &net); // Create pooling primitive and add it to net if (nullptr == workspace) { net.push_back(pooling_backward(pool_bkwd_desc, - input_gradient_diff_dst->GetOpMem(), - output_diff_src->GetOpMem())); + input_gradient_diff_dst->GetOpMem(), + output_diff_src->GetOpMem())); } else { - net.push_back(pooling_backward(pool_bkwd_desc, - input_gradient_diff_dst->GetOpMem(), - workspace->GetOpMem(), - output_diff_src->GetOpMem())); + net.push_back( + pooling_backward(pool_bkwd_desc, input_gradient_diff_dst->GetOpMem(), + workspace->GetOpMem(), output_diff_src->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } @@ -334,77 +326,73 @@ class MklPoolingBackwardOpBase : public MklPoolingOpBase { // Max Pooling and Avg Pooling have slightly different implementations // Takes the Tensor containing original input data and the original // mkl Dnn Shape and populates other data - memory::desc ConfigureOriginalInput(OpKernelContext* context, - const Tensor& tensor_original_input_shape, - const MklDnnShape& original_input_mkl_shape, - memory::dims* original_input_dims_nchw, - MklPoolParameters* pool_params, - const TensorShape& input_tensor_shape) { + memory::desc ConfigureOriginalInput( + OpKernelContext* context, const Tensor& tensor_original_input_shape, + const MklDnnShape& original_input_mkl_shape, + memory::dims* original_input_dims_nchw, MklPoolParameters* pool_params, + const TensorShape& input_tensor_shape) { CHECK_NOTNULL(original_input_dims_nchw); CHECK_NOTNULL(pool_params); - this->InitMklPoolParameters(context, pool_params, - original_input_mkl_shape, - input_tensor_shape); - - *original_input_dims_nchw - = original_input_mkl_shape.IsMklTensor() - ? original_input_mkl_shape.GetSizesAsMklDnnDims() - : TFShapeToMklDnnDimsInNCHW(input_tensor_shape, - this->data_format_tf_); - - return original_input_mkl_shape.IsMklTensor() - ? original_input_mkl_shape.GetMklLayout() - : memory::desc(*original_input_dims_nchw, - MklDnnType(), - this->data_format_mkldnn_); + this->InitMklPoolParameters(context, pool_params, original_input_mkl_shape, + input_tensor_shape); + + *original_input_dims_nchw = + original_input_mkl_shape.IsMklTensor() + ? original_input_mkl_shape.GetSizesAsMklDnnDims() + : TFShapeToMklDnnDimsInNCHW(input_tensor_shape, + this->data_format_tf_); + + return original_input_mkl_shape.IsMklTensor() + ? original_input_mkl_shape.GetMklLayout() + : memory::desc(*original_input_dims_nchw, MklDnnType(), + this->data_format_mkldnn_); } - memory::desc ConfigureOriginalOutput(const MklPoolParameters& pool_params, - const MklDnnShape& original_output_mkl_shape, - memory::dims output_dims_mkl_order) { + memory::desc ConfigureOriginalOutput( + const MklPoolParameters& pool_params, + const MklDnnShape& original_output_mkl_shape, + memory::dims output_dims_mkl_order) { this->GetOutputDims(pool_params, &output_dims_mkl_order); return original_output_mkl_shape.IsMklTensor() - ? original_output_mkl_shape.GetMklLayout() - : memory::desc(output_dims_mkl_order, - MklDnnType(), - this->data_format_mkldnn_); + ? original_output_mkl_shape.GetMklLayout() + : memory::desc(output_dims_mkl_order, MklDnnType(), + this->data_format_mkldnn_); } memory::desc ConfigureInputGradient( - const MklDnnShape& input_gradient_mkl_shape, - const Tensor& input_gradient_tensor, - MklDnnData* input_gradient_dnn_data, - const memory::desc& original_output_md) { + const MklDnnShape& input_gradient_mkl_shape, + const Tensor& input_gradient_tensor, + MklDnnData* input_gradient_dnn_data, + const memory::desc& original_output_md) { // Configure the gradient as is - memory::desc original_input_grad_md - = input_gradient_mkl_shape.IsMklTensor() - ? input_gradient_mkl_shape.GetMklLayout() - : memory::desc(TFShapeToMklDnnDimsInNCHW( - input_gradient_tensor.shape(), - this->data_format_tf_), - MklDnnType(), this->data_format_mkldnn_); + memory::desc original_input_grad_md = + input_gradient_mkl_shape.IsMklTensor() + ? input_gradient_mkl_shape.GetMklLayout() + : memory::desc( + TFShapeToMklDnnDimsInNCHW(input_gradient_tensor.shape(), + this->data_format_tf_), + MklDnnType(), this->data_format_mkldnn_); input_gradient_dnn_data->SetUsrMem(original_input_grad_md, - &input_gradient_tensor); + &input_gradient_tensor); // Check to see if input grad diff dst is in the right format // Create a new memory descriptor with the same shape as the // original, but the format of the other tensors. memory::format original_output_format = - static_cast(original_output_md.data.format); - bool grad_reorder_needed = input_gradient_dnn_data->IsReorderNeeded( - original_output_format); - memory::dims diff_dst_dims = input_gradient_mkl_shape.IsMklTensor() - ? input_gradient_mkl_shape.GetSizesAsMklDnnDims() - : TFShapeToMklDnnDimsInNCHW(input_gradient_tensor.shape(), - this->data_format_tf_); - memory::desc target_diff_dst_md = memory::desc(diff_dst_dims, - MklDnnType(), original_output_format); - - return grad_reorder_needed - ? target_diff_dst_md - : original_input_grad_md; + static_cast(original_output_md.data.format); + bool grad_reorder_needed = + input_gradient_dnn_data->IsReorderNeeded(original_output_format); + memory::dims diff_dst_dims = + input_gradient_mkl_shape.IsMklTensor() + ? input_gradient_mkl_shape.GetSizesAsMklDnnDims() + : TFShapeToMklDnnDimsInNCHW(input_gradient_tensor.shape(), + this->data_format_tf_); + memory::desc target_diff_dst_md = + memory::desc(diff_dst_dims, MklDnnType(), original_output_format); + + return grad_reorder_needed ? target_diff_dst_md : original_input_grad_md; } }; #endif // INTEL_MKL_DNN diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index dc899d8c7e..873aca30ca 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -16,29 +16,29 @@ limitations under the License. // See docs in ../ops/nn_ops.cc. #ifdef INTEL_MKL +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "tensorflow/core/platform/default/logging.h" -#include "tensorflow/core/util/mkl_util.h" #include "mkl_dnn.h" #include "mkl_dnn_types.h" +#include "tensorflow/core/platform/default/logging.h" +#include "tensorflow/core/util/mkl_util.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; -using mkldnn::prop_kind; using mkldnn::algorithm; -using mkldnn::relu_forward; -using mkldnn::relu_backward; -using mkldnn::eltwise_relu; using mkldnn::eltwise_elu; +using mkldnn::eltwise_relu; using mkldnn::eltwise_tanh; +using mkldnn::prop_kind; +using mkldnn::relu_backward; +using mkldnn::relu_forward; +using mkldnn::stream; #endif namespace tensorflow { @@ -180,7 +180,6 @@ class MklReluOp : public OpKernel { } MklReluOpContext; }; - template class MklReluGradOp : public OpKernel { public: @@ -214,10 +213,11 @@ class MklReluGradOp : public OpKernel { if (!dnnLayoutCompare_F32(lt_input, lt_grad)) { AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, lt_grad, &mkl_buffer_convert); - CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input, - lt_grad), E_SUCCESS); + CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input, lt_grad), + E_SUCCESS); CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, buf_input, - mkl_buffer_convert), E_SUCCESS); + mkl_buffer_convert), + E_SUCCESS); relu_res[dnnResourceSrc] = mkl_buffer_convert; dnnDelete_F32(cv_input_to_grad); } else { @@ -325,7 +325,8 @@ void MklReluGradOp::Compute(OpKernelContext* context) { float negative_slope = 0.0; CHECK_EQ(dnnReLUCreateBackward_F32(&mkl_context.prim_relu_bwd, NULL, mkl_context.lt_grad, mkl_context.lt_grad, - negative_slope), E_SUCCESS); + negative_slope), + E_SUCCESS); Tensor mkl_tmp_input_buf_tensor; mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_input_buf_tensor); @@ -348,7 +349,8 @@ void MklReluGradOp::Compute(OpKernelContext* context) { } tf_shape.AddDim(dnnLayoutGetMemorySize_F32(static_cast( - mkl_context.output_shape.GetMklLayout())) / sizeof(T)); + mkl_context.output_shape.GetMklLayout())) / + sizeof(T)); AllocateOutputSetMklShape(context, 0, &output, tf_shape, mkl_context.output_shape); } else { @@ -361,13 +363,11 @@ void MklReluGradOp::Compute(OpKernelContext* context) { mkl_context.relu_res[dnnResourceDiffSrc] = static_cast(output->flat().data()); - CHECK_EQ(dnnExecute_F32(mkl_context.prim_relu_bwd, - mkl_context.relu_res), - E_SUCCESS); + CHECK_EQ(dnnExecute_F32(mkl_context.prim_relu_bwd, mkl_context.relu_res), + E_SUCCESS); mkl_context.MklCleanup(); } - #else // INTEL_MKL_DNN template @@ -375,8 +375,7 @@ class MklReluOpBase : public OpKernel { public: ~MklReluOpBase() {} - explicit MklReluOpBase(OpKernelConstruction* context) : OpKernel(context) { - } + explicit MklReluOpBase(OpKernelConstruction* context) : OpKernel(context) {} virtual void Compute_Scalar(OpKernelContext* context) = 0; @@ -413,12 +412,12 @@ class MklReluOpBase : public OpKernel { T alpha = 0, beta = 0; std::shared_ptr relu_fwd_pd; - auto relu_fwd_desc = relu_forward::desc(prop_kind::forward_training, + auto relu_fwd_desc = relu_forward::desc( + prop_kind::forward_training, // Operator memory descriptor is same as user memory descriptor. - alg_kind, src.GetUsrMemDesc(), - alpha, beta); - relu_fwd_pd.reset(new relu_forward::primitive_desc(relu_fwd_desc, - cpu_engine)); + alg_kind, src.GetUsrMemDesc(), alpha, beta); + relu_fwd_pd.reset( + new relu_forward::primitive_desc(relu_fwd_desc, cpu_engine)); // allocate dst tensor MklDnnShape dnn_shape_dst; @@ -431,7 +430,7 @@ class MklReluOpBase : public OpKernel { dnn_shape_dst.SetTfLayout(dnn_shape_src.GetDimension(), dnn_shape_src.GetSizesAsMklDnnDims(), dnn_shape_src.GetTfDataFormat()); - tf_shape_dst.AddDim(dst_pd.get_size()/sizeof(T)); + tf_shape_dst.AddDim(dst_pd.get_size() / sizeof(T)); } else { dnn_shape_dst.SetMklTensor(false); tf_shape_dst = src_tensor.shape(); @@ -445,34 +444,32 @@ class MklReluOpBase : public OpKernel { // execute net std::vector net; - auto relu_fwd = relu_forward(*relu_fwd_pd, src.GetOpMem(), - dst.GetOpMem()); + auto relu_fwd = + relu_forward(*relu_fwd_pd, src.GetOpMem(), dst.GetOpMem()); net.push_back(relu_fwd); stream(stream::kind::eager).submit(net).wait(); - } catch (mkldnn::error &e) { + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } }; - template class MklReluGradOpBase : public OpKernel { public: ~MklReluGradOpBase() {} - explicit MklReluGradOpBase(OpKernelConstruction* context) : - OpKernel(context) {} + explicit MklReluGradOpBase(OpKernelConstruction* context) + : OpKernel(context) {} virtual void Compute_Scalar(OpKernelContext* context) = 0; - void Compute(OpKernelContext* context) { + void Compute(OpKernelContext* context) { try { auto cpu_engine = engine(engine::cpu, 0); MklDnnData src(&cpu_engine); @@ -483,9 +480,9 @@ class MklReluGradOpBase : public OpKernel { const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); - Tensor* diff_src_tensor = nullptr; + Tensor* diff_src_tensor = nullptr; MklDnnShape dnn_shape_src, dnn_shape_diff_dst; GetMklShape(context, src_index, &dnn_shape_src); @@ -526,25 +523,25 @@ class MklReluGradOpBase : public OpKernel { src_md = dnn_shape_src.GetMklLayout(); memory::format src_mkl_data_format = dnn_shape_src.GetTfDataFormat(); - auto src_tf_data_format = MklDnnDataFormatToTFDataFormat( - src_mkl_data_format); + auto src_tf_data_format = + MklDnnDataFormatToTFDataFormat(src_mkl_data_format); auto diff_dst_dims = TFShapeToMklDnnDimsInNCHW(diff_dst_tensor.shape(), src_tf_data_format); - diff_dst_md = memory::desc(diff_dst_dims, MklDnnType(), - src_mkl_data_format); + diff_dst_md = + memory::desc(diff_dst_dims, MklDnnType(), src_mkl_data_format); } else if (!dnn_shape_src.IsMklTensor() && - dnn_shape_diff_dst.IsMklTensor()) { + dnn_shape_diff_dst.IsMklTensor()) { // Same comment as above. diff_dst_md = dnn_shape_diff_dst.GetMklLayout(); memory::format diff_dst_mkl_data_format = - dnn_shape_diff_dst.GetTfDataFormat(); - auto diff_dst_tf_data_format = MklDnnDataFormatToTFDataFormat( - diff_dst_mkl_data_format); + dnn_shape_diff_dst.GetTfDataFormat(); + auto diff_dst_tf_data_format = + MklDnnDataFormatToTFDataFormat(diff_dst_mkl_data_format); auto src_dims = TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), diff_dst_tf_data_format); - src_md = memory::desc(src_dims, MklDnnType(), - diff_dst_mkl_data_format); + src_md = + memory::desc(src_dims, MklDnnType(), diff_dst_mkl_data_format); } else { // If both the inputs are in MKL format, we use Mkl layout of the input // tensors. @@ -572,12 +569,12 @@ class MklReluGradOpBase : public OpKernel { std::shared_ptr relu_fwd_pd; auto relu_fwd_desc = relu_forward::desc(prop_kind::forward_training, alg_kind, src_md, alpha, beta); - relu_fwd_pd.reset(new relu_forward::primitive_desc(relu_fwd_desc, - cpu_engine)); - auto relu_bwd_desc = relu_backward::desc(alg_kind, common_md, common_md, - alpha, beta); - auto relu_bwd_pd = relu_backward::primitive_desc(relu_bwd_desc, - cpu_engine, *relu_fwd_pd); + relu_fwd_pd.reset( + new relu_forward::primitive_desc(relu_fwd_desc, cpu_engine)); + auto relu_bwd_desc = + relu_backward::desc(alg_kind, common_md, common_md, alpha, beta); + auto relu_bwd_pd = relu_backward::primitive_desc( + relu_bwd_desc, cpu_engine, *relu_fwd_pd); // allocate diff_src tensor MklDnnShape dnn_shape_diff_src; @@ -590,33 +587,32 @@ class MklReluGradOpBase : public OpKernel { dnn_shape_diff_src.SetTfLayout(dnn_shape_src.GetDimension(), dnn_shape_src.GetSizesAsMklDnnDims(), dnn_shape_src.GetTfDataFormat()); - tf_shape_diff_src.AddDim(diff_src_pd.get_size()/sizeof(T)); + tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T)); } else { dnn_shape_diff_src.SetMklTensor(false); tf_shape_diff_src = src_tensor.shape(); } AllocateOutputSetMklShape(context, diff_src_index, &diff_src_tensor, - tf_shape_diff_src, dnn_shape_diff_src); + tf_shape_diff_src, dnn_shape_diff_src); // diff_src memory descriptor is same as memory descriptor for both // inputs. diff_src.SetUsrMem(common_md, diff_src_tensor); PrepareAndExecuteNet(relu_bwd_pd, &src, &diff_src, &diff_dst); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } void PrepareAndExecuteNet(const relu_backward::primitive_desc& relu_prim_desc, - MklDnnData* src, MklDnnData* diff_src, MklDnnData* - diff_dst) { + MklDnnData* src, MklDnnData* diff_src, + MklDnnData* diff_dst) { std::vector net; // Check if we need to reorder original input tensors into common_md layout @@ -632,14 +628,13 @@ class MklReluGradOpBase : public OpKernel { } }; - template class MklReluOp : public MklReluOpBase { public: ~MklReluOp() {} - explicit MklReluOp(OpKernelConstruction* context) : - MklReluOpBase(context) {} + explicit MklReluOp(OpKernelConstruction* context) + : MklReluOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t src_index = 0; // index of src input tensor @@ -649,15 +644,15 @@ class MklReluOp : public MklReluOpBase { GetMklShape(context, src_index, &dnn_shape_src); Tensor* dst_tensor = nullptr; - void* user_i = static_cast(const_cast( - src_tensor.flat().data())); + void* user_i = + static_cast(const_cast(src_tensor.flat().data())); MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); AllocateOutputSetMklShape(context, dst_index, &dst_tensor, src_tensor.shape(), dnn_shape_dst); void* out_o = static_cast(dst_tensor->flat().data()); (static_cast(out_o))[0] = - std::max((static_cast(user_i))[0], static_cast(0)); + std::max((static_cast(user_i))[0], static_cast(0)); return; } }; @@ -667,14 +662,14 @@ class MklReluGradOp : public MklReluGradOpBase { public: ~MklReluGradOp() {} - explicit MklReluGradOp(OpKernelConstruction* context) : - MklReluGradOpBase(context) {} + explicit MklReluGradOp(OpKernelConstruction* context) + : MklReluGradOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t diff_dst_index = 0; // index of diff_dst input tensor const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); Tensor* diff_src_tensor = nullptr; @@ -687,11 +682,11 @@ class MklReluGradOp : public MklReluGradOpBase { diff_dst_tensor.shape(), dnn_shape_diff_src); void* out_o = static_cast(diff_src_tensor->flat().data()); void* user_i = - static_cast(const_cast(src_tensor.flat().data())); + static_cast(const_cast(src_tensor.flat().data())); void* user_g = - static_cast(const_cast(diff_dst_tensor.flat().data())); - (static_cast(out_o))[0] = (static_cast(user_g))[0] * - ((static_cast(user_i))[0] > 0); + static_cast(const_cast(diff_dst_tensor.flat().data())); + (static_cast(out_o))[0] = + (static_cast(user_g))[0] * ((static_cast(user_i))[0] > 0); return; } }; @@ -701,8 +696,8 @@ class MklEluOp : public MklReluOpBase { public: ~MklEluOp() {} - explicit MklEluOp(OpKernelConstruction* context) : - MklReluOpBase(context) {} + explicit MklEluOp(OpKernelConstruction* context) + : MklReluOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t src_index = 0; // index of src input tensor @@ -712,8 +707,8 @@ class MklEluOp : public MklReluOpBase { GetMklShape(context, src_index, &dnn_shape_src); Tensor* dst_tensor = nullptr; - void* user_i = static_cast(const_cast( - src_tensor.flat().data())); + void* user_i = + static_cast(const_cast(src_tensor.flat().data())); MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); AllocateOutputSetMklShape(context, dst_index, &dst_tensor, @@ -734,14 +729,14 @@ class MklEluGradOp : public MklReluGradOpBase { public: ~MklEluGradOp() {} - explicit MklEluGradOp(OpKernelConstruction* context) : - MklReluGradOpBase(context) {} + explicit MklEluGradOp(OpKernelConstruction* context) + : MklReluGradOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t diff_dst_index = 0; // index of diff_dst input tensor const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); Tensor* diff_src_tensor = nullptr; @@ -754,9 +749,9 @@ class MklEluGradOp : public MklReluGradOpBase { diff_dst_tensor.shape(), dnn_shape_diff_src); void* out_o = static_cast(diff_src_tensor->flat().data()); void* user_i = - static_cast(const_cast(src_tensor.flat().data())); + static_cast(const_cast(src_tensor.flat().data())); void* user_g = - static_cast(const_cast(diff_dst_tensor.flat().data())); + static_cast(const_cast(diff_dst_tensor.flat().data())); // gradient of elu(x) = 1 if x > 0; elu(x) + 1 otherwise T feature = (static_cast(user_i))[0]; if (feature > 0) { @@ -773,8 +768,8 @@ class MklTanhOp : public MklReluOpBase { public: ~MklTanhOp() {} - explicit MklTanhOp(OpKernelConstruction* context) : - MklReluOpBase(context) {} + explicit MklTanhOp(OpKernelConstruction* context) + : MklReluOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t src_index = 0; // index of src input tensor @@ -784,8 +779,8 @@ class MklTanhOp : public MklReluOpBase { GetMklShape(context, src_index, &dnn_shape_src); Tensor* dst_tensor = nullptr; - void* user_i = static_cast(const_cast( - src_tensor.flat().data())); + void* user_i = + static_cast(const_cast(src_tensor.flat().data())); MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); AllocateOutputSetMklShape(context, dst_index, &dst_tensor, @@ -795,7 +790,7 @@ class MklTanhOp : public MklReluOpBase { T feature = (static_cast(user_i))[0]; T e1 = std::exp(feature); T e2 = std::exp(-feature); - (static_cast(out_o))[0] = (e1 - e2)/(e1 + e2); + (static_cast(out_o))[0] = (e1 - e2) / (e1 + e2); return; } }; @@ -805,14 +800,14 @@ class MklTanhGradOp : public MklReluGradOpBase { public: ~MklTanhGradOp() {} - explicit MklTanhGradOp(OpKernelConstruction* context) : - MklReluGradOpBase(context) {} + explicit MklTanhGradOp(OpKernelConstruction* context) + : MklReluGradOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t diff_dst_index = 0; // index of diff_dst input tensor const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); Tensor* diff_src_tensor = nullptr; @@ -825,16 +820,16 @@ class MklTanhGradOp : public MklReluGradOpBase { diff_dst_tensor.shape(), dnn_shape_diff_src); void* out_o = static_cast(diff_src_tensor->flat().data()); void* user_i = - static_cast(const_cast(src_tensor.flat().data())); + static_cast(const_cast(src_tensor.flat().data())); // gradient of tanh(x) = 1 - tanh(x)^2 T feature = (static_cast(user_i))[0]; T e1 = std::exp(feature); T e2 = std::exp(-feature); - T tanh = (e1 - e2)/(e1 + e2); + T tanh = (e1 - e2) / (e1 + e2); void* user_g = - static_cast(const_cast(diff_dst_tensor.flat().data())); - (static_cast(out_o))[0] = (static_cast(user_g))[0] * - (1 - tanh * tanh); + static_cast(const_cast(diff_dst_tensor.flat().data())); + (static_cast(out_o))[0] = + (static_cast(user_g))[0] * (1 - tanh * tanh); } }; @@ -857,13 +852,13 @@ TF_CALL_float(REGISTER_RELU_MKL_SUPPORTED_KERNELS_TYPES); #ifdef INTEL_MKL_DNN // register dnn kernels for supported operations and supported types -#define REGISTER_ELU_MKL_SUPPORTED_KERNELS_TYPES(type) \ - REGISTER_KERNEL_BUILDER(Name("_MklElu") \ +#define REGISTER_ELU_MKL_SUPPORTED_KERNELS_TYPES(type) \ + REGISTER_KERNEL_BUILDER(Name("_MklElu") \ .Device(DEVICE_CPU) \ .TypeConstraint("T") \ .Label(mkl_op_registry::kMklOpLabel), \ - MklEluOp); \ - REGISTER_KERNEL_BUILDER(Name("_MklEluGrad") \ + MklEluOp); \ + REGISTER_KERNEL_BUILDER(Name("_MklEluGrad") \ .Device(DEVICE_CPU) \ .TypeConstraint("T") \ .Label(mkl_op_registry::kMklOpLabel), \ @@ -888,4 +883,3 @@ TF_CALL_float(REGISTER_TANH_MKL_SUPPORTED_KERNELS_TYPES); } // namespace tensorflow #endif // INTEL_MKL - diff --git a/tensorflow/core/kernels/mkl_reshape_op.cc b/tensorflow/core/kernels/mkl_reshape_op.cc index b41e529357..7d471e1e4c 100644 --- a/tensorflow/core/kernels/mkl_reshape_op.cc +++ b/tensorflow/core/kernels/mkl_reshape_op.cc @@ -166,9 +166,9 @@ class MklReshapeOp : public OpKernel { MklDnnShape mkl_shape_input; GetMklShape(context, kInputSlotIdx, &mkl_shape_input); bool input_in_mkl_format = mkl_shape_input.IsMklTensor(); - const int64 nelems = input_in_mkl_format ? - mkl_shape_input.GetTfShape().num_elements() - : input_tensor.NumElements(); + const int64 nelems = input_in_mkl_format + ? mkl_shape_input.GetTfShape().num_elements() + : input_tensor.NumElements(); // Preliminary validation of sizes. OP_REQUIRES(context, IsLegacyVector(sizes.shape()), @@ -210,11 +210,11 @@ class MklReshapeOp : public OpKernel { product)); shape.set_dim(unknown_index, missing); } - OP_REQUIRES(context, shape.num_elements() == nelems, - errors::InvalidArgument("Input to reshape is a tensor with ", - nelems, - " values, but the requested shape has ", - shape.num_elements())); + OP_REQUIRES( + context, shape.num_elements() == nelems, + errors::InvalidArgument("Input to reshape is a tensor with ", nelems, + " values, but the requested shape has ", + shape.num_elements())); if (input_in_mkl_format) { TensorShape& shape_to = shape; @@ -237,38 +237,38 @@ class MklReshapeOp : public OpKernel { // need to update MklDnnShape object associated with the input // tensor to reflect the shape change expected by reshape. if (!SkipReorder(mkl_shape_input, shape_to)) { - // If dimensions that are being expanded or collapsed are not - // maintained contiguously by MKLDNN, then we use reorder. - - // Get Mkl layout of input tensor. - auto input_mkl_md = mkl_shape_input.GetMklLayout(); - // Set input Mkl layout as the user layout. - dnn_data_input.SetUsrMem(input_mkl_md, &input_tensor); - // Get expected Tensorflow layout of input tensor. - auto output_tf_md = mkl_shape_input.GetTfLayout(); - auto output_tf_pd = memory::primitive_desc(output_tf_md, - cpu_engine); - - Tensor* output_tensor = nullptr; - MklShape mkl_shape_output; - mkl_shape_output.SetMklTensor(false); - // We allocate output tensor in the shape expected by Reshape. - AllocateOutputSetMklShape(context, kOutputSlotIdx, &output_tensor, - shape_to, mkl_shape_output); - - // Insert reorder between Mkl layout and TensorFlow layout if - // needed. If reorder is not needed but reshape is needed (since - // shape_from != shape_to), then we just copy input tensor to - // output tensor with target shape (we cannot forward Mkl layout - // in such case because shape has changed.) - std::vector net; - if (dnn_data_input.CheckReorderToOpMem(output_tf_pd, - output_tensor, &net)) { - stream(stream::kind::eager).submit(net).wait(); - } else { - output_tensor->CopyFrom(input_tensor, shape_to); - } - return; + // If dimensions that are being expanded or collapsed are not + // maintained contiguously by MKLDNN, then we use reorder. + + // Get Mkl layout of input tensor. + auto input_mkl_md = mkl_shape_input.GetMklLayout(); + // Set input Mkl layout as the user layout. + dnn_data_input.SetUsrMem(input_mkl_md, &input_tensor); + // Get expected Tensorflow layout of input tensor. + auto output_tf_md = mkl_shape_input.GetTfLayout(); + auto output_tf_pd = + memory::primitive_desc(output_tf_md, cpu_engine); + + Tensor* output_tensor = nullptr; + MklShape mkl_shape_output; + mkl_shape_output.SetMklTensor(false); + // We allocate output tensor in the shape expected by Reshape. + AllocateOutputSetMklShape(context, kOutputSlotIdx, &output_tensor, + shape_to, mkl_shape_output); + + // Insert reorder between Mkl layout and TensorFlow layout if + // needed. If reorder is not needed but reshape is needed (since + // shape_from != shape_to), then we just copy input tensor to + // output tensor with target shape (we cannot forward Mkl layout + // in such case because shape has changed.) + std::vector net; + if (dnn_data_input.CheckReorderToOpMem(output_tf_pd, output_tensor, + &net)) { + stream(stream::kind::eager).submit(net).wait(); + } else { + output_tensor->CopyFrom(input_tensor, shape_to); + } + return; } else { // If dimensions that are being expanded or collapsed are // maintained contiguously by MKLDNN, then we skip reorder, just @@ -276,10 +276,10 @@ class MklReshapeOp : public OpKernel { // Tensorflow tensor as it is to the output. auto output_dims = TFShapeToMklDnnDims(shape_to); auto output_strides = CalculateTFStrides(output_dims); - auto output_tf_md = MklDnnData::CreateBlockedMemDesc(output_dims, - output_strides); - auto output_tf_pd = memory::primitive_desc(output_tf_md, - cpu_engine); + auto output_tf_md = MklDnnData::CreateBlockedMemDesc( + output_dims, output_strides); + auto output_tf_pd = + memory::primitive_desc(output_tf_md, cpu_engine); // Set MklDnnShape MklDnnShape mkl_shape_output; @@ -291,18 +291,17 @@ class MklReshapeOp : public OpKernel { // We now simply forward input Mkl tensor to output and change its // output MklDnnShape object. - ForwardMklTensorInToOutWithMklShape(context, kInputSlotIdx, - kOutputSlotIdx, mkl_shape_output); + ForwardMklTensorInToOutWithMklShape( + context, kInputSlotIdx, kOutputSlotIdx, mkl_shape_output); return; } - } catch (mkldnn::error &e) { + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } } else { diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc index b48c735d12..c35f857cfe 100644 --- a/tensorflow/core/kernels/mkl_tfconv_op.cc +++ b/tensorflow/core/kernels/mkl_tfconv_op.cc @@ -28,9 +28,9 @@ limitations under the License. #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/util/tensor_format.h" -#include "tensorflow/core/util/mkl_util.h" #include "mkl_dnn.h" #include "mkl_dnn_types.h" +#include "tensorflow/core/util/mkl_util.h" namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc index 64bdef0008..5d28b87e6b 100644 --- a/tensorflow/core/kernels/non_max_suppression_op.cc +++ b/tensorflow/core/kernels/non_max_suppression_op.cc @@ -92,13 +92,11 @@ static inline bool IOUGreaterThanThreshold( return iou > iou_threshold; } -void DoNonMaxSuppressionOp(OpKernelContext* context, - const Tensor& boxes, - const Tensor& scores, - const Tensor& max_output_size, +void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes, + const Tensor& scores, const Tensor& max_output_size, const float iou_threshold) { OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1, - errors::InvalidArgument("iou_threshold must be in [0, 1]")); + errors::InvalidArgument("iou_threshold must be in [0, 1]")); int num_boxes = 0; ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes); @@ -106,10 +104,8 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, return; } - const int output_size = - std::min(max_output_size.scalar()(), num_boxes); - typename TTypes::ConstTensor boxes_data = - boxes.tensor(); + const int output_size = std::min(max_output_size.scalar()(), num_boxes); + typename TTypes::ConstTensor boxes_data = boxes.tensor(); std::vector scores_data(num_boxes); std::copy_n(scores.flat().data(), num_boxes, scores_data.begin()); @@ -181,8 +177,7 @@ template class NonMaxSuppressionV2Op : public OpKernel { public: explicit NonMaxSuppressionV2Op(OpKernelConstruction* context) - : OpKernel(context) { - } + : OpKernel(context) {} void Compute(OpKernelContext* context) override { // boxes: [num_boxes, 4] @@ -197,10 +192,9 @@ class NonMaxSuppressionV2Op : public OpKernel { max_output_size.shape().DebugString())); // iou_threshold: scalar const Tensor& iou_threshold = context->input(3); - OP_REQUIRES( - context, TensorShapeUtils::IsScalar(iou_threshold.shape()), - errors::InvalidArgument("iou_threshold must be 0-D, got shape ", - iou_threshold.shape().DebugString())); + OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()), + errors::InvalidArgument("iou_threshold must be 0-D, got shape ", + iou_threshold.shape().DebugString())); const float iou_threshold_val = iou_threshold.scalar()(); diff --git a/tensorflow/core/kernels/non_max_suppression_op_test.cc b/tensorflow/core/kernels/non_max_suppression_op_test.cc index fdbcf05b89..67d9217b95 100644 --- a/tensorflow/core/kernels/non_max_suppression_op_test.cc +++ b/tensorflow/core/kernels/non_max_suppression_op_test.cc @@ -43,9 +43,10 @@ class NonMaxSuppressionOpTest : public OpsTestBase { TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClusters) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {3}); TF_ASSERT_OK(RunOpKernel()); @@ -58,7 +59,7 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClusters) { TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClustersFlippedCoordinates) { MakeOp(.5); AddInputFromArray(TensorShape({6, 4}), - {1, 1, 0, 0, 0, 0.1f, 1, 1.1f, 0, .9f, 1, -0.1f, + {1, 1, 0, 0, 0, 0.1f, 1, 1.1f, 0, .9f, 1, -0.1f, 0, 10, 1, 11, 1, 10.1f, 0, 11.1f, 1, 101, 0, 100}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {3}); @@ -71,9 +72,10 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClustersFlippedCoordinates) { TEST_F(NonMaxSuppressionOpTest, TestSelectAtMostTwoBoxesFromThreeClusters) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {2}); TF_ASSERT_OK(RunOpKernel()); @@ -85,9 +87,10 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectAtMostTwoBoxesFromThreeClusters) { TEST_F(NonMaxSuppressionOpTest, TestSelectAtMostThirtyBoxesFromThreeClusters) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {30}); TF_ASSERT_OK(RunOpKernel()); @@ -134,9 +137,10 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectFromTenIdenticalBoxes) { TEST_F(NonMaxSuppressionOpTest, TestInconsistentBoxAndScoreShapes) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({5}), {.9f, .75f, .6f, .95f, .5f}); AddInputFromArray(TensorShape({}), {30}); Status s = RunOpKernel(); diff --git a/tensorflow/core/kernels/nth_element_op.cc b/tensorflow/core/kernels/nth_element_op.cc index da825e408c..7f12eb953a 100644 --- a/tensorflow/core/kernels/nth_element_op.cc +++ b/tensorflow/core/kernels/nth_element_op.cc @@ -16,15 +16,15 @@ limitations under the License. // See docs in ../ops/nn_ops.cc. #include "tensorflow/core/kernels/nth_element_op.h" +#include +#include +#include #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" -#include "tensorflow/core/framework/types.h" #include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/util/work_sharder.h" -#include -#include -#include namespace tensorflow { @@ -54,8 +54,9 @@ class NthElementOp : public OpKernel { errors::InvalidArgument("Input must be >= 1-D, got shape ", input_in.shape().DebugString())); // The last dimension of input tensor must be greater than N. - OP_REQUIRES(context, input_in.dim_size(num_dims-1) > n, - errors::InvalidArgument("Input must have at least n+1 columns")); + OP_REQUIRES( + context, input_in.dim_size(num_dims - 1) > n, + errors::InvalidArgument("Input must have at least n+1 columns")); // std::nth_element only support the nth-smallest selection. if (reverse_) { @@ -64,7 +65,7 @@ class NthElementOp : public OpKernel { // Assume input_shape is [d1,d2,...dk], and output_shape is [d1,d2...dk-1]. TensorShape out_shape; - for (int i = 0; i < num_dims-1; ++i) { + for (int i = 0; i < num_dims - 1; ++i) { out_shape.AddDim(input_in.dim_size(i)); } Tensor* output_tensor = nullptr; @@ -83,32 +84,28 @@ namespace functor { template struct NthElementFunctor { - void operator() (OpKernelContext* context, - const Tensor& input_tensor, - Tensor& output_tensor, - int n, - bool reverse) { + void operator()(OpKernelContext* context, const Tensor& input_tensor, + Tensor& output_tensor, int n, bool reverse) { const T* input = input_tensor.flat().data(); T* output = output_tensor.flat().data(); // Assume input_shape is [d1,d2,...dk], and output_shape is [d1,d2...dk-1], // then num_rows = d1*d2...dk-1, last_dim = dk. const int num_rows = output_tensor.NumElements(); - const int last_dim = input_tensor.dim_size(input_tensor.dims()-1); + const int last_dim = input_tensor.dim_size(input_tensor.dims() - 1); // Allocate each row to different shard. - auto SubNthElement = [&, input, output, last_dim, n](int start, - int limit) { + auto SubNthElement = [&, input, output, last_dim, n](int start, int limit) { // std::nth_element would rearrange the array, so we need a new buffer. std::vector buf(last_dim); for (int b = start; b < limit; ++b) { // Copy from one row of elements to buffer const T* input_start = input + b * last_dim; - const T* input_end = input + (b+1) * last_dim; + const T* input_end = input + (b + 1) * last_dim; std::copy(input_start, input_end, buf.begin()); - std::nth_element(buf.begin(), buf.begin()+n, buf.end()); + std::nth_element(buf.begin(), buf.begin() + n, buf.end()); // The element placed in the nth position is exactly the element that // would occur in this position if the range was fully sorted. output[b] = buf[n]; @@ -116,9 +113,9 @@ struct NthElementFunctor { }; auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - // The average time complexity of partition-based nth_element (BFPRT) is O(n), - // althought the worst time complexity could be O(n^2). - // Here, 20 is a empirical factor of cost_per_unit. + // The average time complexity of partition-based nth_element (BFPRT) is + // O(n), althought the worst time complexity could be O(n^2). Here, 20 is a + // empirical factor of cost_per_unit. Shard(worker_threads.num_threads, worker_threads.workers, num_rows, 20 * last_dim, SubNthElement); } @@ -126,7 +123,6 @@ struct NthElementFunctor { } // namespace functor - #define REGISTER_NTHOP(T) \ REGISTER_KERNEL_BUILDER( \ Name("NthElement").Device(DEVICE_CPU).TypeConstraint("T"), \ @@ -136,4 +132,3 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_NTHOP); #undef REGISTER_NTHOP } // end namespace tensorflow - diff --git a/tensorflow/core/kernels/nth_element_op.h b/tensorflow/core/kernels/nth_element_op.h index 11a6c996b0..e7d25daecc 100644 --- a/tensorflow/core/kernels/nth_element_op.h +++ b/tensorflow/core/kernels/nth_element_op.h @@ -26,10 +26,8 @@ namespace functor { template struct NthElementFunctor { - void operator() (OpKernelContext* context, - const Tensor& input_tensor, - Tensor& output_tensor, - int n); + void operator()(OpKernelContext* context, const Tensor& input_tensor, + Tensor& output_tensor, int n); }; } // namespace functor diff --git a/tensorflow/core/kernels/one_hot_op_gpu.cu.cc b/tensorflow/core/kernels/one_hot_op_gpu.cu.cc index 49fd4bdeba..647515ae38 100644 --- a/tensorflow/core/kernels/one_hot_op_gpu.cu.cc +++ b/tensorflow/core/kernels/one_hot_op_gpu.cu.cc @@ -19,16 +19,16 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/one_hot_op.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/one_hot_op.h" namespace tensorflow { typedef Eigen::GpuDevice GPUDevice; -#define DEFINE_GPU_SPEC_INDEX(T, TI) \ - template class generator::OneGenerator; \ +#define DEFINE_GPU_SPEC_INDEX(T, TI) \ + template class generator::OneGenerator; \ template struct functor::OneHot; #define DEFINE_GPU_SPEC(T) \ diff --git a/tensorflow/core/kernels/ops_util_test.cc b/tensorflow/core/kernels/ops_util_test.cc index 9d53882dee..13427d71ff 100644 --- a/tensorflow/core/kernels/ops_util_test.cc +++ b/tensorflow/core/kernels/ops_util_test.cc @@ -218,7 +218,8 @@ TEST_F(OpsUtilTest, GetBroadcastTest3_3_1_2) { // in_size = 3, ksize = 3, stride = 2, pad_size = 0 TEST_F(OpsUtilTest, GetBroadcastTest3_3_2_0) { bcast_struct bcast[] = { - {{0, 3, 3, 2, 0}, {0, 3}}, {{1, 3, 3, 2, 0}, {2, 1}}, + {{0, 3, 3, 2, 0}, {0, 3}}, + {{1, 3, 3, 2, 0}, {2, 1}}, }; for (size_t i = 0; i < sizeof(bcast) / sizeof(bcast[0]); ++i) { VerifyBcastValues(bcast[i]); @@ -228,7 +229,8 @@ TEST_F(OpsUtilTest, GetBroadcastTest3_3_2_0) { // in_size = 3, ksize = 3, stride = 2, pad_size = 1 TEST_F(OpsUtilTest, GetBroadcastTest3_3_2_1) { bcast_struct bcast[] = { - {{0, 3, 3, 2, 1}, {0, 2}}, {{1, 3, 3, 2, 1}, {1, 2}}, + {{0, 3, 3, 2, 1}, {0, 2}}, + {{1, 3, 3, 2, 1}, {1, 2}}, }; for (size_t i = 0; i < sizeof(bcast) / sizeof(bcast[0]); ++i) { VerifyBcastValues(bcast[i]); @@ -258,7 +260,8 @@ TEST_F(OpsUtilTest, GetBroadcastTest3_3_3_0) { // in_size = 3, ksize = 3, stride = 3, pad_size = 1 TEST_F(OpsUtilTest, GetBroadcastTest3_3_3_1) { bcast_struct bcast[] = { - {{0, 3, 3, 3, 1}, {0, 2}}, {{1, 3, 3, 3, 1}, {2, 1}}, + {{0, 3, 3, 3, 1}, {0, 2}}, + {{1, 3, 3, 3, 1}, {2, 1}}, }; for (size_t i = 0; i < sizeof(bcast) / sizeof(bcast[0]); ++i) { VerifyBcastValues(bcast[i]); @@ -348,8 +351,8 @@ TEST_F(OpsUtilTest, Misaligned1DSlice) { TEST_F(OpsUtilTest, Aligned2DSliceOfDim0) { #if EIGEN_MAX_ALIGN_BYTES == 0 - // When EIGEN_MAX_ALIGN_BYTES is 0 and the size of the first dimension is nonzero, - // a multidimensional tensor is always aligned. + // When EIGEN_MAX_ALIGN_BYTES is 0 and the size of the first dimension is + // nonzero, a multidimensional tensor is always aligned. Tensor t(DT_FLOAT, TensorShape({3, 4})); int64 start = 1; int64 end = 2; diff --git a/tensorflow/core/kernels/pack_op.cc b/tensorflow/core/kernels/pack_op.cc index 2033fbf5dc..e0ae5de0f4 100644 --- a/tensorflow/core/kernels/pack_op.cc +++ b/tensorflow/core/kernels/pack_op.cc @@ -36,7 +36,7 @@ typedef Eigen::GpuDevice GPUDevice; #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // -------------------------------------------------------------------------- template @@ -123,7 +123,7 @@ class PackOp : public OpKernel { ConcatSYCL(c->eigen_sycl_device(), inputs_flat, &output_flat); return; } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL ConcatCPU(c->device(), inputs_flat, &output_flat); } } diff --git a/tensorflow/core/kernels/parameterized_truncated_normal_op.cc b/tensorflow/core/kernels/parameterized_truncated_normal_op.cc index b232ba16a7..0ab9ff9f65 100644 --- a/tensorflow/core/kernels/parameterized_truncated_normal_op.cc +++ b/tensorflow/core/kernels/parameterized_truncated_normal_op.cc @@ -95,9 +95,10 @@ struct TruncatedNormalFunctor { int64 sample = b * samples_per_batch; // On GPU, this check will just fill samples with NAN if it fails. - OP_REQUIRES(ctx, stddev > T(0) && minval < maxval && - (Eigen::numext::isfinite(minval) || - Eigen::numext::isfinite(maxval)), + OP_REQUIRES(ctx, + stddev > T(0) && minval < maxval && + (Eigen::numext::isfinite(minval) || + Eigen::numext::isfinite(maxval)), errors::InvalidArgument("Invalid parameters")); int numIterations = 0; @@ -118,8 +119,9 @@ struct TruncatedNormalFunctor { // Determine the method to use. const T sqrtFactor = Eigen::numext::sqrt((normMin * normMin) + T(4)); const T cutoff = - T(2) * Eigen::numext::exp( - T(0.5) + (normMin * (normMin - sqrtFactor)) / T(4)) / + T(2) * + Eigen::numext::exp(T(0.5) + + (normMin * (normMin - sqrtFactor)) / T(4)) / (normMin + sqrtFactor); const T diff = normMax - normMin; if (diff < cutoff) { @@ -309,30 +311,34 @@ class ParameterizedTruncatedNormalOp : public OpKernel { } else { // Parameters must be broadcastable to the shape [num_batches]. OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(means_tensor.shape()) || - means_tensor.dim_size(0) == 1 || - means_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(means_tensor.shape()) || + means_tensor.dim_size(0) == 1 || + means_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input means should have length 1 or shape[0], got shape: ", means_tensor.shape().DebugString())); OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(stddevs_tensor.shape()) || - stddevs_tensor.dim_size(0) == 1 || - stddevs_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(stddevs_tensor.shape()) || + stddevs_tensor.dim_size(0) == 1 || + stddevs_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input stddevs should have length 1 or shape[0], got shape: ", stddevs_tensor.shape().DebugString())); OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(minvals_tensor.shape()) || - minvals_tensor.dim_size(0) == 1 || - minvals_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(minvals_tensor.shape()) || + minvals_tensor.dim_size(0) == 1 || + minvals_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input minvals should have length 1 or shape[0], got shape: ", minvals_tensor.shape().DebugString())); OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(maxvals_tensor.shape()) || - maxvals_tensor.dim_size(0) == 1 || - maxvals_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(maxvals_tensor.shape()) || + maxvals_tensor.dim_size(0) == 1 || + maxvals_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input maxvals should have length 1 or shape[0], got shape: ", maxvals_tensor.shape().DebugString())); diff --git a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc index 933de65c15..ddfeb1bb79 100644 --- a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc +++ b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc @@ -202,12 +202,13 @@ struct TruncatedNormalFunctor { typename TTypes::Flat output) { const auto config = GetCudaLaunchConfig(num_elements, d); - TruncatedNormalKernel< - T><<>>( - gen, output.data(), num_batches, samples_per_batch, num_elements, - means.data(), means.dimension(0) == 1, stddevs.data(), - stddevs.dimension(0) == 1, minvals.data(), minvals.dimension(0) == 1, - maxvals.data(), maxvals.dimension(0) == 1, kMaxIterations); + TruncatedNormalKernel + <<>>( + gen, output.data(), num_batches, samples_per_batch, num_elements, + means.data(), means.dimension(0) == 1, stddevs.data(), + stddevs.dimension(0) == 1, minvals.data(), + minvals.dimension(0) == 1, maxvals.data(), + maxvals.dimension(0) == 1, kMaxIterations); }; }; diff --git a/tensorflow/core/kernels/parse_tensor_op.cc b/tensorflow/core/kernels/parse_tensor_op.cc index 6b599612ad..dd41744f02 100644 --- a/tensorflow/core/kernels/parse_tensor_op.cc +++ b/tensorflow/core/kernels/parse_tensor_op.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/framework/register_types.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/pooling_ops_3d.cc b/tensorflow/core/kernels/pooling_ops_3d.cc index a406317213..01bcfede1e 100644 --- a/tensorflow/core/kernels/pooling_ops_3d.cc +++ b/tensorflow/core/kernels/pooling_ops_3d.cc @@ -258,7 +258,7 @@ struct LaunchMaxPooling3dGradOp { Eigen::array bcast = {1, csize, rsize, psize, 1}; #else Eigen::IndexList, int, int, int, - Eigen::type2index<1> > + Eigen::type2index<1>> bcast; bcast.set(1, csize); bcast.set(2, rsize); @@ -431,7 +431,7 @@ struct LaunchAvgPooling3dGradOp { Eigen::array bcast = {1, csize, rsize, psize, 1}; #else Eigen::IndexList, int, int, int, - Eigen::type2index<1> > + Eigen::type2index<1>> bcast; bcast.set(1, csize); bcast.set(2, rsize); @@ -833,7 +833,7 @@ TF_CALL_float(REGISTER_GPU_KERNELS) TF_CALL_half(REGISTER_GPU_KERNELS) #ifdef TENSORFLOW_USE_SYCL #define REGISTER_SYCL_KERNELS(T) REGISTER_KERNELS(SYCL, T) -TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS) + TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS) #undef REGISTER_SYCL_KERNELS #endif // TENSORFLOW_USE_SYCL diff --git a/tensorflow/core/kernels/pooling_ops_3d_sycl.h b/tensorflow/core/kernels/pooling_ops_3d_sycl.h index c1bc5af498..b4bead2456 100644 --- a/tensorflow/core/kernels/pooling_ops_3d_sycl.h +++ b/tensorflow/core/kernels/pooling_ops_3d_sycl.h @@ -281,12 +281,11 @@ class MaxPool3DGradSYCL { const T* input_data_n = input_data + n * p_.in_planes_ * p_.in_cols_ * p_.in_rows_ * p_.depth_; - const T* output_data_n = - output_data + - n * p_.out_planes_ * p_.out_cols_ * p_.out_rows_ * p_.depth_; - const T* input_backprop_n = - input_backprop + - n * p_.out_planes_ * p_.out_cols_ * p_.out_rows_ * p_.depth_; + const T* output_data_n = output_data + n * p_.out_planes_ * p_.out_cols_ * + p_.out_rows_ * p_.depth_; + const T* input_backprop_n = input_backprop + n * p_.out_planes_ * + p_.out_cols_ * + p_.out_rows_ * p_.depth_; for (int poolp = poolpstart; poolp < poolpend; ++poolp) { int pstart = poolp * p_.stride_planes_ - p_.pad_planes_; const int pend = std::min(pstart + p_.window_planes_, p_.in_planes_); @@ -678,9 +677,9 @@ class AvgPool3DGradSYCL { n /= p_.in_planes_; T gradient = T(0); - const T* input_backprop_n = - input_backprop + - n * p_.out_planes_ * p_.out_cols_ * p_.out_rows_ * p_.depth_; + const T* input_backprop_n = input_backprop + n * p_.out_planes_ * + p_.out_cols_ * + p_.out_rows_ * p_.depth_; for (int poolp = poolpstart; poolp < poolpend; ++poolp) { int pstart = poolp * p_.stride_planes_ - p_.pad_planes_; const int pend = std::min(pstart + p_.window_planes_, p_.in_planes_); diff --git a/tensorflow/core/kernels/pooling_ops_common.h b/tensorflow/core/kernels/pooling_ops_common.h index e3131b804f..fc7cb437b8 100644 --- a/tensorflow/core/kernels/pooling_ops_common.h +++ b/tensorflow/core/kernels/pooling_ops_common.h @@ -195,7 +195,6 @@ class MaxPoolingOp : public OpKernel { // and updates the corresponding column(s) in output_as_matrix with the // max value. auto shard = [¶ms, &in_mat, &out_mat](int64 start, int64 limit) { - const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; const int32 pad_rows = params.pad_rows; @@ -443,7 +442,6 @@ class MaxPoolingV2Op : public OpKernel { // and updates the corresponding column(s) in output_as_matrix with the // max value. auto shard = [¶ms, &in_mat, &out_mat](int64 start, int64 limit) { - const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; const int32 pad_rows = params.pad_rows; diff --git a/tensorflow/core/kernels/quantization_utils_test.cc b/tensorflow/core/kernels/quantization_utils_test.cc index d148c9f78d..176720c22c 100644 --- a/tensorflow/core/kernels/quantization_utils_test.cc +++ b/tensorflow/core/kernels/quantization_utils_test.cc @@ -385,8 +385,12 @@ void TestQuantizedToFloatInPlaceUsingEigen( // These are the float values we're going to test the conversions on. typedef std::pair FPair; for (FPair min_and_max : std::vector{ - FPair(-255.0f, 255.0f), FPair(-1.0f, 1.0f), FPair(-1.0f, 255.0f), - FPair(0.0f, 1e6), FPair(0.0f, 1.0f), FPair(-31.0f, 13.0f), + FPair(-255.0f, 255.0f), + FPair(-1.0f, 1.0f), + FPair(-1.0f, 255.0f), + FPair(0.0f, 1e6), + FPair(0.0f, 1.0f), + FPair(-31.0f, 13.0f), FPair(-5.89505e+08, 5.89505e+08), }) { const float f_min = min_and_max.first; diff --git a/tensorflow/core/kernels/quantize_and_dequantize_op.h b/tensorflow/core/kernels/quantize_and_dequantize_op.h index 1363c7e325..3b09ea2527 100644 --- a/tensorflow/core/kernels/quantize_and_dequantize_op.h +++ b/tensorflow/core/kernels/quantize_and_dequantize_op.h @@ -71,7 +71,8 @@ struct QuantizeAndDequantizeOneScaleImpl { out.device(d) = ((input.cwiseMin(max_range).cwiseMax(min_range) - min_range) * scale + - T(0.5)).floor() * + T(0.5)) + .floor() * inverse_scale + min_range; } else { diff --git a/tensorflow/core/kernels/quantize_op_test.cc b/tensorflow/core/kernels/quantize_op_test.cc index d2cc55a94d..57982bdf76 100644 --- a/tensorflow/core/kernels/quantize_op_test.cc +++ b/tensorflow/core/kernels/quantize_op_test.cc @@ -250,7 +250,8 @@ TEST_F(QuantizedOpTest, QuantizeV2_32Bit) { Tensor expected(allocator(), DT_QINT32, TensorShape({element_count})); test::FillValues(&expected, { - std::numeric_limits::min(), 0, + std::numeric_limits::min(), + 0, static_cast(1.0f * (1 << 23)), static_cast(1.25f * (1 << 23)), static_cast(1.75f * (1 << 23)), diff --git a/tensorflow/core/kernels/quantized_batch_norm_op.cc b/tensorflow/core/kernels/quantized_batch_norm_op.cc index 18d83b4149..b03da7ad17 100644 --- a/tensorflow/core/kernels/quantized_batch_norm_op.cc +++ b/tensorflow/core/kernels/quantized_batch_norm_op.cc @@ -16,11 +16,11 @@ limitations under the License. #define EIGEN_USE_THREADS #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "tensorflow/core/kernels/quantization_utils.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/kernels/quantization_utils.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/quantized_concat_op.cc b/tensorflow/core/kernels/quantized_concat_op.cc index d67f1ab3ec..b03ac8e87d 100644 --- a/tensorflow/core/kernels/quantized_concat_op.cc +++ b/tensorflow/core/kernels/quantized_concat_op.cc @@ -135,8 +135,8 @@ class QuantizedConcatOp : public OpKernel { context, in.dims() == input_dims || (input_is_scalar && in_is_scalar), errors::InvalidArgument( "ConcatOp : Ranks of all input tensors should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); for (int j = 0; j < input_dims; ++j) { if (j == concat_dim) { continue; @@ -145,8 +145,8 @@ class QuantizedConcatOp : public OpKernel { context, in.dim_size(j) == input_shape.dim_size(j), errors::InvalidArgument( "ConcatOp : Dimensions of inputs should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); } if (in.NumElements() > 0) { int64 inputs_flat_dim1 = in.NumElements() / inputs_flat_dim0; diff --git a/tensorflow/core/kernels/quantized_conv_ops.cc b/tensorflow/core/kernels/quantized_conv_ops.cc index 1921b83d12..5b3570edff 100644 --- a/tensorflow/core/kernels/quantized_conv_ops.cc +++ b/tensorflow/core/kernels/quantized_conv_ops.cc @@ -278,10 +278,9 @@ class Im2ColConvFunctor { *resource = new Im2ColBufferResource(); return Status::OK(); }; - OP_REQUIRES_OK( - context, - context->resource_manager()->LookupOrCreate( - "Conv2d", "im2col_buffer", &im2col_buffer_resource, creator)); + OP_REQUIRES_OK(context, context->resource_manager()->LookupOrCreate( + "Conv2d", "im2col_buffer", + &im2col_buffer_resource, creator)); // This means that multiple ops can't be run simultaneously on different // threads, because we have a single shared resource. The platforms this is // aimed at have intra-op parallelism as their focus though, so it shouldn't diff --git a/tensorflow/core/kernels/quantized_instance_norm.cc b/tensorflow/core/kernels/quantized_instance_norm.cc index c29f534f31..d62094cc9f 100644 --- a/tensorflow/core/kernels/quantized_instance_norm.cc +++ b/tensorflow/core/kernels/quantized_instance_norm.cc @@ -278,10 +278,10 @@ class QuantizedInstanceNorm : public OpKernel { float input_max = context->input(2).flat()(0); float input_scale = (input_max - input_min) / 255.0f; - OP_REQUIRES( - context, input_min < input_max, - errors::InvalidArgument("input_min must be less than input_max : ", - input_min, " >= ", input_max)); + OP_REQUIRES(context, input_min < input_max, + errors::InvalidArgument( + "input_min must be less than input_max : ", input_min, + " >= ", input_max)); auto input_tensor = input.tensor(); auto N = input_tensor.dimension(0); diff --git a/tensorflow/core/kernels/quantized_matmul_op.cc b/tensorflow/core/kernels/quantized_matmul_op.cc index afb30d5f62..da8c46dc51 100644 --- a/tensorflow/core/kernels/quantized_matmul_op.cc +++ b/tensorflow/core/kernels/quantized_matmul_op.cc @@ -104,9 +104,9 @@ class QuantizedMatMulOp : public OpKernel { OP_REQUIRES(context, a.dim_size(dim_pair[0].first) == b.dim_size(dim_pair[0].second), - errors::InvalidArgument("Matrix size-compatible: In[0]: ", - a.shape().DebugString(), ", In[1]: ", - b.shape().DebugString())); + errors::InvalidArgument( + "Matrix size-compatible: In[0]: ", a.shape().DebugString(), + ", In[1]: ", b.shape().DebugString())); OP_REQUIRES(context, ((shift_c >= 0) && (shift_c <= 31)), errors::InvalidArgument("shift_c must be between 0 and 31, " diff --git a/tensorflow/core/kernels/quantized_matmul_op_test.cc b/tensorflow/core/kernels/quantized_matmul_op_test.cc index 535b5115c3..c9f05dbc10 100644 --- a/tensorflow/core/kernels/quantized_matmul_op_test.cc +++ b/tensorflow/core/kernels/quantized_matmul_op_test.cc @@ -206,17 +206,32 @@ TEST_F(QuantizedMatMulTest, Small_WithParams) { // We have set the transpose_a flag to true, so the matrix is transposed, and // for filling the values the in-memory storage order is effectively // column major, rather than the default row-major. - AddInputFromArray(TensorShape({a_rows, a_cols}), - { - 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, - }); + AddInputFromArray(TensorShape({a_rows, a_cols}), { + 11, + 10, + 9, + 8, + 7, + 6, + 5, + 4, + 3, + 2, + 1, + 0, + }); // The B matrix is: // | 1 | 4| // | 2 | 5| // | 3 | 6| AddInputFromArray(TensorShape({b_rows, b_cols}), { - 1, 4, 2, 5, 3, 6, + 1, + 4, + 2, + 5, + 3, + 6, }); AddInputFromArray(TensorShape({1}), {-12.0f}); AddInputFromArray(TensorShape({1}), {243.0f}); @@ -238,10 +253,16 @@ TEST_F(QuantizedMatMulTest, Small_WithParams) { // | -50 | -113 | // | -56 | -128 | Tensor expected(allocator(), DT_QINT32, TensorShape({a_cols, b_cols})); - test::FillValues(&expected, - { - -38, -83, -44, -98, -50, -113, -56, -128, - }); + test::FillValues(&expected, { + -38, + -83, + -44, + -98, + -50, + -113, + -56, + -128, + }); test::ExpectTensorEqual(expected, *GetOutput(0)); } diff --git a/tensorflow/core/kernels/quantized_mul_op.cc b/tensorflow/core/kernels/quantized_mul_op.cc index eaa5e667f7..3c7536e037 100644 --- a/tensorflow/core/kernels/quantized_mul_op.cc +++ b/tensorflow/core/kernels/quantized_mul_op.cc @@ -298,9 +298,8 @@ class QuantizedMulOp : public OpKernel { return; } Tensor* z; - OP_REQUIRES_OK( - context, - context->allocate_output(0, BCast::ToShape(bcast.output_shape()), &z)); + OP_REQUIRES_OK(context, context->allocate_output( + 0, BCast::ToShape(bcast.output_shape()), &z)); // Make sure that we have valid quantization ranges for the input buffers. // If the difference between the min and max is negative or zero, it makes diff --git a/tensorflow/core/kernels/quantized_mul_op_test.cc b/tensorflow/core/kernels/quantized_mul_op_test.cc index b0550c8260..a4e407c7a9 100644 --- a/tensorflow/core/kernels/quantized_mul_op_test.cc +++ b/tensorflow/core/kernels/quantized_mul_op_test.cc @@ -188,11 +188,12 @@ void TestManualScalar() { 10.0f, {1}, {10.0f}, -100.0f, 100.0f, {10}, {10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f, 90.0f, 100.0f}, 3.0f); - TestMul({1}, {10.0f}, -100.0f, 100.0f, {10}, - {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f}, 0.0f, - 10.0f, {10}, {10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f, - 90.0f, 100.0f}, - 3.0f); + TestMul( + {1}, {10.0f}, -100.0f, 100.0f, {10}, + {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f}, 0.0f, + 10.0f, {10}, + {10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f, 90.0f, 100.0f}, + 3.0f); } void TestScalar() { diff --git a/tensorflow/core/kernels/queue_base.cc b/tensorflow/core/kernels/queue_base.cc index 330d161c32..de495c19cb 100644 --- a/tensorflow/core/kernels/queue_base.cc +++ b/tensorflow/core/kernels/queue_base.cc @@ -39,8 +39,8 @@ Status HandleSliceToElement(const Tensor& parent, Tensor* element, return errors::Internal( "HandleSliceToElement Cannot copy slice: number of elements does not " "match. Shapes are: [element]: ", - element->shape().DebugString(), ", [parent slice]: ", - chip_shape.DebugString()); + element->shape().DebugString(), + ", [parent slice]: ", chip_shape.DebugString()); } auto parent_as_matrix = parent.flat_outer_dims(); element->flat() = parent_as_matrix.chip(index, 0); diff --git a/tensorflow/core/kernels/queue_ops.cc b/tensorflow/core/kernels/queue_ops.cc index 17831b7437..46a02854d7 100644 --- a/tensorflow/core/kernels/queue_ops.cc +++ b/tensorflow/core/kernels/queue_ops.cc @@ -428,13 +428,14 @@ REGISTER_KERNEL_BUILDER(Name("QueueSizeV2").Device(DEVICE_CPU), QueueSizeOp); class QueueIsClosedOp : public QueueOpKernel { public: explicit QueueIsClosedOp(OpKernelConstruction* context) - : QueueOpKernel(context) {} + : QueueOpKernel(context) {} protected: void ComputeAsync(OpKernelContext* ctx, QueueInterface* queue, DoneCallback callback) override { Tensor* Tqueue_is_closed = nullptr; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &Tqueue_is_closed)); + OP_REQUIRES_OK(ctx, + ctx->allocate_output(0, TensorShape({}), &Tqueue_is_closed)); Tqueue_is_closed->flat().setConstant(queue->is_closed()); callback(); } @@ -443,8 +444,10 @@ class QueueIsClosedOp : public QueueOpKernel { TF_DISALLOW_COPY_AND_ASSIGN(QueueIsClosedOp); }; -REGISTER_KERNEL_BUILDER(Name("QueueIsClosed").Device(DEVICE_CPU), QueueIsClosedOp); -REGISTER_KERNEL_BUILDER(Name("QueueIsClosedV2").Device(DEVICE_CPU), QueueIsClosedOp); +REGISTER_KERNEL_BUILDER(Name("QueueIsClosed").Device(DEVICE_CPU), + QueueIsClosedOp); +REGISTER_KERNEL_BUILDER(Name("QueueIsClosedV2").Device(DEVICE_CPU), + QueueIsClosedOp); class FakeQueueOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/random_crop_op.cc b/tensorflow/core/kernels/random_crop_op.cc index ba94d6be5c..554909760a 100644 --- a/tensorflow/core/kernels/random_crop_op.cc +++ b/tensorflow/core/kernels/random_crop_op.cc @@ -68,10 +68,10 @@ class RandomCropOp : public OpKernel { // Edge case. The target dimensions are larger then the image, so // zero-pad the image. This guarantees that the image will *always* // be [target_height, target_width] in size. - OP_REQUIRES( - context, width >= target_width, - errors::FailedPrecondition("width must be >= target_width: width = ", - width, ", target_width = ", target_width)); + OP_REQUIRES(context, width >= target_width, + errors::FailedPrecondition( + "width must be >= target_width: width = ", width, + ", target_width = ", target_width)); OP_REQUIRES(context, height >= target_height, errors::FailedPrecondition( "height must be >= target_height: height = ", height, diff --git a/tensorflow/core/kernels/random_op.cc b/tensorflow/core/kernels/random_op.cc index 55a8b9c9b6..78ff7948fb 100644 --- a/tensorflow/core/kernels/random_op.cc +++ b/tensorflow/core/kernels/random_op.cc @@ -50,7 +50,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace functor { using random::PhiloxRandom; @@ -271,9 +271,10 @@ class RandomGammaOp : public OpKernel { const Tensor& shape_t = ctx->input(0); const Tensor& alpha_t = ctx->input(1); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(shape_t.shape()) && - (shape_t.dtype() == DataType::DT_INT32 || - shape_t.dtype() == DataType::DT_INT64), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(shape_t.shape()) && + (shape_t.dtype() == DataType::DT_INT32 || + shape_t.dtype() == DataType::DT_INT64), errors::InvalidArgument( "shape must be a vector of {int32,int64}, got shape: ", shape_t.DebugString())); @@ -325,7 +326,7 @@ class RandomGammaOp : public OpKernel { // avoid a couple flops which can be done on a per-alpha basis. auto DoWork = [num_samples, num_alphas, &rng, samples_flat, alpha_flat]( - int start_output, int limit_output) { + int start_output, int limit_output) { using Eigen::numext::exp; using Eigen::numext::log; using Eigen::numext::pow; @@ -448,40 +449,40 @@ class RandomGammaOp : public OpKernel { } // namespace -#define REGISTER(TYPE) \ - template struct functor::FillPhiloxRandom< \ - CPUDevice, random::UniformDistribution >; \ - template struct functor::FillPhiloxRandom< \ - CPUDevice, random::NormalDistribution >; \ - template struct functor::FillPhiloxRandom< \ - CPUDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >; \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomUniform") \ - .Device(DEVICE_CPU) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomStandardNormal") \ - .Device(DEVICE_CPU) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("TruncatedNormal") \ - .Device(DEVICE_CPU) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp< \ - CPUDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomGamma").Device(DEVICE_CPU).TypeConstraint("T"), \ +#define REGISTER(TYPE) \ + template struct functor::FillPhiloxRandom< \ + CPUDevice, random::UniformDistribution>; \ + template struct functor::FillPhiloxRandom< \ + CPUDevice, random::NormalDistribution>; \ + template struct functor::FillPhiloxRandom< \ + CPUDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>; \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomUniform") \ + .Device(DEVICE_CPU) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomStandardNormal") \ + .Device(DEVICE_CPU) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("TruncatedNormal") \ + .Device(DEVICE_CPU) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp< \ + CPUDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomGamma").Device(DEVICE_CPU).TypeConstraint("T"), \ RandomGammaOp) #define REGISTER_INT(IntType) \ @@ -504,33 +505,33 @@ TF_CALL_int64(REGISTER_INT); #if GOOGLE_CUDA -#define REGISTER(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomUniform") \ - .Device(DEVICE_GPU) \ - .HostMemory("shape") \ - .TypeConstraint("T") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomStandardNormal") \ - .Device(DEVICE_GPU) \ - .HostMemory("shape") \ - .TypeConstraint("T") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("TruncatedNormal") \ - .Device(DEVICE_GPU) \ - .HostMemory("shape") \ - .TypeConstraint("T") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp< \ - GPUDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >); +#define REGISTER(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomUniform") \ + .Device(DEVICE_GPU) \ + .HostMemory("shape") \ + .TypeConstraint("T") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomStandardNormal") \ + .Device(DEVICE_GPU) \ + .HostMemory("shape") \ + .TypeConstraint("T") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("TruncatedNormal") \ + .Device(DEVICE_GPU) \ + .HostMemory("shape") \ + .TypeConstraint("T") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp< \ + GPUDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>); #define REGISTER_INT(IntType) \ REGISTER_KERNEL_BUILDER(Name("RandomUniformInt") \ @@ -565,13 +566,12 @@ struct FillPhiloxRandomKernel; template struct FillPhiloxRandomKernel { typedef typename Distribution::ResultElementType T; - using write_accessor = sycl::accessor; + using write_accessor = sycl::accessor; - FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, Distribution& dist) - : data_(data), - gen_(gen), - dist_(dist) { - } + FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, + Distribution& dist) + : data_(data), gen_(gen), dist_(dist) {} void operator()(sycl::nd_item<1> item) { const size_t kGroupSize = Distribution::kResultElementCount; @@ -597,7 +597,7 @@ struct FillPhiloxRandomKernel { const typename Distribution::ResultType samples = dist_(&gen_); for (size_t i = 0; i < kGroupSize; ++i) { if (offset >= size) { - return; + return; } data[offset] = samples[i]; ++offset; @@ -610,17 +610,15 @@ struct FillPhiloxRandomKernel { Distribution dist_; }; - template struct FillPhiloxRandomKernel { typedef typename Distribution::ResultElementType T; - using write_accessor = sycl::accessor; + using write_accessor = sycl::accessor; - FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, Distribution& dist) - : data_(data), - gen_(gen), - dist_(dist) { - } + FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, + Distribution& dist) + : data_(data), gen_(gen), dist_(dist) {} void operator()(sycl::nd_item<1> item) { using random::PhiloxRandom; @@ -628,9 +626,9 @@ struct FillPhiloxRandomKernel { const size_t kReservedSamplesPerOutput = 256; const size_t kGroupSize = Distribution::kResultElementCount; - const size_t kGeneratorSkipPerOutputGroup = kGroupSize * - kReservedSamplesPerOutput / - PhiloxRandom::kResultElementCount; + const size_t kGeneratorSkipPerOutputGroup = + kGroupSize * kReservedSamplesPerOutput / + PhiloxRandom::kResultElementCount; const size_t item_id = item.get_global(0); const size_t total_item_count = item.get_global_range(); @@ -674,10 +672,9 @@ class FillRandomKernel; // It splits the work into several tasks and run them in parallel template void FillPhiloxRandom::operator()( - OpKernelContext* context, const SYCLDevice& device, random::PhiloxRandom gen, - typename Distribution::ResultElementType* data, int64 size, - Distribution dist) { - + OpKernelContext* context, const SYCLDevice& device, + random::PhiloxRandom gen, typename Distribution::ResultElementType* data, + int64 size, Distribution dist) { const size_t group_size = device.maxSyclThreadsPerBlock(); const size_t group_count = (size + group_size - 1) / group_size; @@ -686,50 +683,52 @@ void FillPhiloxRandom::operator()( device.sycl_queue().submit([&](sycl::handler& cgh) { auto access = buffer.template get_access(cgh); - FillPhiloxRandomKernel task(access, gen, dist); + FillPhiloxRandomKernel + task(access, gen, dist); cgh.parallel_for>( - sycl::nd_range<1>(sycl::range<1>(group_count * group_size), sycl::range<1>(group_size)), - task - ); + sycl::nd_range<1>(sycl::range<1>(group_count * group_size), + sycl::range<1>(group_size)), + task); }); } -} +} // namespace functor + +#define REGISTER(TYPE) \ + template struct functor::FillPhiloxRandom< \ + SYCLDevice, random::UniformDistribution>; \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomUniform") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomStandardNormal") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("TruncatedNormal") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp< \ + SYCLDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>); -#define REGISTER(TYPE) \ - template struct functor::FillPhiloxRandom< \ - SYCLDevice, random::UniformDistribution >; \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomUniform") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomStandardNormal") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("TruncatedNormal") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp< \ - SYCLDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >); - -#define REGISTER_INT(IntType) \ - REGISTER_KERNEL_BUILDER(Name("RandomUniformInt") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .HostMemory("minval") \ - .HostMemory("maxval") \ - .TypeConstraint("Tout"), \ +#define REGISTER_INT(IntType) \ + REGISTER_KERNEL_BUILDER(Name("RandomUniformInt") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .HostMemory("minval") \ + .HostMemory("maxval") \ + .TypeConstraint("Tout"), \ RandomUniformIntOp); TF_CALL_float(REGISTER); @@ -740,6 +739,6 @@ TF_CALL_int64(REGISTER_INT); #undef REGISTER #undef REGISTER_INT -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow diff --git a/tensorflow/core/kernels/random_op_gpu.cu.cc b/tensorflow/core/kernels/random_op_gpu.cu.cc index 7afa6974c6..3393b39faf 100644 --- a/tensorflow/core/kernels/random_op_gpu.cu.cc +++ b/tensorflow/core/kernels/random_op_gpu.cu.cc @@ -222,9 +222,8 @@ void FillPhiloxRandom::operator()( (d.getNumCudaMultiProcessors() * d.maxCudaThreadsPerMultiProcessor()) / block_size; - FillPhiloxRandomKernelLaunch< - Distribution><<>>(gen, data, size, - dist); + FillPhiloxRandomKernelLaunch + <<>>(gen, data, size, dist); }; // Explicit instantiation of the GPU distributions functors diff --git a/tensorflow/core/kernels/random_poisson_op.cc b/tensorflow/core/kernels/random_poisson_op.cc index bf1d83ec75..64fb4a5c22 100644 --- a/tensorflow/core/kernels/random_poisson_op.cc +++ b/tensorflow/core/kernels/random_poisson_op.cc @@ -103,7 +103,7 @@ struct PoissonFunctor { typedef random::UniformDistribution Uniform; auto DoWork = [num_samples, num_rate, &rng, samples_flat, rate_flat]( - int start_output, int limit_output) { + int start_output, int limit_output) { // Capturing "rng" by value would only make a copy for the _shared_ // lambda. Since we want to let each worker have its own copy, we pass // "rng" by reference and explicitly do a copy assignment. diff --git a/tensorflow/core/kernels/random_shuffle_queue_op.cc b/tensorflow/core/kernels/random_shuffle_queue_op.cc index e9695cfde3..87fc943331 100644 --- a/tensorflow/core/kernels/random_shuffle_queue_op.cc +++ b/tensorflow/core/kernels/random_shuffle_queue_op.cc @@ -334,96 +334,95 @@ void RandomShuffleQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx, // TODO(josh11b): This makes two copies of callback, avoid this if possible. dequeue_attempts_.emplace_back( num_elements, [callback]() { callback(Tuple()); }, ctx, cm, token, - [callback, allow_small_batch, this](Attempt* attempt) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - int32 queue_size = queues_[0].size(); - if (closed_ && queue_size < attempt->elements_requested) { - // If we don't have enough for a full dequeue, we have - // to reset the attempt tuple. - if (!attempt->tuple.empty()) { - // Restore already-dequeued elements to the queue. - for (int64 i = attempt->tuple[0].dim_size(0) - - attempt->elements_requested - 1; - i >= 0; --i) { - for (int j = 0; j < num_components(); ++j) { - PersistentTensor element; - Status s = GetElementComponentFromBatch( - attempt->tuple, i, j, attempt->context, &element); - if (!s.ok()) { - attempt->context->SetStatus( - errors::DataLoss("Failed to restore element from " - "partially-dequeued batch " - "to RandomShuffleQueue: ", - s.error_message())); - } - queues_[j].push_back(element); - } - } - } - if (allow_small_batch && !queues_[0].empty()) { - // Request all remaining elements in the queue. - queue_size = queues_[0].size(); - attempt->tuple.clear(); - attempt->elements_requested = queue_size; - } else { - if (allow_small_batch) { - // There may be some other attempts containing - // values. If so, we'll yield and wait for them - // to add elements to the queue. - if (!enqueue_attempts_.empty()) return kProgress; - } - if (attempt->context->status().ok()) { - attempt->context->SetStatus(errors::OutOfRange( - "RandomShuffleQueue '", name_, "' is closed and has ", - "insufficient elements (requested ", - attempt->elements_requested, ", current size ", - queue_size, ")")); + [callback, allow_small_batch, + this](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + int32 queue_size = queues_[0].size(); + if (closed_ && queue_size < attempt->elements_requested) { + // If we don't have enough for a full dequeue, we have + // to reset the attempt tuple. + if (!attempt->tuple.empty()) { + // Restore already-dequeued elements to the queue. + for (int64 i = attempt->tuple[0].dim_size(0) - + attempt->elements_requested - 1; + i >= 0; --i) { + for (int j = 0; j < num_components(); ++j) { + PersistentTensor element; + Status s = GetElementComponentFromBatch( + attempt->tuple, i, j, attempt->context, &element); + if (!s.ok()) { + attempt->context->SetStatus( + errors::DataLoss("Failed to restore element from " + "partially-dequeued batch " + "to RandomShuffleQueue: ", + s.error_message())); } - return kComplete; + queues_[j].push_back(element); } } + } + if (allow_small_batch && !queues_[0].empty()) { + // Request all remaining elements in the queue. + queue_size = queues_[0].size(); + attempt->tuple.clear(); + attempt->elements_requested = queue_size; + } else { + if (allow_small_batch) { + // There may be some other attempts containing + // values. If so, we'll yield and wait for them + // to add elements to the queue. + if (!enqueue_attempts_.empty()) return kProgress; + } + if (attempt->context->status().ok()) { + attempt->context->SetStatus(errors::OutOfRange( + "RandomShuffleQueue '", name_, "' is closed and has ", + "insufficient elements (requested ", + attempt->elements_requested, ", current size ", + queue_size, ")")); + } + return kComplete; + } + } - RunResult result = kNoProgress; - if (!closed_) queue_size -= min_after_dequeue_; - for (; queue_size > 0; --queue_size) { - if (attempt->tuple.empty()) { - // Only allocate tuple when we have something to dequeue - // so we don't use excessive memory when there are many - // blocked dequeue attempts waiting. - attempt->tuple.reserve(num_components()); - for (int i = 0; i < num_components(); ++i) { - const TensorShape shape = - ManyOutShape(i, attempt->elements_requested); - Tensor element; - attempt->context->SetStatus( - attempt->context->allocate_temp(component_dtypes_[i], - shape, &element)); - if (!attempt->context->status().ok()) return kComplete; - attempt->tuple.emplace_back(element); - } - } - result = kProgress; - Tuple tuple; - DequeueLocked(attempt->context, &tuple); - const int index = attempt->tuple[0].dim_size(0) - - attempt->elements_requested; - for (int i = 0; i < num_components(); ++i) { - attempt->context->SetStatus(batch_util::CopyElementToSlice( - std::move(tuple[i]), &attempt->tuple[i], index)); - if (!attempt->context->status().ok()) return kComplete; - } - tuple.clear(); - --attempt->elements_requested; - if (attempt->elements_requested == 0) { - tuple = attempt->tuple; - attempt->done_callback = [callback, tuple]() { - callback(tuple); - }; - return kComplete; - } + RunResult result = kNoProgress; + if (!closed_) queue_size -= min_after_dequeue_; + for (; queue_size > 0; --queue_size) { + if (attempt->tuple.empty()) { + // Only allocate tuple when we have something to dequeue + // so we don't use excessive memory when there are many + // blocked dequeue attempts waiting. + attempt->tuple.reserve(num_components()); + for (int i = 0; i < num_components(); ++i) { + const TensorShape shape = + ManyOutShape(i, attempt->elements_requested); + Tensor element; + attempt->context->SetStatus(attempt->context->allocate_temp( + component_dtypes_[i], shape, &element)); + if (!attempt->context->status().ok()) return kComplete; + attempt->tuple.emplace_back(element); } - return result; - }); + } + result = kProgress; + Tuple tuple; + DequeueLocked(attempt->context, &tuple); + const int index = + attempt->tuple[0].dim_size(0) - attempt->elements_requested; + for (int i = 0; i < num_components(); ++i) { + attempt->context->SetStatus(batch_util::CopyElementToSlice( + std::move(tuple[i]), &attempt->tuple[i], index)); + if (!attempt->context->status().ok()) return kComplete; + } + tuple.clear(); + --attempt->elements_requested; + if (attempt->elements_requested == 0) { + tuple = attempt->tuple; + attempt->done_callback = [callback, tuple]() { + callback(tuple); + }; + return kComplete; + } + } + return result; + }); } } if (!already_cancelled) { diff --git a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h index 36ca7f834f..15ae4c1fc5 100644 --- a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h +++ b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h @@ -312,8 +312,7 @@ __global__ void ColumnReduceKernel( int col = blockIdx.x * 32 + threadIdx.x; value_type sum = initVal; - if (row < num_rows && col < num_cols) - sum = in[row * num_cols + col]; + if (row < num_rows && col < num_cols) sum = in[row * num_cols + col]; // 1D array necessary due to bug in CUDA 9 compiler. // TODO(nluehr) revert to 2D array when compiler is ready. @@ -366,8 +365,7 @@ __global__ void CleanupSegments( const int tid = threadIdx.x + blockIdx.x * blockDim.x; value_type val = initVal; - if (tid < segment_size * num_cols) - val = partial_sums[tid]; + if (tid < segment_size * num_cols) val = partial_sums[tid]; typedef cub::WarpReduce WarpReduce; diff --git a/tensorflow/core/kernels/relu_op.cc b/tensorflow/core/kernels/relu_op.cc index afad288cc0..d52358737f 100644 --- a/tensorflow/core/kernels/relu_op.cc +++ b/tensorflow/core/kernels/relu_op.cc @@ -31,7 +31,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_RELU_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ @@ -113,8 +113,7 @@ namespace functor { \ template <> \ void Selu::operator()( \ - const GPUDevice& d, \ - typename TTypes::ConstTensor features, \ + const GPUDevice& d, typename TTypes::ConstTensor features, \ typename TTypes::Tensor activations); \ extern template struct Selu; \ \ @@ -125,8 +124,6 @@ namespace functor { typename TTypes::Tensor backprops); \ extern template struct SeluGrad; - - TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC); } // namespace functor @@ -157,8 +154,6 @@ TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC); Name("SeluGrad").Device(DEVICE_GPU).TypeConstraint("T"), \ SeluGradOp) - - TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); #undef REGISTER_GPU_KERNELS @@ -192,10 +187,8 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); Name("SeluGrad").Device(DEVICE_SYCL).TypeConstraint("T"), \ SeluGradOp) - - TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS); #undef REGISTER_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/relu_op_functor.h b/tensorflow/core/kernels/relu_op_functor.h index 24b789c543..3bc5ba8a50 100644 --- a/tensorflow/core/kernels/relu_op_functor.h +++ b/tensorflow/core/kernels/relu_op_functor.h @@ -85,10 +85,9 @@ struct Relu6Grad { // make sure not to propagate the associated gradient // value. This allows "features" to be either the input or the output of // the relu6. - backprops.device(d) = - gradients * - ((features > static_cast(0)) * (features < static_cast(6))) - .template cast(); + backprops.device(d) = gradients * ((features > static_cast(0)) * + (features < static_cast(6))) + .template cast(); } }; @@ -161,8 +160,8 @@ struct SeluGrad { const auto scale = static_cast(1.0507009873554804934193349852946); const auto scale_alpha = static_cast(1.7580993408473768599402175208123); backprops.device(d) = - (activations < static_cast(0)).select( - gradients * (activations + scale_alpha), gradients * scale); + (activations < static_cast(0)) + .select(gradients * (activations + scale_alpha), gradients * scale); } }; diff --git a/tensorflow/core/kernels/resize_bicubic_op.cc b/tensorflow/core/kernels/resize_bicubic_op.cc index 1a9cf4c640..86e61bbcef 100644 --- a/tensorflow/core/kernels/resize_bicubic_op.cc +++ b/tensorflow/core/kernels/resize_bicubic_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" @@ -28,7 +29,6 @@ limitations under the License. #include "tensorflow/core/kernels/image_resizer_state.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { namespace { diff --git a/tensorflow/core/kernels/resize_bicubic_op_test.cc b/tensorflow/core/kernels/resize_bicubic_op_test.cc index 9e10fec423..25a37d5e1a 100644 --- a/tensorflow/core/kernels/resize_bicubic_op_test.cc +++ b/tensorflow/core/kernels/resize_bicubic_op_test.cc @@ -286,13 +286,14 @@ BM_ResizeBicubicDev(32, 128, 3); BM_ResizeBicubicDev(32, 512, 3); BM_ResizeBicubicDev(32, 1024, 3); -#define BM_ResizeBicubicExpand(BATCH, SIZE, CHANNELS) \ - static void BM_ResizeBicubicExpand##_##BATCH##_##SIZE##_##CHANNELS(int iters) { \ - testing::ItemsProcessed(static_cast(iters) * BATCH * SIZE * SIZE * \ - CHANNELS * 8 * 8); \ - test::Benchmark("cpu", ResizeBicubic(BATCH, SIZE, CHANNELS, 8, 8)) \ - .Run(iters); \ - } \ +#define BM_ResizeBicubicExpand(BATCH, SIZE, CHANNELS) \ + static void BM_ResizeBicubicExpand##_##BATCH##_##SIZE##_##CHANNELS( \ + int iters) { \ + testing::ItemsProcessed(static_cast(iters) * BATCH * SIZE * SIZE * \ + CHANNELS * 8 * 8); \ + test::Benchmark("cpu", ResizeBicubic(BATCH, SIZE, CHANNELS, 8, 8)) \ + .Run(iters); \ + } \ BENCHMARK(BM_ResizeBicubicExpand##_##BATCH##_##SIZE##_##CHANNELS); BM_ResizeBicubicExpand(12, 48, 1); diff --git a/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc b/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc index a7da7a0777..f82c3fcd9f 100644 --- a/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc +++ b/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc @@ -164,11 +164,11 @@ struct ResizeBilinear { if (total_count == 0) return; CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d); - ResizeBilinearKernel< - T><<>>( - config.virtual_thread_count, images.data(), height_scale, width_scale, - batch, in_height, in_width, channels, out_height, out_width, - output.data()); + ResizeBilinearKernel + <<>>( + config.virtual_thread_count, images.data(), height_scale, + width_scale, batch, in_height, in_width, channels, out_height, + out_width, output.data()); } }; @@ -200,11 +200,11 @@ struct ResizeBilinearGrad { // Accumulate. total_count = batch * resized_height * resized_width * channels; config = GetCudaLaunchConfig(total_count, d); - ResizeBilinearGradKernel< - T><<>>( - config.virtual_thread_count, input_grad.data(), height_scale, - width_scale, batch, original_height, original_width, channels, - resized_height, resized_width, output_grad.data()); + ResizeBilinearGradKernel + <<>>( + config.virtual_thread_count, input_grad.data(), height_scale, + width_scale, batch, original_height, original_width, channels, + resized_height, resized_width, output_grad.data()); } }; diff --git a/tensorflow/core/kernels/reverse_op.cc b/tensorflow/core/kernels/reverse_op.cc index 8f82784d93..bb96c42f10 100644 --- a/tensorflow/core/kernels/reverse_op.cc +++ b/tensorflow/core/kernels/reverse_op.cc @@ -269,10 +269,10 @@ class ReverseV2Op : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(0, input.shape(), &output)); -// TODO(cwhipkey): we can do dimension folding to reduce, e.g., a reverse of -// a single dimension to the dims=3 or dims=2 case, regardless of the number -// of dimensions in the tensor. This would let some ops use faster -// lower-dimension code (and use optimized versions). + // TODO(cwhipkey): we can do dimension folding to reduce, e.g., a reverse + // of a single dimension to the dims=3 or dims=2 case, regardless of the + // number of dimensions in the tensor. This would let some ops use faster + // lower-dimension code (and use optimized versions). #define HANDLE_REVERSE(NDIMS) \ case NDIMS: \ diff --git a/tensorflow/core/kernels/reverse_op_gpu.cu.cc b/tensorflow/core/kernels/reverse_op_gpu.cu.cc index b05a7c5550..3ee49db669 100644 --- a/tensorflow/core/kernels/reverse_op_gpu.cu.cc +++ b/tensorflow/core/kernels/reverse_op_gpu.cu.cc @@ -28,14 +28,14 @@ typedef Eigen::GpuDevice GPUDevice; #define DEFINE_REVERSE(T, DIM) \ template struct functor::Reverse; #define DEFINE_REVERSE_ALL_DIMS(T) \ - DEFINE_REVERSE(T, 0) \ - DEFINE_REVERSE(T, 1) \ - DEFINE_REVERSE(T, 2) \ - DEFINE_REVERSE(T, 3) \ - DEFINE_REVERSE(T, 4) \ - DEFINE_REVERSE(T, 5) \ - DEFINE_REVERSE(T, 6) \ - DEFINE_REVERSE(T, 7) \ + DEFINE_REVERSE(T, 0) \ + DEFINE_REVERSE(T, 1) \ + DEFINE_REVERSE(T, 2) \ + DEFINE_REVERSE(T, 3) \ + DEFINE_REVERSE(T, 4) \ + DEFINE_REVERSE(T, 5) \ + DEFINE_REVERSE(T, 6) \ + DEFINE_REVERSE(T, 7) \ DEFINE_REVERSE(T, 8) TF_CALL_uint8(DEFINE_REVERSE_ALL_DIMS); diff --git a/tensorflow/core/kernels/reverse_sequence_op.cc b/tensorflow/core/kernels/reverse_sequence_op.cc index d1980d4b65..15a707a9c6 100644 --- a/tensorflow/core/kernels/reverse_sequence_op.cc +++ b/tensorflow/core/kernels/reverse_sequence_op.cc @@ -51,8 +51,7 @@ void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) { // Copy seq_len info down for validity checks context->eigen_device().memcpyDeviceToHost( - seq_lens_vec.data(), seq_lens_t.data(), - sizeof(Tlen) * seq_lens_t.size()); + seq_lens_vec.data(), seq_lens_t.data(), sizeof(Tlen) * seq_lens_t.size()); OP_REQUIRES(context, batch_dim != seq_dim, errors::InvalidArgument("batch_dim == seq_dim == ", seq_dim)); @@ -76,8 +75,7 @@ void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) { } } -void CheckErrorsGPU(OpKernelContext* context, int batch_dim, - int seq_dim) { +void CheckErrorsGPU(OpKernelContext* context, int batch_dim, int seq_dim) { const Tensor& input = context->input(0); const Tensor& seq_lens = context->input(1); @@ -98,13 +96,13 @@ void CheckErrorsGPU(OpKernelContext* context, int batch_dim, template <> void CheckErrors(OpKernelContext* context, int batch_dim, - int seq_dim) { + int seq_dim) { CheckErrorsGPU(context, batch_dim, seq_dim); } template <> void CheckErrors(OpKernelContext* context, int batch_dim, - int seq_dim) { + int seq_dim) { CheckErrorsGPU(context, batch_dim, seq_dim); } @@ -164,14 +162,15 @@ class ReverseSequenceOp : public OpKernel { TF_DISALLOW_COPY_AND_ASSIGN(ReverseSequenceOp); }; -#define REGISTER_REVERSE_SEQUENCE(type, len_type) \ - REGISTER_KERNEL_BUILDER( \ - Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint("T"). \ - TypeConstraint("Tlen"), \ - ReverseSequenceOp); +#define REGISTER_REVERSE_SEQUENCE(type, len_type) \ + REGISTER_KERNEL_BUILDER(Name("ReverseSequence") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tlen"), \ + ReverseSequenceOp); -#define REGISTER_REVERSE_SEQUENCE_LEN(type) \ - REGISTER_REVERSE_SEQUENCE(type, int32); \ +#define REGISTER_REVERSE_SEQUENCE_LEN(type) \ + REGISTER_REVERSE_SEQUENCE(type, int32); \ REGISTER_REVERSE_SEQUENCE(type, int64); TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_LEN); @@ -181,23 +180,23 @@ TF_CALL_bool(REGISTER_REVERSE_SEQUENCE_LEN); // Forward declarations of the functor specializations for GPU. namespace functor { -#define DECLARE_GPU_SPEC(T, Tlen, Dims) \ - template <> \ - void ReverseSequence::Compute( \ - const GPUDevice& d, typename TTypes::ConstTensor input, \ - int32 batch_dim, int32 seq_dim, \ - typename TTypes::ConstVec seq_lens, \ - typename TTypes::Tensor output); \ +#define DECLARE_GPU_SPEC(T, Tlen, Dims) \ + template <> \ + void ReverseSequence::Compute( \ + const GPUDevice& d, typename TTypes::ConstTensor input, \ + int32 batch_dim, int32 seq_dim, \ + typename TTypes::ConstVec seq_lens, \ + typename TTypes::Tensor output); \ extern template struct ReverseSequence; -#define DECLARE_GPU_SPEC_LEN(T, Dims) \ - DECLARE_GPU_SPEC(T, int32, Dims); \ +#define DECLARE_GPU_SPEC_LEN(T, Dims) \ + DECLARE_GPU_SPEC(T, int32, Dims); \ DECLARE_GPU_SPEC(T, int64, Dims); -#define DECLARE_GPU_SPECS(T) \ - DECLARE_GPU_SPEC_LEN(T, 2); \ - DECLARE_GPU_SPEC_LEN(T, 3); \ - DECLARE_GPU_SPEC_LEN(T, 4); \ +#define DECLARE_GPU_SPECS(T) \ + DECLARE_GPU_SPEC_LEN(T, 2); \ + DECLARE_GPU_SPEC_LEN(T, 3); \ + DECLARE_GPU_SPEC_LEN(T, 4); \ DECLARE_GPU_SPEC_LEN(T, 5); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); @@ -206,14 +205,15 @@ TF_CALL_bool(DECLARE_GPU_SPECS); } // namespace functor // Registration of the GPU implementations. -#define REGISTER_REVERSE_SEQUENCE_GPU(type, len_type) \ - REGISTER_KERNEL_BUILDER( \ - Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint("T"). \ - TypeConstraint("Tlen"), \ - ReverseSequenceOp); - -#define REGISTER_REVERSE_SEQUENCE_GPU_LEN(type) \ - REGISTER_REVERSE_SEQUENCE_GPU(type, int32); \ +#define REGISTER_REVERSE_SEQUENCE_GPU(type, len_type) \ + REGISTER_KERNEL_BUILDER(Name("ReverseSequence") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tlen"), \ + ReverseSequenceOp); + +#define REGISTER_REVERSE_SEQUENCE_GPU_LEN(type) \ + REGISTER_REVERSE_SEQUENCE_GPU(type, int32); \ REGISTER_REVERSE_SEQUENCE_GPU(type, int64); TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU_LEN); diff --git a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc index cb49f14525..4a2136a2cd 100644 --- a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc +++ b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc @@ -28,14 +28,14 @@ typedef Eigen::GpuDevice GPUDevice; template class generator::ReverseGenerator; \ template struct functor::ReverseSequence; -#define DEFINE_GPU_SPEC_LEN(T, dims) \ - DEFINE_GPU_SPEC(T, int32, dims); \ +#define DEFINE_GPU_SPEC_LEN(T, dims) \ + DEFINE_GPU_SPEC(T, int32, dims); \ DEFINE_GPU_SPEC(T, int64, dims); -#define DEFINE_GPU_SPECS(T) \ - DEFINE_GPU_SPEC_LEN(T, 2); \ - DEFINE_GPU_SPEC_LEN(T, 3); \ - DEFINE_GPU_SPEC_LEN(T, 4); \ +#define DEFINE_GPU_SPECS(T) \ + DEFINE_GPU_SPEC_LEN(T, 2); \ + DEFINE_GPU_SPEC_LEN(T, 3); \ + DEFINE_GPU_SPEC_LEN(T, 4); \ DEFINE_GPU_SPEC_LEN(T, 5); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/save_restore_tensor.cc b/tensorflow/core/kernels/save_restore_tensor.cc index df60eda759..990bd2bff9 100644 --- a/tensorflow/core/kernels/save_restore_tensor.cc +++ b/tensorflow/core/kernels/save_restore_tensor.cc @@ -106,11 +106,11 @@ void SaveTensors( OP_REQUIRES_OK(context, checkpoint::ParseShapeAndSlice( shape_spec, &shape, &slice, &slice_shape)); OP_REQUIRES(context, slice_shape.IsSameSize(input.shape()), - errors::InvalidArgument("Slice in shape_and_slice " - "specification does not match the " - "shape of the tensor to save: ", - shape_spec, ", tensor: ", - input.shape().DebugString())); + errors::InvalidArgument( + "Slice in shape_and_slice " + "specification does not match the " + "shape of the tensor to save: ", + shape_spec, ", tensor: ", input.shape().DebugString())); } #define WRITER_ADD(T) \ diff --git a/tensorflow/core/kernels/scatter_functor.h b/tensorflow/core/kernels/scatter_functor.h index c6e35fe329..079f15e101 100644 --- a/tensorflow/core/kernels/scatter_functor.h +++ b/tensorflow/core/kernels/scatter_functor.h @@ -29,7 +29,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace scatter_op { @@ -117,7 +117,7 @@ struct AssignSYCL { p.device(d) = p / u; } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace internal } // namespace scatter_op @@ -156,7 +156,7 @@ struct ScatterFunctorBase { #ifdef TENSORFLOW_USE_SYCL template -struct ScatterFunctorBase { +struct ScatterFunctorBase { Index operator()(OpKernelContext* c, const SYCLDevice& d, typename TTypes::Matrix params, typename TTypes::ConstMatrix updates, @@ -171,13 +171,13 @@ struct ScatterFunctorBase { const Index index = ::tensorflow::internal::SubtleMustCopy(indices(i)); if (!FastBoundsCheck(index, limit)) return i; // Copy last Ndim-1 dimensions of updates[i] to params[index] - scatter_op::internal::AssignSYCL::Run(d, params.template chip<0>(index), - updates.template chip<0>(i)); + scatter_op::internal::AssignSYCL::Run( + d, params.template chip<0>(index), updates.template chip<0>(i)); } return -1; } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template struct ScatterFunctorBase { @@ -217,7 +217,7 @@ struct ScatterFunctorBase { template struct ScatterFunctor - : ScatterFunctorBase{}; + : ScatterFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template @@ -239,7 +239,7 @@ struct ScatterFunctorSYCL { return -1; } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/scatter_functor_gpu.cu.h b/tensorflow/core/kernels/scatter_functor_gpu.cu.h index e116077d3c..be18658543 100644 --- a/tensorflow/core/kernels/scatter_functor_gpu.cu.h +++ b/tensorflow/core/kernels/scatter_functor_gpu.cu.h @@ -30,9 +30,10 @@ namespace tensorflow { typedef Eigen::GpuDevice GPUDevice; template -__global__ void ScatterOpCustomKernel( - T* params, const T* updates, const Index* indices, - Index first_dim_size, Index updates_size, Index indices_size) { +__global__ void ScatterOpCustomKernel(T* params, const T* updates, + const Index* indices, + Index first_dim_size, Index updates_size, + Index indices_size) { Index update_block = updates_size / indices_size; CUDA_1D_KERNEL_LOOP(i, updates_size) { int indices_i = i / update_block; @@ -85,8 +86,8 @@ struct ScatterFunctor { CudaLaunchConfig config = GetCudaLaunchConfig(updates_size, d); ScatterOpCustomKernel <<>>( - params.data(), updates.data(), indices.data(), - first_dim_size, updates_size, indices_size); + params.data(), updates.data(), indices.data(), first_dim_size, + updates_size, indices_size); return -1; } }; diff --git a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h index c6c9d4e658..e82660dcc1 100644 --- a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h +++ b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h @@ -40,7 +40,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class OpKernelContext; @@ -251,7 +251,7 @@ REGISTER_SCATTER_ND_MATH_SYCL(int32); #undef REGISTER_SCATTER_ND_INDEX_SYCL #undef REGISTER_SCATTER_ND_FULL_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor diff --git a/tensorflow/core/kernels/scatter_op.cc b/tensorflow/core/kernels/scatter_op.cc index 8607c7f95a..282165349f 100644 --- a/tensorflow/core/kernels/scatter_op.cc +++ b/tensorflow/core/kernels/scatter_op.cc @@ -25,7 +25,7 @@ limitations under the License. #ifdef TENSORFLOW_USE_SYCL #include "tensorflow/core/common_runtime/sycl/sycl_util.h" -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace tensorflow { @@ -33,7 +33,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Check whether updates.shape = indices.shape + params.shape[1:] static bool ValidShapes(const Tensor& params, const Tensor& updates, @@ -102,11 +102,12 @@ class ScatterUpdateOp : public OpKernel { // Check that we have enough index space const int64 N_big = indices.NumElements(); - OP_REQUIRES(c, N_big <= std::numeric_limits::max(), - errors::InvalidArgument( - "indices has too many elements for ", - DataTypeString(DataTypeToEnum::v()), " indexing: ", - N_big, " > ", std::numeric_limits::max())); + OP_REQUIRES( + c, N_big <= std::numeric_limits::max(), + errors::InvalidArgument("indices has too many elements for ", + DataTypeString(DataTypeToEnum::v()), + " indexing: ", N_big, " > ", + std::numeric_limits::max())); const Index N = static_cast(indices.NumElements()); OP_REQUIRES( c, params.dim_size(0) <= std::numeric_limits::max(), @@ -137,7 +138,7 @@ class ScatterUpdateOp : public OpKernel { #ifdef TENSORFLOW_USE_SYCL template -class ScatterUpdateOp : public OpKernel { +class ScatterUpdateOp : public OpKernel { public: explicit ScatterUpdateOp(OpKernelConstruction* c) : OpKernel(c) { OP_REQUIRES_OK(c, c->GetAttr("use_locking", &use_exclusive_lock_)); @@ -165,11 +166,12 @@ class ScatterUpdateOp : public OpKernel { // Check that we have enough index space const int64 N_big = indices.NumElements(); - OP_REQUIRES(c, N_big <= std::numeric_limits::max(), - errors::InvalidArgument( - "indices has too many elements for ", - DataTypeString(DataTypeToEnum::v()), " indexing: ", - N_big, " > ", std::numeric_limits::max())); + OP_REQUIRES( + c, N_big <= std::numeric_limits::max(), + errors::InvalidArgument("indices has too many elements for ", + DataTypeString(DataTypeToEnum::v()), + " indexing: ", N_big, " > ", + std::numeric_limits::max())); const Index N = static_cast(indices.NumElements()); OP_REQUIRES( c, params.dim_size(0) <= std::numeric_limits::max(), @@ -206,7 +208,7 @@ class ScatterUpdateOp : public OpKernel { } } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_SCATTER_KERNEL_INDEX(type, index_type, dev, name, op) \ REGISTER_KERNEL_BUILDER(Name(name) \ diff --git a/tensorflow/core/kernels/sdca_internal.cc b/tensorflow/core/kernels/sdca_internal.cc index 863c123b43..066a4b80a2 100644 --- a/tensorflow/core/kernels/sdca_internal.cc +++ b/tensorflow/core/kernels/sdca_internal.cc @@ -37,9 +37,8 @@ void FeatureWeightsDenseStorage::UpdateDenseDeltaWeights( const size_t num_weight_vectors = normalized_bounded_dual_delta.size(); if (num_weight_vectors == 1) { deltas_.device(device) = - deltas_ + - dense_vector.RowAsMatrix() * - deltas_.constant(normalized_bounded_dual_delta[0]); + deltas_ + dense_vector.RowAsMatrix() * + deltas_.constant(normalized_bounded_dual_delta[0]); } else { // Transform the dual vector into a column matrix. const Eigen::TensorMap> @@ -61,9 +60,8 @@ void FeatureWeightsSparseStorage::UpdateSparseDeltaWeights( const Example::SparseFeatures& sparse_features, const std::vector& normalized_bounded_dual_delta) { for (int64 k = 0; k < sparse_features.indices->size(); ++k) { - const double feature_value = sparse_features.values == nullptr - ? 1.0 - : (*sparse_features.values)(k); + const double feature_value = + sparse_features.values == nullptr ? 1.0 : (*sparse_features.values)(k); auto it = indices_to_id_.find((*sparse_features.indices)(k)); for (size_t l = 0; l < normalized_bounded_dual_delta.size(); ++l) { deltas_(l, it->second) += @@ -122,23 +120,24 @@ Status ModelWeights::Initialize(OpKernelContext* const context) { } // Reads in the weights, and allocates and initializes the delta weights. - const auto initialize_weights = [&]( - const OpInputList& weight_inputs, OpOutputList* const weight_outputs, - std::vector* const feature_weights) { - for (int i = 0; i < weight_inputs.size(); ++i) { - Tensor* delta_t; - TF_RETURN_IF_ERROR( - weight_outputs->allocate(i, weight_inputs[i].shape(), &delta_t)); - // Convert the input vector to a row matrix in internal representation. - auto deltas = delta_t->shaped({1, delta_t->NumElements()}); - deltas.setZero(); - feature_weights->emplace_back( - FeatureWeightsDenseStorage{weight_inputs[i].shaped( - {1, weight_inputs[i].NumElements()}), - deltas}); - } - return Status::OK(); - }; + const auto initialize_weights = + [&](const OpInputList& weight_inputs, OpOutputList* const weight_outputs, + std::vector* const feature_weights) { + for (int i = 0; i < weight_inputs.size(); ++i) { + Tensor* delta_t; + TF_RETURN_IF_ERROR( + weight_outputs->allocate(i, weight_inputs[i].shape(), &delta_t)); + // Convert the input vector to a row matrix in internal + // representation. + auto deltas = delta_t->shaped({1, delta_t->NumElements()}); + deltas.setZero(); + feature_weights->emplace_back(FeatureWeightsDenseStorage{ + weight_inputs[i].shaped( + {1, weight_inputs[i].NumElements()}), + deltas}); + } + return Status::OK(); + }; return initialize_weights(dense_weights_inputs, &dense_weights_outputs, &dense_weights_); diff --git a/tensorflow/core/kernels/sdca_internal.h b/tensorflow/core/kernels/sdca_internal.h index 9f07270075..45915693ac 100644 --- a/tensorflow/core/kernels/sdca_internal.h +++ b/tensorflow/core/kernels/sdca_internal.h @@ -149,7 +149,8 @@ class Example { // 1.0f. struct SparseFeatures { std::unique_ptr::UnalignedConstVec> indices; - std::unique_ptr::UnalignedConstVec> values; // nullptr encodes optional. + std::unique_ptr::UnalignedConstVec> + values; // nullptr encodes optional. }; // A dense vector which is a row-slice of the underlying matrix. diff --git a/tensorflow/core/kernels/sdca_ops.cc b/tensorflow/core/kernels/sdca_ops.cc index 0f5c2424b3..dbe0177dda 100644 --- a/tensorflow/core/kernels/sdca_ops.cc +++ b/tensorflow/core/kernels/sdca_ops.cc @@ -57,11 +57,11 @@ namespace tensorflow { namespace { -using sdca::Regularizations; using sdca::Example; using sdca::Examples; using sdca::ExampleStatistics; using sdca::ModelWeights; +using sdca::Regularizations; struct ComputeOptions { explicit ComputeOptions(OpKernelConstruction* const context) { @@ -76,8 +76,9 @@ struct ComputeOptions { } else if (loss_type == "smooth_hinge_loss") { loss_updater.reset(new SmoothHingeLossUpdater); } else { - OP_REQUIRES(context, false, errors::InvalidArgument( - "Unsupported loss type: ", loss_type)); + OP_REQUIRES( + context, false, + errors::InvalidArgument("Unsupported loss type: ", loss_type)); } OP_REQUIRES_OK(context, context->GetAttr("adaptative", &adaptative)); OP_REQUIRES_OK( @@ -90,9 +91,10 @@ struct ComputeOptions { context, num_sparse_features + num_dense_features > 0, errors::InvalidArgument("Requires at least one feature to train.")); - OP_REQUIRES(context, static_cast(num_sparse_features) + - static_cast(num_dense_features) <= - std::numeric_limits::max(), + OP_REQUIRES(context, + static_cast(num_sparse_features) + + static_cast(num_dense_features) <= + std::numeric_limits::max(), errors::InvalidArgument( strings::Printf("Too many feature groups: %lld > %d", static_cast(num_sparse_features) + diff --git a/tensorflow/core/kernels/segment_reduction_ops.cc b/tensorflow/core/kernels/segment_reduction_ops.cc index 3ef1cd1e06..27b8081eb8 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.cc +++ b/tensorflow/core/kernels/segment_reduction_ops.cc @@ -115,7 +115,7 @@ class SegmentReductionOp : public OpKernel { Eigen::DSizes dims_to_reduce; dims_to_reduce[0] = 0; #else - Eigen::IndexList> dims_to_reduce; + Eigen::IndexList > dims_to_reduce; #endif Index start = 0, end = 1; @@ -359,7 +359,8 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_SORTED_KERNELS_ALL); namespace functor { // UnsortedSegmentSumFunctor implementation for CPUDevice. -// todo: Remove duplicate code in UnsortedSegmentSumFunctor and UnsortedSegmentMaxFunctor. +// todo: Remove duplicate code in UnsortedSegmentSumFunctor and +// UnsortedSegmentMaxFunctor. template struct UnsortedSegmentSumFunctor : UnsortedSegmentBaseFunctor { @@ -461,9 +462,10 @@ class UnsortedSegmentBaseOp : public OpKernel { auto data_ptr = data.template flat().data(); reduction_functor_(context, context->template eigen_device(), - output_rows, segment_ids.shape(), segment_flat, - data.NumElements(), data_ptr, output_flat); + output_rows, segment_ids.shape(), segment_flat, + data.NumElements(), data_ptr, output_flat); } + private: functor::UnsortedSegmentBaseFunctor& reduction_functor_; }; @@ -472,22 +474,20 @@ template class UnsortedSegmentSumOp : public UnsortedSegmentBaseOp { public: explicit UnsortedSegmentSumOp(OpKernelConstruction* context) - : UnsortedSegmentBaseOp( - context, - sum_functor_) {} + : UnsortedSegmentBaseOp(context, sum_functor_) {} + private: - functor::UnsortedSegmentSumFunctor sum_functor_; + functor::UnsortedSegmentSumFunctor sum_functor_; }; template class UnsortedSegmentMaxOp : public UnsortedSegmentBaseOp { public: explicit UnsortedSegmentMaxOp(OpKernelConstruction* context) - : UnsortedSegmentBaseOp( - context, - max_functor_) {} + : UnsortedSegmentBaseOp(context, max_functor_) {} + private: - functor::UnsortedSegmentMaxFunctor max_functor_; + functor::UnsortedSegmentMaxFunctor max_functor_; }; #define REGISTER_REAL_CPU_UNSORTED_KERNELS(type, index_type) \ @@ -663,9 +663,9 @@ class SparseSegmentReductionOpBase : public OpKernel { Reduce(input_flat, indices_vec, start, end - start, out); OP_REQUIRES(context, bad_offset < 0, errors::InvalidArgument( - "Bad: indices[", start + bad_offset, "] == ", - indices_vec(start + bad_offset), " out of range [0, ", - input_flat.dimension(0), ")")); + "Bad: indices[", start + bad_offset, + "] == ", indices_vec(start + bad_offset), + " out of range [0, ", input_flat.dimension(0), ")")); start = end; ++end; diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h index bcdd42c80c..5c9cfe0906 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.h +++ b/tensorflow/core/kernels/segment_reduction_ops.h @@ -51,13 +51,14 @@ struct SegmentSumFunctor { // BaseFunctor for definition of UnsorteSegmentReductionOp // for usage without templates. template -struct UnsortedSegmentBaseFunctor{ - virtual ~UnsortedSegmentBaseFunctor(){} +struct UnsortedSegmentBaseFunctor { + virtual ~UnsortedSegmentBaseFunctor() {} virtual void operator()(OpKernelContext* ctx, const Device& d, - const Index output_rows, const TensorShape& segment_ids_shape, - typename TTypes::ConstFlat segment_ids, - const Index data_size, const T* data, - typename TTypes::Tensor output){}; + const Index output_rows, + const TensorShape& segment_ids_shape, + typename TTypes::ConstFlat segment_ids, + const Index data_size, const T* data, + typename TTypes::Tensor output){}; }; // Functor for UnsortedSegmentSumOp. @@ -70,7 +71,8 @@ struct UnsortedSegmentBaseFunctor{ // data: input data tensor. // output: output reshaped to {output_rows, output.size/output_rows} template -struct UnsortedSegmentSumFunctor: public UnsortedSegmentBaseFunctor { +struct UnsortedSegmentSumFunctor + : public UnsortedSegmentBaseFunctor { void operator()(OpKernelContext* ctx, const Device& d, const Index output_rows, const TensorShape& segment_ids_shape, typename TTypes::ConstFlat segment_ids, @@ -88,7 +90,8 @@ struct UnsortedSegmentSumFunctor: public UnsortedSegmentBaseFunctor -struct UnsortedSegmentMaxFunctor: public UnsortedSegmentBaseFunctor { +struct UnsortedSegmentMaxFunctor + : public UnsortedSegmentBaseFunctor { void operator()(OpKernelContext* ctx, const Device& d, const Index output_rows, const TensorShape& segment_ids_shape, typename TTypes::ConstFlat segment_ids, diff --git a/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc b/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc index 159fada621..39d520698e 100644 --- a/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc @@ -194,7 +194,8 @@ void SegmentSumFunctor::operator()( // UnsortedSegmentSumFunctor implementation for GPUDevice. template -struct UnsortedSegmentSumFunctor: UnsortedSegmentBaseFunctor { +struct UnsortedSegmentSumFunctor + : UnsortedSegmentBaseFunctor { void operator()(OpKernelContext* ctx, const GPUDevice& d, const Index output_rows, const TensorShape& segment_ids_shape, typename TTypes::ConstFlat segment_ids, @@ -221,11 +222,10 @@ struct UnsortedSegmentSumFunctor: UnsortedSegmentBaseFuncto const Index input_inner_dim_size = input_total_size / input_outer_dim_size; config = GetCudaLaunchConfig(input_total_size, d); - UnsortedSegmentSumCustomKernel< - T, - Index><<>>( - input_outer_dim_size, input_inner_dim_size, output_rows, - segment_ids.data(), data, output.data()); + UnsortedSegmentSumCustomKernel + <<>>( + input_outer_dim_size, input_inner_dim_size, output_rows, + segment_ids.data(), data, output.data()); } }; diff --git a/tensorflow/core/kernels/self_adjoint_eig_op.cc b/tensorflow/core/kernels/self_adjoint_eig_op.cc index 9765780726..bcd8877390 100644 --- a/tensorflow/core/kernels/self_adjoint_eig_op.cc +++ b/tensorflow/core/kernels/self_adjoint_eig_op.cc @@ -25,7 +25,6 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" - namespace tensorflow { template diff --git a/tensorflow/core/kernels/sendrecv_ops.cc b/tensorflow/core/kernels/sendrecv_ops.cc index 206fd40fa6..688e61fcad 100644 --- a/tensorflow/core/kernels/sendrecv_ops.cc +++ b/tensorflow/core/kernels/sendrecv_ops.cc @@ -114,7 +114,7 @@ REGISTER_KERNEL_BUILDER(Name("_Send").Device(DEVICE_GPU), SendOp); REGISTER_KERNEL_BUILDER(Name("_Send").Device(DEVICE_SYCL), SendOp); REGISTER_KERNEL_BUILDER( Name("_HostSend").Device(DEVICE_SYCL).HostMemory("tensor"), SendOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("_HostSend").Device(DEVICE_CPU), SendOp); REGISTER_KERNEL_BUILDER( @@ -198,7 +198,7 @@ REGISTER_KERNEL_BUILDER(Name("_Recv").Device(DEVICE_GPU), RecvOp); #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("_Recv").Device(DEVICE_SYCL), RecvOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("_HostRecv").Device(DEVICE_CPU), RecvOp); REGISTER_KERNEL_BUILDER( @@ -207,6 +207,6 @@ REGISTER_KERNEL_BUILDER( #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER( Name("_HostRecv").Device(DEVICE_SYCL).HostMemory("tensor"), RecvOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow diff --git a/tensorflow/core/kernels/sequence_ops.cc b/tensorflow/core/kernels/sequence_ops.cc index e2e3758d87..9db0bd4d98 100644 --- a/tensorflow/core/kernels/sequence_ops.cc +++ b/tensorflow/core/kernels/sequence_ops.cc @@ -53,13 +53,13 @@ class RangeOp : public OpKernel { if (delta > 0) { OP_REQUIRES( context, start <= limit, - errors::InvalidArgument("Requires start <= limit when delta > 0: ", - start, "/", limit)); + errors::InvalidArgument( + "Requires start <= limit when delta > 0: ", start, "/", limit)); } else { OP_REQUIRES( context, start >= limit, - errors::InvalidArgument("Requires start >= limit when delta < 0: ", - start, "/", limit)); + errors::InvalidArgument( + "Requires start >= limit when delta < 0: ", start, "/", limit)); } int64 size = (std::is_integral::value ? ((std::abs(limit - start) + std::abs(delta) - 1) / diff --git a/tensorflow/core/kernels/session_ops.cc b/tensorflow/core/kernels/session_ops.cc index 185c5b248f..f2dd2812b5 100644 --- a/tensorflow/core/kernels/session_ops.cc +++ b/tensorflow/core/kernels/session_ops.cc @@ -144,7 +144,7 @@ REGISTER_GPU_KERNEL(bool); TF_CALL_NUMBER_TYPES(REGISTER_SYCL_KERNEL); REGISTER_SYCL_KERNEL(bool); #undef REGISTER_SYCL_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class DeleteSessionTensorOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/shape_ops.h b/tensorflow/core/kernels/shape_ops.h index 8d9d0ea846..55be308901 100644 --- a/tensorflow/core/kernels/shape_ops.h +++ b/tensorflow/core/kernels/shape_ops.h @@ -235,10 +235,10 @@ class SqueezeOp : public OpKernel { if (!wrapped_squeeze_dims.empty()) { if (wrapped_squeeze_dims.count(i) > 0) { OP_REQUIRES(ctx, existing_dim == 1, - errors::InvalidArgument("Tried to explicitly squeeze " - "dimension ", - i, " but dimension was not 1: ", - existing_dim)); + errors::InvalidArgument( + "Tried to explicitly squeeze " + "dimension ", + i, " but dimension was not 1: ", existing_dim)); } else { // This dimension is not being squeezed. new_shape.push_back(existing_dim); diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc index 82595de779..79369fd4a9 100644 --- a/tensorflow/core/kernels/slice_op.cc +++ b/tensorflow/core/kernels/slice_op.cc @@ -58,7 +58,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Shared code that is not dependent on the type of T. We do this to reduce // code size by not duplicating all this for all T (float, double, int32, etc.) @@ -72,10 +72,11 @@ static void SharedValidation(OpKernelContext* context, const Tensor& size_tensor = context->input(2); OP_REQUIRES( - context, context->op_kernel().IsLegacyVector(begin_tensor.shape()) && - context->op_kernel().IsLegacyVector(size_tensor.shape()) && - begin_tensor.NumElements() == input.dims() && - size_tensor.NumElements() == input.dims(), + context, + context->op_kernel().IsLegacyVector(begin_tensor.shape()) && + context->op_kernel().IsLegacyVector(size_tensor.shape()) && + begin_tensor.NumElements() == input.dims() && + size_tensor.NumElements() == input.dims(), errors::InvalidArgument( "Expected begin and size arguments to be 1-D tensors of size ", input.dims(), ", but got shapes ", begin_tensor.shape().DebugString(), @@ -125,8 +126,7 @@ static void SharedSliceCommonCases(OpKernelContext* context, TensorShape* output_shape, gtl::InlinedVector* begin, gtl::InlinedVector* size, - Tensor** result, - bool* done) { + Tensor** result, bool* done) { bool is_identity = true; bool slice_dim0 = true; *done = false; @@ -142,8 +142,8 @@ static void SharedSliceCommonCases(OpKernelContext* context, return; } - if (slice_dim0 && IsDim0SliceAligned(input.shape(), (*begin)[0], - (*size)[0])) { + if (slice_dim0 && + IsDim0SliceAligned(input.shape(), (*begin)[0], (*size)[0])) { VLOG(1) << "Slice dim 0: " << input.shape().DebugString(); CHECK_GE(input.dims(), 1); // Otherwise, is_identity should be true. context->set_output(0, input.Slice((*begin)[0], (*begin)[0] + (*size)[0])); @@ -154,7 +154,6 @@ static void SharedSliceCommonCases(OpKernelContext* context, OP_REQUIRES_OK(context, context->allocate_output(0, *output_shape, result)); } - template class SliceOp : public OpKernel { public: @@ -206,8 +205,9 @@ class SliceOp : public OpKernel { #undef HANDLE_DIM - OP_REQUIRES(context, false, errors::Unimplemented( - "SliceOp : Unhandled input dimensions")); + OP_REQUIRES( + context, false, + errors::Unimplemented("SliceOp : Unhandled input dimensions")); } } @@ -280,8 +280,9 @@ class MklSliceOp : public OpKernel { #undef HANDLE_DIM - OP_REQUIRES(context, false, errors::Unimplemented( - "SliceOp : Unhandled input dimensions")); + OP_REQUIRES( + context, false, + errors::Unimplemented("SliceOp : Unhandled input dimensions")); } } @@ -292,9 +293,9 @@ class MklSliceOp : public OpKernel { // as the sizes of all the dimensions of the input except slice_dim, then // returns True. Otherwise, returns False. bool DoesSliceShapeDifferInOnly1DHelper(const TensorShape& input_shape, - const gtl::ArraySlice& begin, - const gtl::ArraySlice& size, - int slice_dim) { + const gtl::ArraySlice& begin, + const gtl::ArraySlice& size, + int slice_dim) { for (int dim = 0; dim < 4; dim++) { if (dim != slice_dim && (begin[dim] != 0 || size[dim] != input_shape.dim_size(dim))) { @@ -316,9 +317,9 @@ class MklSliceOp : public OpKernel { // Returns True if Slicing over a single dimension, and sets slice_dim // to the number of the dimension that satisfies criteria. bool DoesSliceShapeDifferInOnly1D(const TensorShape& input_shape, - const gtl::ArraySlice& begin, - const gtl::ArraySlice& size, - int* slice_dim) { + const gtl::ArraySlice& begin, + const gtl::ArraySlice& size, + int* slice_dim) { for (int dim = 0; dim < 4; dim++) { if (DoesSliceShapeDifferInOnly1DHelper(input_shape, begin, size, dim)) { *slice_dim = dim; @@ -329,8 +330,7 @@ class MklSliceOp : public OpKernel { } template - void HandleCase(OpKernelContext* context, - const gtl::ArraySlice& begin, + void HandleCase(OpKernelContext* context, const gtl::ArraySlice& begin, const gtl::ArraySlice& size, Tensor* result) { int slice_dim = -1; TensorShape in_shape = context->input(0).shape(); @@ -340,67 +340,63 @@ class MklSliceOp : public OpKernel { // format over channel dimension. if (NDIM == 4 && DoesSliceShapeDifferInOnly1D(in_shape, begin, size, &slice_dim)) { - size_t in_strides[4] = { (size_t) in_shape.dim_size(1) * - in_shape.dim_size(2) * - in_shape.dim_size(3), - (size_t) in_shape.dim_size(2) * - in_shape.dim_size(3), - (size_t) in_shape.dim_size(3), - (size_t) 1 - }; - - size_t out_strides[4] = { (size_t) size[1] * size[2] * size[3], - (size_t) size[2] * size[3], - (size_t) size[3], - (size_t) 1 }; - - T *in_buf = const_cast(const_cast( - context->input(0).flat().data())); - T *op_buf = result->flat().data(); - - if (slice_dim == 1) { - /* data format = NCHW */ - - #pragma omp parallel for - for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { - T *ip = in_buf + (d0 * in_strides[0]); - T *op = op_buf + ((d0 - begin[0]) * out_strides[0]); - #pragma omp parallel for - for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { - T *ip1 = ip + (d1 * in_strides[1]); - T *op1 = op + ((d1 - begin[1]) * out_strides[1]); - // For NCHW, H and W will be contiguous. So we can copy - // both with one memcpy. - memcpy(static_cast(op1), static_cast(ip1), - sizeof(T) * in_strides[1]); - } + size_t in_strides[4] = { + (size_t)in_shape.dim_size(1) * in_shape.dim_size(2) * + in_shape.dim_size(3), + (size_t)in_shape.dim_size(2) * in_shape.dim_size(3), + (size_t)in_shape.dim_size(3), (size_t)1}; + + size_t out_strides[4] = {(size_t)size[1] * size[2] * size[3], + (size_t)size[2] * size[3], (size_t)size[3], + (size_t)1}; + + T* in_buf = const_cast( + const_cast(context->input(0).flat().data())); + T* op_buf = result->flat().data(); + + if (slice_dim == 1) { + /* data format = NCHW */ + +#pragma omp parallel for + for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { + T* ip = in_buf + (d0 * in_strides[0]); + T* op = op_buf + ((d0 - begin[0]) * out_strides[0]); +#pragma omp parallel for + for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { + T* ip1 = ip + (d1 * in_strides[1]); + T* op1 = op + ((d1 - begin[1]) * out_strides[1]); + // For NCHW, H and W will be contiguous. So we can copy + // both with one memcpy. + memcpy(static_cast(op1), static_cast(ip1), + sizeof(T) * in_strides[1]); } - return; - } else if (slice_dim == 3) { - /* data_format = NHWC */ - - #pragma omp parallel for - for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { - T *ip = in_buf + (d0 * in_strides[0]); - T *op = op_buf + ((d0 - begin[0]) * out_strides[0]); - #pragma omp parallel for - for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { - T *ip1 = ip + (d1 * in_strides[1]); - T *op1 = op + ((d1 - begin[1]) * out_strides[1]); - #pragma omp parallel for - for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) { - T *ip2 = ip1 + (d2 * in_strides[2]); - T *ip3 = ip2 + begin[3]; - T *op2 = op1 + ((d2 - begin[2]) * out_strides[2]); - T *op3 = op2; - memcpy(static_cast(op3), static_cast(ip3), - sizeof(T) * size[3]); - } + } + return; + } else if (slice_dim == 3) { + /* data_format = NHWC */ + +#pragma omp parallel for + for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { + T* ip = in_buf + (d0 * in_strides[0]); + T* op = op_buf + ((d0 - begin[0]) * out_strides[0]); +#pragma omp parallel for + for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { + T* ip1 = ip + (d1 * in_strides[1]); + T* op1 = op + ((d1 - begin[1]) * out_strides[1]); +#pragma omp parallel for + for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) { + T* ip2 = ip1 + (d2 * in_strides[2]); + T* ip3 = ip2 + begin[3]; + T* op2 = op1 + ((d2 - begin[2]) * out_strides[2]); + T* op3 = op2; + memcpy(static_cast(op3), static_cast(ip3), + sizeof(T) * size[3]); } } - return; } - // slice_dim is not 1 or 3, then we fallback to Eigen implementation. + return; + } + // slice_dim is not 1 or 3, then we fallback to Eigen implementation. } Eigen::DSizes indices; @@ -535,13 +531,13 @@ REGISTER_KERNEL_BUILDER(Name("Slice") #ifdef TENSORFLOW_USE_SYCL // Forward declarations of the functor specializations for SYCL. namespace functor { -#define DECLARE_SYCL_SPEC(T, NDIM) \ - template <> \ - void Slice::operator()( \ - const SYCLDevice& d, typename TTypes::Tensor output,\ - typename TTypes::ConstTensor input, \ - const Eigen::DSizes& indices, \ - const Eigen::DSizes& sizes); \ +#define DECLARE_SYCL_SPEC(T, NDIM) \ + template <> \ + void Slice::operator()( \ + const SYCLDevice& d, typename TTypes::Tensor output, \ + typename TTypes::ConstTensor input, \ + const Eigen::DSizes& indices, \ + const Eigen::DSizes& sizes); \ extern template struct Slice; #define DECLARE_FOR_N(T) \ diff --git a/tensorflow/core/kernels/slice_op.h b/tensorflow/core/kernels/slice_op.h index 0362a02133..db7eded745 100644 --- a/tensorflow/core/kernels/slice_op.h +++ b/tensorflow/core/kernels/slice_op.h @@ -24,7 +24,6 @@ limitations under the License. namespace tensorflow { namespace functor { - template struct Slice { void operator()(const Device& d, typename TTypes::Tensor output, diff --git a/tensorflow/core/kernels/slice_op_cpu_impl.h b/tensorflow/core/kernels/slice_op_cpu_impl.h index 47f1d5342a..64b6948190 100644 --- a/tensorflow/core/kernels/slice_op_cpu_impl.h +++ b/tensorflow/core/kernels/slice_op_cpu_impl.h @@ -43,7 +43,7 @@ TF_CALL_GPU_NUMBER_TYPES(DEFINE_SYCL_KERNELS); DEFINE_SYCL_KERNELS(int32); #undef DEFINE_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/softmax_op.cc b/tensorflow/core/kernels/softmax_op.cc index 590f01c469..e1712ac239 100644 --- a/tensorflow/core/kernels/softmax_op.cc +++ b/tensorflow/core/kernels/softmax_op.cc @@ -30,7 +30,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Partial specialization for a CPUDevice, that uses the Eigen implementation // from SoftmaxEigenImpl. @@ -48,7 +48,7 @@ struct SoftmaxFunctor : SoftmaxFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template struct SoftmaxFunctor : SoftmaxFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor template @@ -100,5 +100,5 @@ REGISTER_KERNEL_BUILDER( REGISTER_KERNEL_BUILDER( Name("Softmax").Device(DEVICE_SYCL).TypeConstraint("T"), SoftmaxOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/spacetobatch_benchmark_test.cc b/tensorflow/core/kernels/spacetobatch_benchmark_test.cc index c25ce2d8bb..92ddf8edbf 100644 --- a/tensorflow/core/kernels/spacetobatch_benchmark_test.cc +++ b/tensorflow/core/kernels/spacetobatch_benchmark_test.cc @@ -70,7 +70,7 @@ static Graph* ConstructSpaceToBatchGraph( } \ BENCHMARK( \ BM_##OP##_##DEVICE##_##DTYPE##_##B##_##H##_##W##_##D##_bs##BS##_pad##P00##_##P01##_##P10##_##P11); -#define BM_SpaceToBatch(OP, ...) \ +#define BM_SpaceToBatch(OP, ...) \ BM_Expand(BM_SpaceToBatchDev(OP, cpu, DT_FLOAT, __VA_ARGS__)); \ BM_Expand(BM_SpaceToBatchDev(OP, gpu, DT_FLOAT, __VA_ARGS__)); \ BM_Expand(BM_SpaceToBatchDev(OP, cpu, DT_HALF, __VA_ARGS__)); \ diff --git a/tensorflow/core/kernels/spacetobatch_functor.cc b/tensorflow/core/kernels/spacetobatch_functor.cc index 23d8a5f9ed..4c374b8d99 100644 --- a/tensorflow/core/kernels/spacetobatch_functor.cc +++ b/tensorflow/core/kernels/spacetobatch_functor.cc @@ -154,7 +154,7 @@ struct SpaceToBatchFunctor { #define INSTANTIATE(NUM_BLOCK_DIMS, T) \ template struct SpaceToBatchFunctor; \ template struct SpaceToBatchFunctor; \ -/**/ + /**/ #define INSTANTIATE_FOR_T(T) \ TF_SPACETOBATCH_FOR_EACH_NUM_BLOCK_DIMS(INSTANTIATE, T) diff --git a/tensorflow/core/kernels/spacetobatch_functor.h b/tensorflow/core/kernels/spacetobatch_functor.h index 06813650c0..f46a84da1e 100644 --- a/tensorflow/core/kernels/spacetobatch_functor.h +++ b/tensorflow/core/kernels/spacetobatch_functor.h @@ -44,7 +44,7 @@ constexpr int kMaxSpaceToBatchBlockDims = 4; MACRO(2 /**/, ##__VA_ARGS__) \ MACRO(3 /**/, ##__VA_ARGS__) \ MACRO(4 /**/, ##__VA_ARGS__) \ -/**/ + /**/ namespace internal { namespace spacetobatch { diff --git a/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc b/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc index db8d419c38..5687141c9e 100644 --- a/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc @@ -141,10 +141,10 @@ struct SpaceToBatchFunctor { } CudaLaunchConfig config = GetCudaLaunchConfig(static_cast(total_count), d); - S2B<<>>( - config.virtual_thread_count, const_cast(space_tensor.data()), args, - const_cast(batch_tensor.data())); + S2B + <<>>( + config.virtual_thread_count, const_cast(space_tensor.data()), + args, const_cast(batch_tensor.data())); return Status::OK(); } }; @@ -153,7 +153,7 @@ struct SpaceToBatchFunctor { #define INSTANTIATE(NUM_BLOCK_DIMS, T) \ template struct SpaceToBatchFunctor; \ template struct SpaceToBatchFunctor; \ -/**/ + /**/ #define INSTANTIATE_FOR_T(T) \ TF_SPACETOBATCH_FOR_EACH_NUM_BLOCK_DIMS(INSTANTIATE, T) diff --git a/tensorflow/core/kernels/spacetobatch_op.cc b/tensorflow/core/kernels/spacetobatch_op.cc index 95c1f5e7e8..fdc08ec8e3 100644 --- a/tensorflow/core/kernels/spacetobatch_op.cc +++ b/tensorflow/core/kernels/spacetobatch_op.cc @@ -58,9 +58,10 @@ void SpaceToBatchOpCompute(OpKernelContext* context, errors::InvalidArgument("input rank should be >= ", 1 + block_dims, " instead of ", orig_input_tensor.dims())); - OP_REQUIRES(context, TensorShapeUtils::IsMatrix(orig_paddings.shape()) && - block_dims == orig_paddings.dim_size(0) && - 2 == orig_paddings.dim_size(1), + OP_REQUIRES(context, + TensorShapeUtils::IsMatrix(orig_paddings.shape()) && + block_dims == orig_paddings.dim_size(0) && + 2 == orig_paddings.dim_size(1), errors::InvalidArgument("paddings should have shape [", block_dims, ", 2] instead of ", orig_paddings.shape().DebugString())); diff --git a/tensorflow/core/kernels/sparse_add_grad_op.cc b/tensorflow/core/kernels/sparse_add_grad_op.cc index d8ed0c6f0c..8597f3a8f7 100644 --- a/tensorflow/core/kernels/sparse_add_grad_op.cc +++ b/tensorflow/core/kernels/sparse_add_grad_op.cc @@ -35,9 +35,10 @@ class SparseAddGradOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("b_indices", &b_indices)); OP_REQUIRES_OK(ctx, ctx->input("sum_indices", &sum_indices)); - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_indices->shape()) && - TensorShapeUtils::IsMatrix(b_indices->shape()) && - TensorShapeUtils::IsMatrix(sum_indices->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsMatrix(a_indices->shape()) && + TensorShapeUtils::IsMatrix(b_indices->shape()) && + TensorShapeUtils::IsMatrix(sum_indices->shape()), errors::InvalidArgument( "Input indices should be matrices but received shapes: ", a_indices->shape().DebugString(), " and ", @@ -49,8 +50,9 @@ class SparseAddGradOp : public OpKernel { "Input backprop_val_grad should be a vector but received shape: ", backprop_val_grad->shape().DebugString())); OP_REQUIRES( - ctx, a_indices->dim_size(1) == b_indices->dim_size(1) && - b_indices->dim_size(1) == sum_indices->dim_size(1), + ctx, + a_indices->dim_size(1) == b_indices->dim_size(1) && + b_indices->dim_size(1) == sum_indices->dim_size(1), errors::InvalidArgument("The densified operands should have the same " "ndims; for A, B, sum got: ", a_indices->dim_size(1), b_indices->dim_size(1), diff --git a/tensorflow/core/kernels/sparse_add_op.cc b/tensorflow/core/kernels/sparse_add_op.cc index bd91dfdce6..d16317af67 100644 --- a/tensorflow/core/kernels/sparse_add_op.cc +++ b/tensorflow/core/kernels/sparse_add_op.cc @@ -34,8 +34,9 @@ class SparseAddOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("a_indices", &a_indices)); OP_REQUIRES_OK(ctx, ctx->input("b_indices", &b_indices)); - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_indices->shape()) && - TensorShapeUtils::IsMatrix(b_indices->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsMatrix(a_indices->shape()) && + TensorShapeUtils::IsMatrix(b_indices->shape()), errors::InvalidArgument( "Input indices should be matrices but received shapes: ", a_indices->shape().DebugString(), " and ", @@ -46,8 +47,9 @@ class SparseAddOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("a_values", &a_values_t)); OP_REQUIRES_OK(ctx, ctx->input("b_values", &b_values_t)); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_values_t->shape()) && - TensorShapeUtils::IsVector(b_values_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_values_t->shape()) && + TensorShapeUtils::IsVector(b_values_t->shape()), errors::InvalidArgument( "Input values should be vectors but received shapes: ", a_values_t->shape().DebugString(), " and ", @@ -62,8 +64,9 @@ class SparseAddOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("a_shape", &a_shape)); OP_REQUIRES_OK(ctx, ctx->input("b_shape", &b_shape)); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_shape->shape()) && - TensorShapeUtils::IsVector(b_shape->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_shape->shape()) && + TensorShapeUtils::IsVector(b_shape->shape()), errors::InvalidArgument( "Input shapes should be a vector but received shapes ", a_shape->shape().DebugString(), " and ", diff --git a/tensorflow/core/kernels/sparse_add_op_test.cc b/tensorflow/core/kernels/sparse_add_op_test.cc index 4cad02bbee..1f08e6c5ce 100644 --- a/tensorflow/core/kernels/sparse_add_op_test.cc +++ b/tensorflow/core/kernels/sparse_add_op_test.cc @@ -61,9 +61,9 @@ TEST_F(SparseAddOpTest, TwoD_AddSparseTensorWithSelf) { // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); #define ADD_TENSOR_INPUT() \ diff --git a/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc b/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc index c122616cf1..80bc1f1934 100644 --- a/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc +++ b/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc @@ -103,8 +103,9 @@ class SparseAccumulatorTakeGradientOp DoneCallback callback) override { // Check signature OP_REQUIRES_OK_ASYNC( - ctx, ctx->MatchSignature({DT_STRING_REF, DT_INT32}, - {DT_INT64, accumulator->dtype(), DT_INT64}), + ctx, + ctx->MatchSignature({DT_STRING_REF, DT_INT32}, + {DT_INT64, accumulator->dtype(), DT_INT64}), callback); } diff --git a/tensorflow/core/kernels/sparse_cross_op.cc b/tensorflow/core/kernels/sparse_cross_op.cc index 07d935d55f..7cd4532ad6 100644 --- a/tensorflow/core/kernels/sparse_cross_op.cc +++ b/tensorflow/core/kernels/sparse_cross_op.cc @@ -288,8 +288,7 @@ struct CrossTraits { template class SparseCrossOp : public OpKernel { public: - explicit SparseCrossOp(OpKernelConstruction* context) - : OpKernel(context) { + explicit SparseCrossOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_)); // Read signed_hash_key_ as int64 since uint64 attributes are not // supported by REGISTER_OP. @@ -316,8 +315,8 @@ class SparseCrossOp : public OpKernel { GenerateColumnsFromInput(indices_list_in, values_list_in, shapes_list_in, dense_list_in); - typename CrossTraits::Crosser - crosser(columns, num_buckets_, hash_key_); + typename CrossTraits::Crosser crosser( + columns, num_buckets_, hash_key_); Tensor* indices_out; Tensor* values_out; Tensor* shape_out; @@ -326,8 +325,8 @@ class SparseCrossOp : public OpKernel { CreateOutputTensors(columns, batch_size, context, &indices_out, &values_out, &shape_out, &output_start_indices); - typename CrossTraits::Updater - updater(output_start_indices, indices_out, values_out); + typename CrossTraits::Updater updater( + output_start_indices, indices_out, values_out); auto do_work = [this, &columns, crosser, updater](int64 begin, int64 end) { for (int b = begin; b < end; b++) { ProductIterator product_iterator(columns, b); @@ -381,8 +380,9 @@ class SparseCrossOp : public OpKernel { "Input values should be a std::vector but received shape ", values_list_in[i].shape().DebugString(), " at position ", i)); OP_REQUIRES( - context, indices_list_in[i].shape().dim_size(0) == - values_list_in[i].shape().dim_size(0), + context, + indices_list_in[i].shape().dim_size(0) == + values_list_in[i].shape().dim_size(0), errors::InvalidArgument( "Expected size of values to be ", indices_list_in[i].shape().dim_size(0), " got ", diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc index cc0f86ce05..ac48202ada 100644 --- a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc +++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc @@ -70,8 +70,9 @@ class SparseDenseBinaryOpShared : public OpKernel { errors::InvalidArgument( "Input sp_indices should be a matrix but received shape: ", indices_t->shape().DebugString())); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(values_t->shape()) && - TensorShapeUtils::IsVector(shape_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(values_t->shape()) && + TensorShapeUtils::IsVector(shape_t->shape()), errors::InvalidArgument( "Inputs sp_values and sp_shape should be vectors " "but received shapes: ", @@ -150,8 +151,9 @@ class SparseDenseBinaryOpShared : public OpKernel { CASE(4); CASE(5); default: - OP_REQUIRES(ctx, false, errors::InvalidArgument( - "Only tensors with ranks between 1 and 5 " + OP_REQUIRES( + ctx, false, + errors::InvalidArgument("Only tensors with ranks between 1 and 5 " "are currently supported. Tensor rank: ", ndims)); #undef CASE diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc index eaf1884243..fe198af7e6 100644 --- a/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc +++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc @@ -96,9 +96,9 @@ TEST_F(SparseDenseCDivTest, SameShape) { // [2 ] cdiv [dense: same shape, all 1's] // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); // Tensor dense(DT_FLOAT, TensorShape({3, 1})); @@ -125,9 +125,9 @@ TEST_F(SparseDenseCDivTest, BroadcastDenseSameDims) { // [2 ] cdiv [dense: shape [3,1], all 1's] // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); Tensor dense(DT_FLOAT, TensorShape({3, 1})); @@ -152,9 +152,9 @@ TEST_F(SparseDenseCDivTest, BroadcastDenseFewerDims) { // [2 ] cdiv [dense: shape [2]] // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); Tensor dense(DT_FLOAT, TensorShape({2})); @@ -184,9 +184,9 @@ TEST_F(SparseDenseCMulTest, BroadcastDense) { // [1 ?] where ? remains implicitly zero. // [1.5 0] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); Tensor dense(DT_FLOAT, TensorShape({2})); diff --git a/tensorflow/core/kernels/sparse_matmul_op.cc b/tensorflow/core/kernels/sparse_matmul_op.cc index 8ab23b64d3..a1f9667b78 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.cc +++ b/tensorflow/core/kernels/sparse_matmul_op.cc @@ -159,8 +159,8 @@ struct SparseSlice { template template -void SparseSlice::Initialize(const typename SparseSlice::ConstMatrixMap& mat, - int col_offset) { +void SparseSlice::Initialize( + const typename SparseSlice::ConstMatrixMap& mat, int col_offset) { const int mat_rows = Transpose ? mat.dimension(1) : mat.dimension(0); const int mat_cols = Transpose ? mat.dimension(0) : mat.dimension(1); DCHECK_LE(num_rows, mat_rows); @@ -278,9 +278,9 @@ ALWAYS_INLINE float ConvertBfloat16ToFloat(const bfloat16* src) { float out = 0; auto tmp = reinterpret_cast(&out); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - tmp[0] = *src; + tmp[0] = *src; #else - tmp[1] = *src; + tmp[1] = *src; #endif return out; } @@ -970,9 +970,9 @@ class SparseMatMulOp : public OpKernel { const int k2 = transpose_b_ ? b.dim_size(1) : b.dim_size(0); OP_REQUIRES(ctx, k == k2, - errors::InvalidArgument("Matrix size incompatible: a: ", - a.shape().DebugString(), ", b: ", - b.shape().DebugString())); + errors::InvalidArgument( + "Matrix size incompatible: a: ", a.shape().DebugString(), + ", b: ", b.shape().DebugString())); Tensor* output = nullptr; OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({m, n}), &output)); @@ -1224,8 +1224,9 @@ ALWAYS_INLINE void CopyAndMayBeInterleave(void* dst, const void* src, template inline BlockingCounter* SparseMatMul::ShuffleMatrix( - const typename SparseMatMul::ConstMatrixMapR& mat, int slice_row_start, - int slice_num_rows, int slice_col_start, int slice_num_cols, const int N, + const typename SparseMatMul::ConstMatrixMapR& mat, + int slice_row_start, int slice_num_rows, int slice_col_start, + int slice_num_cols, const int N, const DeviceBase::CpuWorkerThreads* thread_pool, MatrixR* buffer) { DCHECK_EQ(N % 2, 0); DCHECK_LE(kNumOperands * sizeof(float) / sizeof(TR), N); @@ -1306,8 +1307,9 @@ inline std::unique_ptr SparseMatMul::CreateDenseSlices( template inline void SparseMatMul::ComputeBlockSizes( const typename SparseMatMul::ConstMatrixMapL& left, - const typename SparseMatMul::ConstMatrixMapR& right, bool transpose_left, - int num_threads, int* KR, int* NR, int* KL, int* JB, int* IB) { + const typename SparseMatMul::ConstMatrixMapR& right, + bool transpose_left, int num_threads, int* KR, int* NR, int* KL, int* JB, + int* IB) { // Heuristics for calculating block sizes // Assume two hyperthreads per core. const int est_num_cores = std::max(1, (num_threads + 1) / 2); diff --git a/tensorflow/core/kernels/sparse_matmul_op.h b/tensorflow/core/kernels/sparse_matmul_op.h index cca52558ae..14ef2ed704 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.h +++ b/tensorflow/core/kernels/sparse_matmul_op.h @@ -159,25 +159,25 @@ EIGEN_STRONG_INLINE Packet4f pload2bf16(const float* from) { // Return a packet with the first value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_first(const Packet4f& a) { - return vec_splat (a, 0); + return vec_splat(a, 0); } // Return a packet with the second value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_second(const Packet4f& a) { - return vec_splat (a, 1); + return vec_splat(a, 1); } // Return a packet with the third value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_third(const Packet4f& a) { - return vec_splat (a, 2); + return vec_splat(a, 2); } // Return a packet with the fourth value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_fourth(const Packet4f& a) { - return vec_splat (a, 3); + return vec_splat(a, 3); } #endif diff --git a/tensorflow/core/kernels/sparse_matmul_op_test.cc b/tensorflow/core/kernels/sparse_matmul_op_test.cc index f815ca9e34..ebc6d8fa4e 100644 --- a/tensorflow/core/kernels/sparse_matmul_op_test.cc +++ b/tensorflow/core/kernels/sparse_matmul_op_test.cc @@ -284,11 +284,11 @@ class SparseMatmulOpTest : public ::testing::Test { uint16_t* data3_bfloat16_p = reinterpret_cast(data3_bfloat16) + i; #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - data3_p[1] = 0; - data3_bfloat16_p[0] = data3_p[0]; + data3_p[1] = 0; + data3_bfloat16_p[0] = data3_p[0]; #else - data3_p[0] = 0; - data3_bfloat16_p[0] = data3_p[1]; + data3_p[0] = 0; + data3_bfloat16_p[0] = data3_p[1]; #endif } } diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc b/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc index 110376be42..96246c7a71 100644 --- a/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc +++ b/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc @@ -51,9 +51,9 @@ TEST_F(SparseReduceSumOpTest, SimpleReduce) { // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); AddInputFromArray(indices_shape, indices); @@ -93,9 +93,9 @@ TEST_F(SparseReduceSumSparseOpTest, SimpleReduce) { // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); AddInputFromArray(indices_shape, indices); diff --git a/tensorflow/core/kernels/sparse_softmax_op.cc b/tensorflow/core/kernels/sparse_softmax_op.cc index 327a94b8a1..444a5f657a 100644 --- a/tensorflow/core/kernels/sparse_softmax_op.cc +++ b/tensorflow/core/kernels/sparse_softmax_op.cc @@ -50,8 +50,9 @@ class SparseSoftmaxOp : public OpKernel { errors::InvalidArgument( "Input sp_indices should be a matrix but received shape: ", indices_t->shape().DebugString())); - OP_REQUIRES(context, TensorShapeUtils::IsVector(values_t->shape()) && - TensorShapeUtils::IsVector(shape_t->shape()), + OP_REQUIRES(context, + TensorShapeUtils::IsVector(values_t->shape()) && + TensorShapeUtils::IsVector(shape_t->shape()), errors::InvalidArgument( "Inputs sp_values and sp_shape should be vectors " "but received shapes: ", diff --git a/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc b/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc index b027adba6b..09cb2a6a71 100644 --- a/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc +++ b/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc @@ -132,14 +132,16 @@ class SparseSparseBinaryOpShared : public OpKernel { // Validations. OP_REQUIRES( - ctx, TensorShapeUtils::IsMatrix(a_indices_t->shape()) && - TensorShapeUtils::IsMatrix(b_indices_t->shape()), + ctx, + TensorShapeUtils::IsMatrix(a_indices_t->shape()) && + TensorShapeUtils::IsMatrix(b_indices_t->shape()), errors::InvalidArgument("Inputs a_indices and b_indices should be " "matrices but received shapes: ", a_indices_t->shape().DebugString(), ", ", b_indices_t->shape().DebugString())); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_values_t->shape()) && - TensorShapeUtils::IsVector(b_values_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_values_t->shape()) && + TensorShapeUtils::IsVector(b_values_t->shape()), errors::InvalidArgument( "Inputs a_values and b_values should be vectors " "but received shapes: ", @@ -157,8 +159,9 @@ class SparseSparseBinaryOpShared : public OpKernel { " non-empty input values, got ", a_values.size(), " and ", b_values.size())); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_shape_t->shape()) && - TensorShapeUtils::IsVector(b_shape_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_shape_t->shape()) && + TensorShapeUtils::IsVector(b_shape_t->shape()), errors::InvalidArgument( "Input shapes should be a vector but received shapes ", a_shape_t->shape().DebugString(), " and ", diff --git a/tensorflow/core/kernels/sparse_split_op.cc b/tensorflow/core/kernels/sparse_split_op.cc index 6171b532aa..67dcf05a6c 100644 --- a/tensorflow/core/kernels/sparse_split_op.cc +++ b/tensorflow/core/kernels/sparse_split_op.cc @@ -48,18 +48,20 @@ class SparseSplitOp : public OpKernel { "Input shape should be a vector but received shape ", input_shape.shape().DebugString())); - OP_REQUIRES(context, input_shape.dim_size(0) && - split_dim < input_shape.vec().size(), - errors::InvalidArgument( - "Input split_dim should be between 0 and rank (", - input_shape.vec().size(), "), got ", split_dim)); - - OP_REQUIRES(context, num_split_ >= 1 && - num_split_ <= input_shape.vec()(split_dim), - errors::InvalidArgument("Input num_split should be between 1 " - "and the splitting dimension size (", - input_shape.vec()(split_dim), - "), got ", num_split_)); + OP_REQUIRES( + context, + input_shape.dim_size(0) && split_dim < input_shape.vec().size(), + errors::InvalidArgument( + "Input split_dim should be between 0 and rank (", + input_shape.vec().size(), "), got ", split_dim)); + + OP_REQUIRES( + context, + num_split_ >= 1 && num_split_ <= input_shape.vec()(split_dim), + errors::InvalidArgument("Input num_split should be between 1 " + "and the splitting dimension size (", + input_shape.vec()(split_dim), "), got ", + num_split_)); sparse::SparseTensor sparse_tensor(input_indices, input_values, TensorShape(input_shape.vec())); diff --git a/tensorflow/core/kernels/sparse_to_dense_op.cc b/tensorflow/core/kernels/sparse_to_dense_op.cc index 6a6cc3d813..ba3da21a43 100644 --- a/tensorflow/core/kernels/sparse_to_dense_op.cc +++ b/tensorflow/core/kernels/sparse_to_dense_op.cc @@ -73,8 +73,9 @@ class SparseToDense : public OpKernel { // sparse_values const Tensor& sparse_values = c->input(2); const int64 num_values = sparse_values.NumElements(); - OP_REQUIRES(c, sparse_values.dims() == 0 || - (sparse_values.dims() == 1 && num_values == num_elems), + OP_REQUIRES(c, + sparse_values.dims() == 0 || + (sparse_values.dims() == 1 && num_values == num_elems), errors::InvalidArgument("sparse_values has incorrect shape ", sparse_values.shape().DebugString(), ", should be [] or [", num_elems, "]")); diff --git a/tensorflow/core/kernels/sparse_to_dense_op_test.cc b/tensorflow/core/kernels/sparse_to_dense_op_test.cc index f0d19da804..d8b0f93082 100644 --- a/tensorflow/core/kernels/sparse_to_dense_op_test.cc +++ b/tensorflow/core/kernels/sparse_to_dense_op_test.cc @@ -38,7 +38,6 @@ namespace { class SparseToDenseTest : public OpsTestBase { protected: - void MakeOp(int dim, DataType index_type, DataType value_type) { TF_ASSERT_OK(NodeDefBuilder("sparsetodense", "SparseToDense") .Input(FakeInput(index_type)) diff --git a/tensorflow/core/kernels/sparse_xent_op.cc b/tensorflow/core/kernels/sparse_xent_op.cc index c35ba42db2..f84ffd5323 100644 --- a/tensorflow/core/kernels/sparse_xent_op.cc +++ b/tensorflow/core/kernels/sparse_xent_op.cc @@ -39,10 +39,10 @@ Status CheckInvalidLabelIndex(const Tensor& labels, int64 max_index) { if (*min_max_dim_value.first < 0 || *min_max_dim_value.second >= max_index) { bad_index = (*min_max_dim_value.first < 0) ? *min_max_dim_value.first : *min_max_dim_value.second; - return errors::InvalidArgument("Received a label value of ", bad_index, - " which is outside the valid range of [0, ", - max_index, "). Label values: ", - labels.SummarizeValue(labels.NumElements())); + return errors::InvalidArgument( + "Received a label value of ", bad_index, + " which is outside the valid range of [0, ", max_index, + "). Label values: ", labels.SummarizeValue(labels.NumElements())); } return Status::OK(); } diff --git a/tensorflow/core/kernels/sparse_xent_op_test.cc b/tensorflow/core/kernels/sparse_xent_op_test.cc index b8ea0d2d7e..afb0bf7626 100644 --- a/tensorflow/core/kernels/sparse_xent_op_test.cc +++ b/tensorflow/core/kernels/sparse_xent_op_test.cc @@ -41,10 +41,10 @@ static Graph* SparseXent(int batch_size, int num_classes) { return g; } -#define BM_SparseXentDev(BATCH, CLASS, DEVICE) \ - static void BM_SparseXent##_##BATCH##_##CLASS##_##DEVICE(int iters) { \ +#define BM_SparseXentDev(BATCH, CLASS, DEVICE) \ + static void BM_SparseXent##_##BATCH##_##CLASS##_##DEVICE(int iters) { \ testing::ItemsProcessed(static_cast(iters) * BATCH * CLASS); \ - test::Benchmark(#DEVICE, SparseXent(BATCH, CLASS)).Run(iters); \ + test::Benchmark(#DEVICE, SparseXent(BATCH, CLASS)).Run(iters); \ } \ BENCHMARK(BM_SparseXent##_##BATCH##_##CLASS##_##DEVICE); diff --git a/tensorflow/core/kernels/split_lib.h b/tensorflow/core/kernels/split_lib.h index ff92ffeeb3..a08949e626 100644 --- a/tensorflow/core/kernels/split_lib.h +++ b/tensorflow/core/kernels/split_lib.h @@ -57,7 +57,7 @@ struct Split { const Eigen::DSizes& slice_indices, const Eigen::DSizes& slice_sizes); }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/split_lib_cpu.cc b/tensorflow/core/kernels/split_lib_cpu.cc index 25026208d1..771c633b15 100644 --- a/tensorflow/core/kernels/split_lib_cpu.cc +++ b/tensorflow/core/kernels/split_lib_cpu.cc @@ -49,13 +49,13 @@ void Split::operator()( typename TTypes::ConstTensor input, const Eigen::DSizes& slice_indices, const Eigen::DSizes& slice_sizes) { - output.device(d) = input.slice(slice_indices, slice_sizes); + output.device(d) = input.slice(slice_indices, slice_sizes); } #define DEFINE_SYCL_KERNELS(T) template struct Split; TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DEFINE_SYCL_KERNELS); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/split_op.cc b/tensorflow/core/kernels/split_op.cc index 78badde27e..85f529326d 100644 --- a/tensorflow/core/kernels/split_op.cc +++ b/tensorflow/core/kernels/split_op.cc @@ -39,7 +39,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class SplitOpBase : public OpKernel { @@ -142,8 +142,9 @@ class SplitOpCPU : public SplitOpBase { // Android also uses int32 indexing, so check here also. OP_REQUIRES( - context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), + context, + FastBoundsCheck(input.NumElements(), + std::numeric_limits::max()), errors::InvalidArgument("Split requires input size < ", std::numeric_limits::max())); @@ -245,10 +246,11 @@ class SplitOpGPU : public SplitOpBase { const int32 split_dim = split_dim_orig < 0 ? split_dim_orig + input.dims() : split_dim_orig; const int32 num_split = Base::num_outputs(); - OP_REQUIRES(context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("Split on GPU requires input size " - "< max int32")); + OP_REQUIRES( + context, + FastBoundsCheck(input.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("Split on GPU requires input size " + "< max int32")); int32 prefix_dim_size; int32 split_dim_size; int32 suffix_dim_size; @@ -304,8 +306,9 @@ class SplitOpSYCL : public SplitOpBase { // Android also uses int32 indexing, so check here also. OP_REQUIRES( - context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), + context, + FastBoundsCheck(input.NumElements(), + std::numeric_limits::max()), errors::InvalidArgument("Split requires input size < ", std::numeric_limits::max())); @@ -342,14 +345,14 @@ class SplitOpSYCL : public SplitOpBase { {prefix_dim_size, split_dim_output_size, suffix_dim_size}); functor::Split()(context->eigen_device(), - result_shaped, input_reshaped, - slice_indices, slice_sizes); + result_shaped, input_reshaped, + slice_indices, slice_sizes); } indices[1] += split_dim_output_size; } } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_SPLIT(type) \ REGISTER_KERNEL_BUILDER(Name("Split") \ @@ -381,11 +384,11 @@ REGISTER_GPU(bfloat16); #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL(type) \ - REGISTER_KERNEL_BUILDER(Name("Split") \ - .Device(DEVICE_SYCL) \ - .TypeConstraint("T") \ - .HostMemory("split_dim"), \ +#define REGISTER_SYCL(type) \ + REGISTER_KERNEL_BUILDER(Name("Split") \ + .Device(DEVICE_SYCL) \ + .TypeConstraint("T") \ + .HostMemory("split_dim"), \ SplitOpSYCL) TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL); diff --git a/tensorflow/core/kernels/split_v_op.cc b/tensorflow/core/kernels/split_v_op.cc index f1078ac349..7ff5df47d7 100644 --- a/tensorflow/core/kernels/split_v_op.cc +++ b/tensorflow/core/kernels/split_v_op.cc @@ -197,8 +197,9 @@ class SplitVOpCPU : public SplitVOpBase { // Android also uses int32 indexing, so check here also. OP_REQUIRES( - context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), + context, + FastBoundsCheck(input.NumElements(), + std::numeric_limits::max()), errors::InvalidArgument("Split requires input size < ", std::numeric_limits::max())); @@ -305,10 +306,11 @@ class SplitVOpGPU : public SplitVOpBase { const int32 split_dim_orig = context->input(2).flat()(0); const int32 split_dim = split_dim_orig < 0 ? split_dim_orig + input.dims() : split_dim_orig; - OP_REQUIRES(context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("Split on GPU requires input size " - "< max int32")); + OP_REQUIRES( + context, + FastBoundsCheck(input.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("Split on GPU requires input size " + "< max int32")); int32 prefix_dim_size; int32 split_dim_size; diff --git a/tensorflow/core/kernels/stack_ops.cc b/tensorflow/core/kernels/stack_ops.cc index affe81a555..65296f61fd 100644 --- a/tensorflow/core/kernels/stack_ops.cc +++ b/tensorflow/core/kernels/stack_ops.cc @@ -42,7 +42,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class Stack : public ResourceBase { public: @@ -242,7 +242,7 @@ REGISTER_KERNEL_BUILDER(Name("StackV2") .HostMemory("max_size") .HostMemory("handle"), StackOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class StackPushOp : public AsyncOpKernel { @@ -274,11 +274,11 @@ class StackPushOp : public AsyncOpKernel { static constexpr int kCopyThreshold = 2048; static constexpr double kOccupancy = 0.7; if (swap_memory_ && !alloc_attrs.on_host() && - ( std::is_same::value + (std::is_same::value #ifdef TENSORFLOW_USE_SYCL - || std::is_same::value -#endif // TENSORFLOW_USE_SYCL - ) && + || std::is_same::value +#endif // TENSORFLOW_USE_SYCL + ) && tensor.TotalBytes() > kCopyThreshold && stack->IsUsefulToSwap(tensor)) { DeviceContext* device_ctxt = ctx->op_device_context(); auto device = static_cast(ctx->device()); @@ -391,7 +391,7 @@ REGISTER_SYCL_HOST_KERNEL(int32); REGISTER_SYCL_HOST_KERNEL(bool); #undef REGISTER_SYCL_KERNEL #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class StackPopOp : public AsyncOpKernel { public: @@ -498,7 +498,7 @@ REGISTER_SYCL_HOST_KERNEL(bool); #undef REGISTER_SYCL_KERNEL #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class StackCloseOp : public OpKernel { public: @@ -526,6 +526,6 @@ REGISTER_KERNEL_BUILDER( REGISTER_KERNEL_BUILDER( Name("StackCloseV2").Device(DEVICE_SYCL).HostMemory("handle"), StackCloseOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/stage_op.cc b/tensorflow/core/kernels/stage_op.cc index 0fae46dea6..03fc4467a1 100644 --- a/tensorflow/core/kernels/stage_op.cc +++ b/tensorflow/core/kernels/stage_op.cc @@ -70,12 +70,11 @@ class Buffer : public ResourceBase { return bytes + current_bytes_ > memory_limit_; } - std::size_t GetTupleBytes(const Tuple & tuple) - { + std::size_t GetTupleBytes(const Tuple& tuple) { return std::accumulate(tuple.begin(), tuple.end(), 0, - [](const std::size_t & lhs, const Tensor & rhs) { - return lhs + rhs.TotalBytes(); - }); + [](const std::size_t& lhs, const Tensor& rhs) { + return lhs + rhs.TotalBytes(); + }); } public: @@ -90,19 +89,22 @@ class Buffer : public ResourceBase { std::size_t tuple_bytes = GetTupleBytes(*tuple); // Sanity check so that we don't block for ever below - if(memory_limit_ > 0 && tuple_bytes > memory_limit_) { - return Status(errors::ResourceExhausted("Attempted to insert " - "tensors with combined size of '", tuple_bytes, "' bytes into " - "Staging Area with a memory limit of '", memory_limit_, "'.")); + if (memory_limit_ > 0 && tuple_bytes > memory_limit_) { + return Status( + errors::ResourceExhausted("Attempted to insert " + "tensors with combined size of '", + tuple_bytes, + "' bytes into " + "Staging Area with a memory limit of '", + memory_limit_, "'.")); } - // If buffer capacity is bounded wait until elements have been removed - if(IsBounded()) { + if (IsBounded()) { full_cond_var_.wait(lock, [tuple_bytes, this]() { // If there's a memory limit, check if there's space for insertion - bool memory_limit_valid = memory_limit_ > 0 ? - !WouldExceedMemoryLimit(tuple_bytes) : true; + bool memory_limit_valid = + memory_limit_ > 0 ? !WouldExceedMemoryLimit(tuple_bytes) : true; // If we're configured for capacity check if there's space for insertion bool capacity_valid = capacity_ > 0 ? !IsCapacityFull() : true; @@ -186,8 +188,7 @@ Status GetBuffer(OpKernelContext* ctx, const NodeDef& ndef, Buffer** buf) { ContainerInfo cinfo; // Lambda for creating the Staging Area - auto create_fn = [&ndef](Buffer** ret) -> Status - { + auto create_fn = [&ndef](Buffer** ret) -> Status { int64 capacity; int64 memory_limit; TF_RETURN_IF_ERROR(GetNodeAttr(ndef, "capacity", &capacity)); @@ -196,7 +197,6 @@ Status GetBuffer(OpKernelContext* ctx, const NodeDef& ndef, Buffer** buf) { return Status::OK(); }; - TF_RETURN_IF_ERROR(cinfo.Init(rm, ndef, true /* use name() */)); TF_RETURN_IF_ERROR(rm->LookupOrCreate(cinfo.container(), cinfo.name(), buf, create_fn)); @@ -228,7 +228,7 @@ REGISTER_KERNEL_BUILDER(Name("Stage").Device(DEVICE_GPU), StageOp); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("Stage").Device(DEVICE_SYCL), StageOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class UnstageOp : public OpKernel { public: @@ -244,7 +244,8 @@ class UnstageOp : public OpKernel { buf->Get(&tuple); - OP_REQUIRES(ctx, tuple.size() == (size_t)ctx->num_outputs(), + OP_REQUIRES( + ctx, tuple.size() == (size_t)ctx->num_outputs(), errors::InvalidArgument("Mismatch stage/unstage: ", tuple.size(), " vs. ", ctx->num_outputs())); @@ -260,7 +261,7 @@ REGISTER_KERNEL_BUILDER(Name("Unstage").Device(DEVICE_GPU), UnstageOp); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("Unstage").Device(DEVICE_SYCL), UnstageOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class StagePeekOp : public OpKernel { public: @@ -278,7 +279,8 @@ class StagePeekOp : public OpKernel { OP_REQUIRES_OK(ctx, buf->Peek(index, &tuple)); - OP_REQUIRES(ctx, tuple.size() == (size_t)ctx->num_outputs(), + OP_REQUIRES( + ctx, tuple.size() == (size_t)ctx->num_outputs(), errors::InvalidArgument("Mismatch stage/unstage: ", tuple.size(), " vs. ", ctx->num_outputs())); @@ -288,17 +290,15 @@ class StagePeekOp : public OpKernel { } }; -REGISTER_KERNEL_BUILDER(Name("StagePeek").Device(DEVICE_CPU), - StagePeekOp); +REGISTER_KERNEL_BUILDER(Name("StagePeek").Device(DEVICE_CPU), StagePeekOp); #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER(Name("StagePeek").HostMemory("index"). - Device(DEVICE_GPU), StagePeekOp); +REGISTER_KERNEL_BUILDER( + Name("StagePeek").HostMemory("index").Device(DEVICE_GPU), StagePeekOp); #endif #ifdef TENSORFLOW_USE_SYCL -REGISTER_KERNEL_BUILDER(Name("StagePeek").HostMemory("index") - .Device(DEVICE_SYCL), StagePeekOp); -#endif // TENSORFLOW_USE_SYCL - +REGISTER_KERNEL_BUILDER( + Name("StagePeek").HostMemory("index").Device(DEVICE_SYCL), StagePeekOp); +#endif // TENSORFLOW_USE_SYCL class StageSizeOp : public OpKernel { public: @@ -312,9 +312,8 @@ class StageSizeOp : public OpKernel { core::ScopedUnref scope(buf); // Allocate size output tensor - Tensor * size = nullptr; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), - &size)); + Tensor* size = nullptr; + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &size)); // Set it to the actual size size->scalar().setConstant(buf->Size()); @@ -323,13 +322,13 @@ class StageSizeOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("StageSize").Device(DEVICE_CPU), StageSizeOp); #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER(Name("StageSize").HostMemory("size") - .Device(DEVICE_GPU), StageSizeOp); +REGISTER_KERNEL_BUILDER(Name("StageSize").HostMemory("size").Device(DEVICE_GPU), + StageSizeOp); #endif #ifdef TENSORFLOW_USE_SYCL -REGISTER_KERNEL_BUILDER(Name("StageSize").HostMemory("size") - .Device(DEVICE_SYCL), StageSizeOp); -#endif // TENSORFLOW_USE_SYCL +REGISTER_KERNEL_BUILDER( + Name("StageSize").HostMemory("size").Device(DEVICE_SYCL), StageSizeOp); +#endif // TENSORFLOW_USE_SYCL class StageClearOp : public OpKernel { public: @@ -352,7 +351,6 @@ REGISTER_KERNEL_BUILDER(Name("StageClear").Device(DEVICE_GPU), StageClearOp); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("StageClear").Device(DEVICE_SYCL), StageClearOp); -#endif // TENSORFLOW_USE_SYCL - +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc index 7c213e14d2..8f7f91c9df 100644 --- a/tensorflow/core/kernels/strided_slice_op.cc +++ b/tensorflow/core/kernels/strided_slice_op.cc @@ -541,5 +541,5 @@ REGISTER_KERNEL_BUILDER(Name("ResourceStridedSliceAssign") .HostMemory("strides"), StridedSliceAssignOp) #undef REGISTER_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/strided_slice_op_impl.h b/tensorflow/core/kernels/strided_slice_op_impl.h index a84ba38ef4..ac1259a9ac 100644 --- a/tensorflow/core/kernels/strided_slice_op_impl.h +++ b/tensorflow/core/kernels/strided_slice_op_impl.h @@ -302,7 +302,7 @@ DECLARE_FOR_N_SYCL(int32); DECLARE_FOR_N_SYCL(int64); #undef DECLARE_FOR_N_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef INSTANTIATE #undef DECLARE_FOR_N_CPU diff --git a/tensorflow/core/kernels/string_join_op.cc b/tensorflow/core/kernels/string_join_op.cc index 721702bec6..28cca9f448 100644 --- a/tensorflow/core/kernels/string_join_op.cc +++ b/tensorflow/core/kernels/string_join_op.cc @@ -50,9 +50,9 @@ class StringJoinOp : public OpKernel { } else { OP_REQUIRES( context, input_shape == input.shape(), - errors::InvalidArgument("Input shapes do not match: ", - input_shape.DebugString(), " vs. ", - input.shape().DebugString())); + errors::InvalidArgument( + "Input shapes do not match: ", input_shape.DebugString(), + " vs. ", input.shape().DebugString())); } } } diff --git a/tensorflow/core/kernels/substr_op.cc b/tensorflow/core/kernels/substr_op.cc index 743f113150..e29f67297f 100644 --- a/tensorflow/core/kernels/substr_op.cc +++ b/tensorflow/core/kernels/substr_op.cc @@ -95,9 +95,9 @@ class SubstrOp : public OpKernel { // Create BCast helper with shape of input and pos/len BCast bcast(BCast::FromShape(input_shape), BCast::FromShape(pos_shape)); OP_REQUIRES(context, bcast.IsValid(), - errors::InvalidArgument("Incompatible shapes: ", - input_shape.DebugString(), " vs. ", - pos_shape.DebugString())); + errors::InvalidArgument( + "Incompatible shapes: ", input_shape.DebugString(), + " vs. ", pos_shape.DebugString())); TensorShape output_shape = BCast::ToShape(bcast.result_shape()); int ndims = output_shape.dims(); Tensor* output_tensor = nullptr; diff --git a/tensorflow/core/kernels/summary_image_op.cc b/tensorflow/core/kernels/summary_image_op.cc index 233b824bcc..29b21ee735 100644 --- a/tensorflow/core/kernels/summary_image_op.cc +++ b/tensorflow/core/kernels/summary_image_op.cc @@ -54,18 +54,20 @@ class SummaryImageOp : public OpKernel { const Tensor& tensor = c->input(1); OP_REQUIRES(c, IsLegacyScalar(tags.shape()), errors::InvalidArgument("Tags must be a scalar")); - OP_REQUIRES(c, tensor.dims() == 4 && - (tensor.dim_size(3) == 1 || tensor.dim_size(3) == 3 || - tensor.dim_size(3) == 4), + OP_REQUIRES(c, + tensor.dims() == 4 && + (tensor.dim_size(3) == 1 || tensor.dim_size(3) == 3 || + tensor.dim_size(3) == 4), errors::InvalidArgument( "Tensor must be 4-D with last dim 1, 3, or 4, not ", tensor.shape().DebugString())); const string& base_tag = tags.scalar()(); - OP_REQUIRES(c, tensor.dim_size(0) < (1LL << 31) && - tensor.dim_size(1) < (1LL << 31) && - tensor.dim_size(2) < (1LL << 31) && - (tensor.dim_size(1) * tensor.dim_size(2)) < (1LL << 29), + OP_REQUIRES(c, + tensor.dim_size(0) < (1LL << 31) && + tensor.dim_size(1) < (1LL << 31) && + tensor.dim_size(2) < (1LL << 31) && + (tensor.dim_size(1) * tensor.dim_size(2)) < (1LL << 29), errors::InvalidArgument("Tensor too large for summary ", tensor.shape().DebugString())); diff --git a/tensorflow/core/kernels/summary_op.cc b/tensorflow/core/kernels/summary_op.cc index b818724ec2..1f4e3418f4 100644 --- a/tensorflow/core/kernels/summary_op.cc +++ b/tensorflow/core/kernels/summary_op.cc @@ -41,11 +41,12 @@ class SummaryScalarOp : public OpKernel { const Tensor& values = c->input(1); OP_REQUIRES( - c, tags.IsSameSize(values) || - (IsLegacyScalar(tags.shape()) && IsLegacyScalar(values.shape())), - errors::InvalidArgument("tags and values not the same shape: ", - tags.shape().DebugString(), " != ", - values.shape().DebugString(), SingleTag(tags))); + c, + tags.IsSameSize(values) || + (IsLegacyScalar(tags.shape()) && IsLegacyScalar(values.shape())), + errors::InvalidArgument( + "tags and values not the same shape: ", tags.shape().DebugString(), + " != ", values.shape().DebugString(), SingleTag(tags))); auto Ttags = tags.flat(); auto Tvalues = values.flat(); Summary s; diff --git a/tensorflow/core/kernels/tile_functor_cpu.cc b/tensorflow/core/kernels/tile_functor_cpu.cc index b2fd669541..f814486701 100644 --- a/tensorflow/core/kernels/tile_functor_cpu.cc +++ b/tensorflow/core/kernels/tile_functor_cpu.cc @@ -15,10 +15,10 @@ limitations under the License. #define EIGEN_USE_THREADS -#include "tensorflow/core/kernels/tile_functor.h" #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/kernels/tile_functor.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/tile_ops_cpu_impl.h b/tensorflow/core/kernels/tile_ops_cpu_impl.h index 054b31ef9e..df6a666cd4 100644 --- a/tensorflow/core/kernels/tile_ops_cpu_impl.h +++ b/tensorflow/core/kernels/tile_ops_cpu_impl.h @@ -63,7 +63,7 @@ TF_CALL_int64(DEFINE_TYPE); #undef DEFINE_DIM #undef DEFINE_TYPE -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace functor } // end namespace tensorflow diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc index 38e77ab60f..07befa27bc 100644 --- a/tensorflow/core/kernels/training_ops.cc +++ b/tensorflow/core/kernels/training_ops.cc @@ -3279,7 +3279,6 @@ REGISTER_KERNELS(double, int64); #undef REGISTER_KERNELS - template class ApplyAddSignOp : public OpKernel { public: @@ -3362,17 +3361,15 @@ TF_CALL_double(REGISTER_CPU_KERNELS); #if GOOGLE_CUDA // Forward declarations of the functor specializations for GPU. namespace functor { -#define DECLARE_GPU_SPEC(T) \ - template <> \ - void ApplyAddSign::operator()( \ - const GPUDevice& d, \ - typename TTypes::Flat var, \ - typename TTypes::Flat m, \ - typename TTypes::ConstScalar lr, \ - typename TTypes::ConstScalar alpha, \ - typename TTypes::ConstScalar sign_decay, \ - typename TTypes::ConstScalar beta, \ - typename TTypes::ConstFlat grad); \ +#define DECLARE_GPU_SPEC(T) \ + template <> \ + void ApplyAddSign::operator()( \ + const GPUDevice& d, typename TTypes::Flat var, \ + typename TTypes::Flat m, typename TTypes::ConstScalar lr, \ + typename TTypes::ConstScalar alpha, \ + typename TTypes::ConstScalar sign_decay, \ + typename TTypes::ConstScalar beta, \ + typename TTypes::ConstFlat grad); \ extern template struct ApplyAddSign; DECLARE_GPU_SPEC(Eigen::half); DECLARE_GPU_SPEC(float); @@ -3387,7 +3384,6 @@ REGISTER_KERNELS(GPU, double); #undef REGISTER_CPU_KERNELS #undef REGISTER_KERNELS - template class ApplyPowerSignOp : public OpKernel { public: @@ -3470,17 +3466,15 @@ TF_CALL_double(REGISTER_CPU_KERNELS); #if GOOGLE_CUDA // Forward declarations of the functor specializations for GPU. namespace functor { -#define DECLARE_GPU_SPEC(T) \ - template <> \ - void ApplyPowerSign::operator()( \ - const GPUDevice& d, \ - typename TTypes::Flat var, \ - typename TTypes::Flat m, \ - typename TTypes::ConstScalar lr, \ - typename TTypes::ConstScalar logbase, \ - typename TTypes::ConstScalar sign_decay, \ - typename TTypes::ConstScalar beta, \ - typename TTypes::ConstFlat grad); \ +#define DECLARE_GPU_SPEC(T) \ + template <> \ + void ApplyPowerSign::operator()( \ + const GPUDevice& d, typename TTypes::Flat var, \ + typename TTypes::Flat m, typename TTypes::ConstScalar lr, \ + typename TTypes::ConstScalar logbase, \ + typename TTypes::ConstScalar sign_decay, \ + typename TTypes::ConstScalar beta, \ + typename TTypes::ConstFlat grad); \ extern template struct ApplyPowerSign; DECLARE_GPU_SPEC(Eigen::half); DECLARE_GPU_SPEC(float); diff --git a/tensorflow/core/kernels/training_ops_gpu.cu.cc b/tensorflow/core/kernels/training_ops_gpu.cu.cc index d443a6b3c1..0376a3b2c6 100644 --- a/tensorflow/core/kernels/training_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/training_ops_gpu.cu.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/training_ops.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/training_ops.h" namespace tensorflow { @@ -115,13 +115,11 @@ struct ApplyAdam { Eigen::Sizes<1> single; const auto one = static_cast(1.0); m.device(d) = - m + - (beta1.constant(one) - beta1).reshape(single).broadcast(bcast) * - (grad - m); + m + (beta1.constant(one) - beta1).reshape(single).broadcast(bcast) * + (grad - m); v.device(d) = - v + - (beta2.constant(one) - beta2).reshape(single).broadcast(bcast) * - (grad.square() - v); + v + (beta2.constant(one) - beta2).reshape(single).broadcast(bcast) * + (grad.square() - v); if (use_nesterov) { var.device(d) -= @@ -157,9 +155,9 @@ struct ApplyRMSProp { bcast[0] = grad.dimension(0); Eigen::Sizes<1> single; const auto one = static_cast(1.0); - ms.device(d) = ms + - (rho.constant(one) - rho).reshape(single).broadcast(bcast) * - (grad.square() - ms); + ms.device(d) = + ms + (rho.constant(one) - rho).reshape(single).broadcast(bcast) * + (grad.square() - ms); mom.device(d) = mom * momentum.reshape(single).broadcast(bcast) + lr.reshape(single).broadcast(bcast) * grad / @@ -212,7 +210,7 @@ struct ApplyAddSign { auto beta_bcast = beta.reshape(single).broadcast(bcast); auto one_minus_beta = (beta.constant(one) - beta).reshape(single).broadcast(bcast); - m.device(d) = m * beta_bcast + grad * one_minus_beta; + m.device(d) = m * beta_bcast + grad * one_minus_beta; // The following is the GPU equivalent of the CPU version: // var.device(d) -= lr() * (alpha() + sign_decay() * sign_gm) * grad; @@ -244,7 +242,7 @@ struct ApplyPowerSign { auto beta_bcast = beta.reshape(single).broadcast(bcast); auto one_minus_beta = (beta.constant(one) - beta).reshape(single).broadcast(bcast); - m.device(d) = m * beta_bcast + grad * one_minus_beta; + m.device(d) = m * beta_bcast + grad * one_minus_beta; // The following is the GPU equivalent of the CPU version: // auto grad_scale = (logbase() * sign_decay() * sign_gm).exp(); @@ -253,7 +251,7 @@ struct ApplyPowerSign { auto lr_bcast = lr.reshape(single).broadcast(bcast); auto logbase_bcast = logbase.reshape(single).broadcast(bcast); auto sign_decay_bcast = sign_decay.reshape(single).broadcast(bcast); - auto grad_scale = (logbase_bcast * sign_decay_bcast * sign_gm).exp(); + auto grad_scale = (logbase_bcast * sign_decay_bcast * sign_gm).exp(); var.device(d) -= lr_bcast * grad_scale * grad; } }; diff --git a/tensorflow/core/kernels/training_ops_test.cc b/tensorflow/core/kernels/training_ops_test.cc index ffa7f87c9e..2dcc4a500e 100644 --- a/tensorflow/core/kernels/training_ops_test.cc +++ b/tensorflow/core/kernels/training_ops_test.cc @@ -176,8 +176,9 @@ static void Adam(int32 n, Graph** init_g, Graph** train_g) { auto beta2 = Scalar(g, 0.99); auto epsilon = Scalar(g, 1e-8); auto grad = Random(g, n); - test::graph::Multi(g, "ApplyAdam", {var, m, v, beta1_power, beta2_power, lr, - beta1, beta2, epsilon, grad}); + test::graph::Multi( + g, "ApplyAdam", + {var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad}); *train_g = g; } } diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc index 2e0d18b634..7177ad7888 100644 --- a/tensorflow/core/kernels/transpose_op.cc +++ b/tensorflow/core/kernels/transpose_op.cc @@ -176,9 +176,10 @@ void TransposeOp::Compute(OpKernelContext* ctx) { } } for (int i = 0; i < dims; ++i) { - OP_REQUIRES(ctx, bits[i], errors::InvalidArgument( - i, " is missing from {", - str_util::Join(permutation, ","), "}.")); + OP_REQUIRES( + ctx, bits[i], + errors::InvalidArgument(i, " is missing from {", + str_util::Join(permutation, ","), "}.")); } // 0-D, 1-D, and identity transposes do nothing. diff --git a/tensorflow/core/kernels/typed_queue.h b/tensorflow/core/kernels/typed_queue.h index 0d608d9b87..43dcb4cef7 100644 --- a/tensorflow/core/kernels/typed_queue.h +++ b/tensorflow/core/kernels/typed_queue.h @@ -58,9 +58,9 @@ Status TypedQueue::Initialize() { if (!component_shapes_.empty() && component_dtypes_.size() != component_shapes_.size()) { return errors::InvalidArgument( - "Different number of component types. ", "Types: ", - DataTypeSliceString(component_dtypes_), ", Shapes: ", - ShapeListString(component_shapes_)); + "Different number of component types. ", + "Types: ", DataTypeSliceString(component_dtypes_), + ", Shapes: ", ShapeListString(component_shapes_)); } mutex_lock lock(mu_); diff --git a/tensorflow/core/kernels/unpack_op.cc b/tensorflow/core/kernels/unpack_op.cc index 397bdd5670..764b6a252a 100644 --- a/tensorflow/core/kernels/unpack_op.cc +++ b/tensorflow/core/kernels/unpack_op.cc @@ -34,7 +34,7 @@ typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class UnpackOp : public OpKernel { @@ -65,8 +65,9 @@ class UnpackOp : public OpKernel { output_shape.RemoveDim(axis); const int64 output_size = output_shape.num_elements(); OP_REQUIRES( - context, FastBoundsCheck(output_size, - std::numeric_limits::max()), + context, + FastBoundsCheck(output_size, + std::numeric_limits::max()), errors::InvalidArgument("output size must fit in Eigen DenseIndex")); // This optimization is currently not applicable for SYCL devices diff --git a/tensorflow/core/kernels/word2vec_kernels.cc b/tensorflow/core/kernels/word2vec_kernels.cc index 2d05d72bff..3477445197 100644 --- a/tensorflow/core/kernels/word2vec_kernels.cc +++ b/tensorflow/core/kernels/word2vec_kernels.cc @@ -188,9 +188,9 @@ class SkipgramOp : public OpKernel { ++corpus_size_; } if (corpus_size_ < window_size_ * 10) { - return errors::InvalidArgument("The text file ", filename, - " contains too little data: ", - corpus_size_, " words"); + return errors::InvalidArgument( + "The text file ", filename, + " contains too little data: ", corpus_size_, " words"); } typedef std::pair WordFreq; std::vector ordered; diff --git a/tensorflow/core/kernels/xent_op.cc b/tensorflow/core/kernels/xent_op.cc index 0f8d027caa..a6a71fdfaf 100644 --- a/tensorflow/core/kernels/xent_op.cc +++ b/tensorflow/core/kernels/xent_op.cc @@ -30,7 +30,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class SoftmaxXentWithLogitsOp : public OpKernel { @@ -44,8 +44,8 @@ class SoftmaxXentWithLogitsOp : public OpKernel { OP_REQUIRES(context, logits_in.IsSameSize(labels_in), errors::InvalidArgument( "logits and labels must be same size: logits_size=", - logits_in.shape().DebugString(), " labels_size=", - labels_in.shape().DebugString())); + logits_in.shape().DebugString(), + " labels_size=", labels_in.shape().DebugString())); OP_REQUIRES(context, TensorShapeUtils::IsMatrix(logits_in.shape()), errors::InvalidArgument("logits must be 2-dimensional")); // As we already tested that both inputs have the same shape no need to @@ -72,7 +72,7 @@ class SoftmaxXentWithLogitsOp : public OpKernel { functor(context->eigen_device(), logits_in.matrix(), labels_in.matrix(), scratch.matrix(), loss_out->vec(), back_out->matrix()); - } + } } }; @@ -87,7 +87,7 @@ struct XentFunctorBase { typename TTypes::Vec loss, typename TTypes::Matrix backprop) { XentEigenImpl::Compute(d, logits, labels, scratch, loss, - backprop); + backprop); } }; @@ -97,7 +97,7 @@ struct XentFunctor : XentFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template struct XentFunctor : XentFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor #define REGISTER_CPU(T) \ @@ -129,6 +129,6 @@ REGISTER_KERNEL_BUILDER(Name("SoftmaxCrossEntropyWithLogits") .Device(DEVICE_SYCL) .TypeConstraint("T"), SoftmaxXentWithLogitsOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/xsmm_conv2d_test.cc b/tensorflow/core/kernels/xsmm_conv2d_test.cc index e294701246..481f3b7ba4 100644 --- a/tensorflow/core/kernels/xsmm_conv2d_test.cc +++ b/tensorflow/core/kernels/xsmm_conv2d_test.cc @@ -13,18 +13,17 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/conv_ops.h" -#include "tensorflow/core/platform/test.h" +#include "include/libxsmm.h" +#include "tensorflow/core/framework/fake_input.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/graph/node_builder.h" +#include "tensorflow/core/kernels/conv_ops.h" #include "tensorflow/core/kernels/ops_testutil.h" -#include "include/libxsmm.h" -#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/platform/test.h" namespace tensorflow { namespace { - typedef struct { int nImg; int nIfm; @@ -49,45 +48,41 @@ typedef struct { int stride_w; } naive_conv_t; - -LIBXSMM_INLINE void naive_copy_NCHW_to_NHWC(const float* nchw, Tensor &nhwc, int N, int H, int W, int C) -{ - LIBXSMM_VLA_DECL(4, const float, input, nchw, C, H, W); +LIBXSMM_INLINE void naive_copy_NCHW_to_NHWC(const float* nchw, Tensor& nhwc, + int N, int H, int W, int C) { + LIBXSMM_VLA_DECL(4, const float, input, nchw, C, H, W); int n, h, w, c; - auto output = nhwc.flat(); - for ( n = 0; n < N; n++ ) { - for ( h = 0; h < H; h++ ) { - for ( w = 0; w < W; w++ ) { - for ( c = 0; c < C; c++ ) { - output(n*H*W*C + h*W*C +w*C + c) = - LIBXSMM_VLA_ACCESS(4, input, n, c, h, w, C, H, W); + auto output = nhwc.flat(); + for (n = 0; n < N; n++) { + for (h = 0; h < H; h++) { + for (w = 0; w < W; w++) { + for (c = 0; c < C; c++) { + output(n * H * W * C + h * W * C + w * C + c) = + LIBXSMM_VLA_ACCESS(4, input, n, c, h, w, C, H, W); } } } } } - -LIBXSMM_INLINE void naive_copy_KCRS_to_RSCK(const float* kcrs, Tensor &rsck, int R, int S, int C, int K) -{ - LIBXSMM_VLA_DECL(4, const float, input, kcrs, C, R, S); +LIBXSMM_INLINE void naive_copy_KCRS_to_RSCK(const float* kcrs, Tensor& rsck, + int R, int S, int C, int K) { + LIBXSMM_VLA_DECL(4, const float, input, kcrs, C, R, S); int r, s, c, k; - auto output = rsck.flat(); - - for ( r = 0; r < R; r++ ) { - for ( s = 0; s < S; s++ ) { - for ( c = 0; c < C; c++ ) { - for ( k = 0; k < K; k++ ) { - output(r*S*C*K + s*C*K + c*K + k) = - LIBXSMM_VLA_ACCESS(4, input, k, c, r, s, C, R, S); + auto output = rsck.flat(); + + for (r = 0; r < R; r++) { + for (s = 0; s < S; s++) { + for (c = 0; c < C; c++) { + for (k = 0; k < K; k++) { + output(r * S * C * K + s * C * K + c * K + k) = + LIBXSMM_VLA_ACCESS(4, input, k, c, r, s, C, R, S); } } } } } - - LIBXSMM_INLINE void zero_buf(float* buf, long size) { int i; for (i = 0; i < size; ++i) { @@ -95,52 +90,53 @@ LIBXSMM_INLINE void zero_buf(float* buf, long size) { } } -LIBXSMM_INLINE void copy_buf(Tensor &dst,float *src,long size) { - long i; - auto output = dst.flat(); - for (i = 0; i < size; ++i) - output(i) = src[i]; +LIBXSMM_INLINE void copy_buf(Tensor& dst, float* src, long size) { + long i; + auto output = dst.flat(); + for (i = 0; i < size; ++i) output(i) = src[i]; } -LIBXSMM_INLINE void init_buf(float* buf, long size, int initPos, int initOne) -{ +LIBXSMM_INLINE void init_buf(float* buf, long size, int initPos, int initOne) { int i; zero_buf(buf, size); for (i = 0; i < size; ++i) { - buf[i] = (float)((initOne != 0) ? 1.0 : ((initPos != 0) ? drand48() : (0.05 - drand48()/10.0))); + buf[i] = + (float)((initOne != 0) + ? 1.0 + : ((initPos != 0) ? drand48() : (0.05 - drand48() / 10.0))); } } - - -LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float* output, const float* filter) -{ - int nImg = param->nImg; - int nIfm = param->nIfm; - int nOfm = param->nOfm; - int ifhp = param->ifhp; - int ifwp = param->ifwp; - int ofhp = param->ofhp; - int ofwp = param->ofwp; - int ifh = param->ifh; - int ifw = param->ifw; - int ofh = param->ofh; - int ofw = param->ofw; - int pad_h = param->pad_h; - int pad_w = param->pad_w; - int pad_h_in = param->pad_h_in; - int pad_w_in = param->pad_w_in; +LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, + float* output, const float* filter) { + int nImg = param->nImg; + int nIfm = param->nIfm; + int nOfm = param->nOfm; + int ifhp = param->ifhp; + int ifwp = param->ifwp; + int ofhp = param->ofhp; + int ofwp = param->ofwp; + int ifh = param->ifh; + int ifw = param->ifw; + int ofh = param->ofh; + int ofw = param->ofw; + int pad_h = param->pad_h; + int pad_w = param->pad_w; + int pad_h_in = param->pad_h_in; + int pad_w_in = param->pad_w_in; int pad_h_out = param->pad_h_out; int pad_w_out = param->pad_w_out; - int kh = param->kh; - int kw = param->kw; - int stride_h = param->stride_h; - int stride_w = param->stride_w; + int kh = param->kh; + int kw = param->kw; + int stride_h = param->stride_h; + int stride_w = param->stride_w; /* loop counters */ int img, ofm, ifm, oj, oi, ij, ii, kj, ki; - LIBXSMM_VLA_DECL(4, float, output_t, output + (pad_w_out * ofwp + pad_h_out), nOfm, ofhp, ofwp); - LIBXSMM_VLA_DECL(4, const float, input_t, input + (pad_w_in * ifwp + pad_h_in), nIfm, ifhp, ifwp); + LIBXSMM_VLA_DECL(4, float, output_t, output + (pad_w_out * ofwp + pad_h_out), + nOfm, ofhp, ofwp); + LIBXSMM_VLA_DECL(4, const float, input_t, + input + (pad_w_in * ifwp + pad_h_in), nIfm, ifhp, ifwp); LIBXSMM_VLA_DECL(4, const float, filter_t, filter, nIfm, kh, kw); for (img = 0; img < nImg; ++img) { @@ -151,12 +147,15 @@ LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float for (oi = 0; oi < ofw; ++oi) { ii = oi * stride_w - pad_w; for (kj = 0; kj < kh; ++kj) { - if(ij+kj < 0 || ij+kj >= ifh) continue; + if (ij + kj < 0 || ij + kj >= ifh) continue; for (ki = 0; ki < kw; ++ki) { - if(ii+ki < 0 || ii+ki >= ifw) continue; - LIBXSMM_VLA_ACCESS( 4, output_t, img, ofm, oj, oi, nOfm, ofhp, ofwp) += - LIBXSMM_VLA_ACCESS(4, input_t, img, ifm, ij + kj, ii + ki, nIfm, ifhp, ifwp) - * LIBXSMM_VLA_ACCESS(4, filter_t, ofm, ifm, kj, ki, nIfm, kh, kw); + if (ii + ki < 0 || ii + ki >= ifw) continue; + LIBXSMM_VLA_ACCESS(4, output_t, img, ofm, oj, oi, nOfm, ofhp, + ofwp) += + LIBXSMM_VLA_ACCESS(4, input_t, img, ifm, ij + kj, ii + ki, + nIfm, ifhp, ifwp) * + LIBXSMM_VLA_ACCESS(4, filter_t, ofm, ifm, kj, ki, nIfm, kh, + kw); } } } @@ -168,134 +167,118 @@ LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float void RunXsmmVsGeneric() {} - class XsmmConv2DTest : public OpsTestBase { protected: void MakeOp(int stride) { - TF_CHECK_OK(NodeDefBuilder("xsmm", "Conv2D") - .Input(FakeInput(DT_FLOAT)) - .Input(FakeInput(DT_FLOAT)) - .Attr("strides", {1, stride,stride, 1}) - .Attr("padding", "VALID" ) - .Finalize(node_def())); - + .Input(FakeInput(DT_FLOAT)) + .Input(FakeInput(DT_FLOAT)) + .Attr("strides", {1, stride, stride, 1}) + .Attr("padding", "VALID") + .Finalize(node_def())); TF_ASSERT_OK(InitOp()); } }; TEST_F(XsmmConv2DTest, Basic) { - MakeOp(1); + MakeOp(1); - // setup scoped allocator, which uses cpu_allocator() for this scope - const libxsmm_tf_allocator tf_allocator; + // setup scoped allocator, which uses cpu_allocator() for this scope + const libxsmm_tf_allocator tf_allocator; - int ifw = 14; /* input width, "W" */ - int ifh = 14; /* input height, "H" */ - int nImg = 32; /* mini-batch size, "N" */ - int nIfm = 64; /* number of input feature maps, "C" */ - int nOfm = 64; /* number of output feature maps, "K" */ - int kh = 3; /* filter height, "R" */ - int kw = 3; /* filter width, "S" */ - int pad = 0; /* padding in output */ - int stride = 1; /* stride when accessing inputs */ + int ifw = 14; /* input width, "W" */ + int ifh = 14; /* input height, "H" */ + int nImg = 32; /* mini-batch size, "N" */ + int nIfm = 64; /* number of input feature maps, "C" */ + int nOfm = 64; /* number of output feature maps, "K" */ + int kh = 3; /* filter height, "R" */ + int kw = 3; /* filter width, "S" */ + int pad = 0; /* padding in output */ + int stride = 1; /* stride when accessing inputs */ + int stride_w = stride; + int stride_h = stride; + int pad_h = pad; + int pad_w = pad; - int stride_w = stride; - int stride_h = stride; - int pad_h = pad; - int pad_w = pad; + int pad_h_in = pad_h; + int pad_w_in = pad_w; - int pad_h_in = pad_h; - int pad_w_in = pad_w; - - int pad_h_out = 0; - int pad_w_out = 0; + int pad_h_out = 0; + int pad_w_out = 0; /* deriving some values for naive code */ - int ofh = (ifh + 2 * pad_h - kh) / stride_h + 1; - int ofw = (ifw + 2 * pad_w - kw) / stride_w + 1; - int ifhp = ifh + 2 * pad_h_in; - int ifwp = ifw + 2 * pad_w_in; - int ofhp = ofh + 2 * pad_h_out; - int ofwp = ofw + 2 * pad_w_out; - - - //Initialization of Filter and Image - - /* allocate data */ - float *naive_input = (float*)libxsmm_aligned_scratch( nImg*nIfm*ifhp*ifwp*sizeof(float), 2097152); - float *naive_output = (float*)libxsmm_aligned_scratch( nImg*nOfm*ofhp*ofwp*sizeof(float), 2097152); - float *naive_filter = (float*)libxsmm_aligned_scratch( nOfm*nIfm*kh*kw* sizeof(float), 2097152); - /* initialize data */ - init_buf(naive_input, nImg*nIfm*ifhp*ifwp, 0, 0); - zero_buf(naive_output, nImg*nOfm*ofhp*ofwp); - init_buf(naive_filter, nOfm*nIfm*kh*kw, 0, 0); - - - Tensor image(DT_FLOAT, - {nImg, ifhp, ifwp, nIfm}); - - - Tensor filter(DT_FLOAT, {kh,kw,nIfm,nOfm}); - - - naive_copy_NCHW_to_NHWC(naive_input, image, nImg, ifhp, ifwp, nIfm); - naive_copy_KCRS_to_RSCK(naive_filter, filter, kh, kw, nIfm, nOfm); - - - //Run naive convolution - - naive_conv_t naive_param; - - naive_param.nImg = nImg; - naive_param.nIfm = nIfm; - naive_param.nOfm = nOfm; - naive_param.ifhp = ifhp; - naive_param.ifwp = ifwp; - naive_param.ofhp = ofhp; - naive_param.ofwp = ofwp; - naive_param.ifh = ifh; - naive_param.ifw = ifw; - naive_param.ofh = ofh; - naive_param.ofw = ofw; - naive_param.pad_h = pad_h; - naive_param.pad_w = pad_w; - naive_param.pad_h_in = pad_h_in; - naive_param.pad_w_in = pad_w_in; - naive_param.pad_h_out = pad_h_out; - naive_param.pad_w_out = pad_w_out; - naive_param.kh = kh; - naive_param.kw = kw; - naive_param.stride_h = stride_h; - naive_param.stride_w = stride_w; - - - naive_conv_fp(&naive_param, naive_input, naive_output, naive_filter); - - - - AddInputFromArray(image.shape(), image.flat()); - AddInputFromArray(filter.shape(), filter.flat()); - - - - //Run Op (TF) - TF_ASSERT_OK(RunOpKernel()); - - // Check the output. - Tensor expected(DT_FLOAT, {nImg,ofhp,ofwp, nOfm}); - naive_copy_NCHW_to_NHWC(naive_output, expected, nImg, ofhp, ofwp, nOfm); - - - test::ExpectTensorNear(expected, *GetOutput(0), 1e-5); - libxsmm_free(naive_input); - libxsmm_free(naive_output); - libxsmm_free(naive_filter); - - - + int ofh = (ifh + 2 * pad_h - kh) / stride_h + 1; + int ofw = (ifw + 2 * pad_w - kw) / stride_w + 1; + int ifhp = ifh + 2 * pad_h_in; + int ifwp = ifw + 2 * pad_w_in; + int ofhp = ofh + 2 * pad_h_out; + int ofwp = ofw + 2 * pad_w_out; + + // Initialization of Filter and Image + + /* allocate data */ + float* naive_input = (float*)libxsmm_aligned_scratch( + nImg * nIfm * ifhp * ifwp * sizeof(float), 2097152); + float* naive_output = (float*)libxsmm_aligned_scratch( + nImg * nOfm * ofhp * ofwp * sizeof(float), 2097152); + float* naive_filter = (float*)libxsmm_aligned_scratch( + nOfm * nIfm * kh * kw * sizeof(float), 2097152); + /* initialize data */ + init_buf(naive_input, nImg * nIfm * ifhp * ifwp, 0, 0); + zero_buf(naive_output, nImg * nOfm * ofhp * ofwp); + init_buf(naive_filter, nOfm * nIfm * kh * kw, 0, 0); + + Tensor image(DT_FLOAT, {nImg, ifhp, ifwp, nIfm}); + + Tensor filter(DT_FLOAT, {kh, kw, nIfm, nOfm}); + + naive_copy_NCHW_to_NHWC(naive_input, image, nImg, ifhp, ifwp, nIfm); + naive_copy_KCRS_to_RSCK(naive_filter, filter, kh, kw, nIfm, nOfm); + + // Run naive convolution + + naive_conv_t naive_param; + + naive_param.nImg = nImg; + naive_param.nIfm = nIfm; + naive_param.nOfm = nOfm; + naive_param.ifhp = ifhp; + naive_param.ifwp = ifwp; + naive_param.ofhp = ofhp; + naive_param.ofwp = ofwp; + naive_param.ifh = ifh; + naive_param.ifw = ifw; + naive_param.ofh = ofh; + naive_param.ofw = ofw; + naive_param.pad_h = pad_h; + naive_param.pad_w = pad_w; + naive_param.pad_h_in = pad_h_in; + naive_param.pad_w_in = pad_w_in; + naive_param.pad_h_out = pad_h_out; + naive_param.pad_w_out = pad_w_out; + naive_param.kh = kh; + naive_param.kw = kw; + naive_param.stride_h = stride_h; + naive_param.stride_w = stride_w; + + naive_conv_fp(&naive_param, naive_input, naive_output, naive_filter); + + AddInputFromArray(image.shape(), image.flat()); + AddInputFromArray(filter.shape(), filter.flat()); + + // Run Op (TF) + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(DT_FLOAT, {nImg, ofhp, ofwp, nOfm}); + naive_copy_NCHW_to_NHWC(naive_output, expected, nImg, ofhp, ofwp, nOfm); + + test::ExpectTensorNear(expected, *GetOutput(0), 1e-5); + libxsmm_free(naive_input); + libxsmm_free(naive_output); + libxsmm_free(naive_filter); } /* @@ -325,7 +308,8 @@ TEST(XsmmConv2DTest, Basic) { desc.threads = num_threads; desc.algo = LIBXSMM_DNN_CONV_ALGO_DIRECT; desc.buffer_format = LIBXSMM_DNN_TENSOR_FORMAT_NHWC; - desc.filter_format = LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;//LIBXSMM_DNN_TENSOR_FORMAT_RSCK; + desc.filter_format = +LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;//LIBXSMM_DNN_TENSOR_FORMAT_RSCK; desc.fuse_ops = LIBXSMM_DNN_CONV_FUSE_NONE; desc.options = LIBXSMM_DNN_CONV_OPTION_NONE; desc.datatype = LIBXSMM_DNN_DATATYPE_F32; -- GitLab From 995378c4c9ff156cae7a365cfdc1480a3ee6d0bf Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 26 Jan 2018 12:08:47 -0800 Subject: [PATCH 140/423] Switch over to max_pool_v2 in Python (#14983) * Switch over to max_pool_v2 in Python This fix is a follow up to 11875 so that MaxPool in Python use v2 version. As 11875 has been merged some time ago, this fix conforms to the deprecation policy. This fix is realted to 11875 and 4746. Signed-off-by: Yong Tang * Update test cases in contrib/specs/python/specs_test due to MaxPool -> MaxPoolV2 Signed-off-by: Yong Tang * Update tensorflow/contrib/receptive_field Update tensorflow/contrib/receptive_field due to max_pool's strides and ksize from attr -> input Signed-off-by: Yong Tang * Remove const restriction for strides and ksize Signed-off-by: Yong Tang * Register MaxPoolV2 with XLA Signed-off-by: Yong Tang * Reformat with clang-format -i --style=Google Signed-off-by: Yong Tang --- .../compiler/tf2xla/kernels/pooling_ops.cc | 67 ++++++++++++++----- .../python/util/parse_layer_parameters.py | 58 +++++++++++----- tensorflow/contrib/specs/python/specs_test.py | 14 ++-- .../python/kernel_tests/pooling_ops_test.py | 21 +++--- tensorflow/python/ops/nn_ops.py | 12 ++-- 5 files changed, 115 insertions(+), 57 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index 0b5a38967a..d092e2e8d6 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -37,21 +37,23 @@ class PoolingOp : public XlaOpKernel { public: PoolingOp(OpKernelConstruction* ctx, int num_spatial_dims) : XlaOpKernel(ctx), num_spatial_dims_(num_spatial_dims) { - std::vector ksize_int; - std::vector stride_int; - OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_int)); - OP_REQUIRES(ctx, ksize_int.size() == num_dims(), - errors::InvalidArgument("Sliding window ksize field must " - "specify ", - num_dims(), " dimensions")); - OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_int)); - OP_REQUIRES(ctx, stride_int.size() == num_dims(), - errors::InvalidArgument("Sliding window stride field must " - "specify ", - num_dims(), " dimensions")); - for (int i = 0; i < num_dims(); ++i) { - ksize_.push_back(ksize_int[i]); - stride_.push_back(stride_int[i]); + if (ctx->num_inputs() == 1) { + std::vector ksize_int; + std::vector stride_int; + OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_int)); + OP_REQUIRES(ctx, ksize_int.size() == num_dims(), + errors::InvalidArgument("Sliding window ksize field must " + "specify ", + num_dims(), " dimensions")); + OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_int)); + OP_REQUIRES(ctx, stride_int.size() == num_dims(), + errors::InvalidArgument("Sliding window stride field must " + "specify ", + num_dims(), " dimensions")); + for (int i = 0; i < num_dims(); ++i) { + ksize_.push_back(ksize_int[i]); + stride_.push_back(stride_int[i]); + } } Padding padding; OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding)); @@ -77,6 +79,33 @@ class PoolingOp : public XlaOpKernel { xla::ComputationDataHandle input = ctx->Input(0); const TensorShape input_shape = ctx->InputShape(0); + std::vector ksize = ksize_; + std::vector stride = stride_; + if (ctx->num_inputs() != 1) { + const TensorShape ksize_shape = ctx->InputShape(1); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(ksize_shape), + errors::InvalidArgument("ksize must be a vector, not shape ", + ksize_shape.DebugString())); + OP_REQUIRES(ctx, ksize_shape.num_elements() == num_dims(), + errors::InvalidArgument("Sliding window ksize field must " + "specify ", + num_dims(), " dimensions")); + ksize.clear(); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(1, &ksize)); + + const TensorShape stride_shape = ctx->InputShape(2); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(stride_shape), + errors::InvalidArgument("stride must be a vector, not shape ", + stride_shape.DebugString())); + OP_REQUIRES(ctx, stride_shape.num_elements() == num_dims(), + errors::InvalidArgument("Sliding window stride field must " + "specify ", + num_dims(), " dimensions")); + stride.clear(); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(2, &stride)); + } OP_REQUIRES(ctx, input_shape.dims() == num_dims(), errors::InvalidArgument("Input to ", type_string(), " operator must have ", num_dims(), @@ -84,8 +113,8 @@ class PoolingOp : public XlaOpKernel { const DataType type = input_type(0); xla::ComputationDataHandle pooled = ctx->builder()->ReduceWindow( - input, InitValue(ctx->builder(), type), *Reduction(ctx, type), ksize_, - stride_, padding_); + input, InitValue(ctx->builder(), type), *Reduction(ctx, type), ksize, + stride, padding_); ctx->SetOutput(0, PostProcessOutput(ctx, pooled, type, input_shape)); } @@ -130,6 +159,10 @@ class MaxPool2DOp : public MaxPoolOp { } }; REGISTER_XLA_OP(Name("MaxPool"), MaxPool2DOp); +REGISTER_XLA_OP(Name("MaxPoolV2") + .CompileTimeConstInput("ksize") + .CompileTimeConstInput("strides"), + MaxPool2DOp); class MaxPool3DOp : public MaxPoolOp { public: diff --git a/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py b/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py index 44998b3b65..69188a461b 100644 --- a/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py +++ b/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py @@ -35,7 +35,7 @@ _VALID_PADDING = ["VALID", b"VALID"] _SAME_PADDING = ["SAME", b"SAME"] -def _stride_size(node): +def _stride_size(node, name_to_node): """Computes stride size given a TF node. Args: @@ -45,10 +45,20 @@ def _stride_size(node): stride_x: Stride size for horizontal direction (integer). stride_y: Stride size for vertical direction (integer). """ - strides_attr = node.attr["strides"] - logging.vlog(4, "strides_attr = %s", strides_attr) - stride_y = strides_attr.list.i[1] - stride_x = strides_attr.list.i[2] + if node.op == "MaxPoolV2": + strides_input_name = node.input[2] + if not strides_input_name.endswith("/strides"): + raise ValueError("Strides name does not end with '/strides'") + strides_node = name_to_node[strides_input_name] + value = strides_node.attr["value"] + t = make_ndarray(value.tensor) + stride_y = t[1] + stride_x = t[2] + else: + strides_attr = node.attr["strides"] + logging.vlog(4, "strides_attr = %s", strides_attr) + stride_y = strides_attr.list.i[1] + stride_x = strides_attr.list.i[2] return stride_x, stride_y @@ -144,7 +154,7 @@ def _padding_size_conv_pool(node, kernel_size, stride, input_resolution=None): return total_padding, padding -def _pool_kernel_size(node): +def _pool_kernel_size(node, name_to_node): """Computes kernel size given a TF pooling node. Args: @@ -157,13 +167,27 @@ def _pool_kernel_size(node): Raises: ValueError: If pooling is invalid. """ - ksize = node.attr["ksize"] - kernel_size_y = ksize.list.i[1] - kernel_size_x = ksize.list.i[2] - if ksize.list.i[0] != 1: - raise ValueError("pool ksize for first dim is not 1") - if ksize.list.i[3] != 1: - raise ValueError("pool ksize for last dim is not 1") + if node.op == "MaxPoolV2": + ksize_input_name = node.input[1] + if not ksize_input_name.endswith("/ksize"): + raise ValueError("Kernel size name does not end with '/ksize'") + ksize_node = name_to_node[ksize_input_name] + value = ksize_node.attr["value"] + t = make_ndarray(value.tensor) + kernel_size_y = t[1] + kernel_size_x = t[2] + if t[0] != 1: + raise ValueError("pool ksize for first dim is not 1") + if t[3] != 1: + raise ValueError("pool ksize for last dim is not 1") + else: + ksize = node.attr["ksize"] + kernel_size_y = ksize.list.i[1] + kernel_size_x = ksize.list.i[2] + if ksize.list.i[0] != 1: + raise ValueError("pool ksize for first dim is not 1") + if ksize.list.i[3] != 1: + raise ValueError("pool ksize for last dim is not 1") return kernel_size_x, kernel_size_y @@ -243,7 +267,7 @@ def get_layer_params(node, name_to_node, input_resolution=None, force=False): logging.vlog(3, "node.op = %s", node.op) logging.vlog(4, "node = %s", node) if node.op == "Conv2D" or node.op == "DepthwiseConv2dNative": - stride_x, stride_y = _stride_size(node) + stride_x, stride_y = _stride_size(node, name_to_node) kernel_size_x, kernel_size_y = _conv_kernel_size(node, name_to_node) # Compute the padding for this node separately for each direction. total_padding_x, padding_x = _padding_size_conv_pool( @@ -260,9 +284,9 @@ def get_layer_params(node, name_to_node, input_resolution=None, force=False): stride_y = 1 total_padding_x, padding_x, total_padding_y, padding_y = ( _padding_size_pad_layer(node, name_to_node)) - elif node.op == "MaxPool" or node.op == "AvgPool": - stride_x, stride_y = _stride_size(node) - kernel_size_x, kernel_size_y = _pool_kernel_size(node) + elif node.op == "MaxPool" or node.op == "MaxPoolV2" or node.op == "AvgPool": + stride_x, stride_y = _stride_size(node, name_to_node) + kernel_size_x, kernel_size_y = _pool_kernel_size(node, name_to_node) # Compute the padding for this node separately for each direction. total_padding_x, padding_x = _padding_size_conv_pool( node, kernel_size_x, stride_x, input_resolution[1] diff --git a/tensorflow/contrib/specs/python/specs_test.py b/tensorflow/contrib/specs/python/specs_test.py index 41782a9fc9..d5f61d1b69 100644 --- a/tensorflow/contrib/specs/python/specs_test.py +++ b/tensorflow/contrib/specs/python/specs_test.py @@ -87,7 +87,7 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (1, 8, 8, 5)) self.assertEqual( summaries.tf_spec_structure(spec, inputs), - "_ maxpool maxpool maxpool") + "_ _ _ maxpoolv2 _ _ maxpoolv2 _ _ maxpoolv2") def testAbbrevPower(self): with self.test_session(): @@ -100,10 +100,10 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (1, 8, 8, 5)) self.assertEqual( summaries.tf_spec_structure(spec, inputs), - "_ variablev2 conv variablev2 biasadd relu maxpool" + "_ variablev2 conv variablev2 biasadd relu _ _ maxpoolv2" " variablev2 conv variablev2" - " biasadd relu maxpool variablev2 conv variablev2" - " biasadd relu maxpool") + " biasadd relu _ _ maxpoolv2 variablev2 conv variablev2" + " biasadd relu _ _ maxpoolv2") def testAbbrevPower2(self): with self.test_session(): @@ -117,10 +117,10 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (1, 8, 8, 5)) self.assertEqual( summaries.tf_spec_structure(spec, inputs), - "_ variablev2 conv variablev2 biasadd relu maxpool" + "_ variablev2 conv variablev2 biasadd relu _ _ maxpoolv2" " variablev2 conv variablev2 biasadd relu" - " maxpool variablev2 conv variablev2 biasadd relu" - " maxpool") + " _ _ maxpoolv2 variablev2 conv variablev2 biasadd relu" + " _ _ maxpoolv2") def testConc(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index 3263ed1a60..4466beeec9 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -1811,16 +1811,17 @@ class PoolingTest(test.TestCase): if test.is_gpu_available(): pool_funcs.append(nn_ops.max_pool_with_argmax) for pool_func in pool_funcs: - # Illegal strides. - with self.assertRaisesRegexp( - errors_impl.UnimplementedError, - "Pooling is not yet supported on the batch"): - sess.run( - pool_func( - array_ops.placeholder(dtypes.float32), - ksize=[1, 1, 1, 1], - strides=[2, 1, 1, 1], - padding="SAME")) + if pool_func != nn_ops.max_pool: + # Illegal strides. + with self.assertRaisesRegexp( + errors_impl.UnimplementedError, + "Pooling is not yet supported on the batch"): + sess.run( + pool_func( + array_ops.placeholder(dtypes.float32), + ksize=[1, 1, 1, 1], + strides=[2, 1, 1, 1], + padding="SAME")) # Filter too large. with self.assertRaisesRegexp(ValueError, "Negative dimension size"): diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 32b14f86b5..644bb3af8a 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -2070,12 +2070,12 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._max_pool(value, - ksize=ksize, - strides=strides, - padding=padding, - data_format=data_format, - name=name) + return gen_nn_ops._max_pool_v2(value, + ksize=ksize, + strides=strides, + padding=padding, + data_format=data_format, + name=name) @ops.RegisterStatistics("Conv2D", "flops") -- GitLab From 4bfaa1135a52e45b592ea60a7691ee0b812e5220 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 12:12:26 -0800 Subject: [PATCH 141/423] Fix the documentation for the dense layer for how rank > 2 inputs are handled. PiperOrigin-RevId: 183425868 --- tensorflow/python/layers/core.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py index e5b93a54f7..7bf62d45b8 100644 --- a/tensorflow/python/layers/core.py +++ b/tensorflow/python/layers/core.py @@ -49,9 +49,6 @@ class Dense(base.Layer): and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). - Note: if the input to the layer has a rank greater than 2, then it is - flattened prior to the initial matrix multiply by `kernel`. - Arguments: units: Integer or Long, dimensionality of the output space. activation: Activation function (callable). Set it to None to maintain a @@ -199,9 +196,6 @@ def dense( and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). - Note: if the `inputs` tensor has a rank greater than 2, then it is - flattened prior to the initial matrix multiply by `kernel`. - Arguments: inputs: Tensor input. units: Integer or Long, dimensionality of the output space. @@ -230,7 +224,8 @@ def dense( by the same name. Returns: - Output tensor. + Output tensor the same shape as `inputs` except the last dimension is of + size `units`. Raises: ValueError: if eager execution is enabled. -- GitLab From 095b1f13c64ddc815b7b47291adcd9553496fca6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 12:40:01 -0800 Subject: [PATCH 142/423] Cleanup: Ran clang-format on all *.{cc,h} in tensorflow/core/ops. PiperOrigin-RevId: 183429339 --- tensorflow/core/ops/array_ops.cc | 17 +++++++++-------- tensorflow/core/ops/array_ops_test.cc | 9 +++++++-- .../core/ops/candidate_sampling_ops_test.cc | 9 ++++++--- tensorflow/core/ops/functional_grad.cc | 2 +- tensorflow/core/ops/math_ops.cc | 8 ++++---- tensorflow/core/ops/nn_ops.cc | 12 ++++++------ tensorflow/core/ops/sdca_ops.cc | 2 +- tensorflow/core/ops/string_ops.cc | 6 +++--- tensorflow/core/ops/training_ops_test.cc | 2 +- 9 files changed, 38 insertions(+), 29 deletions(-) diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index 279a5876f9..fb9e8ad50c 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -977,8 +977,8 @@ REGISTER_OP("GatherNd") if (c->Value(r_dim) > c->Rank(params)) { return errors::InvalidArgument( "indices.shape[-1] must be <= params.rank, but saw indices shape: ", - c->DebugString(indices), " and params shape: ", - c->DebugString(params)); + c->DebugString(indices), + " and params shape: ", c->DebugString(params)); } // Remove r_dim from indices to get output. @@ -1252,12 +1252,12 @@ REGISTER_OP("ReverseSequence") // Validate batch_dim and seq_dim against input. const int32 input_rank = c->Rank(input); if (batch_dim >= input_rank) { - return errors::InvalidArgument("batch_dim must be < input rank: ", - batch_dim, " vs. ", input_rank); + return errors::InvalidArgument( + "batch_dim must be < input rank: ", batch_dim, " vs. ", input_rank); } if (seq_dim >= input_rank) { - return errors::InvalidArgument("seq_dim must be < input rank: ", - seq_dim, " vs. ", input_rank); + return errors::InvalidArgument( + "seq_dim must be < input rank: ", seq_dim, " vs. ", input_rank); } DimensionHandle batch_dim_dim = c->Dim(input, batch_dim); @@ -2638,8 +2638,9 @@ Status ScatterNdShape(InferenceContext* c) { Status s = c->Merge(prefix_indices, prefix_updates, &unused); if (!s.ok()) { return errors::InvalidArgument( - "The outer ", outer_dims, " dimensions of indices.shape=", - c->DebugString(indices_shape), " must match the outer ", outer_dims, + "The outer ", outer_dims, + " dimensions of indices.shape=", c->DebugString(indices_shape), + " must match the outer ", outer_dims, " dimensions of updates.shape=", c->DebugString(updates_shape), ": ", s.error_message()); } diff --git a/tensorflow/core/ops/array_ops_test.cc b/tensorflow/core/ops/array_ops_test.cc index a182fd1c47..86d64635f4 100644 --- a/tensorflow/core/ops/array_ops_test.cc +++ b/tensorflow/core/ops/array_ops_test.cc @@ -142,8 +142,13 @@ TEST(ArrayOpsTest, Const_ShapeFn) { TEST(ArrayOpsTest, UnchangedShapes_ShapeFn) { for (const char* op_name : { - "CheckNumerics", "Identity", "RefIdentity", "QuantizeAndDequantize", - "StopGradient", "ZerosLike", "OnesLike", + "CheckNumerics", + "Identity", + "RefIdentity", + "QuantizeAndDequantize", + "StopGradient", + "ZerosLike", + "OnesLike", }) { ShapeInferenceTestOp op(op_name); INFER_OK(op, "?", "in0"); diff --git a/tensorflow/core/ops/candidate_sampling_ops_test.cc b/tensorflow/core/ops/candidate_sampling_ops_test.cc index c79b443914..f367371604 100644 --- a/tensorflow/core/ops/candidate_sampling_ops_test.cc +++ b/tensorflow/core/ops/candidate_sampling_ops_test.cc @@ -23,9 +23,12 @@ namespace tensorflow { TEST(CandidateSamplerOpsTest, CandidateSampler_ShapeFn) { for (const char* op_name : { - "AllCandidateSampler", "FixedUnigramCandidateSampler", - "LearnedUnigramCandidateSampler", "LogUniformCandidateSampler", - "ThreadUnsafeUnigramCandidateSampler", "UniformCandidateSampler", + "AllCandidateSampler", + "FixedUnigramCandidateSampler", + "LearnedUnigramCandidateSampler", + "LogUniformCandidateSampler", + "ThreadUnsafeUnigramCandidateSampler", + "UniformCandidateSampler", }) { ShapeInferenceTestOp op(op_name); TF_ASSERT_OK(NodeDefBuilder("test", op.name) diff --git a/tensorflow/core/ops/functional_grad.cc b/tensorflow/core/ops/functional_grad.cc index 6df3536795..eeccb72da6 100644 --- a/tensorflow/core/ops/functional_grad.cc +++ b/tensorflow/core/ops/functional_grad.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/framework/function.h" #include +#include "tensorflow/core/framework/function.h" #include "tensorflow/core/lib/core/errors.h" namespace tensorflow { diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index dd484c3ee7..872ebe98c1 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -1172,12 +1172,12 @@ Status RangeSize(const Tensor* start_t, const Tensor* limit_t, T limit = limit_t->scalar()(); T delta = delta_t->scalar()(); if (start > limit && delta > 0) { - return errors::InvalidArgument("Requires start <= limit when delta > 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start <= limit when delta > 0: ", start, "/", limit); } if (start < limit && delta < 0) { - return errors::InvalidArgument("Requires start >= limit when delta < 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start >= limit when delta < 0: ", start, "/", limit); } if (delta == 0) { return errors::InvalidArgument("Requires delta != 0"); diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index 3f72b41569..62661fe4bd 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1155,9 +1155,9 @@ Status TopKShapeFn(InferenceContext* c) { DimensionHandle last_dim = c->Dim(input, -1); if (c->ValueKnown(last_dim) && c->ValueKnown(k_dim) && c->Value(last_dim) < c->Value(k_dim)) { - return errors::InvalidArgument("input must have last dimension >= k = ", - c->Value(k_dim), " but is ", - c->Value(last_dim)); + return errors::InvalidArgument( + "input must have last dimension >= k = ", c->Value(k_dim), " but is ", + c->Value(last_dim)); } // Replace last_dim with k_dim. @@ -1211,9 +1211,9 @@ REGISTER_OP("NthElement") DimensionHandle last_dim = c->Dim(input, -1); if (c->ValueKnown(last_dim) && c->ValueKnown(n_dim) && c->Value(last_dim) <= c->Value(n_dim)) { - return errors::InvalidArgument("Input must have last dimension > n = ", - c->Value(n_dim), " but is ", - c->Value(last_dim)); + return errors::InvalidArgument( + "Input must have last dimension > n = ", c->Value(n_dim), + " but is ", c->Value(last_dim)); } // Reduce last_dim for output tensor diff --git a/tensorflow/core/ops/sdca_ops.cc b/tensorflow/core/ops/sdca_ops.cc index e67d95fa8c..4025070adb 100644 --- a/tensorflow/core/ops/sdca_ops.cc +++ b/tensorflow/core/ops/sdca_ops.cc @@ -19,8 +19,8 @@ limitations under the License. namespace tensorflow { -using shape_inference::ShapeHandle; using shape_inference::InferenceContext; +using shape_inference::ShapeHandle; // -------------------------------------------------------------------------- static Status ApplySdcaOptimizerShapeFn(InferenceContext* c) { diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc index 8beb28de0a..e4c5bcfb54 100644 --- a/tensorflow/core/ops/string_ops.cc +++ b/tensorflow/core/ops/string_ops.cc @@ -137,9 +137,9 @@ REGISTER_OP("Substr") DimensionHandle pos_dim = c->Dim(pos_shape, i); DimensionHandle len_dim = c->Dim(len_shape, i); if (c->Value(pos_dim) != c->Value(len_dim)) { - return errors::InvalidArgument("pos and len shapes must match: ", - c->DebugString(pos_shape), " vs. ", - c->DebugString(len_shape)); + return errors::InvalidArgument( + "pos and len shapes must match: ", c->DebugString(pos_shape), + " vs. ", c->DebugString(len_shape)); } } // c->input(0) is the ShapeHandle to input strings diff --git a/tensorflow/core/ops/training_ops_test.cc b/tensorflow/core/ops/training_ops_test.cc index de4e3cd9e7..0f309c1f4e 100644 --- a/tensorflow/core/ops/training_ops_test.cc +++ b/tensorflow/core/ops/training_ops_test.cc @@ -24,7 +24,7 @@ static void TestGradAndIndicesErrorHandling(const ShapeInferenceTestOp& op, string shape_spec_middle, const string& shape_spec_end = "") { auto shape_spec = [&shape_spec_middle, shape_spec_end]( - const char* var_spec, const char* grad_indices_spec) { + const char* var_spec, const char* grad_indices_spec) { return strings::StrCat(var_spec, ";", shape_spec_middle, ";", grad_indices_spec, shape_spec_end); }; -- GitLab From 16a2a9f6bb9bf4421119b591fa56d58ee95c9a0e Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 26 Jan 2018 12:45:35 -0800 Subject: [PATCH 143/423] Add KafkaReader for processing streaming data with Apache Kafka (#14098) * Add KafkaReader for processing streaming data with Apache Kafka Apache Kafka is a widely used distributed streaming platform in open source community. The goal of this fix is to create a contrib Reader ops (inherits ReaderBase and is similiar to TextLineReader/TFRecordReader) so that it is possible to reader Kafka streaming data from TensorFlow in a similiar fashion. This fix uses a C/C++ Apache Kafka client library librdkafka which is released under the 2-clause BSD license, and is widely used in a number of Kafka bindings such as Go, Python, C#/.Net, etc. Signed-off-by: Yong Tang * Add KafkaReader Python wrapper. Signed-off-by: Yong Tang * Add BUILD file and op registration for KafkaReader. Signed-off-by: Yong Tang * Add C++ Kernel for KafkaReader Signed-off-by: Yong Tang * Add librdkafka to third_party packages in Bazel Signed-off-by: Yong Tang * Add contrib/kafka to part of the contrib bazel file. Signed-off-by: Yong Tang * Update workspace.bzl Signed-off-by: Yong Tang * Comment out clean_deps of `tensorflow/core:framework` and `tensorflow/core:lib` so that it is possible to build with ReaderBase. See 1419 for details. Signed-off-by: Yong Tang * Add group id flag. Signed-off-by: Yong Tang * Sync offset Signed-off-by: Yong Tang * Add test cases and scipt to start and stop Kafka server (with docker) Signed-off-by: Yong Tang * Convert to KafkaConsumer from the legacy Consumer with librdkafka so that thread join does not hang. Signed-off-by: Yong Tang * Only output offset as the key. Signed-off-by: Yong Tang * Add timeout attr so that Kafka Consumer could use Signed-off-by: Yong Tang * Build Kafka kernels by default, so that to get around the linkage issue. Signed-off-by: Yong Tang * Convert KafkaReader to KafkaDataset. Signed-off-by: Yong Tang * Fix workspace.bzl for kafka with tf_http_archive Signed-off-by: Yong Tang * Add public visibility Signed-off-by: Yong Tang * Address review feedbacks Signed-off-by: Yong Tang * Optionally select Kafka support through ./configure Signed-off-by: Yong Tang --- configure.py | 3 + tensorflow/BUILD | 6 + tensorflow/contrib/BUILD | 2 + tensorflow/contrib/kafka/BUILD | 104 ++++++ tensorflow/contrib/kafka/__init__.py | 32 ++ .../kafka/kernels/kafka_dataset_ops.cc | 321 ++++++++++++++++++ tensorflow/contrib/kafka/ops/kafka_ops.cc | 44 +++ .../kafka/python/kernel_tests/kafka_test.py | 117 +++++++ .../kafka/python/kernel_tests/kafka_test.sh | 34 ++ .../kafka/python/ops/kafka_dataset_ops.py | 72 ++++ .../core/platform/default/build_config.bzl | 6 + tensorflow/workspace.bzl | 12 + third_party/kafka/BUILD | 147 ++++++++ third_party/kafka/config.patch | 44 +++ 14 files changed, 944 insertions(+) create mode 100644 tensorflow/contrib/kafka/BUILD create mode 100644 tensorflow/contrib/kafka/__init__.py create mode 100644 tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc create mode 100644 tensorflow/contrib/kafka/ops/kafka_ops.cc create mode 100644 tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py create mode 100644 tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh create mode 100644 tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py create mode 100644 third_party/kafka/BUILD create mode 100644 third_party/kafka/config.patch diff --git a/configure.py b/configure.py index 083fed1710..16763b8c0d 100644 --- a/configure.py +++ b/configure.py @@ -1354,6 +1354,7 @@ def main(): environ_cp['TF_NEED_GCP'] = '0' environ_cp['TF_NEED_HDFS'] = '0' environ_cp['TF_NEED_JEMALLOC'] = '0' + environ_cp['TF_NEED_KAFKA'] = '0' environ_cp['TF_NEED_OPENCL_SYCL'] = '0' environ_cp['TF_NEED_COMPUTECPP'] = '0' environ_cp['TF_NEED_OPENCL'] = '0' @@ -1372,6 +1373,8 @@ def main(): 'with_hdfs_support', True, 'hdfs') set_build_var(environ_cp, 'TF_NEED_S3', 'Amazon S3 File System', 'with_s3_support', True, 's3') + set_build_var(environ_cp, 'TF_NEED_KAFKA', 'Apache Kafka Platform', + 'with_kafka_support', False, 'kafka') set_build_var(environ_cp, 'TF_ENABLE_XLA', 'XLA JIT', 'with_xla_support', False, 'xla') set_build_var(environ_cp, 'TF_NEED_GDR', 'GDR', 'with_gdr_support', diff --git a/tensorflow/BUILD b/tensorflow/BUILD index b26c525525..9e69613c79 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -211,6 +211,12 @@ config_setting( visibility = ["//visibility:public"], ) +config_setting( + name = "with_kafka_support", + define_values = {"with_kafka_support": "true"}, + visibility = ["//visibility:public"], +) + # Crosses between platforms and file system libraries not supported on those # platforms due to limitations in nested select() statements. config_setting( diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index f1e54432fa..5ac5955626 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -48,6 +48,7 @@ py_library( "//tensorflow/contrib/image:single_image_random_dot_stereograms_py", "//tensorflow/contrib/input_pipeline:input_pipeline_py", "//tensorflow/contrib/integrate:integrate_py", + "//tensorflow/contrib/kafka", "//tensorflow/contrib/keras", "//tensorflow/contrib/kernel_methods", "//tensorflow/contrib/kfac", @@ -139,6 +140,7 @@ cc_library( "//tensorflow/contrib/factorization:all_ops", "//tensorflow/contrib/framework:all_ops", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_op_lib", + "//tensorflow/contrib/kafka:kafka_ops_op_lib", "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib", "//tensorflow/contrib/nccl:nccl_ops_op_lib", "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_ops_op_lib", diff --git a/tensorflow/contrib/kafka/BUILD b/tensorflow/contrib/kafka/BUILD new file mode 100644 index 0000000000..f7593aa462 --- /dev/null +++ b/tensorflow/contrib/kafka/BUILD @@ -0,0 +1,104 @@ +package( + default_visibility = ["//visibility:private"], +) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs") +load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py") +load("//tensorflow:tensorflow.bzl", "tf_kernel_library") +load("//tensorflow:tensorflow.bzl", "tf_py_test") + +tf_kernel_library( + name = "kafka_kernels", + srcs = ["kernels/kafka_dataset_ops.cc"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core/kernels:bounds_check_lib", + "//tensorflow/core/kernels:dataset", + "//third_party/eigen3", + "@kafka//:kafka", + ], +) + +tf_gen_op_libs( + op_lib_names = ["kafka_ops"], + deps = [ + "//tensorflow/core:lib", + ], +) + +tf_gen_op_wrapper_py( + name = "gen_kafka_ops", + out = "python/ops/gen_kafka_ops.py", + require_shape_functions = True, + deps = [":kafka_ops_op_lib"], +) + +py_library( + name = "kafka", + srcs = [ + "__init__.py", + "python/ops/kafka_dataset_ops.py", + ], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":gen_kafka_ops", + "//tensorflow/contrib/util:util_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:framework", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform", + "//tensorflow/python:state_ops", + "//tensorflow/python:training", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/ops:iterator_ops", + "//tensorflow/python/data/ops:readers", + ], +) + +# The Kafka server has to be setup before running the test. +# The Kafka server is setup through Docker so the Docker engine +# has to be installed. +# +# Once the Docker engine is ready: +# To setup the Kafka server: +# $ bash tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh start kafka +# +# After the test is complete: +# To team down the Kafka server: +# $ bash tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh stop kafka +tf_py_test( + name = "kafka_test", + srcs = ["python/kernel_tests/kafka_test.py"], + additional_deps = [ + ":kafka", + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + ], + tags = [ + "manual", + ], +) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/contrib/kafka/__init__.py b/tensorflow/contrib/kafka/__init__.py new file mode 100644 index 0000000000..4d755c4056 --- /dev/null +++ b/tensorflow/contrib/kafka/__init__.py @@ -0,0 +1,32 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Kafka Dataset. + +@@KafkaDataset +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.kafka.python.ops.kafka_dataset_ops import KafkaDataset + +from tensorflow.python.util.all_util import remove_undocumented + +_allowed_symbols = [ + "KafkaDataset", +] + +remove_undocumented(__name__) diff --git a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc new file mode 100644 index 0000000000..88ef5f3571 --- /dev/null +++ b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc @@ -0,0 +1,321 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/kernels/dataset.h" + +#include "tensorflow/core/framework/tensor.h" + +#include "src-cpp/rdkafkacpp.h" + +namespace tensorflow { + +class KafkaDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + const Tensor* topics_tensor; + OP_REQUIRES_OK(ctx, ctx->input("topics", &topics_tensor)); + OP_REQUIRES( + ctx, topics_tensor->dims() <= 1, + errors::InvalidArgument("`topics` must be a scalar or a vector.")); + + std::vector topics; + topics.reserve(topics_tensor->NumElements()); + for (int i = 0; i < topics_tensor->NumElements(); ++i) { + topics.push_back(topics_tensor->flat()(i)); + } + + std::string servers = ""; + OP_REQUIRES_OK(ctx, + ParseScalarArgument(ctx, "servers", &servers)); + std::string group = ""; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "group", &group)); + bool eof = false; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "eof", &eof)); + int64 timeout = -1; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "timeout", &timeout)); + OP_REQUIRES(ctx, (timeout > 0), + errors::InvalidArgument( + "Timeout value should be large than 0, got ", timeout)); + *output = new Dataset(ctx, std::move(topics), servers, group, eof, timeout); + } + + private: + class Dataset : public GraphDatasetBase { + public: + Dataset(OpKernelContext* ctx, std::vector topics, + const string& servers, const string& group, const bool eof, + const int64 timeout) + : GraphDatasetBase(ctx), + topics_(std::move(topics)), + servers_(servers), + group_(group), + eof_(eof), + timeout_(timeout) {} + + std::unique_ptr MakeIterator( + const string& prefix) const override { + return std::unique_ptr( + new Iterator({this, strings::StrCat(prefix, "::Kafka")})); + } + + const DataTypeVector& output_dtypes() const override { + static DataTypeVector* dtypes = new DataTypeVector({DT_STRING}); + return *dtypes; + } + + const std::vector& output_shapes() const override { + static std::vector* shapes = + new std::vector({{}}); + return *shapes; + } + + string DebugString() override { return "KafkaDatasetOp::Dataset"; } + + protected: + Status AsGraphDefInternal(DatasetGraphDefBuilder* b, + Node** output) const override { + Node* topics = nullptr; + TF_RETURN_IF_ERROR(b->AddVector(topics_, &topics)); + Node* servers = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(servers_, &servers)); + Node* group = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(group_, &group)); + Node* eof = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(eof_, &eof)); + Node* timeout = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(timeout_, &timeout)); + TF_RETURN_IF_ERROR( + b->AddDataset(this, {topics, servers, group, eof, timeout}, output)); + return Status::OK(); + } + + private: + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params) {} + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + do { + // We are currently processing a topic, so try to read the next line. + if (consumer_.get()) { + while (true) { + if (limit_ >= 0 && + (topic_partition_->offset() >= limit_ || offset_ >= limit_)) { + // EOF current topic + break; + } + std::unique_ptr message( + consumer_->consume(dataset()->timeout_)); + if (message->err() == RdKafka::ERR_NO_ERROR) { + // Produce the line as output. + Tensor line_tensor(cpu_allocator(), DT_STRING, {}); + line_tensor.scalar()() = + std::string(static_cast(message->payload()), + message->len()); + out_tensors->emplace_back(std::move(line_tensor)); + *end_of_sequence = false; + // Sync offset + offset_ = message->offset(); + return Status::OK(); + } + + if (message->err() == RdKafka::ERR__PARTITION_EOF && + dataset()->eof_) { + // EOF current topic + break; + } + if (message->err() != RdKafka::ERR__TIMED_OUT) { + return errors::Internal("Failed to consume:", + message->errstr()); + } + message.reset(nullptr); + consumer_->poll(0); + } + + // We have reached the end of the current topic, so maybe + // move on to next topic. + ResetStreamsLocked(); + ++current_topic_index_; + } + + // Iteration ends when there are no more topic to process. + if (current_topic_index_ == dataset()->topics_.size()) { + *end_of_sequence = true; + return Status::OK(); + } + + TF_RETURN_IF_ERROR(SetupStreamsLocked(ctx->env())); + } while (true); + } + + protected: + Status SaveInternal(IteratorStateWriter* writer) override { + mutex_lock l(mu_); + TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("current_topic_index"), + current_topic_index_)); + + // `consumer_` is empty if + // 1. GetNext has not been called even once. + // 2. All topics have been read and iterator has been exhausted. + if (consumer_.get()) { + TF_RETURN_IF_ERROR( + writer->WriteScalar(full_name("current_pos"), offset_)); + } + return Status::OK(); + } + + Status RestoreInternal(IteratorContext* ctx, + IteratorStateReader* reader) override { + mutex_lock l(mu_); + ResetStreamsLocked(); + int64 current_topic_index; + TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("current_topic_index"), + ¤t_topic_index)); + current_topic_index_ = size_t(current_topic_index); + // The key "current_pos" is written only if the iterator was saved + // with an open topic. + if (reader->Contains(full_name("current_pos"))) { + int64 current_pos; + TF_RETURN_IF_ERROR( + reader->ReadScalar(full_name("current_pos"), ¤t_pos)); + + TF_RETURN_IF_ERROR(SetupStreamsLocked(ctx->env())); + topic_partition_->set_offset(current_pos); + if (topic_partition_->offset() != current_pos) { + return errors::Internal("Failed to restore to offset ", + current_pos); + } + offset_ = current_pos; + } + return Status::OK(); + } + + private: + // Sets up Kafka streams to read from the topic at + // `current_topic_index_`. + Status SetupStreamsLocked(Env* env) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + if (current_topic_index_ >= dataset()->topics_.size()) { + return errors::InvalidArgument( + "current_topic_index_:", current_topic_index_, + " >= topics_.size():", dataset()->topics_.size()); + } + + // Actually move on to next topic. + string entry = dataset()->topics_[current_topic_index_]; + + std::vector parts = str_util::Split(entry, ":"); + if (parts.size() < 1) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + string topic = parts[0]; + int32 partition = 0; + if (parts.size() > 1) { + if (!strings::safe_strto32(parts[1], &partition)) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + } + int64 offset = 0; + if (parts.size() > 2) { + if (!strings::safe_strto64(parts[2], &offset)) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + } + + topic_partition_.reset( + RdKafka::TopicPartition::create(topic, partition, offset)); + + offset_ = topic_partition_->offset(); + limit_ = -1; + if (parts.size() > 3) { + if (!strings::safe_strto64(parts[3], &limit_)) { + return errors::InvalidArgument("Invalid parameters: ", entry); + } + } + + std::unique_ptr conf( + RdKafka::Conf::create(RdKafka::Conf::CONF_GLOBAL)); + std::unique_ptr topic_conf( + RdKafka::Conf::create(RdKafka::Conf::CONF_TOPIC)); + + std::string errstr; + + RdKafka::Conf::ConfResult result = + conf->set("default_topic_conf", topic_conf.get(), errstr); + if (result != RdKafka::Conf::CONF_OK) { + return errors::Internal("Failed to set default_topic_conf:", errstr); + } + + result = conf->set("bootstrap.servers", dataset()->servers_, errstr); + if (result != RdKafka::Conf::CONF_OK) { + return errors::Internal("Failed to set bootstrap.servers ", + dataset()->servers_, ":", errstr); + } + result = conf->set("group.id", dataset()->group_, errstr); + if (result != RdKafka::Conf::CONF_OK) { + return errors::Internal("Failed to set group.id ", dataset()->group_, + ":", errstr); + } + + consumer_.reset(RdKafka::KafkaConsumer::create(conf.get(), errstr)); + if (!consumer_.get()) { + return errors::Internal("Failed to create consumer:", errstr); + } + + std::vector partitions; + partitions.emplace_back(topic_partition_.get()); + RdKafka::ErrorCode err = consumer_->assign(partitions); + if (err != RdKafka::ERR_NO_ERROR) { + return errors::Internal( + "Failed to assign partition [", topic_partition_->topic(), ", ", + topic_partition_->partition(), ", ", topic_partition_->offset(), + "]:", RdKafka::err2str(err)); + } + + return Status::OK(); + } + + // Resets all Kafka streams. + void ResetStreamsLocked() EXCLUSIVE_LOCKS_REQUIRED(mu_) { + consumer_->unassign(); + consumer_->close(); + consumer_.reset(nullptr); + } + + mutex mu_; + size_t current_topic_index_ GUARDED_BY(mu_) = 0; + int64 offset_ GUARDED_BY(mu_) = 0; + int64 limit_ GUARDED_BY(mu_) = -1; + std::unique_ptr topic_partition_ GUARDED_BY(mu_); + std::unique_ptr consumer_ GUARDED_BY(mu_); + }; + + const std::vector topics_; + const std::string servers_; + const std::string group_; + const bool eof_; + const int64 timeout_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("KafkaDataset").Device(DEVICE_CPU), + KafkaDatasetOp); + +} // namespace tensorflow diff --git a/tensorflow/contrib/kafka/ops/kafka_ops.cc b/tensorflow/contrib/kafka/ops/kafka_ops.cc new file mode 100644 index 0000000000..8cdf16103b --- /dev/null +++ b/tensorflow/contrib/kafka/ops/kafka_ops.cc @@ -0,0 +1,44 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("KafkaDataset") + .Input("topics: string") + .Input("servers: string") + .Input("group: string") + .Input("eof: bool") + .Input("timeout: int64") + .Output("handle: variant") + .SetIsStateful() + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Creates a dataset that emits the messages of one or more Kafka topics. + +topics: A `tf.string` tensor containing one or more subscriptions, + in the format of [topic:partition:offset:length], + by default length is -1 for unlimited. +servers: A list of bootstrap servers. +group: The consumer group id. +eof: If True, the kafka reader will stop on EOF. +timeout: The timeout value for the Kafka Consumer to wait + (in millisecond). +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py new file mode 100644 index 0000000000..94cf6b5ace --- /dev/null +++ b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.py @@ -0,0 +1,117 @@ +# 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 KafkaDataset.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import os + +from tensorflow.contrib.kafka.python.ops import kafka_dataset_ops +from tensorflow.python.data.ops import iterator_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 ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.lib.io import python_io +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import io_ops +from tensorflow.python.platform import test +from tensorflow.python.util import compat + +class KafkaDatasetTest(test.TestCase): + + def setUp(self): + # The Kafka server has to be setup before the test + # and tear down after the test manually. + # The docker engine has to be installed. + # + # To setup the Kafka server: + # $ bash kafka_test.sh start kafka + # + # To team down the Kafka server: + # $ bash kafka_test.sh stop kafka + pass + + def testKafkaDataset(self): + topics = array_ops.placeholder(dtypes.string, shape=[None]) + num_epochs = array_ops.placeholder(dtypes.int64, shape=[]) + batch_size = array_ops.placeholder(dtypes.int64, shape=[]) + + repeat_dataset = kafka_dataset_ops.KafkaDataset( + topics, group="test", eof=True).repeat(num_epochs) + batch_dataset = repeat_dataset.batch(batch_size) + + iterator = iterator_ops.Iterator.from_structure(batch_dataset.output_types) + init_op = iterator.make_initializer(repeat_dataset) + init_batch_op = iterator.make_initializer(batch_dataset) + get_next = iterator.get_next() + + with self.test_session() as sess: + # Basic test: read from topic 0. + sess.run( + init_op, feed_dict={topics: ["test:0:0:4"], + num_epochs: 1}) + for i in range(5): + self.assertEqual("D"+str(i), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Basic test: read from topic 1. + sess.run( + init_op, feed_dict={topics: ["test:0:5:-1"], + num_epochs: 1}) + for i in range(5): + self.assertEqual("D"+str(i + 5), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Basic test: read from both topics. + sess.run(init_op, feed_dict={topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 1}) + for j in range(2): + for i in range(5): + self.assertEqual("D"+str(i + j * 5), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Test repeated iteration through both files. + sess.run(init_op, feed_dict={topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 10}) + for _ in range(10): + for j in range(2): + for i in range(5): + self.assertEqual("D"+str(i + j * 5), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + # Test batched and repeated iteration through both files. + sess.run( + init_batch_op, + feed_dict={topics: ["test:0:0:4", "test:0:5:-1"], + num_epochs: 10, + batch_size: 5}) + for _ in range(10): + self.assertAllEqual(["D"+str(i) for i in range(5)], + sess.run(get_next)) + self.assertAllEqual(["D"+str(i + 5) for i in range(5)], + sess.run(get_next)) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh new file mode 100644 index 0000000000..7997c12731 --- /dev/null +++ b/tensorflow/contrib/kafka/python/kernel_tests/kafka_test.sh @@ -0,0 +1,34 @@ +#!/usr/bin/env bash + +set -e +set -o pipefail + +if [ "$#" -ne 2 ]; then + echo "Usage: $0 start|stop " >&2 + exit 1 +fi + +container=$2 +if [ "$1" == "start" ]; then + docker run -d --rm --net=host --name=$container spotify/kafka + echo Wait 5 secs until kafka is up and running + sleep 5 + echo Create test topic + docker exec $container bash -c '/opt/kafka_2.11-0.10.1.0/bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test' + echo Create test message + docker exec $container bash -c 'echo -e "D0\nD1\nD2\nD3\nD4\nD5\nD6\nD7\nD8\nD9" > /test' + echo Produce test message + docker exec $container bash -c '/opt/kafka_2.11-0.10.1.0/bin/kafka-console-producer.sh --topic test --broker-list 127.0.0.1:9092 < /test' + + echo Container $container started successfully +elif [ "$1" == "stop" ]; then + docker rm -f $container + + echo Container $container stopped successfully +else + echo "Usage: $0 start|stop " >&2 + exit 1 +fi + + + diff --git a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py new file mode 100644 index 0000000000..6590d86ebb --- /dev/null +++ b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py @@ -0,0 +1,72 @@ +# 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. +# ============================================================================== +"""Kafka Dataset.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.kafka.python.ops import gen_kafka_ops +from tensorflow.contrib.util import loader +from tensorflow.python.data.ops.readers import Dataset +from tensorflow.python.framework import common_shapes +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.platform import resource_loader + +class KafkaDataset(Dataset): + """A Kafka Dataset that consumes the message. + """ + + def __init__(self, topics, servers="localhost", group="", eof=False, timeout=1000): + """Create a KafkaReader. + + Args: + topics: A `tf.string` tensor containing one or more subscriptions, + in the format of [topic:partition:offset:length], + by default length is -1 for unlimited. + servers: A list of bootstrap servers. + group: The consumer group id. + eof: If True, the kafka reader will stop on EOF. + timeout: The timeout value for the Kafka Consumer to wait + (in millisecond). + """ + super(KafkaDataset, self).__init__() + self._topics = ops.convert_to_tensor( + topics, dtype=dtypes.string, name="topics") + self._servers = ops.convert_to_tensor( + servers, dtype=dtypes.string, name="servers") + self._group = ops.convert_to_tensor( + group, dtype=dtypes.string, name="group") + self._eof = ops.convert_to_tensor( + eof, dtype=dtypes.bool, name="eof") + self._timeout = ops.convert_to_tensor( + timeout, dtype=dtypes.int64, name="timeout") + + def _as_variant_tensor(self): + return gen_kafka_ops.kafka_dataset( + self._topics, self._servers, self._group, self._eof, self._timeout) + + @property + def output_classes(self): + return ops.Tensor + + @property + def output_shapes(self): + return tensor_shape.scalar() + + @property + def output_types(self): + return dtypes.string diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index 2102c5cca3..119ffa3d9e 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -489,6 +489,12 @@ def tf_additional_core_deps(): "//tensorflow/core/platform/s3:s3_file_system", ], "//conditions:default": [], + }) + select({ + "//tensorflow:with_kafka_support": [ + "//tensorflow/contrib/kafka:kafka_kernels", + "//tensorflow/contrib/kafka:kafka_ops_op_lib", + ], + "//conditions:default": [], }) # TODO(jart, jhseu): Delete when GCP is default on. diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index f7d9075032..f9c13e55e6 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -560,6 +560,18 @@ def tf_workspace(path_prefix="", tf_repo_name=""): build_file = str(Label("//third_party:nccl.BUILD")), ) + tf_http_archive( + name = "kafka", + urls = [ + "https://mirror.bazel.build/github.com/edenhill/librdkafka/archive/v0.11.1.tar.gz", + "https://github.com/edenhill/librdkafka/archive/v0.11.1.tar.gz", + ], + sha256 = "dd035d57c8f19b0b612dd6eefe6e5eebad76f506e302cccb7c2066f25a83585e", + strip_prefix = "librdkafka-0.11.1", + build_file = str(Label("//third_party:kafka/BUILD")), + patch_file = str(Label("//third_party/kafka:config.patch")), + ) + tf_http_archive( name = "aws", urls = [ diff --git a/third_party/kafka/BUILD b/third_party/kafka/BUILD new file mode 100644 index 0000000000..a61a9e1f6c --- /dev/null +++ b/third_party/kafka/BUILD @@ -0,0 +1,147 @@ +# Description: +# Kafka C/C++ (librdkafka) client library + +licenses(["notice"]) # 2-clause BSD license + +exports_files(["LICENSE"]) + +cc_library( + name = "kafka", + srcs = [ + "config.h", + "src-cpp/ConfImpl.cpp", + "src-cpp/ConsumerImpl.cpp", + "src-cpp/HandleImpl.cpp", + "src-cpp/KafkaConsumerImpl.cpp", + "src-cpp/MessageImpl.cpp", + "src-cpp/MetadataImpl.cpp", + "src-cpp/QueueImpl.cpp", + "src-cpp/RdKafka.cpp", + "src-cpp/TopicImpl.cpp", + "src-cpp/TopicPartitionImpl.cpp", + "src/crc32c.c", + "src/crc32c.h", + "src/lz4.c", + "src/lz4.h", + "src/lz4frame.c", + "src/lz4frame.h", + "src/lz4frame_static.h", + "src/lz4hc.c", + "src/lz4hc.h", + "src/lz4opt.h", + "src/queue.h", + "src/rd.h", + "src/rdaddr.c", + "src/rdaddr.h", + "src/rdatomic.h", + "src/rdavg.h", + "src/rdavl.c", + "src/rdavl.h", + "src/rdbuf.c", + "src/rdbuf.h", + "src/rdcrc32.h", + "src/rddl.h", + "src/rdendian.h", + "src/rdgz.c", + "src/rdgz.h", + "src/rdinterval.h", + "src/rdkafka.c", + "src/rdkafka.h", + "src/rdkafka_assignor.c", + "src/rdkafka_assignor.h", + "src/rdkafka_broker.c", + "src/rdkafka_broker.h", + "src/rdkafka_buf.c", + "src/rdkafka_buf.h", + "src/rdkafka_cgrp.c", + "src/rdkafka_cgrp.h", + "src/rdkafka_conf.c", + "src/rdkafka_conf.h", + "src/rdkafka_event.h", + "src/rdkafka_feature.c", + "src/rdkafka_feature.h", + "src/rdkafka_int.h", + "src/rdkafka_interceptor.c", + "src/rdkafka_interceptor.h", + "src/rdkafka_lz4.c", + "src/rdkafka_lz4.h", + "src/rdkafka_metadata.c", + "src/rdkafka_metadata.h", + "src/rdkafka_metadata_cache.c", + "src/rdkafka_msg.c", + "src/rdkafka_msg.h", + "src/rdkafka_msgset.h", + "src/rdkafka_msgset_reader.c", + "src/rdkafka_msgset_writer.c", + "src/rdkafka_offset.c", + "src/rdkafka_offset.h", + "src/rdkafka_op.c", + "src/rdkafka_op.h", + "src/rdkafka_partition.c", + "src/rdkafka_partition.h", + "src/rdkafka_pattern.c", + "src/rdkafka_pattern.h", + "src/rdkafka_proto.h", + "src/rdkafka_queue.c", + "src/rdkafka_queue.h", + "src/rdkafka_range_assignor.c", + "src/rdkafka_request.c", + "src/rdkafka_request.h", + "src/rdkafka_roundrobin_assignor.c", + "src/rdkafka_sasl.c", + "src/rdkafka_sasl.h", + "src/rdkafka_sasl_int.h", + "src/rdkafka_sasl_plain.c", + "src/rdkafka_subscription.c", + "src/rdkafka_subscription.h", + "src/rdkafka_timer.c", + "src/rdkafka_timer.h", + "src/rdkafka_topic.c", + "src/rdkafka_topic.h", + "src/rdkafka_transport.c", + "src/rdkafka_transport.h", + "src/rdkafka_transport_int.h", + "src/rdlist.c", + "src/rdlist.h", + "src/rdlog.c", + "src/rdlog.h", + "src/rdports.c", + "src/rdports.h", + "src/rdposix.h", + "src/rdrand.c", + "src/rdrand.h", + "src/rdregex.c", + "src/rdregex.h", + "src/rdstring.c", + "src/rdstring.h", + "src/rdsysqueue.h", + "src/rdtime.h", + "src/rdtypes.h", + "src/rdunittest.c", + "src/rdunittest.h", + "src/rdvarint.c", + "src/rdvarint.h", + "src/snappy.c", + "src/snappy.h", + "src/tinycthread.c", + "src/tinycthread.h", + "src/xxhash.c", + "src/xxhash.h", + ], + hdrs = [ + "config.h", + ], + defines = [ + ], + includes = [ + "src", + "src-cpp", + ], + linkopts = [ + "-lpthread", + ], + visibility = ["//visibility:public"], + deps = [ + "@boringssl//:ssl", + ], +) diff --git a/third_party/kafka/config.patch b/third_party/kafka/config.patch new file mode 100644 index 0000000000..fa5c2d35b4 --- /dev/null +++ b/third_party/kafka/config.patch @@ -0,0 +1,44 @@ +diff -Naur a/config.h b/config.h +--- a/config.h 1970-01-01 00:00:00.000000000 +0000 ++++ b/config.h 2017-10-28 00:57:03.316957390 +0000 +@@ -0,0 +1,40 @@ ++#pragma once ++#define WITHOUT_OPTIMIZATION 0 ++#define ENABLE_DEVEL 0 ++#define ENABLE_REFCNT_DEBUG 0 ++#define ENABLE_SHAREDPTR_DEBUG 0 ++ ++#define HAVE_ATOMICS_32 1 ++#define HAVE_ATOMICS_32_SYNC 1 ++ ++#if (HAVE_ATOMICS_32) ++# if (HAVE_ATOMICS_32_SYNC) ++# define ATOMIC_OP32(OP1,OP2,PTR,VAL) __sync_ ## OP1 ## _and_ ## OP2(PTR, VAL) ++# else ++# define ATOMIC_OP32(OP1,OP2,PTR,VAL) __atomic_ ## OP1 ## _ ## OP2(PTR, VAL, __ATOMIC_SEQ_CST) ++# endif ++#endif ++ ++#define HAVE_ATOMICS_64 1 ++#define HAVE_ATOMICS_64_SYNC 1 ++ ++#if (HAVE_ATOMICS_64) ++# if (HAVE_ATOMICS_64_SYNC) ++# define ATOMIC_OP64(OP1,OP2,PTR,VAL) __sync_ ## OP1 ## _and_ ## OP2(PTR, VAL) ++# else ++# define ATOMIC_OP64(OP1,OP2,PTR,VAL) __atomic_ ## OP1 ## _ ## OP2(PTR, VAL, __ATOMIC_SEQ_CST) ++# endif ++#endif ++ ++ ++#define WITH_ZLIB 1 ++#define WITH_LIBDL 1 ++#define WITH_PLUGINS 0 ++#define WITH_SNAPPY 1 ++#define WITH_SOCKEM 1 ++#define WITH_SSL 1 ++#define WITH_SASL 0 ++#define WITH_SASL_SCRAM 0 ++#define WITH_SASL_CYRUS 0 ++#define HAVE_REGEX 1 ++#define HAVE_STRNDUP 1 -- GitLab From 12cfeb2c5291b1d2af55bf0905374043be599c5a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 12:41:27 -0800 Subject: [PATCH 144/423] Cleanup: Ran clang-format on all *.{h,cc} files in tensorflow/core/framework. PiperOrigin-RevId: 183429540 --- tensorflow/core/framework/bfloat16.cc | 28 ++++++------ tensorflow/core/framework/common_shape_fns.cc | 9 ++-- tensorflow/core/framework/fake_input.cc | 12 ++--- tensorflow/core/framework/function.cc | 4 +- tensorflow/core/framework/function.h | 2 +- tensorflow/core/framework/graph_def_util.cc | 10 ++--- tensorflow/core/framework/op_def_util.cc | 24 +++++----- tensorflow/core/framework/op_def_util_test.cc | 30 +++++++------ tensorflow/core/framework/op_gen_lib.cc | 1 - tensorflow/core/framework/op_gen_lib.h | 1 - tensorflow/core/framework/op_kernel.cc | 3 +- tensorflow/core/framework/op_kernel_test.cc | 28 +++++------- tensorflow/core/framework/reader_base.cc | 6 +-- tensorflow/core/framework/register_types.h | 10 ++--- .../core/framework/register_types_traits.h | 6 +-- tensorflow/core/framework/rendezvous_test.cc | 8 ++-- tensorflow/core/framework/shape_inference.h | 2 +- .../core/framework/shape_inference_test.cc | 5 ++- .../core/framework/tensor_shape_test.cc | 3 +- tensorflow/core/framework/tensor_testutil.cc | 2 +- tensorflow/core/framework/tensor_types.h | 44 ++++++++++++------- tensorflow/core/framework/types_test.cc | 4 +- 22 files changed, 127 insertions(+), 115 deletions(-) diff --git a/tensorflow/core/framework/bfloat16.cc b/tensorflow/core/framework/bfloat16.cc index 0efe43fde2..6025be5170 100644 --- a/tensorflow/core/framework/bfloat16.cc +++ b/tensorflow/core/framework/bfloat16.cc @@ -21,13 +21,13 @@ void FloatToBFloat16(const float* src, bfloat16* dst, int64 size) { const uint16_t* p = reinterpret_cast(src); uint16_t* q = reinterpret_cast(dst); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - for (; size != 0; p += 2, q++, size--) { - *q = p[0]; - } + for (; size != 0; p += 2, q++, size--) { + *q = p[0]; + } #else - for (; size != 0; p += 2, q++, size--) { - *q = p[1]; - } + for (; size != 0; p += 2, q++, size--) { + *q = p[1]; + } #endif } @@ -35,15 +35,15 @@ void BFloat16ToFloat(const bfloat16* src, float* dst, int64 size) { const uint16_t* p = reinterpret_cast(src); uint16_t* q = reinterpret_cast(dst); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - for (; size != 0; p++, q += 2, size--) { - q[0] = *p; - q[1] = 0; - } + for (; size != 0; p++, q += 2, size--) { + q[0] = *p; + q[1] = 0; + } #else - for (; size != 0; p++, q += 2, size--) { - q[0] = 0; - q[1] = *p; - } + for (; size != 0; p++, q += 2, size--) { + q[0] = 0; + q[1] = *p; + } #endif } diff --git a/tensorflow/core/framework/common_shape_fns.cc b/tensorflow/core/framework/common_shape_fns.cc index 7ab8e3ec18..8bb87483e1 100644 --- a/tensorflow/core/framework/common_shape_fns.cc +++ b/tensorflow/core/framework/common_shape_fns.cc @@ -1356,10 +1356,11 @@ Status ScatterNdUpdateShape(InferenceContext* c) { Status s = c->Merge(prefix_indices, prefix_updates, &unused); if (!s.ok()) { return errors::InvalidArgument( - "The outer ", num_outer_dims, " dimensions of indices.shape=", - c->DebugString(indices_shape), " must match the outer ", - num_outer_dims, " dimensions of updates.shape=", - c->DebugString(updates_shape), ": ", s.error_message()); + "The outer ", num_outer_dims, + " dimensions of indices.shape=", c->DebugString(indices_shape), + " must match the outer ", num_outer_dims, + " dimensions of updates.shape=", c->DebugString(updates_shape), + ": ", s.error_message()); } ShapeHandle input_suffix; diff --git a/tensorflow/core/framework/fake_input.cc b/tensorflow/core/framework/fake_input.cc index ad301a8aa4..70d1e20a17 100644 --- a/tensorflow/core/framework/fake_input.cc +++ b/tensorflow/core/framework/fake_input.cc @@ -104,8 +104,8 @@ Status FakeInputImpl::AddInputToBuilder() { Status status = GetNodeAttr(*node_def_, arg_->type_list_attr(), &dts); if (!status.ok()) { return errors::InvalidArgument( - "Could not infer list of types for input '", arg_->name(), "': ", - status.error_message()); + "Could not infer list of types for input '", arg_->name(), + "': ", status.error_message()); } SourceList(dts); return Status::OK(); @@ -131,8 +131,8 @@ Status FakeInputImpl::GetN(int* n) const { Status status = GetNodeAttr(*node_def_, arg_->number_attr(), n); if (!status.ok()) { return errors::InvalidArgument("Could not infer length of input '", - arg_->name(), "': ", - status.error_message()); + arg_->name(), + "': ", status.error_message()); } } return Status::OK(); @@ -153,8 +153,8 @@ Status FakeInputImpl::GetDataType(DataType* dt) const { *dt = attr->default_value().type(); } else { return errors::InvalidArgument("Could not infer type for input '", - arg_->name(), "': ", - status.error_message()); + arg_->name(), + "': ", status.error_message()); } } } else { diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc index 0224f25227..d6b576166c 100644 --- a/tensorflow/core/framework/function.cc +++ b/tensorflow/core/framework/function.cc @@ -1264,8 +1264,8 @@ FunctionDef FunctionDefHelper::Define(const string& name, } for (const string& a : src.arg) { const auto iter = ret_index.find(a); - CHECK(iter != ret_index.end()) << "Node input '" << a << "' in '" - << src.ret[0] << "' of " << name; + CHECK(iter != ret_index.end()) + << "Node input '" << a << "' in '" << src.ret[0] << "' of " << name; n->add_input(iter->second); } for (const string& d : src.dep) { diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h index 3bb5638cdf..b933ee0b0e 100644 --- a/tensorflow/core/framework/function.h +++ b/tensorflow/core/framework/function.h @@ -656,7 +656,7 @@ bool RegisterOp(const string& op, Creator func); // Returns OK the gradient creator for the "op" is found (may be // nullptr if REGISTER_OP_NO_GRADIENT is used. Status GetOpGradientCreator(const string& op, Creator* creator); -}; +}; // namespace gradient // Declare explicit instantiations of GetAttr #define GET_ATTR(T) \ diff --git a/tensorflow/core/framework/graph_def_util.cc b/tensorflow/core/framework/graph_def_util.cc index bd018b7243..1f670535d5 100644 --- a/tensorflow/core/framework/graph_def_util.cc +++ b/tensorflow/core/framework/graph_def_util.cc @@ -35,8 +35,8 @@ namespace tensorflow { string SummarizeGraphDef(const GraphDef& graph_def) { string ret; - strings::StrAppend(&ret, "versions = ", - ProtoShortDebugString(graph_def.versions()), ";\n"); + strings::StrAppend( + &ret, "versions = ", ProtoShortDebugString(graph_def.versions()), ";\n"); for (const NodeDef& node : graph_def.node()) { strings::StrAppend(&ret, SummarizeNodeDef(node), ";\n"); } @@ -90,9 +90,9 @@ static Status RemoveNewDefaultAttrsFromNodeDef( FindAttr(attr.first, *producer_op_def); if (producer_attr_def == nullptr) { return errors::InvalidArgument( - "Attr '", attr.first, "' missing in producer's OpDef: ", - SummarizeOpDef(*producer_op_def), " but found in node: ", - SummarizeNodeDef(*node_def)); + "Attr '", attr.first, + "' missing in producer's OpDef: ", SummarizeOpDef(*producer_op_def), + " but found in node: ", SummarizeNodeDef(*node_def)); } // ...and it has the same value as the default in producer, if (producer_attr_def->has_default_value() && diff --git a/tensorflow/core/framework/op_def_util.cc b/tensorflow/core/framework/op_def_util.cc index a4e8add6c4..2d035ab90d 100644 --- a/tensorflow/core/framework/op_def_util.cc +++ b/tensorflow/core/framework/op_def_util.cc @@ -170,20 +170,20 @@ const OpDef::ArgDef* FindInputArg(StringPiece name, const OpDef& op_def) { return nullptr; } -#define VALIDATE(EXPR, ...) \ - do { \ - if (!(EXPR)) { \ - return errors::InvalidArgument(__VA_ARGS__, "; in OpDef: ", \ - ProtoShortDebugString(op_def)); \ - } \ +#define VALIDATE(EXPR, ...) \ + do { \ + if (!(EXPR)) { \ + return errors::InvalidArgument( \ + __VA_ARGS__, "; in OpDef: ", ProtoShortDebugString(op_def)); \ + } \ } while (false) static Status ValidateArg(const OpDef::ArgDef& arg, const OpDef& op_def, bool output, std::set* names) { const string suffix = strings::StrCat( output ? " for output '" : " for input '", arg.name(), "'"); - VALIDATE(gtl::InsertIfNotPresent(names, arg.name()), "Duplicate name: ", - arg.name()); + VALIDATE(gtl::InsertIfNotPresent(names, arg.name()), + "Duplicate name: ", arg.name()); VALIDATE(HasAttrStyleType(arg), "Missing type", suffix); if (!arg.number_attr().empty()) { @@ -250,8 +250,8 @@ Status ValidateOpDef(const OpDef& op_def) { std::set names; // for detecting duplicate names for (const auto& attr : op_def.attr()) { // Validate name - VALIDATE(gtl::InsertIfNotPresent(&names, attr.name()), "Duplicate name: ", - attr.name()); + VALIDATE(gtl::InsertIfNotPresent(&names, attr.name()), + "Duplicate name: ", attr.name()); DataType dt; VALIDATE(!DataTypeFromString(attr.name(), &dt), "Attr can't have name ", attr.name(), " that matches a data type"); @@ -680,8 +680,8 @@ Status OpDefAddedDefaultsUnchanged(const OpDef& old_op, if (!penultimate_attr.has_default_value() || !new_attr->has_default_value()) { return errors::InvalidArgument("Missing default for attr '", - penultimate_attr.name(), "' in op: ", - SummarizeOpDef(new_op)); + penultimate_attr.name(), + "' in op: ", SummarizeOpDef(new_op)); } // Actually test that the attr's default value hasn't changed. diff --git a/tensorflow/core/framework/op_def_util_test.cc b/tensorflow/core/framework/op_def_util_test.cc index 28809c11c5..2b9812d4fc 100644 --- a/tensorflow/core/framework/op_def_util_test.cc +++ b/tensorflow/core/framework/op_def_util_test.cc @@ -200,10 +200,11 @@ TEST_F(ValidateOpDefTest, BadAttrDefault) { "default_value { list { s: ['foo'] } } }"), "Length for attr 'a' of 1 must be at least minimum 2\n\t in Op " "'BadAttrDef'"); - ExpectFailure(TestBuilder(OpDefBuilder("GoodAttrDef") - .Attr("a: list(type) >=2 = [DT_STRING]")), - "Length for attr 'a' of 1 must be at least minimum 2\n\t in Op " - "'GoodAttrDef'"); + ExpectFailure( + TestBuilder( + OpDefBuilder("GoodAttrDef").Attr("a: list(type) >=2 = [DT_STRING]")), + "Length for attr 'a' of 1 must be at least minimum 2\n\t in Op " + "'GoodAttrDef'"); } TEST_F(ValidateOpDefTest, NoRefTypes) { @@ -213,9 +214,10 @@ TEST_F(ValidateOpDefTest, NoRefTypes) { ExpectFailure( TestBuilder(OpDefBuilder("BadAttrDef").Attr("T: type = DT_INT32_REF")), "AttrValue must not have reference type value of int32_ref"); - ExpectFailure(TestBuilder(OpDefBuilder("BadAttrDef") - .Attr("T: list(type) = [DT_STRING_REF]")), - "AttrValue must not have reference type value of string_ref"); + ExpectFailure( + TestBuilder( + OpDefBuilder("BadAttrDef").Attr("T: list(type) = [DT_STRING_REF]")), + "AttrValue must not have reference type value of string_ref"); } TEST_F(ValidateOpDefTest, BadAttrMin) { @@ -245,9 +247,10 @@ TEST_F(ValidateOpDefTest, BadAttrAllowed) { TF_EXPECT_OK(TestBuilder( OpDefBuilder("GoodAttrtude").Attr("x: numbertype = DT_INT32"))); // Not in list of allowed types. - ExpectFailure(TestBuilder(OpDefBuilder("BadAttrtude") - .Attr("x: numbertype = DT_STRING")), - "attr 'x' of string is not in the list of allowed values"); + ExpectFailure( + TestBuilder( + OpDefBuilder("BadAttrtude").Attr("x: numbertype = DT_STRING")), + "attr 'x' of string is not in the list of allowed values"); ExpectFailure( TestBuilder(OpDefBuilder("BadAttrtude") .Attr("x: list(realnumbertype) = [DT_COMPLEX64]")), @@ -260,9 +263,10 @@ TEST_F(ValidateOpDefTest, BadAttrAllowed) { TF_EXPECT_OK(TestBuilder( OpDefBuilder("GoodAttrtude").Attr("x: {'foo', 'bar'} = 'bar'"))); // Not in list of allowed strings. - ExpectFailure(TestBuilder(OpDefBuilder("BadAttrtude") - .Attr("x: {'foo', 'bar'} = 'baz'")), - "attr 'x' of \"baz\" is not in the list of allowed values"); + ExpectFailure( + TestBuilder( + OpDefBuilder("BadAttrtude").Attr("x: {'foo', 'bar'} = 'baz'")), + "attr 'x' of \"baz\" is not in the list of allowed values"); ExpectFailure(TestBuilder(OpDefBuilder("BadAttrtude") .Attr("x: list({'foo', 'bar'}) = ['baz']")), "attr 'x' of \"baz\" is not in the list of allowed values"); diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc index 870bbb141b..5f2eb9d99a 100644 --- a/tensorflow/core/framework/op_gen_lib.cc +++ b/tensorflow/core/framework/op_gen_lib.cc @@ -296,7 +296,6 @@ static void RenameInDocs(const string& from, const string& to, } } - namespace { // Initializes given ApiDef with data in OpDef. diff --git a/tensorflow/core/framework/op_gen_lib.h b/tensorflow/core/framework/op_gen_lib.h index 94fe194a1a..ff38e4b221 100644 --- a/tensorflow/core/framework/op_gen_lib.h +++ b/tensorflow/core/framework/op_gen_lib.h @@ -47,7 +47,6 @@ string PBTxtToMultiline(StringPiece pbtxt, const std::vector& multi_line_fields); string PBTxtFromMultiline(StringPiece multiline_pbtxt); - // Takes a list of files with ApiDefs text protos, and allows you to // look up the specific ApiDef for any given op. class ApiDefMap { diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc index 16bf5c256f..fd2d06be98 100644 --- a/tensorflow/core/framework/op_kernel.cc +++ b/tensorflow/core/framework/op_kernel.cc @@ -101,7 +101,8 @@ OpKernel::OpKernel(OpKernelConstruction* context) // Kernels executing on GPU/SYCL tie very few resources on the CPU where the // scheduler runs: we consider them as inexpensive. - expensive_ = context->device_type() != DeviceType(DEVICE_GPU) && context->device_type() != DeviceType(DEVICE_SYCL); + expensive_ = context->device_type() != DeviceType(DEVICE_GPU) && + context->device_type() != DeviceType(DEVICE_SYCL); } OpKernel::~OpKernel() {} diff --git a/tensorflow/core/framework/op_kernel_test.cc b/tensorflow/core/framework/op_kernel_test.cc index 94a9d1335a..b53b877f28 100644 --- a/tensorflow/core/framework/op_kernel_test.cc +++ b/tensorflow/core/framework/op_kernel_test.cc @@ -510,10 +510,9 @@ TEST_F(OpKernelBuilderTest, BuilderBoth) { } REGISTER_OP("BuildTypeAttr").Attr("T: type"); -REGISTER_KERNEL_BUILDER(Name("BuildTypeAttr") - .Device(DEVICE_CPU) - .TypeConstraint("T"), - DummyKernel); +REGISTER_KERNEL_BUILDER( + Name("BuildTypeAttr").Device(DEVICE_CPU).TypeConstraint("T"), + DummyKernel); TEST_F(OpKernelBuilderTest, BuilderTypeAttr) { ExpectSuccess("BuildTypeAttr", DEVICE_CPU, {"T|type|DT_FLOAT"}); @@ -525,10 +524,9 @@ TEST_F(OpKernelBuilderTest, BuilderTypeAttr) { } REGISTER_OP("BuildTypeListAttr").Attr("T: list(type)"); -REGISTER_KERNEL_BUILDER(Name("BuildTypeListAttr") - .Device(DEVICE_CPU) - .TypeConstraint("T"), - DummyKernel); +REGISTER_KERNEL_BUILDER( + Name("BuildTypeListAttr").Device(DEVICE_CPU).TypeConstraint("T"), + DummyKernel); TEST_F(OpKernelBuilderTest, BuilderTypeListAttr) { ExpectSuccess("BuildTypeListAttr", DEVICE_CPU, {"T|list(type)|[]"}); @@ -574,14 +572,12 @@ TEST_F(OpKernelBuilderTest, DuplicateKernel) { } REGISTER_OP("DuplicateKernelForT").Attr("T: type"); -REGISTER_KERNEL_BUILDER(Name("DuplicateKernelForT") - .Device(DEVICE_CPU) - .TypeConstraint("T"), - DummyKernel); -REGISTER_KERNEL_BUILDER(Name("DuplicateKernelForT") - .Device(DEVICE_CPU) - .TypeConstraint("T"), - DummyKernel); +REGISTER_KERNEL_BUILDER( + Name("DuplicateKernelForT").Device(DEVICE_CPU).TypeConstraint("T"), + DummyKernel); +REGISTER_KERNEL_BUILDER( + Name("DuplicateKernelForT").Device(DEVICE_CPU).TypeConstraint("T"), + DummyKernel); TEST_F(OpKernelBuilderTest, DuplicateKernelForT) { const NodeDef ndef = diff --git a/tensorflow/core/framework/reader_base.cc b/tensorflow/core/framework/reader_base.cc index b8c771a0a1..f84ef0f953 100644 --- a/tensorflow/core/framework/reader_base.cc +++ b/tensorflow/core/framework/reader_base.cc @@ -178,9 +178,9 @@ void ReaderBase::Read(QueueInterface* queue, string* key, string* value, " must set *at_end=true, *produced=true, or return an error."); } if (!status.ok() && produced) { - status = errors::Internal("ReadLocked() for ", name(), - " set *produced=true *and* returned an error: ", - status.ToString()); + status = errors::Internal( + "ReadLocked() for ", name(), + " set *produced=true *and* returned an error: ", status.ToString()); } if (status.ok() && at_end) { status = OnWorkFinishedLocked(); diff --git a/tensorflow/core/framework/register_types.h b/tensorflow/core/framework/register_types.h index edc93aec7f..f88025fd33 100644 --- a/tensorflow/core/framework/register_types.h +++ b/tensorflow/core/framework/register_types.h @@ -211,14 +211,12 @@ limitations under the License. #define TF_CALL_SYCL_double(m) #else // TENSORFLOW_SYCL_NO_DOUBLE #define TF_CALL_SYCL_double(m) TF_CALL_double(m) -#endif // TENSORFLOW_SYCL_NO_DOUBLE +#endif // TENSORFLOW_SYCL_NO_DOUBLE #ifdef __ANDROID_TYPES_SLIM__ -#define TF_CALL_SYCL_NUMBER_TYPES(m) TF_CALL_float(m) +#define TF_CALL_SYCL_NUMBER_TYPES(m) TF_CALL_float(m) #else // __ANDROID_TYPES_SLIM__ -#define TF_CALL_SYCL_NUMBER_TYPES(m) \ - TF_CALL_float(m) \ - TF_CALL_SYCL_double(m) -#endif // __ANDROID_TYPES_SLIM__ +#define TF_CALL_SYCL_NUMBER_TYPES(m) TF_CALL_float(m) TF_CALL_SYCL_double(m) +#endif // __ANDROID_TYPES_SLIM__ #endif // TENSORFLOW_FRAMEWORK_REGISTER_TYPES_H_ diff --git a/tensorflow/core/framework/register_types_traits.h b/tensorflow/core/framework/register_types_traits.h index c1fe5517c6..ab35c2f095 100644 --- a/tensorflow/core/framework/register_types_traits.h +++ b/tensorflow/core/framework/register_types_traits.h @@ -23,7 +23,7 @@ typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #include "tensorflow/core/framework/numeric_types.h" #include "tensorflow/core/platform/types.h" @@ -79,7 +79,7 @@ template <> struct proxy_type_pod { typedef float type; }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL /// If POD we use proxy_type_pod, otherwise this maps to identiy. template @@ -99,7 +99,7 @@ struct proxy_type { #ifdef TENSORFLOW_USE_SYCL #define TF_CALL_SYCL_PROXY_TYPES(m) \ TF_CALL_double(m) TF_CALL_float(m) TF_CALL_int32(m) -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow #endif // TENSORFLOW_FRAMEWORK_REGISTER_TYPES_TRAITS_H_ diff --git a/tensorflow/core/framework/rendezvous_test.cc b/tensorflow/core/framework/rendezvous_test.cc index 32b8ad784d..de148f0bd3 100644 --- a/tensorflow/core/framework/rendezvous_test.cc +++ b/tensorflow/core/framework/rendezvous_test.cc @@ -69,9 +69,7 @@ class LocalRendezvousTest : public ::testing::Test { rendez_ = NewLocalRendezvous(); } - ~LocalRendezvousTest() override { - rendez_->Unref(); - } + ~LocalRendezvousTest() override { rendez_->Unref(); } void SchedClosure(std::function fn) { threads_.Schedule(std::move(fn)); @@ -99,8 +97,8 @@ string V(const Tensor& tensor) { Rendezvous::ParsedKey MakeKey(const string& name) { string s = Rendezvous::CreateKey("/job:mnist/replica:1/task:2/CPU:0", 7890, - "/job:mnist/replica:1/task:2/device:GPU:0", name, - FrameAndIter(0, 0)); + "/job:mnist/replica:1/task:2/device:GPU:0", + name, FrameAndIter(0, 0)); Rendezvous::ParsedKey k; TF_EXPECT_OK(Rendezvous::ParseKey(s, &k)); return k; diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h index d552ec1693..e3cc848a16 100644 --- a/tensorflow/core/framework/shape_inference.h +++ b/tensorflow/core/framework/shape_inference.h @@ -32,7 +32,7 @@ class ShapeRefinerTest; namespace grappler { class GraphProperties; class SymbolicShapeManager; -} +} // namespace grappler namespace shape_inference { diff --git a/tensorflow/core/framework/shape_inference_test.cc b/tensorflow/core/framework/shape_inference_test.cc index a9b63ca60e..f48a7b9c47 100644 --- a/tensorflow/core/framework/shape_inference_test.cc +++ b/tensorflow/core/framework/shape_inference_test.cc @@ -760,7 +760,10 @@ TEST_F(ShapeInferenceTest, MergePrefix) { NodeDef def; InferenceContext c(kVersion, &def, MakeOpDef(4, 2), { - Unknown(), S({-1, 2}), S({1, -1, 3}), S({2, 4}), + Unknown(), + S({-1, 2}), + S({1, -1, 3}), + S({2, 4}), }, {}, {}, {}); diff --git a/tensorflow/core/framework/tensor_shape_test.cc b/tensorflow/core/framework/tensor_shape_test.cc index d8a9c0bac5..d7517bb311 100644 --- a/tensorflow/core/framework/tensor_shape_test.cc +++ b/tensorflow/core/framework/tensor_shape_test.cc @@ -582,7 +582,8 @@ TEST(TensorShapeTest, Large) { TEST(TensorShapeTest, Overflow) { int64 one = 1; std::vector> overflows = { - {1 << 30, 1 << 30, 1 << 30}, {1 << 5, (one << 60) + 1}, + {1 << 30, 1 << 30, 1 << 30}, + {1 << 5, (one << 60) + 1}, }; for (const auto& overflow : overflows) { TensorShapeProto proto; diff --git a/tensorflow/core/framework/tensor_testutil.cc b/tensorflow/core/framework/tensor_testutil.cc index a8d1412300..8f480d65f2 100644 --- a/tensorflow/core/framework/tensor_testutil.cc +++ b/tensorflow/core/framework/tensor_testutil.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include "tensorflow/core/framework/tensor_testutil.h" +#include namespace tensorflow { namespace test { diff --git a/tensorflow/core/framework/tensor_types.h b/tensorflow/core/framework/tensor_types.h index 921f88dc0b..a5c1a56bfc 100644 --- a/tensorflow/core/framework/tensor_types.h +++ b/tensorflow/core/framework/tensor_types.h @@ -25,7 +25,8 @@ template struct TTypes { // Rank- tensor of scalar type T. typedef Eigen::TensorMap, - Eigen::Aligned> Tensor; + Eigen::Aligned> + Tensor; typedef Eigen::TensorMap< Eigen::Tensor, Eigen::Aligned> ConstTensor; @@ -33,35 +34,42 @@ struct TTypes { // Unaligned Rank- tensor of scalar type T. typedef Eigen::TensorMap > UnalignedTensor; - typedef Eigen::TensorMap > UnalignedConstTensor; + typedef Eigen::TensorMap< + Eigen::Tensor > + UnalignedConstTensor; typedef Eigen::TensorMap, - Eigen::Aligned> Tensor32Bit; + Eigen::Aligned> + Tensor32Bit; // Scalar tensor (implemented as a rank-0 tensor) of scalar type T. typedef Eigen::TensorMap< Eigen::TensorFixedSize, Eigen::RowMajor, IndexType>, - Eigen::Aligned> Scalar; + Eigen::Aligned> + Scalar; typedef Eigen::TensorMap, Eigen::RowMajor, IndexType>, - Eigen::Aligned> ConstScalar; + Eigen::Aligned> + ConstScalar; // Unaligned Scalar tensor of scalar type T. - typedef Eigen::TensorMap, Eigen::RowMajor, IndexType> > UnalignedScalar; + typedef Eigen::TensorMap< + Eigen::TensorFixedSize, Eigen::RowMajor, IndexType> > + UnalignedScalar; typedef Eigen::TensorMap, Eigen::RowMajor, IndexType> > UnalignedConstScalar; // Rank-1 tensor (vector) of scalar type T. typedef Eigen::TensorMap, - Eigen::Aligned> Flat; + Eigen::Aligned> + Flat; typedef Eigen::TensorMap< Eigen::Tensor, Eigen::Aligned> ConstFlat; typedef Eigen::TensorMap, - Eigen::Aligned> Vec; + Eigen::Aligned> + Vec; typedef Eigen::TensorMap< Eigen::Tensor, Eigen::Aligned> ConstVec; @@ -69,16 +77,19 @@ struct TTypes { // Unaligned Rank-1 tensor (vector) of scalar type T. typedef Eigen::TensorMap > UnalignedFlat; - typedef Eigen::TensorMap > UnalignedConstFlat; + typedef Eigen::TensorMap< + Eigen::Tensor > + UnalignedConstFlat; typedef Eigen::TensorMap > UnalignedVec; typedef Eigen::TensorMap< - Eigen::Tensor > UnalignedConstVec; + Eigen::Tensor > + UnalignedConstVec; // Rank-2 tensor (matrix) of scalar type T. typedef Eigen::TensorMap, - Eigen::Aligned> Matrix; + Eigen::Aligned> + Matrix; typedef Eigen::TensorMap< Eigen::Tensor, Eigen::Aligned> ConstMatrix; @@ -86,8 +97,9 @@ struct TTypes { // Unaligned Rank-2 tensor (matrix) of scalar type T. typedef Eigen::TensorMap > UnalignedMatrix; - typedef Eigen::TensorMap > UnalignedConstMatrix; + typedef Eigen::TensorMap< + Eigen::Tensor > + UnalignedConstMatrix; }; typedef typename TTypes::Tensor32Bit::Index Index32; diff --git a/tensorflow/core/framework/types_test.cc b/tensorflow/core/framework/types_test.cc index 5ddc986563..60f2b4135a 100644 --- a/tensorflow/core/framework/types_test.cc +++ b/tensorflow/core/framework/types_test.cc @@ -70,8 +70,8 @@ TEST(TypesTest, kDataTypeRefOffset) { << "Extra reference enum " << enum_descriptor->FindValueByNumber(e_ref)->name() << " without corresponding base enum with value " << e; - ASSERT_LT(DataType_MAX, e_ref) << "Gap in reference types, missing value for " - << e_ref; + ASSERT_LT(DataType_MAX, e_ref) + << "Gap in reference types, missing value for " << e_ref; // Make sure there are no enums defined after the last regular type before // the first reference type. -- GitLab From f6a53e7abd54afdff4d1377535d61dbc1efd174c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 12:52:54 -0800 Subject: [PATCH 145/423] Make the graph generation of TFBT deterministic. PiperOrigin-RevId: 183431139 --- .../python/ops/batch_ops_utils.py | 18 +++++++++--------- .../python/training/functions/gbdt_batch.py | 7 ++++--- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py index b281a4c6d1..7a5f329b7a 100644 --- a/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py +++ b/tensorflow/contrib/boosted_trees/python/ops/batch_ops_utils.py @@ -81,32 +81,32 @@ def _scheduled_stamp_resource_op_runner(batch, stamp): if not batch: return arg_keys = set(batch[0].args.keys()) - grouped_args = collections.defaultdict(list) + grouped_args = collections.OrderedDict() resource_handles = [] # Check that the set of arguments is the same across all the scheduled ops. for op in batch: if set(op.args.keys()) != arg_keys: raise ValueError("Mismatching arguments: %s, %s.", op.args, arg_keys) for key in arg_keys: - grouped_args[key].append(op.args[key]) + grouped_args.setdefault(key, []).append(op.args[key]) resource_handles.append(op.resource_handle) # Move all the inputs to the op device in one RPC. - grouped_args = { - k: _move_tensors(v, resource_handles[0].device) - for k, v in grouped_args.items() - } + grouped_args = collections.OrderedDict( + (k, _move_tensors(v, resource_handles[0].device)) + for k, v in sorted(grouped_args.items())) with ops.device(resource_handles[0].device): return batch[0].op(resource_handles, stamp, **grouped_args) def run_handler_scheduled_ops(per_handler_ops, stamp, worker_device): """Given a dictionary of ops for each handler, runs them in batch.""" - batched_ops = collections.defaultdict(list) + batched_ops = collections.OrderedDict() # Group the ops by their batching_key. Ops that share the same batching key # can be executed together. - for handler in sorted(per_handler_ops.keys()): + for handler in per_handler_ops.keys(): for op in per_handler_ops[handler]: - batched_ops[(op.batching_key(), op.batch_runner_fn())].append(op) + key = (op.batching_key(), op.batch_runner_fn()) + batched_ops.setdefault(key, []).append(op) op_results = {} for batch in batched_ops.values(): # Run each of the batched ops using its runner. 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 b95956dae2..f0b66dcbbe 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections import copy from tensorflow.contrib import learn @@ -163,7 +164,7 @@ def extract_features(features, feature_columns): scope = "gbdt" with variable_scope.variable_scope(scope): feature_columns = list(feature_columns) - transformed_features = {} + transformed_features = collections.OrderedDict() for fc in feature_columns: # pylint: disable=protected-access if isinstance(fc, feature_column_lib._EmbeddingColumn): @@ -681,13 +682,13 @@ class GradientBoostedDecisionTreeModel(object): control_flow_ops.no_op)) # Update handler stats. - handler_reads = {} + handler_reads = collections.OrderedDict() for handler in handlers: handler_reads[handler] = handler.scheduled_reads() handler_results = batch_ops_utils.run_handler_scheduled_ops( handler_reads, ensemble_stamp, worker_device) - per_handler_updates = {} + per_handler_updates = collections.OrderedDict() # Two values per handler. First one is if the handler is active for the # current layer. The second one is if the handler is going to be active # for the next layer. -- GitLab From f84623507b46d1226385bffbf115cafa9c25e892 Mon Sep 17 00:00:00 2001 From: ted chang Date: Fri, 26 Jan 2018 13:15:47 -0800 Subject: [PATCH 146/423] Fix a bug in PR #15906 (#16467) --- tensorflow/python/tools/freeze_graph.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py index a2e86a1c43..fd78f44c99 100644 --- a/tensorflow/python/tools/freeze_graph.py +++ b/tensorflow/python/tools/freeze_graph.py @@ -251,7 +251,7 @@ def main(unused_args): FLAGS.output_graph, FLAGS.clear_devices, FLAGS.initializer_nodes, FLAGS.variable_names_whitelist, FLAGS.variable_names_blacklist, FLAGS.input_meta_graph, FLAGS.input_saved_model_dir, - FLAGS.saved_model_tags, checkpoint_version=checkpoint_version) + FLAGS.saved_model_tags, FLAGS.checkpoint_version) if __name__ == "__main__": -- GitLab From d1910fa9eb274717719c4dcff3247498ea30caa4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 13:20:42 -0800 Subject: [PATCH 147/423] Add more tests to validate the bucket boundaries for inputs with equal distributions. PiperOrigin-RevId: 183435084 --- .../python/kernel_tests/quantile_ops_test.py | 30 +++++++++++++++++-- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py index eefa7ef0dc..81f58de28c 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py @@ -183,11 +183,10 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): self.assertEqual(num_quantiles + 1, len(buckets)) self.assertAllEqual([2030, 2040, 2050, 2060], buckets) - def _testStreamingQuantileBucketsHelper(self, inputs): + def _testStreamingQuantileBucketsHelper( + self, inputs, num_quantiles=3, expected_buckets=None): """Helper to test quantile buckets on different inputs.""" - # Use 3 quantiles, 4 boundaries for simplicity. - num_quantiles = 3 # set generate_quantiles to True since the test will generate fewer # boundaries otherwise. with self.test_session() as sess: @@ -213,7 +212,10 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): buckets, are_ready_flush = (sess.run( [buckets, are_ready_flush])) self.assertEqual(True, are_ready_flush) + # By default, use 3 quantiles, 4 boundaries for simplicity. self.assertEqual(num_quantiles + 1, len(buckets)) + if expected_buckets: + self.assertAllEqual(buckets, expected_buckets) def testStreamingQuantileBucketsRepeatedSingleValue(self): inputs = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] @@ -231,6 +233,28 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): inputs = [5] self._testStreamingQuantileBucketsHelper(inputs) + def testStreamingQuantileBucketsEqualDistributionInSequence(self): + # Input pattern is of the form [1, 1, 1, 2, 2, 2, 3, 3, 3, ...] + ones = 100 * [1] + inputs = [] + for i in range(1, 101): + inputs += [i * k for k in ones] + # Expect 100 equally spaced buckets. + expected_buckets = range(1, 101) + self._testStreamingQuantileBucketsHelper( + inputs, num_quantiles=99, expected_buckets=expected_buckets) + + def testStreamingQuantileBucketsEqualDistributionInterleaved(self): + # Input pattern is of the form [1, 2, 3, 1, 2, 3, 1, 2, 3, ...] + sequence = range(1, 101) + inputs = [] + for _ in range(1, 101): + inputs += sequence + # Expect 100 equally spaced buckets. + expected_buckets = range(1, 101) + self._testStreamingQuantileBucketsHelper( + inputs, num_quantiles=99, expected_buckets=expected_buckets) + def testStreamingQuantileBuckets(self): """Sets up the quantile summary op test as follows. -- GitLab From 079a0e53311b4cf913be5ec5bd26bbb2b0649e93 Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Fri, 26 Jan 2018 13:23:02 -0800 Subject: [PATCH 148/423] Improved heuristics for swapping PiperOrigin-RevId: 183435438 --- .../core/grappler/costs/cost_estimator.h | 3 + tensorflow/core/grappler/optimizers/BUILD | 1 + .../grappler/optimizers/memory_optimizer.cc | 149 ++++++++++++------ .../optimizers/memory_optimizer_test.cc | 3 +- 4 files changed, 108 insertions(+), 48 deletions(-) diff --git a/tensorflow/core/grappler/costs/cost_estimator.h b/tensorflow/core/grappler/costs/cost_estimator.h index b7eaf8dc63..d442861339 100644 --- a/tensorflow/core/grappler/costs/cost_estimator.h +++ b/tensorflow/core/grappler/costs/cost_estimator.h @@ -78,6 +78,9 @@ struct Costs { MilliSeconds asMilliSeconds() const { return std::chrono::duration_cast(*this); } + static NanoSeconds infinity() { + return NanoSeconds(std::chrono::nanoseconds::max()); + } }; // We store all our times in nanoseconds. If needs be, we can always switch to diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index 791ad34bbe..68de03e81c 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -285,6 +285,7 @@ cc_library( "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", + "//tensorflow/core/grappler/clusters:virtual_cluster", "//tensorflow/core/grappler/costs:graph_memory", "//tensorflow/core/grappler/costs:graph_properties", "//tensorflow/core/grappler/utils:topological_sort", diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc index f537ecc41b..6f95a00fa3 100644 --- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/tensor_shape.pb.h" +#include "tensorflow/core/grappler/clusters/virtual_cluster.h" #include "tensorflow/core/grappler/costs/graph_memory.h" #include "tensorflow/core/grappler/costs/graph_properties.h" #include "tensorflow/core/grappler/graph_view.h" @@ -828,8 +829,7 @@ static NodeDef* FindSwapOutTrigger( const std::unordered_set& fanout = view.GetFanout(generator); NodeDef* trigger = nullptr; - Costs::NanoSeconds earliest_fanout( - static_cast(std::numeric_limits::max() >> 2)); + Costs::NanoSeconds earliest_fanout(Costs::NanoSeconds::infinity()); for (const auto& port : fanout) { if (port.node == node) { @@ -861,6 +861,15 @@ static bool IsSwappable(GraphView::InputPort input) { return !IsRefType(dtype); } +struct MemInfo { + GraphView::OutputPort port; + int64 memory_used; + std::vector uses_left; + double fitness; + + bool operator<(const MemInfo& other) const { return fitness < other.fitness; } +}; + static bool IdentifySwappingCandidates( Cluster* cluster, GrapplerItem* item, std::unordered_set* skip_list, std::unordered_map* nodes_to_swap) { @@ -890,31 +899,56 @@ static bool IdentifySwappingCandidates( continue; } int64 required_savings = mem_usage.used_memory - prop.memory_size(); - // TODO(bsteiner): sort the tensors by how long they're live. - std::unordered_map execution_times; + std::unordered_map op_completion_times; { - std::unordered_map - tmp_execution_times; - if (!EstimateEarliestExecutionTimes(*item, cluster, &tmp_execution_times) - .ok()) { + VirtualCluster vcluster(cluster->GetDevices()); + if (!vcluster.Provision().ok()) { return false; } - for (const auto& exec_time : tmp_execution_times) { - execution_times.emplace(exec_time.first->name(), exec_time.second); + if (!vcluster.Initialize(*item).ok()) { + return false; + } + RunMetadata metadata; + Status s = vcluster.Run(item->graph, item->feed, item->fetch, &metadata); + if (!s.ok() && s.code() != error::RESOURCE_EXHAUSTED) { + return false; + } + + for (const auto& dev_stats : metadata.step_stats().dev_stats()) { + for (const auto& node_stats : dev_stats.node_stats()) { + Costs::NanoSeconds exec_time = + Costs::NanoSeconds(1) + + Costs::MicroSeconds(node_stats.all_start_micros() + + node_stats.op_end_rel_micros()); + op_completion_times.emplace(node_stats.node_name(), exec_time); + } } } + Costs::Duration peak_time = -1; + for (const auto& live_tensor : mem_usage.live_tensors) { + if (live_tensor.allocation_time > peak_time) { + peak_time = live_tensor.allocation_time; + } + } + + std::vector mem_state; + GraphView graph(&item->graph); for (const auto& live_tensor : mem_usage.live_tensors) { + if (live_tensor.memory_used <= 1024) { + // Don't bother with small tensors. + continue; + } if (live_tensor.deallocation_time - live_tensor.allocation_time <= Costs::Duration(1e6)) { // Not enough time to swap. VLOG(1) << "Not enough time to swap: skipping " << live_tensor.node; continue; } - if (live_tensor.memory_used <= 1024) { - // Don't bother with small tensors. + + if (skip_list->find(live_tensor.node) != skip_list->end()) { continue; } GraphView::OutputPort port = @@ -922,56 +956,77 @@ static bool IdentifySwappingCandidates( if (!IsSwappable(graph, port)) { continue; } - Costs::NanoSeconds execution_time(-1); - GraphView::InputPort fanout_to_swap; + MemInfo mem_info; + mem_info.port = port; + mem_info.memory_used = live_tensor.memory_used; + Costs::Duration allocation_time = live_tensor.allocation_time; + Costs::Duration earliest_use(Costs::Duration::infinity()); + bool valid = true; for (GraphView::InputPort input : graph.GetFanout(port)) { - if (skip_list->find(input.node->name()) != skip_list->end()) { + // Get execution time. + auto it = op_completion_times.find(input.node->name()); + if (it == op_completion_times.end()) { + valid = false; + break; + } + if (it->second <= peak_time) { continue; } + + if (skip_list->find(input.node->name()) != skip_list->end()) { + valid = false; + break; + } string input_name = strings::StrCat(input.node->name(), ":", input.port_id); if (skip_list->find(input_name) != skip_list->end()) { - continue; + valid = false; + break; } if (!IsSwappable(input)) { - continue; - } - auto it = execution_times.find(input.node->name()); - if (it != execution_times.end()) { - if (it->second > execution_time) { - fanout_to_swap = input; - execution_time = it->second; - } + valid = false; + break; } + + // Set earliest use time that's after peak. + mem_info.uses_left.emplace_back(input); + earliest_use = std::min(earliest_use, it->second); } - // Annotate the fanout to request the tensor to be swapped if it's not - // already been done. - bool found = false; - if (!fanout_to_swap.node) { - continue; - } - auto it = fanout_to_swap.node->attr().find("_swap_to_host"); - if (it != fanout_to_swap.node->attr().end()) { - const AttrValue& val = it->second; - for (int port_id : val.list().i()) { - if (port_id == fanout_to_swap.port_id) { - found = true; - break; - } - } + if (valid && !mem_info.uses_left.empty()) { + // Compute the fitness: we need the tensor to be generated way away of + // the time of peak memory usage (to ensure there is enough time to swap + // it out). We also need to ensure it's used way after the peak time, to + // ensure that swapping the tensor back in won't recreate the memory + // bottleneck. Last but not least, we want the tensor to have as few + // remaining uses as possible. + mem_info.fitness = std::pow((earliest_use - peak_time).count(), 2); + mem_info.fitness /= std::pow(mem_info.uses_left.size(), 2); + mem_info.fitness += std::pow((allocation_time - peak_time).count(), 2); + mem_info.fitness = -mem_info.fitness; + mem_state.push_back(mem_info); } - if (!found) { + } + + // Sort by fitness + std::sort(mem_state.begin(), mem_state.end()); + + for (const MemInfo& mem_info : mem_state) { + for (const GraphView::InputPort fanout_to_swap : mem_info.uses_left) { + VLOG(1) << "Will swap fanout " << fanout_to_swap.node->name() << ":" + << fanout_to_swap.port_id << " of tensor " + << mem_info.port.node->name() << ":" << mem_info.port.port_id + << " of size " << mem_info.memory_used; + (*nodes_to_swap)[fanout_to_swap.node].inputs_to_swap.push_back( fanout_to_swap.port_id); - required_savings -= live_tensor.memory_used; - updated_graph = true; - if (required_savings < 0) { - break; - } + } + required_savings -= mem_info.memory_used; + updated_graph = true; + if (required_savings < 0) { + break; } } } - return updated_graph; } @@ -1011,7 +1066,7 @@ bool SwappingPass(RewriterConfig::MemOptType optimization_level, } for (auto& swap : nodes_to_swap) { const NodeDef* node = swap.first; - std::vector props = + const std::vector& props = properties.GetInputProperties(node->name()); SwapInfo& swap_info = swap.second; int64 bytes_to_swap = 0; diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc index dd2d20d8d6..f5d9c87992 100644 --- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc @@ -337,8 +337,9 @@ TEST_F(MemoryOptimizerTest, UnswappableInputs) { for (const auto& node : output.node()) { if (node.name() == "e") { // The d node isn't swappable. - EXPECT_EQ(4, node.input_size()); + EXPECT_EQ(5, node.input_size()); EXPECT_EQ("d", node.input(2)); + EXPECT_EQ("^swap_out_d_2", node.input(4)); } } } -- GitLab From 982549ea3423df4270ff154e5c764beb43d472da Mon Sep 17 00:00:00 2001 From: Rasmus Munk Larsen Date: Fri, 26 Jan 2018 13:32:16 -0800 Subject: [PATCH 149/423] Branch 183429339 (#16469) * Change `reduce_logsumexp` to internally use `reshape` rather than `squeeze` since the latter requires the `axis` arg to be a Python `list`. PiperOrigin-RevId: 183396533 * Kernel utils to support broadcast add and mul. PiperOrigin-RevId: 183397494 * Updating sparsify_gather. PiperOrigin-RevId: 183402917 * [tf.data] Move slow-path-related code into the slow path in IteratorHandleOp::Compute(). This slightly reduces the amount of work performed when an iterator is accessed (after the first access), and potentially reduces contention if concurrent steps are accessing the same iterator. PiperOrigin-RevId: 183406221 * Cleanup: Ran clang-format on all *.{cc,h} in under grappler. PiperOrigin-RevId: 183406440 * Increase shard count of //third_party/tensorflow/python:nn_batchnorm_test to avoid timeouts When run under asan, the test runs for about 5 minutes, and sometimes longer, causing frequent timeouts. This change increases the shard count of the test to 4, which brings the run time of the longest running shard under asan to about 2 minutes. PiperOrigin-RevId: 183414888 * Add available choices to toco flags and fix minor formatting issues. PiperOrigin-RevId: 183415713 * Performance improvements to some GPU code to use shared locks instead of unique locks for some hotspot cases. PiperOrigin-RevId: 183418559 * [XLA] Improve error message for bad slices. PiperOrigin-RevId: 183420038 * Fix py3 build rules for all py tests under py2tf. PiperOrigin-RevId: 183422144 * Fix bug with Operation._control_inputs setter. PiperOrigin-RevId: 183422192 * Make softmax_op_test.py work with C API enabled. PiperOrigin-RevId: 183422829 * Cleanup: Ran clang-format on all *.{cc,h} files in tensorflow/core/kernels. PiperOrigin-RevId: 183423961 * Fix the documentation for the dense layer for how rank > 2 inputs are handled. PiperOrigin-RevId: 183425868 * Cleanup: Ran clang-format on all *.{cc,h} in tensorflow/core/ops. PiperOrigin-RevId: 183429339 --- .../compiler/xla/service/shape_inference.cc | 55 +- .../xla/service/shape_inference_test.cc | 15 + tensorflow/contrib/lite/kernels/BUILD | 29 +- .../contrib/lite/kernels/kernel_util.cc | 26 + tensorflow/contrib/lite/kernels/kernel_util.h | 17 + .../contrib/lite/kernels/kernel_util_test.cc | 150 ++++ .../contrib/lite/toco/toco_cmdline_flags.cc | 36 +- tensorflow/contrib/py2tf/BUILD | 3 + tensorflow/contrib/py2tf/converters/BUILD | 8 + tensorflow/contrib/py2tf/pyct/BUILD | 5 + .../contrib/py2tf/pyct/static_analysis/BUILD | 3 + .../core/common_runtime/gpu/process_state.cc | 18 +- tensorflow/core/grappler/clusters/cluster.cc | 3 +- .../core/grappler/costs/virtual_scheduler.h | 2 +- .../core/grappler/optimizers/auto_parallel.h | 2 +- tensorflow/core/kernels/adjust_contrast_op.cc | 4 +- .../core/kernels/adjust_contrast_op_test.cc | 3 +- .../core/kernels/adjust_saturation_op.cc | 5 +- tensorflow/core/kernels/aggregate_ops_cpu.h | 12 +- tensorflow/core/kernels/attention_ops.cc | 5 +- tensorflow/core/kernels/avgpooling_op.h | 5 +- .../core/kernels/avgpooling_op_gpu.cu.cc | 14 +- tensorflow/core/kernels/barrier_ops.cc | 34 +- tensorflow/core/kernels/batch_kernels.cc | 4 +- .../core/kernels/batch_matmul_op_impl.h | 27 +- .../core/kernels/batch_matmul_op_real.cc | 2 +- .../core/kernels/batch_matmul_op_test.cc | 7 +- tensorflow/core/kernels/batch_norm_op.cc | 2 +- tensorflow/core/kernels/batch_norm_op_test.cc | 2 +- tensorflow/core/kernels/batchtospace_op.cc | 7 +- tensorflow/core/kernels/bcast_ops.cc | 2 +- tensorflow/core/kernels/bias_op_gpu.cu.cc | 43 +- tensorflow/core/kernels/bounds_check.h | 2 +- .../core/kernels/candidate_sampler_ops.cc | 17 +- tensorflow/core/kernels/cast_op.cc | 17 +- tensorflow/core/kernels/cast_op.h | 3 +- tensorflow/core/kernels/cast_op_impl.h | 28 +- tensorflow/core/kernels/cast_op_test.cc | 12 +- tensorflow/core/kernels/colorspace_op.cc | 65 +- tensorflow/core/kernels/colorspace_op.h | 7 +- .../core/kernels/colorspace_op_gpu.cu.cc | 4 +- tensorflow/core/kernels/colorspace_op_test.cc | 56 +- tensorflow/core/kernels/concat_lib.h | 20 +- tensorflow/core/kernels/concat_lib_cpu.cc | 32 +- tensorflow/core/kernels/concat_lib_cpu.h | 6 +- tensorflow/core/kernels/concat_op.cc | 21 +- tensorflow/core/kernels/concat_op_test.cc | 3 +- .../kernels/conditional_accumulator_base.h | 2 +- .../kernels/conditional_accumulator_op.cc | 8 +- tensorflow/core/kernels/constant_op.cc | 1 - tensorflow/core/kernels/control_flow_ops.cc | 96 +-- .../core/kernels/control_flow_ops_test.cc | 1 + tensorflow/core/kernels/conv_ops.cc | 2 +- tensorflow/core/kernels/conv_ops_fused.cc | 78 ++- tensorflow/core/kernels/conv_ops_gpu.h | 1 - tensorflow/core/kernels/conv_ops_gpu_3.cu.cc | 31 +- .../core/kernels/conv_ops_using_gemm.cc | 29 +- tensorflow/core/kernels/cross_op_gpu.cu.cc | 2 +- tensorflow/core/kernels/ctc_decoder_ops.cc | 11 +- tensorflow/core/kernels/ctc_loss_op.cc | 4 +- tensorflow/core/kernels/cwise_op_abs.cc | 2 +- tensorflow/core/kernels/cwise_op_acos.cc | 2 +- tensorflow/core/kernels/cwise_op_acosh.cc | 6 +- tensorflow/core/kernels/cwise_op_add_1.cc | 3 +- tensorflow/core/kernels/cwise_op_add_2.cc | 4 +- tensorflow/core/kernels/cwise_op_asin.cc | 2 +- tensorflow/core/kernels/cwise_op_asinh.cc | 8 +- tensorflow/core/kernels/cwise_op_atan.cc | 2 +- tensorflow/core/kernels/cwise_op_atanh.cc | 4 +- tensorflow/core/kernels/cwise_op_ceil.cc | 2 +- tensorflow/core/kernels/cwise_op_cos.cc | 2 +- tensorflow/core/kernels/cwise_op_cosh.cc | 16 +- tensorflow/core/kernels/cwise_op_div.cc | 2 +- tensorflow/core/kernels/cwise_op_exp.cc | 2 +- tensorflow/core/kernels/cwise_op_expm1.cc | 2 +- tensorflow/core/kernels/cwise_op_floor.cc | 2 +- tensorflow/core/kernels/cwise_op_floor_div.cc | 2 +- tensorflow/core/kernels/cwise_op_floor_mod.cc | 2 +- .../core/kernels/cwise_op_gpu_conj.cu.cc | 4 +- .../core/kernels/cwise_op_gpu_equal_to.cu.cc | 2 +- .../core/kernels/cwise_op_gpu_select.cu.cc | 24 +- tensorflow/core/kernels/cwise_op_greater.cc | 2 +- .../core/kernels/cwise_op_greater_equal.cc | 5 +- tensorflow/core/kernels/cwise_op_invert.cc | 2 +- tensorflow/core/kernels/cwise_op_isfinite.cc | 2 +- tensorflow/core/kernels/cwise_op_isinf.cc | 2 +- tensorflow/core/kernels/cwise_op_isnan.cc | 2 +- tensorflow/core/kernels/cwise_op_less.cc | 2 +- .../core/kernels/cwise_op_less_equal.cc | 2 +- tensorflow/core/kernels/cwise_op_log.cc | 2 +- tensorflow/core/kernels/cwise_op_log1p.cc | 2 +- tensorflow/core/kernels/cwise_op_maximum.cc | 2 +- tensorflow/core/kernels/cwise_op_minimum.cc | 2 +- tensorflow/core/kernels/cwise_op_mul_1.cc | 8 +- tensorflow/core/kernels/cwise_op_mul_2.cc | 6 +- tensorflow/core/kernels/cwise_op_neg.cc | 2 +- .../core/kernels/cwise_op_not_equal_to_1.cc | 2 +- .../core/kernels/cwise_op_not_equal_to_2.cc | 2 +- .../core/kernels/cwise_op_reciprocal.cc | 4 +- tensorflow/core/kernels/cwise_op_select.cc | 30 +- tensorflow/core/kernels/cwise_op_sigmoid.cc | 4 +- tensorflow/core/kernels/cwise_op_sign.cc | 2 +- tensorflow/core/kernels/cwise_op_sin.cc | 2 +- tensorflow/core/kernels/cwise_op_sinh.cc | 16 +- tensorflow/core/kernels/cwise_op_sqrt.cc | 4 +- tensorflow/core/kernels/cwise_op_square.cc | 2 +- tensorflow/core/kernels/cwise_op_sub.cc | 2 +- tensorflow/core/kernels/cwise_op_tan.cc | 2 +- tensorflow/core/kernels/cwise_op_tanh.cc | 2 +- tensorflow/core/kernels/cwise_ops_common.cc | 6 +- .../core/kernels/cwise_ops_gpu_gradients.cu.h | 14 +- tensorflow/core/kernels/cwise_ops_gradients.h | 3 +- .../core/kernels/cwise_ops_sycl_common.h | 10 +- tensorflow/core/kernels/cwise_ops_test.cc | 42 +- tensorflow/core/kernels/data/iterator_ops.cc | 95 ++- tensorflow/core/kernels/debug_ops.cc | 6 +- tensorflow/core/kernels/debug_ops.h | 4 +- tensorflow/core/kernels/decode_csv_op.cc | 40 +- tensorflow/core/kernels/decode_image_op.cc | 32 +- tensorflow/core/kernels/deep_conv2d.cc | 6 +- tensorflow/core/kernels/dense_update_ops.cc | 16 +- .../core/kernels/depthwise_conv_grad_op.cc | 4 +- tensorflow/core/kernels/depthwise_conv_op.cc | 15 +- .../core/kernels/depthwise_conv_op_gpu.cu.cc | 4 +- tensorflow/core/kernels/diag_op.cc | 56 +- tensorflow/core/kernels/diag_op.h | 8 +- tensorflow/core/kernels/diag_op_gpu.cu.cc | 52 +- tensorflow/core/kernels/diag_op_test.cc | 5 +- tensorflow/core/kernels/dilation_ops.cc | 26 +- .../core/kernels/dilation_ops_gpu.cu.cc | 15 +- .../core/kernels/draw_bounding_box_op.cc | 28 +- .../core/kernels/dynamic_partition_op.cc | 6 +- .../kernels/dynamic_partition_op_gpu.cu.cc | 57 +- tensorflow/core/kernels/eigen_activations.h | 38 +- .../core/kernels/eigen_activations_test.cc | 2 +- tensorflow/core/kernels/eigen_attention.h | 83 ++- .../core/kernels/eigen_attention_test.cc | 2 +- .../eigen_backward_spatial_convolutions.h | 60 +- ...igen_backward_spatial_convolutions_test.cc | 2 +- tensorflow/core/kernels/eigen_pooling.h | 8 +- tensorflow/core/kernels/eigen_pooling_test.cc | 2 +- tensorflow/core/kernels/eigen_softmax.h | 61 +- tensorflow/core/kernels/eigen_softmax_test.cc | 2 +- .../core/kernels/eigen_spatial_convolutions.h | 32 +- tensorflow/core/kernels/encode_jpeg_op.cc | 16 +- .../core/kernels/example_parsing_ops.cc | 29 +- tensorflow/core/kernels/fact_op.cc | 12 +- .../core/kernels/fake_quant_ops_test.cc | 44 +- tensorflow/core/kernels/fifo_queue.cc | 169 +++-- tensorflow/core/kernels/fill_functor.cc | 10 +- .../core/kernels/fractional_avg_pool_op.cc | 5 +- tensorflow/core/kernels/function_ops.cc | 31 +- .../core/kernels/fused_batch_norm_op.cu.cc | 3 +- tensorflow/core/kernels/gather_functor.cc | 10 +- tensorflow/core/kernels/gather_functor.h | 52 +- tensorflow/core/kernels/gather_op.cc | 3 +- tensorflow/core/kernels/hinge-loss.h | 5 +- .../core/kernels/histogram_op_gpu.cu.cc | 8 +- tensorflow/core/kernels/image_resizer_state.h | 10 +- tensorflow/core/kernels/in_topk_op.cc | 64 +- tensorflow/core/kernels/inplace_ops.cc | 10 +- tensorflow/core/kernels/l2loss_op.cc | 2 +- tensorflow/core/kernels/linalg_ops_common.cc | 1 - tensorflow/core/kernels/lmdb_reader_op.cc | 15 +- tensorflow/core/kernels/logistic-loss.h | 7 +- tensorflow/core/kernels/loss_test.cc | 201 +++--- tensorflow/core/kernels/lrn_op.cc | 25 +- tensorflow/core/kernels/matching_files_op.cc | 11 +- tensorflow/core/kernels/matmul_op.cc | 4 +- tensorflow/core/kernels/matmul_op.h | 3 +- .../core/kernels/matrix_exponential_op.cc | 12 +- .../core/kernels/matrix_logarithm_op.cc | 12 +- tensorflow/core/kernels/matrix_set_diag_op.cc | 4 +- tensorflow/core/kernels/maxpooling_op.cc | 6 +- .../core/kernels/maxpooling_op_gpu.cu.cc | 4 +- tensorflow/core/kernels/meta_support.cc | 6 +- tensorflow/core/kernels/mfcc.cc | 26 +- tensorflow/core/kernels/mfcc.h | 5 +- .../core/kernels/mfcc_mel_filterbank.cc | 46 +- tensorflow/core/kernels/mfcc_mel_filterbank.h | 8 +- .../core/kernels/mfcc_mel_filterbank_test.cc | 15 +- tensorflow/core/kernels/mfcc_test.cc | 9 +- tensorflow/core/kernels/mirror_pad_op.cc | 8 +- tensorflow/core/kernels/mkl_avgpooling_op.cc | 279 ++++---- .../core/kernels/mkl_batch_matmul_op.cc | 1 - tensorflow/core/kernels/mkl_concat_op.cc | 132 ++-- .../core/kernels/mkl_conv_grad_bias_ops.cc | 2 +- .../core/kernels/mkl_conv_grad_filter_ops.cc | 182 ++--- .../core/kernels/mkl_conv_grad_input_ops.cc | 107 ++- tensorflow/core/kernels/mkl_conv_ops.cc | 252 +++---- tensorflow/core/kernels/mkl_conv_ops.h | 230 +++---- .../core/kernels/mkl_fused_batch_norm_op.cc | 506 ++++++-------- .../core/kernels/mkl_input_conversion_op.cc | 10 +- tensorflow/core/kernels/mkl_lrn_op.cc | 647 +++++++++--------- tensorflow/core/kernels/mkl_maxpooling_op.cc | 497 +++++++------- .../core/kernels/mkl_pooling_ops_common.cc | 14 +- .../core/kernels/mkl_pooling_ops_common.h | 328 +++++---- tensorflow/core/kernels/mkl_relu_op.cc | 212 +++--- tensorflow/core/kernels/mkl_reshape_op.cc | 105 ++- tensorflow/core/kernels/mkl_tfconv_op.cc | 124 ++++ .../core/kernels/non_max_suppression_op.cc | 24 +- .../kernels/non_max_suppression_op_test.cc | 30 +- tensorflow/core/kernels/nth_element_op.cc | 39 +- tensorflow/core/kernels/nth_element_op.h | 6 +- tensorflow/core/kernels/one_hot_op_gpu.cu.cc | 6 +- tensorflow/core/kernels/ops_util_test.cc | 13 +- tensorflow/core/kernels/pack_op.cc | 4 +- .../parameterized_truncated_normal_op.cc | 40 +- ...arameterized_truncated_normal_op_gpu.cu.cc | 13 +- tensorflow/core/kernels/parse_tensor_op.cc | 1 - tensorflow/core/kernels/pooling_ops_3d.cc | 6 +- tensorflow/core/kernels/pooling_ops_3d_sycl.h | 17 +- tensorflow/core/kernels/pooling_ops_common.h | 2 - .../core/kernels/quantization_utils_test.cc | 8 +- .../core/kernels/quantize_and_dequantize_op.h | 3 +- tensorflow/core/kernels/quantize_op_test.cc | 3 +- .../core/kernels/quantized_batch_norm_op.cc | 2 +- .../core/kernels/quantized_concat_op.cc | 8 +- tensorflow/core/kernels/quantized_conv_ops.cc | 7 +- .../core/kernels/quantized_instance_norm.cc | 8 +- .../core/kernels/quantized_matmul_op.cc | 6 +- .../core/kernels/quantized_matmul_op_test.cc | 39 +- tensorflow/core/kernels/quantized_mul_op.cc | 5 +- .../core/kernels/quantized_mul_op_test.cc | 11 +- tensorflow/core/kernels/queue_base.cc | 4 +- tensorflow/core/kernels/queue_ops.cc | 11 +- tensorflow/core/kernels/random_crop_op.cc | 8 +- tensorflow/core/kernels/random_op.cc | 253 ++++--- tensorflow/core/kernels/random_op_gpu.cu.cc | 5 +- tensorflow/core/kernels/random_poisson_op.cc | 2 +- .../core/kernels/random_shuffle_queue_op.cc | 169 +++-- .../core/kernels/reduction_gpu_kernels.cu.h | 6 +- tensorflow/core/kernels/relu_op.cc | 13 +- tensorflow/core/kernels/relu_op_functor.h | 11 +- tensorflow/core/kernels/resize_bicubic_op.cc | 2 +- .../core/kernels/resize_bicubic_op_test.cc | 15 +- .../core/kernels/resize_bilinear_op_gpu.cu.cc | 20 +- tensorflow/core/kernels/reverse_op.cc | 8 +- tensorflow/core/kernels/reverse_op_gpu.cu.cc | 16 +- .../core/kernels/reverse_sequence_op.cc | 68 +- .../kernels/reverse_sequence_op_gpu.cu.cc | 12 +- .../core/kernels/save_restore_tensor.cc | 10 +- tensorflow/core/kernels/scatter_functor.h | 16 +- .../core/kernels/scatter_functor_gpu.cu.h | 11 +- .../core/kernels/scatter_nd_op_cpu_impl.h | 4 +- tensorflow/core/kernels/scatter_op.cc | 30 +- tensorflow/core/kernels/sdca_internal.cc | 45 +- tensorflow/core/kernels/sdca_internal.h | 3 +- tensorflow/core/kernels/sdca_ops.cc | 14 +- .../core/kernels/segment_reduction_ops.cc | 30 +- .../core/kernels/segment_reduction_ops.h | 19 +- .../kernels/segment_reduction_ops_gpu.cu.cc | 12 +- .../core/kernels/self_adjoint_eig_op.cc | 1 - tensorflow/core/kernels/sendrecv_ops.cc | 6 +- tensorflow/core/kernels/sequence_ops.cc | 8 +- tensorflow/core/kernels/session_ops.cc | 2 +- tensorflow/core/kernels/shape_ops.h | 8 +- tensorflow/core/kernels/slice_op.cc | 168 +++-- tensorflow/core/kernels/slice_op.h | 1 - tensorflow/core/kernels/slice_op_cpu_impl.h | 2 +- tensorflow/core/kernels/softmax_op.cc | 6 +- .../kernels/spacetobatch_benchmark_test.cc | 2 +- .../core/kernels/spacetobatch_functor.cc | 2 +- .../core/kernels/spacetobatch_functor.h | 2 +- .../kernels/spacetobatch_functor_gpu.cu.cc | 10 +- tensorflow/core/kernels/spacetobatch_op.cc | 7 +- tensorflow/core/kernels/sparse_add_grad_op.cc | 12 +- tensorflow/core/kernels/sparse_add_op.cc | 15 +- tensorflow/core/kernels/sparse_add_op_test.cc | 4 +- .../sparse_conditional_accumulator_op.cc | 5 +- tensorflow/core/kernels/sparse_cross_op.cc | 16 +- .../kernels/sparse_dense_binary_op_shared.cc | 10 +- .../sparse_dense_binary_op_shared_test.cc | 16 +- tensorflow/core/kernels/sparse_matmul_op.cc | 24 +- tensorflow/core/kernels/sparse_matmul_op.h | 8 +- .../core/kernels/sparse_matmul_op_test.cc | 8 +- .../core/kernels/sparse_reduce_sum_op_test.cc | 8 +- tensorflow/core/kernels/sparse_softmax_op.cc | 5 +- .../kernels/sparse_sparse_binary_op_shared.cc | 15 +- tensorflow/core/kernels/sparse_split_op.cc | 26 +- tensorflow/core/kernels/sparse_to_dense_op.cc | 5 +- .../core/kernels/sparse_to_dense_op_test.cc | 1 - tensorflow/core/kernels/sparse_xent_op.cc | 8 +- .../core/kernels/sparse_xent_op_test.cc | 6 +- tensorflow/core/kernels/split_lib.h | 2 +- tensorflow/core/kernels/split_lib_cpu.cc | 4 +- tensorflow/core/kernels/split_op.cc | 37 +- tensorflow/core/kernels/split_v_op.cc | 14 +- tensorflow/core/kernels/stack_ops.cc | 18 +- tensorflow/core/kernels/stage_op.cc | 74 +- tensorflow/core/kernels/strided_slice_op.cc | 2 +- .../core/kernels/strided_slice_op_impl.h | 2 +- tensorflow/core/kernels/string_join_op.cc | 6 +- tensorflow/core/kernels/substr_op.cc | 6 +- tensorflow/core/kernels/summary_image_op.cc | 16 +- tensorflow/core/kernels/summary_op.cc | 11 +- tensorflow/core/kernels/tile_functor_cpu.cc | 2 +- tensorflow/core/kernels/tile_ops_cpu_impl.h | 2 +- tensorflow/core/kernels/training_ops.cc | 42 +- .../core/kernels/training_ops_gpu.cu.cc | 24 +- tensorflow/core/kernels/training_ops_test.cc | 5 +- tensorflow/core/kernels/transpose_op.cc | 7 +- tensorflow/core/kernels/typed_queue.h | 6 +- tensorflow/core/kernels/unpack_op.cc | 7 +- tensorflow/core/kernels/word2vec_kernels.cc | 6 +- tensorflow/core/kernels/xent_op.cc | 14 +- tensorflow/core/kernels/xsmm_conv2d_test.cc | 344 +++++----- tensorflow/core/ops/array_ops.cc | 17 +- tensorflow/core/ops/array_ops_test.cc | 9 +- .../core/ops/candidate_sampling_ops_test.cc | 9 +- tensorflow/core/ops/functional_grad.cc | 2 +- tensorflow/core/ops/math_ops.cc | 8 +- tensorflow/core/ops/nn_ops.cc | 12 +- tensorflow/core/ops/sdca_ops.cc | 2 +- tensorflow/core/ops/string_ops.cc | 6 +- tensorflow/core/ops/training_ops_test.cc | 2 +- tensorflow/python/BUILD | 1 + tensorflow/python/framework/ops.py | 4 + .../python/kernel_tests/softmax_op_test.py | 7 +- tensorflow/python/layers/core.py | 9 +- tensorflow/python/ops/math_ops.py | 9 +- tensorflow/stream_executor/executor_cache.cc | 23 +- .../stream_executor/multi_platform_manager.cc | 4 +- .../tools/graph_transforms/sparsify_gather.cc | 109 ++- .../graph_transforms/sparsify_gather_test.cc | 40 +- 325 files changed, 4747 insertions(+), 4414 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/kernel_util_test.cc create mode 100644 tensorflow/core/kernels/mkl_tfconv_op.cc diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index a6d6c8b27f..4ba6da6ccc 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -37,6 +37,9 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/protobuf.h" +using tensorflow::str_util::Join; +using tensorflow::strings::Printf; + namespace xla { namespace { @@ -934,7 +937,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "inferring shape for <%s>(%s, %s) with broadcast_dimensions={%s}", BinaryOperation_Name(operation).c_str(), ShapeUtil::HumanString(lhs).c_str(), ShapeUtil::HumanString(rhs).c_str(), - tensorflow::str_util::Join(broadcast_dimensions, ", ").c_str()); + Join(broadcast_dimensions, ", ").c_str()); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(rhs)); @@ -1097,7 +1100,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Map operation requires all operands to have the same shape; got: " "%s", - tensorflow::str_util::Join(pieces, ", ").c_str()); + Join(pieces, ", ").c_str()); } // Check that dimensions.size == arg_shape.dimensions_size() (we currently @@ -1114,7 +1117,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( if (dimensions[i] != i) { return InvalidArgument( "Map requires monotonically increasing dimension numbers, found: %s ", - tensorflow::str_util::Join(dimensions, ", ").c_str()); + Join(dimensions, ", ").c_str()); } } @@ -1914,21 +1917,28 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( const Shape& arg, tensorflow::gtl::ArraySlice starts, tensorflow::gtl::ArraySlice limits, tensorflow::gtl::ArraySlice strides) { + auto error = [&](const string& message) { + return InvalidArgument( + "%s in slice operation; argument shape: %s; starts: {%s}; limits: " + "{%s}; strides: {%s}", + message.c_str(), ShapeUtil::HumanString(arg).c_str(), + Join(starts, ",").c_str(), Join(limits, ",").c_str(), + Join(strides, ",").c_str()); + }; TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(arg, "operand of slice")); VLOG(2) << tensorflow::strings::Printf( "slicing shape %s starts={%s} limits={%s}", - ShapeUtil::HumanString(arg).c_str(), - tensorflow::str_util::Join(starts, ", ").c_str(), - tensorflow::str_util::Join(limits, ", ").c_str()); + ShapeUtil::HumanString(arg).c_str(), Join(starts, ", ").c_str(), + Join(limits, ", ").c_str()); if (starts.size() != limits.size()) { - return InvalidArgument("slice start and limit sizes differ: %zu vs %zu", - starts.size(), limits.size()); + return error(Printf("slice start and limit sizes differ: %zu vs %zu", + starts.size(), limits.size())); } if (starts.size() != strides.size()) { - return InvalidArgument("slice start and strides sizes differ: %zu vs %zu", - starts.size(), strides.size()); + return error(Printf("slice start and strides sizes differ: %zu vs %zu", + starts.size(), strides.size())); } if (starts.size() != ShapeUtil::Rank(arg)) { @@ -1947,20 +1957,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( start_index); } if (limit_index > arg.dimensions(dimension)) { - return InvalidArgument( - "limit index (%lld) must be less than or equal to dimension " - "size (%lld)", - limit_index, arg.dimensions(dimension)); + return error( + Printf("limit index (%lld) must be less than or equal to dimension " + "size (%lld)", + limit_index, arg.dimensions(dimension))); } VLOG(2) << tensorflow::strings::Printf("starts[%lld] = %lld", dimension, start_index); VLOG(2) << tensorflow::strings::Printf("limits[%lld] = %lld", dimension, limit_index); if (start_index > limit_index) { - return InvalidArgument( - "limit index (%lld) must be greater or equal to " - "start index (%lld) in slice with positive stride", - limit_index, start_index); + return error( + Printf("limit index (%lld) must be greater or equal to " + "start index (%lld) in slice with positive stride", + limit_index, start_index)); } if (stride <= 0) { return InvalidArgument("stride (%lld) must be positive", stride); @@ -1983,7 +1993,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( "slicing shape %s at dynamic start_indices %s with slice_sizes={%s}", ShapeUtil::HumanString(operand_shape).c_str(), ShapeUtil::HumanString(start_indices_shape).c_str(), - tensorflow::str_util::Join(slice_sizes, ", ").c_str()); + Join(slice_sizes, ", ").c_str()); if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( @@ -2280,8 +2290,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( return InvalidArgument( "Reshape dimensions [%s] are not a permutation of the operand " "dimensions (operand shape is %s).", - tensorflow::str_util::Join(dimensions, ",").c_str(), - ShapeUtil::HumanString(operand).c_str()); + Join(dimensions, ",").c_str(), ShapeUtil::HumanString(operand).c_str()); } return inferred_shape; @@ -2373,8 +2382,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape( // The applied function's arity equals the number of arguments. if (arg_shapes.size() != to_apply.parameters_size()) { string computation_signature = ShapeUtil::HumanString(to_apply); - string argument_shapes = tensorflow::str_util::Join( - arg_shapes, ", ", [](string* out, const Shape* shape) { + string argument_shapes = + Join(arg_shapes, ", ", [](string* out, const Shape* shape) { tensorflow::strings::StrAppend(out, ShapeUtil::HumanString(*shape)); }); return InvalidArgument( diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index 99d87f3b55..026c021165 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -1512,5 +1512,20 @@ TEST_F(ShapeInferenceTest, Conditional) { "must have the same shape")); } +TEST_F(ShapeInferenceTest, BadSlice) { + auto arg = ShapeUtil::MakeShape(F32, {4}); + StatusOr statusor = + ShapeInference::InferSliceShape(arg, {0}, {5}, {1}); + ASSERT_FALSE(statusor.ok()); + + LOG(INFO) << statusor.status(); + + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("less than or equal to dimension size")) + << statusor.status(); + EXPECT_THAT(statusor.status().error_message(), HasSubstr("argument shape")) + << statusor.status(); +} + } // namespace } // namespace xla diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 4195e7553c..b5428d3246 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -71,6 +71,32 @@ cc_library( ], ) +cc_library( + name = "kernel_util", + srcs = [ + "kernel_util.cc", + ], + hdrs = [ + "kernel_util.h", + ], + deps = [ + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:context", + "//tensorflow/contrib/lite/kernels/internal:round", + ], +) + +tf_cc_test( + name = "kernel_util_test", + size = "small", + srcs = ["kernel_util_test.cc"], + deps = [ + ":kernel_util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + cc_library( name = "builtin_ops", srcs = [ @@ -87,7 +113,6 @@ cc_library( "fully_connected.cc", "gather.cc", "hashtable_lookup.cc", - "kernel_util.cc", "l2norm.cc", "local_response_norm.cc", "lsh_projection.cc", @@ -111,7 +136,6 @@ cc_library( "unidirectional_sequence_rnn.cc", ], hdrs = [ - "kernel_util.h", "padding.h", "register.h", ], @@ -125,6 +149,7 @@ cc_library( }), deps = [ ":activation_functor", + ":kernel_util", ":op_macros", "//tensorflow/contrib/lite:builtin_op_data", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/kernels/kernel_util.cc b/tensorflow/contrib/lite/kernels/kernel_util.cc index b0546c00cf..955e8c5764 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.cc +++ b/tensorflow/contrib/lite/kernels/kernel_util.cc @@ -13,8 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/kernels/kernel_util.h" + #include #include +#include + #include "tensorflow/contrib/lite/kernels/internal/round.h" namespace tflite { @@ -84,4 +87,27 @@ void CalculateActivationRangeFloat(TfLiteFusedActivation activation, } } +bool HaveSameShapes(TfLiteTensor* input1, TfLiteTensor* input2) { + return TfLiteIntArrayEqual(input1->dims, input2->dims); +} + +TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, + TfLiteTensor* input1, + TfLiteTensor* input2, + TfLiteIntArray** output_shape) { + int64_t dims1 = NumDimensions(input1); + int64_t dims2 = NumDimensions(input2); + int64_t out_dims = std::max(dims1, dims2); + std::unique_ptr shape( + TfLiteIntArrayCreate(out_dims), TfLiteIntArrayFree); + for (int i = 0; i < out_dims; ++i) { + int64_t d1 = i >= dims1 ? 1 : SizeOfDimension(input1, dims1 - i - 1); + int64_t d2 = i >= dims2 ? 1 : SizeOfDimension(input2, dims2 - i - 1); + TF_LITE_ENSURE(context, d1 == d2 || d1 == 1 || d2 == 1); + shape->data[out_dims - i - 1] = std::max(d1, d2); + } + *output_shape = shape.release(); + return kTfLiteOk; +} + } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/kernel_util.h b/tensorflow/contrib/lite/kernels/kernel_util.h index bfdfba00f5..3cfa72615a 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util.h +++ b/tensorflow/contrib/lite/kernels/kernel_util.h @@ -35,6 +35,14 @@ inline TfLiteTensor* GetOutput(TfLiteContext* context, TfLiteNode* node, inline int NumInputs(const TfLiteNode* node) { return node->inputs->size; } inline int NumOutputs(const TfLiteNode* node) { return node->outputs->size; } +inline int64_t NumElements(const TfLiteTensor* t) { + int64_t count = 1; + for (int i = 0; i < NumDimensions(t); ++i) { + count *= SizeOfDimension(t, i); + } + return count; +} + inline TfLiteTensor* GetOptionalInputTensor(TfLiteContext* context, const TfLiteNode* node, int index) { const bool use_tensor = node->inputs->data[index] != kOptionalTensor; @@ -76,6 +84,15 @@ void CalculateActivationRangeFloat(TfLiteFusedActivation activation, float* activation_min, float* activation_max); +// Return true if the given tensors have the same shape. +bool HaveSameShapes(TfLiteTensor* input1, TfLiteTensor* input2); + +// Calculate the output_shape that is necessary for element-wise operations +// with broadcasting involving the two input tensors. +TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, + TfLiteTensor* input1, + TfLiteTensor* input2, + TfLiteIntArray** output_shape); } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_KERNEL_UTIL_H_ diff --git a/tensorflow/contrib/lite/kernels/kernel_util_test.cc b/tensorflow/contrib/lite/kernels/kernel_util_test.cc new file mode 100644 index 0000000000..63a317f338 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/kernel_util_test.cc @@ -0,0 +1,150 @@ +/* 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/kernels/kernel_util.h" + +#include +#include +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { +namespace { + +void ReportError(TfLiteContext* context, const char* format, ...) {} + +class KernelUtilTest : public ::testing::Test { + public: + KernelUtilTest() { + context_.ReportError = ReportError; + + tensor1_.dims = nullptr; + tensor2_.dims = nullptr; + } + ~KernelUtilTest() { + TfLiteTensorFree(&tensor1_); + TfLiteTensorFree(&tensor2_); + } + + void SetShape(TfLiteTensor* tensor, std::initializer_list dims) { + TfLiteTensorFree(tensor); + tensor->dims = TfLiteIntArrayCreate(dims.size()); + int i = 0; + for (int d : dims) { + tensor->dims->data[i] = d; + ++i; + } + } + + std::vector GetShape(TfLiteIntArray* dims) { + std::vector result; + for (int i = 0; i < dims->size; ++i) { + result.push_back(dims->data[i]); + } + return result; + } + + protected: + TfLiteContext context_; + TfLiteTensor tensor1_; + TfLiteTensor tensor2_; +}; + +TEST_F(KernelUtilTest, SameShapeEmpty) { + EXPECT_TRUE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor1_, {1, 2, 3}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {1, 2}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {1, 2, 3, 4}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {1, 2, 3}); + EXPECT_TRUE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor2_, {}); + EXPECT_FALSE(HaveSameShapes(&tensor1_, &tensor2_)); + + SetShape(&tensor1_, {}); + EXPECT_TRUE(HaveSameShapes(&tensor1_, &tensor2_)); +} + +TEST_F(KernelUtilTest, BroadcastShapeIncompatibleDim) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {1, 3}); + EXPECT_NE(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_EQ(output, nullptr); +} + +TEST_F(KernelUtilTest, BroadcastShapeOnes) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 1}); + SetShape(&tensor2_, {1, 3}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + TfLiteIntArrayFree(output); + + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {1, 1}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + TfLiteIntArrayFree(output); +} + +TEST_F(KernelUtilTest, BroadcastShapeScalars) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(1, 2)); + TfLiteIntArrayFree(output); + + SetShape(&tensor1_, {}); + SetShape(&tensor2_, {2}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(2)); + TfLiteIntArrayFree(output); +} + +TEST_F(KernelUtilTest, BroadcastShapeDifferentSizes) { + TfLiteIntArray* output = nullptr; + SetShape(&tensor1_, {1, 2}); + SetShape(&tensor2_, {3, 1, 1}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(3, 1, 2)); + TfLiteIntArrayFree(output); + + SetShape(&tensor1_, {1, 2, 3, 4}); + SetShape(&tensor2_, {1, 3, 1}); + EXPECT_EQ(kTfLiteOk, CalculateShapeForBroadcast(&context_, &tensor1_, + &tensor2_, &output)); + EXPECT_THAT(GetShape(output), ::testing::ElementsAre(1, 2, 3, 4)); + TfLiteIntArrayFree(output); +} + +} // 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/toco/toco_cmdline_flags.cc b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc index f8281f3a57..c5a62fdb62 100644 --- a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc @@ -44,9 +44,11 @@ bool ParseTocoFlagsFromCommandLineFlags( "For Protobuf formats, the binary format will be used."), Flag("input_format", parsed_flags.input_format.bind(), parsed_flags.input_format.default_value(), - "Input file format. One of: tensorflow_graphdef, "), + "Input file format. One of: TENSORFLOW_GRAPHDEF, TFLITE."), Flag("output_format", parsed_flags.output_format.bind(), - parsed_flags.output_format.default_value(), "Output file format."), + parsed_flags.output_format.default_value(), + "Output file format. " + "One of TENSORFLOW_GRAPHDEF, TFLITE, GRAPHVIZ_DOT."), Flag("default_ranges_min", parsed_flags.default_ranges_min.bind(), parsed_flags.default_ranges_min.default_value(), "If defined, will be used as the default value for the min bound " @@ -58,11 +60,13 @@ bool ParseTocoFlagsFromCommandLineFlags( Flag("inference_type", parsed_flags.inference_type.bind(), parsed_flags.inference_type.default_value(), "Target data type of arrays in the output file (for input_arrays, " - "this may be overridden by inference_input_type)."), + "this may be overridden by inference_input_type). " + "One of FLOAT, QUANTIZED_UINT8."), Flag("inference_input_type", parsed_flags.inference_input_type.bind(), parsed_flags.inference_input_type.default_value(), - "Target data type of input arrays. If not specified, inference_type " - "is used."), + "Target data type of input arrays. " + "If not specified, inference_type is used. " + "One of FLOAT, QUANTIZED_UINT8."), Flag("input_type", parsed_flags.input_type.bind(), parsed_flags.input_type.default_value(), "Deprecated ambiguous flag that set both --input_data_types and " @@ -76,35 +80,31 @@ bool ParseTocoFlagsFromCommandLineFlags( Flag("drop_fake_quant", parsed_flags.drop_fake_quant.bind(), parsed_flags.drop_fake_quant.default_value(), - "Ignore and discard FakeQuant nodes. For instance, that can be used " - "to " + "Ignore and discard FakeQuant nodes. For instance, to " "generate plain float code without fake-quantization from a " - "quantized " - "graph."), + "quantized graph."), Flag( "reorder_across_fake_quant", parsed_flags.reorder_across_fake_quant.bind(), parsed_flags.reorder_across_fake_quant.default_value(), "Normally, FakeQuant nodes must be strict boundaries for graph " "transformations, in order to ensure that quantized inference has " - "the " - "exact same arithmetic behavior as quantized training --- which is " - "the " - "whole point of quantized training and of FakeQuant nodes in the " - "first " - "place. However, that entails subtle requirements on where exactly " + "the exact same arithmetic behavior as quantized training --- which " + "is the whole point of quantized training and of FakeQuant nodes in " + "the first place. " + "However, that entails subtle requirements on where exactly " "FakeQuant nodes must be placed in the graph. Some quantized graphs " "have FakeQuant nodes at unexpected locations, that prevent graph " "transformations that are necessary in order to generate inference " "code for these graphs. Such graphs should be fixed, but as a " "temporary work-around, setting this reorder_across_fake_quant flag " - "allows toco to perform necessary graph transformaitons on them, " + "allows TOCO to perform necessary graph transformaitons on them, " "at the cost of no longer faithfully matching inference and training " "arithmetic."), Flag("allow_custom_ops", parsed_flags.allow_custom_ops.bind(), parsed_flags.allow_custom_ops.default_value(), - "If true, allow TOCO to create TF Lite Custom operators for all the" - "unsupported Tensorflow ops."), + "If true, allow TOCO to create TF Lite Custom operators for all the " + "unsupported TensorFlow ops."), Flag( "drop_control_dependency", parsed_flags.drop_control_dependency.bind(), diff --git a/tensorflow/contrib/py2tf/BUILD b/tensorflow/contrib/py2tf/BUILD index d395de986d..3e846aefeb 100644 --- a/tensorflow/contrib/py2tf/BUILD +++ b/tensorflow/contrib/py2tf/BUILD @@ -57,6 +57,7 @@ py_library( py_test( name = "api_test", srcs = ["api_test.py"], + srcs_version = "PY2AND3", deps = [ ":py2tf_internal", "//tensorflow/python:client_testlib", @@ -66,6 +67,7 @@ py_test( py_test( name = "conversion_test", srcs = ["conversion_test.py"], + srcs_version = "PY2AND3", deps = [ ":py2tf_internal", "//tensorflow/python:client_testlib", @@ -76,6 +78,7 @@ py_test( py_test( name = "naming_test", srcs = ["naming_test.py"], + srcs_version = "PY2AND3", deps = [ ":py2tf_internal", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD index 2b0a1234e6..4f90f94e09 100644 --- a/tensorflow/contrib/py2tf/converters/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -52,6 +52,7 @@ py_library( py_test( name = "break_canonicalization_test", srcs = ["break_canonicalization_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -62,6 +63,7 @@ py_test( py_test( name = "call_trees_test", srcs = ["call_trees_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -72,6 +74,7 @@ py_test( py_test( name = "continue_canonicalization_test", srcs = ["continue_canonicalization_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -82,6 +85,7 @@ py_test( py_test( name = "control_flow_test", srcs = ["control_flow_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -92,6 +96,7 @@ py_test( py_test( name = "builtin_functions_test", srcs = ["builtin_functions_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -112,6 +117,7 @@ py_test( py_test( name = "logical_expressions_test", srcs = ["logical_expressions_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -122,6 +128,7 @@ py_test( py_test( name = "print_functions_test", srcs = ["print_functions_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", @@ -133,6 +140,7 @@ py_test( py_test( name = "side_effect_guards_test", srcs = ["side_effect_guards_test.py"], + srcs_version = "PY2AND3", deps = [ ":test_lib", "//tensorflow/contrib/py2tf/pyct", diff --git a/tensorflow/contrib/py2tf/pyct/BUILD b/tensorflow/contrib/py2tf/pyct/BUILD index e0331dbc97..88902dea84 100644 --- a/tensorflow/contrib/py2tf/pyct/BUILD +++ b/tensorflow/contrib/py2tf/pyct/BUILD @@ -38,6 +38,7 @@ py_library( py_test( name = "anno_test", srcs = ["anno_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -47,6 +48,7 @@ py_test( py_test( name = "compiler_test", srcs = ["compiler_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -57,6 +59,7 @@ py_test( py_test( name = "parser_test", srcs = ["parser_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -66,6 +69,7 @@ py_test( py_test( name = "pretty_printer_test", srcs = ["pretty_printer_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", @@ -75,6 +79,7 @@ py_test( py_test( name = "templates_test", srcs = ["templates_test.py"], + srcs_version = "PY2AND3", deps = [ ":pyct", "//tensorflow/python:client_testlib", diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD index abaf953678..32e2954fff 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/BUILD @@ -32,6 +32,7 @@ py_library( py_test( name = "access_test", srcs = ["access_test.py"], + srcs_version = "PY2AND3", deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", @@ -43,6 +44,7 @@ py_test( py_test( name = "live_values_test", srcs = ["live_values_test.py"], + srcs_version = "PY2AND3", deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", @@ -53,6 +55,7 @@ py_test( py_test( name = "type_info_test", srcs = ["type_info_test.py"], + srcs_version = "PY2AND3", deps = [ ":static_analysis", "//tensorflow/contrib/py2tf/pyct", diff --git a/tensorflow/core/common_runtime/gpu/process_state.cc b/tensorflow/core/common_runtime/gpu/process_state.cc index 995fd1253f..2f13cf8bd7 100644 --- a/tensorflow/core/common_runtime/gpu/process_state.cc +++ b/tensorflow/core/common_runtime/gpu/process_state.cc @@ -230,8 +230,24 @@ Allocator* ProcessState::GetCUDAHostAllocator(int numa_node) { // TODO(tucker): actually maintain separate CPUAllocators for // different numa_nodes. For now, just one. numa_node = 0; - mutex_lock lock(mu_); + { + // Here we optimize the most common use case where cuda_host_allocators_ + // and cuda_al_ have already been populated and since we're only reading + // these vectors, we can get by with a shared lock. In the slower case, + // we take a unique lock and populate these vectors. + tf_shared_lock lock(mu_); + + if (FLAGS_brain_gpu_record_mem_types && + static_cast(cuda_al_.size()) > 0) { + return cuda_al_[0]; + } + if (static_cast(cuda_host_allocators_.size()) > numa_node) { + return cuda_host_allocators_[0]; + } + } + + mutex_lock lock(mu_); // Find the first valid StreamExecutor to request CUDA host memory // through, since any will work. // diff --git a/tensorflow/core/grappler/clusters/cluster.cc b/tensorflow/core/grappler/clusters/cluster.cc index 01a618ed77..39bfca244e 100644 --- a/tensorflow/core/grappler/clusters/cluster.cc +++ b/tensorflow/core/grappler/clusters/cluster.cc @@ -23,8 +23,7 @@ Cluster::Cluster(int timeout_s) : timeout_s_(timeout_s) { DisableDetailedStats(false); } -Cluster::~Cluster() { -} +Cluster::~Cluster() {} void Cluster::AllowSoftPlacement(bool soft_placement_state) { options_.config.set_allow_soft_placement(soft_placement_state); diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.h b/tensorflow/core/grappler/costs/virtual_scheduler.h index 9db6d46266..5116c8183c 100644 --- a/tensorflow/core/grappler/costs/virtual_scheduler.h +++ b/tensorflow/core/grappler/costs/virtual_scheduler.h @@ -325,7 +325,7 @@ class VirtualScheduler { // Boolean field for whether the cost is accurate. std::map> op_costs_; - Costs graph_costs_; // Graph cost. + Costs graph_costs_; // Graph cost. std::map op_to_cost_; // Per-op cost. // Auxilliary data structures for constructing NodeState and DeviceState. diff --git a/tensorflow/core/grappler/optimizers/auto_parallel.h b/tensorflow/core/grappler/optimizers/auto_parallel.h index c5d2d47782..8d1098d877 100644 --- a/tensorflow/core/grappler/optimizers/auto_parallel.h +++ b/tensorflow/core/grappler/optimizers/auto_parallel.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ #define TENSORFLOW_GRAPPLER_OPTIMIZERS_AUTO_PARALLEL_H_ -#include "tensorflow/core/grappler/optimizers/graph_optimizer.h" #include "tensorflow/core/framework/variable.pb.h" +#include "tensorflow/core/grappler/optimizers/graph_optimizer.h" #include "tensorflow/core/lib/core/status.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/adjust_contrast_op.cc b/tensorflow/core/kernels/adjust_contrast_op.cc index 37976f7183..72155fd037 100644 --- a/tensorflow/core/kernels/adjust_contrast_op.cc +++ b/tensorflow/core/kernels/adjust_contrast_op.cc @@ -40,8 +40,8 @@ typedef Eigen::SyclDevice SYCLDevice; template class AdjustContrastOp : public OpKernel { public: - explicit AdjustContrastOp(OpKernelConstruction* context) : OpKernel(context) { - } + explicit AdjustContrastOp(OpKernelConstruction* context) + : OpKernel(context) {} void Compute(OpKernelContext* context) override { const Tensor& input = context->input(0); diff --git a/tensorflow/core/kernels/adjust_contrast_op_test.cc b/tensorflow/core/kernels/adjust_contrast_op_test.cc index 0fc03b5a23..7522b32040 100644 --- a/tensorflow/core/kernels/adjust_contrast_op_test.cc +++ b/tensorflow/core/kernels/adjust_contrast_op_test.cc @@ -29,8 +29,7 @@ limitations under the License. namespace tensorflow { -class AdjustContrastOpTest : public OpsTestBase { -}; +class AdjustContrastOpTest : public OpsTestBase {}; TEST_F(AdjustContrastOpTest, Simple_1113) { TF_EXPECT_OK(NodeDefBuilder("adjust_contrast_op", "AdjustContrastv2") diff --git a/tensorflow/core/kernels/adjust_saturation_op.cc b/tensorflow/core/kernels/adjust_saturation_op.cc index 4643d4e6ef..f0c6ae499d 100644 --- a/tensorflow/core/kernels/adjust_saturation_op.cc +++ b/tensorflow/core/kernels/adjust_saturation_op.cc @@ -192,8 +192,9 @@ class AdjustSaturationOp : public AdjustSaturationOpBase { const DeviceBase::CpuWorkerThreads& worker_threads = *context->device()->tensorflow_cpu_worker_threads(); Shard(worker_threads.num_threads, worker_threads.workers, channel_count, - kCostPerChannel, [channel_count, &input_data, &output_data, scale_h]( - int64 start_channel, int64 end_channel) { + kCostPerChannel, + [channel_count, &input_data, &output_data, scale_h]( + int64 start_channel, int64 end_channel) { const float* p = input_data.data() + start_channel * kChannelSize; float* q = output_data.data() + start_channel * kChannelSize; for (int i = start_channel; i < end_channel; i++) { diff --git a/tensorflow/core/kernels/aggregate_ops_cpu.h b/tensorflow/core/kernels/aggregate_ops_cpu.h index dfa3fe585e..aa1cead928 100644 --- a/tensorflow/core/kernels/aggregate_ops_cpu.h +++ b/tensorflow/core/kernels/aggregate_ops_cpu.h @@ -25,7 +25,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace tensorflow { @@ -201,7 +201,7 @@ struct Add7Functor { typename TTypes::ConstFlat in6, typename TTypes::ConstFlat in7) { Add7EigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7); + in7); } }; @@ -214,7 +214,7 @@ struct Add8Functor { typename TTypes::ConstFlat in5, typename TTypes::ConstFlat in6, typename TTypes::ConstFlat in7, typename TTypes::ConstFlat in8) { Add8EigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7, in8); + in7, in8); } }; @@ -227,7 +227,7 @@ struct Add8pFunctor { typename TTypes::ConstFlat in5, typename TTypes::ConstFlat in6, typename TTypes::ConstFlat in7, typename TTypes::ConstFlat in8) { Add8pEigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7, in8); + in7, in8); } }; @@ -241,10 +241,10 @@ struct Add9Functor { typename TTypes::ConstFlat in7, typename TTypes::ConstFlat in8, typename TTypes::ConstFlat in9) { Add9EigenImpl::Compute(d, out, in1, in2, in3, in4, in5, in6, - in7, in8, in9); + in7, in8, in9); } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor diff --git a/tensorflow/core/kernels/attention_ops.cc b/tensorflow/core/kernels/attention_ops.cc index cc8f122cab..ce2fce92e4 100644 --- a/tensorflow/core/kernels/attention_ops.cc +++ b/tensorflow/core/kernels/attention_ops.cc @@ -52,8 +52,9 @@ class ExtractGlimpseOp : public OpKernel { const int64 batch_size = input_shape.dim_size(0); const Tensor& window_size = context->input(1); - OP_REQUIRES(context, (window_size.shape().dims() == 1) && - window_size.shape().dim_size(0) == 2, + OP_REQUIRES(context, + (window_size.shape().dims() == 1) && + window_size.shape().dim_size(0) == 2, errors::InvalidArgument( "input must be a vector of size 2 (height, width)", window_size.shape().DebugString())); diff --git a/tensorflow/core/kernels/avgpooling_op.h b/tensorflow/core/kernels/avgpooling_op.h index dea2683184..f5e81dbc09 100644 --- a/tensorflow/core/kernels/avgpooling_op.h +++ b/tensorflow/core/kernels/avgpooling_op.h @@ -48,9 +48,8 @@ struct SpatialAvgPooling { typedef Eigen::GpuDevice GPUDevice; -// Launch a custom GPU kernels from Yanqing for the avgpooling backward operation -// that works NHWC data formats. -// Arguments: +// Launch a custom GPU kernels from Yanqing for the avgpooling backward +// operation that works NHWC data formats. Arguments: // top_diff: backprop to the output of the pooling layer // num: number of input batches // height: input height diff --git a/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc b/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc index 2be330d142..6537b42f1e 100644 --- a/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc +++ b/tensorflow/core/kernels/avgpooling_op_gpu.cu.cc @@ -71,8 +71,8 @@ __global__ void AvePoolBackwardNHWC(const int nthreads, hstart = max(hstart, 0); wstart = max(wstart, 0); int pool_size = (hend - hstart) * (wend - wstart); - gradient += - top_diff_slice[(ph * pooled_width + pw) * channels] / dtype(pool_size); + gradient += top_diff_slice[(ph * pooled_width + pw) * channels] / + dtype(pool_size); } } bottom_diff[index] = gradient; @@ -90,11 +90,11 @@ bool RunAvePoolBackwardNHWC(const T* const top_diff, const int num, const GPUDevice& d) { int x_size = num * height * width * channels; CudaLaunchConfig config = GetCudaLaunchConfig(x_size, d); - AvePoolBackwardNHWC< - T><<>>( - config.virtual_thread_count, top_diff, num, height, width, channels, - pooled_height, pooled_width, kernel_h, kernel_w, stride_h, stride_w, - pad_t, pad_t, bottom_diff); + AvePoolBackwardNHWC + <<>>( + config.virtual_thread_count, top_diff, num, height, width, channels, + pooled_height, pooled_width, kernel_h, kernel_w, stride_h, stride_w, + pad_t, pad_t, bottom_diff); return d.ok(); } diff --git a/tensorflow/core/kernels/barrier_ops.cc b/tensorflow/core/kernels/barrier_ops.cc index d0bbea9fe2..944564dfba 100644 --- a/tensorflow/core/kernels/barrier_ops.cc +++ b/tensorflow/core/kernels/barrier_ops.cc @@ -111,13 +111,14 @@ class Barrier : public ResourceBase { mutex_lock lock(mu_); if (closed_) { OP_REQUIRES_ASYNC( - ctx, !cancel_pending_enqueues_ && - (num_inserted == 0 || !incomplete_.empty()), + ctx, + !cancel_pending_enqueues_ && + (num_inserted == 0 || !incomplete_.empty()), errors::Cancelled( "Barrier ", name_, " is closed. Pending enqueues cancelled: ", - cancel_pending_enqueues_, ". Number of new insertions: ", - num_inserted, ". Number of incomplete keys: ", - incomplete_.size(), "."), + cancel_pending_enqueues_, + ". Number of new insertions: ", num_inserted, + ". Number of incomplete keys: ", incomplete_.size(), "."), callback); } @@ -128,9 +129,10 @@ class Barrier : public ResourceBase { for (int i = 0; i < num_inserted; ++i) { OP_REQUIRES_OK_ASYNC( - ctx, InsertOneLocked(ctx, keys, values, element_shape, - component_index, i, &ready_tuples, - &new_elements), + ctx, + InsertOneLocked(ctx, keys, values, element_shape, + component_index, i, &ready_tuples, + &new_elements), callback); } @@ -317,8 +319,9 @@ class Barrier : public ResourceBase { return errors::Cancelled( "Barrier ", name_, " is closed, but attempted to insert a brand new key: ", - keys_vec(i), ". Pending enqueues cancelled: ", - cancel_pending_enqueues_, ". Insertion index: ", i, + keys_vec(i), + ". Pending enqueues cancelled: ", cancel_pending_enqueues_, + ". Insertion index: ", i, ". Number of incomplete keys: ", incomplete_.size(), "."); } } else { @@ -532,13 +535,14 @@ class InsertManyOp : public BarrierOpKernel { OP_REQUIRES_ASYNC( ctx, component_index_ < barrier->num_components(), errors::InvalidArgument("The component ID is out of range ", - component_index_, " > num_components", " (= ", - barrier->num_components(), ")"), + component_index_, " > num_components", + " (= ", barrier->num_components(), ")"), callback); OP_REQUIRES_OK_ASYNC( - ctx, ctx->MatchSignature({DT_STRING_REF, DT_STRING, - barrier->component_type(component_index_)}, - {}), + ctx, + ctx->MatchSignature({DT_STRING_REF, DT_STRING, + barrier->component_type(component_index_)}, + {}), callback); const Tensor* keys; diff --git a/tensorflow/core/kernels/batch_kernels.cc b/tensorflow/core/kernels/batch_kernels.cc index 5b4e1a809f..c447db842d 100644 --- a/tensorflow/core/kernels/batch_kernels.cc +++ b/tensorflow/core/kernels/batch_kernels.cc @@ -13,22 +13,20 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ - #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_util.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/batching_util/shared_batch_scheduler.h" #include "tensorflow/core/kernels/batching_util/periodic_function.h" +#include "tensorflow/core/kernels/batching_util/shared_batch_scheduler.h" #include "tensorflow/core/kernels/concat_lib.h" #include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/kernels/split_lib.h" #include "tensorflow/core/lib/random/random.h" #include "tensorflow/core/platform/macros.h" - namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; diff --git a/tensorflow/core/kernels/batch_matmul_op_impl.h b/tensorflow/core/kernels/batch_matmul_op_impl.h index 93c3918319..43e716c542 100644 --- a/tensorflow/core/kernels/batch_matmul_op_impl.h +++ b/tensorflow/core/kernels/batch_matmul_op_impl.h @@ -41,7 +41,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace { @@ -429,14 +429,13 @@ template struct LaunchBatchMatMul { static void Launch(OpKernelContext* context, const Tensor& in_x, const Tensor& in_y, bool adj_x, bool adj_y, Tensor* out) { - - // Number of matrix multiplies i.e. size of the batch. - const int64 batch_size = in_x.dim_size(0); - ParallelMatMulKernelSYCL::Run(context, in_x, in_y, adj_x, adj_y, out, - 0, batch_size); + // Number of matrix multiplies i.e. size of the batch. + const int64 batch_size = in_x.dim_size(0); + ParallelMatMulKernelSYCL::Run(context, in_x, in_y, adj_x, adj_y, + out, 0, batch_size); } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class BatchMatMul : public OpKernel { @@ -462,10 +461,10 @@ class BatchMatMul : public OpKernel { TensorShape out_shape; for (int i = 0; i < ndims - 2; ++i) { OP_REQUIRES(ctx, in0.dim_size(i) == in1.dim_size(i), - errors::InvalidArgument("In[0].dim(", i, ") and In[1].dim(", - i, ") must be the same: ", - in0.shape().DebugString(), " vs ", - in1.shape().DebugString())); + errors::InvalidArgument( + "In[0].dim(", i, ") and In[1].dim(", i, + ") must be the same: ", in0.shape().DebugString(), " vs ", + in1.shape().DebugString())); out_shape.AddDim(in0.dim_size(i)); } auto n = (ndims == 2) ? 1 : out_shape.num_elements(); @@ -507,12 +506,12 @@ class BatchMatMul : public OpKernel { bool adj_y_; }; -#define REGISTER_BATCH_MATMUL_CPU(TYPE) \ +#define REGISTER_BATCH_MATMUL_CPU(TYPE) \ REGISTER_KERNEL_BUILDER( \ Name("BatchMatMul").Device(DEVICE_CPU).TypeConstraint("T"), \ BatchMatMul) -#define REGISTER_BATCH_MATMUL_GPU(TYPE) \ +#define REGISTER_BATCH_MATMUL_GPU(TYPE) \ REGISTER_KERNEL_BUILDER( \ Name("BatchMatMul").Device(DEVICE_GPU).TypeConstraint("T"), \ BatchMatMul) @@ -522,5 +521,5 @@ class BatchMatMul : public OpKernel { REGISTER_KERNEL_BUILDER( \ Name("BatchMatMul").Device(DEVICE_SYCL).TypeConstraint("T"), \ BatchMatMul) -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow diff --git a/tensorflow/core/kernels/batch_matmul_op_real.cc b/tensorflow/core/kernels/batch_matmul_op_real.cc index 8d155ca62b..7e1e2aa4ec 100644 --- a/tensorflow/core/kernels/batch_matmul_op_real.cc +++ b/tensorflow/core/kernels/batch_matmul_op_real.cc @@ -35,5 +35,5 @@ TF_CALL_half(REGISTER_BATCH_MATMUL_GPU); #ifdef TENSORFLOW_USE_SYCL TF_CALL_float(REGISTER_BATCH_MATMUL_SYCL); TF_CALL_double(REGISTER_BATCH_MATMUL_SYCL); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/batch_matmul_op_test.cc b/tensorflow/core/kernels/batch_matmul_op_test.cc index 7923f34155..c3932cd7b9 100644 --- a/tensorflow/core/kernels/batch_matmul_op_test.cc +++ b/tensorflow/core/kernels/batch_matmul_op_test.cc @@ -53,9 +53,10 @@ static Graph* BatchMatmul(int b, int m, int k, int n, bool adjoint_a, /* Uncomment to enable benchmarks for double & complex types: */ // BM_BatchMatmulDev(B, M, K, N, TA, TB, std::complex, DT_COMPLEX64, // gpu); -// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, cpu); \ -// BM_BatchMatmulDev(M, K, N, TA, TB, std::complex, DT_COMPLEX128, cpu); \ -// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, gpu); \ +// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, cpu); \ +// BM_BatchMatmulDev(M, K, N, TA, TB, std::complex, DT_COMPLEX128, cpu); +// \ +// BM_BatchMatmulDev(M, K, N, TA, TB, double, DT_DOUBLE, gpu); \ // BM_BatchMatmulDev(M, K, N, TA, TB, std::complex, DT_COMPLEX128, gpu); // Typical fully connected layers diff --git a/tensorflow/core/kernels/batch_norm_op.cc b/tensorflow/core/kernels/batch_norm_op.cc index d3ed617f71..c34ea14bf6 100644 --- a/tensorflow/core/kernels/batch_norm_op.cc +++ b/tensorflow/core/kernels/batch_norm_op.cc @@ -30,7 +30,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class BatchNormOp : public OpKernel { diff --git a/tensorflow/core/kernels/batch_norm_op_test.cc b/tensorflow/core/kernels/batch_norm_op_test.cc index 5e3fcd2114..45ddc85329 100644 --- a/tensorflow/core/kernels/batch_norm_op_test.cc +++ b/tensorflow/core/kernels/batch_norm_op_test.cc @@ -54,7 +54,7 @@ TEST_F(BatchNormOpTest, Simple) { Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 6, 2})); test::FillValues( &expected, {-17.86f, -22.00f, -15.87f, -20.59f, -13.87f, -19.18f, -21.86f, - -33.31f, -23.85f, -34.72f, -25.85f, -36.13f }); + -33.31f, -23.85f, -34.72f, -25.85f, -36.13f}); test::ExpectTensorNear(expected, *GetOutput(0), 0.01); } diff --git a/tensorflow/core/kernels/batchtospace_op.cc b/tensorflow/core/kernels/batchtospace_op.cc index c1c0d6d329..b07c5fd718 100644 --- a/tensorflow/core/kernels/batchtospace_op.cc +++ b/tensorflow/core/kernels/batchtospace_op.cc @@ -56,9 +56,10 @@ static void BatchToSpaceOpCompute(OpKernelContext* context, errors::InvalidArgument("input rank should be >= ", 1 + block_dims, " instead of ", orig_input_tensor.dims())); - OP_REQUIRES(context, TensorShapeUtils::IsMatrix(orig_crops.shape()) && - block_dims == orig_crops.dim_size(0) && - 2 == orig_crops.dim_size(1), + OP_REQUIRES(context, + TensorShapeUtils::IsMatrix(orig_crops.shape()) && + block_dims == orig_crops.dim_size(0) && + 2 == orig_crops.dim_size(1), errors::InvalidArgument("crops should have shape [", block_dims, ", 2] instead of ", orig_crops.shape().DebugString())); diff --git a/tensorflow/core/kernels/bcast_ops.cc b/tensorflow/core/kernels/bcast_ops.cc index 7fc4b1762d..8e4f08e473 100644 --- a/tensorflow/core/kernels/bcast_ops.cc +++ b/tensorflow/core/kernels/bcast_ops.cc @@ -13,11 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/util/bcast.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/util/bcast.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/bias_op_gpu.cu.cc b/tensorflow/core/kernels/bias_op_gpu.cu.cc index 2ca194a77f..754b93b073 100644 --- a/tensorflow/core/kernels/bias_op_gpu.cu.cc +++ b/tensorflow/core/kernels/bias_op_gpu.cu.cc @@ -77,14 +77,14 @@ void BiasGPU::compute(const GPUDevice& d, const T* input, const T* bias, } CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d); if (data_format == FORMAT_NHWC) { - BiasNHWCKernel< - T><<>>( - config.virtual_thread_count, input, bias, output, bias_size); + BiasNHWCKernel + <<>>( + config.virtual_thread_count, input, bias, output, bias_size); } else { - BiasNCHWKernel< - T><<>>( - config.virtual_thread_count, input, bias, output, bias_size, - image_size); + BiasNCHWKernel + <<>>( + config.virtual_thread_count, input, bias, output, bias_size, + image_size); } } @@ -206,10 +206,10 @@ void BiasGradGPU::compute(const GPUDevice& d, const T* output_backprop, // Check if we have enough shared memory. if (shared_memory_size <= max_shared_memory_size) { if (data_format == FORMAT_NHWC) { - BiasGradNHWC_SharedAtomics< - T><<>>(total_count, output_backprop, bias_backprop, - bias_size); + BiasGradNHWC_SharedAtomics + <<>>(total_count, output_backprop, bias_backprop, + bias_size); } else { // Round up the block count to multiple of bias_size. int group_size = (config.block_count + bias_size - 1) / bias_size; @@ -217,23 +217,24 @@ void BiasGradGPU::compute(const GPUDevice& d, const T* output_backprop, if (config.thread_per_block < kWarpSize) { config.thread_per_block = kWarpSize; } - BiasGradNCHW_SharedAtomics< - T><<>>( - output_backprop, bias_backprop, batch, bias_size, image_size, - group_size); + BiasGradNCHW_SharedAtomics + <<>>( + output_backprop, bias_backprop, batch, bias_size, image_size, + group_size); } } else { // Note that even if we don't have enough shared memory to fit the entire // output block, it is possible to process one group of elements at a time. // But for now, we simply fall back to the naive implementation. if (data_format == FORMAT_NHWC) { - BiasGradNHWC_Naive< - T><<>>( - total_count, output_backprop, bias_backprop, bias_size); + BiasGradNHWC_Naive + <<>>( + total_count, output_backprop, bias_backprop, bias_size); } else { - BiasGradNCHW_Naive< - T><<>>( - total_count, output_backprop, bias_backprop, bias_size, image_size); + BiasGradNCHW_Naive + <<>>( + total_count, output_backprop, bias_backprop, bias_size, + image_size); } } } diff --git a/tensorflow/core/kernels/bounds_check.h b/tensorflow/core/kernels/bounds_check.h index e35f42ad41..c8c60c5524 100644 --- a/tensorflow/core/kernels/bounds_check.h +++ b/tensorflow/core/kernels/bounds_check.h @@ -48,7 +48,7 @@ EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC const T SubtleMustCopy(const T &x) { auto *to_x = reinterpret_cast(&x); return *to_x; } -} // namespace tensorflow::internal +} // namespace internal } // namespace tensorflow #endif // TENSORFLOW_UTIL_BOUNDS_CHECK_H_ diff --git a/tensorflow/core/kernels/candidate_sampler_ops.cc b/tensorflow/core/kernels/candidate_sampler_ops.cc index e937c4f11b..654d99301a 100644 --- a/tensorflow/core/kernels/candidate_sampler_ops.cc +++ b/tensorflow/core/kernels/candidate_sampler_ops.cc @@ -126,13 +126,13 @@ REGISTER_KERNEL_BUILDER(Name("UniformCandidateSampler").Device(DEVICE_CPU), REGISTER_KERNEL_BUILDER(Name("LogUniformCandidateSampler").Device(DEVICE_CPU), SimpleCandidateSamplerOp); -REGISTER_KERNEL_BUILDER(Name("LearnedUnigramCandidateSampler") - .Device(DEVICE_CPU), - SimpleCandidateSamplerOp); +REGISTER_KERNEL_BUILDER( + Name("LearnedUnigramCandidateSampler").Device(DEVICE_CPU), + SimpleCandidateSamplerOp); -REGISTER_KERNEL_BUILDER(Name("ThreadUnsafeUnigramCandidateSampler") - .Device(DEVICE_CPU), - SimpleCandidateSamplerOp); +REGISTER_KERNEL_BUILDER( + Name("ThreadUnsafeUnigramCandidateSampler").Device(DEVICE_CPU), + SimpleCandidateSamplerOp); class AllCandidateSamplerOp : public BaseCandidateSamplerOp { public: @@ -197,8 +197,9 @@ class ComputeAccidentalHitsOp : public OpKernel { void Compute(OpKernelContext* context) override { const Tensor& in_true_candidates = context->input(0); const TensorShape& in_true_candidates_shape = in_true_candidates.shape(); - OP_REQUIRES(context, TensorShapeUtils::IsMatrix(in_true_candidates_shape) && - in_true_candidates_shape.dim_size(1) == num_true_, + OP_REQUIRES(context, + TensorShapeUtils::IsMatrix(in_true_candidates_shape) && + in_true_candidates_shape.dim_size(1) == num_true_, errors::InvalidArgument( "true_candidates must be a batch_size * num_true matrix")); diff --git a/tensorflow/core/kernels/cast_op.cc b/tensorflow/core/kernels/cast_op.cc index f16abb2b79..626db9131a 100644 --- a/tensorflow/core/kernels/cast_op.cc +++ b/tensorflow/core/kernels/cast_op.cc @@ -36,7 +36,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define CURRY_TYPES2(FN, arg0) \ FN(arg0, bool); \ @@ -223,11 +223,11 @@ class SyclCastOp : public CastOpBase { } }; -#define REGISTER_CAST_SYCL(srctype, dsttype) \ - REGISTER_KERNEL_BUILDER(Name("Cast") \ - .TypeConstraint("SrcT") \ - .TypeConstraint("DstT") \ - .Device(DEVICE_SYCL), \ +#define REGISTER_CAST_SYCL(srctype, dsttype) \ + REGISTER_KERNEL_BUILDER(Name("Cast") \ + .TypeConstraint("SrcT") \ + .TypeConstraint("DstT") \ + .Device(DEVICE_SYCL), \ SyclCastOp) CURRY_TYPES2(REGISTER_CAST_SYCL, bool); CURRY_TYPES2(REGISTER_CAST_SYCL, int32); @@ -237,7 +237,7 @@ CURRY_TYPES2(REGISTER_CAST_SYCL, double); #undef REGISTER_CAST_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef CURRY_TYPES2 @@ -250,6 +250,5 @@ REGISTER_KERNEL_BUILDER( REGISTER_KERNEL_BUILDER( Name("_HostCast").Device(DEVICE_SYCL).HostMemory("x").HostMemory("y"), CpuCastOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow - diff --git a/tensorflow/core/kernels/cast_op.h b/tensorflow/core/kernels/cast_op.h index 8fedf2c271..fd4e75d26f 100644 --- a/tensorflow/core/kernels/cast_op.h +++ b/tensorflow/core/kernels/cast_op.h @@ -131,7 +131,8 @@ struct scalar_cast_op<::tensorflow::bfloat16, float> { p[0] = a.value; p[1] = 0; #else - static_assert(::tensorflow::port::kLittleEndian, "Not a little endian system!"); + static_assert(::tensorflow::port::kLittleEndian, + "Not a little endian system!"); p[0] = 0; p[1] = a.value; #endif diff --git a/tensorflow/core/kernels/cast_op_impl.h b/tensorflow/core/kernels/cast_op_impl.h index 470e9e0804..3ae9f2ab4d 100644 --- a/tensorflow/core/kernels/cast_op_impl.h +++ b/tensorflow/core/kernels/cast_op_impl.h @@ -41,25 +41,25 @@ struct CastFunctor { o.device(d) = i.template cast(); } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor -#define CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ - FN(arg0, arg1, bool); \ - FN(arg0, arg1, uint8); \ - FN(arg0, arg1, int8); \ - FN(arg0, arg1, uint16); \ - FN(arg0, arg1, int16); \ - FN(arg0, arg1, int32); \ - FN(arg0, arg1, int64); \ - FN(arg0, arg1, float); \ - FN(arg0, arg1, double); \ - FN(arg0, arg1, std::complex); \ +#define CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ + FN(arg0, arg1, bool); \ + FN(arg0, arg1, uint8); \ + FN(arg0, arg1, int8); \ + FN(arg0, arg1, uint16); \ + FN(arg0, arg1, int16); \ + FN(arg0, arg1, int32); \ + FN(arg0, arg1, int64); \ + FN(arg0, arg1, float); \ + FN(arg0, arg1, double); \ + FN(arg0, arg1, std::complex); \ FN(arg0, arg1, std::complex) -#define CURRY_TYPES3(FN, arg0, arg1) \ - CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ +#define CURRY_TYPES3(FN, arg0, arg1) \ + CURRY_TYPES3_NO_HALF(FN, arg0, arg1) \ FN(arg0, arg1, Eigen::half); #define CAST_CASE(DEVICE, IN, OUT) \ diff --git a/tensorflow/core/kernels/cast_op_test.cc b/tensorflow/core/kernels/cast_op_test.cc index a106f287c1..057e209a71 100644 --- a/tensorflow/core/kernels/cast_op_test.cc +++ b/tensorflow/core/kernels/cast_op_test.cc @@ -107,10 +107,10 @@ static void BM_gpu_float_int64(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL test::Benchmark("sycl", Cast(num)).Run(iters); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } BENCHMARK(BM_gpu_float_int64)->Arg(64 << 10)->Arg(32 << 20); @@ -130,10 +130,10 @@ static void BM_gpu_bool_float(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL test::Benchmark("sycl", Cast(num)).Run(iters); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } BENCHMARK(BM_gpu_bool_float)->Arg(64 << 10)->Arg(32 << 20); @@ -180,7 +180,7 @@ static void BM_gpu_float_half(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA } BENCHMARK(BM_gpu_float_half)->Arg(64 << 10)->Arg(32 << 20); @@ -191,7 +191,7 @@ static void BM_gpu_half_float(int iters, int num) { testing::UseRealTime(); #if GOOGLE_CUDA test::Benchmark("gpu", Cast(num)).Run(iters); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA } BENCHMARK(BM_gpu_half_float)->Arg(64 << 10)->Arg(32 << 20); diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc index ba100b32e7..9cc2e67bbe 100644 --- a/tensorflow/core/kernels/colorspace_op.cc +++ b/tensorflow/core/kernels/colorspace_op.cc @@ -107,14 +107,14 @@ class HSVToRGBOp : public OpKernel { } }; -#define REGISTER_CPU(T) \ - REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - RGBToHSVOp); \ - template class RGBToHSVOp; \ - REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - HSVToRGBOp); \ +#define REGISTER_CPU(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("RGBToHSV").Device(DEVICE_CPU).TypeConstraint("T"), \ + RGBToHSVOp); \ + template class RGBToHSVOp; \ + REGISTER_KERNEL_BUILDER( \ + Name("HSVToRGB").Device(DEVICE_CPU).TypeConstraint("T"), \ + HSVToRGBOp); \ template class HSVToRGBOp; TF_CALL_float(REGISTER_CPU); TF_CALL_double(REGISTER_CPU); @@ -123,40 +123,39 @@ TF_CALL_double(REGISTER_CPU); // Forward declarations of the function specializations for GPU (to prevent // building the GPU versions here, they will be built compiling _gpu.cu.cc). namespace functor { -#define DECLARE_GPU(T) \ - template <> \ - void RGBToHSV::operator()(const GPUDevice& d, \ - TTypes::ConstTensor input_data, \ - TTypes::Tensor range, \ - TTypes::Tensor output_data); \ - extern template struct RGBToHSV; \ - template <> \ - void HSVToRGB::operator()(const GPUDevice& d, \ - TTypes::ConstTensor input_data, \ - TTypes::Tensor output_data); \ +#define DECLARE_GPU(T) \ + template <> \ + void RGBToHSV::operator()( \ + const GPUDevice& d, TTypes::ConstTensor input_data, \ + TTypes::Tensor range, TTypes::Tensor output_data); \ + extern template struct RGBToHSV; \ + template <> \ + void HSVToRGB::operator()( \ + const GPUDevice& d, TTypes::ConstTensor input_data, \ + TTypes::Tensor output_data); \ extern template struct HSVToRGB; TF_CALL_float(DECLARE_GPU); TF_CALL_double(DECLARE_GPU); } // namespace functor -#define REGISTER_GPU(T) \ - REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_GPU) \ - .TypeConstraint("T"), \ - RGBToHSVOp); \ - REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_GPU) \ - .TypeConstraint("T"), \ - HSVToRGBOp); +#define REGISTER_GPU(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("RGBToHSV").Device(DEVICE_GPU).TypeConstraint("T"), \ + RGBToHSVOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("HSVToRGB").Device(DEVICE_GPU).TypeConstraint("T"), \ + HSVToRGBOp); TF_CALL_float(REGISTER_GPU); TF_CALL_double(REGISTER_GPU); #endif #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL(T) \ - REGISTER_KERNEL_BUILDER(Name("RGBToHSV").Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - RGBToHSVOp); \ - REGISTER_KERNEL_BUILDER(Name("HSVToRGB").Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - HSVToRGBOp); +#define REGISTER_SYCL(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("RGBToHSV").Device(DEVICE_SYCL).TypeConstraint("T"), \ + RGBToHSVOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("HSVToRGB").Device(DEVICE_SYCL).TypeConstraint("T"), \ + HSVToRGBOp); TF_CALL_float(REGISTER_SYCL); TF_CALL_double(REGISTER_SYCL); #endif diff --git a/tensorflow/core/kernels/colorspace_op.h b/tensorflow/core/kernels/colorspace_op.h index c5721ef6dd..90bfce1419 100644 --- a/tensorflow/core/kernels/colorspace_op.h +++ b/tensorflow/core/kernels/colorspace_op.h @@ -54,10 +54,9 @@ struct RGBToHSV { // TODO(wicke): all these assignments are only necessary because a combined // expression is larger than kernel parameter space. A custom kernel is // probably in order. - H.device(d) = (R == V).select(norm * (G - B), - (G == V).select( - norm * (B - R) + T(2) / T(6), - norm * (R - G) + T(4) / T(6))); + H.device(d) = (R == V).select( + norm * (G - B), (G == V).select(norm * (B - R) + T(2) / T(6), + norm * (R - G) + T(4) / T(6))); H.device(d) = (range > T(0)).select(H, H.constant(T(0))); H.device(d) = (H < T(0)).select(H + T(1), H); } diff --git a/tensorflow/core/kernels/colorspace_op_gpu.cu.cc b/tensorflow/core/kernels/colorspace_op_gpu.cu.cc index e19d0b14d5..61f9ba44c4 100644 --- a/tensorflow/core/kernels/colorspace_op_gpu.cu.cc +++ b/tensorflow/core/kernels/colorspace_op_gpu.cu.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/colorspace_op.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/colorspace_op.h" namespace tensorflow { @@ -29,6 +29,6 @@ typedef Eigen::GpuDevice GPUDevice; template class functor::HSVToRGB; TF_CALL_float(INSTANTIATE_GPU); TF_CALL_double(INSTANTIATE_GPU); -} +} // namespace tensorflow #endif // GOOGLE_CUDA diff --git a/tensorflow/core/kernels/colorspace_op_test.cc b/tensorflow/core/kernels/colorspace_op_test.cc index 8c6fb732ab..bd82826770 100644 --- a/tensorflow/core/kernels/colorspace_op_test.cc +++ b/tensorflow/core/kernels/colorspace_op_test.cc @@ -224,34 +224,34 @@ class HSVToRGBOpTest : public OpsTestBase { } }; -#define TEST_COLORSPACE(test, dt) \ - TEST_F(test, CheckBlack) { \ - MakeOp(dt); \ - CheckBlack(dt); \ - } \ - TEST_F(test, CheckGray) { \ - MakeOp(dt); \ - CheckGray(dt); \ - } \ - TEST_F(test, CheckWhite) { \ - MakeOp(dt); \ - CheckWhite(dt); \ - } \ - TEST_F(test, CheckRedMax) { \ - MakeOp(dt); \ - CheckRedMax(dt); \ - } \ - TEST_F(test, CheckGreenMax) { \ - MakeOp(dt); \ - CheckGreenMax(dt); \ - } \ - TEST_F(test, CheckBlueMax) { \ - MakeOp(dt); \ - CheckBlueMax(dt); \ - } \ - TEST_F(test, CheckNegativeDifference) { \ - MakeOp(dt); \ - CheckNegativeDifference(dt); \ +#define TEST_COLORSPACE(test, dt) \ + TEST_F(test, CheckBlack) { \ + MakeOp(dt); \ + CheckBlack(dt); \ + } \ + TEST_F(test, CheckGray) { \ + MakeOp(dt); \ + CheckGray(dt); \ + } \ + TEST_F(test, CheckWhite) { \ + MakeOp(dt); \ + CheckWhite(dt); \ + } \ + TEST_F(test, CheckRedMax) { \ + MakeOp(dt); \ + CheckRedMax(dt); \ + } \ + TEST_F(test, CheckGreenMax) { \ + MakeOp(dt); \ + CheckGreenMax(dt); \ + } \ + TEST_F(test, CheckBlueMax) { \ + MakeOp(dt); \ + CheckBlueMax(dt); \ + } \ + TEST_F(test, CheckNegativeDifference) { \ + MakeOp(dt); \ + CheckNegativeDifference(dt); \ } typedef RGBToHSVOpTest rgb_to_hsv_float; diff --git a/tensorflow/core/kernels/concat_lib.h b/tensorflow/core/kernels/concat_lib.h index 526f9420d7..16784c4770 100644 --- a/tensorflow/core/kernels/concat_lib.h +++ b/tensorflow/core/kernels/concat_lib.h @@ -41,10 +41,11 @@ namespace tensorflow { // Assumes all inputs are nonempty template -void ConcatCPU(DeviceBase* d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output); +void ConcatCPU( + DeviceBase* d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output); #if GOOGLE_CUDA template void ConcatGPU( @@ -57,11 +58,12 @@ void ConcatGPU( #ifdef TENSORFLOW_USE_SYCL template -void ConcatSYCL(const Eigen::SyclDevice& d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output); -#endif // TENSORFLOW_USE_SYCL +void ConcatSYCL( + const Eigen::SyclDevice& d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output); +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow #endif // TENSORFLOW_KERNELS_CONCAT_LIB_H_ diff --git a/tensorflow/core/kernels/concat_lib_cpu.cc b/tensorflow/core/kernels/concat_lib_cpu.cc index 43731114c0..fc5a3e6288 100644 --- a/tensorflow/core/kernels/concat_lib_cpu.cc +++ b/tensorflow/core/kernels/concat_lib_cpu.cc @@ -48,10 +48,11 @@ struct MemCpyCopier { } // namespace template -void ConcatCPU(DeviceBase* d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output) { +void ConcatCPU( + DeviceBase* d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output) { if (std::is_same::value) { // use a large cost here to force strings to be handled by separate threads ConcatCPUImpl(d, inputs, 100000, MemCpyCopier(), output); @@ -86,21 +87,22 @@ TF_CALL_variant(REGISTER) #ifdef TENSORFLOW_USE_SYCL template -void ConcatSYCL(const Eigen::SyclDevice& d, - const std::vector< - std::unique_ptr::ConstMatrix>>& inputs, - typename TTypes::Matrix* output) { +void ConcatSYCL( + const Eigen::SyclDevice& d, + const std::vector::ConstMatrix>>& + inputs, + typename TTypes::Matrix* output) { ConcatSYCLImpl(d, inputs, sizeof(T) /* cost_per_unit */, MemCpyCopier(), - output); + output); } -#define REGISTER_SYCL(T) \ - template void ConcatSYCL( \ - const Eigen::SyclDevice&, \ - const std::vector::ConstMatrix>>&, \ - typename TTypes::Matrix* output); +#define REGISTER_SYCL(T) \ + template void ConcatSYCL( \ + const Eigen::SyclDevice&, \ + const std::vector::ConstMatrix>>&, \ + typename TTypes::Matrix* output); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL) #undef REGISTER_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/concat_lib_cpu.h b/tensorflow/core/kernels/concat_lib_cpu.h index 6a933efde4..720b506537 100644 --- a/tensorflow/core/kernels/concat_lib_cpu.h +++ b/tensorflow/core/kernels/concat_lib_cpu.h @@ -15,9 +15,9 @@ limitations under the License. #define EIGEN_USE_THREADS -#include "tensorflow/core/kernels/concat_lib.h" #include #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/concat_lib.h" #include "tensorflow/core/util/work_sharder.h" namespace tensorflow { @@ -73,7 +73,7 @@ void ConcatCPUImpl( // Sharded mode. auto work = [&row_size, &sizes, &inputs, &output, &copier, &num_inputs]( - int64 start, int64 end) { + int64 start, int64 end) { int64 skipped_rows = start / row_size; T* out = output->data() + skipped_rows * row_size; T* out_start = output->data() + start; @@ -160,5 +160,5 @@ void ConcatSYCLImpl( } } } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/concat_op.cc b/tensorflow/core/kernels/concat_op.cc index ae1b5da32e..7011550f7e 100644 --- a/tensorflow/core/kernels/concat_op.cc +++ b/tensorflow/core/kernels/concat_op.cc @@ -37,7 +37,7 @@ typedef Eigen::GpuDevice GPUDevice; #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL enum AxisArgumentName { NAME_IS_AXIS, NAME_IS_CONCAT_DIM }; @@ -71,8 +71,9 @@ class ConcatBaseOp : public OpKernel { const TensorShape& input_shape = values[0].shape(); int32 axis = concat_dim < 0 ? concat_dim + input_dims : concat_dim; - OP_REQUIRES(c, (0 <= axis && axis < input_dims) || - (allow_legacy_scalars() && concat_dim == 0), + OP_REQUIRES(c, + (0 <= axis && axis < input_dims) || + (allow_legacy_scalars() && concat_dim == 0), errors::InvalidArgument( "ConcatOp : Expected concatenating dimensions in the range " "[", @@ -97,8 +98,8 @@ class ConcatBaseOp : public OpKernel { c, in.dims() == input_dims || (input_is_scalar && in_is_scalar), errors::InvalidArgument( "ConcatOp : Ranks of all input tensors should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); for (int j = 0; j < input_dims; ++j) { if (j == axis) { continue; @@ -107,8 +108,8 @@ class ConcatBaseOp : public OpKernel { c, in.dim_size(j) == input_shape.dim_size(j), errors::InvalidArgument( "ConcatOp : Dimensions of inputs should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); } if (in.NumElements() > 0) { int64 inputs_flat_dim1 = in.NumElements() / inputs_flat_dim0; @@ -142,7 +143,7 @@ class ConcatBaseOp : public OpKernel { ConcatSYCL(c->eigen_sycl_device(), inputs_flat, &output_flat); return; } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL ConcatCPU(c->device(), inputs_flat, &output_flat); } } @@ -252,7 +253,7 @@ REGISTER_KERNEL_BUILDER(Name("ConcatV2") ConcatV2Op); #undef REGISTER_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class ConcatOffsetOp : public OpKernel { public: @@ -347,5 +348,5 @@ REGISTER_KERNEL_BUILDER(Name("ConcatOffset") .HostMemory("shape") .HostMemory("offset"), ConcatOffsetOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/concat_op_test.cc b/tensorflow/core/kernels/concat_op_test.cc index c5bded9daf..e3ba8ae9f6 100644 --- a/tensorflow/core/kernels/concat_op_test.cc +++ b/tensorflow/core/kernels/concat_op_test.cc @@ -157,7 +157,8 @@ BENCHMARK(BM_MemcpyAlternativeDim0)->Arg(1000)->Arg(100000)->Arg(1000000); BENCHMARK(BM_MemcpyAlternativeDim1)->Arg(1000)->Arg(100000)->Arg(1000000); typedef Eigen::TensorMap, - Eigen::Unaligned> EigenMap; + Eigen::Unaligned> + EigenMap; static void MemcpyManyAlternative1(int iters, int dim2) { testing::StopTiming(); diff --git a/tensorflow/core/kernels/conditional_accumulator_base.h b/tensorflow/core/kernels/conditional_accumulator_base.h index 794ac6fa6d..c7c7c98369 100644 --- a/tensorflow/core/kernels/conditional_accumulator_base.h +++ b/tensorflow/core/kernels/conditional_accumulator_base.h @@ -160,7 +160,7 @@ class ConditionalAccumulatorBase : public ResourceBase { * Modifications to convenience macros defined in core/framework/op_kernel.h. * The below macros return a boolean if the test fails, so that the calling * function can get an indication that a failure has occurred. -*/ + */ #define OP_REQUIRES_BOOLEAN(CTX, EXP, STATUS) \ do { \ if (!TF_PREDICT_TRUE(EXP)) { \ diff --git a/tensorflow/core/kernels/conditional_accumulator_op.cc b/tensorflow/core/kernels/conditional_accumulator_op.cc index fa37916eab..e13bf8a4c6 100644 --- a/tensorflow/core/kernels/conditional_accumulator_op.cc +++ b/tensorflow/core/kernels/conditional_accumulator_op.cc @@ -99,9 +99,10 @@ class AccumulatorTakeGradientOp ConditionalAccumulatorBase* accumulator, DoneCallback callback) override { // Check signature - OP_REQUIRES_OK_ASYNC(ctx, ctx->MatchSignature({DT_STRING_REF, DT_INT32}, - {accumulator->dtype()}), - callback); + OP_REQUIRES_OK_ASYNC( + ctx, + ctx->MatchSignature({DT_STRING_REF, DT_INT32}, {accumulator->dtype()}), + callback); } private: @@ -111,5 +112,4 @@ class AccumulatorTakeGradientOp REGISTER_KERNEL_BUILDER(Name("AccumulatorTakeGradient").Device(DEVICE_CPU), AccumulatorTakeGradientOp); - } // namespace tensorflow diff --git a/tensorflow/core/kernels/constant_op.cc b/tensorflow/core/kernels/constant_op.cc index 59f9f69315..920cd87858 100644 --- a/tensorflow/core/kernels/constant_op.cc +++ b/tensorflow/core/kernels/constant_op.cc @@ -146,7 +146,6 @@ typedef Eigen::GpuDevice GPUDevice; typedef Eigen::SyclDevice SYCLDevice; #endif // TENSORFLOW_USE_SYCL - template class FillOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/control_flow_ops.cc b/tensorflow/core/kernels/control_flow_ops.cc index 8fe82d118a..7d5d54e5be 100644 --- a/tensorflow/core/kernels/control_flow_ops.cc +++ b/tensorflow/core/kernels/control_flow_ops.cc @@ -113,47 +113,47 @@ REGISTER_GPU_HOST_REF_KERNEL(string); #undef REGISTER_GPU_HOST_REF_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_SWITCH(type) \ - REGISTER_KERNEL_BUILDER(Name("Switch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("pred") \ - .TypeConstraint("T"),\ +#define REGISTER_SYCL_SWITCH(type) \ + REGISTER_KERNEL_BUILDER(Name("Switch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("pred") \ + .TypeConstraint("T"), \ SwitchOp) TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_SWITCH); -#define REGISTER_SYCL_REF_SWITCH(type) \ - REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("pred") \ - .TypeConstraint("T"), \ +#define REGISTER_SYCL_REF_SWITCH(type) \ + REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("pred") \ + .TypeConstraint("T"), \ SwitchOp) TF_CALL_REAL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_SWITCH); #undef REGISTER_SYCL_SWITCH #undef REGISTER_SYCL_REF_SWITCH -#define REGISTER_SYCL_HOST_KERNEL(type) \ - REGISTER_KERNEL_BUILDER(Name("Switch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("data") \ - .HostMemory("pred") \ - .HostMemory("output_false")\ - .HostMemory("output_true") \ - .TypeConstraint("T"),\ +#define REGISTER_SYCL_HOST_KERNEL(type) \ + REGISTER_KERNEL_BUILDER(Name("Switch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("data") \ + .HostMemory("pred") \ + .HostMemory("output_false") \ + .HostMemory("output_true") \ + .TypeConstraint("T"), \ SwitchOp) REGISTER_SYCL_HOST_KERNEL(bool); REGISTER_SYCL_HOST_KERNEL(string); REGISTER_SYCL_HOST_KERNEL(int32); -#define REGISTER_SYCL_HOST_REF_KERNEL(type) \ - REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ - .Device(DEVICE_SYCL) \ - .HostMemory("data") \ - .HostMemory("pred") \ - .HostMemory("output_false") \ - .HostMemory("output_true") \ - .TypeConstraint("T"), \ +#define REGISTER_SYCL_HOST_REF_KERNEL(type) \ + REGISTER_KERNEL_BUILDER(Name("RefSwitch") \ + .Device(DEVICE_SYCL) \ + .HostMemory("data") \ + .HostMemory("pred") \ + .HostMemory("output_false") \ + .HostMemory("output_true") \ + .TypeConstraint("T"), \ SwitchOp) REGISTER_SYCL_HOST_REF_KERNEL(int32); @@ -162,7 +162,7 @@ REGISTER_SYCL_HOST_REF_KERNEL(string); #undef REGISTER_SYCL_HOST_KERNEL #undef REGISTER_SYCL_HOST_REF_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class RefSelectOp : public OpKernel { public: @@ -282,7 +282,7 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL); #undef REGISTER_SYCL_KERNEL #undef REGISTER_SYCL_REF_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Special GPU kernels for int32 and string. // TODO(b/25387198): Also enable int32 in device memory. This kernel @@ -331,7 +331,7 @@ REGISTER_SYCL_HOST_KERNEL(string); REGISTER_SYCL_HOST_KERNEL(ResourceHandle); #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL void EnterOp::Compute(OpKernelContext* context) { if (IsRefType(context->input_dtype(0))) { @@ -360,14 +360,14 @@ REGISTER_GPU_REF_KERNEL(bool); #undef REGISTER_GPU_REF_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ +#define REGISTER_SYCL_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ Name("Enter").Device(DEVICE_SYCL).TypeConstraint("T"), EnterOp) REGISTER_SYCL_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); -#define REGISTER_SYCL_REF_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ +#define REGISTER_SYCL_REF_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ Name("RefEnter").Device(DEVICE_SYCL).TypeConstraint("T"), EnterOp) REGISTER_SYCL_REF_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_REF_KERNEL); @@ -398,7 +398,7 @@ REGISTER_SYCL_HOST_KERNEL(ResourceHandle); #undef REGISTER_SYCL_HOST_KERNEL #undef REGISTER_SYCL_HOST_REF_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Special GPU kernels for int32 and string. // TODO(b/25387198): Also enable int32 in device memory. This kernel @@ -455,10 +455,10 @@ REGISTER_GPU_REF_KERNEL(bool); #undef REGISTER_GPU_REF_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ - Name("Exit").Device(DEVICE_SYCL).TypeConstraint("T"), ExitOp); \ - REGISTER_KERNEL_BUILDER( \ +#define REGISTER_SYCL_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("Exit").Device(DEVICE_SYCL).TypeConstraint("T"), ExitOp); \ + REGISTER_KERNEL_BUILDER( \ Name("RefExit").Device(DEVICE_SYCL).TypeConstraint("T"), ExitOp); REGISTER_SYCL_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); @@ -483,7 +483,7 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); REGISTER_SYCL_HOST_KERNEL(int32); REGISTER_SYCL_HOST_KERNEL(string); #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Special GPU kernels for int32 and string. // TODO(b/25387198): Also enable int32 in device memory. This kernel @@ -556,12 +556,12 @@ REGISTER_GPU_HOST_KERNEL(string); #undef REGISTER_GPU_HOST_KERNEL #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(type) \ - REGISTER_KERNEL_BUILDER( \ - Name("NextIteration").Device(DEVICE_SYCL).TypeConstraint("T"), \ - NextIterationOp); \ - REGISTER_KERNEL_BUILDER( \ - Name("RefNextIteration").Device(DEVICE_SYCL).TypeConstraint("T"),\ +#define REGISTER_SYCL_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("NextIteration").Device(DEVICE_SYCL).TypeConstraint("T"), \ + NextIterationOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("RefNextIteration").Device(DEVICE_SYCL).TypeConstraint("T"), \ NextIterationOp) REGISTER_SYCL_KERNEL(bool); TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); @@ -585,7 +585,7 @@ TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL); REGISTER_SYCL_HOST_KERNEL(int32); REGISTER_SYCL_HOST_KERNEL(string); #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // A LoopCond op has one input and one output. The input is a boolean // scalar representing the taken branches of the "pivot" Switch that @@ -619,7 +619,7 @@ REGISTER_KERNEL_BUILDER(Name("LoopCond") .HostMemory("input") .HostMemory("output"), LoopCondOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // ControlTrigger kernels REGISTER_KERNEL_BUILDER(Name("ControlTrigger").Device(DEVICE_CPU), @@ -631,7 +631,7 @@ REGISTER_KERNEL_BUILDER(Name("ControlTrigger").Device(DEVICE_GPU), #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("ControlTrigger").Device(DEVICE_SYCL), ControlTriggerOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // When called, abort op will abort the current process. This can be used to // abort remote PSs when needed. diff --git a/tensorflow/core/kernels/control_flow_ops_test.cc b/tensorflow/core/kernels/control_flow_ops_test.cc index affa0e8ca6..a2f7bd4069 100644 --- a/tensorflow/core/kernels/control_flow_ops_test.cc +++ b/tensorflow/core/kernels/control_flow_ops_test.cc @@ -91,6 +91,7 @@ class KilledBySignal { public: explicit KilledBySignal(int signum) : signum_(signum) {} bool operator()(int exit_status) const { return exit_status == signum_; } + private: const int signum_; }; diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc index 985586d626..dbddaf3dc6 100644 --- a/tensorflow/core/kernels/conv_ops.cc +++ b/tensorflow/core/kernels/conv_ops.cc @@ -688,7 +688,7 @@ void LaunchConv2DOp::operator()( static int64 ConvolveScratchSize = GetCudnnWorkspaceLimit( // default value is in bytes despite the name of the environment variable "TF_CUDNN_WORKSPACE_LIMIT_IN_MB", 1LL << 32 // 4GB - ); + ); int device_id = stream->parent()->device_ordinal(); DataType dtype = input.dtype(); diff --git a/tensorflow/core/kernels/conv_ops_fused.cc b/tensorflow/core/kernels/conv_ops_fused.cc index 291ebf2298..1b40ad81f4 100644 --- a/tensorflow/core/kernels/conv_ops_fused.cc +++ b/tensorflow/core/kernels/conv_ops_fused.cc @@ -679,8 +679,9 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { const int dims = resized_shape.dims(); OP_REQUIRES( - context, TensorShapeUtils::IsMatrix(paddings.shape()) && - paddings.dim_size(1) == 2, + context, + TensorShapeUtils::IsMatrix(paddings.shape()) && + paddings.dim_size(1) == 2, errors::InvalidArgument("paddings must be a matrix with 2 columns: ", paddings.shape().DebugString())); const int fixed_dims = @@ -715,20 +716,22 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { const int32 after = paddings_matrix(d, 1); // Pad after existing elements. OP_REQUIRES(context, before >= 0 && after >= 0, - errors::InvalidArgument("paddings must be non-negative: ", - before, " ", after)); + errors::InvalidArgument( + "paddings must be non-negative: ", before, " ", after)); if (offset_ == 0) { // SYMMETRIC mode. OP_REQUIRES( - context, before <= resized_shape.dim_size(d) && - after <= resized_shape.dim_size(d), + context, + before <= resized_shape.dim_size(d) && + after <= resized_shape.dim_size(d), errors::InvalidArgument("paddings must be no greater " "than the dimension size: ", before, ", ", after, " greater than ", resized_shape.dim_size(d))); } else if (offset_ == 1) { // REFLECT mode. OP_REQUIRES( - context, before < resized_shape.dim_size(d) && - after < resized_shape.dim_size(d), + context, + before < resized_shape.dim_size(d) && + after < resized_shape.dim_size(d), errors::InvalidArgument("paddings must be less than" " the dimension size: ", before, ", ", after, " not less than ", @@ -767,18 +770,19 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { // We only check the first three dims, since the depth is accessed as an // int64 below. for (int i = 0; i < 3; i++) { - OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES( + context, + FastBoundsCheck(filter.dim_size(i), std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } // The last dimension for input is in_depth. It must be the same as the // filter's in_depth. const int64 in_depth = padded_shape.dim_size(3); - OP_REQUIRES( - context, in_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - in_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, in_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", in_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -786,9 +790,10 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { // The second dimension for input is rows/height. // The first dimension for filter is rows/height. const int64 padded_rows_raw = padded_shape.dim_size(1); - OP_REQUIRES(context, FastBoundsCheck(padded_rows_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input rows too large")); + OP_REQUIRES( + context, + FastBoundsCheck(padded_rows_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input rows too large")); const int padded_rows = static_cast(padded_rows_raw); const int filter_rows = static_cast(filter.dim_size(0)); const int resized_rows = static_cast(resized_shape.dim_size(1)); @@ -796,9 +801,10 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { // The third dimension for input is columns/width. // The second dimension for filter is columns/width. const int64 padded_cols_raw = padded_shape.dim_size(2); - OP_REQUIRES(context, FastBoundsCheck(padded_cols_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input cols too large")); + OP_REQUIRES( + context, + FastBoundsCheck(padded_cols_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input cols too large")); const int padded_cols = static_cast(padded_cols_raw); const int filter_cols = static_cast(filter.dim_size(1)); const int resized_cols = static_cast(resized_shape.dim_size(2)); @@ -864,24 +870,26 @@ class FusedResizeConv2DUsingGemmOp : public OpKernel { TF_DISALLOW_COPY_AND_ASSIGN(FusedResizeConv2DUsingGemmOp); }; -#define REGISTER_FUSED(T) \ - REGISTER_KERNEL_BUILDER( \ - Name("FusedResizeAndPadConv2D") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - FusedResizeConv2DUsingGemmOp< \ - T, FusedResizeAndPadConvFunctor, \ - BILINEAR>, \ +#define REGISTER_FUSED(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("FusedResizeAndPadConv2D") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T"), \ + FusedResizeConv2DUsingGemmOp< \ + T, \ + FusedResizeAndPadConvFunctor, \ + BILINEAR>, \ true>); TF_CALL_float(REGISTER_FUSED); -#define REGISTER_PAD_ONLY_FUSED(T) \ - REGISTER_KERNEL_BUILDER( \ - Name("FusedPadConv2D").Device(DEVICE_CPU).TypeConstraint("T"), \ - FusedResizeConv2DUsingGemmOp< \ - T, FusedResizeAndPadConvFunctor, \ - NEAREST>, \ +#define REGISTER_PAD_ONLY_FUSED(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("FusedPadConv2D").Device(DEVICE_CPU).TypeConstraint("T"), \ + FusedResizeConv2DUsingGemmOp< \ + T, \ + FusedResizeAndPadConvFunctor, \ + NEAREST>, \ false>); TF_CALL_float(REGISTER_PAD_ONLY_FUSED); diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h index 57e196c67c..f0085be3a5 100644 --- a/tensorflow/core/kernels/conv_ops_gpu.h +++ b/tensorflow/core/kernels/conv_ops_gpu.h @@ -27,7 +27,6 @@ limitations under the License. namespace tensorflow { - // Get the Cudnn workspace limit from the environment variable, which is in MB. // Return the workspace memory limit in bytes. If no value is set, return the // default value. diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc index af6013c974..e58f5f61f3 100644 --- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc +++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc @@ -25,9 +25,9 @@ limitations under the License. #include "cuda/include/cuda.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/kernels/conv_2d.h" +#include "tensorflow/core/lib/math/math_util.h" #include "tensorflow/core/util/cuda_kernel_helper.h" #include "tensorflow/core/util/tensor_format.h" -#include "tensorflow/core/lib/math/math_util.h" namespace tensorflow { @@ -252,11 +252,14 @@ __global__ void SwapDimension1And2InTensor3UsingTiles( int x = threadIdx.x; Dimension<3> output_dims = { - input_dims[0], input_dims[2], input_dims[1], + input_dims[0], + input_dims[2], + input_dims[1], }; Dimension<3> input_dims_in_tiles = { - input_dims[0], (input_dims[1] + TileSizeI - 1) / TileSizeI, + input_dims[0], + (input_dims[1] + TileSizeI - 1) / TileSizeI, (input_dims[2] + TileSizeJ - 1) / TileSizeJ, }; @@ -264,7 +267,8 @@ __global__ void SwapDimension1And2InTensor3UsingTiles( FlatToTensorIndex(blockIdx.x, input_dims_in_tiles); Index<3> input_tile_origin = { - input_tile_index[0], input_tile_index[1] * TileSizeI, + input_tile_index[0], + input_tile_index[1] * TileSizeI, input_tile_index[2] * TileSizeJ, }; @@ -322,11 +326,14 @@ __global__ void SwapDimension1And2InTensor3UsingTiles( __syncthreads(); Index<3> output_tile_index = { - input_tile_index[0], input_tile_index[2], input_tile_index[1], + input_tile_index[0], + input_tile_index[2], + input_tile_index[1], }; Index<3> output_tile_origin = { - output_tile_index[0], output_tile_index[1] * TileSizeJ, + output_tile_index[0], + output_tile_index[1] * TileSizeJ, output_tile_index[2] * TileSizeI, }; @@ -799,7 +806,7 @@ struct TransposeElemType<16> { // A helper function to make RunSwapDimension1And2InTensor3 concise. This // helper function looks at the data type and input matrix sizes and decides // the thread numbers and tile sizes to use. -template +template void SwapDimension1And2InTensor3WithNarrowMatrices( const GPUDevice& d, const T* input, const Dimension<3>& input_dims, T* output, const int kMinDimensionToUseTiles) { @@ -902,19 +909,21 @@ void RunSwapDimension1And2InTensor3(const GPUDevice& d, const T* input, constexpr int kNumThreads = 256; Dimension<3> input_dims_in_tiles = { - input_dims[0], MathUtil::CeilOfRatio(input_dims[1], kTileSize), + input_dims[0], + MathUtil::CeilOfRatio(input_dims[1], kTileSize), MathUtil::CeilOfRatio(input_dims[2], kTileSize), }; int total_tiles_count = input_dims_in_tiles[0] * input_dims_in_tiles[1] * input_dims_in_tiles[2]; - SwapDimension1And2InTensor3UsingTiles + SwapDimension1And2InTensor3UsingTiles <<>>(input, input_dims, output); } else if (narrow_matrix) { - SwapDimension1And2InTensor3WithNarrowMatrices(d, input, input_dims, output, - kMinDimensionToUseTiles); + SwapDimension1And2InTensor3WithNarrowMatrices( + d, input, input_dims, output, kMinDimensionToUseTiles); } else { int total_element_count = input_dims[0] * input_dims[1] * input_dims[2]; CudaLaunchConfig config = GetCudaLaunchConfig(total_element_count, d); diff --git a/tensorflow/core/kernels/conv_ops_using_gemm.cc b/tensorflow/core/kernels/conv_ops_using_gemm.cc index 20da77c36f..af0a9fa82e 100644 --- a/tensorflow/core/kernels/conv_ops_using_gemm.cc +++ b/tensorflow/core/kernels/conv_ops_using_gemm.cc @@ -468,18 +468,19 @@ class Conv2DUsingGemmOp : public BinaryOp { filter.shape().DebugString())); for (int i = 0; i < 3; i++) { - OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES( + context, + FastBoundsCheck(filter.dim_size(i), std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } // The last dimension for input is in_depth. It must be the same as the // filter's in_depth. const int64 in_depth = GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES( - context, in_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - in_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, in_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", in_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -487,18 +488,20 @@ class Conv2DUsingGemmOp : public BinaryOp { // The second dimension for input is rows/height. // The first dimension for filter is rows/height. const int64 input_rows_raw = GetTensorDim(input, data_format_, 'H'); - OP_REQUIRES(context, FastBoundsCheck(input_rows_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input rows too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_rows_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input rows too large")); const int input_rows = static_cast(input_rows_raw); const int filter_rows = static_cast(filter.dim_size(0)); // The third dimension for input is columns/width. // The second dimension for filter is columns/width. const int64 input_cols_raw = GetTensorDim(input, data_format_, 'W'); - OP_REQUIRES(context, FastBoundsCheck(input_cols_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input cols too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_cols_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input cols too large")); const int input_cols = static_cast(input_cols_raw); const int filter_cols = static_cast(filter.dim_size(1)); diff --git a/tensorflow/core/kernels/cross_op_gpu.cu.cc b/tensorflow/core/kernels/cross_op_gpu.cu.cc index 7ea0b3be0c..4a37f6cfbb 100644 --- a/tensorflow/core/kernels/cross_op_gpu.cu.cc +++ b/tensorflow/core/kernels/cross_op_gpu.cu.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/cross_op.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/cross_op.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/ctc_decoder_ops.cc b/tensorflow/core/kernels/ctc_decoder_ops.cc index 73ee310604..96bdb6a241 100644 --- a/tensorflow/core/kernels/ctc_decoder_ops.cc +++ b/tensorflow/core/kernels/ctc_decoder_ops.cc @@ -19,13 +19,13 @@ limitations under the License. #include -#include "tensorflow/core/util/ctc/ctc_beam_search.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/util/ctc/ctc_beam_search.h" #include "tensorflow/core/util/sparse/sparse_tensor.h" namespace tensorflow { @@ -80,16 +80,17 @@ class CTCDecodeHelper { if (!(batch_size == (*seq_len)->dim_size(0))) { return errors::FailedPrecondition( - "len(sequence_length) != batch_size. ", "len(sequence_length): ", - (*seq_len)->dim_size(0), " batch_size: ", batch_size); + "len(sequence_length) != batch_size. ", + "len(sequence_length): ", (*seq_len)->dim_size(0), + " batch_size: ", batch_size); } auto seq_len_t = (*seq_len)->vec(); for (int b = 0; b < batch_size; ++b) { if (!(seq_len_t(b) <= max_time)) { - return errors::FailedPrecondition("sequence_length(", b, ") <= ", - max_time); + return errors::FailedPrecondition("sequence_length(", b, + ") <= ", max_time); } } diff --git a/tensorflow/core/kernels/ctc_loss_op.cc b/tensorflow/core/kernels/ctc_loss_op.cc index fb03adb7a5..b38d838bf1 100644 --- a/tensorflow/core/kernels/ctc_loss_op.cc +++ b/tensorflow/core/kernels/ctc_loss_op.cc @@ -113,8 +113,8 @@ class CTCLossOp : public OpKernel { const int64 batch_indices = g.group()[0]; OP_REQUIRES(ctx, FastBoundsCheck(batch_indices, batch_size), errors::InvalidArgument("labels batch index must be between ", - 0, " and ", batch_size, " but saw: ", - batch_indices)); + 0, " and ", batch_size, + " but saw: ", batch_indices)); auto values = g.values(); std::vector* b_values = &labels_t[batch_indices]; diff --git a/tensorflow/core/kernels/cwise_op_abs.cc b/tensorflow/core/kernels/cwise_op_abs.cc index 5fd38d9dc2..1466f24202 100644 --- a/tensorflow/core/kernels/cwise_op_abs.cc +++ b/tensorflow/core/kernels/cwise_op_abs.cc @@ -45,5 +45,5 @@ REGISTER_KERNEL_BUILDER(Name("Abs") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_acos.cc b/tensorflow/core/kernels/cwise_op_acos.cc index 12cc6c8bdd..4919122607 100644 --- a/tensorflow/core/kernels/cwise_op_acos.cc +++ b/tensorflow/core/kernels/cwise_op_acos.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Acos", functor::acos, float, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Acos", functor::acos, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_acosh.cc b/tensorflow/core/kernels/cwise_op_acosh.cc index 39c8814073..c2b355ab7f 100644 --- a/tensorflow/core/kernels/cwise_op_acosh.cc +++ b/tensorflow/core/kernels/cwise_op_acosh.cc @@ -17,12 +17,12 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_gradients.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Acosh", functor::acosh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Acosh", functor::acosh, float, double, complex64, + complex128); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Acosh", functor::acosh, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA REGISTER2(UnaryOp, GPU, "Acosh", functor::acosh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_add_1.cc b/tensorflow/core/kernels/cwise_op_add_1.cc index 608a6dce3d..bf32c8a54b 100644 --- a/tensorflow/core/kernels/cwise_op_add_1.cc +++ b/tensorflow/core/kernels/cwise_op_add_1.cc @@ -44,7 +44,6 @@ REGISTER_KERNEL_BUILDER(Name("AddV2") BinaryOp>); #endif - #if TENSORFLOW_USE_SYCL #define REGISTER_KERNEL(type) \ REGISTER(BinaryOp, SYCL, "Add", functor::add, type); \ @@ -66,5 +65,5 @@ REGISTER_KERNEL_BUILDER(Name("AddV2") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_add_2.cc b/tensorflow/core/kernels/cwise_op_add_2.cc index ac21ca06c9..e8acbac285 100644 --- a/tensorflow/core/kernels/cwise_op_add_2.cc +++ b/tensorflow/core/kernels/cwise_op_add_2.cc @@ -22,8 +22,8 @@ namespace tensorflow { // sharded files, only make its register calls when not __ANDROID_TYPES_SLIM__. #if !defined(__ANDROID_TYPES_SLIM__) -REGISTER6(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64, - uint8, complex128, string); +REGISTER6(BinaryOp, CPU, "Add", functor::add, int8, int16, complex64, uint8, + complex128, string); // Notice: String is excluded to allow marking AddV2 is_commutative and // is_aggregate. REGISTER5(BinaryOp, CPU, "AddV2", functor::add, int8, int16, complex64, uint8, diff --git a/tensorflow/core/kernels/cwise_op_asin.cc b/tensorflow/core/kernels/cwise_op_asin.cc index c28e27d95a..fe8dfea117 100644 --- a/tensorflow/core/kernels/cwise_op_asin.cc +++ b/tensorflow/core/kernels/cwise_op_asin.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Asin", functor::asin, float, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Asin", functor::asin, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_asinh.cc b/tensorflow/core/kernels/cwise_op_asinh.cc index 0aec6aac34..7cf0405f52 100644 --- a/tensorflow/core/kernels/cwise_op_asinh.cc +++ b/tensorflow/core/kernels/cwise_op_asinh.cc @@ -1,10 +1,10 @@ - /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. +/* 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 +http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, @@ -17,8 +17,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_gradients.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Asinh", functor::asinh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Asinh", functor::asinh, float, double, complex64, + complex128); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Asinh", functor::asinh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_atan.cc b/tensorflow/core/kernels/cwise_op_atan.cc index 7d73de4810..09f0448874 100644 --- a/tensorflow/core/kernels/cwise_op_atan.cc +++ b/tensorflow/core/kernels/cwise_op_atan.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Atan", functor::atan, float, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Atan", functor::atan, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_atanh.cc b/tensorflow/core/kernels/cwise_op_atanh.cc index 7b688db4c5..6170683fa6 100644 --- a/tensorflow/core/kernels/cwise_op_atanh.cc +++ b/tensorflow/core/kernels/cwise_op_atanh.cc @@ -17,8 +17,8 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_gradients.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Atanh", functor::atanh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Atanh", functor::atanh, float, double, complex64, + complex128); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Atanh", functor::atanh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_ceil.cc b/tensorflow/core/kernels/cwise_op_ceil.cc index 0111e9d5fd..816eadc80e 100644 --- a/tensorflow/core/kernels/cwise_op_ceil.cc +++ b/tensorflow/core/kernels/cwise_op_ceil.cc @@ -24,5 +24,5 @@ REGISTER3(UnaryOp, GPU, "Ceil", functor::ceil, float, Eigen::half, double); #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Ceil", functor::ceil, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_cos.cc b/tensorflow/core/kernels/cwise_op_cos.cc index d4b3b0e393..71ad0ff0dc 100644 --- a/tensorflow/core/kernels/cwise_op_cos.cc +++ b/tensorflow/core/kernels/cwise_op_cos.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Cos", functor::cos, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Cos", functor::cos, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_cosh.cc b/tensorflow/core/kernels/cwise_op_cosh.cc index bca99a4f89..31b4bb3cad 100644 --- a/tensorflow/core/kernels/cwise_op_cosh.cc +++ b/tensorflow/core/kernels/cwise_op_cosh.cc @@ -16,20 +16,18 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Cosh", functor::cosh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Cosh", functor::cosh, float, double, complex64, + complex128); #if TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("Cosh") \ - .Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - UnaryOp>); +#define REGISTER_SYCL_KERNEL(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("Cosh").Device(DEVICE_SYCL).TypeConstraint("T"), \ + UnaryOp>); REGISTER_SYCL_KERNEL(float); REGISTER_SYCL_KERNEL(double); #undef REGISTER_SYCL_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA REGISTER2(UnaryOp, GPU, "Cosh", functor::cosh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_div.cc b/tensorflow/core/kernels/cwise_op_div.cc index d44c1bf473..c71c756e44 100644 --- a/tensorflow/core/kernels/cwise_op_div.cc +++ b/tensorflow/core/kernels/cwise_op_div.cc @@ -54,5 +54,5 @@ REGISTER_KERNEL_BUILDER(Name("Div") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_exp.cc b/tensorflow/core/kernels/cwise_op_exp.cc index 66d7b7d22e..8f4ac98016 100644 --- a/tensorflow/core/kernels/cwise_op_exp.cc +++ b/tensorflow/core/kernels/cwise_op_exp.cc @@ -26,5 +26,5 @@ REGISTER5(UnaryOp, GPU, "Exp", functor::exp, float, Eigen::half, double, #if TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Exp", functor::exp, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_expm1.cc b/tensorflow/core/kernels/cwise_op_expm1.cc index 4f72308006..ce03ad5de6 100644 --- a/tensorflow/core/kernels/cwise_op_expm1.cc +++ b/tensorflow/core/kernels/cwise_op_expm1.cc @@ -23,5 +23,5 @@ REGISTER3(UnaryOp, GPU, "Expm1", functor::expm1, float, Eigen::half, double); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Expm1", functor::expm1, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_floor.cc b/tensorflow/core/kernels/cwise_op_floor.cc index 5a142b9ce9..d554d41c41 100644 --- a/tensorflow/core/kernels/cwise_op_floor.cc +++ b/tensorflow/core/kernels/cwise_op_floor.cc @@ -23,5 +23,5 @@ REGISTER3(UnaryOp, GPU, "Floor", functor::floor, float, Eigen::half, double); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Floor", functor::floor, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_floor_div.cc b/tensorflow/core/kernels/cwise_op_floor_div.cc index fa81ef0872..fecbf85989 100644 --- a/tensorflow/core/kernels/cwise_op_floor_div.cc +++ b/tensorflow/core/kernels/cwise_op_floor_div.cc @@ -49,5 +49,5 @@ REGISTER_KERNEL_BUILDER(Name("FloorDiv") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_floor_mod.cc b/tensorflow/core/kernels/cwise_op_floor_mod.cc index 55f8a30461..29340b8850 100644 --- a/tensorflow/core/kernels/cwise_op_floor_mod.cc +++ b/tensorflow/core/kernels/cwise_op_floor_mod.cc @@ -40,5 +40,5 @@ REGISTER_KERNEL_BUILDER(Name("FloorMod") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc index e7dff5d0ac..77723b3169 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_conj.cu.cc @@ -19,8 +19,8 @@ limitations under the License. namespace tensorflow { namespace functor { - DEFINE_UNARY1(conj, complex64); - DEFINE_UNARY1(conj, complex128); +DEFINE_UNARY1(conj, complex64); +DEFINE_UNARY1(conj, complex128); } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc index 3675398126..26748ef0e7 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_equal_to.cu.cc @@ -20,7 +20,7 @@ limitations under the License. namespace tensorflow { namespace functor { DEFINE_BINARY10(equal_to, float, Eigen::half, double, uint8, int8, int16, int64, - complex64, complex128, bool); + complex64, complex128, bool); DEFINE_APPROXIMATE_EQUAL2(float, double); } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc index a54dbdfc24..627ecc8c80 100644 --- a/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc +++ b/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc @@ -15,8 +15,10 @@ limitations under the License. #if GOOGLE_CUDA -#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" +#define EIGEN_USE_GPU + #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" namespace tensorflow { namespace functor { @@ -38,19 +40,17 @@ struct SelectScalarFunctor { typename TTypes::ConstScalar cond, typename TTypes::ConstFlat then_flat, typename TTypes::ConstFlat else_flat) { - #if !defined(EIGEN_HAS_INDEX_LIST) - Eigen::array rank1{1}; + Eigen::array rank1{1}; #else - Eigen::IndexList> rank1; + Eigen::IndexList > rank1; #endif - const int size = then_flat.dimension(0); - Eigen::array broadcast_dims{size}; - - To32Bit(out).device(d) = cond.reshape(rank1) - .broadcast(broadcast_dims) - .select(then_flat, else_flat); + const int size = then_flat.dimension(0); + Eigen::array broadcast_dims{size}; + To32Bit(out).device(d) = cond.reshape(rank1) + .broadcast(broadcast_dims) + .select(then_flat, else_flat); } }; @@ -89,8 +89,8 @@ struct BatchSelectFunctor { } }; -#define SELECT_FUNCTOR(T) \ - template struct SelectFunctor; \ +#define SELECT_FUNCTOR(T) \ + template struct SelectFunctor; \ template struct SelectScalarFunctor; \ template struct BatchSelectFunctor; diff --git a/tensorflow/core/kernels/cwise_op_greater.cc b/tensorflow/core/kernels/cwise_op_greater.cc index ba89899fb3..a4ea408836 100644 --- a/tensorflow/core/kernels/cwise_op_greater.cc +++ b/tensorflow/core/kernels/cwise_op_greater.cc @@ -43,5 +43,5 @@ REGISTER_KERNEL_BUILDER(Name("Greater") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_greater_equal.cc b/tensorflow/core/kernels/cwise_op_greater_equal.cc index 8f0c483aec..3f34d6269e 100644 --- a/tensorflow/core/kernels/cwise_op_greater_equal.cc +++ b/tensorflow/core/kernels/cwise_op_greater_equal.cc @@ -35,7 +35,8 @@ REGISTER_KERNEL_BUILDER(Name("GreaterEqual") #endif #ifdef TENSORFLOW_USE_SYCL -REGISTER2(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float, double); +REGISTER2(BinaryOp, SYCL, "GreaterEqual", functor::greater_equal, float, + double); REGISTER_KERNEL_BUILDER(Name("GreaterEqual") .Device(DEVICE_SYCL) @@ -44,5 +45,5 @@ REGISTER_KERNEL_BUILDER(Name("GreaterEqual") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc index df2c02e42e..f5cafcc780 100644 --- a/tensorflow/core/kernels/cwise_op_invert.cc +++ b/tensorflow/core/kernels/cwise_op_invert.cc @@ -21,7 +21,7 @@ REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64, #ifdef TENSORFLOW_USE_SYCL REGISTER6(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64, - uint8, uint16); + uint8, uint16); #endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA diff --git a/tensorflow/core/kernels/cwise_op_isfinite.cc b/tensorflow/core/kernels/cwise_op_isfinite.cc index 53ec1c1c63..ae1e590d24 100644 --- a/tensorflow/core/kernels/cwise_op_isfinite.cc +++ b/tensorflow/core/kernels/cwise_op_isfinite.cc @@ -26,5 +26,5 @@ REGISTER3(UnaryOp, GPU, "IsFinite", functor::isfinite, float, Eigen::half, #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "IsFinite", functor::isfinite, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_isinf.cc b/tensorflow/core/kernels/cwise_op_isinf.cc index 4b34744304..f22ca21e1c 100644 --- a/tensorflow/core/kernels/cwise_op_isinf.cc +++ b/tensorflow/core/kernels/cwise_op_isinf.cc @@ -24,5 +24,5 @@ REGISTER3(UnaryOp, GPU, "IsInf", functor::isinf, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "IsInf", functor::isinf, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_isnan.cc b/tensorflow/core/kernels/cwise_op_isnan.cc index ad2dd3f722..aa180c247e 100644 --- a/tensorflow/core/kernels/cwise_op_isnan.cc +++ b/tensorflow/core/kernels/cwise_op_isnan.cc @@ -24,5 +24,5 @@ REGISTER3(UnaryOp, GPU, "IsNan", functor::isnan, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "IsNan", functor::isnan, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_less.cc b/tensorflow/core/kernels/cwise_op_less.cc index 136c3666df..00cdecdbd1 100644 --- a/tensorflow/core/kernels/cwise_op_less.cc +++ b/tensorflow/core/kernels/cwise_op_less.cc @@ -42,5 +42,5 @@ REGISTER_KERNEL_BUILDER(Name("Less") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_less_equal.cc b/tensorflow/core/kernels/cwise_op_less_equal.cc index 97a2508d12..11806c5fc7 100644 --- a/tensorflow/core/kernels/cwise_op_less_equal.cc +++ b/tensorflow/core/kernels/cwise_op_less_equal.cc @@ -44,5 +44,5 @@ REGISTER_KERNEL_BUILDER(Name("LessEqual") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_log.cc b/tensorflow/core/kernels/cwise_op_log.cc index 7fdfdff0e3..98936e0f96 100644 --- a/tensorflow/core/kernels/cwise_op_log.cc +++ b/tensorflow/core/kernels/cwise_op_log.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Log", functor::log, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Log", functor::log, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_log1p.cc b/tensorflow/core/kernels/cwise_op_log1p.cc index 25ad7b24bb..162ca9e07c 100644 --- a/tensorflow/core/kernels/cwise_op_log1p.cc +++ b/tensorflow/core/kernels/cwise_op_log1p.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Log1p", functor::log1p, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Log1p", functor::log1p, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_maximum.cc b/tensorflow/core/kernels/cwise_op_maximum.cc index 87d54e380b..8c54f22f10 100644 --- a/tensorflow/core/kernels/cwise_op_maximum.cc +++ b/tensorflow/core/kernels/cwise_op_maximum.cc @@ -43,5 +43,5 @@ REGISTER_KERNEL_BUILDER(Name("Maximum") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_minimum.cc b/tensorflow/core/kernels/cwise_op_minimum.cc index 442171193b..dff83df828 100644 --- a/tensorflow/core/kernels/cwise_op_minimum.cc +++ b/tensorflow/core/kernels/cwise_op_minimum.cc @@ -43,6 +43,6 @@ REGISTER_KERNEL_BUILDER(Name("Minimum") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_mul_1.cc b/tensorflow/core/kernels/cwise_op_mul_1.cc index 023eb07ca3..0e8d2e3735 100644 --- a/tensorflow/core/kernels/cwise_op_mul_1.cc +++ b/tensorflow/core/kernels/cwise_op_mul_1.cc @@ -17,8 +17,8 @@ limitations under the License. namespace tensorflow { -REGISTER5(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double, - uint8, int32); +REGISTER5(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double, uint8, + int32); #if defined(__ANDROID_TYPES_SLIM__) // We only register the first type when we have multi-argument calls in the // case where we're trying to reduce executable size, but it turns out that the @@ -28,7 +28,7 @@ REGISTER(BinaryOp, CPU, "Mul", functor::mul, int32); #if GOOGLE_CUDA REGISTER4(BinaryOp, GPU, "Mul", functor::mul, float, Eigen::half, double, - uint8); + uint8); // A special GPU kernel for int32. // TODO(b/25387198): Also enable int32 in device memory. This kernel // registration requires all int32 inputs and outputs to be in host memory. @@ -50,5 +50,5 @@ REGISTER_KERNEL_BUILDER(Name("Mul") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_mul_2.cc b/tensorflow/core/kernels/cwise_op_mul_2.cc index 7be5857cc0..6aa8f88364 100644 --- a/tensorflow/core/kernels/cwise_op_mul_2.cc +++ b/tensorflow/core/kernels/cwise_op_mul_2.cc @@ -22,11 +22,11 @@ namespace tensorflow { // sharded files, only make its register calls when not __ANDROID_TYPES_SLIM__. #if !defined(__ANDROID_TYPES_SLIM__) -REGISTER6(BinaryOp, CPU, "Mul", functor::mul, - int8, uint16, int16, int64, complex64, complex128); +REGISTER6(BinaryOp, CPU, "Mul", functor::mul, int8, uint16, int16, int64, + complex64, complex128); #if GOOGLE_CUDA REGISTER6(BinaryOp, GPU, "Mul", functor::mul, int8, uint16, int16, int64, - complex64, complex128); + complex64, complex128); #endif // GOOGLE_CUDA diff --git a/tensorflow/core/kernels/cwise_op_neg.cc b/tensorflow/core/kernels/cwise_op_neg.cc index 536891b548..a136769b91 100644 --- a/tensorflow/core/kernels/cwise_op_neg.cc +++ b/tensorflow/core/kernels/cwise_op_neg.cc @@ -27,7 +27,7 @@ REGISTER_KERNEL_BUILDER(Name("Neg") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA REGISTER6(UnaryOp, GPU, "Neg", functor::neg, float, Eigen::half, double, int64, diff --git a/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc b/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc index 7bd81ee127..02cd298745 100644 --- a/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc +++ b/tensorflow/core/kernels/cwise_op_not_equal_to_1.cc @@ -17,7 +17,7 @@ limitations under the License. namespace tensorflow { REGISTER6(BinaryOp, CPU, "NotEqual", functor::not_equal_to, float, Eigen::half, - double, uint8, int8, int16); + double, uint8, int8, int16); #if GOOGLE_CUDA REGISTER4(BinaryOp, GPU, "NotEqual", functor::not_equal_to, float, Eigen::half, double, uint8); diff --git a/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc b/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc index 7d4ecec59f..05bdea6636 100644 --- a/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc +++ b/tensorflow/core/kernels/cwise_op_not_equal_to_2.cc @@ -30,5 +30,5 @@ REGISTER6(BinaryOp, GPU, "NotEqual", functor::not_equal_to, int8, int16, int64, #endif // GOOGLE_CUDA -#endif // !defined(__ANDROID_TYPES_SLIM__) +#endif // !defined(__ANDROID_TYPES_SLIM__) } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_reciprocal.cc b/tensorflow/core/kernels/cwise_op_reciprocal.cc index 8c0e21f9cf..aee25747b8 100644 --- a/tensorflow/core/kernels/cwise_op_reciprocal.cc +++ b/tensorflow/core/kernels/cwise_op_reciprocal.cc @@ -38,7 +38,7 @@ REGISTER4(UnaryOp, GPU, "Reciprocal", functor::inverse, float, Eigen::half, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(UnaryOp, SYCL, "Reciprocal", functor::inverse, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "ReciprocalGrad", functor::inverse_grad, float, Eigen::half, double, complex64, complex128); @@ -48,5 +48,5 @@ REGISTER3(SimpleBinaryOp, GPU, "ReciprocalGrad", functor::inverse_grad, float, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(SimpleBinaryOp, SYCL, "ReciprocalGrad", functor::inverse_grad, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_select.cc b/tensorflow/core/kernels/cwise_op_select.cc index 3dd9de8d89..e259daaba4 100644 --- a/tensorflow/core/kernels/cwise_op_select.cc +++ b/tensorflow/core/kernels/cwise_op_select.cc @@ -30,7 +30,7 @@ typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class SelectOp : public OpKernel { @@ -185,7 +185,7 @@ REGISTER_SELECT_SYCL(double); REGISTER_SELECT_SYCL(int32); REGISTER_SELECT_SYCL(int64); #undef REGISTER_SELECT_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace functor { @@ -201,13 +201,11 @@ struct SelectFunctorBase { }; template -struct SelectFunctor - : SelectFunctorBase {}; +struct SelectFunctor : SelectFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template -struct SelectFunctor - : SelectFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL +struct SelectFunctor : SelectFunctorBase {}; +#endif // TENSORFLOW_USE_SYCL template struct SelectScalarFunctorBase { @@ -222,12 +220,12 @@ struct SelectScalarFunctorBase { // CPU Specializations of Select functors with scalar template struct SelectScalarFunctor - : SelectScalarFunctorBase {}; + : SelectScalarFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template struct SelectScalarFunctor - : SelectScalarFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL + : SelectScalarFunctorBase {}; +#endif // TENSORFLOW_USE_SYCL template struct BatchSelectFunctorBase { @@ -240,8 +238,8 @@ struct BatchSelectFunctorBase { const Eigen::DenseIndex all_but_batch = then_flat_outer_dims.dimension(1); #if !defined(EIGEN_HAS_INDEX_LIST) - Eigen::array broadcast_dims{{ 1, all_but_batch }}; - Eigen::Tensor::Dimensions reshape_dims{{ batch, 1 }}; + Eigen::array broadcast_dims{{1, all_but_batch}}; + Eigen::Tensor::Dimensions reshape_dims{{batch, 1}}; #else Eigen::IndexList, Eigen::DenseIndex> broadcast_dims; broadcast_dims.set(1, all_but_batch); @@ -257,13 +255,13 @@ struct BatchSelectFunctorBase { }; template -struct BatchSelectFunctor - : BatchSelectFunctorBase {}; +struct BatchSelectFunctor : BatchSelectFunctorBase { +}; #ifdef TENSORFLOW_USE_SYCL template struct BatchSelectFunctor - : BatchSelectFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL + : BatchSelectFunctorBase {}; +#endif // TENSORFLOW_USE_SYCL } // namespace functor diff --git a/tensorflow/core/kernels/cwise_op_sigmoid.cc b/tensorflow/core/kernels/cwise_op_sigmoid.cc index a76a088ac8..c132fdb63f 100644 --- a/tensorflow/core/kernels/cwise_op_sigmoid.cc +++ b/tensorflow/core/kernels/cwise_op_sigmoid.cc @@ -25,7 +25,7 @@ REGISTER3(UnaryOp, GPU, "Sigmoid", functor::sigmoid, float, Eigen::half, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(UnaryOp, SYCL, "Sigmoid", functor::sigmoid, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "SigmoidGrad", functor::sigmoid_grad, float, Eigen::half, double, complex64, complex128); @@ -35,6 +35,6 @@ REGISTER3(SimpleBinaryOp, GPU, "SigmoidGrad", functor::sigmoid_grad, float, #endif #ifdef TENSORFLOW_USE_SYCL REGISTER(SimpleBinaryOp, SYCL, "SigmoidGrad", functor::sigmoid_grad, float); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sign.cc b/tensorflow/core/kernels/cwise_op_sign.cc index a4084d5ad1..02915ff4ce 100644 --- a/tensorflow/core/kernels/cwise_op_sign.cc +++ b/tensorflow/core/kernels/cwise_op_sign.cc @@ -41,6 +41,6 @@ REGISTER_KERNEL_BUILDER(Name("Sign") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sin.cc b/tensorflow/core/kernels/cwise_op_sin.cc index b91ff1ac30..16c6057864 100644 --- a/tensorflow/core/kernels/cwise_op_sin.cc +++ b/tensorflow/core/kernels/cwise_op_sin.cc @@ -25,5 +25,5 @@ REGISTER3(UnaryOp, GPU, "Sin", functor::sin, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Sin", functor::sin, float, double); -#endif // TENSORFLOW_USE_SYC +#endif // TENSORFLOW_USE_SYC } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sinh.cc b/tensorflow/core/kernels/cwise_op_sinh.cc index 055f0b12e1..26b7a940aa 100644 --- a/tensorflow/core/kernels/cwise_op_sinh.cc +++ b/tensorflow/core/kernels/cwise_op_sinh.cc @@ -16,20 +16,18 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER4(UnaryOp, CPU, "Sinh", functor::sinh, float, double, - complex64, complex128); +REGISTER4(UnaryOp, CPU, "Sinh", functor::sinh, float, double, complex64, + complex128); #if TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNEL(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("Sinh") \ - .Device(DEVICE_SYCL) \ - .TypeConstraint("T"), \ - UnaryOp>); +#define REGISTER_SYCL_KERNEL(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("Sinh").Device(DEVICE_SYCL).TypeConstraint("T"), \ + UnaryOp>); REGISTER_SYCL_KERNEL(float); REGISTER_SYCL_KERNEL(double); #undef REGISTER_SYCL_KERNEL -#endif // TENSORFLOW_USE_SYC +#endif // TENSORFLOW_USE_SYC #if GOOGLE_CUDA REGISTER2(UnaryOp, GPU, "Sinh", functor::sinh, float, double); diff --git a/tensorflow/core/kernels/cwise_op_sqrt.cc b/tensorflow/core/kernels/cwise_op_sqrt.cc index 00efbb00f1..497756133d 100644 --- a/tensorflow/core/kernels/cwise_op_sqrt.cc +++ b/tensorflow/core/kernels/cwise_op_sqrt.cc @@ -25,7 +25,7 @@ REGISTER3(UnaryOp, GPU, "Sqrt", functor::sqrt, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Sqrt", functor::sqrt, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "SqrtGrad", functor::sqrt_grad, float, Eigen::half, double, complex64, complex128); @@ -36,5 +36,5 @@ REGISTER3(SimpleBinaryOp, GPU, "SqrtGrad", functor::sqrt_grad, float, #ifdef TENSORFLOW_USE_SYCL REGISTER2(SimpleBinaryOp, SYCL, "SqrtGrad", functor::sqrt_grad, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_square.cc b/tensorflow/core/kernels/cwise_op_square.cc index 07a4b0b084..7fc2f6bf08 100644 --- a/tensorflow/core/kernels/cwise_op_square.cc +++ b/tensorflow/core/kernels/cwise_op_square.cc @@ -42,5 +42,5 @@ REGISTER_KERNEL_BUILDER(Name("Square") .HostMemory("y") .TypeConstraint("T"), UnaryOp>); -#endif // TENSORFLOW_USE_SYC +#endif // TENSORFLOW_USE_SYC } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_sub.cc b/tensorflow/core/kernels/cwise_op_sub.cc index 6adaecba04..025041946a 100644 --- a/tensorflow/core/kernels/cwise_op_sub.cc +++ b/tensorflow/core/kernels/cwise_op_sub.cc @@ -53,5 +53,5 @@ REGISTER_KERNEL_BUILDER(Name("Sub") .HostMemory("z") .TypeConstraint("T"), BinaryOp>); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_tan.cc b/tensorflow/core/kernels/cwise_op_tan.cc index 7891b1183d..c1a25767d3 100644 --- a/tensorflow/core/kernels/cwise_op_tan.cc +++ b/tensorflow/core/kernels/cwise_op_tan.cc @@ -24,5 +24,5 @@ REGISTER2(UnaryOp, GPU, "Tan", functor::tan, float, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Tan", functor::tan, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_tanh.cc b/tensorflow/core/kernels/cwise_op_tanh.cc index 8b3900892c..c5005f5ea8 100644 --- a/tensorflow/core/kernels/cwise_op_tanh.cc +++ b/tensorflow/core/kernels/cwise_op_tanh.cc @@ -26,7 +26,7 @@ REGISTER3(UnaryOp, GPU, "Tanh", functor::tanh, float, Eigen::half, double); #ifdef TENSORFLOW_USE_SYCL REGISTER2(UnaryOp, SYCL, "Tanh", functor::tanh, float, double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER5(SimpleBinaryOp, CPU, "TanhGrad", functor::tanh_grad, float, Eigen::half, double, complex64, complex128); diff --git a/tensorflow/core/kernels/cwise_ops_common.cc b/tensorflow/core/kernels/cwise_ops_common.cc index e561e59cf5..980edffceb 100644 --- a/tensorflow/core/kernels/cwise_ops_common.cc +++ b/tensorflow/core/kernels/cwise_ops_common.cc @@ -57,9 +57,9 @@ BinaryOpShared::BinaryOpState::BinaryOpState(OpKernelContext* ctx) in1(ctx->input(1)), bcast(BCast::FromShape(in0.shape()), BCast::FromShape(in1.shape())) { if (!bcast.IsValid()) { - ctx->SetStatus(errors::InvalidArgument("Incompatible shapes: ", - in0.shape().DebugString(), " vs. ", - in1.shape().DebugString())); + ctx->SetStatus(errors::InvalidArgument( + "Incompatible shapes: ", in0.shape().DebugString(), " vs. ", + in1.shape().DebugString())); return; } const TensorShape output_shape = BCast::ToShape(bcast.output_shape()); diff --git a/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h b/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h index 4394770708..e81b840a50 100644 --- a/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h +++ b/tensorflow/core/kernels/cwise_ops_gpu_gradients.cu.h @@ -50,16 +50,16 @@ struct SimpleBinaryFunctor { // Macros to explicitly instantiate kernels on GPU for multiple types // (T0, T1, etc.) for SimpleBiaryFunctor (e.g., functor::tanh_grad). -#define DEFINE_SIMPLE_BINARY1(F, T) \ +#define DEFINE_SIMPLE_BINARY1(F, T) \ template struct SimpleBinaryFunctor > -#define DEFINE_SIMPLE_BINARY2(F, T0, T1) \ - DEFINE_SIMPLE_BINARY1(F, T0); \ +#define DEFINE_SIMPLE_BINARY2(F, T0, T1) \ + DEFINE_SIMPLE_BINARY1(F, T0); \ DEFINE_SIMPLE_BINARY1(F, T1) -#define DEFINE_SIMPLE_BINARY3(F, T0, T1, T2) \ - DEFINE_SIMPLE_BINARY2(F, T0, T1); \ +#define DEFINE_SIMPLE_BINARY3(F, T0, T1, T2) \ + DEFINE_SIMPLE_BINARY2(F, T0, T1); \ DEFINE_SIMPLE_BINARY1(F, T2) -#define DEFINE_SIMPLE_BINARY4(F, T0, T1, T2, T3) \ - DEFINE_SIMPLE_BINARY2(F, T0, T1); \ +#define DEFINE_SIMPLE_BINARY4(F, T0, T1, T2, T3) \ + DEFINE_SIMPLE_BINARY2(F, T0, T1); \ DEFINE_SIMPLE_BINARY2(F, T2, T3) #define DEFINE_SIMPLE_BINARY5(F, T0, T1, T2, T3, T4) \ DEFINE_SIMPLE_BINARY2(F, T0, T1); \ diff --git a/tensorflow/core/kernels/cwise_ops_gradients.h b/tensorflow/core/kernels/cwise_ops_gradients.h index 77b330f589..82cdae9a34 100644 --- a/tensorflow/core/kernels/cwise_ops_gradients.h +++ b/tensorflow/core/kernels/cwise_ops_gradients.h @@ -171,7 +171,6 @@ struct SimpleBinaryFunctor { } }; - #ifdef TENSORFLOW_USE_SYCL // Partial specialization of BinaryFunctor for SYCL devices typedef Eigen::SyclDevice SYCLDevice; @@ -184,7 +183,7 @@ struct SimpleBinaryFunctor { } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template struct tanh_grad : base> {}; diff --git a/tensorflow/core/kernels/cwise_ops_sycl_common.h b/tensorflow/core/kernels/cwise_ops_sycl_common.h index 3f6ff7303d..3e107cee04 100644 --- a/tensorflow/core/kernels/cwise_ops_sycl_common.h +++ b/tensorflow/core/kernels/cwise_ops_sycl_common.h @@ -51,7 +51,8 @@ struct BinaryFunctor { void operator()(const SYCLDevice& d, typename Functor::tout_type out, typename Functor::tin_type in0, typename Functor::tin_type in1, bool* error) { - To32Bit(out).device(d) = To32Bit(in0).binaryExpr(To32Bit(in1), typename Functor::func()); + To32Bit(out).device(d) = + To32Bit(in0).binaryExpr(To32Bit(in1), typename Functor::func()); } void Left(const SYCLDevice& d, typename Functor::tout_type out, @@ -61,7 +62,9 @@ struct BinaryFunctor { constexpr int NumDims = Functor::tin_type::NumDimensions; static_assert(NumDims == 1, "Unexpected size"); Eigen::Sizes<1> scalar_dim; - out.device(d) = scalar.reshape(scalar_dim).broadcast(in.dimensions()).binaryExpr(in, Binary()); + out.device(d) = scalar.reshape(scalar_dim) + .broadcast(in.dimensions()) + .binaryExpr(in, Binary()); } void Right(const SYCLDevice& d, typename Functor::tout_type out, @@ -71,7 +74,8 @@ struct BinaryFunctor { constexpr int NumDims = Functor::tin_type::NumDimensions; static_assert(NumDims == 1, "Unexpected size"); Eigen::Sizes<1> scalar_dim; - out.device(d) = in.binaryExpr(scalar.reshape(scalar_dim).broadcast(in.dimensions()), Binary()); + out.device(d) = in.binaryExpr( + scalar.reshape(scalar_dim).broadcast(in.dimensions()), Binary()); } void BCast(const SYCLDevice& d, diff --git a/tensorflow/core/kernels/cwise_ops_test.cc b/tensorflow/core/kernels/cwise_ops_test.cc index bca0f1004d..39f497e716 100644 --- a/tensorflow/core/kernels/cwise_ops_test.cc +++ b/tensorflow/core/kernels/cwise_ops_test.cc @@ -54,36 +54,36 @@ int ColsFromArg(int arg) { return (arg % kRows); } BM_UNARY(cpu, Floor, float, DT_FLOAT); #if GOOGLE_CUDA BM_UNARY(gpu, Floor, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_UNARY(sycl, Floor, float, DT_FLOAT); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL BM_UNARY(cpu, Floor, double, DT_DOUBLE); #if GOOGLE_CUDA BM_UNARY(gpu, Floor, double, DT_DOUBLE); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_UNARY(sycl, Floor, double, DT_DOUBLE); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL BM_UNARY(cpu, Conj, std::complex, DT_COMPLEX64); #if GOOGLE_CUDA BM_UNARY(gpu, Conj, std::complex, DT_COMPLEX64); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_UNARY(cpu, Conj, std::complex, DT_COMPLEX128); #if GOOGLE_CUDA BM_UNARY(gpu, Conj, std::complex, DT_COMPLEX128); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_UNARY(cpu, Rint, double, DT_DOUBLE); #if GOOGLE_CUDA BM_UNARY(gpu, Rint, double, DT_DOUBLE); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_UNARY(cpu, Rint, float, DT_FLOAT); #if GOOGLE_CUDA BM_UNARY(gpu, Rint, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA // data func scalar. Graph* BinaryScalar(int num, const string& func) { @@ -113,18 +113,18 @@ Graph* BinaryScalar(int num, const string& func) { BM_BINARY_SCALAR(cpu, Less); #if GOOGLE_CUDA BM_BINARY_SCALAR(gpu, Less); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BINARY_SCALAR(sycl, Less); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL BM_BINARY_SCALAR(cpu, Add); #if GOOGLE_CUDA BM_BINARY_SCALAR(gpu, Add); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BINARY_SCALAR(sycl, Add); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef BM_BINARY_SCALAR template @@ -163,11 +163,11 @@ using Eigen::half; BM_BIAS_ADD_ALL(cpu, float, DT_FLOAT); #if GOOGLE_CUDA BM_BIAS_ADD_ALL(gpu, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_BIAS_ADD_ALL(cpu, half, DT_HALF); #if GOOGLE_CUDA BM_BIAS_ADD_ALL(gpu, half, DT_HALF); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #undef BM_BIAS_ADD_ALL #undef BM_BIAS_ADD @@ -217,15 +217,15 @@ using Eigen::half; #if GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(gpu, NCHW, float, DT_FLOAT); BM_BIAS_ADD_GRAD_ALL(gpu, NCHW, half, DT_HALF); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(cpu, NHWC, float, DT_FLOAT); #if GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(gpu, NHWC, float, DT_FLOAT); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(cpu, NHWC, half, DT_HALF); #if GOOGLE_CUDA BM_BIAS_ADD_GRAD_ALL(gpu, NHWC, half, DT_HALF); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #undef BM_BIAS_ADD_GRAD_ALL #undef BM_BIAS_ADD_GRAD @@ -265,10 +265,10 @@ Graph* BcastAdd(int rows, int cols, int dim) { BM_BCAST_ADD_ROW_ALL(cpu); #if GOOGLE_CUDA BM_BCAST_ADD_ROW_ALL(gpu); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BCAST_ADD_ROW_ALL(sycl); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef BM_BCAST_ADD_ROW_ALL #undef BM_BCAST_ADD_ROW @@ -291,10 +291,10 @@ BM_BCAST_ADD_ROW_ALL(sycl); BM_BCAST_ADD_COL_ALL(cpu); #if GOOGLE_CUDA BM_BCAST_ADD_COL_ALL(gpu); -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL BM_BCAST_ADD_COL_ALL(sycl); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef BM_BCAST_ADD_COL_ALL #undef BM_BCAST_ADD_COL diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index 56044a3d41..ca22f10a85 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -430,13 +430,10 @@ class IteratorStateVariant { REGISTER_UNARY_VARIANT_DECODE_FUNCTION(IteratorStateVariant, kIteratorVariantTypeName); -// TODO(mrry): Can we simply use the template kernel here? class IteratorHandleOp : public OpKernel { public: explicit IteratorHandleOp(OpKernelConstruction* ctx) : OpKernel(ctx), graph_def_version_(ctx->graph_def_version()) { - OP_REQUIRES_OK(ctx, ctx->allocate_persistent(DT_STRING, TensorShape({2}), - &handle_, nullptr)); OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_dtypes_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("shared_name", &name_)); @@ -460,56 +457,51 @@ class IteratorHandleOp : public OpKernel { } void Compute(OpKernelContext* context) override LOCKS_EXCLUDED(mu_) { - mutex_lock l(mu_); - FunctionLibraryRuntime* lib = context->function_library(); - std::unique_ptr device_mgr(nullptr); - std::unique_ptr flib_def(nullptr); - std::unique_ptr pflr(nullptr); - // If the iterator is shared then we construct a new FLR, and pass that in. - // NOTE(mrry,rohanj): In this case it is not possible to call remote - // functions from the iterator. We may add this functionality if there - // is sufficient demand, but it will require a significant refactoring. - if (!name_.empty()) { - lib = CreateFLR(context, &device_mgr, &flib_def, &pflr); - } + { + mutex_lock l(mu_); + if (resource_ == nullptr) { + FunctionLibraryRuntime* lib = context->function_library(); + std::unique_ptr device_mgr(nullptr); + std::unique_ptr flib_def(nullptr); + std::unique_ptr pflr(nullptr); + // If the iterator is shared then we construct a new FLR, and pass that + // in. NOTE(mrry,rohanj): In this case it is not possible to call remote + // functions from the iterator. We may add this functionality if there + // is sufficient demand, but it will require a significant refactoring. + if (!name_.empty()) { + lib = CreatePrivateFLR(context, &device_mgr, &flib_def, &pflr); + } - if (resource_ == nullptr) { - ResourceMgr* mgr = context->resource_manager(); - OP_REQUIRES_OK(context, cinfo_.Init(mgr, def())); + ResourceMgr* mgr = context->resource_manager(); + OP_REQUIRES_OK(context, cinfo_.Init(mgr, def())); + + IteratorResource* resource; + OP_REQUIRES_OK( + context, + mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [lib, &device_mgr, &flib_def, &pflr, + this](IteratorResource** ret) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + *ret = new IteratorResource( + output_dtypes_, output_shapes_, graph_def_version_, + std::move(device_mgr), 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; + } - IteratorResource* resource; - OP_REQUIRES_OK( - context, - mgr->LookupOrCreate( - cinfo_.container(), cinfo_.name(), &resource, - [lib, &device_mgr, &flib_def, &pflr, this](IteratorResource** ret) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - *ret = new IteratorResource( - output_dtypes_, output_shapes_, graph_def_version_, - std::move(device_mgr), 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; } - - auto h = handle_.AccessTensor(context)->template flat(); - h(0) = cinfo_.container(); - h(1) = cinfo_.name(); - resource_ = resource; - } - if (context->expected_output_dtype(0) == DT_RESOURCE) { - OP_REQUIRES_OK(context, MakeResourceHandleToOutput( - context, 0, cinfo_.container(), cinfo_.name(), - MakeTypeIndex())); - } else { - context->set_output_ref(0, &mu_, handle_.AccessTensor(context)); } + OP_REQUIRES_OK(context, MakeResourceHandleToOutput( + context, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); } private: @@ -526,7 +518,7 @@ class IteratorHandleOp : public OpKernel { return Status::OK(); } - FunctionLibraryRuntime* CreateFLR( + FunctionLibraryRuntime* CreatePrivateFLR( OpKernelContext* ctx, std::unique_ptr* device_mgr, std::unique_ptr* flib_def, std::unique_ptr* pflr) { @@ -546,9 +538,8 @@ class IteratorHandleOp : public OpKernel { } mutex mu_; - ContainerInfo cinfo_ GUARDED_BY(mu_); + ContainerInfo cinfo_; // Written once under mu_ then constant afterwards. IteratorResource* resource_ GUARDED_BY(mu_) = nullptr; - PersistentTensor handle_ GUARDED_BY(mu_); DataTypeVector output_dtypes_; std::vector output_shapes_; const int graph_def_version_; diff --git a/tensorflow/core/kernels/debug_ops.cc b/tensorflow/core/kernels/debug_ops.cc index 965a60c7e0..1b94ea0544 100644 --- a/tensorflow/core/kernels/debug_ops.cc +++ b/tensorflow/core/kernels/debug_ops.cc @@ -46,7 +46,7 @@ REGISTER_KERNEL_BUILDER(Name("CopyHost") .HostMemory("input") .HostMemory("output"), CopyOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register debug identity (non-ref and ref) ops. REGISTER_KERNEL_BUILDER(Name("DebugIdentity").Device(DEVICE_CPU), @@ -66,7 +66,7 @@ REGISTER_KERNEL_BUILDER(Name("DebugIdentity") .HostMemory("input") .HostMemory("output"), DebugIdentityOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register debug NaN-counter (non-ref and ref) ops. #define REGISTER_DEBUG_NAN_COUNT(type) \ @@ -98,7 +98,7 @@ REGISTER_GPU_DEBUG_NAN_COUNT(double); DebugNanCountOp); REGISTER_GPU_DEBUG_NAN_COUNT(float); REGISTER_GPU_DEBUG_NAN_COUNT(double); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register debug numeric summary ops. #define REGISTER_DEBUG_NUMERIC_SUMMARY_COUNT(type) \ diff --git a/tensorflow/core/kernels/debug_ops.h b/tensorflow/core/kernels/debug_ops.h index 381add3fb3..53a23b1306 100644 --- a/tensorflow/core/kernels/debug_ops.h +++ b/tensorflow/core/kernels/debug_ops.h @@ -21,7 +21,7 @@ limitations under the License. #endif #ifdef TENSORFLOW_USE_SYCL #include "tensorflow/core/common_runtime/sycl/sycl_util.h" -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #include "tensorflow/core/debug/debug_io_utils.h" #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/framework/op_kernel.h" @@ -91,7 +91,7 @@ class CopyOp : public OpKernel { Device* device = static_cast(context->device()); // Determine if the input tensor is not on CPU (e.g., on GPU). const bool off_host_input = device->device_type() == DEVICE_SYCL && - !context->input_alloc_attr(0).on_host(); + !context->input_alloc_attr(0).on_host(); if (off_host_input) { SYCLmemcpy(context->eigen_sycl_device(), src_tensor, copied_tensor); diff --git a/tensorflow/core/kernels/decode_csv_op.cc b/tensorflow/core/kernels/decode_csv_op.cc index c4555db453..0c42f63252 100644 --- a/tensorflow/core/kernels/decode_csv_op.cc +++ b/tensorflow/core/kernels/decode_csv_op.cc @@ -91,9 +91,9 @@ class DecodeCSVOp : public OpKernel { } else { int32 value; OP_REQUIRES(ctx, strings::safe_strto32(fields[f], &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid int32: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid int32: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -111,9 +111,9 @@ class DecodeCSVOp : public OpKernel { } else { int64 value; OP_REQUIRES(ctx, strings::safe_strto64(fields[f], &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid int64: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid int64: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -130,9 +130,9 @@ class DecodeCSVOp : public OpKernel { } else { float value; OP_REQUIRES(ctx, strings::safe_strtof(fields[f].c_str(), &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid float: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid float: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -150,9 +150,9 @@ class DecodeCSVOp : public OpKernel { } else { double value; OP_REQUIRES(ctx, strings::safe_strtod(fields[f].c_str(), &value), - errors::InvalidArgument("Field ", f, " in record ", i, - " is not a valid double: ", - fields[f])); + errors::InvalidArgument( + "Field ", f, " in record ", i, + " is not a valid double: ", fields[f])); output[f]->flat()(i) = value; } break; @@ -208,9 +208,10 @@ class DecodeCSVOp : public OpKernel { if (!quoted) { while (static_cast(current_idx) < input.size() && input[current_idx] != delim_) { - OP_REQUIRES(ctx, (!use_quote_delim_ || input[current_idx] != '"') && - input[current_idx] != '\n' && - input[current_idx] != '\r', + OP_REQUIRES(ctx, + (!use_quote_delim_ || input[current_idx] != '"') && + input[current_idx] != '\n' && + input[current_idx] != '\r', errors::InvalidArgument( "Unquoted fields cannot have quotes/CRLFs inside")); field += input[current_idx]; @@ -238,10 +239,11 @@ class DecodeCSVOp : public OpKernel { } OP_REQUIRES( - ctx, (static_cast(current_idx) < input.size() && - input[current_idx] == '"' && - (static_cast(current_idx) == input.size() - 1 || - input[current_idx + 1] == delim_)), + ctx, + (static_cast(current_idx) < input.size() && + input[current_idx] == '"' && + (static_cast(current_idx) == input.size() - 1 || + input[current_idx + 1] == delim_)), errors::InvalidArgument("Quoted field has to end with quote " "followed by delim or end")); diff --git a/tensorflow/core/kernels/decode_image_op.cc b/tensorflow/core/kernels/decode_image_op.cc index 44dcbf834c..912d04c153 100644 --- a/tensorflow/core/kernels/decode_image_op.cc +++ b/tensorflow/core/kernels/decode_image_op.cc @@ -87,10 +87,11 @@ class DecodeImageOp : public OpKernel { channels_ = 3; } else { OP_REQUIRES_OK(context, context->GetAttr("channels", &channels_)); - OP_REQUIRES(context, channels_ == 0 || channels_ == 1 || channels_ == 3 || - channels_ == 4, - errors::InvalidArgument( - "channels must be 0, 1, 3, or 4, got ", channels_)); + OP_REQUIRES( + context, + channels_ == 0 || channels_ == 1 || channels_ == 3 || channels_ == 4, + errors::InvalidArgument("channels must be 0, 1, 3, or 4, got ", + channels_)); } flags_.components = channels_; @@ -114,8 +115,9 @@ class DecodeImageOp : public OpKernel { if (format_ == kJpgFormat) { OP_REQUIRES_OK(context, context->GetAttr("ratio", &flags_.ratio)); - OP_REQUIRES(context, flags_.ratio == 1 || flags_.ratio == 2 || - flags_.ratio == 4 || flags_.ratio == 8, + OP_REQUIRES(context, + flags_.ratio == 1 || flags_.ratio == 2 || flags_.ratio == 4 || + flags_.ratio == 8, errors::InvalidArgument("ratio must be 1, 2, 4, or 8, got ", flags_.ratio)); OP_REQUIRES_OK(context, context->GetAttr("fancy_upscaling", @@ -130,8 +132,9 @@ class DecodeImageOp : public OpKernel { string dct_method; OP_REQUIRES_OK(context, context->GetAttr("dct_method", &dct_method)); OP_REQUIRES( - context, (dct_method.empty() || dct_method == "INTEGER_FAST" || - dct_method == "INTEGER_ACCURATE"), + context, + (dct_method.empty() || dct_method == "INTEGER_FAST" || + dct_method == "INTEGER_ACCURATE"), errors::InvalidArgument("dct_method must be one of " "{'', 'INTEGER_FAST', 'INTEGER_ACCURATE'}")); if (dct_method == "INTEGER_FAST") { @@ -157,9 +160,9 @@ class DecodeImageOp : public OpKernel { errors::InvalidArgument("Expected image (JPEG, PNG, or GIF), got ", FileFormatString(magic, input))); OP_REQUIRES(context, input.size() <= std::numeric_limits::max(), - errors::InvalidArgument(FileFormatString(magic, input), - " contents are too large for int: ", - input.size())); + errors::InvalidArgument( + FileFormatString(magic, input), + " contents are too large for int: ", input.size())); OP_REQUIRES(context, magic == kPngFormat || channel_bits_ == 8, errors::InvalidArgument(FileFormatString(magic, input), " does not support uint16 output")); @@ -212,9 +215,10 @@ class DecodeImageOp : public OpKernel { input.data(), input.size(), flags, nullptr /* nwarn */, [=, &output](int width, int height, int channels) -> uint8* { Status status(context->allocate_output( - 0, format_ == kGifFormat - ? TensorShape({1, height, width, channels}) - : TensorShape({height, width, channels}), + 0, + format_ == kGifFormat + ? TensorShape({1, height, width, channels}) + : TensorShape({height, width, channels}), &output)); if (!status.ok()) { VLOG(1) << status; diff --git a/tensorflow/core/kernels/deep_conv2d.cc b/tensorflow/core/kernels/deep_conv2d.cc index 8e9b8a7e2e..829155fb31 100644 --- a/tensorflow/core/kernels/deep_conv2d.cc +++ b/tensorflow/core/kernels/deep_conv2d.cc @@ -120,9 +120,9 @@ bool CanUseDeepConv2D(int stride_rows, int stride_cols, int filter_rows, VLOG(2) << "CanUseDeepConv2D" << " deep_conv_cost: " << deep_conv_cost - << " direct_conv_cost: " << direct_conv_cost - << " deep_direct_ratio: " << (static_cast(deep_conv_cost) / - static_cast(direct_conv_cost)) + << " direct_conv_cost: " << direct_conv_cost << " deep_direct_ratio: " + << (static_cast(deep_conv_cost) / + static_cast(direct_conv_cost)) << " use_deep_conv: " << (deep_conv_cost < direct_conv_cost); return deep_conv_cost < direct_conv_cost; } diff --git a/tensorflow/core/kernels/dense_update_ops.cc b/tensorflow/core/kernels/dense_update_ops.cc index 6d44a92fa3..6497c8f371 100644 --- a/tensorflow/core/kernels/dense_update_ops.cc +++ b/tensorflow/core/kernels/dense_update_ops.cc @@ -89,7 +89,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ @@ -113,14 +113,14 @@ TF_CALL_GPU_ALL_TYPES(REGISTER_GPU_KERNELS); #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNELS(type) \ -REGISTER_KERNEL_BUILDER( \ - Name("Assign").Device(DEVICE_SYCL).TypeConstraint("T"), \ - AssignOpT); +#define REGISTER_SYCL_KERNELS(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("Assign").Device(DEVICE_SYCL).TypeConstraint("T"), \ + AssignOpT); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS); #undef REGISTER_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ @@ -146,7 +146,7 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); #endif // end GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL_KERNELS(type) \ +#define REGISTER_SYCL_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ Name("AssignAdd").Device(DEVICE_SYCL).TypeConstraint("T"), \ DenseUpdateOp); \ @@ -156,5 +156,5 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS); #undef REGISTER_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/depthwise_conv_grad_op.cc b/tensorflow/core/kernels/depthwise_conv_grad_op.cc index 9347978d51..91a9587174 100644 --- a/tensorflow/core/kernels/depthwise_conv_grad_op.cc +++ b/tensorflow/core/kernels/depthwise_conv_grad_op.cc @@ -400,7 +400,7 @@ struct LaunchDepthwiseConvBackpropInputOp { // Computes one shard of depthwise conv2d backprop input. auto shard = [&ctx, &args, &out_backprop, &filter_data, &in_backprop]( - int64 start, int64 limit) { + int64 start, int64 limit) { static const int64 kPacketSize = (sizeof(Packet) / sizeof(T)); const int64 input_image_size = @@ -750,7 +750,7 @@ struct LaunchDepthwiseConvBackpropFilterOp { // Computes one shard of depthwise conv2d backprop filter. auto shard = [&ctx, &args, &out_backprop, &input, &output_buffer_data]( - int64 start, int64 limit) { + int64 start, int64 limit) { static const int64 kPacketSize = (sizeof(Packet) / sizeof(T)); const int64 filter_spatial_size = args.filter_rows * args.filter_cols; const int64 padded_out_depth_size = diff --git a/tensorflow/core/kernels/depthwise_conv_op.cc b/tensorflow/core/kernels/depthwise_conv_op.cc index a5fd07fbe1..c060b2e14d 100644 --- a/tensorflow/core/kernels/depthwise_conv_op.cc +++ b/tensorflow/core/kernels/depthwise_conv_op.cc @@ -308,10 +308,10 @@ class DepthwiseConv2dNativeOp : public BinaryOp { // in_depth for input and filter must match. const int64 in_depth = GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES( - context, in_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - in_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, in_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", in_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is depth multiplier. const int32 depth_multiplier = filter.dim_size(3); @@ -430,9 +430,10 @@ TF_CALL_double(REGISTER_CPU_KERNEL); #endif #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER( - Name("DepthwiseConv2dNative").Device(DEVICE_GPU).TypeConstraint("T"), - DepthwiseConv2dNativeOp); +REGISTER_KERNEL_BUILDER(Name("DepthwiseConv2dNative") + .Device(DEVICE_GPU) + .TypeConstraint("T"), + DepthwiseConv2dNativeOp); REGISTER_KERNEL_BUILDER( Name("DepthwiseConv2dNative").Device(DEVICE_GPU).TypeConstraint("T"), diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc index 5493e33532..126b64f73d 100644 --- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc +++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc @@ -17,12 +17,12 @@ limitations under the License. #define EIGEN_USE_GPU #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "external/cub_archive/cub/util_ptx.cuh" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/kernels/depthwise_conv_op.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/cuda_kernel_helper.h" #include "tensorflow/core/util/tensor_format.h" -#include "external/cub_archive/cub/util_ptx.cuh" #if !defined(_MSC_VER) #define UNROLL _Pragma("unroll") @@ -1021,7 +1021,7 @@ __global__ void __launch_bounds__(640, 2) // Device function to compute sub-warp sum reduction for a power-of-two group of // neighboring threads. -template +template __device__ __forceinline__ T WarpSumReduce(T val) { // support only power-of-two widths. assert(__popc(kWidth) == 1); diff --git a/tensorflow/core/kernels/diag_op.cc b/tensorflow/core/kernels/diag_op.cc index 86fa7dce36..d228153d4c 100644 --- a/tensorflow/core/kernels/diag_op.cc +++ b/tensorflow/core/kernels/diag_op.cc @@ -29,8 +29,8 @@ limitations under the License. #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_types.h" -#include "tensorflow/core/platform/types.h" #include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/work_sharder.h" namespace tensorflow { @@ -47,8 +47,9 @@ class DiagOp : public OpKernel { void Compute(OpKernelContext* context) override { const Tensor& diagonal = context->input(0); const int num_dims = diagonal.dims(); - OP_REQUIRES(context, 0 != num_dims, errors::InvalidArgument( - "Input must be at least rank 1, got 0")); + OP_REQUIRES( + context, 0 != num_dims, + errors::InvalidArgument("Input must be at least rank 1, got 0")); TensorShape out_shape; for (int i = 0; i < num_dims; ++i) { out_shape.AddDim(diagonal.dim_size(i)); @@ -60,10 +61,9 @@ class DiagOp : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output_tensor)); functor::DiagFunctor diagFunc; - Status s = diagFunc(context, - diagonal.NumElements(), - diagonal.flat().data(), - output_tensor->flat().data()); + Status s = + diagFunc(context, diagonal.NumElements(), diagonal.flat().data(), + output_tensor->flat().data()); OP_REQUIRES_OK(context, s); } }; @@ -82,12 +82,12 @@ class DiagPartOp : public OpKernel { errors::InvalidArgument("The rank of the tensor should be \ even and positive, got shape ", tensor.shape().DebugString())); - for (int i = 0; i < out_dims; i++){ - OP_REQUIRES(context, tensor.dim_size(i) == tensor.dim_size(i + out_dims), - errors::InvalidArgument( - "Invalid shape ", tensor.shape().DebugString(), - ": dimensions ", i, " and ", i + out_dims, " do not match.") - ); + for (int i = 0; i < out_dims; i++) { + OP_REQUIRES( + context, tensor.dim_size(i) == tensor.dim_size(i + out_dims), + errors::InvalidArgument("Invalid shape ", + tensor.shape().DebugString(), ": dimensions ", + i, " and ", i + out_dims, " do not match.")); } TensorShape out_shape; @@ -96,13 +96,10 @@ class DiagPartOp : public OpKernel { } Tensor* output = nullptr; - OP_REQUIRES_OK(context, - context->allocate_output(0, out_shape, &output)); + OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output)); functor::DiagPartFunctor diagPartFunc; - Status s = diagPartFunc(context, - out_shape.num_elements(), - tensor.flat().data(), - output->flat().data()); + Status s = diagPartFunc(context, out_shape.num_elements(), + tensor.flat().data(), output->flat().data()); OP_REQUIRES_OK(context, s); } }; @@ -129,9 +126,8 @@ class DiagPartOp : public OpKernel { namespace functor { template struct DiagFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // This subprocess is responsible for writing values in index range // [start*size, limit*size) auto subDiag = [in, out, size](int64 start, int64 limit) { @@ -143,17 +139,16 @@ struct DiagFunctor { // Here, 5 is a empirical factor of cost_per_unit. auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - Shard(worker_threads.num_threads, worker_threads.workers, size, - 5 * size, subDiag); + Shard(worker_threads.num_threads, worker_threads.workers, size, 5 * size, + subDiag); return Status::OK(); } }; template struct DiagPartFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // This subprocess is responsible for extracting values in index range // [start, limit) auto subDiagPart = [in, out, size](int64 start, int64 limit) { @@ -164,14 +159,13 @@ struct DiagPartFunctor { // Here, 5 is a empirical factor of cost_per_unit. auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - Shard(worker_threads.num_threads, worker_threads.workers, size, - 5, subDiagPart); + Shard(worker_threads.num_threads, worker_threads.workers, size, 5, + subDiagPart); return Status::OK(); } }; } // namespace functor - // Register the CPU kernels. #define REGISTER_DIAGOP(T) \ REGISTER_KERNEL_BUILDER( \ @@ -250,6 +244,4 @@ TF_CALL_complex128(REGISTER_DIAGPARTOP_GPU); #endif // GOOGLE_CUDA - } // namespace tensorflow - diff --git a/tensorflow/core/kernels/diag_op.h b/tensorflow/core/kernels/diag_op.h index c6ca6a2047..baf16ddb4b 100644 --- a/tensorflow/core/kernels/diag_op.h +++ b/tensorflow/core/kernels/diag_op.h @@ -26,14 +26,14 @@ namespace functor { template struct DiagFunctor { - Status operator() (OpKernelContext* context, const int64 size, - const T* in, T* out); + Status operator()(OpKernelContext* context, const int64 size, const T* in, + T* out); }; template struct DiagPartFunctor { - Status operator() (OpKernelContext* context, const int64 size, - const T* in, T* out); + Status operator()(OpKernelContext* context, const int64 size, const T* in, + T* out); }; } // namespace functor diff --git a/tensorflow/core/kernels/diag_op_gpu.cu.cc b/tensorflow/core/kernels/diag_op_gpu.cu.cc index d3c529d784..910f3093b2 100644 --- a/tensorflow/core/kernels/diag_op_gpu.cu.cc +++ b/tensorflow/core/kernels/diag_op_gpu.cu.cc @@ -19,8 +19,8 @@ limitations under the License. #include #include "tensorflow/core/framework/register_types.h" -#include "tensorflow/core/util/cuda_kernel_helper.h" #include "tensorflow/core/kernels/diag_op.h" +#include "tensorflow/core/util/cuda_kernel_helper.h" namespace tensorflow { namespace functor { @@ -28,10 +28,8 @@ namespace functor { typedef Eigen::GpuDevice GPUDevice; template -__global__ void DiagCudaKernel(const int num_threads, - const int64 size, - const T* in, - T* out) { +__global__ void DiagCudaKernel(const int num_threads, const int64 size, + const T* in, T* out) { CUDA_1D_KERNEL_LOOP(index, num_threads) { // Fill the diagonal elements or set to zero in other place. if (index % (1 + size) == 0) { @@ -44,9 +42,8 @@ __global__ void DiagCudaKernel(const int num_threads, template struct DiagFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // Empty tensor couldn't launch the kernel. if (size == 0) { return Status::OK(); @@ -56,25 +53,22 @@ struct DiagFunctor { // so this may overflow for `size*size` in extreme cases, // here is checking the multiplication overflow for integer. if (size && (int(size * size) / size) != size) { - return errors::Internal( - "DiagOp got input size too large."); + return errors::Internal("DiagOp got input size too large."); } int virtual_thread_count = int(size * size); // Launch the GPU kernel. const GPUDevice& device = context->eigen_device(); - CudaLaunchConfig diag_config = GetCudaLaunchConfig( - virtual_thread_count, device); - DiagCudaKernel<<>>( - diag_config.virtual_thread_count, size, in, out); + CudaLaunchConfig diag_config = + GetCudaLaunchConfig(virtual_thread_count, device); + DiagCudaKernel<<>>(diag_config.virtual_thread_count, size, + in, out); auto err = cudaGetLastError(); if (err != cudaSuccess) { return errors::Internal( - "Could not launch DiagOp kernel: ", - cudaGetErrorString(err), "."); + "Could not launch DiagOp kernel: ", cudaGetErrorString(err), "."); } return Status::OK(); } @@ -87,12 +81,9 @@ template struct DiagFunctor; template struct DiagFunctor; template struct DiagFunctor; - template -__global__ void DiagPartCudaKernel(const int num_threads, - const int64 size, - const T* in, - T* out) { +__global__ void DiagPartCudaKernel(const int num_threads, const int64 size, + const T* in, T* out) { CUDA_1D_KERNEL_LOOP(index, num_threads) { out[index] = in[(1 + size) * index]; } @@ -100,9 +91,8 @@ __global__ void DiagPartCudaKernel(const int num_threads, template struct DiagPartFunctor { - EIGEN_ALWAYS_INLINE Status - operator() (OpKernelContext* context, const int64 size, - const T* in, T* out) { + EIGEN_ALWAYS_INLINE Status operator()(OpKernelContext* context, + const int64 size, const T* in, T* out) { // Empty tensor couldn't launch the kernel. if (size == 0) { return Status::OK(); @@ -111,16 +101,14 @@ struct DiagPartFunctor { // Extract the diagonal elements. CudaLaunchConfig diag_config = GetCudaLaunchConfig(size, device); - DiagPartCudaKernel<<>>( - diag_config.virtual_thread_count, size, in, out); + DiagPartCudaKernel<<>>(diag_config.virtual_thread_count, + size, in, out); auto err = cudaGetLastError(); if (err != cudaSuccess) { return errors::Internal( - "Could not launch DiagPartOp kernel: ", - cudaGetErrorString(err), "."); + "Could not launch DiagPartOp kernel: ", cudaGetErrorString(err), "."); } return Status::OK(); } diff --git a/tensorflow/core/kernels/diag_op_test.cc b/tensorflow/core/kernels/diag_op_test.cc index 2d1417854c..a708e53dd0 100644 --- a/tensorflow/core/kernels/diag_op_test.cc +++ b/tensorflow/core/kernels/diag_op_test.cc @@ -30,8 +30,8 @@ static Graph* Diag(int n, DataType type) { return g; } -#define BM_DiagDev(N, T, TFTYPE, DEVICE) \ - static void BM_Diag##_##N##_##TFTYPE##_##DEVICE(int iters) { \ +#define BM_DiagDev(N, T, TFTYPE, DEVICE) \ + static void BM_Diag##_##N##_##TFTYPE##_##DEVICE(int iters) { \ testing::UseRealTime(); \ testing::ItemsProcessed(static_cast(iters) * N * N); \ test::Benchmark(#DEVICE, Diag(N, TFTYPE)).Run(iters); \ @@ -51,4 +51,3 @@ BM_Diag(128); BM_Diag(512); } // end namespace tensorflow - diff --git a/tensorflow/core/kernels/dilation_ops.cc b/tensorflow/core/kernels/dilation_ops.cc index 6f5c0e9156..441a63465c 100644 --- a/tensorflow/core/kernels/dilation_ops.cc +++ b/tensorflow/core/kernels/dilation_ops.cc @@ -91,10 +91,10 @@ void ParseSizes(OpKernelContext* context, const std::vector& strides, filter.shape().DebugString())); const int filter_rows = filter.dim_size(0); const int filter_cols = filter.dim_size(1); - OP_REQUIRES( - context, depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", depth, " vs ", + filter.dim_size(2))); // Effective filter size, after introducing rate - 1 zeros between each // non-zero filter element. @@ -234,10 +234,11 @@ class DilationBackpropInputOp : public OpKernel { // [ batch, out_rows, out_cols, depth ] const int batch = input.dim_size(0); const int depth = input.dim_size(3); - OP_REQUIRES(context, batch == out_backprop.dim_size(0) && - out_rows == out_backprop.dim_size(1) && - out_cols == out_backprop.dim_size(2) && - depth == out_backprop.dim_size(3), + OP_REQUIRES(context, + batch == out_backprop.dim_size(0) && + out_rows == out_backprop.dim_size(1) && + out_cols == out_backprop.dim_size(2) && + depth == out_backprop.dim_size(3), errors::InvalidArgument("out_backprop has incompatible size.")); // The computed in_backprop has the same dimensions as the input: @@ -353,10 +354,11 @@ class DilationBackpropFilterOp : public OpKernel { // [ batch, out_rows, out_cols, depth ] const int batch = input.dim_size(0); const int depth = input.dim_size(3); - OP_REQUIRES(context, batch == out_backprop.dim_size(0) && - out_rows == out_backprop.dim_size(1) && - out_cols == out_backprop.dim_size(2) && - depth == out_backprop.dim_size(3), + OP_REQUIRES(context, + batch == out_backprop.dim_size(0) && + out_rows == out_backprop.dim_size(1) && + out_cols == out_backprop.dim_size(2) && + depth == out_backprop.dim_size(3), errors::InvalidArgument("out_backprop has incompatible size.")); // The computed filter_backprop has the same dimensions as the filter: diff --git a/tensorflow/core/kernels/dilation_ops_gpu.cu.cc b/tensorflow/core/kernels/dilation_ops_gpu.cu.cc index ac0775fbef..c63806a7f6 100644 --- a/tensorflow/core/kernels/dilation_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/dilation_ops_gpu.cu.cc @@ -61,9 +61,8 @@ __global__ void DilationKernel(const int32 nthreads, const T* input_ptr, const int w_in = w_beg + w * rate_cols; if (w_in >= 0 && w_in < input_cols) { const T val = - input_ptr[d + - depth * - (w_in + input_cols * (h_in + input_rows * b))] + + input_ptr[d + depth * (w_in + + input_cols * (h_in + input_rows * b))] + filter_ptr[d + depth * (w + filter_cols * h)]; if (val > cur_val) { cur_val = val; @@ -106,9 +105,8 @@ __global__ void DilationBackpropInputKernel( const int w_in = w_beg + w * rate_cols; if (w_in >= 0 && w_in < input_cols) { const T val = - input_ptr[d + - depth * - (w_in + input_cols * (h_in + input_rows * b))] + + input_ptr[d + depth * (w_in + + input_cols * (h_in + input_rows * b))] + filter_ptr[d + depth * (w + filter_cols * h)]; if (val > cur_val) { cur_val = val; @@ -156,9 +154,8 @@ __global__ void DilationBackpropFilterKernel( const int w_in = w_beg + w * rate_cols; if (w_in >= 0 && w_in < input_cols) { const T val = - input_ptr[d + - depth * - (w_in + input_cols * (h_in + input_rows * b))] + + input_ptr[d + depth * (w_in + + input_cols * (h_in + input_rows * b))] + filter_ptr[d + depth * (w + filter_cols * h)]; if (val > cur_val) { cur_val = val; diff --git a/tensorflow/core/kernels/draw_bounding_box_op.cc b/tensorflow/core/kernels/draw_bounding_box_op.cc index a8818b7385..b5d5b880bb 100644 --- a/tensorflow/core/kernels/draw_bounding_box_op.cc +++ b/tensorflow/core/kernels/draw_bounding_box_op.cc @@ -29,8 +29,7 @@ template class DrawBoundingBoxesOp : public OpKernel { public: explicit DrawBoundingBoxesOp(OpKernelConstruction* context) - : OpKernel(context) { - } + : OpKernel(context) {} void Compute(OpKernelContext* context) override { const Tensor& images = context->input(0); @@ -94,35 +93,28 @@ class DrawBoundingBoxesOp : public OpKernel { int64 color_index = bb % color_table_length; const int64 min_box_row = static_cast(tboxes(b, bb, 0)) * (height - 1); - const int64 min_box_row_clamp = - std::max(min_box_row, 0); + const int64 min_box_row_clamp = std::max(min_box_row, 0); const int64 max_box_row = static_cast(tboxes(b, bb, 2)) * (height - 1); const int64 max_box_row_clamp = std::min(max_box_row, height - 1); const int64 min_box_col = static_cast(tboxes(b, bb, 1)) * (width - 1); - const int64 min_box_col_clamp = - std::max(min_box_col, 0); + const int64 min_box_col_clamp = std::max(min_box_col, 0); const int64 max_box_col = static_cast(tboxes(b, bb, 3)) * (width - 1); - const int64 max_box_col_clamp = - std::min(max_box_col, width - 1); + const int64 max_box_col_clamp = std::min(max_box_col, width - 1); if (min_box_row > max_box_row || min_box_col > max_box_col) { - LOG(WARNING) << "Bounding box (" << min_box_row - << "," << min_box_col - << "," << max_box_row - << "," << max_box_col + LOG(WARNING) << "Bounding box (" << min_box_row << "," << min_box_col + << "," << max_box_row << "," << max_box_col << ") is inverted and will not be drawn."; continue; } - if (min_box_row >= height || max_box_row < 0 || - min_box_col >= width || max_box_col < 0) { - LOG(WARNING) << "Bounding box (" << min_box_row - << "," << min_box_col - << "," << max_box_row - << "," << max_box_col + if (min_box_row >= height || max_box_row < 0 || min_box_col >= width || + max_box_col < 0) { + LOG(WARNING) << "Bounding box (" << min_box_row << "," << min_box_col + << "," << max_box_row << "," << max_box_col << ") is completely outside the image" << " and will not be drawn."; continue; diff --git a/tensorflow/core/kernels/dynamic_partition_op.cc b/tensorflow/core/kernels/dynamic_partition_op.cc index 861e16b2fd..3c988db5e6 100644 --- a/tensorflow/core/kernels/dynamic_partition_op.cc +++ b/tensorflow/core/kernels/dynamic_partition_op.cc @@ -103,7 +103,8 @@ class DynamicPartitionOp : public DynamicPartitionOp_Shared { // Walk through data and copy the data to the appropriate output tensor const auto data_flat = data->flat(); std::vector, - Eigen::Aligned> > out_vec; + Eigen::Aligned> > + out_vec; out_vec.reserve(num_partitions_); for (int p = 0; p < num_partitions_; p++) { out_vec.push_back(outputs[p]->vec()); @@ -124,7 +125,8 @@ class DynamicPartitionOp : public DynamicPartitionOp_Shared { } else { // If data has extra dimensions, use Eigen slices std::vector, - Eigen::Aligned> > out_flat; + Eigen::Aligned> > + out_flat; out_flat.reserve(num_partitions_); for (int p = 0; p < num_partitions_; p++) { out_flat.push_back(outputs[p]->flat_outer_dims()); diff --git a/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc b/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc index 9bb58b13f3..9dfeccff0e 100644 --- a/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc +++ b/tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc @@ -79,9 +79,9 @@ template void RangeInit(const GPUDevice& d, const T start, const T delta, const int32 size, typename TTypes::Flat out) { CudaLaunchConfig config = GetCudaLaunchConfig(size, d); - RangeInitKernel< - T><<>>( - start, delta, size, out.data()); + RangeInitKernel + <<>>( + start, delta, size, out.data()); } // Given *num_runs pairs (key, value), this function moves the value @@ -103,11 +103,10 @@ void CallGatherKernel(const GPUDevice& d, const T* params, const int32* indices, T* out, int64 gather_dim_size, int64 indices_size, int64 slice_size, int64 out_size) { CudaLaunchConfig config = GetCudaLaunchConfig(out_size, d); - GatherOpKernel< - T, int32, - true><<>>( - params, indices, out, gather_dim_size, indices_size, slice_size, - out_size); + GatherOpKernel + <<>>( + params, indices, out, gather_dim_size, indices_size, slice_size, + out_size); } struct IdentityOp { @@ -231,10 +230,10 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { OP_REQUIRES_ASYNC( c, TensorShapeUtils::StartsWith(data.shape(), partitions.shape()), - errors::InvalidArgument("data.shape must start with partitions.shape, ", - "got data.shape = ", data.shape().DebugString(), - ", partitions.shape = ", - partitions.shape().DebugString()), + errors::InvalidArgument( + "data.shape must start with partitions.shape, ", + "got data.shape = ", data.shape().DebugString(), + ", partitions.shape = ", partitions.shape().DebugString()), done); Tensor partition_count; @@ -245,8 +244,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { AllocatorAttributes alloc_attr; alloc_attr.set_on_host(true); OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), - &partition_count, alloc_attr), + c, + c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), + &partition_count, alloc_attr), done); auto e_part_count = partition_count.flat(); for (int i = 0; i < num_partitions_; i++) e_part_count(i) = 0; @@ -259,8 +259,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { // Prepare for counting. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), - &partition_count), + c, + c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), + &partition_count), done); Tensor indices_out; // Count how many times each partition index occurs. @@ -280,8 +281,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { alloc_attr.set_on_host(true); alloc_attr.set_gpu_compatible(true); OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(partition_count.dtype(), partition_count.shape(), - &cpu_tensor, alloc_attr), + c, + c->allocate_temp(partition_count.dtype(), partition_count.shape(), + &cpu_tensor, alloc_attr), done); perftools::gputools::DeviceMemoryBase wrapped( partition_count.flat().data(), num_partitions_ * sizeof(int32)); @@ -340,9 +342,10 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { indices_in_ptr, indices_out_ptr, N, 0, sizeof(int32) * 8, cu_stream); // Allocate temporary storage. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &cub_temp_storage), + c, + c->allocate_temp(DT_INT8, + TensorShape({static_cast(temp_storage_bytes)}), + &cub_temp_storage), done); // Radix-sort the partition information. cub::DeviceRadixSort::SortPairs( @@ -376,8 +379,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { zero_functor(device, partition_count->flat()); // Allocate memory for aggregates_out. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), - &aggregates_out), + c, + c->allocate_temp(DT_INT32, TensorShape({num_partitions_}), + &aggregates_out), done); // Obtain the pointers to inner buffers. int32* keys_in_ptr = partitions_out.flat().data(); @@ -408,9 +412,10 @@ class DynamicPartitionOpGPU : public AsyncOpKernel { num_runs_ptr, reduction_op, N, cu_stream); // Allocate temporary storage. OP_REQUIRES_OK_ASYNC( - c, c->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &cub_temp_storage), + c, + c->allocate_temp(DT_INT8, + TensorShape({static_cast(temp_storage_bytes)}), + &cub_temp_storage), done); // Run reduce-by-key. The effect is that we count how many times // each index appears in partitions. The distinct indices are stored diff --git a/tensorflow/core/kernels/eigen_activations.h b/tensorflow/core/kernels/eigen_activations.h index 99b4b2abe6..302033e47c 100644 --- a/tensorflow/core/kernels/eigen_activations.h +++ b/tensorflow/core/kernels/eigen_activations.h @@ -21,13 +21,13 @@ limitations under the License. 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() - */ + * \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) @@ -55,13 +55,13 @@ struct functor_traits > { } // 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() - */ + * \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) @@ -89,11 +89,11 @@ struct functor_traits > { } // namespace internal /** - * \ingroup CXX11_NeuralNetworks_Module - * \brief Template functor to clip the magnitude of the first scalar. - * - * \sa class CwiseBinaryOp, MatrixBase::Clip - */ + * \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) diff --git a/tensorflow/core/kernels/eigen_activations_test.cc b/tensorflow/core/kernels/eigen_activations_test.cc index 907233103d..34952f5abb 100644 --- a/tensorflow/core/kernels/eigen_activations_test.cc +++ b/tensorflow/core/kernels/eigen_activations_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenBackwardSpatialConvolutionsTest, SigmoidFastDerivative) { const ptrdiff_t depth = 3; diff --git a/tensorflow/core/kernels/eigen_attention.h b/tensorflow/core/kernels/eigen_attention.h index 3a94b8c993..4d86f9deb9 100644 --- a/tensorflow/core/kernels/eigen_attention.h +++ b/tensorflow/core/kernels/eigen_attention.h @@ -21,35 +21,47 @@ limitations under the License. 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. - */ + * \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) { } + 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; + internal::traits::Layout, IndexType> > + Ref; Ref in(input); DSizes dims = in.dimensions(); @@ -62,12 +74,12 @@ struct GlimpseExtractionOp { } template - EIGEN_DEVICE_FUNC - void eval(const Input& input, Output& output, const Device& device) const - { + 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; + internal::traits::Layout, IndexType> > + Ref; Ref in(input); const Index num_channels = in.dimension(0); const Index input_width = in.dimension(1); @@ -97,8 +109,8 @@ struct GlimpseExtractionOp { x -= width_ / 2.0f; y -= height_ / 2.0f; - const Index offset_x = (Index) x; - const Index offset_y = (Index) y; + const Index offset_x = (Index)x; + const Index offset_y = (Index)y; Index glimpse_width = width_; Index glimpse_height = height_; bool partial_overlap = false; @@ -135,7 +147,7 @@ struct GlimpseExtractionOp { if (uniform_noise_) { // Initialize the glimpse with uniform noise. typedef typename internal::remove_const< - typename internal::traits::Scalar>::type Scalar; + typename internal::traits::Scalar>::type Scalar; TensorFixedSize > mini; mini.device(device) = input.template chip<3>(i).minimum(); TensorFixedSize > range; @@ -215,21 +227,22 @@ struct GlimpseExtractionOp { const bool centered_; const bool uniform_noise_; }; -} - +} // namespace template -EIGEN_ALWAYS_INLINE -static const TensorCustomUnaryOp::Index>, const Input> +EIGEN_ALWAYS_INLINE static const TensorCustomUnaryOp< + const GlimpseExtractionOp::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); + 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, @@ -237,6 +250,6 @@ ExtractGlimpses(const Input& input, return input.customOp(op); } -} // end namespace Eigen +} // end namespace Eigen #endif // TENSORFLOW_CORE_KERNELS_EIGEN_ATTENTION_H_ diff --git a/tensorflow/core/kernels/eigen_attention_test.cc b/tensorflow/core/kernels/eigen_attention_test.cc index 3a2eeb0595..08f6187718 100644 --- a/tensorflow/core/kernels/eigen_attention_test.cc +++ b/tensorflow/core/kernels/eigen_attention_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenAttentionTest, Simple) { const ptrdiff_t depth = 3; diff --git a/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h b/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h index aec7697810..099696105b 100644 --- a/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h +++ b/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h @@ -21,29 +21,29 @@ limitations under the License. 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 + * \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, + * 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 + * The output_backward and the kernel must both be in col-major layout. The * result will also be in col-major layout. - * - * If row_in_stride, col_in_stride > 1, then applies convolution with holes + * + * If row_in_stride, col_in_stride > 1, then applies convolution with holes * (aka atrous convolution), sampling every row_in_stride, col_in_stride input * pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the + * + * 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 + * + * 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. - * - */ + * + */ #ifdef EIGEN_HAS_INDEX_LIST typedef IndexList, type2index<0>, type2index<1>, type2index<1> > ReverseColMajor; @@ -293,29 +293,29 @@ SpatialConvolutionBackwardInput( } /** 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 + * \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, + * 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 + * The output_backward and the kernel must both be in col-major layout. The * result will also be in col-major layout. - * - * If row_in_stride, col_stride > 1, then applies convolution with holes (aka + * + * If row_in_stride, col_stride > 1, then applies convolution with holes (aka * atrous convolution), sampling every row_in_stride, col_in_stride input * pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the + * + * 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 + * + * 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< diff --git a/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc b/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc index 1758067829..2229ec9659 100644 --- a/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc +++ b/tensorflow/core/kernels/eigen_backward_spatial_convolutions_test.cc @@ -25,7 +25,7 @@ void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } static int ceil_div(int a, int b) { return (a + b - 1) / b; } -} +} // namespace TEST(EigenBackwardSpatialConvolutionsTest, test_simple_spatial_convolution_backward_input_valid) { diff --git a/tensorflow/core/kernels/eigen_pooling.h b/tensorflow/core/kernels/eigen_pooling.h index 972036833f..896c995761 100644 --- a/tensorflow/core/kernels/eigen_pooling.h +++ b/tensorflow/core/kernels/eigen_pooling.h @@ -309,10 +309,10 @@ struct AvgPoolMeanReducer { _mm512_castsi512_ps( \ _mm512_maskz_set1_epi32(_mm512_cmp_ps_mask(a, b, _CMP_EQ_UQ), -1)) -// The ternarylogic function immediate determines the values in the result -// In the case below, 0xd8 implies (false_mask) ? (b) : (a) -// For details, refer to the vpternlogd instruction table at -// http://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-software-developer-vol-2c-manual.pdf + // The ternarylogic function immediate determines the values in the result + // In the case below, 0xd8 implies (false_mask) ? (b) : (a) + // For details, refer to the vpternlogd instruction table at + // http://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-software-developer-vol-2c-manual.pdf #define psel(a, b, false_mask) \ _mm512_castsi512_ps(_mm512_ternarylogic_epi32( \ diff --git a/tensorflow/core/kernels/eigen_pooling_test.cc b/tensorflow/core/kernels/eigen_pooling_test.cc index 9383972b9f..47b6665e68 100644 --- a/tensorflow/core/kernels/eigen_pooling_test.cc +++ b/tensorflow/core/kernels/eigen_pooling_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenPoolingTest, Simple) { const int depth = 10; diff --git a/tensorflow/core/kernels/eigen_softmax.h b/tensorflow/core/kernels/eigen_softmax.h index a2930a726f..12148c54b3 100644 --- a/tensorflow/core/kernels/eigen_softmax.h +++ b/tensorflow/core/kernels/eigen_softmax.h @@ -21,19 +21,21 @@ limitations under the License. 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. - * -*/ + * \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 { struct SoftmaxOp { - SoftmaxOp(const float beta) : beta_(beta) { } + SoftmaxOp(const float beta) : beta_(beta) {} template typename Input::Dimensions dimensions(const Input& input) const { @@ -41,8 +43,7 @@ struct SoftmaxOp { } template - void eval(const Input& input, Output& output, const Device& device) const - { + 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; @@ -56,35 +57,43 @@ struct SoftmaxOp { #else // Take advantage of cxx11 to give the compiler information it can use to // optimize the code. - Eigen::IndexList> depth_dim; - Eigen::IndexList> bcast; + Eigen::IndexList > depth_dim; + Eigen::IndexList > bcast; bcast.set(0, dimensions(input)[0]); - Eigen::IndexList, typename internal::traits::Index> dims2d; + 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)); + 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_; }; -} - +} // namespace 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); +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 +} // end namespace Eigen #endif // TENSORFLOW_CORE_KERNELS_EIGEN_SOFTMAX_H_ diff --git a/tensorflow/core/kernels/eigen_softmax_test.cc b/tensorflow/core/kernels/eigen_softmax_test.cc index ba681d68ab..7f985d7136 100644 --- a/tensorflow/core/kernels/eigen_softmax_test.cc +++ b/tensorflow/core/kernels/eigen_softmax_test.cc @@ -23,7 +23,7 @@ namespace { void EigenApprox(float a, float b) { ASSERT_TRUE(std::abs(a - b) <= std::min(std::abs(a), std::abs(b)) * 1e-3); } -} +} // namespace TEST(EigenSoftmaxTest, Simple) { const int depth = 1024; diff --git a/tensorflow/core/kernels/eigen_spatial_convolutions.h b/tensorflow/core/kernels/eigen_spatial_convolutions.h index 2fe64cd72a..1acbe3a658 100644 --- a/tensorflow/core/kernels/eigen_spatial_convolutions.h +++ b/tensorflow/core/kernels/eigen_spatial_convolutions.h @@ -877,29 +877,29 @@ struct gemm_pack_rhs< } // end namespace internal /** SpatialConvolution - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies a 2D convolution over a multichannel input image. - * - * The input parameter is expected to be a tensor with a rank of 3 or more + * \ingroup CXX11_NeuralNetworks_Module + * + * \brief Applies a 2D convolution over a multichannel input image. + * + * The input 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, + * The kernel parameter is expected to be a 4D tensor (filters, channels, * kernel_height, kernel_width) - * The input and the kernel must both be in col-major layout. The result will + * The input and the kernel must both be in col-major layout. The result will * also be in col-major layout. - * - * If col_in_stride, row_in_stride > 1, then applies convolution with holes + * + * If col_in_stride, row_in_stride > 1, then applies convolution with holes * (aka atrous convolution), sampling every col_in_stride, row_in_stride input * pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the + * + * 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 + * + * 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_DEVICE_FUNC EIGEN_ALWAYS_INLINE static const typename internal::conditional< @@ -993,7 +993,7 @@ EIGEN_DEVICE_FUNC default: // Initialize unused variables to avoid a compiler warning out_height = 0; - out_width = 0; + out_width = 0; eigen_assert(false && "unexpected padding"); } diff --git a/tensorflow/core/kernels/encode_jpeg_op.cc b/tensorflow/core/kernels/encode_jpeg_op.cc index 4fcae25aa6..1a5b0f2b67 100644 --- a/tensorflow/core/kernels/encode_jpeg_op.cc +++ b/tensorflow/core/kernels/encode_jpeg_op.cc @@ -80,10 +80,11 @@ class EncodeJpegOp : public OpKernel { errors::InvalidArgument("image must be 3-dimensional", image.shape().DebugString())); - OP_REQUIRES(context, FastBoundsCheck(image.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument( - "Cannot encode images with >= max int32 elements")); + OP_REQUIRES( + context, + FastBoundsCheck(image.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument( + "Cannot encode images with >= max int32 elements")); const int32 dim_size0 = static_cast(image.dim_size(0)); const int32 dim_size1 = static_cast(image.dim_size(1)); @@ -100,9 +101,10 @@ class EncodeJpegOp : public OpKernel { } else if (channels == 3) { adjusted_flags.format = jpeg::FORMAT_RGB; } else { - OP_REQUIRES(context, false, errors::InvalidArgument( - "image must have 1 or 3 channels, got ", - image.shape().DebugString())); + OP_REQUIRES( + context, false, + errors::InvalidArgument("image must have 1 or 3 channels, got ", + image.shape().DebugString())); } } else { if (flags_.format == jpeg::FORMAT_GRAYSCALE) { diff --git a/tensorflow/core/kernels/example_parsing_ops.cc b/tensorflow/core/kernels/example_parsing_ops.cc index 268a059275..83cd0e9b47 100644 --- a/tensorflow/core/kernels/example_parsing_ops.cc +++ b/tensorflow/core/kernels/example_parsing_ops.cc @@ -346,8 +346,9 @@ class SingleSequenceExampleParserOp : public OpKernel { feature_list_sparse_keys[di].scalar()(); } OP_REQUIRES( - ctx, TensorShapeUtils::IsVector( - feature_list_dense_missing_assumed_empty->shape()), + ctx, + TensorShapeUtils::IsVector( + feature_list_dense_missing_assumed_empty->shape()), errors::InvalidArgument( "Expected feature_list_dense_missing_assumed_empty ", "to be a vector, got shape: ", @@ -386,12 +387,12 @@ class SingleSequenceExampleParserOp : public OpKernel { required[d] = (def_value.NumElements() == 0); // No default provided. if (def_value.NumElements() > 0) { - OP_REQUIRES( - ctx, def_value.shape() == attrs_.context_dense_shapes[d], - errors::InvalidArgument( - "def_value[", d, "].shape() == ", - def_value.shape().DebugString(), " != context_dense_shapes_[", - d, "] == ", attrs_.context_dense_shapes[d].DebugString())); + OP_REQUIRES(ctx, def_value.shape() == attrs_.context_dense_shapes[d], + errors::InvalidArgument( + "def_value[", d, + "].shape() == ", def_value.shape().DebugString(), + " != context_dense_shapes_[", d, + "] == ", attrs_.context_dense_shapes[d].DebugString())); OP_REQUIRES( ctx, def_value.dtype() == attrs_.context_dense_types[d], errors::InvalidArgument( @@ -576,12 +577,12 @@ class SingleSequenceExampleParserOp : public OpKernel { const Feature& f = fl.feature(t); bool types_match; OP_REQUIRES_OK(ctx, CheckTypesMatch(f, dtype, &types_match)); - OP_REQUIRES( - ctx, types_match, - errors::InvalidArgument( - "Name: ", name, ", Feature list: ", key, ", Index: ", t, - ". Data types don't match. ", "Expected type: ", - DataTypeString(dtype), " Feature is: ", ProtoDebugString(f))); + OP_REQUIRES(ctx, types_match, + errors::InvalidArgument( + "Name: ", name, ", Feature list: ", key, ", Index: ", t, + ". Data types don't match. ", + "Expected type: ", DataTypeString(dtype), + " Feature is: ", ProtoDebugString(f))); OP_REQUIRES_OK(ctx, FeatureDenseCopy(t, name, key, dtype, shape, f, feature_list_dense_values[d])); } diff --git a/tensorflow/core/kernels/fact_op.cc b/tensorflow/core/kernels/fact_op.cc index 4fbf76d2d0..4a1aa433bc 100644 --- a/tensorflow/core/kernels/fact_op.cc +++ b/tensorflow/core/kernels/fact_op.cc @@ -122,13 +122,9 @@ static string D(const char* s) { return ret; } -REGISTER_KERNEL_BUILDER(Name("Fact") - .Device(DEVICE_CPU) - .Label(D("Yoxmos").c_str()), - FactOpKernel2); -REGISTER_KERNEL_BUILDER(Name("Fact") - .Device(DEVICE_CPU) - .Label(D("yoxmos").c_str()), - FactOpKernel2); +REGISTER_KERNEL_BUILDER( + Name("Fact").Device(DEVICE_CPU).Label(D("Yoxmos").c_str()), FactOpKernel2); +REGISTER_KERNEL_BUILDER( + Name("Fact").Device(DEVICE_CPU).Label(D("yoxmos").c_str()), FactOpKernel2); } // namespace tensorflow diff --git a/tensorflow/core/kernels/fake_quant_ops_test.cc b/tensorflow/core/kernels/fake_quant_ops_test.cc index 5953db1476..af3a42135d 100644 --- a/tensorflow/core/kernels/fake_quant_ops_test.cc +++ b/tensorflow/core/kernels/fake_quant_ops_test.cc @@ -378,9 +378,8 @@ TEST_F(QuantOpsTest, WithArgsGradient_RegularRange) { Tensor* output = GetOutput(0); auto input_flat = GetInput(0).flat(); Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 3})); - FillValues(&expected, - {0.0f, input_flat(1), input_flat(2), - input_flat(3), input_flat(4), 0.0f}); + FillValues(&expected, {0.0f, input_flat(1), input_flat(2), + input_flat(3), input_flat(4), 0.0f}); ExpectClose(expected, *output); } @@ -2167,21 +2166,19 @@ TEST_F(QuantOpsTest, Tensor* output_bprop_wrt_input = GetOutput(0); Tensor expected_bprop_wrt_input(allocator(), DT_FLOAT, TensorShape({2, 3})); auto grad_flat = GetInput(0).flat(); - FillValues(&expected_bprop_wrt_input, - {0.0f, grad_flat(1), grad_flat(2), - grad_flat(3), grad_flat(4), 0.0f}); + FillValues( + &expected_bprop_wrt_input, + {0.0f, grad_flat(1), grad_flat(2), grad_flat(3), grad_flat(4), 0.0f}); ExpectClose(expected_bprop_wrt_input, *output_bprop_wrt_input); Tensor* output_bprop_wrt_min = GetOutput(1); Tensor expected_bprop_wrt_min(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_min, - {grad_flat(0), 0.0f, 0.0f}); + FillValues(&expected_bprop_wrt_min, {grad_flat(0), 0.0f, 0.0f}); ExpectClose(expected_bprop_wrt_min, *output_bprop_wrt_min); Tensor* output_bprop_wrt_max = GetOutput(2); Tensor expected_bprop_wrt_max(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_max, - {0.0f, 0.0f, grad_flat(5)}); + FillValues(&expected_bprop_wrt_max, {0.0f, 0.0f, grad_flat(5)}); ExpectClose(expected_bprop_wrt_max, *output_bprop_wrt_max); } @@ -2215,21 +2212,19 @@ TEST_F(QuantOpsTest, WithVarsPerChannelDim2GradientNudgedUp_4Bits_NarrowRange) { Tensor* output_bprop_wrt_input = GetOutput(0); Tensor expected_bprop_wrt_input(allocator(), DT_FLOAT, TensorShape({2, 3})); auto grad_flat = GetInput(0).flat(); - FillValues(&expected_bprop_wrt_input, - {0.0f, grad_flat(1), grad_flat(2), - grad_flat(3), grad_flat(4), 0.0f}); + FillValues( + &expected_bprop_wrt_input, + {0.0f, grad_flat(1), grad_flat(2), grad_flat(3), grad_flat(4), 0.0f}); ExpectClose(expected_bprop_wrt_input, *output_bprop_wrt_input); Tensor* output_bprop_wrt_min = GetOutput(1); Tensor expected_bprop_wrt_min(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_min, - {grad_flat(0), 0.0f, 0.0f}); + FillValues(&expected_bprop_wrt_min, {grad_flat(0), 0.0f, 0.0f}); ExpectClose(expected_bprop_wrt_min, *output_bprop_wrt_min); Tensor* output_bprop_wrt_max = GetOutput(2); Tensor expected_bprop_wrt_max(allocator(), DT_FLOAT, TensorShape({3})); - FillValues(&expected_bprop_wrt_max, - {0.0f, 0.0f, grad_flat(5)}); + FillValues(&expected_bprop_wrt_max, {0.0f, 0.0f, grad_flat(5)}); ExpectClose(expected_bprop_wrt_max, *output_bprop_wrt_max); } @@ -2270,14 +2265,13 @@ TEST_F(QuantOpsTest, Tensor expected_bprop_wrt_input(allocator(), DT_FLOAT, TensorShape({1, 2, 3, 4})); auto grad_flat = GetInput(0).flat(); - FillValues( - &expected_bprop_wrt_input, - {0.0f, grad_flat(1), grad_flat(2), 0.0f, - 0.0f, grad_flat(5), grad_flat(6), 0.0f, - 0.0f, grad_flat(9), grad_flat(10), 0.0f, - 0.0f, grad_flat(13), grad_flat(14), 0.0f, - 0.0f, grad_flat(17), grad_flat(18), 0.0f, - 0.0f, grad_flat(21), grad_flat(22), 0.0f}); + FillValues(&expected_bprop_wrt_input, + {0.0f, grad_flat(1), grad_flat(2), 0.0f, + 0.0f, grad_flat(5), grad_flat(6), 0.0f, + 0.0f, grad_flat(9), grad_flat(10), 0.0f, + 0.0f, grad_flat(13), grad_flat(14), 0.0f, + 0.0f, grad_flat(17), grad_flat(18), 0.0f, + 0.0f, grad_flat(21), grad_flat(22), 0.0f}); ExpectClose(expected_bprop_wrt_input, *output_bprop_wrt_input); Tensor* output_bprop_wrt_min = GetOutput(1); diff --git a/tensorflow/core/kernels/fifo_queue.cc b/tensorflow/core/kernels/fifo_queue.cc index 82ec879119..479f7be4b5 100644 --- a/tensorflow/core/kernels/fifo_queue.cc +++ b/tensorflow/core/kernels/fifo_queue.cc @@ -255,97 +255,96 @@ void FIFOQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx, // TODO(josh11b): This makes two copies of callback, avoid this if possible. dequeue_attempts_.emplace_back( num_elements, [callback]() { callback(Tuple()); }, ctx, cm, token, - [callback, allow_small_batch, this](Attempt* attempt) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - int64 queue_size = queues_[0].size(); + [callback, allow_small_batch, + this](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + int64 queue_size = queues_[0].size(); - if (closed_ && queue_size < attempt->elements_requested) { - // If we don't have enough for a full dequeue, we have - // to reset the attempt tuple. - if (!attempt->tuple.empty()) { - // Restore already-dequeued elements to the front of the - // queue. - for (int64 i = attempt->tuple[0].dim_size(0) - - attempt->elements_requested - 1; - i >= 0; --i) { - for (int j = 0; j < num_components(); ++j) { - PersistentTensor element; - Status s = GetElementComponentFromBatch( - attempt->tuple, i, j, attempt->context, &element); - if (!s.ok()) { - attempt->context->SetStatus( - errors::DataLoss("Failed to restore element from " - "partially-dequeued batch " - "to FIFOQueue: ", - s.error_message())); - } - queues_[j].push_front(element); - } - } - } - if (allow_small_batch && !queues_[0].empty()) { - // Request all remaining elements in the queue. - queue_size = queues_[0].size(); - attempt->tuple.clear(); - attempt->elements_requested = queue_size; - } else { - if (allow_small_batch) { - // There may be some other attempts containing - // values. If so, we'll yield and wait for them - // to add elements to the queue. - if (!enqueue_attempts_.empty()) return kProgress; - } - if (attempt->context->status().ok()) { - attempt->context->SetStatus(errors::OutOfRange( - "FIFOQueue '", name_, "' is closed and has ", - "insufficient elements (requested ", - attempt->elements_requested, ", current size ", - queue_size, ")")); + if (closed_ && queue_size < attempt->elements_requested) { + // If we don't have enough for a full dequeue, we have + // to reset the attempt tuple. + if (!attempt->tuple.empty()) { + // Restore already-dequeued elements to the front of the + // queue. + for (int64 i = attempt->tuple[0].dim_size(0) - + attempt->elements_requested - 1; + i >= 0; --i) { + for (int j = 0; j < num_components(); ++j) { + PersistentTensor element; + Status s = GetElementComponentFromBatch( + attempt->tuple, i, j, attempt->context, &element); + if (!s.ok()) { + attempt->context->SetStatus( + errors::DataLoss("Failed to restore element from " + "partially-dequeued batch " + "to FIFOQueue: ", + s.error_message())); } - return kComplete; + queues_[j].push_front(element); } } + } + if (allow_small_batch && !queues_[0].empty()) { + // Request all remaining elements in the queue. + queue_size = queues_[0].size(); + attempt->tuple.clear(); + attempt->elements_requested = queue_size; + } else { + if (allow_small_batch) { + // There may be some other attempts containing + // values. If so, we'll yield and wait for them + // to add elements to the queue. + if (!enqueue_attempts_.empty()) return kProgress; + } + if (attempt->context->status().ok()) { + attempt->context->SetStatus(errors::OutOfRange( + "FIFOQueue '", name_, "' is closed and has ", + "insufficient elements (requested ", + attempt->elements_requested, ", current size ", + queue_size, ")")); + } + return kComplete; + } + } - RunResult result = kNoProgress; - for (; queue_size > 0; --queue_size) { - if (attempt->tuple.empty()) { - // Only allocate tuple when we have something to dequeue - // so we don't use excessive memory when there are many - // blocked dequeue attempts waiting. - attempt->tuple.reserve(num_components()); - for (int i = 0; i < num_components(); ++i) { - const TensorShape shape = - ManyOutShape(i, attempt->elements_requested); - Tensor element; - attempt->context->SetStatus( - attempt->context->allocate_temp(component_dtypes_[i], - shape, &element)); - if (!attempt->context->status().ok()) return kComplete; - attempt->tuple.emplace_back(element); - } - } - result = kProgress; - Tuple tuple; - DequeueLocked(attempt->context, &tuple); - const int64 index = attempt->tuple[0].dim_size(0) - - attempt->elements_requested; - for (int i = 0; i < num_components(); ++i) { - attempt->context->SetStatus(batch_util::CopyElementToSlice( - std::move(tuple[i]), &attempt->tuple[i], index)); - if (!attempt->context->status().ok()) return kComplete; - } - tuple.clear(); - --attempt->elements_requested; - if (attempt->elements_requested == 0) { - tuple = attempt->tuple; - attempt->done_callback = [callback, tuple]() { - callback(tuple); - }; - return kComplete; - } + RunResult result = kNoProgress; + for (; queue_size > 0; --queue_size) { + if (attempt->tuple.empty()) { + // Only allocate tuple when we have something to dequeue + // so we don't use excessive memory when there are many + // blocked dequeue attempts waiting. + attempt->tuple.reserve(num_components()); + for (int i = 0; i < num_components(); ++i) { + const TensorShape shape = + ManyOutShape(i, attempt->elements_requested); + Tensor element; + attempt->context->SetStatus(attempt->context->allocate_temp( + component_dtypes_[i], shape, &element)); + if (!attempt->context->status().ok()) return kComplete; + attempt->tuple.emplace_back(element); } - return result; - }); + } + result = kProgress; + Tuple tuple; + DequeueLocked(attempt->context, &tuple); + const int64 index = + attempt->tuple[0].dim_size(0) - attempt->elements_requested; + for (int i = 0; i < num_components(); ++i) { + attempt->context->SetStatus(batch_util::CopyElementToSlice( + std::move(tuple[i]), &attempt->tuple[i], index)); + if (!attempt->context->status().ok()) return kComplete; + } + tuple.clear(); + --attempt->elements_requested; + if (attempt->elements_requested == 0) { + tuple = attempt->tuple; + attempt->done_callback = [callback, tuple]() { + callback(tuple); + }; + return kComplete; + } + } + return result; + }); } } if (!already_cancelled) { diff --git a/tensorflow/core/kernels/fill_functor.cc b/tensorflow/core/kernels/fill_functor.cc index bde39770de..7090417dfd 100644 --- a/tensorflow/core/kernels/fill_functor.cc +++ b/tensorflow/core/kernels/fill_functor.cc @@ -18,8 +18,8 @@ limitations under the License. #define EIGEN_USE_THREADS #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/framework/variant_encode_decode.h" @@ -60,7 +60,7 @@ DEFINE_SETZERO_CPU(Variant); template void SetZeroFunctor::operator()( const Eigen::SyclDevice& d, typename TTypes::Flat out) { - To32Bit(out).device(d) = To32Bit(out).constant(T(0)); + To32Bit(out).device(d) = To32Bit(out).constant(T(0)); } #define DEFINE_SETZERO_SYCL(T) \ @@ -118,7 +118,8 @@ DEFINE_SETONE_SYCL(double); template struct FillFunctor { - void operator()(const Eigen::ThreadPoolDevice& d, typename TTypes::Flat out, + void operator()(const Eigen::ThreadPoolDevice& d, + typename TTypes::Flat out, typename TTypes::ConstScalar in) { out.device(d) = out.constant(in()); } @@ -150,8 +151,7 @@ struct FillFunctor { } }; -#define DEFINE_FILL_SYCL(T) \ - template struct FillFunctor; +#define DEFINE_FILL_SYCL(T) template struct FillFunctor; DEFINE_FILL_SYCL(float); DEFINE_FILL_SYCL(double); TF_CALL_INTEGRAL_TYPES(DEFINE_FILL_SYCL) diff --git a/tensorflow/core/kernels/fractional_avg_pool_op.cc b/tensorflow/core/kernels/fractional_avg_pool_op.cc index 47f4189c30..135d002345 100644 --- a/tensorflow/core/kernels/fractional_avg_pool_op.cc +++ b/tensorflow/core/kernels/fractional_avg_pool_op.cc @@ -232,8 +232,9 @@ class FractionalAvgPoolGradOp : public OpKernel { // Grab the inputs. const Tensor& orig_input_tensor_shape = context->input(0); - OP_REQUIRES(context, orig_input_tensor_shape.dims() == 1 && - orig_input_tensor_shape.NumElements() == 4, + OP_REQUIRES(context, + orig_input_tensor_shape.dims() == 1 && + orig_input_tensor_shape.NumElements() == 4, errors::InvalidArgument("original input tensor shape must be" "1-dimensional and 4 elements")); const Tensor& out_backprop = context->input(1); diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc index ef9e848413..9d4bc35ba8 100644 --- a/tensorflow/core/kernels/function_ops.cc +++ b/tensorflow/core/kernels/function_ops.cc @@ -253,22 +253,21 @@ class SymbolicGradientOp : public AsyncOpKernel { args.push_back(ctx->input(i)); } std::vector* rets = new std::vector; - lib->Run( - opts, handle, args, rets, [ctx, done, rets](const Status& status) { - if (!status.ok()) { - ctx->SetStatus(status); - } else if (rets->size() != ctx->num_outputs()) { - ctx->SetStatus(errors::InvalidArgument( - "SymGrad expects to return ", ctx->num_outputs(), - " tensor(s), but get ", rets->size(), " tensor(s) instead.")); - } else { - for (size_t i = 0; i < rets->size(); ++i) { - ctx->set_output(i, (*rets)[i]); - } - } - delete rets; - done(); - }); + lib->Run(opts, handle, args, rets, [ctx, done, rets](const Status& status) { + if (!status.ok()) { + ctx->SetStatus(status); + } else if (rets->size() != ctx->num_outputs()) { + ctx->SetStatus(errors::InvalidArgument( + "SymGrad expects to return ", ctx->num_outputs(), + " tensor(s), but get ", rets->size(), " tensor(s) instead.")); + } else { + for (size_t i = 0; i < rets->size(); ++i) { + ctx->set_output(i, (*rets)[i]); + } + } + delete rets; + done(); + }); } private: diff --git a/tensorflow/core/kernels/fused_batch_norm_op.cu.cc b/tensorflow/core/kernels/fused_batch_norm_op.cu.cc index a8484390b9..4a67b2b3a3 100644 --- a/tensorflow/core/kernels/fused_batch_norm_op.cu.cc +++ b/tensorflow/core/kernels/fused_batch_norm_op.cu.cc @@ -68,7 +68,8 @@ void InvVarianceToVariance::operator()(const Eigen::GpuDevice& d, template void SetNanFunctor::operator()(const Eigen::GpuDevice& d, typename TTypes::Flat out) { - To32Bit(out).device(d) = To32Bit(out).constant(Eigen::NumTraits::quiet_NaN()); + To32Bit(out).device(d) = + To32Bit(out).constant(Eigen::NumTraits::quiet_NaN()); } template class VarianceToInvVariance; diff --git a/tensorflow/core/kernels/gather_functor.cc b/tensorflow/core/kernels/gather_functor.cc index dde08b37ea..e6fefe643b 100644 --- a/tensorflow/core/kernels/gather_functor.cc +++ b/tensorflow/core/kernels/gather_functor.cc @@ -25,12 +25,12 @@ typedef Eigen::GpuDevice GPUDevice; namespace functor { // Forward declarations of the functor specializations for GPU. -#define DECLARE_GPU_SPECS_INDEX(T, Index) \ - template <> \ - int64 GatherFunctor::operator()( \ +#define DECLARE_GPU_SPECS_INDEX(T, Index) \ + template <> \ + int64 GatherFunctor::operator()( \ OpKernelContext* ctx, typename TTypes::ConstTensor Tparams, \ - typename TTypes::ConstFlat Tindices, \ - typename TTypes::Tensor Tout); \ + typename TTypes::ConstFlat Tindices, \ + typename TTypes::Tensor Tout); \ extern template struct GatherFunctor; #define DECLARE_GPU_SPECS(T) \ diff --git a/tensorflow/core/kernels/gather_functor.h b/tensorflow/core/kernels/gather_functor.h index 1e429a037e..16ccb03b85 100644 --- a/tensorflow/core/kernels/gather_functor.h +++ b/tensorflow/core/kernels/gather_functor.h @@ -18,12 +18,12 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/type_traits.h" #include "tensorflow/core/kernels/bounds_check.h" #include "tensorflow/core/platform/prefetch.h" #include "tensorflow/core/platform/types.h" -#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/work_sharder.h" namespace tensorflow { @@ -52,21 +52,23 @@ SliceIndex HandleCopies(OpKernelContext* ctx, const size_t slice_bytes = slice_elems * sizeof(T); auto worker_threads = ctx->device()->tensorflow_cpu_worker_threads(); mutex mu; - // Store the value of invalidate index for printing error information, it's a shared variable. + // Store the value of invalidate index for printing error information, it's a + // shared variable. SliceIndex result = -1; - auto work = [&] (int64 start, int64 end) { + auto work = [&](int64 start, int64 end) { SliceIndex batch_idx = static_cast(start / indices_size); SliceIndex indices_idx = static_cast(start % indices_size); SliceIndex batch_idx_end = static_cast(end / indices_size); SliceIndex indices_idx_end = static_cast(end % indices_size); while ((batch_idx < batch_idx_end) || - (batch_idx == batch_idx_end && indices_idx < indices_idx_end)) { + (batch_idx == batch_idx_end && indices_idx < indices_idx_end)) { SliceIndex i_next = indices_idx + 1; SliceIndex b_next = batch_idx + 1; if ((batch_idx == batch_idx_end && i_next < indices_idx_end) || - (i_next < indices_size)) { - port::prefetch(¶ms(batch_idx, indices(i_next), 0)); + (i_next < indices_size)) { + port::prefetch( + ¶ms(batch_idx, indices(i_next), 0)); port::prefetch(&out(batch_idx, i_next, 0)); b_next = batch_idx; } else if (b_next <= batch_idx_end) { @@ -85,11 +87,12 @@ SliceIndex HandleCopies(OpKernelContext* ctx, // ahead-of-time compilation binary size). if (is_simple_type::value) { // Avoid auto-promotion to Index from SliceIndex by casting. - memcpy(out_base + (batch_idx * indices_size + indices_idx) * slice_elems, - params_base + (batch_idx * static_cast(limit) + - static_cast(index)) * - slice_elems, - slice_bytes); + memcpy( + out_base + (batch_idx * indices_size + indices_idx) * slice_elems, + params_base + (batch_idx * static_cast(limit) + + static_cast(index)) * + slice_elems, + slice_bytes); } else { // For non-"simple" types (e.g. strings). out.template chip<1>(indices_idx) = params.template chip<1>(index); @@ -99,8 +102,8 @@ SliceIndex HandleCopies(OpKernelContext* ctx, } }; - Shard(worker_threads->num_threads, worker_threads->workers, batch_size*indices_size, - slice_elems * sizeof(T), work); + Shard(worker_threads->num_threads, worker_threads->workers, + batch_size * indices_size, slice_elems * sizeof(T), work); return result; } @@ -117,16 +120,16 @@ struct GatherFunctorCPU { bool use_large = (slice_size > std::numeric_limits::max() || params.size() > std::numeric_limits::max() || N > std::numeric_limits::max()); -#define CALL(elems) \ - do { \ - if (use_large) { \ - bad_i = HandleCopies(ctx, params, indices, \ - slice_size, out); \ - } else { \ - const int32 small_slice = static_cast(slice_size); \ - bad_i = HandleCopies(ctx, params, indices, \ - small_slice, out); \ - } \ +#define CALL(elems) \ + do { \ + if (use_large) { \ + bad_i = HandleCopies(ctx, params, indices, \ + slice_size, out); \ + } else { \ + const int32 small_slice = static_cast(slice_size); \ + bad_i = HandleCopies(ctx, params, indices, \ + small_slice, out); \ + } \ } while (0) if (slice_size == 10) @@ -143,7 +146,8 @@ struct GatherFunctorCPU { template struct GatherFunctor { - int64 operator()(OpKernelContext* ctx, typename TTypes::ConstTensor params, + int64 operator()(OpKernelContext* ctx, + typename TTypes::ConstTensor params, typename TTypes::ConstFlat indices, typename TTypes::Tensor out); }; diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc index 239d5d2e99..d6cbcf1d93 100644 --- a/tensorflow/core/kernels/gather_op.cc +++ b/tensorflow/core/kernels/gather_op.cc @@ -106,8 +106,7 @@ class GatherOp : public OpKernel { auto out_flat = out->shaped({outer_size, N, inner_size}); functor::GatherFunctor functor; - int64 bad_i = functor(c, params_flat, - indices_flat, out_flat); + int64 bad_i = functor(c, params_flat, indices_flat, out_flat); OP_REQUIRES( c, bad_i < 0, diff --git a/tensorflow/core/kernels/hinge-loss.h b/tensorflow/core/kernels/hinge-loss.h index 789a7ce7a3..d303e9c877 100644 --- a/tensorflow/core/kernels/hinge-loss.h +++ b/tensorflow/core/kernels/hinge-loss.h @@ -50,9 +50,8 @@ class HingeLossUpdater : public DualLossUpdater { // valid value for new dual = 0 // c. new optimal value > 1.0. Then new optimal value should be set to 1.0. const double candidate_optimal_dual = - current_dual + - (label - wx) / - (num_loss_partitions * example_weight * weighted_example_norm); + current_dual + (label - wx) / (num_loss_partitions * example_weight * + weighted_example_norm); if (label * candidate_optimal_dual < 0) { return 0.0; } diff --git a/tensorflow/core/kernels/histogram_op_gpu.cu.cc b/tensorflow/core/kernels/histogram_op_gpu.cu.cc index c2bb958be8..a88e9b0ddc 100644 --- a/tensorflow/core/kernels/histogram_op_gpu.cu.cc +++ b/tensorflow/core/kernels/histogram_op_gpu.cu.cc @@ -17,16 +17,16 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/histogram_op.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "external/cub_archive/cub/device/device_histogram.cuh" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/kernels/histogram_op.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/cuda_kernel_helper.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { @@ -104,8 +104,8 @@ struct HistogramFixedWidthFunctor { /* num_samples */ num_samples, /* stream */ stream); if (err != cudaSuccess) { - return errors::Internal("Could not launch HistogramRange: ", - cudaGetErrorString(err), "."); + return errors::Internal( + "Could not launch HistogramRange: ", cudaGetErrorString(err), "."); } return Status::OK(); diff --git a/tensorflow/core/kernels/image_resizer_state.h b/tensorflow/core/kernels/image_resizer_state.h index f088315ff5..faf997be05 100644 --- a/tensorflow/core/kernels/image_resizer_state.h +++ b/tensorflow/core/kernels/image_resizer_state.h @@ -109,8 +109,9 @@ struct ImageResizerState { ValidateAndCalculateOutputSize(context, input); if (!context->status().ok()) return; OP_REQUIRES_OK(context, context->allocate_output( - 0, TensorShape({input.dim_size(0), out_height, - out_width, input.dim_size(3)}), + 0, + TensorShape({input.dim_size(0), out_height, + out_width, input.dim_size(3)}), &output)); } @@ -168,8 +169,9 @@ struct ImageResizerGradientState { CalculateResizeScale(original_width, resized_width, align_corners_); output = nullptr; OP_REQUIRES_OK(context, context->allocate_output( - 0, TensorShape({batch_size, original_height, - original_width, channels}), + 0, + TensorShape({batch_size, original_height, + original_width, channels}), &output)); } diff --git a/tensorflow/core/kernels/in_topk_op.cc b/tensorflow/core/kernels/in_topk_op.cc index e2861ae090..c37055239c 100644 --- a/tensorflow/core/kernels/in_topk_op.cc +++ b/tensorflow/core/kernels/in_topk_op.cc @@ -17,11 +17,11 @@ limitations under the License. #define EIGEN_USE_THREADS +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/kernels/bounds_check.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { @@ -98,36 +98,36 @@ class InTopK : public OpKernel { int k_; }; -REGISTER_KERNEL_BUILDER( - Name("InTopK").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); -REGISTER_KERNEL_BUILDER( - Name("InTopK").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); - -REGISTER_KERNEL_BUILDER( - Name("InTopKV2").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("k") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); -REGISTER_KERNEL_BUILDER( - Name("InTopKV2").Device(DEVICE_CPU) - .HostMemory("predictions") - .HostMemory("targets") - .HostMemory("k") - .HostMemory("precision") - .TypeConstraint("T"), - InTopK); +REGISTER_KERNEL_BUILDER(Name("InTopK") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); +REGISTER_KERNEL_BUILDER(Name("InTopK") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); + +REGISTER_KERNEL_BUILDER(Name("InTopKV2") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("k") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); +REGISTER_KERNEL_BUILDER(Name("InTopKV2") + .Device(DEVICE_CPU) + .HostMemory("predictions") + .HostMemory("targets") + .HostMemory("k") + .HostMemory("precision") + .TypeConstraint("T"), + InTopK); } // namespace tensorflow diff --git a/tensorflow/core/kernels/inplace_ops.cc b/tensorflow/core/kernels/inplace_ops.cc index 7728ba850c..a71d047ed1 100644 --- a/tensorflow/core/kernels/inplace_ops.cc +++ b/tensorflow/core/kernels/inplace_ops.cc @@ -27,13 +27,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SyclDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace functor { template -Status DoParallelConcatUpdate(const Device& d, const Tensor& value, - int32 loc, Tensor* output) { +Status DoParallelConcatUpdate(const Device& d, const Tensor& value, int32 loc, + Tensor* output) { auto Tvalue = value.shaped({1, value.NumElements()}); auto Toutput = output->flat_outer_dims(); auto nrows = Toutput.dimension(0); @@ -74,7 +74,7 @@ Status DoParallelConcat(const SyclDevice& d, const Tensor& value, int32 loc, return errors::InvalidArgument("Unsupported data type: ", value.dtype()); } } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace functor @@ -207,7 +207,7 @@ REGISTER_KERNEL_BUILDER(Name("_ParallelConcatUpdate") .HostMemory("output") .TypeConstraint("T"), ParallelConcatUpdate); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #if GOOGLE_CUDA diff --git a/tensorflow/core/kernels/l2loss_op.cc b/tensorflow/core/kernels/l2loss_op.cc index f8ed935157..f561287f7a 100644 --- a/tensorflow/core/kernels/l2loss_op.cc +++ b/tensorflow/core/kernels/l2loss_op.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_THREADS -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/kernels/l2loss_op.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/core/kernels/linalg_ops_common.cc b/tensorflow/core/kernels/linalg_ops_common.cc index 36907fb571..b58bcf5834 100644 --- a/tensorflow/core/kernels/linalg_ops_common.cc +++ b/tensorflow/core/kernels/linalg_ops_common.cc @@ -108,7 +108,6 @@ void LinearAlgebraOp::Compute(OpKernelContext* context) { auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); Shard(worker_threads.num_threads, worker_threads.workers, batch_shape.num_elements(), GetCostPerUnit(input_matrix_shapes), shard); - } template diff --git a/tensorflow/core/kernels/lmdb_reader_op.cc b/tensorflow/core/kernels/lmdb_reader_op.cc index 31a427f2c9..1335a95dce 100755 --- a/tensorflow/core/kernels/lmdb_reader_op.cc +++ b/tensorflow/core/kernels/lmdb_reader_op.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/framework/reader_op_kernel.h" #include "tensorflow/core/framework/reader_base.h" +#include "tensorflow/core/framework/reader_op_kernel.h" #include "tensorflow/core/lib/core/errors.h" #include @@ -77,15 +77,13 @@ class LMDBReader : public ReaderBase { *at_end = true; return Status::OK(); } - } - else { + } else { if (Seek(MDB_NEXT) == false) { *at_end = true; return Status::OK(); } } - *key = string(static_cast(mdb_key_.mv_data), - mdb_key_.mv_size); + *key = string(static_cast(mdb_key_.mv_data), mdb_key_.mv_size); *value = string(static_cast(mdb_value_.mv_data), mdb_value_.mv_size); *produced = true; @@ -123,13 +121,10 @@ class LMDBReaderOp : public ReaderOpKernel { explicit LMDBReaderOp(OpKernelConstruction* context) : ReaderOpKernel(context) { Env* env = context->env(); - SetReaderFactory([this, env]() { - return new LMDBReader(name(), env); - }); + SetReaderFactory([this, env]() { return new LMDBReader(name(), env); }); } }; -REGISTER_KERNEL_BUILDER(Name("LMDBReader").Device(DEVICE_CPU), - LMDBReaderOp); +REGISTER_KERNEL_BUILDER(Name("LMDBReader").Device(DEVICE_CPU), LMDBReaderOp); } // namespace tensorflow diff --git a/tensorflow/core/kernels/logistic-loss.h b/tensorflow/core/kernels/logistic-loss.h index 2765f42bbd..6479e6f5dc 100644 --- a/tensorflow/core/kernels/logistic-loss.h +++ b/tensorflow/core/kernels/logistic-loss.h @@ -122,10 +122,9 @@ class LogisticLossUpdater : public DualLossUpdater { num_loss_partitions * weighted_example_norm * example_weight * (0.5 * (1 + tanhx) / label - current_dual); - const double denominator = -2 * label - - num_loss_partitions * weighted_example_norm * - example_weight * (1 - tanhx * tanhx) * 0.5 / - label; + const double denominator = + -2 * label - num_loss_partitions * weighted_example_norm * + example_weight * (1 - tanhx * tanhx) * 0.5 / label; return x - numerator / denominator; } }; diff --git a/tensorflow/core/kernels/loss_test.cc b/tensorflow/core/kernels/loss_test.cc index 89f0677e1f..460d65c5c2 100644 --- a/tensorflow/core/kernels/loss_test.cc +++ b/tensorflow/core/kernels/loss_test.cc @@ -32,14 +32,17 @@ namespace { TEST(LogisticLoss, ComputePrimalLoss) { LogisticLossUpdater loss_updater; - EXPECT_NEAR(0.693147, loss_updater.ComputePrimalLoss( - 0 /* wx */, 1 /* label */, 1 /* example weight */), + EXPECT_NEAR(0.693147, + loss_updater.ComputePrimalLoss(0 /* wx */, 1 /* label */, + 1 /* example weight */), 1e-3); - EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(70 /* wx */, 1 /* label */, - 1 /* example weight */), + EXPECT_NEAR(0.0, + loss_updater.ComputePrimalLoss(70 /* wx */, 1 /* label */, + 1 /* example weight */), 1e-3); - EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(-70 /* wx */, -1 /* label */, - 1 /* example weight */), + EXPECT_NEAR(0.0, + loss_updater.ComputePrimalLoss(-70 /* wx */, -1 /* label */, + 1 /* example weight */), 1e-3); } @@ -53,31 +56,35 @@ TEST(LogisticLoss, ComputeDualLoss) { loss_updater.ComputeDualLoss(1 /* current dual */, 1 /* label */, 1 /* example weight */), 1e-3); - EXPECT_NEAR(-0.693147, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1 /* label */, - 1 /* example weight */), - 1e-3); + EXPECT_NEAR( + -0.693147, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1 /* label */, + 1 /* example weight */), + 1e-3); } TEST(LogisticLoss, ComputeUpdatedDual) { LogisticLossUpdater loss_updater; - EXPECT_NEAR(0.479, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.5 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.479, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.5 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); - EXPECT_NEAR(-0.031, loss_updater.ComputeUpdatedDual( - 2 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, 0.1 /* current_dual */, - -0.8 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.031, + loss_updater.ComputeUpdatedDual( + 2 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, 0.1 /* current_dual */, + -0.8 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } TEST(SquaredLoss, ComputePrimalLoss) { SquaredLossUpdater loss_updater; - EXPECT_NEAR(0.5, loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, - 1.0 /* example weight */), + EXPECT_NEAR(0.5, + loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, + 1.0 /* example weight */), 1e-3); EXPECT_NEAR(40.5, loss_updater.ComputePrimalLoss(10.0 /* wx */, 1.0 /* label */, @@ -95,43 +102,50 @@ TEST(SquaredLoss, ComputePrimalLoss) { TEST(SquaredLoss, ComputeDualLoss) { SquaredLossUpdater loss_updater; - EXPECT_NEAR(0.0, loss_updater.ComputeDualLoss(0.0 /* current dual */, - -1.0 /* label */, - 1.0 /* example weight */), - 1e-3); - EXPECT_NEAR(0.66, loss_updater.ComputeDualLoss(0.2 /* current dual */, - -1.0 /* label */, - 3.0 /* example weight */), - 1e-3); - EXPECT_NEAR(-0.375, loss_updater.ComputeDualLoss(1.5 /* current dual */, - 1.0 /* label */, - 1.0 /* example weight */), - 1e-3); - EXPECT_NEAR(-1.125, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1.0 /* label */, - 3.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + 0.0, + loss_updater.ComputeDualLoss(0.0 /* current dual */, -1.0 /* label */, + 1.0 /* example weight */), + 1e-3); + EXPECT_NEAR( + 0.66, + loss_updater.ComputeDualLoss(0.2 /* current dual */, -1.0 /* label */, + 3.0 /* example weight */), + 1e-3); + EXPECT_NEAR( + -0.375, + loss_updater.ComputeDualLoss(1.5 /* current dual */, 1.0 /* label */, + 1.0 /* example weight */), + 1e-3); + EXPECT_NEAR( + -1.125, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1.0 /* label */, + 3.0 /* example weight */), + 1e-3); } TEST(SquaredLoss, ComputeUpdatedDual) { SquaredLossUpdater loss_updater; - EXPECT_NEAR(0.336, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.3 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.336, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.3 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); - EXPECT_NEAR(-0.427, loss_updater.ComputeUpdatedDual( - 5 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, -0.4 /* current_dual */, - 0.8 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.427, + loss_updater.ComputeUpdatedDual( + 5 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, -0.4 /* current_dual */, + 0.8 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } TEST(HingeLoss, ComputePrimalLoss) { HingeLossUpdater loss_updater; - EXPECT_NEAR(1.0, loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, - 1.0 /* example weight */), + EXPECT_NEAR(1.0, + loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, + 1.0 /* example weight */), 1e-3); EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(10.0 /* wx */, 1.0 /* label */, @@ -149,10 +163,11 @@ TEST(HingeLoss, ComputePrimalLoss) { TEST(HingeLoss, ComputeDualLoss) { HingeLossUpdater loss_updater; - EXPECT_NEAR(0.0, loss_updater.ComputeDualLoss(0.0 /* current dual */, - -1.0 /* label */, - 1.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + 0.0, + loss_updater.ComputeDualLoss(0.0 /* current dual */, -1.0 /* label */, + 1.0 /* example weight */), + 1e-3); EXPECT_NEAR( std::numeric_limits::max(), loss_updater.ComputeDualLoss(0.2 /* current dual */, -1.0 /* label */, @@ -163,10 +178,11 @@ TEST(HingeLoss, ComputeDualLoss) { loss_updater.ComputeDualLoss(1.5 /* current dual */, 1.0 /* label */, 1.0 /* example weight */), 1e-3); - EXPECT_NEAR(-1.5, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1.0 /* label */, - 3.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + -1.5, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1.0 /* label */, + 3.0 /* example weight */), + 1e-3); } TEST(HingeLoss, ConvertLabel) { @@ -195,28 +211,31 @@ TEST(HingeLoss, ComputeUpdatedDual) { // weighted_example_norm=100.0, it turns out that the optimal value to update // the dual to is 0.507 which is within the permitted range and thus should be // the value returned. - EXPECT_NEAR(0.507, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.5 /* current_dual */, - 0.3 /* wx */, 100.0 /* weighted_example_norm */), + EXPECT_NEAR(0.507, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.5 /* current_dual */, + 0.3 /* wx */, 100.0 /* weighted_example_norm */), 1e-3); // When label=-1.0, example_weight=1.0, current_dual=0.4, wx=0.6, // weighted_example_norm=10.0 and num_loss_partitions=10, it turns out that // the optimal value to update the dual to is 0.384 which is within the // permitted range and thus should be the value returned. - EXPECT_NEAR(-0.416, loss_updater.ComputeUpdatedDual( - 10 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, -0.4 /* current_dual */, - 0.6 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.416, + loss_updater.ComputeUpdatedDual( + 10 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, -0.4 /* current_dual */, + 0.6 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); // When label=1.0, example_weight=1.0, current_dual=-0.5, wx=0.3 and // weighted_example_norm=10.0, it turns out that the optimal value to update // the dual to is -0.43. However, this is outside the allowed [0.0, 1.0] range // and hence the closest permitted value (0.0) should be returned instead. - EXPECT_NEAR(0.0, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, -0.5 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.0, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, -0.5 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); // When label=-1.0, example_weight=2.0, current_dual=-1.0, wx=0.3 and @@ -224,17 +243,19 @@ TEST(HingeLoss, ComputeUpdatedDual) { // the dual to is -1.065. However, this is outside the allowed [-1.0, 0.0] // range and hence the closest permitted value (-1.0) should be returned // instead. - EXPECT_NEAR(-1.0, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, -1.0 /* label */, - 2.0 /* example weight */, -1.0 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-1.0, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, -1.0 /* label */, + 2.0 /* example weight */, -1.0 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } TEST(SmoothHingeLoss, ComputePrimalLoss) { SmoothHingeLossUpdater loss_updater; - EXPECT_NEAR(0.5, loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, - 1.0 /* example weight */), + EXPECT_NEAR(0.5, + loss_updater.ComputePrimalLoss(0.0 /* wx */, 1.0 /* label */, + 1.0 /* example weight */), 1e-3); EXPECT_NEAR(0.0, loss_updater.ComputePrimalLoss(10.0 /* wx */, 1.0 /* label */, @@ -252,10 +273,11 @@ TEST(SmoothHingeLoss, ComputePrimalLoss) { TEST(SmoothHingeLoss, ComputeDualLoss) { SmoothHingeLossUpdater loss_updater; - EXPECT_NEAR(0.0, loss_updater.ComputeDualLoss(0.0 /* current dual */, - -1.0 /* label */, - 1.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + 0.0, + loss_updater.ComputeDualLoss(0.0 /* current dual */, -1.0 /* label */, + 1.0 /* example weight */), + 1e-3); EXPECT_NEAR( std::numeric_limits::max(), loss_updater.ComputeDualLoss(0.2 /* current dual */, -1.0 /* label */, @@ -266,24 +288,27 @@ TEST(SmoothHingeLoss, ComputeDualLoss) { loss_updater.ComputeDualLoss(1.5 /* current dual */, 1.0 /* label */, 1.0 /* example weight */), 1e-3); - EXPECT_NEAR(-1.125, loss_updater.ComputeDualLoss(0.5 /* current dual */, - 1.0 /* label */, - 3.0 /* example weight */), - 1e-3); + EXPECT_NEAR( + -1.125, + loss_updater.ComputeDualLoss(0.5 /* current dual */, 1.0 /* label */, + 3.0 /* example weight */), + 1e-3); } TEST(SmoothHingeLoss, ComputeUpdatedDual) { SmoothHingeLossUpdater loss_updater; - EXPECT_NEAR(0.336, loss_updater.ComputeUpdatedDual( - 1 /* num partitions */, 1.0 /* label */, - 1.0 /* example weight */, 0.3 /* current_dual */, - 0.3 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(0.336, + loss_updater.ComputeUpdatedDual( + 1 /* num partitions */, 1.0 /* label */, + 1.0 /* example weight */, 0.3 /* current_dual */, + 0.3 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); - EXPECT_NEAR(-0.427, loss_updater.ComputeUpdatedDual( - 5 /* num partitions */, -1.0 /* label */, - 1.0 /* example weight */, -0.4 /* current_dual */, - 0.8 /* wx */, 10.0 /* weighted_example_norm */), + EXPECT_NEAR(-0.427, + loss_updater.ComputeUpdatedDual( + 5 /* num partitions */, -1.0 /* label */, + 1.0 /* example weight */, -0.4 /* current_dual */, + 0.8 /* wx */, 10.0 /* weighted_example_norm */), 1e-3); } diff --git a/tensorflow/core/kernels/lrn_op.cc b/tensorflow/core/kernels/lrn_op.cc index c905ebc84a..c3a59c9576 100644 --- a/tensorflow/core/kernels/lrn_op.cc +++ b/tensorflow/core/kernels/lrn_op.cc @@ -229,10 +229,11 @@ class LRNOp : public OpKernel { explicit LRNOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); float tmp; OP_REQUIRES_OK(context, context->GetAttr("bias", &tmp)); @@ -247,9 +248,10 @@ class LRNOp : public OpKernel { const Tensor& in = context->input(0); OP_REQUIRES(context, in.dims() == 4, errors::InvalidArgument("in must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(in.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES( + context, + FastBoundsCheck(in.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); // Cast to platform-specific int to avoid conversion warnings. const int batch = static_cast(in.dim_size(0)); const int rows = static_cast(in.dim_size(1)); @@ -448,10 +450,11 @@ class LRNGradOp : public OpKernel { explicit LRNGradOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); float tmp; OP_REQUIRES_OK(context, context->GetAttr("bias", &tmp)); diff --git a/tensorflow/core/kernels/matching_files_op.cc b/tensorflow/core/kernels/matching_files_op.cc index 5eb060f664..cdff7bad5f 100644 --- a/tensorflow/core/kernels/matching_files_op.cc +++ b/tensorflow/core/kernels/matching_files_op.cc @@ -45,15 +45,14 @@ class MatchingFilesOp : public OpKernel { int num_files = 0; std::vector> all_fnames(num_patterns); for (int i = 0; i < num_patterns; i++) { - OP_REQUIRES_OK( - context, - context->env()->GetMatchingPaths(patterns(i), &all_fnames[i])); + OP_REQUIRES_OK(context, context->env()->GetMatchingPaths(patterns(i), + &all_fnames[i])); num_files += all_fnames[i].size(); } Tensor* output_t = nullptr; - OP_REQUIRES_OK(context, - context->allocate_output( - "filenames", TensorShape({num_files}), &output_t)); + OP_REQUIRES_OK( + context, context->allocate_output("filenames", TensorShape({num_files}), + &output_t)); auto output = output_t->vec(); int index = 0; for (int i = 0; i < num_patterns; ++i) { diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc index cb68690f28..f499ce6519 100644 --- a/tensorflow/core/kernels/matmul_op.cc +++ b/tensorflow/core/kernels/matmul_op.cc @@ -261,12 +261,12 @@ struct LaunchMatMul { std::vector* algorithms, bool use_autotune, Tensor* out) { using perftools::gputools::blas::AlgorithmConfig; using perftools::gputools::blas::ComputationType; - using perftools::gputools::blas::ProfileResult; - using perftools::gputools::blas::Transpose; using perftools::gputools::blas::kDefaultAlgorithm; using perftools::gputools::blas::kDefaultBlasGemm; using perftools::gputools::blas::kDefaultBlasGemv; using perftools::gputools::blas::kNoAlgorithm; + using perftools::gputools::blas::ProfileResult; + using perftools::gputools::blas::Transpose; Transpose trans[] = {Transpose::kNoTranspose, Transpose::kTranspose}; const uint64 m = a.dim_size(1 - dim_pair[0].first); const uint64 k = a.dim_size(dim_pair[0].first); diff --git a/tensorflow/core/kernels/matmul_op.h b/tensorflow/core/kernels/matmul_op.h index 6398da2fb9..628895ca86 100644 --- a/tensorflow/core/kernels/matmul_op.h +++ b/tensorflow/core/kernels/matmul_op.h @@ -30,7 +30,8 @@ struct MatMulTypes { typedef Eigen::TensorMap, Eigen::Aligned> out_type; typedef Eigen::TensorMap, - Eigen::Aligned> in_type; + Eigen::Aligned> + in_type; }; template @@ -40,7 +39,8 @@ class MatrixExponentialOp : public LinearAlgebraOp { MatrixMaps* outputs) final { const ConstMatrixMap& input = inputs[0]; if (input.rows() == 0) return; - using Matrix = Eigen::Matrix; + using Matrix = + Eigen::Matrix; Matrix tmp = input; outputs->at(0) = tmp.exp(); } @@ -51,9 +51,9 @@ class MatrixExponentialOp : public LinearAlgebraOp { REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), float); REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), double); -REGISTER_LINALG_OP("MatrixExponential", - (MatrixExponentialOp), complex64); -REGISTER_LINALG_OP("MatrixExponential", - (MatrixExponentialOp), complex128); +REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), + complex64); +REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp), + complex128); } // namespace tensorflow diff --git a/tensorflow/core/kernels/matrix_logarithm_op.cc b/tensorflow/core/kernels/matrix_logarithm_op.cc index cf0007b5b6..22ca094e24 100644 --- a/tensorflow/core/kernels/matrix_logarithm_op.cc +++ b/tensorflow/core/kernels/matrix_logarithm_op.cc @@ -26,7 +26,6 @@ limitations under the License. #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/types.h" - namespace tensorflow { template @@ -40,7 +39,8 @@ class MatrixLogarithmOp : public LinearAlgebraOp { MatrixMaps* outputs) final { const ConstMatrixMap& input = inputs[0]; if (input.rows() == 0) return; - using Matrix = Eigen::Matrix; + using Matrix = + Eigen::Matrix; Matrix tmp = input; outputs->at(0) = tmp.log(); } @@ -53,9 +53,9 @@ class MatrixLogarithmOp : public LinearAlgebraOp { // logarithm. If all eigenvalues are positive, then this returns the correct // logarithm, however checking for positive definiteness adds significant // overhead. Therefore at present we only register this Op for complex types. -REGISTER_LINALG_OP("MatrixLogarithm", - (MatrixLogarithmOp), complex64); -REGISTER_LINALG_OP("MatrixLogarithm", - (MatrixLogarithmOp), complex128); +REGISTER_LINALG_OP("MatrixLogarithm", (MatrixLogarithmOp), + complex64); +REGISTER_LINALG_OP("MatrixLogarithm", (MatrixLogarithmOp), + complex128); } // namespace tensorflow diff --git a/tensorflow/core/kernels/matrix_set_diag_op.cc b/tensorflow/core/kernels/matrix_set_diag_op.cc index 9dd665392b..502d593474 100644 --- a/tensorflow/core/kernels/matrix_set_diag_op.cc +++ b/tensorflow/core/kernels/matrix_set_diag_op.cc @@ -69,8 +69,8 @@ class MatrixSetDiagOp : public OpKernel { errors::InvalidArgument( "must have diagonal.shape == input.shape[:-2] + " "min(input.shape[-2:]), but received input shape: ", - input_shape.DebugString(), " and diagonal shape: ", - diag_shape.DebugString())); + input_shape.DebugString(), + " and diagonal shape: ", diag_shape.DebugString())); if (input.NumElements() == 0) { // This is a no-op. diff --git a/tensorflow/core/kernels/maxpooling_op.cc b/tensorflow/core/kernels/maxpooling_op.cc index 2eefadad49..9be7408012 100644 --- a/tensorflow/core/kernels/maxpooling_op.cc +++ b/tensorflow/core/kernels/maxpooling_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/core/kernels/maxpooling_op.h" #include +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -37,7 +38,6 @@ limitations under the License. #include "tensorflow/core/util/padding.h" #include "tensorflow/core/util/tensor_format.h" #include "tensorflow/core/util/use_cudnn.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #if GOOGLE_CUDA #include "tensorflow/core/kernels/maxpooling_op_gpu.h" @@ -89,7 +89,6 @@ static void SpatialMaxPoolWithArgMaxHelper( // max value. auto shard = [¶ms, &in_mat, &out_mat, &out_arg_max_mat, &input_backprop, &output_arg_max, &out_backprop](int64 start, int64 limit) { - const int32 depth = params.depth; const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; @@ -180,7 +179,6 @@ static void SpatialMaxPoolWithArgMaxHelper( input_backprop_flat(input_backprop_index) += out_backprop_flat(index); } } - }; const int64 shard_cost = params.tensor_in_rows * params.tensor_in_cols * @@ -567,7 +565,7 @@ class MaxPoolingGradGradOp : public OpKernel { // tensor_out_as_matrix with the corresponding values in // top_diff_as_matrix. auto shard = [¶ms, &in_mat, &out_mat, &top_diff_mat, &bottom_diff_mat]( - int64 start, int64 limit) { + int64 start, int64 limit) { const int32 depth = params.depth; const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; diff --git a/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc b/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc index f8daaca4c9..0c7a236b2f 100644 --- a/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc +++ b/tensorflow/core/kernels/maxpooling_op_gpu.cu.cc @@ -450,10 +450,10 @@ bool MaxPoolBackwardWithArgmax::operator()( T* bottom_diff, const Eigen::GpuDevice& d) { const int kThreadsPerBlock = 1024; SetZero<<<(input_size + kThreadsPerBlock - 1) / kThreadsPerBlock, - kThreadsPerBlock, 0, d.stream()>>>(input_size, bottom_diff); + kThreadsPerBlock, 0, d.stream()>>>(input_size, bottom_diff); MaxPoolBackward<<<(output_size + kThreadsPerBlock - 1) / kThreadsPerBlock, kThreadsPerBlock, 0, d.stream()>>>( - output_size, top_diff, mask, top_offset, bottom_offset, bottom_diff); + output_size, top_diff, mask, top_offset, bottom_offset, bottom_diff); return d.ok(); } diff --git a/tensorflow/core/kernels/meta_support.cc b/tensorflow/core/kernels/meta_support.cc index 9fed01189f..39e60c9fce 100644 --- a/tensorflow/core/kernels/meta_support.cc +++ b/tensorflow/core/kernels/meta_support.cc @@ -98,9 +98,9 @@ typedef gemmlowp::meta::SimpleContext LocalContext; template void MultiThreadGemm(Context* context, const Params& params) { if (params.m <= 4) { - gemmlowp::meta::MultiThreadGemm< - Context, gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>, Params, - 1, 8, 8>(context, params); + gemmlowp::meta::MultiThreadGemm< + Context, gemmlowp::meta::GemmExecutorPackLHSCacheFriendly<>, Params, 1, + 8, 8>(context, params); } else { if (params.m >= params.n) { gemmlowp::meta::MultiThreadGemm< diff --git a/tensorflow/core/kernels/mfcc.cc b/tensorflow/core/kernels/mfcc.cc index 2793005aa2..8c755e0df8 100644 --- a/tensorflow/core/kernels/mfcc.cc +++ b/tensorflow/core/kernels/mfcc.cc @@ -27,21 +27,19 @@ const double kFilterbankFloor = 1e-12; const int kDefaultFilterbankChannelCount = 40; const int kDefaultDCTCoefficientCount = 13; -Mfcc::Mfcc() : initialized_(false), - lower_frequency_limit_(kDefaultLowerFrequencyLimit), - upper_frequency_limit_(kDefaultUpperFrequencyLimit), - filterbank_channel_count_(kDefaultFilterbankChannelCount), - dct_coefficient_count_(kDefaultDCTCoefficientCount) { } +Mfcc::Mfcc() + : initialized_(false), + lower_frequency_limit_(kDefaultLowerFrequencyLimit), + upper_frequency_limit_(kDefaultUpperFrequencyLimit), + filterbank_channel_count_(kDefaultFilterbankChannelCount), + dct_coefficient_count_(kDefaultDCTCoefficientCount) {} -bool Mfcc::Initialize(int input_length, - double input_sample_rate) { - bool initialized = mel_filterbank_.Initialize(input_length, - input_sample_rate, - filterbank_channel_count_, - lower_frequency_limit_, - upper_frequency_limit_); - initialized &= dct_.Initialize(filterbank_channel_count_, - dct_coefficient_count_); +bool Mfcc::Initialize(int input_length, double input_sample_rate) { + bool initialized = mel_filterbank_.Initialize( + input_length, input_sample_rate, filterbank_channel_count_, + lower_frequency_limit_, upper_frequency_limit_); + initialized &= + dct_.Initialize(filterbank_channel_count_, dct_coefficient_count_); initialized_ = initialized; return initialized; } diff --git a/tensorflow/core/kernels/mfcc.h b/tensorflow/core/kernels/mfcc.h index 8268f47203..8eee76f7f0 100644 --- a/tensorflow/core/kernels/mfcc.h +++ b/tensorflow/core/kernels/mfcc.h @@ -20,18 +20,17 @@ limitations under the License. #include +#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/kernels/mfcc_dct.h" #include "tensorflow/core/kernels/mfcc_mel_filterbank.h" #include "tensorflow/core/platform/logging.h" -#include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { class Mfcc { public: Mfcc(); - bool Initialize(int input_length, - double input_sample_rate); + bool Initialize(int input_length, double input_sample_rate); // Input is a single squared-magnitude spectrogram frame. The input spectrum // is converted to linear magnitude and weighted into bands using a diff --git a/tensorflow/core/kernels/mfcc_mel_filterbank.cc b/tensorflow/core/kernels/mfcc_mel_filterbank.cc index 630de8a5a3..3db3b51e8b 100644 --- a/tensorflow/core/kernels/mfcc_mel_filterbank.cc +++ b/tensorflow/core/kernels/mfcc_mel_filterbank.cc @@ -38,13 +38,12 @@ namespace tensorflow { MfccMelFilterbank::MfccMelFilterbank() : initialized_(false) {} -bool MfccMelFilterbank::Initialize(int input_length, - double input_sample_rate, - int output_channel_count, - double lower_frequency_limit, - double upper_frequency_limit) { +bool MfccMelFilterbank::Initialize(int input_length, double input_sample_rate, + int output_channel_count, + double lower_frequency_limit, + double upper_frequency_limit) { num_channels_ = output_channel_count; - sample_rate_ = input_sample_rate; + sample_rate_ = input_sample_rate; input_length_ = input_length; if (num_channels_ < 1) { @@ -85,10 +84,9 @@ bool MfccMelFilterbank::Initialize(int input_length, } // Always exclude DC; emulate HTK. - const double hz_per_sbin = 0.5 * sample_rate_ / - static_cast(input_length_ - 1); - start_index_ = static_cast(1.5 + (lower_frequency_limit / - hz_per_sbin)); + const double hz_per_sbin = + 0.5 * sample_rate_ / static_cast(input_length_ - 1); + start_index_ = static_cast(1.5 + (lower_frequency_limit / hz_per_sbin)); end_index_ = static_cast(upper_frequency_limit / hz_per_sbin); // Maps the input spectrum bin indices to filter bank channels/indices. For @@ -121,12 +119,12 @@ bool MfccMelFilterbank::Initialize(int input_length, weights_[i] = 0.0; } else { if (channel >= 0) { - weights_[i] = (center_frequencies_[channel + 1] - - FreqToMel(i * hz_per_sbin)) / + weights_[i] = + (center_frequencies_[channel + 1] - FreqToMel(i * hz_per_sbin)) / (center_frequencies_[channel + 1] - center_frequencies_[channel]); } else { weights_[i] = (center_frequencies_[0] - FreqToMel(i * hz_per_sbin)) / - (center_frequencies_[0] - mel_low); + (center_frequencies_[0] - mel_low); } } } @@ -152,16 +150,16 @@ bool MfccMelFilterbank::Initialize(int input_length, } } if (!bad_channels.empty()) { - LOG(ERROR) << "Missing " << bad_channels.size() << " bands " << - " starting at " << bad_channels[0] << - " in mel-frequency design. " << - "Perhaps too many channels or " << - "not enough frequency resolution in spectrum. (" << - "input_length: " << input_length << - " input_sample_rate: " << input_sample_rate << - " output_channel_count: " << output_channel_count << - " lower_frequency_limit: " << lower_frequency_limit << - " upper_frequency_limit: " << upper_frequency_limit; + LOG(ERROR) << "Missing " << bad_channels.size() << " bands " + << " starting at " << bad_channels[0] + << " in mel-frequency design. " + << "Perhaps too many channels or " + << "not enough frequency resolution in spectrum. (" + << "input_length: " << input_length + << " input_sample_rate: " << input_sample_rate + << " output_channel_count: " << output_channel_count + << " lower_frequency_limit: " << lower_frequency_limit + << " upper_frequency_limit: " << upper_frequency_limit; } initialized_ = true; return true; @@ -171,7 +169,7 @@ bool MfccMelFilterbank::Initialize(int input_length, // square root, then summing FFT magnitudes under triangular integration windows // whose widths increase with frequency. void MfccMelFilterbank::Compute(const std::vector &input, - std::vector *output) const { + std::vector *output) const { if (!initialized_) { LOG(ERROR) << "Mel Filterbank not initialized."; return; diff --git a/tensorflow/core/kernels/mfcc_mel_filterbank.h b/tensorflow/core/kernels/mfcc_mel_filterbank.h index 1bdc2dc93b..37c3936e80 100644 --- a/tensorflow/core/kernels/mfcc_mel_filterbank.h +++ b/tensorflow/core/kernels/mfcc_mel_filterbank.h @@ -27,10 +27,8 @@ class MfccMelFilterbank { public: MfccMelFilterbank(); bool Initialize(int input_length, // Number of unique FFT bins fftsize/2+1. - double input_sample_rate, - int output_channel_count, - double lower_frequency_limit, - double upper_frequency_limit); + double input_sample_rate, int output_channel_count, + double lower_frequency_limit, double upper_frequency_limit); // Takes a squared-magnitude spectrogram slice as input, computes a // triangular-mel-weighted linear-magnitude filterbank, and places the result @@ -56,7 +54,7 @@ class MfccMelFilterbank { // FFT bin i contributes to the upper side of mel channel band_mapper_[i] std::vector band_mapper_; int start_index_; // Lowest FFT bin used to calculate mel spectrum. - int end_index_; // Highest FFT bin used to calculate mel spectrum. + int end_index_; // Highest FFT bin used to calculate mel spectrum. TF_DISALLOW_COPY_AND_ASSIGN(MfccMelFilterbank); }; diff --git a/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc b/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc index 602dfeb4e5..54f31e1699 100644 --- a/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc +++ b/tensorflow/core/kernels/mfcc_mel_filterbank_test.cc @@ -34,11 +34,9 @@ TEST(MfccMelFilterbankTest, AgreesWithPythonGoldenValues) { input.push_back(i + 1); } const int kChannelCount = 20; - filterbank.Initialize(input.size(), - 22050 /* sample rate */, - kChannelCount /* channels */, - 20.0 /* lower frequency limit */, - 4000.0 /* upper frequency limit */); + filterbank.Initialize( + input.size(), 22050 /* sample rate */, kChannelCount /* channels */, + 20.0 /* lower frequency limit */, 4000.0 /* upper frequency limit */); std::vector output; filterbank.Compute(input, &output); @@ -65,13 +63,10 @@ TEST(MfccMelFilterbankTest, IgnoresExistingContentOfOutputVector) { std::vector input; std::vector output; - filterbank.Initialize(kSampleCount, - 22050 /* sample rate */, - 20 /* channels */, - 20.0 /* lower frequency limit */, + filterbank.Initialize(kSampleCount, 22050 /* sample rate */, + 20 /* channels */, 20.0 /* lower frequency limit */, 4000.0 /* upper frequency limit */); - // First call with nonzero input value, and an empty output vector, // will resize the output and fill it with the correct, nonzero outputs. input.assign(kSampleCount, 1.0); diff --git a/tensorflow/core/kernels/mfcc_test.cc b/tensorflow/core/kernels/mfcc_test.cc index cb32df8811..72c1d331d6 100644 --- a/tensorflow/core/kernels/mfcc_test.cc +++ b/tensorflow/core/kernels/mfcc_test.cc @@ -36,11 +36,10 @@ TEST(MfccTest, AgreesWithPythonGoldenValues) { std::vector output; mfcc.Compute(input, &output); - std::vector expected = {29.13970072, -6.41568601, -0.61903012, - -0.96778652, -0.26819878, -0.40907028, - -0.15614748, -0.23203119, -0.10481487, - -0.1543029, -0.0769791, -0.10806114, - -0.06047613}; + std::vector expected = { + 29.13970072, -6.41568601, -0.61903012, -0.96778652, -0.26819878, + -0.40907028, -0.15614748, -0.23203119, -0.10481487, -0.1543029, + -0.0769791, -0.10806114, -0.06047613}; ASSERT_EQ(expected.size(), output.size()); for (int i = 0; i < output.size(); ++i) { diff --git a/tensorflow/core/kernels/mirror_pad_op.cc b/tensorflow/core/kernels/mirror_pad_op.cc index fbdeaf43eb..26e1082989 100644 --- a/tensorflow/core/kernels/mirror_pad_op.cc +++ b/tensorflow/core/kernels/mirror_pad_op.cc @@ -87,8 +87,8 @@ class MirrorPadOp : public OpKernel { const Tpaddings before = paddings(d, 0); // Pad before existing elements. const Tpaddings after = paddings(d, 1); // Pad after existing elements. OP_REQUIRES(context, before >= 0 && after >= 0, - errors::InvalidArgument("paddings must be non-negative: ", - before, " ", after)); + errors::InvalidArgument( + "paddings must be non-negative: ", before, " ", after)); if (offset_ == 0) { // SYMMETRIC mode. OP_REQUIRES(context, before <= in0.dim_size(d) && after <= in0.dim_size(d), @@ -296,8 +296,8 @@ class MirrorPadGradOp : public OpKernel { const Tpaddings before = paddings(d, 0); // Pad before existing elements. const Tpaddings after = paddings(d, 1); // Pad after existing elements. OP_REQUIRES(context, before >= 0 && after >= 0, - errors::InvalidArgument("Paddings must be non-negative: ", - before, ", ", after)); + errors::InvalidArgument( + "Paddings must be non-negative: ", before, ", ", after)); const int64 out_size = in0.dim_size(d) - (before + after); if (offset_ == 0) { // SYMMETRIC mode. diff --git a/tensorflow/core/kernels/mkl_avgpooling_op.cc b/tensorflow/core/kernels/mkl_avgpooling_op.cc index d751a70fc8..a7c569ee05 100644 --- a/tensorflow/core/kernels/mkl_avgpooling_op.cc +++ b/tensorflow/core/kernels/mkl_avgpooling_op.cc @@ -26,14 +26,14 @@ #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::memory; +using mkldnn::algorithm; +using mkldnn::engine; using mkldnn::error; -using mkldnn::pooling_forward; -using mkldnn::pooling_backward; +using mkldnn::memory; using mkldnn::padding_kind; -using mkldnn::engine; +using mkldnn::pooling_backward; +using mkldnn::pooling_forward; using mkldnn::prop_kind; -using mkldnn::algorithm; #endif namespace tensorflow { @@ -358,10 +358,11 @@ class MklAvgPoolingGradOp : public OpKernel { if (!outbackprop_in_mkl_format) { // For avgpooling, tensor_in_shape should have 1 dimension, and 4 // elements. - OP_REQUIRES(context, tensor_in_shape.dims() == 1 && - tensor_in_shape.NumElements() == 4, - errors::InvalidArgument("original input shape must be " - "1-dimensional and 4 elements")); + OP_REQUIRES( + context, + tensor_in_shape.dims() == 1 && tensor_in_shape.NumElements() == 4, + errors::InvalidArgument("original input shape must be " + "1-dimensional and 4 elements")); // For avgpooling, out_backprop should have 4 dimensions. OP_REQUIRES(context, out_backprop.dims() == 4, @@ -428,14 +429,13 @@ class MklAvgPoolingGradOp : public OpKernel { TensorFormat data_format_; }; // MklAvgPoolingGradOp - #else // INTEL_MKL_DNN is defined template class MklAvgPoolingOp : public MklPoolingForwardOpBase { public: explicit MklAvgPoolingOp(OpKernelConstruction* context) - : MklPoolingForwardOpBase(context) { + : MklPoolingForwardOpBase(context) { // Workspace is an MKLDNN construct that is only used in Max Pooling. // So set workspace_enabled_ to false. this->workspace_enabled_ = false; @@ -444,8 +444,8 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase { void Compute(OpKernelContext* context) override { try { auto cpu_engine = engine(engine::cpu, 0); - const Tensor& input_tensor = MklGetInput(context, - this->kInputTensorIndexInput); + const Tensor& input_tensor = + MklGetInput(context, this->kInputTensorIndexInput); MklDnnShape dnn_shape_input; GetMklShape(context, this->kInputTensorIndexInput, &dnn_shape_input); this->SanityCheckInput(context, input_tensor, dnn_shape_input); @@ -457,9 +457,8 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase { // initialize variables for the pooling op MklPoolParameters pool_params; // Get the input tensor and initialize the pooling parameters - this->ConfigureInput(context, dnn_shape_input, - input_tensor, &pool_params, - &dnn_data_input); + this->ConfigureInput(context, dnn_shape_input, input_tensor, &pool_params, + &dnn_data_input); OP_REQUIRES_OK(context, context->status()); // Declare output tensor @@ -470,56 +469,52 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase { // If input is in Mkl layout, then just get the memory format from it // directly, instead of using input data_format to AvgPool. if (dnn_shape_input.IsMklTensor()) { - dnn_data_output.SetUsrMem(output_dims_mkl_order, - static_cast(dnn_data_input.GetUsrMemDesc() - .data.format)); + dnn_data_output.SetUsrMem( + output_dims_mkl_order, + static_cast( + dnn_data_input.GetUsrMemDesc().data.format)); } else { - dnn_data_output.SetUsrMem(output_dims_mkl_order, - this->data_format_mkldnn_); + dnn_data_output.SetUsrMem(output_dims_mkl_order, + this->data_format_mkldnn_); } - // describe the memory layout + // describe the memory layout dnn_data_output.SetOpMemDesc(output_dims_mkl_order, memory::format::any); // 3. create a pooling primitive descriptor - auto pool_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_avg_exclude_padding, - dnn_data_input.GetUsrMemDesc(), - dnn_data_output.GetUsrMemDesc(), - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, - cpu_engine); + auto pool_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_avg_exclude_padding, + dnn_data_input.GetUsrMemDesc(), dnn_data_output.GetUsrMemDesc(), + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_prim_desc = + pooling_forward::primitive_desc(pool_desc, cpu_engine); this->AllocateOutputTensor(context, pool_prim_desc, output_dims_mkl_order, - this->data_format_mkldnn_, &output_tensor); + this->data_format_mkldnn_, &output_tensor); CHECK_NOTNULL(output_tensor); OP_REQUIRES_OK(context, context->status()); dnn_data_output.SetUsrMemDataHandle(output_tensor); - this->PrepareAndExecuteNet(pool_prim_desc, - &dnn_data_input, - &dnn_data_output); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + this->PrepareAndExecuteNet(pool_prim_desc, &dnn_data_input, + &dnn_data_output); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } // Compute -}; // MklAvgPoolingOp +}; // MklAvgPoolingOp //----------------------------------------------------------------------------- @@ -527,27 +522,23 @@ template class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { public: explicit MklAvgPoolingGradOp(OpKernelConstruction* context) - : MklPoolingBackwardOpBase(context) { - } + : MklPoolingBackwardOpBase(context) {} void Compute(OpKernelContext* context) override { try { auto cpu_engine = engine(engine::cpu, 0); MklDnnShape original_input_mkl_shape, input_gradient_mkl_shape; - const Tensor& tensor_in_shape = MklGetInput(context, - kInputTensorIndexInputShape); - const Tensor& input_gradient_tensor = MklGetInput(context, - kInputTensorIndexInputGradient); + const Tensor& tensor_in_shape = + MklGetInput(context, kInputTensorIndexInputShape); + const Tensor& input_gradient_tensor = + MklGetInput(context, kInputTensorIndexInputGradient); GetMklShape(context, kInputTensorIndexInputShape, - &original_input_mkl_shape); + &original_input_mkl_shape); GetMklShape(context, kInputTensorIndexInputGradient, - &input_gradient_mkl_shape); - + &input_gradient_mkl_shape); - SanityCheckInputs(context, tensor_in_shape, - input_gradient_tensor, - original_input_mkl_shape, - input_gradient_mkl_shape); + SanityCheckInputs(context, tensor_in_shape, input_gradient_tensor, + original_input_mkl_shape, input_gradient_mkl_shape); if (!context->status().ok()) return; // Used to allocate output_diff_src/diff_src @@ -562,90 +553,70 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { MklPoolParameters pool_params; memory::dims output_dims_mkl_order, original_input_dims_nchw; // Configure the original input memory descriptor - memory::desc original_input_md = ConfigureOriginalInput(context, - tensor_in_shape, - original_input_mkl_shape, - &original_input_dims_nchw, - &pool_params, - &original_input_shape); + memory::desc original_input_md = ConfigureOriginalInput( + context, tensor_in_shape, original_input_mkl_shape, + &original_input_dims_nchw, &pool_params, &original_input_shape); // configure the original output memory descriptor // by definition, the shape of the original output is the same // as the shape of the gradient diff_dst memory::desc original_output_md = this->ConfigureOriginalOutput( - pool_params, input_gradient_mkl_shape, output_dims_mkl_order); + pool_params, input_gradient_mkl_shape, output_dims_mkl_order); memory::desc target_diff_dst_md = this->ConfigureInputGradient( - input_gradient_mkl_shape, - input_gradient_tensor, - &input_gradient_diff_dst, - original_output_md); + input_gradient_mkl_shape, input_gradient_tensor, + &input_gradient_diff_dst, original_output_md); // The shape of the output diff src needs to be the same shape as the // original input. But we will set its format to be same as the format of // input gradient. We won't use format of original input since it will // always be in Tensorflow layout (given that AvgPoolGrad gets shape of // the input rather than actual input). - output_diff_src.SetUsrMem(original_input_dims_nchw, - static_cast( - target_diff_dst_md.data.format)); + output_diff_src.SetUsrMem( + original_input_dims_nchw, + static_cast(target_diff_dst_md.data.format)); // Create the forward pooling primitive descriptor so we can reference it // in the backward pooling primitive descriptor - auto pool_fwd_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_avg_exclude_padding, - original_input_md, - original_output_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_fwd_prim_desc - = pooling_forward::primitive_desc(pool_fwd_desc, - cpu_engine); + auto pool_fwd_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_avg_exclude_padding, + original_input_md, original_output_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_fwd_prim_desc = + pooling_forward::primitive_desc(pool_fwd_desc, cpu_engine); auto pool_bkwd_desc = pooling_backward::desc( - algorithm::pooling_avg_exclude_padding, - output_diff_src.GetUsrMemDesc(), - target_diff_dst_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_bkwd_prim_desc - = pooling_backward::primitive_desc(pool_bkwd_desc, - cpu_engine, - pool_fwd_prim_desc); - this->AllocateOutputTensor(context, pool_bkwd_prim_desc, - original_input_dims_nchw, - this->data_format_mkldnn_, - &output_tensor_diff_src); + algorithm::pooling_avg_exclude_padding, + output_diff_src.GetUsrMemDesc(), target_diff_dst_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_bkwd_prim_desc = pooling_backward::primitive_desc( + pool_bkwd_desc, cpu_engine, pool_fwd_prim_desc); + this->AllocateOutputTensor( + context, pool_bkwd_prim_desc, original_input_dims_nchw, + this->data_format_mkldnn_, &output_tensor_diff_src); output_diff_src.SetUsrMemDataHandle(output_tensor_diff_src); - this->PrepareAndExecuteNet(pool_bkwd_prim_desc, - &input_gradient_diff_dst, - &output_diff_src, - memory::primitive_desc( - target_diff_dst_md, - cpu_engine)); - } catch (mkldnn::error &e) { + this->PrepareAndExecuteNet( + pool_bkwd_prim_desc, &input_gradient_diff_dst, &output_diff_src, + memory::primitive_desc(target_diff_dst_md, cpu_engine)); + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Compute received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK(context, errors::Aborted("Compute received an exception:", + error_msg)); } } // Compute @@ -655,12 +626,11 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { const int kInputTensorIndexInputShape = 0; const int kInputTensorIndexInputGradient = 1; - memory::desc ConfigureOriginalInput(OpKernelContext* context, - const Tensor& tensor_original_input_shape, - const MklDnnShape& original_input_mkl_shape, - memory::dims* original_input_dims_mkl_order, - MklPoolParameters* pool_params, - TensorShape* input_tensor_shape) { + memory::desc ConfigureOriginalInput( + OpKernelContext* context, const Tensor& tensor_original_input_shape, + const MklDnnShape& original_input_mkl_shape, + memory::dims* original_input_dims_mkl_order, + MklPoolParameters* pool_params, TensorShape* input_tensor_shape) { CHECK_NOTNULL(original_input_dims_mkl_order); CHECK_NOTNULL(pool_params); CHECK_NOTNULL(input_tensor_shape); @@ -672,46 +642,42 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { } return MklPoolingBackwardOpBase::ConfigureOriginalInput( - context, - tensor_original_input_shape, - original_input_mkl_shape, - original_input_dims_mkl_order, - pool_params, - *input_tensor_shape); -} + context, tensor_original_input_shape, original_input_mkl_shape, + original_input_dims_mkl_order, pool_params, *input_tensor_shape); + } void SanityCheckInputs(OpKernelContext* context, - const Tensor& tensor_in_shape, - const Tensor& input_gradient_tensor, - const MklDnnShape& original_input_mkl_shape, - const MklDnnShape& input_gradient_mkl_shape) { + const Tensor& tensor_in_shape, + const Tensor& input_gradient_tensor, + const MklDnnShape& original_input_mkl_shape, + const MklDnnShape& input_gradient_mkl_shape) { if (!original_input_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, tensor_in_shape.dims() == 1 && - tensor_in_shape.NumElements() == 4, + OP_REQUIRES( + context, + tensor_in_shape.dims() == 1 && tensor_in_shape.NumElements() == 4, errors::InvalidArgument("original input shape must be " - "1-dimensional and 4 elements")); + "1-dimensional and 4 elements")); } else { - OP_REQUIRES(context, original_input_mkl_shape.GetDimension() == 1 && - original_input_mkl_shape.DimSize(0) == 4, - errors::InvalidArgument("original input shape must be " - "1-dimensional and 4 elements")); + OP_REQUIRES(context, + original_input_mkl_shape.GetDimension() == 1 && + original_input_mkl_shape.DimSize(0) == 4, + errors::InvalidArgument("original input shape must be " + "1-dimensional and 4 elements")); } if (!input_gradient_mkl_shape.IsMklTensor()) { // For avgpooling, input_gradient_diff_dst should have 4 dimensions. OP_REQUIRES(context, input_gradient_tensor.dims() == 4, - errors::InvalidArgument("Gradient shape must be " - "4-dimensional")); + errors::InvalidArgument("Gradient shape must be " + "4-dimensional")); } else { OP_REQUIRES(context, input_gradient_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Gradient shape must be " - "4-dimensional")); + errors::InvalidArgument("Gradient shape must be " + "4-dimensional")); } } }; // MklAvgPoolingGradOp - - #endif // INTEL_MKL_DNN REGISTER_KERNEL_BUILDER(Name("_MklAvgPool") @@ -728,4 +694,3 @@ REGISTER_KERNEL_BUILDER(Name("_MklAvgPoolGrad") } // namespace tensorflow #endif // INTEL_MKL - diff --git a/tensorflow/core/kernels/mkl_batch_matmul_op.cc b/tensorflow/core/kernels/mkl_batch_matmul_op.cc index 9fee94f946..d9713075be 100644 --- a/tensorflow/core/kernels/mkl_batch_matmul_op.cc +++ b/tensorflow/core/kernels/mkl_batch_matmul_op.cc @@ -40,7 +40,6 @@ limitations under the License. #include "tensorflow/core/kernels/fill_functor.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #define MKL_Complex8 tensorflow::complex64 #define MKL_Complex16 tensorflow::complex128 diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index d109bb6bcf..7da63604d2 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -33,8 +33,8 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; using mkldnn::concat; +using mkldnn::stream; #endif namespace tensorflow { @@ -45,7 +45,6 @@ typedef std::vector TensorShapeList; enum AxisArgumentName { NAME_IS_AXIS, NAME_IS_CONCAT_DIM }; - // TODO(intelft) Check if we can reuse existing EigenConcatOp using Mutable // reference inputs. // -------------------------------------------------------------------------- @@ -152,8 +151,8 @@ class EigenConcatBaseOp : public OpKernel { #else // MKL_DNN -void Compute(OpKernelContext* c, const std::vector& values, - const TensorShapeList& input_shapes) { + void Compute(OpKernelContext* c, const std::vector& values, + const TensorShapeList& input_shapes) { const Tensor* concat_dim_tensor; const char* axis_attribute_name = AxisArgName == NAME_IS_AXIS @@ -197,7 +196,8 @@ void Compute(OpKernelContext* c, const std::vector& values, const auto in = values[i]; const bool in_is_scalar = IsLegacyScalar(input_shapes[i]); OP_REQUIRES( - c, (input_shapes[i].dims() == input_dims) || + c, + (input_shapes[i].dims() == input_dims) || (input_is_scalar && in_is_scalar), errors::InvalidArgument( "ConcatOp : Ranks of all input tensors should match: shape[0] = ", @@ -208,8 +208,8 @@ void Compute(OpKernelContext* c, const std::vector& values, inputs_flat.emplace_back(new typename TTypes::ConstMatrix( in.shaped({inputs_flat_dim0, inputs_flat_dim1}))); } - output_concat_dim += input_shapes[i].dims() > 0 ? - input_shapes[i].dim_size(axis) : 1; + output_concat_dim += + input_shapes[i].dims() > 0 ? input_shapes[i].dim_size(axis) : 1; } TensorShape output_shape(input_shape); @@ -418,7 +418,6 @@ class MklConcatOp : public OpKernel { OP_REQUIRES_OK(context, context->status()); } - private: typedef struct { TensorFormat data_format; @@ -590,39 +589,45 @@ class MklConcatOp : public OpKernel { GetMklShapeList(context, "values", &input_shapes); const Tensor& concat_dim_tensor = (AxisArgName == NAME_IS_CONCAT_DIM) - ? MklGetInput(context, 0) : MklGetInput(context, N); + ? MklGetInput(context, 0) + : MklGetInput(context, N); // Sanity checks - OP_REQUIRES(context, IsLegacyScalar(concat_dim_tensor.shape()), - errors::InvalidArgument( - "Concat dim tensor should be a scalar integer, but got shape ", - concat_dim_tensor.shape().DebugString())); - int32 concat_dim = internal::SubtleMustCopy( - concat_dim_tensor.scalar()()); + OP_REQUIRES( + context, IsLegacyScalar(concat_dim_tensor.shape()), + errors::InvalidArgument( + "Concat dim tensor should be a scalar integer, but got shape ", + concat_dim_tensor.shape().DebugString())); + int32 concat_dim = + internal::SubtleMustCopy(concat_dim_tensor.scalar()()); // check that ranks of all tensors match // and that their shapes match except for concat_dim. int i = 0; bool invoke_eigen = false; bool are_all_mkl_inputs = true, are_all_tf_inputs = true; - const TensorShape expected_shape = input_shapes[0].IsMklTensor() ? - input_shapes[0].GetTfShape() : - input_tensors[0].shape(); + const TensorShape expected_shape = input_shapes[0].IsMklTensor() + ? input_shapes[0].GetTfShape() + : input_tensors[0].shape(); size_t expected_dims = expected_shape.dims(); if (concat_dim < 0) concat_dim = expected_dims + concat_dim; for (auto& s : input_shapes) { - if (s == expected_shape) {++i; continue;} + if (s == expected_shape) { + ++i; + continue; + } - TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : - input_tensors[i].shape(); + TensorShape s_shape = + s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); size_t s_dims = s_shape.dims(); - OP_REQUIRES(context, s_dims == expected_dims, - errors::InvalidArgument( - "_MklConcatOp : Ranks of all input tensors should match:" - " input dimensions = ", - s_dims, " vs. expected rank = ", expected_dims)); + OP_REQUIRES( + context, s_dims == expected_dims, + errors::InvalidArgument( + "_MklConcatOp : Ranks of all input tensors should match:" + " input dimensions = ", + s_dims, " vs. expected rank = ", expected_dims)); for (int d = 0; d < expected_dims; ++d) { if (d == concat_dim) continue; @@ -630,10 +635,11 @@ class MklConcatOp : public OpKernel { size_t expected_size = expected_shape.dim_size(d); size_t s_size = s_shape.dim_size(d); OP_REQUIRES( - context, expected_size == s_size, - errors::InvalidArgument("_MklConcatOp : Dimensions of inputs " - "should match: shape[0][", d, "]= ", expected_size, - " vs. shape[", i, "][", d, "] = ", s_size)); + context, expected_size == s_size, + errors::InvalidArgument("_MklConcatOp : Dimensions of inputs " + "should match: shape[0][", + d, "]= ", expected_size, " vs. shape[", i, + "][", d, "] = ", s_size)); } if (s.IsMklTensor()) @@ -657,8 +663,8 @@ class MklConcatOp : public OpKernel { TensorShapeList tf_input_shapes; i = 0; for (auto& s : input_shapes) { - TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : - input_tensors[i].shape(); + TensorShape s_shape = + s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); tf_input_shapes.push_back(s_shape); ++i; } @@ -678,21 +684,22 @@ class MklConcatOp : public OpKernel { std::vector srcs_pd; std::vector> srcs(N, MklDnnData(&cpu_engine)); int64 dst_concat_dim_size = 0; - for (int k =0; k < N; k++) { + for (int k = 0; k < N; k++) { bool is_mkl_tensor = input_shapes[k].IsMklTensor(); memory::dims src_dims; // Same comment as dst_dims for src_dims. - src_dims = (is_mkl_tensor) ? - TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) : - TFShapeToMklDnnDims(input_tensors[k].shape()); + src_dims = (is_mkl_tensor) + ? TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) + : TFShapeToMklDnnDims(input_tensors[k].shape()); dst_concat_dim_size += src_dims[concat_dim]; - auto src_md = is_mkl_tensor ? input_shapes[k].GetMklLayout() : - // It does not matter what data format we use here (NHWC or NCHW). - // We just need to ensure that output of Concat uses same data format - // as input. - memory::desc(src_dims, MklDnnType(), memory::format::nchw); + auto src_md = + is_mkl_tensor ? input_shapes[k].GetMklLayout() : + // It does not matter what data format we use here + // (NHWC or NCHW). We just need to ensure that output + // of Concat uses same data format as input. + memory::desc(src_dims, MklDnnType(), memory::format::nchw); srcs[k].SetUsrMem(src_md, &input_tensors[k]); auto src_mpd = srcs[k].GetUsrMemPrimDesc(); @@ -707,14 +714,15 @@ class MklConcatOp : public OpKernel { // Since we are passing a specific format for destination, // we need to have dst_dims in MklDnn order (NCHW). auto orig_tf_format = input_shapes[0].GetTfDataFormat(); - dst_dims_in_nchw = MklDnnDimsInNCHW(dst_dims, - MklDnnDataFormatToTFDataFormat(orig_tf_format)); + dst_dims_in_nchw = MklDnnDimsInNCHW( + dst_dims, MklDnnDataFormatToTFDataFormat(orig_tf_format)); // We will set the output in the same format as input to avoid layout // conversions. // Currently we are setting dst format same as input format. // See if we can make this choice in a better way. - dst_md = memory::desc(dst_dims_in_nchw, MklDnnType(), - (memory::format) input_shapes[0].GetMklLayout().data.format); + dst_md = memory::desc( + dst_dims_in_nchw, MklDnnType(), + (memory::format)input_shapes[0].GetMklLayout().data.format); } else { // Again, format does not matter here. We just need to make it same as // input format. @@ -722,7 +730,7 @@ class MklConcatOp : public OpKernel { } std::vector inputs; - for (int k=0; k < input_tensors.size(); k++) + for (int k = 0; k < input_tensors.size(); k++) inputs.push_back(srcs[k].GetOpMem()); // If all inputs are in MKL format, then meaning of concat_dim needs to @@ -732,8 +740,7 @@ class MklConcatOp : public OpKernel { // But ifinput tensors are in NHWC order, then semantics need to change. // E.g., if we are concatinating over Channel (dimension 3 for NHWC), // then since MklDnn order is NCHW, concat_dim needs to be 1. - if (are_all_mkl_inputs) - concat_dim = input_shapes[0].TfDimIdx(concat_dim); + if (are_all_mkl_inputs) concat_dim = input_shapes[0].TfDimIdx(concat_dim); auto concat_pd = concat::primitive_desc(dst_md, concat_dim, srcs_pd); @@ -752,24 +759,25 @@ class MklConcatOp : public OpKernel { dnn_shape_dst.SetMklTensor(false); tf_shape_dst = MklDnnDimsToTFShape(dst_dims); } - AllocateOutputSetMklShape(context, 0, &dst_tensor, - tf_shape_dst, dnn_shape_dst); + AllocateOutputSetMklShape(context, 0, &dst_tensor, tf_shape_dst, + dnn_shape_dst); CHECK_NOTNULL(dst_tensor); - dst_md = dnn_shape_dst.IsMklTensor() ? - dnn_shape_dst.GetMklLayout() : dst_md; + dst_md = + dnn_shape_dst.IsMklTensor() ? dnn_shape_dst.GetMklLayout() : dst_md; dst.SetUsrMem(dst_md, dst_tensor); auto concat_op = concat(concat_pd, inputs, dst.GetOpMem()); std::vector net; net.push_back(concat_op); stream(stream::kind::eager).submit(net).wait(); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + ", in file " + - string(__FILE__) + ":" + std::to_string(__LINE__); - OP_REQUIRES_OK(context, errors::Aborted( - "Operation received an exception:", error_msg)); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -790,11 +798,9 @@ class MklConcatOp : public OpKernel { dnn_shape_output.SetDimensions(4); Tensor* output_tensor = nullptr; TensorShape tf_shape_output; - tf_shape_output.AddDim( - dnn_shape_output.GetSerializeBufferSize()); - context->allocate_output( - GetTensorMetaDataIndex(0, context->num_outputs()), - tf_shape_output, &output_tensor); + tf_shape_output.AddDim(dnn_shape_output.GetSerializeBufferSize()); + context->allocate_output(GetTensorMetaDataIndex(0, context->num_outputs()), + tf_shape_output, &output_tensor); dnn_shape_output.SerializeMklDnnShape( output_tensor->flat().data(), output_tensor->flat().size() * sizeof(uint8)); diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc index 0f1a218fe6..25c2573741 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc @@ -38,9 +38,9 @@ limitations under the License. #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#include "tensorflow/core/util/mkl_util.h" #include "mkl_dnn.h" #include "mkl_dnn_types.h" +#include "tensorflow/core/util/mkl_util.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc index 54d4916d49..ef3f8cfec1 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc @@ -38,17 +38,17 @@ limitations under the License. #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#include "tensorflow/core/util/mkl_util.h" #include "mkl_dnn.h" #include "mkl_dnn_types.h" +#include "tensorflow/core/util/mkl_util.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; -using mkldnn::prop_kind; using mkldnn::convolution_backward_weights; using mkldnn::memory; +using mkldnn::prop_kind; +using mkldnn::stream; #endif namespace tensorflow { @@ -360,8 +360,8 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel { (mkl_convert_input) ? mkl_buf_convert_input : mkl_buf_input; const Tensor& out_backprop = MklGetInput(context, 2); - void* mkl_buf_out_backprop = const_cast(static_cast( - out_backprop.flat().data())); + void* mkl_buf_out_backprop = const_cast( + static_cast(out_backprop.flat().data())); CHECK_EQ(dnnLayoutCreateFromPrimitive_F32(&mkl_lt_internal_out_backprop, prim_conv_bwdfilter, @@ -371,10 +371,11 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel { !dnnLayoutCompare_F32(mkl_lt_internal_out_backprop, lt_out_backprop); if (mkl_convert_out_backprop) { CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_out_backprop, - lt_out_backprop, mkl_lt_internal_out_backprop), + lt_out_backprop, + mkl_lt_internal_out_backprop), E_SUCCESS); AllocTmpBuffer(context, mkl_tmp_out_backprop_buf_tensor, - lt_out_backprop, &mkl_buf_convert_out_backprop); + lt_out_backprop, &mkl_buf_convert_out_backprop); CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_out_backprop, mkl_buf_out_backprop, mkl_buf_convert_out_backprop), @@ -428,18 +429,18 @@ class MklConv2DCustomBackpropFilterOp : public OpKernel { .Device(DEVICE_CPU) \ .TypeConstraint("T") \ .Label(mkl_op_registry::kMklOpLabel), \ - MklConv2DCustomBackpropFilterOp); + MklConv2DCustomBackpropFilterOp); TF_CALL_float(REGISTER_MKL_FILTER_KERNELS); #undef REGISTER_MKL_FILTER_KERNELS #else template -class MklConv2DCustomBackpropFilterOp : - public MklConv2DBackpropCommonOp { +class MklConv2DCustomBackpropFilterOp + : public MklConv2DBackpropCommonOp { public: explicit MklConv2DCustomBackpropFilterOp(OpKernelConstruction* context) - : MklConv2DBackpropCommonOp(context) { } + : MklConv2DBackpropCommonOp(context) {} ~MklConv2DCustomBackpropFilterOp() {} private: @@ -447,7 +448,7 @@ class MklConv2DCustomBackpropFilterOp : const MklDnnShape& filter_mkl_shape, const MklDnnShape& obp_mkl_shape) { CHECK(!filter_mkl_shape.IsMklTensor()) - << "Conv2DBackpropFilter: filter should not be in MKL Layout"; + << "Conv2DBackpropFilter: filter should not be in MKL Layout"; } size_t GetInputTensorIndexWithSizes() { return 1; /* filter index */ } @@ -462,8 +463,10 @@ class MklConv2DCustomBackpropFilterOp : const Tensor& filter_tensor) { TensorShape filter_tf_shape; CHECK_EQ(TensorShapeUtils::IsVector(filter_tensor.shape()), true); - CHECK_EQ(TensorShapeUtils::MakeShape( - filter_tensor.vec(), &filter_tf_shape).ok(), true); + CHECK_EQ(TensorShapeUtils::MakeShape(filter_tensor.vec(), + &filter_tf_shape) + .ok(), + true); return filter_tf_shape; } @@ -485,16 +488,13 @@ class MklConv2DCustomBackpropFilterOp : return memory::format::hwio; } - void CreatePrimitive(OpKernelContext* context, - const engine& cpu_engine, + void CreatePrimitive(OpKernelContext* context, const engine& cpu_engine, const convolution_forward::primitive_desc& conv_fwd_pd, MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, MklDnnData* output, - Tensor** output_tensor, - const memory::dims& strides, + Tensor** output_tensor, const memory::dims& strides, const memory::dims& padding_l, - const memory::dims& padding_r, - padding_kind padding, + const memory::dims& padding_r, padding_kind padding, const memory::dims& bwd_output_dims, memory::format bwd_output_format) { CHECK_NOTNULL(context); @@ -508,34 +508,35 @@ class MklConv2DCustomBackpropFilterOp : int depth = 0; if (biasEnabled) { // Data structure for bias_grad - bias_grad = new MklDnnData (&cpu_engine); + bias_grad = new MklDnnData(&cpu_engine); TensorShape obp_tf_shape = GetTfShape(context, 2); - depth = (MklConv2DBackpropCommonOp::GetTFDataFormat() - == FORMAT_NCHW) ? - obp_tf_shape.dim_size(1) : obp_tf_shape.dim_size(3); + depth = (MklConv2DBackpropCommonOp::GetTFDataFormat() == + FORMAT_NCHW) + ? obp_tf_shape.dim_size(1) + : obp_tf_shape.dim_size(3); memory::dims bias_grad_dims = {depth}; bias_grad->SetOpMemDesc(bias_grad_dims, memory::format::x); } // Create convolution backward weights primitive. - auto bwd_desc = (biasEnabled && (bias_grad != nullptr))? - convolution_backward_weights::desc(convolution_direct, - input->GetOpMemDesc(), output->GetOpMemDesc(), - bias_grad->GetOpMemDesc(), - outbackprop->GetOpMemDesc(), strides, padding_l, - padding_r, padding) : - convolution_backward_weights::desc(convolution_direct, - input->GetOpMemDesc(), output->GetOpMemDesc(), - outbackprop->GetOpMemDesc(), strides, padding_l, - padding_r, padding); - - auto bwd_pd = convolution_backward_weights::primitive_desc(bwd_desc, - cpu_engine, - conv_fwd_pd); + auto bwd_desc = + (biasEnabled && (bias_grad != nullptr)) + ? convolution_backward_weights::desc( + convolution_direct, input->GetOpMemDesc(), + output->GetOpMemDesc(), bias_grad->GetOpMemDesc(), + outbackprop->GetOpMemDesc(), strides, padding_l, padding_r, + padding) + : convolution_backward_weights::desc( + convolution_direct, input->GetOpMemDesc(), + output->GetOpMemDesc(), outbackprop->GetOpMemDesc(), strides, + padding_l, padding_r, padding); + + auto bwd_pd = convolution_backward_weights::primitive_desc( + bwd_desc, cpu_engine, conv_fwd_pd); // Allocate output tensor. - AllocateOutputTensor(context, bwd_pd, bwd_output_dims, - bwd_output_format, output_tensor); + AllocateOutputTensor(context, bwd_pd, bwd_output_dims, bwd_output_format, + output_tensor); CHECK_NOTNULL(*output_tensor); // Set buffer handle using allocated output tensor. @@ -548,8 +549,8 @@ class MklConv2DCustomBackpropFilterOp : AllocateBiasGradTensor(context, bias_grad_shape, &bias_grad_tensor); memory::dims bias_grad_dims = {depth}; // Since Bias is 1D, we use format::x from MKLDNN to represent it. - auto bias_grad_md = memory::desc({bias_grad_dims}, MklDnnType(), - memory::format::x); + auto bias_grad_md = + memory::desc({bias_grad_dims}, MklDnnType(), memory::format::x); bias_grad->SetUsrMem(bias_grad_md, bias_grad_tensor); bias_grad->SetUsrMemDataHandle(bias_grad_tensor); } @@ -562,28 +563,29 @@ class MklConv2DCustomBackpropFilterOp : } // Allocate output tensor. - void AllocateOutputTensor(OpKernelContext* context, - const convolution_backward_weights::primitive_desc& conv_pd, - const memory::dims& output_dims_mkl_order, - memory::format output_tf_format, Tensor** output_tensor) { - CHECK_NOTNULL(output_tensor); - - // For BackpropFilter, we convert the output tensor back in Tensorflow - // layout. Because typically, BackpropFilter is the last operator in the - // graph that emit filter gradient that is provided to ApplyGradient - // method to update the filter. But it may be possible to eliminate this - // by forwarding filter in MKL layout if we support ApplyGradient method - // for MKL layout propagation. - MklDnnShape output_mkl_shape; - output_mkl_shape.SetMklTensor(false); - // output_dims_mkl_order is in OIHW format. - // Allocate shape of TF tensor in HWIO format. - TensorShape output_tf_shape({output_dims_mkl_order[MklDnnDims::Dim_H], - output_dims_mkl_order[MklDnnDims::Dim_W], - output_dims_mkl_order[MklDnnDims::Dim_I], - output_dims_mkl_order[MklDnnDims::Dim_O]}); - AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, - output_mkl_shape); + void AllocateOutputTensor( + OpKernelContext* context, + const convolution_backward_weights::primitive_desc& conv_pd, + const memory::dims& output_dims_mkl_order, + memory::format output_tf_format, Tensor** output_tensor) { + CHECK_NOTNULL(output_tensor); + + // For BackpropFilter, we convert the output tensor back in Tensorflow + // layout. Because typically, BackpropFilter is the last operator in the + // graph that emit filter gradient that is provided to ApplyGradient + // method to update the filter. But it may be possible to eliminate this + // by forwarding filter in MKL layout if we support ApplyGradient method + // for MKL layout propagation. + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(false); + // output_dims_mkl_order is in OIHW format. + // Allocate shape of TF tensor in HWIO format. + TensorShape output_tf_shape({output_dims_mkl_order[MklDnnDims::Dim_H], + output_dims_mkl_order[MklDnnDims::Dim_W], + output_dims_mkl_order[MklDnnDims::Dim_I], + output_dims_mkl_order[MklDnnDims::Dim_O]}); + AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, + output_mkl_shape); } // Allocate tensor for bias grad @@ -600,9 +602,9 @@ class MklConv2DCustomBackpropFilterOp : // Prepare and execute net - checks for input and output reorders. void PrepareAndExecutePrimitive( - const convolution_backward_weights::primitive_desc& conv_pd, - MklDnnData* input, MklDnnData* obp, - MklDnnData* output, MklDnnData* bias_grad = nullptr) { + const convolution_backward_weights::primitive_desc& conv_pd, + MklDnnData* input, MklDnnData* obp, MklDnnData* output, + MklDnnData* bias_grad = nullptr) { // Create reorders between user layout and MKL layout if it is needed and // add it to the net before convolution. std::vector net; @@ -612,15 +614,15 @@ class MklConv2DCustomBackpropFilterOp : // For BackpropFilter, we convert the output tensor back in Tensorflow // layout. bool output_reorder_required = output->PrepareReorderToUserMemIfReq( - conv_pd.diff_weights_primitive_desc()); + conv_pd.diff_weights_primitive_desc()); if (biasEnabled && (bias_grad != nullptr)) { - net.push_back(convolution_backward_weights(conv_pd, input->GetOpMem(), - obp->GetOpMem(), output->GetOpMem(), - bias_grad->GetOpMem())); + net.push_back(convolution_backward_weights( + conv_pd, input->GetOpMem(), obp->GetOpMem(), output->GetOpMem(), + bias_grad->GetOpMem())); } else { - net.push_back(convolution_backward_weights(conv_pd, input->GetOpMem(), - obp->GetOpMem(), output->GetOpMem())); + net.push_back(convolution_backward_weights( + conv_pd, input->GetOpMem(), obp->GetOpMem(), output->GetOpMem())); } if (output_reorder_required) { @@ -631,22 +633,24 @@ class MklConv2DCustomBackpropFilterOp : } }; -#define REGISTER_MKL_FILTER_KERNELS(T) \ - REGISTER_KERNEL_BUILDER(Name("_MklConv2DBackpropFilter") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ - MklConv2DCustomBackpropFilterOp);\ - REGISTER_KERNEL_BUILDER(Name("_MklConv2DBackpropFilterWithBias") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ - MklConv2DCustomBackpropFilterOp); \ - REGISTER_KERNEL_BUILDER(Name("__MklDummyConv2DBackpropFilterWithBias") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ - MklDummyOp); +#define REGISTER_MKL_FILTER_KERNELS(T) \ + REGISTER_KERNEL_BUILDER( \ + Name("_MklConv2DBackpropFilter") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklConv2DCustomBackpropFilterOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("_MklConv2DBackpropFilterWithBias") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklConv2DCustomBackpropFilterOp); \ + REGISTER_KERNEL_BUILDER(Name("__MklDummyConv2DBackpropFilterWithBias") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklDummyOp); TF_CALL_float(REGISTER_MKL_FILTER_KERNELS); #undef REGISTER_MKL_FILTER_KERNELS diff --git a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc index ef6db58d31..a6745489f4 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc @@ -23,6 +23,8 @@ limitations under the License. #define EIGEN_USE_THREADS #include #include +#include "mkl_dnn.h" +#include "mkl_dnn_types.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -41,15 +43,13 @@ limitations under the License. #include "tensorflow/core/util/tensor_format.h" #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#include "mkl_dnn.h" -#include "mkl_dnn_types.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; -using mkldnn::prop_kind; using mkldnn::convolution_backward_data; +using mkldnn::prop_kind; +using mkldnn::stream; #endif namespace tensorflow { @@ -359,16 +359,15 @@ class MklConv2DCustomBackpropInputOp : public OpKernel { #else template -class MklConv2DCustomBackpropInputOp : - public MklConv2DBackpropCommonOp { +class MklConv2DCustomBackpropInputOp + : public MklConv2DBackpropCommonOp { public: explicit MklConv2DCustomBackpropInputOp(OpKernelConstruction* context) - : MklConv2DBackpropCommonOp(context) { } + : MklConv2DBackpropCommonOp(context) {} ~MklConv2DCustomBackpropInputOp() {} private: - const int kInputIndex_Filter = 1, - kInputIndex_InputSizes = 0, + const int kInputIndex_Filter = 1, kInputIndex_InputSizes = 0, kInputIndex_OutBackProp = 2; void ValidateMklShapes(const MklDnnShape& input_mkl_shape, const MklDnnShape& filter_mkl_shape, @@ -377,7 +376,7 @@ class MklConv2DCustomBackpropInputOp : // of the Tensor and never an actual tensor. So it will never be in MKL // layout. CHECK(!input_mkl_shape.IsMklTensor()) - << "Conv2DBackpropInput: input should not be in MKL Layout"; + << "Conv2DBackpropInput: input should not be in MKL Layout"; } size_t GetInputTensorIndexWithSizes() { return kInputIndex_InputSizes; } @@ -386,8 +385,10 @@ class MklConv2DCustomBackpropInputOp : const Tensor& input_tensor) { TensorShape input_tf_shape; CHECK_EQ(TensorShapeUtils::IsVector(input_tensor.shape()), true); - CHECK_EQ(TensorShapeUtils::MakeShape(input_tensor.vec(), - &input_tf_shape).ok(), true); + CHECK_EQ( + TensorShapeUtils::MakeShape(input_tensor.vec(), &input_tf_shape) + .ok(), + true); return input_tf_shape; } @@ -414,16 +415,13 @@ class MklConv2DCustomBackpropInputOp : return data_format; } - void CreatePrimitive(OpKernelContext* context, - const engine& cpu_engine, + void CreatePrimitive(OpKernelContext* context, const engine& cpu_engine, const convolution_forward::primitive_desc& conv_fwd_pd, MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, MklDnnData* output, - Tensor** output_tensor, - const memory::dims& strides, + Tensor** output_tensor, const memory::dims& strides, const memory::dims& padding_l, - const memory::dims& padding_r, - padding_kind padding, + const memory::dims& padding_r, padding_kind padding, const memory::dims& bwd_output_dims, memory::format bwd_output_format) { CHECK_NOTNULL(context); @@ -434,19 +432,16 @@ class MklConv2DCustomBackpropInputOp : CHECK_NOTNULL(output_tensor); // Create convolution backward data primitive. - auto bwd_desc = convolution_backward_data::desc(convolution_direct, - output->GetOpMemDesc(), filter->GetOpMemDesc(), - outbackprop->GetOpMemDesc(), strides, padding_l, - padding_r, padding); - - auto bwd_pd = convolution_backward_data::primitive_desc(bwd_desc, - cpu_engine, - conv_fwd_pd); + auto bwd_desc = convolution_backward_data::desc( + convolution_direct, output->GetOpMemDesc(), filter->GetOpMemDesc(), + outbackprop->GetOpMemDesc(), strides, padding_l, padding_r, padding); + auto bwd_pd = convolution_backward_data::primitive_desc( + bwd_desc, cpu_engine, conv_fwd_pd); // Allocate output tensor in TensorFlow and MKL layout. - AllocateOutputTensor(context, bwd_pd, bwd_output_dims, - bwd_output_format, output_tensor); + AllocateOutputTensor(context, bwd_pd, bwd_output_dims, bwd_output_format, + output_tensor); CHECK_NOTNULL(*output_tensor); // Set buffer handle using allocated output tensor. output->SetUsrMemDataHandle(*output_tensor); @@ -455,44 +450,44 @@ class MklConv2DCustomBackpropInputOp : } // Allocate output tensor. - void AllocateOutputTensor(OpKernelContext* context, - const convolution_backward_data::primitive_desc& conv_pd, - const memory::dims& output_dims_mkl_order, - memory::format output_tf_format, Tensor** output_tensor) { - CHECK_NOTNULL(output_tensor); - - // Output primitive descriptor for backward data is diff_src. - auto dst_pd = conv_pd.diff_src_primitive_desc(); - - // Allocate shape of Mkl tensor. - MklDnnShape output_mkl_shape; - output_mkl_shape.SetMklTensor(true); - output_mkl_shape.SetMklLayout(&dst_pd); - output_mkl_shape.SetElemType(MklDnnType()); - output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, output_tf_format); - - // Allocate shape of TF tensor. - TensorShape output_tf_shape; - output_tf_shape.AddDim(dst_pd.get_size() / sizeof(T)); - - AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, - output_mkl_shape); + void AllocateOutputTensor( + OpKernelContext* context, + const convolution_backward_data::primitive_desc& conv_pd, + const memory::dims& output_dims_mkl_order, + memory::format output_tf_format, Tensor** output_tensor) { + CHECK_NOTNULL(output_tensor); + + // Output primitive descriptor for backward data is diff_src. + auto dst_pd = conv_pd.diff_src_primitive_desc(); + + // Allocate shape of Mkl tensor. + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(true); + output_mkl_shape.SetMklLayout(&dst_pd); + output_mkl_shape.SetElemType(MklDnnType()); + output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), + output_dims_mkl_order, output_tf_format); + + // Allocate shape of TF tensor. + TensorShape output_tf_shape; + output_tf_shape.AddDim(dst_pd.get_size() / sizeof(T)); + + AllocateOutputSetMklShape(context, 0, output_tensor, output_tf_shape, + output_mkl_shape); } // Prepare and execute net - checks for input and output reorders. void PrepareAndExecutePrimitive( - const convolution_backward_data::primitive_desc& conv_pd, - MklDnnData* filter, MklDnnData* obp, - MklDnnData* output) { + const convolution_backward_data::primitive_desc& conv_pd, + MklDnnData* filter, MklDnnData* obp, MklDnnData* output) { // Create reorders between user layout and MKL layout if it is needed and // add it to the net before convolution. std::vector net; filter->CheckReorderToOpMem(conv_pd.weights_primitive_desc(), &net); obp->CheckReorderToOpMem(conv_pd.diff_dst_primitive_desc(), &net); - net.push_back(convolution_backward_data(conv_pd, obp->GetOpMem(), - filter->GetOpMem(), output->GetOpMem())); + net.push_back(convolution_backward_data( + conv_pd, obp->GetOpMem(), filter->GetOpMem(), output->GetOpMem())); stream(stream::kind::eager).submit(net).wait(); } diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index 0e77b45993..e44fba754b 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -18,8 +18,8 @@ limitations under the License. #include #include -#include #include +#include #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -41,15 +41,14 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" - #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; using mkldnn::prop_kind; +using mkldnn::stream; -using mkldnn::convolution_forward; using mkldnn::convolution_direct; +using mkldnn::convolution_forward; #else #include "mkl_dnn.h" #include "mkl_dnn_types.h" @@ -116,18 +115,19 @@ class MklConv2DOp : public OpKernel { filter.shape().DebugString())); for (int i = 0; i < 3; i++) { - OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES( + context, + FastBoundsCheck(filter.dim_size(i), std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } const int64 input_depth = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'C') : GetTensorDim(input, data_format_, 'C'); - OP_REQUIRES( - context, input_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - input_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, input_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", input_depth, + " vs ", filter.dim_size(2))); // The last dimension for filter is out_depth. const int out_depth = static_cast(filter.dim_size(3)); @@ -136,9 +136,10 @@ class MklConv2DOp : public OpKernel { const int64 input_rows_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'H') : GetTensorDim(input, data_format_, 'H'); - OP_REQUIRES(context, FastBoundsCheck(input_rows_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input rows too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_rows_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input rows too large")); const int input_rows = static_cast(input_rows_raw); const int filter_rows = static_cast(filter.dim_size(0)); @@ -147,9 +148,10 @@ class MklConv2DOp : public OpKernel { const int64 input_cols_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'W') : GetTensorDim(input, data_format_, 'W'); - OP_REQUIRES(context, FastBoundsCheck(input_cols_raw, - std::numeric_limits::max()), - errors::InvalidArgument("Input cols too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_cols_raw, std::numeric_limits::max()), + errors::InvalidArgument("Input cols too large")); const int input_cols = static_cast(input_cols_raw); const int filter_cols = static_cast(filter.dim_size(1)); @@ -157,9 +159,10 @@ class MklConv2DOp : public OpKernel { const int64 input_batch_raw = input_in_mkl_format ? GetMklTensorDim(mkl_context.input_shape, 'N') : GetTensorDim(input, data_format_, 'N'); - OP_REQUIRES(context, FastBoundsCheck(input_batch_raw, - std::numeric_limits::max()), - errors::InvalidArgument("batch is too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input_batch_raw, std::numeric_limits::max()), + errors::InvalidArgument("batch is too large")); const int batch = static_cast(input_batch_raw); // For now we take the stride from the second and third dimensions only (we @@ -313,8 +316,7 @@ class MklConv2DOp : public OpKernel { // Temp tensor used to allocate tmp buffers Tensor mkl_tmp_input_buf_tensor, mkl_tmp_filter_buf_tensor, mkl_tmp_bias_buf_tensor; - mkl_context.MklPrepareConvolutionInputs(context, - &mkl_tmp_input_buf_tensor, + mkl_context.MklPrepareConvolutionInputs(context, &mkl_tmp_input_buf_tensor, &mkl_tmp_filter_buf_tensor, &mkl_tmp_bias_buf_tensor); @@ -398,8 +400,9 @@ class MklConv2DOp : public OpKernel { mkl_convert_input = !dnnLayoutCompare_F32(mkl_lt_internal_input, lt_input); if (mkl_convert_input) { - CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, - lt_input, mkl_lt_internal_input), E_SUCCESS); + CHECK_EQ(dnnConversionCreate_F32(&mkl_prim_convert_input, lt_input, + mkl_lt_internal_input), + E_SUCCESS); AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, mkl_lt_internal_input, &mkl_buf_convert_input); CHECK_EQ(dnnConversionExecute_F32(mkl_prim_convert_input, mkl_buf_input, @@ -517,8 +520,8 @@ class MklConv2DOp : public OpKernel { GetMklShape(context, kInputIndex_Src, &src_mkl_shape); GetMklShape(context, kInputIndex_Filter, &filter_mkl_shape); OP_REQUIRES(context, filter_mkl_shape.IsMklTensor() == false, - errors::InvalidArgument("Filter should not be in " - "Mkl Layout")); + errors::InvalidArgument("Filter should not be in " + "Mkl Layout")); MklDnnData src(&cpu_engine); MklDnnData filter(&cpu_engine); @@ -531,11 +534,10 @@ class MklConv2DOp : public OpKernel { MklDnnConvUtil conv_utl(context, strides_, padding_, data_format_); auto src_tf_shape = GetTfShape(context, kInputIndex_Src); auto filter_tf_shape = GetTfShape(context, kInputIndex_Filter); - conv_utl.GetConvFwdSizesInMklOrder(src_tf_shape, filter_tf_shape, - &src_dims, &filter_dims, &strides, - &output_dims_tf_order, - &output_dims_mkl_order, &padding_l, - &padding_r); + conv_utl.GetConvFwdSizesInMklOrder( + src_tf_shape, filter_tf_shape, &src_dims, &filter_dims, &strides, + &output_dims_tf_order, &output_dims_mkl_order, &padding_l, + &padding_r); if (!context->status().ok()) return; // Check for corner case - if there is nothing to compute, return. @@ -543,21 +545,20 @@ class MklConv2DOp : public OpKernel { // Corner cases: output with 0 elements and 0 batch size. Tensor* output_tensor = nullptr; - if (output_tf_shape.num_elements() == 0 || - output_dims_tf_order[0] == 0) { + if (output_tf_shape.num_elements() == 0 || output_dims_tf_order[0] == 0) { // TODO(jbobba): Verify correctness here // Need semantics for Null MKL tensor MklDnnShape output_mkl_shape; output_mkl_shape.SetMklTensor(false); AllocateOutputSetMklShape(context, kOutputIndex_Dst, &output_tensor, - src_tf_shape, output_mkl_shape); + src_tf_shape, output_mkl_shape); // MklConv2D also outputs converted filter as 2nd output of Conv2D. filter_mkl_shape.SetMklTensor(false); Tensor* output_filter_tensor = nullptr; AllocateOutputSetMklShape(context, kOutputIndex_Filter, - &output_filter_tensor, - filter_tf_shape, filter_mkl_shape); + &output_filter_tensor, filter_tf_shape, + filter_mkl_shape); return; } @@ -570,14 +571,15 @@ class MklConv2DOp : public OpKernel { // (src_dims) required is in MKL-DNN order, the layout is Tensorflow's // layout (NHWC or NCHW depending on data format). auto src_md = src_mkl_shape.IsMklTensor() - ? src_mkl_shape.GetMklLayout() - : memory::desc(src_dims, MklDnnType(), tf_fmt); + ? src_mkl_shape.GetMklLayout() + : memory::desc(src_dims, MklDnnType(), tf_fmt); src.SetUsrMem(src_md, &src_tensor); // Although filter shape (filter_dims) required is in MKL-DNN order, // the layout is Tensorflow's layout (HWIO). auto filter_md = filter_mkl_shape.IsMklTensor() // Should NEVER be true - ? filter_mkl_shape.GetMklLayout() - : memory::desc(filter_dims, MklDnnType(), memory::format::hwio); + ? filter_mkl_shape.GetMklLayout() + : memory::desc(filter_dims, MklDnnType(), + memory::format::hwio); filter.SetUsrMem(filter_md, &filter_tensor); // Set output shape (output_dims) required in MKL-DNN order. @@ -601,34 +603,34 @@ class MklConv2DOp : public OpKernel { bias.SetOpMemDesc(bias_size, memory::format::any); // Create convolution primitive with Bias. - auto conv_desc = convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), filter.GetOpMemDesc(), - bias.GetOpMemDesc(), output.GetOpMemDesc(), strides, - padding_l, padding_r, TFPaddingToMklDnnPadding(padding_)); - - auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, - cpu_engine); - AllocateOutputTensor(context, conv_prim_desc, - output_dims_mkl_order, tf_fmt, &output_tensor); + auto conv_desc = convolution_forward::desc( + prop_kind::forward, convolution_direct, src.GetOpMemDesc(), + filter.GetOpMemDesc(), bias.GetOpMemDesc(), output.GetOpMemDesc(), + strides, padding_l, padding_r, TFPaddingToMklDnnPadding(padding_)); + + auto conv_prim_desc = + convolution_forward::primitive_desc(conv_desc, cpu_engine); + AllocateOutputTensor(context, conv_prim_desc, output_dims_mkl_order, + tf_fmt, &output_tensor); // Set data handle for output. output.SetUsrMemDataHandle(output_tensor); Tensor* filter_out_tensor = nullptr; AllocateFilterOutputTensor(context, conv_prim_desc, - TFShapeToMklDnnDims(filter_tf_shape), - &filter_out_tensor); + TFShapeToMklDnnDims(filter_tf_shape), + &filter_out_tensor); - PrepareAndExecuteNet(conv_prim_desc, &src, &filter, - &bias, &output, filter_out_tensor); + PrepareAndExecuteNet(conv_prim_desc, &src, &filter, &bias, &output, + filter_out_tensor); } else { // Create convolution primitive without Bias. - auto conv_desc = convolution_forward::desc(prop_kind::forward, - convolution_direct, src.GetOpMemDesc(), filter.GetOpMemDesc(), - output.GetOpMemDesc(), strides, padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_)); + auto conv_desc = convolution_forward::desc( + prop_kind::forward, convolution_direct, src.GetOpMemDesc(), + filter.GetOpMemDesc(), output.GetOpMemDesc(), strides, padding_l, + padding_r, TFPaddingToMklDnnPadding(padding_)); - auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, - cpu_engine); + auto conv_prim_desc = + convolution_forward::primitive_desc(conv_desc, cpu_engine); AllocateOutputTensor(context, conv_prim_desc, output_dims_mkl_order, tf_fmt, &output_tensor); // Set data handle for output. @@ -636,18 +638,18 @@ class MklConv2DOp : public OpKernel { Tensor* filter_out_tensor = nullptr; AllocateFilterOutputTensor(context, conv_prim_desc, - TFShapeToMklDnnDims(filter_tf_shape), - &filter_out_tensor); - PrepareAndExecuteNet(conv_prim_desc, &src, &filter, - nullptr, &output, filter_out_tensor); + TFShapeToMklDnnDims(filter_tf_shape), + &filter_out_tensor); + PrepareAndExecuteNet(conv_prim_desc, &src, &filter, nullptr, &output, + filter_out_tensor); } - } catch (mkldnn::error &e) { + } 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__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", error_msg)); + ", message: " + std::string(e.message) + ", in file " + + std::string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -655,71 +657,67 @@ class MklConv2DOp : public OpKernel { std::vector strides_; Padding padding_; TensorFormat data_format_; - const int kInputIndex_Src = 0, - kInputIndex_Filter = 1, - kInputIndex_Bias = 2; + const int kInputIndex_Src = 0, kInputIndex_Filter = 1, kInputIndex_Bias = 2; const int kOutputIndex_Dst = 0, kOutputIndex_Filter = 1; // Allocate output tensor. void AllocateOutputTensor( - OpKernelContext* context, - const convolution_forward::primitive_desc& conv_prim_desc, - const memory::dims& output_dims_mkl_order, - memory::format output_tf_format, Tensor** output_tensor) { - CHECK_NOTNULL(output_tensor); - auto dst_pd = conv_prim_desc.dst_primitive_desc(); - - // Allocate shape of Mkl tensor. - MklDnnShape output_mkl_shape; - output_mkl_shape.SetMklTensor(true); - output_mkl_shape.SetMklLayout(&dst_pd); - output_mkl_shape.SetElemType(MklDnnType()); - output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, output_tf_format); - - // Allocate shape of TF tensor. - TensorShape output_tf_shape; - output_tf_shape.AddDim((dst_pd.get_size() / sizeof(T))); - - AllocateOutputSetMklShape(context, kOutputIndex_Dst, output_tensor, - output_tf_shape, output_mkl_shape); + OpKernelContext* context, + const convolution_forward::primitive_desc& conv_prim_desc, + const memory::dims& output_dims_mkl_order, + memory::format output_tf_format, Tensor** output_tensor) { + CHECK_NOTNULL(output_tensor); + auto dst_pd = conv_prim_desc.dst_primitive_desc(); + + // Allocate shape of Mkl tensor. + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(true); + output_mkl_shape.SetMklLayout(&dst_pd); + output_mkl_shape.SetElemType(MklDnnType()); + output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), + output_dims_mkl_order, output_tf_format); + + // Allocate shape of TF tensor. + TensorShape output_tf_shape; + output_tf_shape.AddDim((dst_pd.get_size() / sizeof(T))); + + AllocateOutputSetMklShape(context, kOutputIndex_Dst, output_tensor, + output_tf_shape, output_mkl_shape); } // Allocate output tensor. void AllocateFilterOutputTensor( - OpKernelContext* context, - const convolution_forward::primitive_desc& conv_prim_desc, - const memory::dims& filter_dims_tf_order, - Tensor** filter_tensor) { - CHECK_NOTNULL(filter_tensor); - auto filter_pd = conv_prim_desc.weights_primitive_desc(); - - // Allocate shape of Mkl tensor. - MklDnnShape filter_mkl_shape; - filter_mkl_shape.SetMklTensor(true); - filter_mkl_shape.SetMklLayout(&filter_pd); - filter_mkl_shape.SetElemType(MklDnnType()); - - // The format of the filter is actually OIhw8i8o, but TF doesn't support - // this format. Just use format::blocked for now because the layout - // is stored in the MKL data. - filter_mkl_shape.SetTfLayout(filter_dims_tf_order.size(), - filter_dims_tf_order, memory::format::blocked); - - // Allocate the data space for the filter to propagate as TF tensor. - TensorShape filter_tf_shape; - filter_tf_shape.AddDim((filter_pd.get_size() / sizeof(T))); - - AllocateOutputSetMklShape(context, kOutputIndex_Filter, filter_tensor, - filter_tf_shape, filter_mkl_shape); + OpKernelContext* context, + const convolution_forward::primitive_desc& conv_prim_desc, + const memory::dims& filter_dims_tf_order, Tensor** filter_tensor) { + CHECK_NOTNULL(filter_tensor); + auto filter_pd = conv_prim_desc.weights_primitive_desc(); + + // Allocate shape of Mkl tensor. + MklDnnShape filter_mkl_shape; + filter_mkl_shape.SetMklTensor(true); + filter_mkl_shape.SetMklLayout(&filter_pd); + filter_mkl_shape.SetElemType(MklDnnType()); + + // The format of the filter is actually OIhw8i8o, but TF doesn't support + // this format. Just use format::blocked for now because the layout + // is stored in the MKL data. + filter_mkl_shape.SetTfLayout(filter_dims_tf_order.size(), + filter_dims_tf_order, memory::format::blocked); + + // Allocate the data space for the filter to propagate as TF tensor. + TensorShape filter_tf_shape; + filter_tf_shape.AddDim((filter_pd.get_size() / sizeof(T))); + + AllocateOutputSetMklShape(context, kOutputIndex_Filter, filter_tensor, + filter_tf_shape, filter_mkl_shape); } // Prepare and execute net - checks for input and output reorders. void PrepareAndExecuteNet( - const convolution_forward::primitive_desc& conv_prim_desc, - MklDnnData* src, MklDnnData* filter, - MklDnnData* bias, MklDnnData* output, - Tensor* filter_out_tensor) { + const convolution_forward::primitive_desc& conv_prim_desc, + MklDnnData* src, MklDnnData* filter, MklDnnData* bias, + MklDnnData* output, Tensor* filter_out_tensor) { CHECK_NOTNULL(filter_out_tensor); // Create reorders between user layout and MKL layout if it is needed and @@ -731,18 +729,20 @@ class MklConv2DOp : public OpKernel { // rather than re-order to a temp buffer, reorder directly to the // filter output tensor filter->CheckReorderToOpMem(conv_prim_desc.weights_primitive_desc(), - filter->GetTensorBuffer(filter_out_tensor), &net); + filter->GetTensorBuffer(filter_out_tensor), + &net); // Create convolution primitive and add it to net. if (bias) { CHECK_EQ(biasEnabled, true); net.push_back(convolution_forward(conv_prim_desc, src->GetOpMem(), - filter->GetOpMem(), bias->GetOpMem(), - output->GetOpMem())); + filter->GetOpMem(), bias->GetOpMem(), + output->GetOpMem())); } else { CHECK_EQ(biasEnabled, false); net.push_back(convolution_forward(conv_prim_desc, src->GetOpMem(), - filter->GetOpMem(), output->GetOpMem())); + filter->GetOpMem(), + output->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); diff --git a/tensorflow/core/kernels/mkl_conv_ops.h b/tensorflow/core/kernels/mkl_conv_ops.h index c6456bd5c3..8b65eaea0d 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.h +++ b/tensorflow/core/kernels/mkl_conv_ops.h @@ -16,9 +16,9 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_MKL_CONV_OPS_H_ #define TENSORFLOW_CORE_KERNELS_MKL_CONV_OPS_H_ -#include #include #include +#include #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" @@ -27,8 +27,8 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/tensor_slice.h" #include "tensorflow/core/kernels/bounds_check.h" -#include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/kernels/conv_grad_ops.h" +#include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/strings/numbers.h" @@ -43,11 +43,11 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; using mkldnn::prop_kind; +using mkldnn::stream; -using mkldnn::convolution_forward; using mkldnn::convolution_direct; +using mkldnn::convolution_forward; #endif namespace tensorflow { @@ -63,13 +63,13 @@ class MklDnnConvUtil { public: MklDnnConvUtil(OpKernelContext* context, const std::vector& strides, - Padding pad, TensorFormat fm) : context_(context), - strides_(strides), padding_(pad), data_format_(fm) {} + Padding pad, TensorFormat fm) + : context_(context), strides_(strides), padding_(pad), data_format_(fm) {} virtual ~MklDnnConvUtil() { context_ = nullptr; } // Calculate Convolution strides - virtual inline void GetStridesInMklOrder(memory::dims *strides) { + virtual inline void GetStridesInMklOrder(memory::dims* strides) { // For now we take the stride from the second and third dimensions only // (we do not support striding on the batch or depth dimension). CHECK_NOTNULL(strides); @@ -82,14 +82,14 @@ class MklDnnConvUtil { // requires input in NCHW format. Function does not return anything. // But errors arising from sanity checks are returned in context's // status. - virtual inline void - GetInputSizeInMklOrder(const TensorShape& input_shape, - memory::dims *input_dims) { - #define CHECK_BOUNDS(val, err_msg) do { \ - OP_REQUIRES(context_, FastBoundsCheck(val, \ - std::numeric_limits::max()), \ - errors::InvalidArgument(err_msg)); \ - }while(0) + virtual inline void GetInputSizeInMklOrder(const TensorShape& input_shape, + memory::dims* input_dims) { +#define CHECK_BOUNDS(val, err_msg) \ + do { \ + OP_REQUIRES(context_, \ + FastBoundsCheck(val, std::numeric_limits::max()), \ + errors::InvalidArgument(err_msg)); \ + } while (0) CHECK_NOTNULL(input_dims); @@ -112,7 +112,7 @@ class MklDnnConvUtil { CHECK_BOUNDS(input_batch_raw, "Input batch too large"); int input_batch = static_cast(input_batch_raw); - #undef CHECK_BOUNDS +#undef CHECK_BOUNDS // MKL-DNN always requires input in NCHW format. std::vector mkldnn_sizes(4, -1); @@ -138,10 +138,9 @@ class MklDnnConvUtil { // forward gets actual tensor as input). // // TODO(nhasabni): Add similar function for input and filter in MklShape. - virtual inline void - GetFilterSizeInMklOrder(const TensorShape& input_shape, - const TensorShape& filter_shape, - memory::dims *filter_dims) { + virtual inline void GetFilterSizeInMklOrder(const TensorShape& input_shape, + const TensorShape& filter_shape, + memory::dims* filter_dims) { CHECK_NOTNULL(filter_dims); OP_REQUIRES(context_, filter_shape.dims() == 4, @@ -149,17 +148,18 @@ class MklDnnConvUtil { filter_shape.DebugString())); for (int i = 0; i < 3; i++) { - OP_REQUIRES(context_, FastBoundsCheck(filter_shape.dim_size(i), - std::numeric_limits::max()), - errors::InvalidArgument("filter too large")); + OP_REQUIRES(context_, + FastBoundsCheck(filter_shape.dim_size(i), + std::numeric_limits::max()), + errors::InvalidArgument("filter too large")); } int input_depth = GetTensorDim(input_shape, data_format_, 'C'); - OP_REQUIRES( - context_, input_depth == filter_shape.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - input_depth, " vs ", filter_shape.dim_size(2))); + OP_REQUIRES(context_, input_depth == filter_shape.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", input_depth, + " vs ", filter_shape.dim_size(2))); // TF filter is always in (rows, cols, in_depth, out_depth) order. int filter_rows = static_cast(filter_shape.dim_size(0)); @@ -182,25 +182,24 @@ class MklDnnConvUtil { // requires filter in OIHW format. Function does not return anything. // But errors arising from sanity checks are returned in context's // status. - virtual inline void - GetFilterSizeInMklOrder(size_t src_index, size_t filter_index, - memory::dims *filter_dims) { + virtual inline void GetFilterSizeInMklOrder(size_t src_index, + size_t filter_index, + memory::dims* filter_dims) { CHECK_NOTNULL(filter_dims); GetFilterSizeInMklOrder(GetTfShape(context_, src_index), - GetTfShape(context_, filter_index), - filter_dims); + GetTfShape(context_, filter_index), filter_dims); } // Calculate Bias size for 2D Convolution. Function does not return // anything, but sets error in context status. - virtual inline void - GetBiasSizeInMklOrder(size_t bias_index, memory::dims *bias_dims) { + virtual inline void GetBiasSizeInMklOrder(size_t bias_index, + memory::dims* bias_dims) { const Tensor& bias = MklGetInput(context_, bias_index); OP_REQUIRES(context_, bias.dims() == 1, errors::InvalidArgument("bias must be 1-dimensional: ", bias.shape().DebugString())); - *bias_dims = { static_cast(bias.dim_size(0)) }; + *bias_dims = {static_cast(bias.dim_size(0))}; } // Function to calculate output and padding size for 2D convolution. @@ -212,13 +211,11 @@ class MklDnnConvUtil { // status is returned via context status. // // TODO(nhasabni): Add similar function for input and filter in MklShape. - virtual inline void - GetOutputAndPadSizeInMklOrder(const TensorShape& input_shape, - const TensorShape& filter_shape, - const memory::dims& strides, - memory::dims *output_dims_tf_order, - memory::dims *output_dims_mkl_order, - memory::dims *pad_l, memory::dims *pad_r) { + virtual inline void GetOutputAndPadSizeInMklOrder( + const TensorShape& input_shape, const TensorShape& filter_shape, + const memory::dims& strides, memory::dims* output_dims_tf_order, + memory::dims* output_dims_mkl_order, memory::dims* pad_l, + memory::dims* pad_r) { CHECK_NOTNULL(output_dims_tf_order); CHECK_NOTNULL(output_dims_mkl_order); CHECK_NOTNULL(pad_l); @@ -244,16 +241,16 @@ class MklDnnConvUtil { int64 out_rows = 0, out_cols = 0; int64 pad_top = 0, pad_bottom = 0, pad_left, pad_right; - OP_REQUIRES_OK(context_, - GetWindowedOutputSizeVerbose(input_rows, filter_rows, stride_rows, - padding_, &out_rows, &pad_top, &pad_bottom)); - OP_REQUIRES_OK(context_, - GetWindowedOutputSizeVerbose(input_cols, filter_cols, stride_cols, - padding_, &out_cols, &pad_left, &pad_right)); + OP_REQUIRES_OK(context_, GetWindowedOutputSizeVerbose( + input_rows, filter_rows, stride_rows, padding_, + &out_rows, &pad_top, &pad_bottom)); + OP_REQUIRES_OK(context_, GetWindowedOutputSizeVerbose( + input_cols, filter_cols, stride_cols, padding_, + &out_cols, &pad_left, &pad_right)); // Tensorflow output is in data_format order. (NHWC or NCHW) - TensorShape out_shape = ShapeFromFormat(data_format_, out_batch, - out_rows, out_cols, out_depth); + TensorShape out_shape = + ShapeFromFormat(data_format_, out_batch, out_rows, out_cols, out_depth); *output_dims_tf_order = TFShapeToMklDnnDims(out_shape); // MKL-DNN always needs output in NCHW format. @@ -273,12 +270,10 @@ class MklDnnConvUtil { // See comment on GetConvOutputAndPadSizeInMklOrder for parameters. // // Function does not return anything, but sets error in context status. - inline void - GetOutputAndPadSizeInMklOrder(size_t src_index, size_t filter_index, - const memory::dims& strides, - memory::dims *output_dims_tf_order, - memory::dims *output_dims_mkl_order, - memory::dims *pad_l, memory::dims *pad_r) { + inline void GetOutputAndPadSizeInMklOrder( + size_t src_index, size_t filter_index, const memory::dims& strides, + memory::dims* output_dims_tf_order, memory::dims* output_dims_mkl_order, + memory::dims* pad_l, memory::dims* pad_r) { CHECK_NOTNULL(output_dims_tf_order); CHECK_NOTNULL(output_dims_mkl_order); CHECK_NOTNULL(pad_l); @@ -289,11 +284,11 @@ class MklDnnConvUtil { OP_REQUIRES(context_, input_tf_shape.dims() == 4, errors::InvalidArgument("input must be 4-dimensional", - input_tf_shape.DebugString())); + input_tf_shape.DebugString())); - GetOutputAndPadSizeInMklOrder(input_tf_shape, filter_tf_shape, - strides, output_dims_tf_order, - output_dims_mkl_order, pad_l, pad_r); + GetOutputAndPadSizeInMklOrder(input_tf_shape, filter_tf_shape, strides, + output_dims_tf_order, output_dims_mkl_order, + pad_l, pad_r); } // Wrapper function to calculate input, filter, and output sizes of @@ -302,15 +297,12 @@ class MklDnnConvUtil { // also calculates strides and paddings for 2D Convolution. // // Function does not return anything, but sets error in context status. - inline void GetConvFwdSizesInMklOrder(const TensorShape& input_shape, - const TensorShape& filter_shape, - memory::dims *input_dims, - memory::dims *filter_dims, - memory::dims *strides, - memory::dims *output_dims_tf_order, - memory::dims *output_dims_mkl_order, - memory::dims *pad_l, - memory::dims *pad_r) { + inline void GetConvFwdSizesInMklOrder( + const TensorShape& input_shape, const TensorShape& filter_shape, + memory::dims* input_dims, memory::dims* filter_dims, + memory::dims* strides, memory::dims* output_dims_tf_order, + memory::dims* output_dims_mkl_order, memory::dims* pad_l, + memory::dims* pad_r) { CHECK_NOTNULL(input_dims); CHECK_NOTNULL(filter_dims); CHECK_NOTNULL(strides); @@ -325,8 +317,7 @@ class MklDnnConvUtil { if (!context_->status().ok()) return; GetStridesInMklOrder(strides); GetOutputAndPadSizeInMklOrder(input_shape, filter_shape, *strides, - output_dims_tf_order, - output_dims_mkl_order, + output_dims_tf_order, output_dims_mkl_order, pad_l, pad_r); if (!context_->status().ok()) return; } @@ -337,7 +328,7 @@ class MklDnnConvUtil { ///////////////////////////////////////////////////////////////////// template -class MklConv2DBackpropCommonOp : public OpKernel { +class MklConv2DBackpropCommonOp : public OpKernel { public: ~MklConv2DBackpropCommonOp() {} explicit MklConv2DBackpropCommonOp(OpKernelConstruction* context) @@ -397,12 +388,11 @@ class MklConv2DBackpropCommonOp : public OpKernel { outbprop_tf_shape.num_elements() == 0) { MklDnnShape output_mkl_shape; output_mkl_shape.SetMklTensor(false); - TensorShape output_tf_shape = GetOutputTfShape(input_tf_shape, - filter_tf_shape, - outbprop_tf_shape); + TensorShape output_tf_shape = GetOutputTfShape( + input_tf_shape, filter_tf_shape, outbprop_tf_shape); const int kOutputIdx = 0; AllocateOutputSetMklShape(context, kOutputIdx, &output_tensor, - output_tf_shape, output_mkl_shape); + output_tf_shape, output_mkl_shape); CHECK_NOTNULL(output_tensor); // if output tensor has more than 0 elements, we need to 0 them out. @@ -421,12 +411,10 @@ class MklConv2DBackpropCommonOp : public OpKernel { // Get forward convolution parameters. MklDnnConvUtil conv_utl(context, strides_, padding_, data_format_); - conv_utl.GetConvFwdSizesInMklOrder(input_tf_shape, filter_tf_shape, - &fwd_input_dims, &fwd_filter_dims, - &strides, - &fwd_output_dims_tf_order, - &fwd_output_dims, - &padding_l, &padding_r); + conv_utl.GetConvFwdSizesInMklOrder( + input_tf_shape, filter_tf_shape, &fwd_input_dims, &fwd_filter_dims, + &strides, &fwd_output_dims_tf_order, &fwd_output_dims, &padding_l, + &padding_r); if (!context->status().ok()) return; // Create Convolution forward descriptor since Convolution backward @@ -437,20 +425,22 @@ class MklConv2DBackpropCommonOp : public OpKernel { // construct input TF layout. For TF layout, although input shape // required is in MKL-DNN order, the layout is Tensorflow's layout // (NHWC or NCHW depending on data format). - auto fwd_input_md = input_mkl_shape.IsMklTensor() ? - input_mkl_shape.GetMklLayout() : - memory::desc(fwd_input_dims, MklDnnType(), tf_fmt); + auto fwd_input_md = + input_mkl_shape.IsMklTensor() + ? input_mkl_shape.GetMklLayout() + : memory::desc(fwd_input_dims, MklDnnType(), tf_fmt); // If filter is in MKL layout, then simply grab filter layout; otherwise // construct filter in TF layout. For TF layout, filter is in HWIO format. - auto fwd_filter_md = filter_mkl_shape.IsMklTensor() ? - filter_mkl_shape.GetMklLayout() : - memory::desc(fwd_filter_dims, MklDnnType(), - memory::format::hwio); + auto fwd_filter_md = filter_mkl_shape.IsMklTensor() + ? filter_mkl_shape.GetMklLayout() + : memory::desc(fwd_filter_dims, MklDnnType(), + memory::format::hwio); // Tensorflow Output of Conv2D is in data_format order. auto fwd_out_md = memory::desc(fwd_output_dims, MklDnnType(), tf_fmt); - auto fwd_desc = convolution_forward::desc(prop_kind::forward, - convolution_direct, fwd_input_md, fwd_filter_md, fwd_out_md, - strides, padding_l, padding_r, TFPaddingToMklDnnPadding(padding_)); + auto fwd_desc = convolution_forward::desc( + prop_kind::forward, convolution_direct, fwd_input_md, fwd_filter_md, + fwd_out_md, strides, padding_l, padding_r, + TFPaddingToMklDnnPadding(padding_)); auto fwd_pd = convolution_forward::primitive_desc(fwd_desc, cpu_engine); // Create memory for user data. Describe how the inputs and outputs of @@ -495,17 +485,16 @@ class MklConv2DBackpropCommonOp : public OpKernel { // Operator-specific call to create and execute primitive. CreatePrimitive(context, cpu_engine, fwd_pd, &input, &filter, - &outbackprop, &output, &output_tensor, - strides, padding_l, padding_r, - TFPaddingToMklDnnPadding(padding_), + &outbackprop, &output, &output_tensor, strides, padding_l, + padding_r, TFPaddingToMklDnnPadding(padding_), bwd_output_dims, bwd_output_format); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, errors::Aborted("Operation received an exception:", - error_msg)); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -523,11 +512,11 @@ class MklConv2DBackpropCommonOp : public OpKernel { /// Get TensorFlow shape of input tensor. virtual TensorShape MakeInputTfShape(OpKernelContext* context, - const Tensor& input_tensor) = 0; + const Tensor& input_tensor) = 0; /// Get TensorFlow shape of filter tensor. virtual TensorShape MakeFilterTfShape(OpKernelContext* context, - const Tensor& filter_tensor) = 0; + const Tensor& filter_tensor) = 0; /// Get the TensorFlow shape of output tensor. virtual TensorShape GetOutputTfShape(const TensorShape& input_shape, @@ -536,9 +525,9 @@ class MklConv2DBackpropCommonOp : public OpKernel { /// Get shape of output in MKL-DNN order. Computes shape of output from /// input shape (fwd_input_dims) and filter shape (fwd_filter_dims). - virtual - const memory::dims& GetOutputDims(const memory::dims& fwd_input_dims, - const memory::dims& fwd_filter_dims) = 0; + virtual const memory::dims& GetOutputDims( + const memory::dims& fwd_input_dims, + const memory::dims& fwd_filter_dims) = 0; /// Get data_format of output in MKL-DNN order. If output data format is /// same as input data format, then it simply returns value of data_format @@ -546,17 +535,18 @@ class MklConv2DBackpropCommonOp : public OpKernel { virtual memory::format GetOutputFormat(const memory::format data_format) = 0; /// Create and execute the primitive storing output in the output_tensor. - virtual void CreatePrimitive(OpKernelContext* context, - const engine& cpu_engine, - const convolution_forward::primitive_desc& conv_fwd_pd, - MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, - MklDnnData* output, Tensor** output_tensor, const memory::dims& strides, - const memory::dims& padding_l, const memory::dims& padding_r, - padding_kind padding, const memory::dims& bwd_output_dims, - memory::format bwd_output_format) = 0; + virtual void CreatePrimitive( + OpKernelContext* context, const engine& cpu_engine, + const convolution_forward::primitive_desc& conv_fwd_pd, + MklDnnData* input, MklDnnData* filter, MklDnnData* outbackprop, + MklDnnData* output, Tensor** output_tensor, + const memory::dims& strides, const memory::dims& padding_l, + const memory::dims& padding_r, padding_kind padding, + const memory::dims& bwd_output_dims, + memory::format bwd_output_format) = 0; // Get the data_format {NCHW, NHWC} - TensorFormat GetTFDataFormat () { return data_format_; } + TensorFormat GetTFDataFormat() { return data_format_; } private: std::vector strides_; @@ -575,12 +565,12 @@ class MklDummyOp : public OpKernel { public: ~MklDummyOp() {} - explicit MklDummyOp(OpKernelConstruction* context) : - OpKernel(context) {} + explicit MklDummyOp(OpKernelConstruction* context) : OpKernel(context) {} void Compute(OpKernelContext* context) override { - TF_CHECK_OK(errors::Unimplemented("This is a dummy op." - "It should not have been invoked.")); + TF_CHECK_OK( + errors::Unimplemented("This is a dummy op." + "It should not have been invoked.")); } }; diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc index 8340a91d05..0b6d838e09 100644 --- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc +++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc @@ -28,12 +28,12 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; +using mkldnn::batch_normalization_backward; +using mkldnn::batch_normalization_forward; using mkldnn::prop_kind; -using mkldnn::use_scale_shift; +using mkldnn::stream; using mkldnn::use_global_stats; -using mkldnn::batch_normalization_forward; -using mkldnn::batch_normalization_backward; +using mkldnn::use_scale_shift; #endif // TODO(inteltf) Address comments from PR 8968. @@ -601,7 +601,7 @@ class MklFusedBatchNormGradOp : public OpKernel { mkl_res_batchnorm_bwd[dnnResourceSrc] = (mkl_convert_input) ? mkl_buf_converted_input : mkl_buf_input; - bool mkl_convert_out_backprop; + bool mkl_convert_out_backprop; dnnPrimitive_t mkl_prim_convert_out_backprop = nullptr; dnnLayout_t mkl_lt_internal_out_backprop = nullptr; void* mkl_buf_converted_out_backprop = nullptr; @@ -709,12 +709,11 @@ class MklFusedBatchNormOp : public OpKernel { const size_t kMeanIndex = 3; // index of est_mean tensor const size_t kVarianceIndex = 4; // index of est_variance tensor - const Tensor& src_tensor = MklGetInput(context, kSrcIndex); - const Tensor& scale_tensor = MklGetInput(context, kScaleIndex); - const Tensor& shift_tensor = MklGetInput(context, kShiftIndex); - const Tensor& est_mean_tensor = MklGetInput(context, kMeanIndex); - const Tensor& est_variance_tensor = MklGetInput(context, - kVarianceIndex); + const Tensor& src_tensor = MklGetInput(context, kSrcIndex); + const Tensor& scale_tensor = MklGetInput(context, kScaleIndex); + const Tensor& shift_tensor = MklGetInput(context, kShiftIndex); + const Tensor& est_mean_tensor = MklGetInput(context, kMeanIndex); + const Tensor& est_variance_tensor = MklGetInput(context, kVarianceIndex); TensorShape tf_shape_src; MklDnnShape dnn_shape_src; @@ -723,37 +722,34 @@ class MklFusedBatchNormOp : public OpKernel { if (dnn_shape_src.IsMklTensor()) { tf_shape_src = dnn_shape_src.GetTfShape(); OP_REQUIRES(context, dnn_shape_src.GetDimension() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } else { tf_shape_src = src_tensor.shape(); OP_REQUIRES(context, src_tensor.dims() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } OP_REQUIRES(context, scale_tensor.dims() == 1, - errors::InvalidArgument( - "scale must be 1-dimensional", - scale_tensor.shape().DebugString())); + errors::InvalidArgument("scale must be 1-dimensional", + scale_tensor.shape().DebugString())); OP_REQUIRES(context, shift_tensor.dims() == 1, errors::InvalidArgument("offset must be 1-dimensional", - shift_tensor.shape().DebugString())); - OP_REQUIRES(context, est_mean_tensor.dims() == 1, - errors::InvalidArgument( - "estimated_mean must be 1-dimensional", - est_mean_tensor.shape().DebugString())); - OP_REQUIRES(context, est_variance_tensor.dims() == 1, - errors::InvalidArgument( - "estimated_variance must be 1-dimensional", - est_variance_tensor.shape().DebugString())); + shift_tensor.shape().DebugString())); + OP_REQUIRES( + context, est_mean_tensor.dims() == 1, + errors::InvalidArgument("estimated_mean must be 1-dimensional", + est_mean_tensor.shape().DebugString())); + OP_REQUIRES( + context, est_variance_tensor.dims() == 1, + errors::InvalidArgument("estimated_variance must be 1-dimensional", + est_variance_tensor.shape().DebugString())); if (is_training_) { - OP_REQUIRES(context, est_mean_tensor.dim_size(0) == 0, - errors::InvalidArgument( - "estimated_mean must be empty for training", - est_mean_tensor.shape().DebugString())); + OP_REQUIRES( + context, est_mean_tensor.dim_size(0) == 0, + errors::InvalidArgument("estimated_mean must be empty for training", + est_mean_tensor.shape().DebugString())); OP_REQUIRES(context, est_variance_tensor.dim_size(0) == 0, errors::InvalidArgument( "estimated_variance must be empty for training", @@ -763,11 +759,9 @@ class MklFusedBatchNormOp : public OpKernel { // special case: input with 0 element and 0 batch size Tensor* dst_tensor = nullptr; if (tf_shape_src.num_elements() == 0) { - HandleEmptyInput(context, - tf_shape_src, - scale_tensor.shape(), - &dst_tensor); - return; + HandleEmptyInput(context, tf_shape_src, scale_tensor.shape(), + &dst_tensor); + return; } if (dnn_shape_src.IsMklTensor()) @@ -783,11 +777,8 @@ class MklFusedBatchNormOp : public OpKernel { Tensor* batch_variance_tensor = nullptr; Tensor* saved_mean_tensor = nullptr; Tensor* saved_variance_tensor = nullptr; - AllocateTFOutputs(context, - scale_tensor.shape(), - &batch_mean_tensor, - &batch_variance_tensor, - &saved_mean_tensor, + AllocateTFOutputs(context, scale_tensor.shape(), &batch_mean_tensor, + &batch_variance_tensor, &saved_mean_tensor, &saved_variance_tensor); if (is_training_) @@ -815,69 +806,63 @@ class MklFusedBatchNormOp : public OpKernel { src_dims = TFShapeToMklDnnDimsInNCHW(dnn_shape_src.GetTfShape(), tensor_format_); } else { - src_dims = TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), - tensor_format_); + src_dims = + TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), tensor_format_); } auto src_md = dnn_shape_src.IsMklTensor() - ? dnn_shape_src.GetMklLayout() - : memory::desc(src_dims, MklDnnType(), format_m); + ? dnn_shape_src.GetMklLayout() + : memory::desc(src_dims, MklDnnType(), format_m); src.SetUsrMem(src_md, &src_tensor); // set weights primitive // MKL-DNN packs scale & shift as "weights": // ...... - auto weights_desc = memory::desc({2, depth_}, - MklDnnType(), - memory::format::nc); + auto weights_desc = + memory::desc({2, depth_}, MklDnnType(), memory::format::nc); auto weights_pd = memory::primitive_desc(weights_desc, cpu_engine); auto weights_m = memory(weights_pd); - T* weights_data = reinterpret_cast( - weights_m.get_data_handle()); - T* scale_tf = reinterpret_cast( - const_cast(scale_tensor.flat().data())); - T* shift_tf = reinterpret_cast( - const_cast(shift_tensor.flat().data())); - - for (int k=0; k < depth_; k++) { + T* weights_data = reinterpret_cast(weights_m.get_data_handle()); + T* scale_tf = + reinterpret_cast(const_cast(scale_tensor.flat().data())); + T* shift_tf = + reinterpret_cast(const_cast(shift_tensor.flat().data())); + + for (int k = 0; k < depth_; k++) { weights_data[k] = scale_tf[k]; weights_data[k + depth_] = shift_tf[k]; } // set mean primitive - auto mean_desc = memory::desc({1, depth_}, - MklDnnType(), - memory::format::nc); + auto mean_desc = + memory::desc({1, depth_}, MklDnnType(), memory::format::nc); auto mean_pd = memory::primitive_desc(mean_desc, cpu_engine); - char* saved_mean_data_tf = reinterpret_cast - (saved_mean_tensor->flat().data()); - std::memcpy(saved_mean_data_tf, - reinterpret_cast(mean_values_), - depth_*sizeof(T)); - auto mean_m = memory(mean_pd, - reinterpret_cast(saved_mean_data_tf)); + char* saved_mean_data_tf = + reinterpret_cast(saved_mean_tensor->flat().data()); + std::memcpy(saved_mean_data_tf, reinterpret_cast(mean_values_), + depth_ * sizeof(T)); + auto mean_m = + memory(mean_pd, reinterpret_cast(saved_mean_data_tf)); // set variance primitive - auto variance_desc = memory::desc({1, depth_}, - MklDnnType(), - memory::format::nc); + auto variance_desc = + memory::desc({1, depth_}, MklDnnType(), memory::format::nc); auto variance_pd = memory::primitive_desc(variance_desc, cpu_engine); - char* saved_variance_data_tf = reinterpret_cast - (saved_variance_tensor->flat().data()); + char* saved_variance_data_tf = + reinterpret_cast(saved_variance_tensor->flat().data()); std::memcpy(saved_variance_data_tf, reinterpret_cast(variance_values_), - depth_*sizeof(T)); + depth_ * sizeof(T)); auto variance_m = memory(variance_pd, saved_variance_data_tf); - prop_kind pk = (is_training_) ? - prop_kind::forward_training : - prop_kind::forward_scoring; + prop_kind pk = (is_training_) ? prop_kind::forward_training + : prop_kind::forward_scoring; auto bnrm_fwd_desc = batch_normalization_forward::desc( - pk, src.GetUsrMemDesc(), epsilon_, - is_training_ ? use_scale_shift : - (use_scale_shift | use_global_stats)); + pk, src.GetUsrMemDesc(), epsilon_, + is_training_ ? use_scale_shift + : (use_scale_shift | use_global_stats)); auto bnrm_fwd_pd = batch_normalization_forward::primitive_desc( - bnrm_fwd_desc, cpu_engine); + bnrm_fwd_desc, cpu_engine); // allocate dst tensor MklDnnShape dnn_shape_dst; @@ -887,47 +872,39 @@ class MklFusedBatchNormOp : public OpKernel { auto dst_pd = bnrm_fwd_pd.dst_primitive_desc(); dnn_shape_dst.SetMklLayout(&dst_pd); dnn_shape_dst.SetElemType(MklDnnType()); - dnn_shape_dst.SetTfLayout(dnn_shape_src.GetDimension(), - src_dims, format_m); - tf_shape_dst.AddDim(dst_pd.get_size()/sizeof(T)); + dnn_shape_dst.SetTfLayout(dnn_shape_src.GetDimension(), src_dims, + format_m); + tf_shape_dst.AddDim(dst_pd.get_size() / sizeof(T)); } else { dnn_shape_dst.SetMklTensor(false); tf_shape_dst = src_tensor.shape(); } - AllocateOutputSetMklShape(context, kDstIndex, &dst_tensor, - tf_shape_dst, dnn_shape_dst); + AllocateOutputSetMklShape(context, kDstIndex, &dst_tensor, tf_shape_dst, + dnn_shape_dst); // Output of batchnorm has same shape as input. dst.SetUsrMem(src_md, dst_tensor); primitive bnrm_fwd_op; if (is_training_) { - bnrm_fwd_op = batch_normalization_forward( - bnrm_fwd_pd, - src.GetOpMem(), - weights_m, - dst.GetOpMem(), - mean_m, - variance_m); + bnrm_fwd_op = + batch_normalization_forward(bnrm_fwd_pd, src.GetOpMem(), weights_m, + dst.GetOpMem(), mean_m, variance_m); } else { bnrm_fwd_op = batch_normalization_forward( - bnrm_fwd_pd, - src.GetOpMem(), - mean_m, - variance_m, - (const primitive::at) weights_m, - dst.GetOpMem()); + bnrm_fwd_pd, src.GetOpMem(), mean_m, variance_m, + (const primitive::at)weights_m, dst.GetOpMem()); } std::vector net; net.push_back(bnrm_fwd_op); stream(stream::kind::eager).submit(net).wait(); // copy batch_mean data - T* batch_mean_data_tf = reinterpret_cast( - batch_mean_tensor->flat().data()); + T* batch_mean_data_tf = + reinterpret_cast(batch_mean_tensor->flat().data()); std::memcpy(reinterpret_cast(batch_mean_data_tf), reinterpret_cast(mean_m.get_data_handle()), - depth_*sizeof(T)); + depth_ * sizeof(T)); // copy batch_variance data with Bessel's correction // if training mode is on @@ -937,18 +914,17 @@ class MklFusedBatchNormOp : public OpKernel { size_t adjust_size = orig_size - 1; adjust_factor = (static_cast(orig_size)) / adjust_size; } - for (int k=0; k < depth_; k++) + for (int k = 0; k < depth_; k++) batch_variance_tensor->flat().data()[k] = - (reinterpret_cast(variance_m.get_data_handle()))[k] - * adjust_factor; - } catch (mkldnn::error &e) { + (reinterpret_cast(variance_m.get_data_handle()))[k] * + adjust_factor; + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } @@ -958,7 +934,7 @@ class MklFusedBatchNormOp : public OpKernel { bool is_training_; T* mean_values_; T* variance_values_; - size_t depth_; // batch normalization is done for per channel. + size_t depth_; // batch normalization is done for per channel. void ExtractParams(OpKernelContext* context) { const Tensor& input = MklGetInput(context, 0); @@ -966,23 +942,20 @@ class MklFusedBatchNormOp : public OpKernel { } void SetMeanVariance(const Tensor& mean, const Tensor& variance) { - mean_values_ = reinterpret_cast( - const_cast(mean.flat().data())); - variance_values_ = reinterpret_cast( - const_cast(variance.flat().data())); + mean_values_ = reinterpret_cast(const_cast(mean.flat().data())); + variance_values_ = + reinterpret_cast(const_cast(variance.flat().data())); } - void HandleEmptyInput(OpKernelContext* context, - TensorShape tf_shape_src, - TensorShape tf_shape_scale, - Tensor** dst_tensor) { + void HandleEmptyInput(OpKernelContext* context, TensorShape tf_shape_src, + TensorShape tf_shape_scale, Tensor** dst_tensor) { CHECK_NOTNULL(dst_tensor); const size_t kDstIndex = 0; MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); - AllocateOutputSetMklShape(context, kDstIndex, dst_tensor, - tf_shape_src, dnn_shape_dst); + AllocateOutputSetMklShape(context, kDstIndex, dst_tensor, tf_shape_src, + dnn_shape_dst); CHECK_NOTNULL(*dst_tensor); memset(const_cast((*dst_tensor)->tensor_data().data()), 0, (*dst_tensor)->tensor_data().size()); @@ -991,15 +964,12 @@ class MklFusedBatchNormOp : public OpKernel { Tensor* batch_variance_tensor = nullptr; Tensor* saved_mean_tensor = nullptr; Tensor* saved_variance_tensor = nullptr; - AllocateTFOutputs(context, tf_shape_scale, - &batch_mean_tensor, - &batch_variance_tensor, - &saved_mean_tensor, + AllocateTFOutputs(context, tf_shape_scale, &batch_mean_tensor, + &batch_variance_tensor, &saved_mean_tensor, &saved_variance_tensor); } - void AllocateTFOutputs(OpKernelContext* context, - TensorShape tf_shape_scale, + void AllocateTFOutputs(OpKernelContext* context, TensorShape tf_shape_scale, Tensor** batch_mean_tensor, Tensor** batch_variance_tensor, Tensor** saved_mean_tensor, @@ -1017,51 +987,43 @@ class MklFusedBatchNormOp : public OpKernel { // allocate batch mean output tensor MklDnnShape mkl_shape_batch_mean; mkl_shape_batch_mean.SetMklTensor(false); - AllocateOutputSetMklShape(context, - kBatchMeanIndex, - batch_mean_tensor, - tf_shape_scale, - mkl_shape_batch_mean); + AllocateOutputSetMklShape(context, kBatchMeanIndex, batch_mean_tensor, + tf_shape_scale, mkl_shape_batch_mean); CHECK_NOTNULL(*batch_mean_tensor); // set NAN mean value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*batch_mean_tensor)->flat().data()[k] = NAN; // allocate batch variance output tensor MklDnnShape mkl_shape_batch_variance; mkl_shape_batch_variance.SetMklTensor(false); - AllocateOutputSetMklShape(context, - kBatchVarianceIndex, - batch_variance_tensor, - tf_shape_scale, + AllocateOutputSetMklShape(context, kBatchVarianceIndex, + batch_variance_tensor, tf_shape_scale, mkl_shape_batch_variance); CHECK_NOTNULL(*batch_variance_tensor); // set NAN variance value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*batch_variance_tensor)->flat().data()[k] = NAN; // Mean and variance (without Bessel's correction) saved for backward // computation to serve as pre-computed mean and variance. MklDnnShape mkl_shape_saved_mean; mkl_shape_saved_mean.SetMklTensor(false); - AllocateOutputSetMklShape(context, kSavedMeanIndex, - saved_mean_tensor, - tf_shape_scale, - mkl_shape_saved_mean); + AllocateOutputSetMklShape(context, kSavedMeanIndex, saved_mean_tensor, + tf_shape_scale, mkl_shape_saved_mean); CHECK_NOTNULL(*saved_mean_tensor); // set NAN mean value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*saved_mean_tensor)->flat().data()[k] = NAN; MklDnnShape mkl_shape_saved_variance; mkl_shape_saved_variance.SetMklTensor(false); AllocateOutputSetMklShape(context, kSavedVarianceIndex, - saved_variance_tensor, - tf_shape_scale, + saved_variance_tensor, tf_shape_scale, mkl_shape_saved_variance); CHECK_NOTNULL(*saved_variance_tensor); // set NAN variance value in case of empty input tensor - for (int k=0; k < tf_shape_scale.num_elements(); k++) + for (int k = 0; k < tf_shape_scale.num_elements(); k++) (*saved_variance_tensor)->flat().data()[k] = NAN; } }; @@ -1093,8 +1055,8 @@ class MklFusedBatchNormGradOp : public OpKernel { const Tensor& src_tensor = MklGetInput(context, kSrcIndex); const Tensor& scale_tensor = MklGetInput(context, kScaleIndex); const Tensor& saved_mean_tensor = MklGetInput(context, kMeanIndex); - const Tensor& saved_variance_tensor = MklGetInput(context, - kVarianceIndex); + const Tensor& saved_variance_tensor = + MklGetInput(context, kVarianceIndex); MklDnnShape dnn_shape_src, dnn_shape_diff_dst; GetMklShape(context, kSrcIndex, &dnn_shape_src); @@ -1103,53 +1065,49 @@ class MklFusedBatchNormGradOp : public OpKernel { if (dnn_shape_diff_dst.IsMklTensor()) { tf_shape_diff_dst = dnn_shape_diff_dst.GetTfShape(); - OP_REQUIRES(context, dnn_shape_diff_dst.GetDimension() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - diff_dst_tensor.shape().DebugString())); + OP_REQUIRES( + context, dnn_shape_diff_dst.GetDimension() == 4, + errors::InvalidArgument("input must be 4-dimensional", + diff_dst_tensor.shape().DebugString())); } else { tf_shape_diff_dst = diff_dst_tensor.shape(); - OP_REQUIRES(context, diff_dst_tensor.dims() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - diff_dst_tensor.shape().DebugString())); + OP_REQUIRES( + context, diff_dst_tensor.dims() == 4, + errors::InvalidArgument("input must be 4-dimensional", + diff_dst_tensor.shape().DebugString())); } if (dnn_shape_src.IsMklTensor()) { tf_shape_src = dnn_shape_src.GetTfShape(); OP_REQUIRES(context, dnn_shape_src.GetDimension() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } else { tf_shape_src = src_tensor.shape(); OP_REQUIRES(context, src_tensor.dims() == 4, - errors::InvalidArgument( - "input must be 4-dimensional", - src_tensor.shape().DebugString())); + errors::InvalidArgument("input must be 4-dimensional", + src_tensor.shape().DebugString())); } OP_REQUIRES(context, scale_tensor.dims() == 1, - errors::InvalidArgument( - "scale must be 1-dimensional", - scale_tensor.shape().DebugString())); - OP_REQUIRES(context, saved_mean_tensor.dims() == 1, - errors::InvalidArgument( - "saved mean must be 1-dimensional", - saved_mean_tensor.shape().DebugString())); - - OP_REQUIRES(context, saved_variance_tensor.dims() == 1, - errors::InvalidArgument( - "saved variance must be 1-dimensional", - saved_variance_tensor.shape().DebugString())); + errors::InvalidArgument("scale must be 1-dimensional", + scale_tensor.shape().DebugString())); + OP_REQUIRES( + context, saved_mean_tensor.dims() == 1, + errors::InvalidArgument("saved mean must be 1-dimensional", + saved_mean_tensor.shape().DebugString())); + + OP_REQUIRES( + context, saved_variance_tensor.dims() == 1, + errors::InvalidArgument("saved variance must be 1-dimensional", + saved_variance_tensor.shape().DebugString())); Tensor* diff_src_tensor = nullptr; if (tf_shape_src.num_elements() == 0 || tf_shape_diff_dst.num_elements() == 0) { - HandleEmptyInput(context, tf_shape_src, - scale_tensor.shape(), - &diff_src_tensor); - return; + HandleEmptyInput(context, tf_shape_src, scale_tensor.shape(), + &diff_src_tensor); + return; } if (dnn_shape_src.IsMklTensor()) @@ -1175,20 +1133,18 @@ class MklFusedBatchNormGradOp : public OpKernel { memory::dims src_dims, diff_dst_dims; if (dnn_shape_src.IsMklTensor()) - src_dims = TFShapeToMklDnnDimsInNCHW( - dnn_shape_src.GetTfShape(), tensor_format_); + src_dims = TFShapeToMklDnnDimsInNCHW(dnn_shape_src.GetTfShape(), + tensor_format_); else - src_dims = TFShapeToMklDnnDimsInNCHW( - src_tensor.shape(), tensor_format_); + src_dims = + TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), tensor_format_); if (dnn_shape_diff_dst.IsMklTensor()) diff_dst_dims = TFShapeToMklDnnDimsInNCHW( - dnn_shape_diff_dst.GetTfShape(), - tensor_format_); + dnn_shape_diff_dst.GetTfShape(), tensor_format_); else - diff_dst_dims = TFShapeToMklDnnDimsInNCHW( - diff_dst_tensor.shape(), - tensor_format_); + diff_dst_dims = + TFShapeToMklDnnDimsInNCHW(diff_dst_tensor.shape(), tensor_format_); // set src and diff_dst primitives memory::desc src_md({}, memory::data_undef, memory::format_undef); @@ -1202,7 +1158,7 @@ class MklFusedBatchNormGradOp : public OpKernel { src_md = diff_dst_md; } } else { - src_md = memory::desc(src_dims, MklDnnType(), format_m); + src_md = memory::desc(src_dims, MklDnnType(), format_m); diff_dst_md = src_md; } src.SetUsrMem(src_md, &src_tensor); @@ -1210,55 +1166,47 @@ class MklFusedBatchNormGradOp : public OpKernel { // weights -- DNN packs scales/shifts as weights in order of // scale, ..., scale, shift, ..., shift - auto weights_desc = memory::desc({2, depth_}, - MklDnnType(), - memory::format::nc); + auto weights_desc = + memory::desc({2, depth_}, MklDnnType(), memory::format::nc); auto weights_pd = memory::primitive_desc(weights_desc, cpu_engine); auto weights_m = memory(weights_pd); T* weights_data = reinterpret_cast(weights_m.get_data_handle()); - T* scale_tf = reinterpret_cast(const_cast - (scale_tensor.flat().data())); - for (int k=0; k < depth_; k++) { + T* scale_tf = + reinterpret_cast(const_cast(scale_tensor.flat().data())); + for (int k = 0; k < depth_; k++) { weights_data[k] = scale_tf[k]; weights_data[k + depth_] = 0; } // set mean primitive memory::dims mv_dims = GetMeanVarianceDims(); - mean.SetUsrMem(mv_dims, - memory::format::nc, - const_cast(static_cast - (saved_mean_tensor.flat().data()))); + mean.SetUsrMem(mv_dims, memory::format::nc, + const_cast(static_cast( + saved_mean_tensor.flat().data()))); mean.SetOpMemDesc(mv_dims, memory::format::nc); // set variance primitive - variance.SetUsrMem(mv_dims, memory::format::nc, - const_cast(static_cast - (saved_variance_tensor.flat().data()))); + variance.SetUsrMem(mv_dims, memory::format::nc, + const_cast(static_cast( + saved_variance_tensor.flat().data()))); variance.SetOpMemDesc(mv_dims, memory::format::nc); // set diff_weight primitive - auto diff_weights_desc = memory::desc( - {2, depth_}, - MklDnnType(), - memory::format::nc); - auto diff_weights_pd = memory::primitive_desc( - diff_weights_desc, - cpu_engine); + auto diff_weights_desc = + memory::desc({2, depth_}, MklDnnType(), memory::format::nc); + auto diff_weights_pd = + memory::primitive_desc(diff_weights_desc, cpu_engine); auto diff_weights_m = memory(diff_weights_pd); auto bnrm_fwd_desc = batch_normalization_forward::desc( - prop_kind::forward_training, - src.GetUsrMemDesc(), - epsilon_, - is_training_ ? use_scale_shift : - (use_scale_shift | use_global_stats)); + prop_kind::forward_training, src.GetUsrMemDesc(), epsilon_, + is_training_ ? use_scale_shift + : (use_scale_shift | use_global_stats)); auto bnrm_fwd_pd = batch_normalization_forward::primitive_desc( - bnrm_fwd_desc, - cpu_engine); + bnrm_fwd_desc, cpu_engine); // Indices of output tensors - const size_t kDiffSrcIndex = 0; // index of diff_src tensor + const size_t kDiffSrcIndex = 0; // index of diff_src tensor // allocate diff_src tensor MklDnnShape dnn_shape_diff_src; @@ -1268,14 +1216,11 @@ class MklFusedBatchNormGradOp : public OpKernel { auto diff_src_pd = bnrm_fwd_pd.dst_primitive_desc(); dnn_shape_diff_src.SetMklLayout(&diff_src_pd); dnn_shape_diff_src.SetElemType(MklDnnType()); - dnn_shape_diff_src.SetTfLayout( - dnn_shape_src.GetDimension(), - src_dims, - format_m); - dnn_shape_diff_src.SetTfDimOrder( - dnn_shape_src.GetDimension(), - tensor_format_); - tf_shape_diff_src.AddDim(diff_src_pd.get_size()/sizeof(T)); + dnn_shape_diff_src.SetTfLayout(dnn_shape_src.GetDimension(), src_dims, + format_m); + dnn_shape_diff_src.SetTfDimOrder(dnn_shape_src.GetDimension(), + tensor_format_); + tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T)); } else { dnn_shape_diff_src.SetMklTensor(false); tf_shape_diff_src = src_tensor.shape(); @@ -1287,33 +1232,22 @@ class MklFusedBatchNormGradOp : public OpKernel { prop_kind pk = prop_kind::backward; auto bnrm_bwd_desc = batch_normalization_backward::desc( - pk, - diff_src.GetUsrMemDesc(), - src.GetUsrMemDesc(), - epsilon_, - /* for inference, specify use_global_stats - 1. on fwd prop, use mean and variance - provided as inputs - 2. on bwd prop, mean and variance are - considered as constants. Thus, - reduce the amout of MKL computations - */ - is_training_ ? use_scale_shift : - (use_scale_shift | use_global_stats)); + pk, diff_src.GetUsrMemDesc(), src.GetUsrMemDesc(), epsilon_, + /* for inference, specify use_global_stats + 1. on fwd prop, use mean and variance + provided as inputs + 2. on bwd prop, mean and variance are + considered as constants. Thus, + reduce the amout of MKL computations + */ + is_training_ ? use_scale_shift + : (use_scale_shift | use_global_stats)); auto bnrm_bwd_pd = batch_normalization_backward::primitive_desc( - bnrm_bwd_desc, - cpu_engine, - bnrm_fwd_pd); + bnrm_bwd_desc, cpu_engine, bnrm_fwd_pd); auto bnrm_bwd_op = batch_normalization_backward( - bnrm_bwd_pd, - src.GetOpMem(), - mean.GetOpMem(), - variance.GetOpMem(), - diff_dst.GetOpMem(), - weights_m, - diff_src.GetOpMem(), - diff_weights_m); + bnrm_bwd_pd, src.GetOpMem(), mean.GetOpMem(), variance.GetOpMem(), + diff_dst.GetOpMem(), weights_m, diff_src.GetOpMem(), diff_weights_m); std::vector net; net.push_back(bnrm_bwd_op); @@ -1322,43 +1256,39 @@ class MklFusedBatchNormGradOp : public OpKernel { // allocate 4 output TF tensors Tensor* diff_scale_tensor = nullptr; Tensor* diff_shift_tensor = nullptr; - AllocateTFOutputs(context, scale_tensor.shape(), - &diff_scale_tensor, + AllocateTFOutputs(context, scale_tensor.shape(), &diff_scale_tensor, &diff_shift_tensor); // copy data: diff_scale and diff_shift - T* diff_weights_data_dnn = reinterpret_cast - (diff_weights_m.get_data_handle()); + T* diff_weights_data_dnn = + reinterpret_cast(diff_weights_m.get_data_handle()); for (int i = 0; i < depth_; i++) { - diff_scale_tensor->flat().data()[i] = - diff_weights_data_dnn[i]; + diff_scale_tensor->flat().data()[i] = diff_weights_data_dnn[i]; diff_shift_tensor->flat().data()[i] = - diff_weights_data_dnn[i + depth_]; + diff_weights_data_dnn[i + depth_]; } - } catch (mkldnn::error &e) { + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } private: T epsilon_; TensorFormat tensor_format_; - int depth_; // batch normalization is done for per channel. + int depth_; // batch normalization is done for per channel. bool is_training_; void ExtractParams(OpKernelContext* context) { - const Tensor& input = MklGetInput(context, 0); - depth_ = static_cast(GetTensorDim(input, tensor_format_, 'C')); + const Tensor& input = MklGetInput(context, 0); + depth_ = static_cast(GetTensorDim(input, tensor_format_, 'C')); } - void HandleEmptyInput(OpKernelContext* context, - TensorShape tf_shape_src, + void HandleEmptyInput(OpKernelContext* context, TensorShape tf_shape_src, TensorShape tf_shape_scale_shift, Tensor** diff_src_tensor) { const size_t kDiffSrcIndex = 0; @@ -1366,22 +1296,20 @@ class MklFusedBatchNormGradOp : public OpKernel { MklDnnShape dnn_shape_diff_src; dnn_shape_diff_src.SetMklTensor(false); AllocateOutputSetMklShape(context, kDiffSrcIndex, diff_src_tensor, - tf_shape_src, dnn_shape_diff_src); - for (size_t i=0; i < (*diff_src_tensor)->shape().num_elements(); i++) - (*diff_src_tensor)->flat().data()[i] = 0; + tf_shape_src, dnn_shape_diff_src); + for (size_t i = 0; i < (*diff_src_tensor)->shape().num_elements(); i++) + (*diff_src_tensor)->flat().data()[i] = 0; Tensor* diff_scale_tensor = nullptr; Tensor* diff_shift_tensor = nullptr; - AllocateTFOutputs(context, - tf_shape_scale_shift, - &diff_scale_tensor, + AllocateTFOutputs(context, tf_shape_scale_shift, &diff_scale_tensor, &diff_shift_tensor); } void AllocateTFOutputs(OpKernelContext* context, - TensorShape tf_shape_scale_shift, - Tensor** diff_scale_tensor, - Tensor** diff_shift_tensor) { + TensorShape tf_shape_scale_shift, + Tensor** diff_scale_tensor, + Tensor** diff_shift_tensor) { CHECK_NOTNULL(diff_scale_tensor); CHECK_NOTNULL(diff_shift_tensor); @@ -1396,31 +1324,29 @@ class MklFusedBatchNormGradOp : public OpKernel { AllocateOutputSetMklShape(context, kDiffScaleIndex, diff_scale_tensor, tf_shape_scale_shift, mkl_shape_diff_scale); CHECK_NOTNULL(*diff_scale_tensor); - for (size_t i=0; i < (*diff_scale_tensor)->shape().num_elements(); i++) - (*diff_scale_tensor)->flat().data()[i] = 0; + for (size_t i = 0; i < (*diff_scale_tensor)->shape().num_elements(); i++) + (*diff_scale_tensor)->flat().data()[i] = 0; MklDnnShape mkl_shape_diff_shift; mkl_shape_diff_shift.SetMklTensor(false); AllocateOutputSetMklShape(context, kDiffShiftIndex, diff_shift_tensor, tf_shape_scale_shift, mkl_shape_diff_shift); CHECK_NOTNULL(*diff_shift_tensor); - for (size_t i=0; i < (*diff_shift_tensor)->shape().num_elements(); i++) - (*diff_shift_tensor)->flat().data()[i] = 0; + for (size_t i = 0; i < (*diff_shift_tensor)->shape().num_elements(); i++) + (*diff_shift_tensor)->flat().data()[i] = 0; // Placeholders for estimated_mean and estimated_variance, which are // used for inference and thus not needed here for gradient computation. - Tensor* p1_tensor = nullptr, *p2_tensor = nullptr; + Tensor *p1_tensor = nullptr, *p2_tensor = nullptr; MklDnnShape mkl_shape_p; mkl_shape_p.SetMklTensor(false); - AllocateOutputSetMklShape(context, kP1Index, &p1_tensor, - TensorShape({}), mkl_shape_p); - AllocateOutputSetMklShape(context, kP2Index, &p2_tensor, - TensorShape({}), mkl_shape_p); + AllocateOutputSetMklShape(context, kP1Index, &p1_tensor, TensorShape({}), + mkl_shape_p); + AllocateOutputSetMklShape(context, kP2Index, &p2_tensor, TensorShape({}), + mkl_shape_p); } - memory::dims GetMeanVarianceDims() { - return memory::dims({1, depth_}); - } + memory::dims GetMeanVarianceDims() { return memory::dims({1, depth_}); } }; #endif diff --git a/tensorflow/core/kernels/mkl_input_conversion_op.cc b/tensorflow/core/kernels/mkl_input_conversion_op.cc index 4b5f7b8310..73d41efce1 100644 --- a/tensorflow/core/kernels/mkl_input_conversion_op.cc +++ b/tensorflow/core/kernels/mkl_input_conversion_op.cc @@ -271,8 +271,8 @@ class MklInputConversionOp : public OpKernel { MklDnnShape input_shape_1; GetMklShape(context, 1, &input_shape_1); - bool tf_shapes_are_same = context->input(0).shape() == - context->input(1).shape(); + bool tf_shapes_are_same = + context->input(0).shape() == context->input(1).shape(); VLOG(1) << "MklInputConversionOp: Input shapes are " << (tf_shapes_are_same ? "*same*" : "*different*") << ": " @@ -400,9 +400,9 @@ class MklInputConversionOp : public OpKernel { // Create reorder between tensorflow layout and Mkl layout. std::vector net; - CHECK_EQ(tf_input.CheckReorderToOpMem(memory::primitive_desc( - output_mkl_md, cpu_engine), - tensor_out, &net), + CHECK_EQ(tf_input.CheckReorderToOpMem( + memory::primitive_desc(output_mkl_md, cpu_engine), + tensor_out, &net), true); stream(stream::kind::eager).submit(net).wait(); diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc index 95e0404ba8..a8b45004b7 100644 --- a/tensorflow/core/kernels/mkl_lrn_op.cc +++ b/tensorflow/core/kernels/mkl_lrn_op.cc @@ -22,6 +22,9 @@ limitations under the License. #define EIGEN_USE_THREADS #include +#include "mkl_dnn.h" +#include "mkl_dnn_types.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" @@ -30,9 +33,6 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/tensor_format.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "mkl_dnn.h" -#include "mkl_dnn_types.h" #if !defined(IS_MOBILE_PLATFORM) #include "tensorflow/core/util/work_sharder.h" @@ -40,10 +40,10 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::lrn_forward; +using mkldnn::lrn_across_channels; using mkldnn::lrn_backward; +using mkldnn::lrn_forward; using mkldnn::prop_kind; -using mkldnn::lrn_across_channels; using mkldnn::stream; #endif @@ -77,10 +77,11 @@ class MklLRNOp : public OpKernel { explicit MklLRNOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); @@ -103,9 +104,10 @@ class MklLRNOp : public OpKernel { : input.dims(); OP_REQUIRES(context, mkl_context.in_dims == 4, errors::InvalidArgument("input must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES( + context, + FastBoundsCheck(input.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); if (!input_in_mkl_format) { mkl_context.MklDefaultToEigen(context, depth_radius_, bias_, alpha_, @@ -339,17 +341,17 @@ class MklLRNOp : public OpKernel { float beta_; }; - template class MklLRNGradOp : public OpKernel { public: explicit MklLRNGradOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_)); @@ -740,10 +742,11 @@ class MklLRNOp : public OpKernel { explicit MklLRNOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); @@ -773,10 +776,10 @@ class MklLRNOp : public OpKernel { if (!src_dnn_shape.IsMklTensor()) { MklDefaultToEigen(context, src_tensor); return; - } else if (!src_dnn_shape.IsMklChannelDim( - src_dnn_shape.GetDimension() - 1) ) { + } else if (!src_dnn_shape.IsMklChannelDim(src_dnn_shape.GetDimension() - + 1)) { Tensor converted_tensor = - ConvertMklToTF(context, src_tensor, src_dnn_shape); + ConvertMklToTF(context, src_tensor, src_dnn_shape); MklDefaultToEigen(context, converted_tensor); return; } @@ -807,18 +810,16 @@ class MklLRNOp : public OpKernel { // Create LRN primitive descriptor. // Tensorflow's normalization semantics is across channels. // MKL-DNN also supports normalization within channel. - auto lrn_desc = lrn_forward::desc(prop_kind::forward, - lrn_across_channels, + auto lrn_desc = lrn_forward::desc(prop_kind::forward, lrn_across_channels, src_dnn_data.GetUsrMemDesc(), - kernel_size, - new_alpha, beta_, bias_); + kernel_size, new_alpha, beta_, bias_); auto lrn_prim_desc = lrn_forward::primitive_desc(lrn_desc, cpu_engine); // Allocate output_dnn_data tensor. Tensor* output_tensor = nullptr; memory::format input_format = src_dnn_shape.GetTfDataFormat(); - AllocateOutputTensor(context, lrn_prim_desc, input_dims, - input_format, &output_tensor); + AllocateOutputTensor(context, lrn_prim_desc, input_dims, input_format, + &output_tensor); OP_REQUIRES_OK(context, context->status()); CHECK_NOTNULL(output_tensor); dst_dnn_data.SetUsrMemDataHandle(output_tensor); @@ -827,25 +828,23 @@ class MklLRNOp : public OpKernel { AllocateWorkspaceTensor(context, lrn_prim_desc, &workspace_dnn_data); OP_REQUIRES_OK(context, context->status()); - PrepareAndExecuteNet(lrn_prim_desc, &src_dnn_data, - &dst_dnn_data, &workspace_dnn_data); - } catch (mkldnn::error &e) { + PrepareAndExecuteNet(lrn_prim_desc, &src_dnn_data, &dst_dnn_data, + &workspace_dnn_data); + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } private: - void PrepareAndExecuteNet( - const lrn_forward::primitive_desc& lrn_fwd_desc, - MklDnnData* src_dnn_data, - MklDnnData* dst_dnn_data, - MklDnnData* wksp_dnn_data = nullptr) { + void PrepareAndExecuteNet(const lrn_forward::primitive_desc& lrn_fwd_desc, + MklDnnData* src_dnn_data, + MklDnnData* dst_dnn_data, + MklDnnData* wksp_dnn_data = nullptr) { std::vector net; // Check for input reorder @@ -853,23 +852,21 @@ class MklLRNOp : public OpKernel { // Create pooling primitive and add it to net if (wksp_dnn_data != nullptr) { - net.push_back(lrn_forward(lrn_fwd_desc, - src_dnn_data->GetOpMem(), - wksp_dnn_data->GetOpMem(), - dst_dnn_data->GetOpMem())); + net.push_back(lrn_forward(lrn_fwd_desc, src_dnn_data->GetOpMem(), + wksp_dnn_data->GetOpMem(), + dst_dnn_data->GetOpMem())); } else { - net.push_back(lrn_forward(lrn_fwd_desc, - src_dnn_data->GetOpMem(), - dst_dnn_data->GetOpMem())); + net.push_back(lrn_forward(lrn_fwd_desc, src_dnn_data->GetOpMem(), + dst_dnn_data->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } - void AllocateOutputTensor(OpKernelContext* context, - const lrn_forward::primitive_desc& lrn_fwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const lrn_forward::primitive_desc& lrn_fwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); memory::primitive_desc dst_pd = lrn_fwd_prim_desc.dst_primitive_desc(); @@ -880,111 +877,106 @@ class MklLRNOp : public OpKernel { output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; // only allocate enough space for the elements we need. size_t num_bytes = dst_pd.get_size(); CHECK_EQ(num_bytes % sizeof(T), 0); output_tf_shape.AddDim(num_bytes / sizeof(T)); - AllocateOutputSetMklShape(context, kIdxOutput, - output_tensor, - output_tf_shape, output_mkl_shape); - } - - // Fallback implementation - Taken from lrn_op.cc - // TODO(inteltf) Check if we can use EigenLRNOp directly instead of making a - // copy. - void MklDefaultToEigen(OpKernelContext* context, - const Tensor& input) { - const int batch = static_cast(input.dim_size(0)); - const int rows = static_cast(input.dim_size(1)); - const int cols = static_cast(input.dim_size(2)); - const int depth = static_cast(input.dim_size(3)); - const int nodes = cols * rows; - - auto in_shaped = input.shaped({nodes * batch, depth}); - // Multiplying the input with the band matrix has the effect of reducing - // the - // correct patch along the depth. - Eigen::Tensor multiplier(depth, depth); - GetBandMatrix(depth, depth_radius_, &multiplier); + AllocateOutputSetMklShape(context, kIdxOutput, output_tensor, + output_tf_shape, output_mkl_shape); + } - Tensor *output_dnn_data = nullptr; - MklDnnShape mkl_output_mkl_shape; - mkl_output_mkl_shape.SetMklTensor(false); - mkl_output_mkl_shape.SetDimensions(4); - AllocateOutputSetMklShape(context, kIdxOutput, &output_dnn_data, - input.shape(), mkl_output_mkl_shape); - CHECK_NOTNULL(output_dnn_data); - - Tensor* workspace_tensor = nullptr; - MklDnnShape workspace_mkl_shape; - workspace_mkl_shape.SetMklTensor(false); - TensorShape workspace_tf_shape; - workspace_tf_shape.AddDim(0); - AllocateOutputSetMklShape(context, kIdxWorkspace, - &workspace_tensor, + // Fallback implementation - Taken from lrn_op.cc + // TODO(inteltf) Check if we can use EigenLRNOp directly instead of making a + // copy. + void MklDefaultToEigen(OpKernelContext* context, const Tensor& input) { + const int batch = static_cast(input.dim_size(0)); + const int rows = static_cast(input.dim_size(1)); + const int cols = static_cast(input.dim_size(2)); + const int depth = static_cast(input.dim_size(3)); + const int nodes = cols * rows; + + auto in_shaped = input.shaped({nodes * batch, depth}); + // Multiplying the input with the band matrix has the effect of reducing + // the + // correct patch along the depth. + Eigen::Tensor multiplier(depth, depth); + GetBandMatrix(depth, depth_radius_, &multiplier); + + Tensor* output_dnn_data = nullptr; + MklDnnShape mkl_output_mkl_shape; + mkl_output_mkl_shape.SetMklTensor(false); + mkl_output_mkl_shape.SetDimensions(4); + AllocateOutputSetMklShape(context, kIdxOutput, &output_dnn_data, + input.shape(), mkl_output_mkl_shape); + CHECK_NOTNULL(output_dnn_data); + + Tensor* workspace_tensor = nullptr; + MklDnnShape workspace_mkl_shape; + workspace_mkl_shape.SetMklTensor(false); + TensorShape workspace_tf_shape; + workspace_tf_shape.AddDim(0); + AllocateOutputSetMklShape(context, kIdxWorkspace, &workspace_tensor, workspace_tf_shape, workspace_mkl_shape); - CHECK_NOTNULL(workspace_tensor); - - auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); - Eigen::array dims = {{DimPair(1, 0)}}; - auto tmp = in_shaped.square().contract(multiplier, dims) * alpha_ + bias_; - if (beta_ == T(1)) { - out_shaped.device(context->eigen_cpu_device()) = - in_shaped * tmp.inverse(); - } else if (beta_ == T(0.5)) { - out_shaped.device(context->eigen_cpu_device()) = - in_shaped * tmp.rsqrt(); - } else { - out_shaped.device(context->eigen_cpu_device()) = - in_shaped * (tmp.log() * -beta_).exp(); - } + CHECK_NOTNULL(workspace_tensor); + + auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); + Eigen::array dims = {{DimPair(1, 0)}}; + auto tmp = in_shaped.square().contract(multiplier, dims) * alpha_ + bias_; + if (beta_ == T(1)) { + out_shaped.device(context->eigen_cpu_device()) = + in_shaped * tmp.inverse(); + } else if (beta_ == T(0.5)) { + out_shaped.device(context->eigen_cpu_device()) = in_shaped * tmp.rsqrt(); + } else { + out_shaped.device(context->eigen_cpu_device()) = + in_shaped * (tmp.log() * -beta_).exp(); } + } - void AllocateWorkspaceTensor(OpKernelContext* context, - const lrn_forward::primitive_desc& lrn_fwd_prim_desc, - MklDnnData* dnn_data_wksp) { - CHECK_NOTNULL(dnn_data_wksp); - Tensor* workspace_tensor = nullptr; - memory::primitive_desc workspace_pd - = lrn_fwd_prim_desc.workspace_primitive_desc(); - size_t workspace_bytes = workspace_pd.get_size(); - MklDnnShape workspace_mkl_shape; - // the workspace tensor is a uint8 tensor that has - // exactly the number of bytes necessary - workspace_mkl_shape.SetMklTensor(false); - TensorShape workspace_tf_shape; - workspace_tf_shape.AddDim(workspace_bytes); - AllocateOutputSetMklShape(context, kIdxWorkspace, - &workspace_tensor, + void AllocateWorkspaceTensor( + OpKernelContext* context, + const lrn_forward::primitive_desc& lrn_fwd_prim_desc, + MklDnnData* dnn_data_wksp) { + CHECK_NOTNULL(dnn_data_wksp); + Tensor* workspace_tensor = nullptr; + memory::primitive_desc workspace_pd = + lrn_fwd_prim_desc.workspace_primitive_desc(); + size_t workspace_bytes = workspace_pd.get_size(); + MklDnnShape workspace_mkl_shape; + // the workspace tensor is a uint8 tensor that has + // exactly the number of bytes necessary + workspace_mkl_shape.SetMklTensor(false); + TensorShape workspace_tf_shape; + workspace_tf_shape.AddDim(workspace_bytes); + AllocateOutputSetMklShape(context, kIdxWorkspace, &workspace_tensor, workspace_tf_shape, workspace_mkl_shape); - CHECK_NOTNULL(workspace_tensor); - dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); - } + CHECK_NOTNULL(workspace_tensor); + dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); + } void SanityCheckInputs(OpKernelContext* context) { const Tensor& src_tensor = MklGetInput(context, kIdxInput); MklDnnShape src_dnn_shape; GetMklShape(context, kIdxInput, &src_dnn_shape); if (src_dnn_shape.IsMklTensor()) { - OP_REQUIRES(context, src_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("input must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(src_tensor.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES(context, src_dnn_shape.GetDimension() == 4, + errors::InvalidArgument("input must be 4-dimensional")); + OP_REQUIRES(context, + FastBoundsCheck(src_tensor.NumElements(), + std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); } else { - OP_REQUIRES(context, src_tensor.dims() == 4, - errors::InvalidArgument("input must be 4-dimensional")); - OP_REQUIRES(context, FastBoundsCheck(src_tensor.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("argument to LRN too large")); + OP_REQUIRES(context, src_tensor.dims() == 4, + errors::InvalidArgument("input must be 4-dimensional")); + OP_REQUIRES(context, + FastBoundsCheck(src_tensor.NumElements(), + std::numeric_limits::max()), + errors::InvalidArgument("argument to LRN too large")); } } - const int kIdxInput = 0, - kIdxOutput = 0, - kIdxWorkspace = 1; + const int kIdxInput = 0, kIdxOutput = 0, kIdxWorkspace = 1; typedef typename Eigen::Tensor::DimensionPair DimPair; bool workspace_enabled_; @@ -994,17 +986,17 @@ class MklLRNOp : public OpKernel { float beta_; }; - template class MklLRNGradOp : public OpKernel { public: explicit MklLRNGradOp(OpKernelConstruction* context) : OpKernel(context) { int64 depth_radius64; OP_REQUIRES_OK(context, context->GetAttr("depth_radius", &depth_radius64)); - OP_REQUIRES(context, FastBoundsCheck(depth_radius64, - std::numeric_limits::max()), - errors::InvalidArgument("depth_radius = ", depth_radius64, - " larger than int max")); + OP_REQUIRES( + context, + FastBoundsCheck(depth_radius64, std::numeric_limits::max()), + errors::InvalidArgument("depth_radius = ", depth_radius64, + " larger than int max")); depth_radius_ = static_cast(depth_radius64); OP_REQUIRES_OK(context, context->GetAttr("bias", &bias_)); OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_)); @@ -1025,7 +1017,7 @@ class MklLRNGradOp : public OpKernel { MklDnnData output_dnn_data(&cpu_engine); MklDnnShape input_grad_dnn_shape, orig_input_dnn_shape, - orig_output_dnn_shape; + orig_output_dnn_shape; GetMklShape(context, kIdxGradient, &input_grad_dnn_shape); GetMklShape(context, kIdxOrigInput, &orig_input_dnn_shape); GetMklShape(context, kIdxOrigOutput, &orig_output_dnn_shape); @@ -1037,16 +1029,16 @@ class MklLRNGradOp : public OpKernel { orig_input_dnn_shape.IsMklTensor() && orig_output_dnn_shape.IsMklTensor() && input_grad_dnn_shape.IsMklChannelDim( - input_grad_dnn_shape.GetDimension() - 1) && + input_grad_dnn_shape.GetDimension() - 1) && orig_input_dnn_shape.IsMklChannelDim( - orig_input_dnn_shape.GetDimension() - 1) && + orig_input_dnn_shape.GetDimension() - 1) && orig_output_dnn_shape.IsMklChannelDim( - orig_output_dnn_shape.GetDimension() - 1); + orig_output_dnn_shape.GetDimension() - 1); if (!can_use_mkldnn) { - // Fallback to eigen - MklDefaultToEigen(context); - return; + // Fallback to eigen + MklDefaultToEigen(context); + return; } // At this point, we have the all clear to use MklDnn constructs // Naming: diff_dst is input_gradient_tensor; src is orig_input_tensor. @@ -1059,13 +1051,11 @@ class MklLRNGradOp : public OpKernel { // NHWC format. memory::desc original_output_md = orig_output_dnn_shape.GetCurLayout(); memory::desc target_diff_dst_md = ConfigureInputGradient( - input_grad_tensor, - input_grad_dnn_shape, - &input_grad_dnn_data); + input_grad_tensor, input_grad_dnn_shape, &input_grad_dnn_data); memory::desc orig_input_md = orig_input_dnn_shape.GetCurLayout(); memory::dims orig_input_dims = - orig_input_dnn_shape.GetSizesAsMklDnnDims(); + orig_input_dnn_shape.GetSizesAsMklDnnDims(); orig_input_dnn_data.SetUsrMem(orig_input_md, &orig_input_tensor); orig_input_dnn_data.SetOpMemDesc(orig_input_dims, memory::format::nhwc); @@ -1079,27 +1069,21 @@ class MklLRNGradOp : public OpKernel { // Create LRN backward primitive descriptor. It requires LRN forward // primitive descriptor also. - auto lrn_fwd_desc = lrn_forward::desc(prop_kind::forward, - lrn_across_channels, - orig_input_md, - kernel_size, - new_alpha, beta_, bias_); - auto lrn_fwd_prim_desc = lrn_forward::primitive_desc(lrn_fwd_desc, - cpu_engine); - auto lrn_bwd_desc = lrn_backward::desc(lrn_across_channels, - original_output_md, - target_diff_dst_md, - kernel_size, - new_alpha, beta_, bias_); - auto lrn_bwd_prim_desc = lrn_backward::primitive_desc(lrn_bwd_desc, - cpu_engine, - lrn_fwd_prim_desc); + auto lrn_fwd_desc = lrn_forward::desc( + prop_kind::forward, lrn_across_channels, orig_input_md, kernel_size, + new_alpha, beta_, bias_); + auto lrn_fwd_prim_desc = + lrn_forward::primitive_desc(lrn_fwd_desc, cpu_engine); + auto lrn_bwd_desc = lrn_backward::desc( + lrn_across_channels, original_output_md, target_diff_dst_md, + kernel_size, new_alpha, beta_, bias_); + auto lrn_bwd_prim_desc = lrn_backward::primitive_desc( + lrn_bwd_desc, cpu_engine, lrn_fwd_prim_desc); Tensor* output_tensor = nullptr; - memory::format orig_input_format - = orig_input_dnn_shape.GetTfDataFormat(); - AllocateOutputTensor(context, lrn_bwd_prim_desc, - orig_input_dims, orig_input_format, &output_tensor); + memory::format orig_input_format = orig_input_dnn_shape.GetTfDataFormat(); + AllocateOutputTensor(context, lrn_bwd_prim_desc, orig_input_dims, + orig_input_format, &output_tensor); OP_REQUIRES_OK(context, context->status()); CHECK_NOTNULL(output_tensor); output_dnn_data.SetUsrMemDataHandle(output_tensor); @@ -1110,35 +1094,32 @@ class MklLRNGradOp : public OpKernel { const Tensor& workspace_tensor = MklGetInput(context, kIdxWorkspace); MklDnnData workspace_dnn_data(&cpu_engine); ConfigureWorkspace(workspace_tensor, - lrn_fwd_prim_desc.workspace_primitive_desc(), - &workspace_dnn_data); - - PrepareAndExecuteNet(lrn_bwd_prim_desc, - lrn_fwd_prim_desc, - &orig_input_dnn_data, - &input_grad_dnn_data, - &output_dnn_data, - memory::primitive_desc(target_diff_dst_md, cpu_engine), - &workspace_dnn_data); - } catch (mkldnn::error &e) { + lrn_fwd_prim_desc.workspace_primitive_desc(), + &workspace_dnn_data); + + PrepareAndExecuteNet( + lrn_bwd_prim_desc, lrn_fwd_prim_desc, &orig_input_dnn_data, + &input_grad_dnn_data, &output_dnn_data, + memory::primitive_desc(target_diff_dst_md, cpu_engine), + &workspace_dnn_data); + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } - void AllocateOutputTensor(OpKernelContext* context, - const lrn_backward::primitive_desc& lrn_bkwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const lrn_backward::primitive_desc& lrn_bkwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); - memory::primitive_desc dst_pd - = lrn_bkwd_prim_desc.diff_src_primitive_desc(); + memory::primitive_desc dst_pd = + lrn_bkwd_prim_desc.diff_src_primitive_desc(); MklDnnShape output_mkl_shape; // We assume that all outputs at this point are MKL Tensors @@ -1146,170 +1127,153 @@ class MklLRNGradOp : public OpKernel { output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; size_t num_bytes = dst_pd.get_size(); CHECK_EQ(num_bytes % sizeof(T), 0); output_tf_shape.AddDim(num_bytes / sizeof(T)); - AllocateOutputSetMklShape(context, kIdxOutput, - output_tensor, - output_tf_shape, output_mkl_shape); + AllocateOutputSetMklShape(context, kIdxOutput, output_tensor, + output_tf_shape, output_mkl_shape); } memory::desc ConfigureInputGradient(const Tensor& input_grad_tensor, - const MklDnnShape& input_grad_dnn_shape, - MklDnnData *input_grad_dnn_data) { + const MklDnnShape& input_grad_dnn_shape, + MklDnnData* input_grad_dnn_data) { CHECK_NOTNULL(input_grad_dnn_data); // This shouldn't be necessary at this point, but just in case CHECK_EQ(input_grad_dnn_shape.IsMklTensor(), true); memory::desc input_grad_md = input_grad_dnn_shape.GetCurLayout(); - memory::dims orig_input_dims = - input_grad_dnn_shape.GetSizesAsMklDnnDims(); + memory::dims orig_input_dims = input_grad_dnn_shape.GetSizesAsMklDnnDims(); input_grad_dnn_data->SetUsrMem(input_grad_md, &input_grad_tensor); input_grad_dnn_data->SetOpMemDesc(orig_input_dims, memory::format::nhwc); return input_grad_md; } void PrepareAndExecuteNet( - const lrn_backward::primitive_desc& lrn_bkwd_desc, - const lrn_forward::primitive_desc& lrn_fwd_desc, - MklDnnData* src_dnn_data, - MklDnnData* input_gradient_diff_dst, - MklDnnData* output_diff_src, - const memory::primitive_desc& target_diff_dst_pd, - const MklDnnData* workspace_dnn_data = nullptr) { + const lrn_backward::primitive_desc& lrn_bkwd_desc, + const lrn_forward::primitive_desc& lrn_fwd_desc, + MklDnnData* src_dnn_data, MklDnnData* input_gradient_diff_dst, + MklDnnData* output_diff_src, + const memory::primitive_desc& target_diff_dst_pd, + const MklDnnData* workspace_dnn_data = nullptr) { std::vector net; // Check for input reordering on the diff dst input input_gradient_diff_dst->CheckReorderToOpMem( - lrn_bkwd_desc.diff_dst_primitive_desc(), &net); + lrn_bkwd_desc.diff_dst_primitive_desc(), &net); // Check for input reordering on the original input - src_dnn_data->CheckReorderToOpMem(lrn_fwd_desc.src_primitive_desc(), - &net); + src_dnn_data->CheckReorderToOpMem(lrn_fwd_desc.src_primitive_desc(), &net); // Create pooling primitive and add it to net if (nullptr == workspace_dnn_data) { - net.push_back(lrn_backward(lrn_bkwd_desc, - src_dnn_data->GetOpMem(), - input_gradient_diff_dst->GetOpMem(), - output_diff_src->GetOpMem())); + net.push_back(lrn_backward(lrn_bkwd_desc, src_dnn_data->GetOpMem(), + input_gradient_diff_dst->GetOpMem(), + output_diff_src->GetOpMem())); } else { - net.push_back(lrn_backward(lrn_bkwd_desc, - src_dnn_data->GetOpMem(), - input_gradient_diff_dst->GetOpMem(), - workspace_dnn_data->GetOpMem(), - output_diff_src->GetOpMem())); + net.push_back(lrn_backward(lrn_bkwd_desc, src_dnn_data->GetOpMem(), + input_gradient_diff_dst->GetOpMem(), + workspace_dnn_data->GetOpMem(), + output_diff_src->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } void ConfigureWorkspace(const Tensor& workspace_tensor, - memory::primitive_desc workspace_pd, - MklDnnData *workspace_dnn_data) { + memory::primitive_desc workspace_pd, + MklDnnData* workspace_dnn_data) { CHECK_NOTNULL(workspace_dnn_data); workspace_dnn_data->SetUsrMem(workspace_pd, &workspace_tensor); } - // Fallback implementation - Taken from lrn_op.cc - // TODO(intelft) Check if we can use EigenLRNOp directly instead of making a - // copy. - void MklDefaultToEigen(OpKernelContext* context) { - Tensor input_gradient_tensor; - Tensor orig_input_tensor; - Tensor orig_output_tensor; - - MklDnnShape input_grad_dnn_shape, orig_input_dnn_shape, - orig_output_dnn_shape; - GetMklShape(context, kIdxGradient, &input_grad_dnn_shape); - GetMklShape(context, kIdxOrigInput, &orig_input_dnn_shape); - GetMklShape(context, kIdxOrigOutput, &orig_output_dnn_shape); - - if (input_grad_dnn_shape.IsMklTensor()) { - input_gradient_tensor = - ConvertMklToTF(context, - MklGetInput(context, kIdxGradient), - input_grad_dnn_shape); - } else { - input_gradient_tensor = MklGetInput(context, kIdxGradient); - } - - if (orig_input_dnn_shape.IsMklTensor()) { - orig_input_tensor = - ConvertMklToTF(context, - MklGetInput(context, kIdxOrigInput), - orig_input_dnn_shape); - } else { - orig_input_tensor = MklGetInput(context, kIdxOrigInput); - } + // Fallback implementation - Taken from lrn_op.cc + // TODO(intelft) Check if we can use EigenLRNOp directly instead of making a + // copy. + void MklDefaultToEigen(OpKernelContext* context) { + Tensor input_gradient_tensor; + Tensor orig_input_tensor; + Tensor orig_output_tensor; + + MklDnnShape input_grad_dnn_shape, orig_input_dnn_shape, + orig_output_dnn_shape; + GetMklShape(context, kIdxGradient, &input_grad_dnn_shape); + GetMklShape(context, kIdxOrigInput, &orig_input_dnn_shape); + GetMklShape(context, kIdxOrigOutput, &orig_output_dnn_shape); + + if (input_grad_dnn_shape.IsMklTensor()) { + input_gradient_tensor = ConvertMklToTF( + context, MklGetInput(context, kIdxGradient), input_grad_dnn_shape); + } else { + input_gradient_tensor = MklGetInput(context, kIdxGradient); + } - if (orig_output_dnn_shape.IsMklTensor()) { - orig_output_tensor = - ConvertMklToTF(context, - MklGetInput(context, kIdxOrigOutput), - orig_output_dnn_shape); - } else { - orig_output_tensor = MklGetInput(context, kIdxOrigOutput); - } + if (orig_input_dnn_shape.IsMklTensor()) { + orig_input_tensor = ConvertMklToTF( + context, MklGetInput(context, kIdxOrigInput), orig_input_dnn_shape); + } else { + orig_input_tensor = MklGetInput(context, kIdxOrigInput); + } - const int64 batch = static_cast(input_gradient_tensor.dim_size(0)); - const int64 rows = static_cast(input_gradient_tensor.dim_size(1)); - const int64 cols = static_cast(input_gradient_tensor.dim_size(2)); - const int64 depth = static_cast(input_gradient_tensor.dim_size(3)); - const auto nodes = cols * rows; + if (orig_output_dnn_shape.IsMklTensor()) { + orig_output_tensor = ConvertMklToTF( + context, MklGetInput(context, kIdxOrigOutput), orig_output_dnn_shape); + } else { + orig_output_tensor = MklGetInput(context, kIdxOrigOutput); + } - auto grads_shaped = - input_gradient_tensor.shaped({nodes * batch, depth}); + const int64 batch = static_cast(input_gradient_tensor.dim_size(0)); + const int64 rows = static_cast(input_gradient_tensor.dim_size(1)); + const int64 cols = static_cast(input_gradient_tensor.dim_size(2)); + const int64 depth = static_cast(input_gradient_tensor.dim_size(3)); + const auto nodes = cols * rows; - auto in_shaped = orig_input_tensor.shaped({nodes * batch, depth}); - auto activations = - orig_output_tensor.shaped({nodes * batch, depth}); + auto grads_shaped = + input_gradient_tensor.shaped({nodes * batch, depth}); - Tensor* output_dnn_data; - MklShape mkl_output_mkl_shape; - mkl_output_mkl_shape.SetMklTensor(false); - mkl_output_mkl_shape.SetDimensions(4); - AllocateOutputSetMklShape(context, kIdxOutput, - &output_dnn_data, - input_gradient_tensor.shape(), - mkl_output_mkl_shape); + auto in_shaped = orig_input_tensor.shaped({nodes * batch, depth}); + auto activations = orig_output_tensor.shaped({nodes * batch, depth}); - auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); - out_shaped.setZero(); - auto shard = [this, activations, in_shaped, grads_shaped, out_shaped, - depth](int64 begin, int64 end) { - for (int64 i = begin; i < end; ++i) { - for (int64 j = 0; j < depth; ++j) { - int64 depth_begin = std::max(0, j - depth_radius_); - int64 depth_end = std::min(depth, j + depth_radius_ + 1); + Tensor* output_dnn_data; + MklShape mkl_output_mkl_shape; + mkl_output_mkl_shape.SetMklTensor(false); + mkl_output_mkl_shape.SetDimensions(4); + AllocateOutputSetMklShape(context, kIdxOutput, &output_dnn_data, + input_gradient_tensor.shape(), + mkl_output_mkl_shape); - T norm(0); - for (int64 k = depth_begin; k < depth_end; ++k) { - norm += in_shaped(i, k) * in_shaped(i, k); - } - norm = alpha_ * norm + bias_; - DCHECK_GT(norm, T(1e-6)); - for (int64 k = depth_begin; k < depth_end; ++k) { - T dyi = T(-2) * alpha_ * beta_ * in_shaped(i, k) * - activations(i, j) / norm; - if (k == j) { - dyi += Eigen::numext::pow(norm, -beta_); - } - dyi *= grads_shaped(i, j); - const_cast::Tensor&>(out_shaped)(i, k) += - dyi; + auto out_shaped = output_dnn_data->shaped({nodes * batch, depth}); + out_shaped.setZero(); + auto shard = [this, activations, in_shaped, grads_shaped, out_shaped, + depth](int64 begin, int64 end) { + for (int64 i = begin; i < end; ++i) { + for (int64 j = 0; j < depth; ++j) { + int64 depth_begin = std::max(0, j - depth_radius_); + int64 depth_end = std::min(depth, j + depth_radius_ + 1); + + T norm(0); + for (int64 k = depth_begin; k < depth_end; ++k) { + norm += in_shaped(i, k) * in_shaped(i, k); + } + norm = alpha_ * norm + bias_; + DCHECK_GT(norm, T(1e-6)); + for (int64 k = depth_begin; k < depth_end; ++k) { + T dyi = T(-2) * alpha_ * beta_ * in_shaped(i, k) * + activations(i, j) / norm; + if (k == j) { + dyi += Eigen::numext::pow(norm, -beta_); } + dyi *= grads_shaped(i, j); + const_cast::Tensor&>(out_shaped)(i, k) += dyi; } } - }; - auto worker_threads = - *(context->device()->tensorflow_cpu_worker_threads()); - Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch, - depth * depth, shard); - } + } + }; + auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); + Shard(worker_threads.num_threads, worker_threads.workers, nodes * batch, + depth * depth, shard); + } void SanityCheckInputs(OpKernelContext* context) { const Tensor& input_gradient_tensor = MklGetInput(context, kIdxGradient); @@ -1317,59 +1281,59 @@ class MklLRNGradOp : public OpKernel { const Tensor& orig_output_tensor = MklGetInput(context, kIdxOrigOutput); const Tensor& workspace_tensor = MklGetInput(context, kIdxWorkspace); MklDnnShape in_grads_dnn_shape, in_image_dnn_shape, out_image_dnn_shape, - workspace_dnn_shape; + workspace_dnn_shape; GetMklShape(context, kIdxGradient, &in_grads_dnn_shape); GetMklShape(context, kIdxOrigInput, &in_image_dnn_shape); GetMklShape(context, kIdxOrigOutput, &out_image_dnn_shape); GetMklShape(context, kIdxWorkspace, &workspace_dnn_shape); if (in_grads_dnn_shape.IsMklTensor()) { OP_REQUIRES(context, in_grads_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("Input gradient must be " - "4-dimensional")); + errors::InvalidArgument("Input gradient must be " + "4-dimensional")); } else { - OP_REQUIRES(context, input_gradient_tensor.dims() == 4, - errors::InvalidArgument("input gradient must be 4-dimensional")); + OP_REQUIRES( + context, input_gradient_tensor.dims() == 4, + errors::InvalidArgument("input gradient must be 4-dimensional")); } if (in_image_dnn_shape.IsMklTensor()) { OP_REQUIRES(context, in_image_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("input images must be " - "4-dimensional")); + errors::InvalidArgument("input images must be " + "4-dimensional")); } else { OP_REQUIRES(context, orig_input_tensor.dims() == 4, errors::InvalidArgument("input images must be " - "4-dimensional")); + "4-dimensional")); } if (out_image_dnn_shape.IsMklTensor()) { OP_REQUIRES(context, out_image_dnn_shape.GetDimension() == 4, - errors::InvalidArgument("Output image must be " - "4-dimensional")); + errors::InvalidArgument("Output image must be " + "4-dimensional")); } else { - OP_REQUIRES(context, orig_output_tensor.dims() == 4, - errors::InvalidArgument("Output image must be 4-dimensional")); + OP_REQUIRES( + context, orig_output_tensor.dims() == 4, + errors::InvalidArgument("Output image must be 4-dimensional")); } if (workspace_enabled_) { if (workspace_dnn_shape.IsMklTensor()) { - OP_REQUIRES(context, workspace_dnn_shape.IsMklTensor() == false, - errors::InvalidArgument("Workspace should not be MKL Tensor.")); + OP_REQUIRES( + context, workspace_dnn_shape.IsMklTensor() == false, + errors::InvalidArgument("Workspace should not be MKL Tensor.")); } else { OP_REQUIRES(context, workspace_tensor.dims() == 1, - errors::InvalidArgument("Workspace must be 1-dimensional")); + errors::InvalidArgument("Workspace must be 1-dimensional")); } } } -// Input("input_grads: T") -// Input("input_image: T") -// Input("output_image: T") -// Input("workspace: uint8") - const int kIdxGradient = 0, - kIdxOrigInput = 1, - kIdxOrigOutput = 2, - kIdxWorkspace = 3, - kIdxOutput = 0; + // Input("input_grads: T") + // Input("input_image: T") + // Input("output_image: T") + // Input("workspace: uint8") + const int kIdxGradient = 0, kIdxOrigInput = 1, kIdxOrigOutput = 2, + kIdxWorkspace = 3, kIdxOutput = 0; typedef typename Eigen::Tensor::DimensionPair DimPair; bool workspace_enabled_; @@ -1393,7 +1357,6 @@ class MklLRNGradOp : public OpKernel { .Label(mkl_op_registry::kMklOpLabel), \ MklLRNGradOp); - TF_CALL_float(REGISTER_MKL_LRN_CPU); } // namespace tensorflow diff --git a/tensorflow/core/kernels/mkl_maxpooling_op.cc b/tensorflow/core/kernels/mkl_maxpooling_op.cc index 82c5229bab..0de27ccd60 100644 --- a/tensorflow/core/kernels/mkl_maxpooling_op.cc +++ b/tensorflow/core/kernels/mkl_maxpooling_op.cc @@ -25,14 +25,14 @@ limitations under the License. #ifdef INTEL_MKL_DNN #include #include "mkldnn.hpp" -using mkldnn::memory; +using mkldnn::algorithm; +using mkldnn::engine; using mkldnn::error; -using mkldnn::pooling_forward; -using mkldnn::pooling_backward; +using mkldnn::memory; using mkldnn::padding_kind; -using mkldnn::engine; +using mkldnn::pooling_backward; +using mkldnn::pooling_forward; using mkldnn::prop_kind; -using mkldnn::algorithm; #endif namespace tensorflow { @@ -397,18 +397,19 @@ class MklMaxPoolingGradOp : public OpKernel { if (workspace_enabled == false) { if (convert_input != nullptr) { if (input_in_mkl_format == false) { - CHECK_EQ( - dnnConversionExecute_F32( - convert_input, const_cast(static_cast( - tensor_in.flat().data())), - input_buf), - E_SUCCESS); + CHECK_EQ(dnnConversionExecute_F32( + convert_input, + const_cast(static_cast( + tensor_in.flat().data())), + input_buf), + E_SUCCESS); CHECK_EQ(dnnDelete_F32(convert_input), E_SUCCESS); convert_input = nullptr; } else { input_shape.GetConvertedFlatData( - lt_input_prim, const_cast(static_cast( - tensor_in.flat().data())), + lt_input_prim, + const_cast( + static_cast(tensor_in.flat().data())), input_buf); } pooling_resfwd[dnnResourceSrc] = input_buf; @@ -453,8 +454,9 @@ class MklMaxPoolingGradOp : public OpKernel { CHECK_EQ(dnnDelete_F32(convert_outbackprop), E_SUCCESS); } else { output_backprop_shape.GetConvertedFlatData( - lt_outbackprop_prim, const_cast(static_cast( - out_backprop.flat().data())), + lt_outbackprop_prim, + const_cast( + static_cast(out_backprop.flat().data())), outbackprop_buf); } pooling_res[dnnResourceDiffDst] = outbackprop_buf; @@ -499,7 +501,7 @@ template class MklMaxPoolingOp : public MklPoolingForwardOpBase { public: explicit MklMaxPoolingOp(OpKernelConstruction* context) - : MklPoolingForwardOpBase(context) { + : MklPoolingForwardOpBase(context) { // In Max Pooling, MKLDNN does not allow passing workspace as NULL. // So we set workspace_enabled_ to true. this->workspace_enabled_ = true; @@ -508,8 +510,8 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { void Compute(OpKernelContext* context) override { try { auto cpu_engine = engine(engine::cpu, 0); - const Tensor& input_tensor = MklGetInput(context, - this->kInputTensorIndexInput); + const Tensor& input_tensor = + MklGetInput(context, this->kInputTensorIndexInput); MklDnnShape dnn_shape_input; GetMklShape(context, this->kInputTensorIndexInput, &dnn_shape_input); this->SanityCheckInput(context, input_tensor, dnn_shape_input); @@ -522,9 +524,8 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { // initialize variables for the pooling op MklPoolParameters pool_params; // Get the input tensor and initialize the pooling parameters - this->ConfigureInput(context, dnn_shape_input, - input_tensor, &pool_params, - &dnn_data_input); + this->ConfigureInput(context, dnn_shape_input, input_tensor, &pool_params, + &dnn_data_input); OP_REQUIRES_OK(context, context->status()); // Declare output tensor @@ -535,9 +536,10 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { // If input is in Mkl layout, then just get the memory format from it // directly, instead of using input data_format to MaxPool. if (dnn_shape_input.IsMklTensor()) { - dnn_data_output.SetUsrMem(output_dims_mkl_order, - static_cast( - dnn_data_input.GetUsrMemDesc().data.format)); + dnn_data_output.SetUsrMem( + output_dims_mkl_order, + static_cast( + dnn_data_input.GetUsrMemDesc().data.format)); } else { dnn_data_output.SetUsrMem(output_dims_mkl_order, this->data_format_mkldnn_); @@ -546,24 +548,21 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { // describe the memory layout; let mkl-dnn choose the best for the op dnn_data_output.SetOpMemDesc(output_dims_mkl_order, memory::format::any); - auto pool_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_max, - dnn_data_input.GetUsrMemDesc(), - dnn_data_output.GetUsrMemDesc(), - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_fwd_desc = pooling_forward::primitive_desc(pool_desc, - cpu_engine); + auto pool_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_max, + dnn_data_input.GetUsrMemDesc(), dnn_data_output.GetUsrMemDesc(), + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_fwd_desc = + pooling_forward::primitive_desc(pool_desc, cpu_engine); this->AllocateOutputTensor(context, pool_fwd_desc, output_dims_mkl_order, - this->data_format_mkldnn_, &output_tensor); + this->data_format_mkldnn_, &output_tensor); OP_REQUIRES_OK(context, context->status()); dnn_data_output.SetUsrMemDataHandle(output_tensor); @@ -571,39 +570,38 @@ class MklMaxPoolingOp : public MklPoolingForwardOpBase { OP_REQUIRES_OK(context, context->status()); this->PrepareAndExecuteNet(pool_fwd_desc, &dnn_data_input, - &dnn_data_output, &dnn_data_wksp); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Compute received an exception:", - error_msg)); + &dnn_data_output, &dnn_data_wksp); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK(context, errors::Aborted("Compute received an exception:", + error_msg)); } } // Compute private: - const int kOutputTensorIndexWorkspace = 1; - - void AllocateWorkspaceTensor(OpKernelContext* context, - const pooling_forward::primitive_desc& pool_fwd_prim_desc, - MklDnnData* dnn_data_wksp) { - CHECK_NOTNULL(dnn_data_wksp); - Tensor* workspace_tensor = nullptr; - memory::primitive_desc workspace_pd - = pool_fwd_prim_desc.workspace_primitive_desc(); - size_t workspace_bytes = workspace_pd.get_size(); - MklDnnShape workspace_mkl_shape; - workspace_mkl_shape.SetMklTensor(false); - TensorShape workspace_tf_shape; - workspace_tf_shape.AddDim(workspace_bytes); - AllocateOutputSetMklShape(context, kOutputTensorIndexWorkspace, - &workspace_tensor, - workspace_tf_shape, workspace_mkl_shape); - CHECK_NOTNULL(workspace_tensor); - dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); - } + const int kOutputTensorIndexWorkspace = 1; + + void AllocateWorkspaceTensor( + OpKernelContext* context, + const pooling_forward::primitive_desc& pool_fwd_prim_desc, + MklDnnData* dnn_data_wksp) { + CHECK_NOTNULL(dnn_data_wksp); + Tensor* workspace_tensor = nullptr; + memory::primitive_desc workspace_pd = + pool_fwd_prim_desc.workspace_primitive_desc(); + size_t workspace_bytes = workspace_pd.get_size(); + MklDnnShape workspace_mkl_shape; + workspace_mkl_shape.SetMklTensor(false); + TensorShape workspace_tf_shape; + workspace_tf_shape.AddDim(workspace_bytes); + AllocateOutputSetMklShape(context, kOutputTensorIndexWorkspace, + &workspace_tensor, workspace_tf_shape, + workspace_mkl_shape); + CHECK_NOTNULL(workspace_tensor); + dnn_data_wksp->SetUsrMem(workspace_pd, workspace_tensor); + } }; // The operation to compute MaxPool gradients. @@ -616,218 +614,183 @@ template class MklMaxPoolingGradOp : public MklPoolingBackwardOpBase { public: explicit MklMaxPoolingGradOp(OpKernelConstruction* context) - : MklPoolingBackwardOpBase(context) { - } + : MklPoolingBackwardOpBase(context) {} void Compute(OpKernelContext* context) override { try { - auto cpu_engine = engine(engine::cpu, 0); - const Tensor& orig_input_tensor = MklGetInput(context, - kInputTensorIndexOrigInput); - const Tensor& orig_output_tensor = MklGetInput(context, - kInputTensorIndexOrigOutput); - const Tensor& grad_tensor = MklGetInput(context, - kInputTensorIndexGradient); - const Tensor& workspace_tensor = MklGetInput(context, - kInputTensorIndexWorkspace); - MklDnnShape orig_input_mkl_shape, - orig_output_mkl_shape, - grad_mkl_shape, - workspace_mkl_shape; - GetMklShape(context, kInputTensorIndexOrigInput, - &orig_input_mkl_shape); - GetMklShape(context, kInputTensorIndexOrigOutput, - &orig_output_mkl_shape); - GetMklShape(context, kInputTensorIndexGradient, - &grad_mkl_shape); - GetMklShape(context, kInputTensorIndexWorkspace, - &workspace_mkl_shape); - - SanityCheckInputs(context, - orig_input_tensor, orig_output_tensor, - grad_tensor, workspace_tensor, - orig_input_mkl_shape, orig_output_mkl_shape, - grad_mkl_shape, workspace_mkl_shape); - if (!context->status().ok()) return; - - MklDnnData grad_dnn_data(&cpu_engine); - MklDnnData workspace_dnn_data(&cpu_engine); - MklDnnData output_dnn_data(&cpu_engine); - Tensor* output_tensor = nullptr; - MklPoolParameters pool_params; - TensorShape orig_input_shape; - memory::dims output_dims_mkl_order, orig_input_dims_mkl_order; - memory::desc original_input_md = ConfigureOriginalInput(context, - orig_input_tensor, - orig_input_mkl_shape, - &orig_input_dims_mkl_order, - &pool_params, - &orig_input_shape); - - memory::desc original_output_md = this->ConfigureOriginalOutput( - pool_params, - orig_output_mkl_shape, - output_dims_mkl_order); - - memory::desc target_diff_dst_md = this->ConfigureInputGradient( - grad_mkl_shape, - grad_tensor, - &grad_dnn_data, - original_output_md); - - output_dnn_data.SetUsrMem(original_input_md); - - // Create the forward pooling primitive descriptor so we can - // pass it as a hint to the backward pooling primitive descriptor - auto pool_fwd_desc = pooling_forward::desc(prop_kind::forward, - algorithm::pooling_max, - original_input_md, - original_output_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_fwd_prim_desc - = pooling_forward::primitive_desc(pool_fwd_desc, - cpu_engine); - - auto pool_bkwd_desc = pooling_backward::desc( - algorithm::pooling_max, - output_dnn_data.GetUsrMemDesc(), - target_diff_dst_md, - memory::dims({ pool_params.row_stride, - pool_params.col_stride}), - memory::dims({ pool_params.window_rows, - pool_params.window_cols}), - memory::dims({ static_cast(pool_params.pad_top), - static_cast(pool_params.pad_left)}), - memory::dims({ static_cast(pool_params.pad_bottom), - static_cast(pool_params.pad_right)}), - TFPaddingToMklDnnPadding(this->padding_)); - auto pool_bkwd_prim_desc - = pooling_backward::primitive_desc(pool_bkwd_desc, - cpu_engine, - pool_fwd_prim_desc); - - this->AllocateOutputTensor(context, pool_bkwd_prim_desc, - orig_input_dims_mkl_order, - this->data_format_mkldnn_, - &output_tensor); - output_dnn_data.SetUsrMemDataHandle(output_tensor); - - ConfigureWorkspace(workspace_tensor, - pool_fwd_prim_desc.workspace_primitive_desc(), - &workspace_dnn_data); - this->PrepareAndExecuteNet(pool_bkwd_prim_desc, - &grad_dnn_data, - &output_dnn_data, - memory::primitive_desc( - target_diff_dst_md, - cpu_engine), - &workspace_dnn_data); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Compute received an exception:", - error_msg)); + auto cpu_engine = engine(engine::cpu, 0); + const Tensor& orig_input_tensor = + MklGetInput(context, kInputTensorIndexOrigInput); + const Tensor& orig_output_tensor = + MklGetInput(context, kInputTensorIndexOrigOutput); + const Tensor& grad_tensor = + MklGetInput(context, kInputTensorIndexGradient); + const Tensor& workspace_tensor = + MklGetInput(context, kInputTensorIndexWorkspace); + MklDnnShape orig_input_mkl_shape, orig_output_mkl_shape, grad_mkl_shape, + workspace_mkl_shape; + GetMklShape(context, kInputTensorIndexOrigInput, &orig_input_mkl_shape); + GetMklShape(context, kInputTensorIndexOrigOutput, &orig_output_mkl_shape); + GetMklShape(context, kInputTensorIndexGradient, &grad_mkl_shape); + GetMklShape(context, kInputTensorIndexWorkspace, &workspace_mkl_shape); + + SanityCheckInputs(context, orig_input_tensor, orig_output_tensor, + grad_tensor, workspace_tensor, orig_input_mkl_shape, + orig_output_mkl_shape, grad_mkl_shape, + workspace_mkl_shape); + if (!context->status().ok()) return; + + MklDnnData grad_dnn_data(&cpu_engine); + MklDnnData workspace_dnn_data(&cpu_engine); + MklDnnData output_dnn_data(&cpu_engine); + Tensor* output_tensor = nullptr; + MklPoolParameters pool_params; + TensorShape orig_input_shape; + memory::dims output_dims_mkl_order, orig_input_dims_mkl_order; + memory::desc original_input_md = ConfigureOriginalInput( + context, orig_input_tensor, orig_input_mkl_shape, + &orig_input_dims_mkl_order, &pool_params, &orig_input_shape); + + memory::desc original_output_md = this->ConfigureOriginalOutput( + pool_params, orig_output_mkl_shape, output_dims_mkl_order); + + memory::desc target_diff_dst_md = this->ConfigureInputGradient( + grad_mkl_shape, grad_tensor, &grad_dnn_data, original_output_md); + + output_dnn_data.SetUsrMem(original_input_md); + + // Create the forward pooling primitive descriptor so we can + // pass it as a hint to the backward pooling primitive descriptor + auto pool_fwd_desc = pooling_forward::desc( + prop_kind::forward, algorithm::pooling_max, original_input_md, + original_output_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_fwd_prim_desc = + pooling_forward::primitive_desc(pool_fwd_desc, cpu_engine); + + auto pool_bkwd_desc = pooling_backward::desc( + algorithm::pooling_max, output_dnn_data.GetUsrMemDesc(), + target_diff_dst_md, + memory::dims({pool_params.row_stride, pool_params.col_stride}), + memory::dims({pool_params.window_rows, pool_params.window_cols}), + memory::dims({static_cast(pool_params.pad_top), + static_cast(pool_params.pad_left)}), + memory::dims({static_cast(pool_params.pad_bottom), + static_cast(pool_params.pad_right)}), + TFPaddingToMklDnnPadding(this->padding_)); + auto pool_bkwd_prim_desc = pooling_backward::primitive_desc( + pool_bkwd_desc, cpu_engine, pool_fwd_prim_desc); + + this->AllocateOutputTensor(context, pool_bkwd_prim_desc, + orig_input_dims_mkl_order, + this->data_format_mkldnn_, &output_tensor); + output_dnn_data.SetUsrMemDataHandle(output_tensor); + + ConfigureWorkspace(workspace_tensor, + pool_fwd_prim_desc.workspace_primitive_desc(), + &workspace_dnn_data); + this->PrepareAndExecuteNet( + pool_bkwd_prim_desc, &grad_dnn_data, &output_dnn_data, + memory::primitive_desc(target_diff_dst_md, cpu_engine), + &workspace_dnn_data); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK(context, errors::Aborted("Compute received an exception:", + error_msg)); } } // Compute private: - // .Input("orig_input: T") - // .Input("orig_output: T") - // .Input("grad: T") - // .Input("workspace: T") - const int kInputTensorIndexOrigInput = 0; - const int kInputTensorIndexOrigOutput = 1; - const int kInputTensorIndexGradient = 2; - const int kInputTensorIndexWorkspace = 3; - // Output("output: T") in Base Class - - memory::desc ConfigureOriginalInput(OpKernelContext* context, - const Tensor& tensor_original_input, - const MklDnnShape& original_input_mkl_shape, - memory::dims* original_input_dims_mkl_order, - MklPoolParameters* pool_params, - TensorShape* input_tensor_shape) { - *input_tensor_shape = tensor_original_input.shape(); - return MklPoolingBackwardOpBase::ConfigureOriginalInput( - context, - tensor_original_input, - original_input_mkl_shape, - original_input_dims_mkl_order, - pool_params, - *input_tensor_shape); - } + // .Input("orig_input: T") + // .Input("orig_output: T") + // .Input("grad: T") + // .Input("workspace: T") + const int kInputTensorIndexOrigInput = 0; + const int kInputTensorIndexOrigOutput = 1; + const int kInputTensorIndexGradient = 2; + const int kInputTensorIndexWorkspace = 3; + // Output("output: T") in Base Class + + memory::desc ConfigureOriginalInput( + OpKernelContext* context, const Tensor& tensor_original_input, + const MklDnnShape& original_input_mkl_shape, + memory::dims* original_input_dims_mkl_order, + MklPoolParameters* pool_params, TensorShape* input_tensor_shape) { + *input_tensor_shape = tensor_original_input.shape(); + return MklPoolingBackwardOpBase::ConfigureOriginalInput( + context, tensor_original_input, original_input_mkl_shape, + original_input_dims_mkl_order, pool_params, *input_tensor_shape); + } - void ConfigureWorkspace(const Tensor& workspace_tensor, - memory::primitive_desc workspace_pd, - MklDnnData *workspace_dnn_data) { - CHECK_NOTNULL(workspace_dnn_data); + void ConfigureWorkspace(const Tensor& workspace_tensor, + memory::primitive_desc workspace_pd, + MklDnnData* workspace_dnn_data) { + CHECK_NOTNULL(workspace_dnn_data); - workspace_dnn_data->SetUsrMem(workspace_pd, &workspace_tensor); - } + workspace_dnn_data->SetUsrMem(workspace_pd, &workspace_tensor); + } - void SanityCheckInputs(OpKernelContext* context, - const Tensor& orig_input_tensor, - const Tensor& orig_output_tensor, - const Tensor& grad_tensor, - const Tensor& workspace_tensor, - const MklDnnShape& orig_input_mkl_shape, - const MklDnnShape& orig_output_mkl_shape, - const MklDnnShape& grad_mkl_shape, - const MklDnnShape& workspace_mkl_shape) { - if (!orig_input_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, orig_input_tensor.dims() == 4, - errors::InvalidArgument("Original input shape must be " - "4-dimensional")); - } else { - OP_REQUIRES(context, orig_input_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Original input shape must be " - "4-dimensional")); - } - if (!orig_output_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, orig_output_tensor.dims() == 4, - errors::InvalidArgument("Original output must be " - "4-dimensional")); - } else { - OP_REQUIRES(context, orig_output_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Original output must be " - "4-dimensional")); - } - if (!grad_mkl_shape.IsMklTensor()) { - OP_REQUIRES(context, grad_tensor.dims() == 4, - errors::InvalidArgument("Gradient must be 4-dimensional")); - } else { - OP_REQUIRES(context, grad_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Gradient must be " - "4-dimensional")); - } - if (this->workspace_enabled_) { - // The workspace should not be an MKL tensor - OP_REQUIRES(context, workspace_mkl_shape.IsMklTensor() == false, - errors::InvalidArgument("Workspace tensor should not" - " be an MKL Tensor.")); - // It should only have one dimension - OP_REQUIRES(context, workspace_tensor.dims() == 1, - errors::InvalidArgument("Workspace tensor must be " - "1-dimensional")); - } else { - OP_REQUIRES(context, this->workspace_enabled_, - errors::Unimplemented("MKL-DNN Max Pooling does not " + void SanityCheckInputs(OpKernelContext* context, + const Tensor& orig_input_tensor, + const Tensor& orig_output_tensor, + const Tensor& grad_tensor, + const Tensor& workspace_tensor, + const MklDnnShape& orig_input_mkl_shape, + const MklDnnShape& orig_output_mkl_shape, + const MklDnnShape& grad_mkl_shape, + const MklDnnShape& workspace_mkl_shape) { + if (!orig_input_mkl_shape.IsMklTensor()) { + OP_REQUIRES(context, orig_input_tensor.dims() == 4, + errors::InvalidArgument("Original input shape must be " + "4-dimensional")); + } else { + OP_REQUIRES(context, orig_input_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Original input shape must be " + "4-dimensional")); + } + if (!orig_output_mkl_shape.IsMklTensor()) { + OP_REQUIRES(context, orig_output_tensor.dims() == 4, + errors::InvalidArgument("Original output must be " + "4-dimensional")); + } else { + OP_REQUIRES(context, orig_output_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Original output must be " + "4-dimensional")); + } + if (!grad_mkl_shape.IsMklTensor()) { + OP_REQUIRES(context, grad_tensor.dims() == 4, + errors::InvalidArgument("Gradient must be 4-dimensional")); + } else { + OP_REQUIRES(context, grad_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Gradient must be " + "4-dimensional")); + } + if (this->workspace_enabled_) { + // The workspace should not be an MKL tensor + OP_REQUIRES(context, workspace_mkl_shape.IsMklTensor() == false, + errors::InvalidArgument("Workspace tensor should not" + " be an MKL Tensor.")); + // It should only have one dimension + OP_REQUIRES(context, workspace_tensor.dims() == 1, + errors::InvalidArgument("Workspace tensor must be " + "1-dimensional")); + } else { + OP_REQUIRES( + context, this->workspace_enabled_, + errors::Unimplemented("MKL-DNN Max Pooling does not " "yet support the use case " "where MaxPoolGrad is called without first" " calling MaxPool.")); - } } + } }; // MklMaxPoolingGradOp #endif // INTEL_MKL_DNN diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.cc b/tensorflow/core/kernels/mkl_pooling_ops_common.cc index f7cadffd39..ef8597b057 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.cc +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.cc @@ -15,9 +15,9 @@ limitations under the License. #ifdef INTEL_MKL -#include -#include #include "tensorflow/core/kernels/mkl_pooling_ops_common.h" +#include +#include #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/framework/common_shape_fns.h" #include "tensorflow/core/kernels/bounds_check.h" @@ -111,17 +111,17 @@ void MklPoolParameters::Init(OpKernelContext* context, // TF can work with int64, but mkldnn only supports int32 // Fail if the height or width are greater than MAX_INT - OP_REQUIRES(context, FastBoundsCheck(out_height, - std::numeric_limits::max()), + OP_REQUIRES(context, + FastBoundsCheck(out_height, std::numeric_limits::max()), errors::InvalidArgument("output height is too large")); - OP_REQUIRES(context, FastBoundsCheck(out_width, - std::numeric_limits::max()), + OP_REQUIRES(context, + FastBoundsCheck(out_width, std::numeric_limits::max()), errors::InvalidArgument("output width is too large")); #endif out_depth = depth; // output will have the same depth as the input - } else { // we are pooling in the depth dimension + } else { // we are pooling in the depth dimension // Our current version of depthwise max pooling does not support // any padding, and expects the depth_window to equal the depth // stride (no overlapping). diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index b974b2c59a..880e45ab1e 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -17,16 +17,16 @@ limitations under the License. #define TENSORFLOW_CORE_KERNELS_MKL_POOLING_OPS_COMMON_H_ #ifdef INTEL_MKL -#include #include +#include #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/padding.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" using mkldnn::memory; -using mkldnn::pooling_forward; using mkldnn::pooling_backward; +using mkldnn::pooling_forward; using mkldnn::stream; #endif @@ -61,13 +61,25 @@ struct MklPoolParameters { TensorFormat data_format; MklPoolParameters() - : depth(0) - , tensor_in_cols(0), tensor_in_rows(0), tensor_in_batch(0) - , window_rows(0), window_cols(0), depth_window(0) - , row_stride(0), col_stride(0), depth_stride(0) - , out_height(0), out_width(0), out_depth(0) - , pad_left(0), pad_right(0), pad_top(0), pad_bottom(0), pad_depth(0) - , data_format(TensorFormat::FORMAT_NCHW) {} + : depth(0), + tensor_in_cols(0), + tensor_in_rows(0), + tensor_in_batch(0), + window_rows(0), + window_cols(0), + depth_window(0), + row_stride(0), + col_stride(0), + depth_stride(0), + out_height(0), + out_width(0), + out_depth(0), + pad_left(0), + pad_right(0), + pad_top(0), + pad_bottom(0), + pad_depth(0), + data_format(TensorFormat::FORMAT_NCHW) {} // Updates context->status if there is an invalid input. void Init(OpKernelContext* context, const std::vector& ksize, @@ -96,33 +108,31 @@ template class MklPoolingOpBase : public OpKernel { public: explicit MklPoolingOpBase(OpKernelConstruction* context) - : OpKernel(context) - , workspace_enabled_(false) { - string data_format; - OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format)); - OP_REQUIRES(context, - FormatFromString(data_format, &this->data_format_tf_), - errors::InvalidArgument("Invalid data format")); - this->data_format_mkldnn_ - = TFDataFormatToMklDnnDataFormat(this->data_format_tf_); - OP_REQUIRES_OK(context, context->GetAttr("ksize", &this->ksize_)); - OP_REQUIRES(context, this->ksize_.size() == 4, - errors::InvalidArgument("Sliding window ksize field must " - "specify 4 dimensions")); - OP_REQUIRES_OK(context, context->GetAttr("strides", &this->stride_)); - OP_REQUIRES(context, this->stride_.size() == 4, - errors::InvalidArgument("Sliding window strides field must " - "specify 4 dimensions")); - OP_REQUIRES_OK(context, context->GetAttr("padding", &this->padding_)); - OP_REQUIRES(context, this->ksize_[0] == 1 && this->stride_[0] == 1, - errors::Unimplemented("Pooling is not yet supported on the " - "batch dimension.")); - - // We may not get this attribute for this node if it does not go through - // graph rewrite pass. So we do not check for error while retrieving this - // attribute value. - context->GetAttr("workspace_enabled", &this->workspace_enabled_); - } + : OpKernel(context), workspace_enabled_(false) { + string data_format; + OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format)); + OP_REQUIRES(context, FormatFromString(data_format, &this->data_format_tf_), + errors::InvalidArgument("Invalid data format")); + this->data_format_mkldnn_ = + TFDataFormatToMklDnnDataFormat(this->data_format_tf_); + OP_REQUIRES_OK(context, context->GetAttr("ksize", &this->ksize_)); + OP_REQUIRES(context, this->ksize_.size() == 4, + errors::InvalidArgument("Sliding window ksize field must " + "specify 4 dimensions")); + OP_REQUIRES_OK(context, context->GetAttr("strides", &this->stride_)); + OP_REQUIRES(context, this->stride_.size() == 4, + errors::InvalidArgument("Sliding window strides field must " + "specify 4 dimensions")); + OP_REQUIRES_OK(context, context->GetAttr("padding", &this->padding_)); + OP_REQUIRES(context, this->ksize_[0] == 1 && this->stride_[0] == 1, + errors::Unimplemented("Pooling is not yet supported on the " + "batch dimension.")); + + // We may not get this attribute for this node if it does not go through + // graph rewrite pass. So we do not check for error while retrieving this + // attribute value. + context->GetAttr("workspace_enabled", &this->workspace_enabled_); + } void Compute(OpKernelContext* context) override = 0; protected: @@ -132,24 +142,24 @@ class MklPoolingOpBase : public OpKernel { // output height and output width to have already been int32 // bounds-checked void GetOutputDims(const MklPoolParameters& mkl_pool_params, - memory::dims* output_dims_mkl_order) { + memory::dims* output_dims_mkl_order) { // MKL-DNN always needs output in NCHW format. - *output_dims_mkl_order = { mkl_pool_params.tensor_in_batch, + *output_dims_mkl_order = {mkl_pool_params.tensor_in_batch, mkl_pool_params.out_depth, static_cast(mkl_pool_params.out_height), static_cast(mkl_pool_params.out_width)}; } void InitMklPoolParameters(OpKernelContext* context, - MklPoolParameters* pool_params, - const MklDnnShape& original_input_mkl_shape, - const TensorShape& input_tensor_shape) { + MklPoolParameters* pool_params, + const MklDnnShape& original_input_mkl_shape, + const TensorShape& input_tensor_shape) { if (!original_input_mkl_shape.IsMklTensor()) { pool_params->Init(context, this->ksize_, this->stride_, this->padding_, - this->data_format_tf_, input_tensor_shape); + this->data_format_tf_, input_tensor_shape); } else { pool_params->Init(context, this->ksize_, this->stride_, this->padding_, - this->data_format_tf_, &original_input_mkl_shape); + this->data_format_tf_, &original_input_mkl_shape); } } @@ -159,13 +169,12 @@ class MklPoolingOpBase : public OpKernel { size_t GetNumTElements(const memory::primitive_desc& pd) { size_t num_bytes = pd.get_size(); size_t ret_val = num_bytes / sizeof(T); - if ( num_bytes % sizeof(T) != 0 ) { - ret_val++; + if (num_bytes % sizeof(T) != 0) { + ret_val++; } return ret_val; } - std::vector ksize_; std::vector stride_; Padding padding_; @@ -183,30 +192,29 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { protected: void ConfigureInput(OpKernelContext* context, - const MklDnnShape& input_mkl_shape, - const Tensor& input_tensor, - MklPoolParameters* pool_params, - MklDnnData* dnn_data_input) { + const MklDnnShape& input_mkl_shape, + const Tensor& input_tensor, + MklPoolParameters* pool_params, + MklDnnData* dnn_data_input) { CHECK_NOTNULL(pool_params); CHECK_NOTNULL(dnn_data_input); TensorShape input_tensor_shape = input_tensor.shape(); - memory::desc input_md = input_mkl_shape.IsMklTensor() - ? input_mkl_shape.GetMklLayout() - : memory::desc( - TFShapeToMklDnnDimsInNCHW( - input_tensor_shape, this->data_format_tf_), - MklDnnType(), - this->data_format_mkldnn_); + memory::desc input_md = + input_mkl_shape.IsMklTensor() + ? input_mkl_shape.GetMklLayout() + : memory::desc(TFShapeToMklDnnDimsInNCHW(input_tensor_shape, + this->data_format_tf_), + MklDnnType(), this->data_format_mkldnn_); dnn_data_input->SetUsrMem(input_md, &input_tensor); - this->InitMklPoolParameters(context, pool_params, - input_mkl_shape, input_tensor_shape); + this->InitMklPoolParameters(context, pool_params, input_mkl_shape, + input_tensor_shape); } - void AllocateOutputTensor(OpKernelContext* context, - const pooling_forward::primitive_desc& pool_fwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const pooling_forward::primitive_desc& pool_fwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); memory::primitive_desc dst_pd = pool_fwd_prim_desc.dst_primitive_desc(); @@ -215,50 +223,42 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; // only allocate enough space for the elements we need. output_tf_shape.AddDim(this->GetNumTElements(dst_pd)); - AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, - output_tensor, - output_tf_shape, output_mkl_shape); + AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, output_tensor, + output_tf_shape, output_mkl_shape); CHECK_NOTNULL(*output_tensor); } void PrepareAndExecuteNet( - const pooling_forward::primitive_desc& pool_fwd_desc, - const MklDnnData* src, - MklDnnData* dst, - MklDnnData* wksp = nullptr) { + const pooling_forward::primitive_desc& pool_fwd_desc, + const MklDnnData* src, MklDnnData* dst, + MklDnnData* wksp = nullptr) { std::vector net; // Create pooling primitive and add it to net if (wksp != nullptr) { - net.push_back(pooling_forward(pool_fwd_desc, - src->GetOpMem(), - dst->GetOpMem(), - wksp->GetOpMem())); + net.push_back(pooling_forward(pool_fwd_desc, src->GetOpMem(), + dst->GetOpMem(), wksp->GetOpMem())); } else { - net.push_back(pooling_forward(pool_fwd_desc, - src->GetOpMem(), - dst->GetOpMem())); + net.push_back( + pooling_forward(pool_fwd_desc, src->GetOpMem(), dst->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } - - void SanityCheckInput(OpKernelContext* context, - const Tensor& input_tensor, - const MklDnnShape& input_mkl_shape) { + void SanityCheckInput(OpKernelContext* context, const Tensor& input_tensor, + const MklDnnShape& input_mkl_shape) { if (!input_mkl_shape.IsMklTensor()) { OP_REQUIRES(context, input_tensor.dims() == 4, - errors::InvalidArgument("Input must be 4-dimensional")); + errors::InvalidArgument("Input must be 4-dimensional")); } else { - OP_REQUIRES(context, input_mkl_shape.GetDimension() == 4, - errors::InvalidArgument("Input shape must be " - "4-dimensional")); + OP_REQUIRES(context, input_mkl_shape.GetDimension() == 4, + errors::InvalidArgument("Input shape must be " + "4-dimensional")); } } // .Input("value: T") @@ -267,66 +267,58 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase { const int kOutputTensorIndexOutput = 0; }; // MklPoolingForwardBaseOp - template class MklPoolingBackwardOpBase : public MklPoolingOpBase { public: explicit MklPoolingBackwardOpBase(OpKernelConstruction* context) - : MklPoolingOpBase(context) { } + : MklPoolingOpBase(context) {} void Compute(OpKernelContext* context) override = 0; protected: const int kOutputTensorIndexOutput = 0; - void AllocateOutputTensor(OpKernelContext* context, - const pooling_backward::primitive_desc& pool_bkwd_prim_desc, - const memory::dims output_dims_mkl_order, - const memory::format& output_tf_format, - Tensor** output_tensor) { + void AllocateOutputTensor( + OpKernelContext* context, + const pooling_backward::primitive_desc& pool_bkwd_prim_desc, + const memory::dims output_dims_mkl_order, + const memory::format& output_tf_format, Tensor** output_tensor) { CHECK_NOTNULL(output_tensor); - memory::primitive_desc dst_pd - = pool_bkwd_prim_desc.diff_src_primitive_desc(); + memory::primitive_desc dst_pd = + pool_bkwd_prim_desc.diff_src_primitive_desc(); MklDnnShape output_mkl_shape; output_mkl_shape.SetMklTensor(true); output_mkl_shape.SetMklLayout(&dst_pd); output_mkl_shape.SetElemType(MklDnnType()); output_mkl_shape.SetTfLayout(output_dims_mkl_order.size(), - output_dims_mkl_order, - output_tf_format); + output_dims_mkl_order, output_tf_format); TensorShape output_tf_shape; output_tf_shape.AddDim(this->GetNumTElements(dst_pd)); - AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, - output_tensor, - output_tf_shape, output_mkl_shape); + AllocateOutputSetMklShape(context, kOutputTensorIndexOutput, output_tensor, + output_tf_shape, output_mkl_shape); CHECK_NOTNULL(*output_tensor); } void PrepareAndExecuteNet( - const pooling_backward::primitive_desc& pool_bkwd_desc, - MklDnnData* input_gradient_diff_dst, - MklDnnData* output_diff_src, - const memory::primitive_desc& target_diff_dst_pd, - const MklDnnData* workspace = nullptr) { - + const pooling_backward::primitive_desc& pool_bkwd_desc, + MklDnnData* input_gradient_diff_dst, MklDnnData* output_diff_src, + const memory::primitive_desc& target_diff_dst_pd, + const MklDnnData* workspace = nullptr) { std::vector net; // If the input gradient isn't in the same format as the output // reorder it to the same format as the output - input_gradient_diff_dst->CheckReorderToOpMem( - target_diff_dst_pd, - &net); + input_gradient_diff_dst->CheckReorderToOpMem(target_diff_dst_pd, &net); // Create pooling primitive and add it to net if (nullptr == workspace) { net.push_back(pooling_backward(pool_bkwd_desc, - input_gradient_diff_dst->GetOpMem(), - output_diff_src->GetOpMem())); + input_gradient_diff_dst->GetOpMem(), + output_diff_src->GetOpMem())); } else { - net.push_back(pooling_backward(pool_bkwd_desc, - input_gradient_diff_dst->GetOpMem(), - workspace->GetOpMem(), - output_diff_src->GetOpMem())); + net.push_back( + pooling_backward(pool_bkwd_desc, input_gradient_diff_dst->GetOpMem(), + workspace->GetOpMem(), output_diff_src->GetOpMem())); } stream(stream::kind::eager).submit(net).wait(); } @@ -334,77 +326,73 @@ class MklPoolingBackwardOpBase : public MklPoolingOpBase { // Max Pooling and Avg Pooling have slightly different implementations // Takes the Tensor containing original input data and the original // mkl Dnn Shape and populates other data - memory::desc ConfigureOriginalInput(OpKernelContext* context, - const Tensor& tensor_original_input_shape, - const MklDnnShape& original_input_mkl_shape, - memory::dims* original_input_dims_nchw, - MklPoolParameters* pool_params, - const TensorShape& input_tensor_shape) { + memory::desc ConfigureOriginalInput( + OpKernelContext* context, const Tensor& tensor_original_input_shape, + const MklDnnShape& original_input_mkl_shape, + memory::dims* original_input_dims_nchw, MklPoolParameters* pool_params, + const TensorShape& input_tensor_shape) { CHECK_NOTNULL(original_input_dims_nchw); CHECK_NOTNULL(pool_params); - this->InitMklPoolParameters(context, pool_params, - original_input_mkl_shape, - input_tensor_shape); - - *original_input_dims_nchw - = original_input_mkl_shape.IsMklTensor() - ? original_input_mkl_shape.GetSizesAsMklDnnDims() - : TFShapeToMklDnnDimsInNCHW(input_tensor_shape, - this->data_format_tf_); - - return original_input_mkl_shape.IsMklTensor() - ? original_input_mkl_shape.GetMklLayout() - : memory::desc(*original_input_dims_nchw, - MklDnnType(), - this->data_format_mkldnn_); + this->InitMklPoolParameters(context, pool_params, original_input_mkl_shape, + input_tensor_shape); + + *original_input_dims_nchw = + original_input_mkl_shape.IsMklTensor() + ? original_input_mkl_shape.GetSizesAsMklDnnDims() + : TFShapeToMklDnnDimsInNCHW(input_tensor_shape, + this->data_format_tf_); + + return original_input_mkl_shape.IsMklTensor() + ? original_input_mkl_shape.GetMklLayout() + : memory::desc(*original_input_dims_nchw, MklDnnType(), + this->data_format_mkldnn_); } - memory::desc ConfigureOriginalOutput(const MklPoolParameters& pool_params, - const MklDnnShape& original_output_mkl_shape, - memory::dims output_dims_mkl_order) { + memory::desc ConfigureOriginalOutput( + const MklPoolParameters& pool_params, + const MklDnnShape& original_output_mkl_shape, + memory::dims output_dims_mkl_order) { this->GetOutputDims(pool_params, &output_dims_mkl_order); return original_output_mkl_shape.IsMklTensor() - ? original_output_mkl_shape.GetMklLayout() - : memory::desc(output_dims_mkl_order, - MklDnnType(), - this->data_format_mkldnn_); + ? original_output_mkl_shape.GetMklLayout() + : memory::desc(output_dims_mkl_order, MklDnnType(), + this->data_format_mkldnn_); } memory::desc ConfigureInputGradient( - const MklDnnShape& input_gradient_mkl_shape, - const Tensor& input_gradient_tensor, - MklDnnData* input_gradient_dnn_data, - const memory::desc& original_output_md) { + const MklDnnShape& input_gradient_mkl_shape, + const Tensor& input_gradient_tensor, + MklDnnData* input_gradient_dnn_data, + const memory::desc& original_output_md) { // Configure the gradient as is - memory::desc original_input_grad_md - = input_gradient_mkl_shape.IsMklTensor() - ? input_gradient_mkl_shape.GetMklLayout() - : memory::desc(TFShapeToMklDnnDimsInNCHW( - input_gradient_tensor.shape(), - this->data_format_tf_), - MklDnnType(), this->data_format_mkldnn_); + memory::desc original_input_grad_md = + input_gradient_mkl_shape.IsMklTensor() + ? input_gradient_mkl_shape.GetMklLayout() + : memory::desc( + TFShapeToMklDnnDimsInNCHW(input_gradient_tensor.shape(), + this->data_format_tf_), + MklDnnType(), this->data_format_mkldnn_); input_gradient_dnn_data->SetUsrMem(original_input_grad_md, - &input_gradient_tensor); + &input_gradient_tensor); // Check to see if input grad diff dst is in the right format // Create a new memory descriptor with the same shape as the // original, but the format of the other tensors. memory::format original_output_format = - static_cast(original_output_md.data.format); - bool grad_reorder_needed = input_gradient_dnn_data->IsReorderNeeded( - original_output_format); - memory::dims diff_dst_dims = input_gradient_mkl_shape.IsMklTensor() - ? input_gradient_mkl_shape.GetSizesAsMklDnnDims() - : TFShapeToMklDnnDimsInNCHW(input_gradient_tensor.shape(), - this->data_format_tf_); - memory::desc target_diff_dst_md = memory::desc(diff_dst_dims, - MklDnnType(), original_output_format); - - return grad_reorder_needed - ? target_diff_dst_md - : original_input_grad_md; + static_cast(original_output_md.data.format); + bool grad_reorder_needed = + input_gradient_dnn_data->IsReorderNeeded(original_output_format); + memory::dims diff_dst_dims = + input_gradient_mkl_shape.IsMklTensor() + ? input_gradient_mkl_shape.GetSizesAsMklDnnDims() + : TFShapeToMklDnnDimsInNCHW(input_gradient_tensor.shape(), + this->data_format_tf_); + memory::desc target_diff_dst_md = + memory::desc(diff_dst_dims, MklDnnType(), original_output_format); + + return grad_reorder_needed ? target_diff_dst_md : original_input_grad_md; } }; #endif // INTEL_MKL_DNN diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index dc899d8c7e..873aca30ca 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -16,29 +16,29 @@ limitations under the License. // See docs in ../ops/nn_ops.cc. #ifdef INTEL_MKL +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "tensorflow/core/platform/default/logging.h" -#include "tensorflow/core/util/mkl_util.h" #include "mkl_dnn.h" #include "mkl_dnn_types.h" +#include "tensorflow/core/platform/default/logging.h" +#include "tensorflow/core/util/mkl_util.h" #ifdef INTEL_MKL_DNN #include "mkldnn.hpp" -using mkldnn::stream; -using mkldnn::prop_kind; using mkldnn::algorithm; -using mkldnn::relu_forward; -using mkldnn::relu_backward; -using mkldnn::eltwise_relu; using mkldnn::eltwise_elu; +using mkldnn::eltwise_relu; using mkldnn::eltwise_tanh; +using mkldnn::prop_kind; +using mkldnn::relu_backward; +using mkldnn::relu_forward; +using mkldnn::stream; #endif namespace tensorflow { @@ -180,7 +180,6 @@ class MklReluOp : public OpKernel { } MklReluOpContext; }; - template class MklReluGradOp : public OpKernel { public: @@ -214,10 +213,11 @@ class MklReluGradOp : public OpKernel { if (!dnnLayoutCompare_F32(lt_input, lt_grad)) { AllocTmpBuffer(context, mkl_tmp_input_buf_tensor, lt_grad, &mkl_buffer_convert); - CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input, - lt_grad), E_SUCCESS); + CHECK_EQ(dnnConversionCreate_F32(&cv_input_to_grad, lt_input, lt_grad), + E_SUCCESS); CHECK_EQ(dnnConversionExecute_F32(cv_input_to_grad, buf_input, - mkl_buffer_convert), E_SUCCESS); + mkl_buffer_convert), + E_SUCCESS); relu_res[dnnResourceSrc] = mkl_buffer_convert; dnnDelete_F32(cv_input_to_grad); } else { @@ -325,7 +325,8 @@ void MklReluGradOp::Compute(OpKernelContext* context) { float negative_slope = 0.0; CHECK_EQ(dnnReLUCreateBackward_F32(&mkl_context.prim_relu_bwd, NULL, mkl_context.lt_grad, mkl_context.lt_grad, - negative_slope), E_SUCCESS); + negative_slope), + E_SUCCESS); Tensor mkl_tmp_input_buf_tensor; mkl_context.MklPrepareReluGradInputs(context, &mkl_tmp_input_buf_tensor); @@ -348,7 +349,8 @@ void MklReluGradOp::Compute(OpKernelContext* context) { } tf_shape.AddDim(dnnLayoutGetMemorySize_F32(static_cast( - mkl_context.output_shape.GetMklLayout())) / sizeof(T)); + mkl_context.output_shape.GetMklLayout())) / + sizeof(T)); AllocateOutputSetMklShape(context, 0, &output, tf_shape, mkl_context.output_shape); } else { @@ -361,13 +363,11 @@ void MklReluGradOp::Compute(OpKernelContext* context) { mkl_context.relu_res[dnnResourceDiffSrc] = static_cast(output->flat().data()); - CHECK_EQ(dnnExecute_F32(mkl_context.prim_relu_bwd, - mkl_context.relu_res), - E_SUCCESS); + CHECK_EQ(dnnExecute_F32(mkl_context.prim_relu_bwd, mkl_context.relu_res), + E_SUCCESS); mkl_context.MklCleanup(); } - #else // INTEL_MKL_DNN template @@ -375,8 +375,7 @@ class MklReluOpBase : public OpKernel { public: ~MklReluOpBase() {} - explicit MklReluOpBase(OpKernelConstruction* context) : OpKernel(context) { - } + explicit MklReluOpBase(OpKernelConstruction* context) : OpKernel(context) {} virtual void Compute_Scalar(OpKernelContext* context) = 0; @@ -413,12 +412,12 @@ class MklReluOpBase : public OpKernel { T alpha = 0, beta = 0; std::shared_ptr relu_fwd_pd; - auto relu_fwd_desc = relu_forward::desc(prop_kind::forward_training, + auto relu_fwd_desc = relu_forward::desc( + prop_kind::forward_training, // Operator memory descriptor is same as user memory descriptor. - alg_kind, src.GetUsrMemDesc(), - alpha, beta); - relu_fwd_pd.reset(new relu_forward::primitive_desc(relu_fwd_desc, - cpu_engine)); + alg_kind, src.GetUsrMemDesc(), alpha, beta); + relu_fwd_pd.reset( + new relu_forward::primitive_desc(relu_fwd_desc, cpu_engine)); // allocate dst tensor MklDnnShape dnn_shape_dst; @@ -431,7 +430,7 @@ class MklReluOpBase : public OpKernel { dnn_shape_dst.SetTfLayout(dnn_shape_src.GetDimension(), dnn_shape_src.GetSizesAsMklDnnDims(), dnn_shape_src.GetTfDataFormat()); - tf_shape_dst.AddDim(dst_pd.get_size()/sizeof(T)); + tf_shape_dst.AddDim(dst_pd.get_size() / sizeof(T)); } else { dnn_shape_dst.SetMklTensor(false); tf_shape_dst = src_tensor.shape(); @@ -445,34 +444,32 @@ class MklReluOpBase : public OpKernel { // execute net std::vector net; - auto relu_fwd = relu_forward(*relu_fwd_pd, src.GetOpMem(), - dst.GetOpMem()); + auto relu_fwd = + relu_forward(*relu_fwd_pd, src.GetOpMem(), dst.GetOpMem()); net.push_back(relu_fwd); stream(stream::kind::eager).submit(net).wait(); - } catch (mkldnn::error &e) { + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } }; - template class MklReluGradOpBase : public OpKernel { public: ~MklReluGradOpBase() {} - explicit MklReluGradOpBase(OpKernelConstruction* context) : - OpKernel(context) {} + explicit MklReluGradOpBase(OpKernelConstruction* context) + : OpKernel(context) {} virtual void Compute_Scalar(OpKernelContext* context) = 0; - void Compute(OpKernelContext* context) { + void Compute(OpKernelContext* context) { try { auto cpu_engine = engine(engine::cpu, 0); MklDnnData src(&cpu_engine); @@ -483,9 +480,9 @@ class MklReluGradOpBase : public OpKernel { const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); - Tensor* diff_src_tensor = nullptr; + Tensor* diff_src_tensor = nullptr; MklDnnShape dnn_shape_src, dnn_shape_diff_dst; GetMklShape(context, src_index, &dnn_shape_src); @@ -526,25 +523,25 @@ class MklReluGradOpBase : public OpKernel { src_md = dnn_shape_src.GetMklLayout(); memory::format src_mkl_data_format = dnn_shape_src.GetTfDataFormat(); - auto src_tf_data_format = MklDnnDataFormatToTFDataFormat( - src_mkl_data_format); + auto src_tf_data_format = + MklDnnDataFormatToTFDataFormat(src_mkl_data_format); auto diff_dst_dims = TFShapeToMklDnnDimsInNCHW(diff_dst_tensor.shape(), src_tf_data_format); - diff_dst_md = memory::desc(diff_dst_dims, MklDnnType(), - src_mkl_data_format); + diff_dst_md = + memory::desc(diff_dst_dims, MklDnnType(), src_mkl_data_format); } else if (!dnn_shape_src.IsMklTensor() && - dnn_shape_diff_dst.IsMklTensor()) { + dnn_shape_diff_dst.IsMklTensor()) { // Same comment as above. diff_dst_md = dnn_shape_diff_dst.GetMklLayout(); memory::format diff_dst_mkl_data_format = - dnn_shape_diff_dst.GetTfDataFormat(); - auto diff_dst_tf_data_format = MklDnnDataFormatToTFDataFormat( - diff_dst_mkl_data_format); + dnn_shape_diff_dst.GetTfDataFormat(); + auto diff_dst_tf_data_format = + MklDnnDataFormatToTFDataFormat(diff_dst_mkl_data_format); auto src_dims = TFShapeToMklDnnDimsInNCHW(src_tensor.shape(), diff_dst_tf_data_format); - src_md = memory::desc(src_dims, MklDnnType(), - diff_dst_mkl_data_format); + src_md = + memory::desc(src_dims, MklDnnType(), diff_dst_mkl_data_format); } else { // If both the inputs are in MKL format, we use Mkl layout of the input // tensors. @@ -572,12 +569,12 @@ class MklReluGradOpBase : public OpKernel { std::shared_ptr relu_fwd_pd; auto relu_fwd_desc = relu_forward::desc(prop_kind::forward_training, alg_kind, src_md, alpha, beta); - relu_fwd_pd.reset(new relu_forward::primitive_desc(relu_fwd_desc, - cpu_engine)); - auto relu_bwd_desc = relu_backward::desc(alg_kind, common_md, common_md, - alpha, beta); - auto relu_bwd_pd = relu_backward::primitive_desc(relu_bwd_desc, - cpu_engine, *relu_fwd_pd); + relu_fwd_pd.reset( + new relu_forward::primitive_desc(relu_fwd_desc, cpu_engine)); + auto relu_bwd_desc = + relu_backward::desc(alg_kind, common_md, common_md, alpha, beta); + auto relu_bwd_pd = relu_backward::primitive_desc( + relu_bwd_desc, cpu_engine, *relu_fwd_pd); // allocate diff_src tensor MklDnnShape dnn_shape_diff_src; @@ -590,33 +587,32 @@ class MklReluGradOpBase : public OpKernel { dnn_shape_diff_src.SetTfLayout(dnn_shape_src.GetDimension(), dnn_shape_src.GetSizesAsMklDnnDims(), dnn_shape_src.GetTfDataFormat()); - tf_shape_diff_src.AddDim(diff_src_pd.get_size()/sizeof(T)); + tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T)); } else { dnn_shape_diff_src.SetMklTensor(false); tf_shape_diff_src = src_tensor.shape(); } AllocateOutputSetMklShape(context, diff_src_index, &diff_src_tensor, - tf_shape_diff_src, dnn_shape_diff_src); + tf_shape_diff_src, dnn_shape_diff_src); // diff_src memory descriptor is same as memory descriptor for both // inputs. diff_src.SetUsrMem(common_md, diff_src_tensor); PrepareAndExecuteNet(relu_bwd_pd, &src, &diff_src, &diff_dst); - } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + } catch (mkldnn::error& e) { + string error_msg = "Status: " + std::to_string(e.status) + + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } void PrepareAndExecuteNet(const relu_backward::primitive_desc& relu_prim_desc, - MklDnnData* src, MklDnnData* diff_src, MklDnnData* - diff_dst) { + MklDnnData* src, MklDnnData* diff_src, + MklDnnData* diff_dst) { std::vector net; // Check if we need to reorder original input tensors into common_md layout @@ -632,14 +628,13 @@ class MklReluGradOpBase : public OpKernel { } }; - template class MklReluOp : public MklReluOpBase { public: ~MklReluOp() {} - explicit MklReluOp(OpKernelConstruction* context) : - MklReluOpBase(context) {} + explicit MklReluOp(OpKernelConstruction* context) + : MklReluOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t src_index = 0; // index of src input tensor @@ -649,15 +644,15 @@ class MklReluOp : public MklReluOpBase { GetMklShape(context, src_index, &dnn_shape_src); Tensor* dst_tensor = nullptr; - void* user_i = static_cast(const_cast( - src_tensor.flat().data())); + void* user_i = + static_cast(const_cast(src_tensor.flat().data())); MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); AllocateOutputSetMklShape(context, dst_index, &dst_tensor, src_tensor.shape(), dnn_shape_dst); void* out_o = static_cast(dst_tensor->flat().data()); (static_cast(out_o))[0] = - std::max((static_cast(user_i))[0], static_cast(0)); + std::max((static_cast(user_i))[0], static_cast(0)); return; } }; @@ -667,14 +662,14 @@ class MklReluGradOp : public MklReluGradOpBase { public: ~MklReluGradOp() {} - explicit MklReluGradOp(OpKernelConstruction* context) : - MklReluGradOpBase(context) {} + explicit MklReluGradOp(OpKernelConstruction* context) + : MklReluGradOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t diff_dst_index = 0; // index of diff_dst input tensor const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); Tensor* diff_src_tensor = nullptr; @@ -687,11 +682,11 @@ class MklReluGradOp : public MklReluGradOpBase { diff_dst_tensor.shape(), dnn_shape_diff_src); void* out_o = static_cast(diff_src_tensor->flat().data()); void* user_i = - static_cast(const_cast(src_tensor.flat().data())); + static_cast(const_cast(src_tensor.flat().data())); void* user_g = - static_cast(const_cast(diff_dst_tensor.flat().data())); - (static_cast(out_o))[0] = (static_cast(user_g))[0] * - ((static_cast(user_i))[0] > 0); + static_cast(const_cast(diff_dst_tensor.flat().data())); + (static_cast(out_o))[0] = + (static_cast(user_g))[0] * ((static_cast(user_i))[0] > 0); return; } }; @@ -701,8 +696,8 @@ class MklEluOp : public MklReluOpBase { public: ~MklEluOp() {} - explicit MklEluOp(OpKernelConstruction* context) : - MklReluOpBase(context) {} + explicit MklEluOp(OpKernelConstruction* context) + : MklReluOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t src_index = 0; // index of src input tensor @@ -712,8 +707,8 @@ class MklEluOp : public MklReluOpBase { GetMklShape(context, src_index, &dnn_shape_src); Tensor* dst_tensor = nullptr; - void* user_i = static_cast(const_cast( - src_tensor.flat().data())); + void* user_i = + static_cast(const_cast(src_tensor.flat().data())); MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); AllocateOutputSetMklShape(context, dst_index, &dst_tensor, @@ -734,14 +729,14 @@ class MklEluGradOp : public MklReluGradOpBase { public: ~MklEluGradOp() {} - explicit MklEluGradOp(OpKernelConstruction* context) : - MklReluGradOpBase(context) {} + explicit MklEluGradOp(OpKernelConstruction* context) + : MklReluGradOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t diff_dst_index = 0; // index of diff_dst input tensor const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); Tensor* diff_src_tensor = nullptr; @@ -754,9 +749,9 @@ class MklEluGradOp : public MklReluGradOpBase { diff_dst_tensor.shape(), dnn_shape_diff_src); void* out_o = static_cast(diff_src_tensor->flat().data()); void* user_i = - static_cast(const_cast(src_tensor.flat().data())); + static_cast(const_cast(src_tensor.flat().data())); void* user_g = - static_cast(const_cast(diff_dst_tensor.flat().data())); + static_cast(const_cast(diff_dst_tensor.flat().data())); // gradient of elu(x) = 1 if x > 0; elu(x) + 1 otherwise T feature = (static_cast(user_i))[0]; if (feature > 0) { @@ -773,8 +768,8 @@ class MklTanhOp : public MklReluOpBase { public: ~MklTanhOp() {} - explicit MklTanhOp(OpKernelConstruction* context) : - MklReluOpBase(context) {} + explicit MklTanhOp(OpKernelConstruction* context) + : MklReluOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t src_index = 0; // index of src input tensor @@ -784,8 +779,8 @@ class MklTanhOp : public MklReluOpBase { GetMklShape(context, src_index, &dnn_shape_src); Tensor* dst_tensor = nullptr; - void* user_i = static_cast(const_cast( - src_tensor.flat().data())); + void* user_i = + static_cast(const_cast(src_tensor.flat().data())); MklDnnShape dnn_shape_dst; dnn_shape_dst.SetMklTensor(false); AllocateOutputSetMklShape(context, dst_index, &dst_tensor, @@ -795,7 +790,7 @@ class MklTanhOp : public MklReluOpBase { T feature = (static_cast(user_i))[0]; T e1 = std::exp(feature); T e2 = std::exp(-feature); - (static_cast(out_o))[0] = (e1 - e2)/(e1 + e2); + (static_cast(out_o))[0] = (e1 - e2) / (e1 + e2); return; } }; @@ -805,14 +800,14 @@ class MklTanhGradOp : public MklReluGradOpBase { public: ~MklTanhGradOp() {} - explicit MklTanhGradOp(OpKernelConstruction* context) : - MklReluGradOpBase(context) {} + explicit MklTanhGradOp(OpKernelConstruction* context) + : MklReluGradOpBase(context) {} virtual void Compute_Scalar(OpKernelContext* context) { const size_t diff_dst_index = 0; // index of diff_dst input tensor const size_t src_index = 1; // index of src input tensor const size_t diff_src_index = 0; // index of diff_src output tensor - const Tensor& src_tensor = MklGetInput(context, src_index); + const Tensor& src_tensor = MklGetInput(context, src_index); const Tensor& diff_dst_tensor = MklGetInput(context, diff_dst_index); Tensor* diff_src_tensor = nullptr; @@ -825,16 +820,16 @@ class MklTanhGradOp : public MklReluGradOpBase { diff_dst_tensor.shape(), dnn_shape_diff_src); void* out_o = static_cast(diff_src_tensor->flat().data()); void* user_i = - static_cast(const_cast(src_tensor.flat().data())); + static_cast(const_cast(src_tensor.flat().data())); // gradient of tanh(x) = 1 - tanh(x)^2 T feature = (static_cast(user_i))[0]; T e1 = std::exp(feature); T e2 = std::exp(-feature); - T tanh = (e1 - e2)/(e1 + e2); + T tanh = (e1 - e2) / (e1 + e2); void* user_g = - static_cast(const_cast(diff_dst_tensor.flat().data())); - (static_cast(out_o))[0] = (static_cast(user_g))[0] * - (1 - tanh * tanh); + static_cast(const_cast(diff_dst_tensor.flat().data())); + (static_cast(out_o))[0] = + (static_cast(user_g))[0] * (1 - tanh * tanh); } }; @@ -857,13 +852,13 @@ TF_CALL_float(REGISTER_RELU_MKL_SUPPORTED_KERNELS_TYPES); #ifdef INTEL_MKL_DNN // register dnn kernels for supported operations and supported types -#define REGISTER_ELU_MKL_SUPPORTED_KERNELS_TYPES(type) \ - REGISTER_KERNEL_BUILDER(Name("_MklElu") \ +#define REGISTER_ELU_MKL_SUPPORTED_KERNELS_TYPES(type) \ + REGISTER_KERNEL_BUILDER(Name("_MklElu") \ .Device(DEVICE_CPU) \ .TypeConstraint("T") \ .Label(mkl_op_registry::kMklOpLabel), \ - MklEluOp); \ - REGISTER_KERNEL_BUILDER(Name("_MklEluGrad") \ + MklEluOp); \ + REGISTER_KERNEL_BUILDER(Name("_MklEluGrad") \ .Device(DEVICE_CPU) \ .TypeConstraint("T") \ .Label(mkl_op_registry::kMklOpLabel), \ @@ -888,4 +883,3 @@ TF_CALL_float(REGISTER_TANH_MKL_SUPPORTED_KERNELS_TYPES); } // namespace tensorflow #endif // INTEL_MKL - diff --git a/tensorflow/core/kernels/mkl_reshape_op.cc b/tensorflow/core/kernels/mkl_reshape_op.cc index b41e529357..7d471e1e4c 100644 --- a/tensorflow/core/kernels/mkl_reshape_op.cc +++ b/tensorflow/core/kernels/mkl_reshape_op.cc @@ -166,9 +166,9 @@ class MklReshapeOp : public OpKernel { MklDnnShape mkl_shape_input; GetMklShape(context, kInputSlotIdx, &mkl_shape_input); bool input_in_mkl_format = mkl_shape_input.IsMklTensor(); - const int64 nelems = input_in_mkl_format ? - mkl_shape_input.GetTfShape().num_elements() - : input_tensor.NumElements(); + const int64 nelems = input_in_mkl_format + ? mkl_shape_input.GetTfShape().num_elements() + : input_tensor.NumElements(); // Preliminary validation of sizes. OP_REQUIRES(context, IsLegacyVector(sizes.shape()), @@ -210,11 +210,11 @@ class MklReshapeOp : public OpKernel { product)); shape.set_dim(unknown_index, missing); } - OP_REQUIRES(context, shape.num_elements() == nelems, - errors::InvalidArgument("Input to reshape is a tensor with ", - nelems, - " values, but the requested shape has ", - shape.num_elements())); + OP_REQUIRES( + context, shape.num_elements() == nelems, + errors::InvalidArgument("Input to reshape is a tensor with ", nelems, + " values, but the requested shape has ", + shape.num_elements())); if (input_in_mkl_format) { TensorShape& shape_to = shape; @@ -237,38 +237,38 @@ class MklReshapeOp : public OpKernel { // need to update MklDnnShape object associated with the input // tensor to reflect the shape change expected by reshape. if (!SkipReorder(mkl_shape_input, shape_to)) { - // If dimensions that are being expanded or collapsed are not - // maintained contiguously by MKLDNN, then we use reorder. - - // Get Mkl layout of input tensor. - auto input_mkl_md = mkl_shape_input.GetMklLayout(); - // Set input Mkl layout as the user layout. - dnn_data_input.SetUsrMem(input_mkl_md, &input_tensor); - // Get expected Tensorflow layout of input tensor. - auto output_tf_md = mkl_shape_input.GetTfLayout(); - auto output_tf_pd = memory::primitive_desc(output_tf_md, - cpu_engine); - - Tensor* output_tensor = nullptr; - MklShape mkl_shape_output; - mkl_shape_output.SetMklTensor(false); - // We allocate output tensor in the shape expected by Reshape. - AllocateOutputSetMklShape(context, kOutputSlotIdx, &output_tensor, - shape_to, mkl_shape_output); - - // Insert reorder between Mkl layout and TensorFlow layout if - // needed. If reorder is not needed but reshape is needed (since - // shape_from != shape_to), then we just copy input tensor to - // output tensor with target shape (we cannot forward Mkl layout - // in such case because shape has changed.) - std::vector net; - if (dnn_data_input.CheckReorderToOpMem(output_tf_pd, - output_tensor, &net)) { - stream(stream::kind::eager).submit(net).wait(); - } else { - output_tensor->CopyFrom(input_tensor, shape_to); - } - return; + // If dimensions that are being expanded or collapsed are not + // maintained contiguously by MKLDNN, then we use reorder. + + // Get Mkl layout of input tensor. + auto input_mkl_md = mkl_shape_input.GetMklLayout(); + // Set input Mkl layout as the user layout. + dnn_data_input.SetUsrMem(input_mkl_md, &input_tensor); + // Get expected Tensorflow layout of input tensor. + auto output_tf_md = mkl_shape_input.GetTfLayout(); + auto output_tf_pd = + memory::primitive_desc(output_tf_md, cpu_engine); + + Tensor* output_tensor = nullptr; + MklShape mkl_shape_output; + mkl_shape_output.SetMklTensor(false); + // We allocate output tensor in the shape expected by Reshape. + AllocateOutputSetMklShape(context, kOutputSlotIdx, &output_tensor, + shape_to, mkl_shape_output); + + // Insert reorder between Mkl layout and TensorFlow layout if + // needed. If reorder is not needed but reshape is needed (since + // shape_from != shape_to), then we just copy input tensor to + // output tensor with target shape (we cannot forward Mkl layout + // in such case because shape has changed.) + std::vector net; + if (dnn_data_input.CheckReorderToOpMem(output_tf_pd, output_tensor, + &net)) { + stream(stream::kind::eager).submit(net).wait(); + } else { + output_tensor->CopyFrom(input_tensor, shape_to); + } + return; } else { // If dimensions that are being expanded or collapsed are // maintained contiguously by MKLDNN, then we skip reorder, just @@ -276,10 +276,10 @@ class MklReshapeOp : public OpKernel { // Tensorflow tensor as it is to the output. auto output_dims = TFShapeToMklDnnDims(shape_to); auto output_strides = CalculateTFStrides(output_dims); - auto output_tf_md = MklDnnData::CreateBlockedMemDesc(output_dims, - output_strides); - auto output_tf_pd = memory::primitive_desc(output_tf_md, - cpu_engine); + auto output_tf_md = MklDnnData::CreateBlockedMemDesc( + output_dims, output_strides); + auto output_tf_pd = + memory::primitive_desc(output_tf_md, cpu_engine); // Set MklDnnShape MklDnnShape mkl_shape_output; @@ -291,18 +291,17 @@ class MklReshapeOp : public OpKernel { // We now simply forward input Mkl tensor to output and change its // output MklDnnShape object. - ForwardMklTensorInToOutWithMklShape(context, kInputSlotIdx, - kOutputSlotIdx, mkl_shape_output); + ForwardMklTensorInToOutWithMklShape( + context, kInputSlotIdx, kOutputSlotIdx, mkl_shape_output); return; } - } catch (mkldnn::error &e) { + } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + - ", in file " + string(__FILE__) + ":" + - std::to_string(__LINE__); - OP_REQUIRES_OK(context, - errors::Aborted("Operation received an exception:", - error_msg)); + ", message: " + string(e.message) + ", in file " + + string(__FILE__) + ":" + std::to_string(__LINE__); + OP_REQUIRES_OK( + context, + errors::Aborted("Operation received an exception:", error_msg)); } } } else { diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc new file mode 100644 index 0000000000..c35f857cfe --- /dev/null +++ b/tensorflow/core/kernels/mkl_tfconv_op.cc @@ -0,0 +1,124 @@ +/* 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. +==============================================================================*/ + +#ifdef INTEL_MKL + +#include +#include +#include "tensorflow/core/framework/numeric_op.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/platform/cpu_info.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/util/tensor_format.h" + +#include "mkl_dnn.h" +#include "mkl_dnn_types.h" +#include "tensorflow/core/util/mkl_util.h" + +namespace tensorflow { +typedef Eigen::ThreadPoolDevice CPUDevice; + +/////////////////////////////////////////////////////////// +// Op kernel +/////////////////////////////////////////////////////////// + +template +class MklToTfOp : public OpKernel { + public: + explicit MklToTfOp(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format_str)); + OP_REQUIRES_OK(context, context->GetAttr("T", &op_data_type)); + has_avx512f_ = port::TestCPUFeature(port::CPUFeature::AVX512F); + } + + void Compute(OpKernelContext* context) override { + // Check that input tensor is in MKL format. + const Tensor& input_tensor = MklGetInput(context, 0); + MklShape input_shape; + GetMklShape(context, 0, &input_shape); + + // if input is already in Tf format, then just copy input tensor to output. + if (!input_shape.IsMklTensor()) { + context->set_output(0, input_tensor); + VLOG(1) << "MKLToTFConversion: No conversion needed, " + << "copying input to output"; + return; + } + + // Check that input data type is same as operator data type and that it is + // same as output data type. + DataType input_data_type = input_type(0); + DataType output_data_type = output_type(0); + CHECK_EQ(op_data_type, input_data_type); + CHECK_EQ(op_data_type, output_data_type); + + TensorShape output_shape; + size_t ndims = input_shape.GetDimension(); + size_t* in_sizes = new size_t[ndims]; + for (size_t i = 0; i < ndims; i++) { + // Outermost to innermost dimension + output_shape.AddDim(input_shape.GetSizes()[input_shape.tf_dim_idx(i)]); + in_sizes[i] = input_shape.GetSizes()[i]; + } + + // Allocate output tensor. + Tensor* output_tensor = NULL; + OP_REQUIRES_OK(context, + context->allocate_output(0, output_shape, &output_tensor)); + + dnnLayout_t output_layout = + static_cast(input_shape.GetTfLayout()); + // Execute DNNConversion. + void* input_buffer = + static_cast(const_cast(input_tensor.flat().data())); + delete[] in_sizes; + void* output_buffer = + static_cast(const_cast(output_tensor->flat().data())); + input_shape.GetConvertedFlatData(output_layout, input_buffer, + output_buffer); + VLOG(1) << "MKLToTFConversion complete successfully."; + } + + private: + /// Data format of the operation + string data_format_str; + + /// Data type of the operation + DataType op_data_type; + + /// CPUIDInfo + bool has_avx512f_ = false; +}; + +/////////////////////////////////////////////////////////// +// Register kernel +/////////////////////////////////////////////////////////// + +#define REGISTER_CPU(T) \ + REGISTER_KERNEL_BUILDER(Name("_MklToTf") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .Label(mkl_op_registry::kMklOpLabel), \ + MklToTfOp); + +TF_CALL_float(REGISTER_CPU); +#undef REGISTER_CPU +} // namespace tensorflow +#endif /* INTEL_MKL */ diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc index 64bdef0008..5d28b87e6b 100644 --- a/tensorflow/core/kernels/non_max_suppression_op.cc +++ b/tensorflow/core/kernels/non_max_suppression_op.cc @@ -92,13 +92,11 @@ static inline bool IOUGreaterThanThreshold( return iou > iou_threshold; } -void DoNonMaxSuppressionOp(OpKernelContext* context, - const Tensor& boxes, - const Tensor& scores, - const Tensor& max_output_size, +void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes, + const Tensor& scores, const Tensor& max_output_size, const float iou_threshold) { OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1, - errors::InvalidArgument("iou_threshold must be in [0, 1]")); + errors::InvalidArgument("iou_threshold must be in [0, 1]")); int num_boxes = 0; ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes); @@ -106,10 +104,8 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, return; } - const int output_size = - std::min(max_output_size.scalar()(), num_boxes); - typename TTypes::ConstTensor boxes_data = - boxes.tensor(); + const int output_size = std::min(max_output_size.scalar()(), num_boxes); + typename TTypes::ConstTensor boxes_data = boxes.tensor(); std::vector scores_data(num_boxes); std::copy_n(scores.flat().data(), num_boxes, scores_data.begin()); @@ -181,8 +177,7 @@ template class NonMaxSuppressionV2Op : public OpKernel { public: explicit NonMaxSuppressionV2Op(OpKernelConstruction* context) - : OpKernel(context) { - } + : OpKernel(context) {} void Compute(OpKernelContext* context) override { // boxes: [num_boxes, 4] @@ -197,10 +192,9 @@ class NonMaxSuppressionV2Op : public OpKernel { max_output_size.shape().DebugString())); // iou_threshold: scalar const Tensor& iou_threshold = context->input(3); - OP_REQUIRES( - context, TensorShapeUtils::IsScalar(iou_threshold.shape()), - errors::InvalidArgument("iou_threshold must be 0-D, got shape ", - iou_threshold.shape().DebugString())); + OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()), + errors::InvalidArgument("iou_threshold must be 0-D, got shape ", + iou_threshold.shape().DebugString())); const float iou_threshold_val = iou_threshold.scalar()(); diff --git a/tensorflow/core/kernels/non_max_suppression_op_test.cc b/tensorflow/core/kernels/non_max_suppression_op_test.cc index fdbcf05b89..67d9217b95 100644 --- a/tensorflow/core/kernels/non_max_suppression_op_test.cc +++ b/tensorflow/core/kernels/non_max_suppression_op_test.cc @@ -43,9 +43,10 @@ class NonMaxSuppressionOpTest : public OpsTestBase { TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClusters) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {3}); TF_ASSERT_OK(RunOpKernel()); @@ -58,7 +59,7 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClusters) { TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClustersFlippedCoordinates) { MakeOp(.5); AddInputFromArray(TensorShape({6, 4}), - {1, 1, 0, 0, 0, 0.1f, 1, 1.1f, 0, .9f, 1, -0.1f, + {1, 1, 0, 0, 0, 0.1f, 1, 1.1f, 0, .9f, 1, -0.1f, 0, 10, 1, 11, 1, 10.1f, 0, 11.1f, 1, 101, 0, 100}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {3}); @@ -71,9 +72,10 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectFromThreeClustersFlippedCoordinates) { TEST_F(NonMaxSuppressionOpTest, TestSelectAtMostTwoBoxesFromThreeClusters) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {2}); TF_ASSERT_OK(RunOpKernel()); @@ -85,9 +87,10 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectAtMostTwoBoxesFromThreeClusters) { TEST_F(NonMaxSuppressionOpTest, TestSelectAtMostThirtyBoxesFromThreeClusters) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f}); AddInputFromArray(TensorShape({}), {30}); TF_ASSERT_OK(RunOpKernel()); @@ -134,9 +137,10 @@ TEST_F(NonMaxSuppressionOpTest, TestSelectFromTenIdenticalBoxes) { TEST_F(NonMaxSuppressionOpTest, TestInconsistentBoxAndScoreShapes) { MakeOp(.5); - AddInputFromArray(TensorShape({6, 4}), - {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, - 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); + AddInputFromArray( + TensorShape({6, 4}), + {0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f, + 0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101}); AddInputFromArray(TensorShape({5}), {.9f, .75f, .6f, .95f, .5f}); AddInputFromArray(TensorShape({}), {30}); Status s = RunOpKernel(); diff --git a/tensorflow/core/kernels/nth_element_op.cc b/tensorflow/core/kernels/nth_element_op.cc index da825e408c..7f12eb953a 100644 --- a/tensorflow/core/kernels/nth_element_op.cc +++ b/tensorflow/core/kernels/nth_element_op.cc @@ -16,15 +16,15 @@ limitations under the License. // See docs in ../ops/nn_ops.cc. #include "tensorflow/core/kernels/nth_element_op.h" +#include +#include +#include #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" -#include "tensorflow/core/framework/types.h" #include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/util/work_sharder.h" -#include -#include -#include namespace tensorflow { @@ -54,8 +54,9 @@ class NthElementOp : public OpKernel { errors::InvalidArgument("Input must be >= 1-D, got shape ", input_in.shape().DebugString())); // The last dimension of input tensor must be greater than N. - OP_REQUIRES(context, input_in.dim_size(num_dims-1) > n, - errors::InvalidArgument("Input must have at least n+1 columns")); + OP_REQUIRES( + context, input_in.dim_size(num_dims - 1) > n, + errors::InvalidArgument("Input must have at least n+1 columns")); // std::nth_element only support the nth-smallest selection. if (reverse_) { @@ -64,7 +65,7 @@ class NthElementOp : public OpKernel { // Assume input_shape is [d1,d2,...dk], and output_shape is [d1,d2...dk-1]. TensorShape out_shape; - for (int i = 0; i < num_dims-1; ++i) { + for (int i = 0; i < num_dims - 1; ++i) { out_shape.AddDim(input_in.dim_size(i)); } Tensor* output_tensor = nullptr; @@ -83,32 +84,28 @@ namespace functor { template struct NthElementFunctor { - void operator() (OpKernelContext* context, - const Tensor& input_tensor, - Tensor& output_tensor, - int n, - bool reverse) { + void operator()(OpKernelContext* context, const Tensor& input_tensor, + Tensor& output_tensor, int n, bool reverse) { const T* input = input_tensor.flat().data(); T* output = output_tensor.flat().data(); // Assume input_shape is [d1,d2,...dk], and output_shape is [d1,d2...dk-1], // then num_rows = d1*d2...dk-1, last_dim = dk. const int num_rows = output_tensor.NumElements(); - const int last_dim = input_tensor.dim_size(input_tensor.dims()-1); + const int last_dim = input_tensor.dim_size(input_tensor.dims() - 1); // Allocate each row to different shard. - auto SubNthElement = [&, input, output, last_dim, n](int start, - int limit) { + auto SubNthElement = [&, input, output, last_dim, n](int start, int limit) { // std::nth_element would rearrange the array, so we need a new buffer. std::vector buf(last_dim); for (int b = start; b < limit; ++b) { // Copy from one row of elements to buffer const T* input_start = input + b * last_dim; - const T* input_end = input + (b+1) * last_dim; + const T* input_end = input + (b + 1) * last_dim; std::copy(input_start, input_end, buf.begin()); - std::nth_element(buf.begin(), buf.begin()+n, buf.end()); + std::nth_element(buf.begin(), buf.begin() + n, buf.end()); // The element placed in the nth position is exactly the element that // would occur in this position if the range was fully sorted. output[b] = buf[n]; @@ -116,9 +113,9 @@ struct NthElementFunctor { }; auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - // The average time complexity of partition-based nth_element (BFPRT) is O(n), - // althought the worst time complexity could be O(n^2). - // Here, 20 is a empirical factor of cost_per_unit. + // The average time complexity of partition-based nth_element (BFPRT) is + // O(n), althought the worst time complexity could be O(n^2). Here, 20 is a + // empirical factor of cost_per_unit. Shard(worker_threads.num_threads, worker_threads.workers, num_rows, 20 * last_dim, SubNthElement); } @@ -126,7 +123,6 @@ struct NthElementFunctor { } // namespace functor - #define REGISTER_NTHOP(T) \ REGISTER_KERNEL_BUILDER( \ Name("NthElement").Device(DEVICE_CPU).TypeConstraint("T"), \ @@ -136,4 +132,3 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_NTHOP); #undef REGISTER_NTHOP } // end namespace tensorflow - diff --git a/tensorflow/core/kernels/nth_element_op.h b/tensorflow/core/kernels/nth_element_op.h index 11a6c996b0..e7d25daecc 100644 --- a/tensorflow/core/kernels/nth_element_op.h +++ b/tensorflow/core/kernels/nth_element_op.h @@ -26,10 +26,8 @@ namespace functor { template struct NthElementFunctor { - void operator() (OpKernelContext* context, - const Tensor& input_tensor, - Tensor& output_tensor, - int n); + void operator()(OpKernelContext* context, const Tensor& input_tensor, + Tensor& output_tensor, int n); }; } // namespace functor diff --git a/tensorflow/core/kernels/one_hot_op_gpu.cu.cc b/tensorflow/core/kernels/one_hot_op_gpu.cu.cc index 49fd4bdeba..647515ae38 100644 --- a/tensorflow/core/kernels/one_hot_op_gpu.cu.cc +++ b/tensorflow/core/kernels/one_hot_op_gpu.cu.cc @@ -19,16 +19,16 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/one_hot_op.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/one_hot_op.h" namespace tensorflow { typedef Eigen::GpuDevice GPUDevice; -#define DEFINE_GPU_SPEC_INDEX(T, TI) \ - template class generator::OneGenerator; \ +#define DEFINE_GPU_SPEC_INDEX(T, TI) \ + template class generator::OneGenerator; \ template struct functor::OneHot; #define DEFINE_GPU_SPEC(T) \ diff --git a/tensorflow/core/kernels/ops_util_test.cc b/tensorflow/core/kernels/ops_util_test.cc index 9d53882dee..13427d71ff 100644 --- a/tensorflow/core/kernels/ops_util_test.cc +++ b/tensorflow/core/kernels/ops_util_test.cc @@ -218,7 +218,8 @@ TEST_F(OpsUtilTest, GetBroadcastTest3_3_1_2) { // in_size = 3, ksize = 3, stride = 2, pad_size = 0 TEST_F(OpsUtilTest, GetBroadcastTest3_3_2_0) { bcast_struct bcast[] = { - {{0, 3, 3, 2, 0}, {0, 3}}, {{1, 3, 3, 2, 0}, {2, 1}}, + {{0, 3, 3, 2, 0}, {0, 3}}, + {{1, 3, 3, 2, 0}, {2, 1}}, }; for (size_t i = 0; i < sizeof(bcast) / sizeof(bcast[0]); ++i) { VerifyBcastValues(bcast[i]); @@ -228,7 +229,8 @@ TEST_F(OpsUtilTest, GetBroadcastTest3_3_2_0) { // in_size = 3, ksize = 3, stride = 2, pad_size = 1 TEST_F(OpsUtilTest, GetBroadcastTest3_3_2_1) { bcast_struct bcast[] = { - {{0, 3, 3, 2, 1}, {0, 2}}, {{1, 3, 3, 2, 1}, {1, 2}}, + {{0, 3, 3, 2, 1}, {0, 2}}, + {{1, 3, 3, 2, 1}, {1, 2}}, }; for (size_t i = 0; i < sizeof(bcast) / sizeof(bcast[0]); ++i) { VerifyBcastValues(bcast[i]); @@ -258,7 +260,8 @@ TEST_F(OpsUtilTest, GetBroadcastTest3_3_3_0) { // in_size = 3, ksize = 3, stride = 3, pad_size = 1 TEST_F(OpsUtilTest, GetBroadcastTest3_3_3_1) { bcast_struct bcast[] = { - {{0, 3, 3, 3, 1}, {0, 2}}, {{1, 3, 3, 3, 1}, {2, 1}}, + {{0, 3, 3, 3, 1}, {0, 2}}, + {{1, 3, 3, 3, 1}, {2, 1}}, }; for (size_t i = 0; i < sizeof(bcast) / sizeof(bcast[0]); ++i) { VerifyBcastValues(bcast[i]); @@ -348,8 +351,8 @@ TEST_F(OpsUtilTest, Misaligned1DSlice) { TEST_F(OpsUtilTest, Aligned2DSliceOfDim0) { #if EIGEN_MAX_ALIGN_BYTES == 0 - // When EIGEN_MAX_ALIGN_BYTES is 0 and the size of the first dimension is nonzero, - // a multidimensional tensor is always aligned. + // When EIGEN_MAX_ALIGN_BYTES is 0 and the size of the first dimension is + // nonzero, a multidimensional tensor is always aligned. Tensor t(DT_FLOAT, TensorShape({3, 4})); int64 start = 1; int64 end = 2; diff --git a/tensorflow/core/kernels/pack_op.cc b/tensorflow/core/kernels/pack_op.cc index 2033fbf5dc..e0ae5de0f4 100644 --- a/tensorflow/core/kernels/pack_op.cc +++ b/tensorflow/core/kernels/pack_op.cc @@ -36,7 +36,7 @@ typedef Eigen::GpuDevice GPUDevice; #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // -------------------------------------------------------------------------- template @@ -123,7 +123,7 @@ class PackOp : public OpKernel { ConcatSYCL(c->eigen_sycl_device(), inputs_flat, &output_flat); return; } -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL ConcatCPU(c->device(), inputs_flat, &output_flat); } } diff --git a/tensorflow/core/kernels/parameterized_truncated_normal_op.cc b/tensorflow/core/kernels/parameterized_truncated_normal_op.cc index b232ba16a7..0ab9ff9f65 100644 --- a/tensorflow/core/kernels/parameterized_truncated_normal_op.cc +++ b/tensorflow/core/kernels/parameterized_truncated_normal_op.cc @@ -95,9 +95,10 @@ struct TruncatedNormalFunctor { int64 sample = b * samples_per_batch; // On GPU, this check will just fill samples with NAN if it fails. - OP_REQUIRES(ctx, stddev > T(0) && minval < maxval && - (Eigen::numext::isfinite(minval) || - Eigen::numext::isfinite(maxval)), + OP_REQUIRES(ctx, + stddev > T(0) && minval < maxval && + (Eigen::numext::isfinite(minval) || + Eigen::numext::isfinite(maxval)), errors::InvalidArgument("Invalid parameters")); int numIterations = 0; @@ -118,8 +119,9 @@ struct TruncatedNormalFunctor { // Determine the method to use. const T sqrtFactor = Eigen::numext::sqrt((normMin * normMin) + T(4)); const T cutoff = - T(2) * Eigen::numext::exp( - T(0.5) + (normMin * (normMin - sqrtFactor)) / T(4)) / + T(2) * + Eigen::numext::exp(T(0.5) + + (normMin * (normMin - sqrtFactor)) / T(4)) / (normMin + sqrtFactor); const T diff = normMax - normMin; if (diff < cutoff) { @@ -309,30 +311,34 @@ class ParameterizedTruncatedNormalOp : public OpKernel { } else { // Parameters must be broadcastable to the shape [num_batches]. OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(means_tensor.shape()) || - means_tensor.dim_size(0) == 1 || - means_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(means_tensor.shape()) || + means_tensor.dim_size(0) == 1 || + means_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input means should have length 1 or shape[0], got shape: ", means_tensor.shape().DebugString())); OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(stddevs_tensor.shape()) || - stddevs_tensor.dim_size(0) == 1 || - stddevs_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(stddevs_tensor.shape()) || + stddevs_tensor.dim_size(0) == 1 || + stddevs_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input stddevs should have length 1 or shape[0], got shape: ", stddevs_tensor.shape().DebugString())); OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(minvals_tensor.shape()) || - minvals_tensor.dim_size(0) == 1 || - minvals_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(minvals_tensor.shape()) || + minvals_tensor.dim_size(0) == 1 || + minvals_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input minvals should have length 1 or shape[0], got shape: ", minvals_tensor.shape().DebugString())); OP_REQUIRES( - ctx, TensorShapeUtils::IsScalar(maxvals_tensor.shape()) || - maxvals_tensor.dim_size(0) == 1 || - maxvals_tensor.dim_size(0) == num_batches, + ctx, + TensorShapeUtils::IsScalar(maxvals_tensor.shape()) || + maxvals_tensor.dim_size(0) == 1 || + maxvals_tensor.dim_size(0) == num_batches, errors::InvalidArgument( "Input maxvals should have length 1 or shape[0], got shape: ", maxvals_tensor.shape().DebugString())); diff --git a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc index 933de65c15..ddfeb1bb79 100644 --- a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc +++ b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc @@ -202,12 +202,13 @@ struct TruncatedNormalFunctor { typename TTypes::Flat output) { const auto config = GetCudaLaunchConfig(num_elements, d); - TruncatedNormalKernel< - T><<>>( - gen, output.data(), num_batches, samples_per_batch, num_elements, - means.data(), means.dimension(0) == 1, stddevs.data(), - stddevs.dimension(0) == 1, minvals.data(), minvals.dimension(0) == 1, - maxvals.data(), maxvals.dimension(0) == 1, kMaxIterations); + TruncatedNormalKernel + <<>>( + gen, output.data(), num_batches, samples_per_batch, num_elements, + means.data(), means.dimension(0) == 1, stddevs.data(), + stddevs.dimension(0) == 1, minvals.data(), + minvals.dimension(0) == 1, maxvals.data(), + maxvals.dimension(0) == 1, kMaxIterations); }; }; diff --git a/tensorflow/core/kernels/parse_tensor_op.cc b/tensorflow/core/kernels/parse_tensor_op.cc index 6b599612ad..dd41744f02 100644 --- a/tensorflow/core/kernels/parse_tensor_op.cc +++ b/tensorflow/core/kernels/parse_tensor_op.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/framework/register_types.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/pooling_ops_3d.cc b/tensorflow/core/kernels/pooling_ops_3d.cc index a406317213..01bcfede1e 100644 --- a/tensorflow/core/kernels/pooling_ops_3d.cc +++ b/tensorflow/core/kernels/pooling_ops_3d.cc @@ -258,7 +258,7 @@ struct LaunchMaxPooling3dGradOp { Eigen::array bcast = {1, csize, rsize, psize, 1}; #else Eigen::IndexList, int, int, int, - Eigen::type2index<1> > + Eigen::type2index<1>> bcast; bcast.set(1, csize); bcast.set(2, rsize); @@ -431,7 +431,7 @@ struct LaunchAvgPooling3dGradOp { Eigen::array bcast = {1, csize, rsize, psize, 1}; #else Eigen::IndexList, int, int, int, - Eigen::type2index<1> > + Eigen::type2index<1>> bcast; bcast.set(1, csize); bcast.set(2, rsize); @@ -833,7 +833,7 @@ TF_CALL_float(REGISTER_GPU_KERNELS) TF_CALL_half(REGISTER_GPU_KERNELS) #ifdef TENSORFLOW_USE_SYCL #define REGISTER_SYCL_KERNELS(T) REGISTER_KERNELS(SYCL, T) -TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS) + TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS) #undef REGISTER_SYCL_KERNELS #endif // TENSORFLOW_USE_SYCL diff --git a/tensorflow/core/kernels/pooling_ops_3d_sycl.h b/tensorflow/core/kernels/pooling_ops_3d_sycl.h index c1bc5af498..b4bead2456 100644 --- a/tensorflow/core/kernels/pooling_ops_3d_sycl.h +++ b/tensorflow/core/kernels/pooling_ops_3d_sycl.h @@ -281,12 +281,11 @@ class MaxPool3DGradSYCL { const T* input_data_n = input_data + n * p_.in_planes_ * p_.in_cols_ * p_.in_rows_ * p_.depth_; - const T* output_data_n = - output_data + - n * p_.out_planes_ * p_.out_cols_ * p_.out_rows_ * p_.depth_; - const T* input_backprop_n = - input_backprop + - n * p_.out_planes_ * p_.out_cols_ * p_.out_rows_ * p_.depth_; + const T* output_data_n = output_data + n * p_.out_planes_ * p_.out_cols_ * + p_.out_rows_ * p_.depth_; + const T* input_backprop_n = input_backprop + n * p_.out_planes_ * + p_.out_cols_ * + p_.out_rows_ * p_.depth_; for (int poolp = poolpstart; poolp < poolpend; ++poolp) { int pstart = poolp * p_.stride_planes_ - p_.pad_planes_; const int pend = std::min(pstart + p_.window_planes_, p_.in_planes_); @@ -678,9 +677,9 @@ class AvgPool3DGradSYCL { n /= p_.in_planes_; T gradient = T(0); - const T* input_backprop_n = - input_backprop + - n * p_.out_planes_ * p_.out_cols_ * p_.out_rows_ * p_.depth_; + const T* input_backprop_n = input_backprop + n * p_.out_planes_ * + p_.out_cols_ * + p_.out_rows_ * p_.depth_; for (int poolp = poolpstart; poolp < poolpend; ++poolp) { int pstart = poolp * p_.stride_planes_ - p_.pad_planes_; const int pend = std::min(pstart + p_.window_planes_, p_.in_planes_); diff --git a/tensorflow/core/kernels/pooling_ops_common.h b/tensorflow/core/kernels/pooling_ops_common.h index e3131b804f..fc7cb437b8 100644 --- a/tensorflow/core/kernels/pooling_ops_common.h +++ b/tensorflow/core/kernels/pooling_ops_common.h @@ -195,7 +195,6 @@ class MaxPoolingOp : public OpKernel { // and updates the corresponding column(s) in output_as_matrix with the // max value. auto shard = [¶ms, &in_mat, &out_mat](int64 start, int64 limit) { - const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; const int32 pad_rows = params.pad_rows; @@ -443,7 +442,6 @@ class MaxPoolingV2Op : public OpKernel { // and updates the corresponding column(s) in output_as_matrix with the // max value. auto shard = [¶ms, &in_mat, &out_mat](int64 start, int64 limit) { - const int32 in_rows = params.tensor_in_rows; const int32 in_cols = params.tensor_in_cols; const int32 pad_rows = params.pad_rows; diff --git a/tensorflow/core/kernels/quantization_utils_test.cc b/tensorflow/core/kernels/quantization_utils_test.cc index d148c9f78d..176720c22c 100644 --- a/tensorflow/core/kernels/quantization_utils_test.cc +++ b/tensorflow/core/kernels/quantization_utils_test.cc @@ -385,8 +385,12 @@ void TestQuantizedToFloatInPlaceUsingEigen( // These are the float values we're going to test the conversions on. typedef std::pair FPair; for (FPair min_and_max : std::vector{ - FPair(-255.0f, 255.0f), FPair(-1.0f, 1.0f), FPair(-1.0f, 255.0f), - FPair(0.0f, 1e6), FPair(0.0f, 1.0f), FPair(-31.0f, 13.0f), + FPair(-255.0f, 255.0f), + FPair(-1.0f, 1.0f), + FPair(-1.0f, 255.0f), + FPair(0.0f, 1e6), + FPair(0.0f, 1.0f), + FPair(-31.0f, 13.0f), FPair(-5.89505e+08, 5.89505e+08), }) { const float f_min = min_and_max.first; diff --git a/tensorflow/core/kernels/quantize_and_dequantize_op.h b/tensorflow/core/kernels/quantize_and_dequantize_op.h index 1363c7e325..3b09ea2527 100644 --- a/tensorflow/core/kernels/quantize_and_dequantize_op.h +++ b/tensorflow/core/kernels/quantize_and_dequantize_op.h @@ -71,7 +71,8 @@ struct QuantizeAndDequantizeOneScaleImpl { out.device(d) = ((input.cwiseMin(max_range).cwiseMax(min_range) - min_range) * scale + - T(0.5)).floor() * + T(0.5)) + .floor() * inverse_scale + min_range; } else { diff --git a/tensorflow/core/kernels/quantize_op_test.cc b/tensorflow/core/kernels/quantize_op_test.cc index d2cc55a94d..57982bdf76 100644 --- a/tensorflow/core/kernels/quantize_op_test.cc +++ b/tensorflow/core/kernels/quantize_op_test.cc @@ -250,7 +250,8 @@ TEST_F(QuantizedOpTest, QuantizeV2_32Bit) { Tensor expected(allocator(), DT_QINT32, TensorShape({element_count})); test::FillValues(&expected, { - std::numeric_limits::min(), 0, + std::numeric_limits::min(), + 0, static_cast(1.0f * (1 << 23)), static_cast(1.25f * (1 << 23)), static_cast(1.75f * (1 << 23)), diff --git a/tensorflow/core/kernels/quantized_batch_norm_op.cc b/tensorflow/core/kernels/quantized_batch_norm_op.cc index 18d83b4149..b03da7ad17 100644 --- a/tensorflow/core/kernels/quantized_batch_norm_op.cc +++ b/tensorflow/core/kernels/quantized_batch_norm_op.cc @@ -16,11 +16,11 @@ limitations under the License. #define EIGEN_USE_THREADS #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "tensorflow/core/kernels/quantization_utils.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/kernels/quantization_utils.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/quantized_concat_op.cc b/tensorflow/core/kernels/quantized_concat_op.cc index d67f1ab3ec..b03ac8e87d 100644 --- a/tensorflow/core/kernels/quantized_concat_op.cc +++ b/tensorflow/core/kernels/quantized_concat_op.cc @@ -135,8 +135,8 @@ class QuantizedConcatOp : public OpKernel { context, in.dims() == input_dims || (input_is_scalar && in_is_scalar), errors::InvalidArgument( "ConcatOp : Ranks of all input tensors should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); for (int j = 0; j < input_dims; ++j) { if (j == concat_dim) { continue; @@ -145,8 +145,8 @@ class QuantizedConcatOp : public OpKernel { context, in.dim_size(j) == input_shape.dim_size(j), errors::InvalidArgument( "ConcatOp : Dimensions of inputs should match: shape[0] = ", - input_shape.DebugString(), " vs. shape[", i, "] = ", - in.shape().DebugString())); + input_shape.DebugString(), " vs. shape[", i, + "] = ", in.shape().DebugString())); } if (in.NumElements() > 0) { int64 inputs_flat_dim1 = in.NumElements() / inputs_flat_dim0; diff --git a/tensorflow/core/kernels/quantized_conv_ops.cc b/tensorflow/core/kernels/quantized_conv_ops.cc index 1921b83d12..5b3570edff 100644 --- a/tensorflow/core/kernels/quantized_conv_ops.cc +++ b/tensorflow/core/kernels/quantized_conv_ops.cc @@ -278,10 +278,9 @@ class Im2ColConvFunctor { *resource = new Im2ColBufferResource(); return Status::OK(); }; - OP_REQUIRES_OK( - context, - context->resource_manager()->LookupOrCreate( - "Conv2d", "im2col_buffer", &im2col_buffer_resource, creator)); + OP_REQUIRES_OK(context, context->resource_manager()->LookupOrCreate( + "Conv2d", "im2col_buffer", + &im2col_buffer_resource, creator)); // This means that multiple ops can't be run simultaneously on different // threads, because we have a single shared resource. The platforms this is // aimed at have intra-op parallelism as their focus though, so it shouldn't diff --git a/tensorflow/core/kernels/quantized_instance_norm.cc b/tensorflow/core/kernels/quantized_instance_norm.cc index c29f534f31..d62094cc9f 100644 --- a/tensorflow/core/kernels/quantized_instance_norm.cc +++ b/tensorflow/core/kernels/quantized_instance_norm.cc @@ -278,10 +278,10 @@ class QuantizedInstanceNorm : public OpKernel { float input_max = context->input(2).flat()(0); float input_scale = (input_max - input_min) / 255.0f; - OP_REQUIRES( - context, input_min < input_max, - errors::InvalidArgument("input_min must be less than input_max : ", - input_min, " >= ", input_max)); + OP_REQUIRES(context, input_min < input_max, + errors::InvalidArgument( + "input_min must be less than input_max : ", input_min, + " >= ", input_max)); auto input_tensor = input.tensor(); auto N = input_tensor.dimension(0); diff --git a/tensorflow/core/kernels/quantized_matmul_op.cc b/tensorflow/core/kernels/quantized_matmul_op.cc index afb30d5f62..da8c46dc51 100644 --- a/tensorflow/core/kernels/quantized_matmul_op.cc +++ b/tensorflow/core/kernels/quantized_matmul_op.cc @@ -104,9 +104,9 @@ class QuantizedMatMulOp : public OpKernel { OP_REQUIRES(context, a.dim_size(dim_pair[0].first) == b.dim_size(dim_pair[0].second), - errors::InvalidArgument("Matrix size-compatible: In[0]: ", - a.shape().DebugString(), ", In[1]: ", - b.shape().DebugString())); + errors::InvalidArgument( + "Matrix size-compatible: In[0]: ", a.shape().DebugString(), + ", In[1]: ", b.shape().DebugString())); OP_REQUIRES(context, ((shift_c >= 0) && (shift_c <= 31)), errors::InvalidArgument("shift_c must be between 0 and 31, " diff --git a/tensorflow/core/kernels/quantized_matmul_op_test.cc b/tensorflow/core/kernels/quantized_matmul_op_test.cc index 535b5115c3..c9f05dbc10 100644 --- a/tensorflow/core/kernels/quantized_matmul_op_test.cc +++ b/tensorflow/core/kernels/quantized_matmul_op_test.cc @@ -206,17 +206,32 @@ TEST_F(QuantizedMatMulTest, Small_WithParams) { // We have set the transpose_a flag to true, so the matrix is transposed, and // for filling the values the in-memory storage order is effectively // column major, rather than the default row-major. - AddInputFromArray(TensorShape({a_rows, a_cols}), - { - 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, - }); + AddInputFromArray(TensorShape({a_rows, a_cols}), { + 11, + 10, + 9, + 8, + 7, + 6, + 5, + 4, + 3, + 2, + 1, + 0, + }); // The B matrix is: // | 1 | 4| // | 2 | 5| // | 3 | 6| AddInputFromArray(TensorShape({b_rows, b_cols}), { - 1, 4, 2, 5, 3, 6, + 1, + 4, + 2, + 5, + 3, + 6, }); AddInputFromArray(TensorShape({1}), {-12.0f}); AddInputFromArray(TensorShape({1}), {243.0f}); @@ -238,10 +253,16 @@ TEST_F(QuantizedMatMulTest, Small_WithParams) { // | -50 | -113 | // | -56 | -128 | Tensor expected(allocator(), DT_QINT32, TensorShape({a_cols, b_cols})); - test::FillValues(&expected, - { - -38, -83, -44, -98, -50, -113, -56, -128, - }); + test::FillValues(&expected, { + -38, + -83, + -44, + -98, + -50, + -113, + -56, + -128, + }); test::ExpectTensorEqual(expected, *GetOutput(0)); } diff --git a/tensorflow/core/kernels/quantized_mul_op.cc b/tensorflow/core/kernels/quantized_mul_op.cc index eaa5e667f7..3c7536e037 100644 --- a/tensorflow/core/kernels/quantized_mul_op.cc +++ b/tensorflow/core/kernels/quantized_mul_op.cc @@ -298,9 +298,8 @@ class QuantizedMulOp : public OpKernel { return; } Tensor* z; - OP_REQUIRES_OK( - context, - context->allocate_output(0, BCast::ToShape(bcast.output_shape()), &z)); + OP_REQUIRES_OK(context, context->allocate_output( + 0, BCast::ToShape(bcast.output_shape()), &z)); // Make sure that we have valid quantization ranges for the input buffers. // If the difference between the min and max is negative or zero, it makes diff --git a/tensorflow/core/kernels/quantized_mul_op_test.cc b/tensorflow/core/kernels/quantized_mul_op_test.cc index b0550c8260..a4e407c7a9 100644 --- a/tensorflow/core/kernels/quantized_mul_op_test.cc +++ b/tensorflow/core/kernels/quantized_mul_op_test.cc @@ -188,11 +188,12 @@ void TestManualScalar() { 10.0f, {1}, {10.0f}, -100.0f, 100.0f, {10}, {10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f, 90.0f, 100.0f}, 3.0f); - TestMul({1}, {10.0f}, -100.0f, 100.0f, {10}, - {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f}, 0.0f, - 10.0f, {10}, {10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f, - 90.0f, 100.0f}, - 3.0f); + TestMul( + {1}, {10.0f}, -100.0f, 100.0f, {10}, + {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f}, 0.0f, + 10.0f, {10}, + {10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0f, 90.0f, 100.0f}, + 3.0f); } void TestScalar() { diff --git a/tensorflow/core/kernels/queue_base.cc b/tensorflow/core/kernels/queue_base.cc index 330d161c32..de495c19cb 100644 --- a/tensorflow/core/kernels/queue_base.cc +++ b/tensorflow/core/kernels/queue_base.cc @@ -39,8 +39,8 @@ Status HandleSliceToElement(const Tensor& parent, Tensor* element, return errors::Internal( "HandleSliceToElement Cannot copy slice: number of elements does not " "match. Shapes are: [element]: ", - element->shape().DebugString(), ", [parent slice]: ", - chip_shape.DebugString()); + element->shape().DebugString(), + ", [parent slice]: ", chip_shape.DebugString()); } auto parent_as_matrix = parent.flat_outer_dims(); element->flat() = parent_as_matrix.chip(index, 0); diff --git a/tensorflow/core/kernels/queue_ops.cc b/tensorflow/core/kernels/queue_ops.cc index 17831b7437..46a02854d7 100644 --- a/tensorflow/core/kernels/queue_ops.cc +++ b/tensorflow/core/kernels/queue_ops.cc @@ -428,13 +428,14 @@ REGISTER_KERNEL_BUILDER(Name("QueueSizeV2").Device(DEVICE_CPU), QueueSizeOp); class QueueIsClosedOp : public QueueOpKernel { public: explicit QueueIsClosedOp(OpKernelConstruction* context) - : QueueOpKernel(context) {} + : QueueOpKernel(context) {} protected: void ComputeAsync(OpKernelContext* ctx, QueueInterface* queue, DoneCallback callback) override { Tensor* Tqueue_is_closed = nullptr; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &Tqueue_is_closed)); + OP_REQUIRES_OK(ctx, + ctx->allocate_output(0, TensorShape({}), &Tqueue_is_closed)); Tqueue_is_closed->flat().setConstant(queue->is_closed()); callback(); } @@ -443,8 +444,10 @@ class QueueIsClosedOp : public QueueOpKernel { TF_DISALLOW_COPY_AND_ASSIGN(QueueIsClosedOp); }; -REGISTER_KERNEL_BUILDER(Name("QueueIsClosed").Device(DEVICE_CPU), QueueIsClosedOp); -REGISTER_KERNEL_BUILDER(Name("QueueIsClosedV2").Device(DEVICE_CPU), QueueIsClosedOp); +REGISTER_KERNEL_BUILDER(Name("QueueIsClosed").Device(DEVICE_CPU), + QueueIsClosedOp); +REGISTER_KERNEL_BUILDER(Name("QueueIsClosedV2").Device(DEVICE_CPU), + QueueIsClosedOp); class FakeQueueOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/random_crop_op.cc b/tensorflow/core/kernels/random_crop_op.cc index ba94d6be5c..554909760a 100644 --- a/tensorflow/core/kernels/random_crop_op.cc +++ b/tensorflow/core/kernels/random_crop_op.cc @@ -68,10 +68,10 @@ class RandomCropOp : public OpKernel { // Edge case. The target dimensions are larger then the image, so // zero-pad the image. This guarantees that the image will *always* // be [target_height, target_width] in size. - OP_REQUIRES( - context, width >= target_width, - errors::FailedPrecondition("width must be >= target_width: width = ", - width, ", target_width = ", target_width)); + OP_REQUIRES(context, width >= target_width, + errors::FailedPrecondition( + "width must be >= target_width: width = ", width, + ", target_width = ", target_width)); OP_REQUIRES(context, height >= target_height, errors::FailedPrecondition( "height must be >= target_height: height = ", height, diff --git a/tensorflow/core/kernels/random_op.cc b/tensorflow/core/kernels/random_op.cc index 55a8b9c9b6..78ff7948fb 100644 --- a/tensorflow/core/kernels/random_op.cc +++ b/tensorflow/core/kernels/random_op.cc @@ -50,7 +50,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace functor { using random::PhiloxRandom; @@ -271,9 +271,10 @@ class RandomGammaOp : public OpKernel { const Tensor& shape_t = ctx->input(0); const Tensor& alpha_t = ctx->input(1); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(shape_t.shape()) && - (shape_t.dtype() == DataType::DT_INT32 || - shape_t.dtype() == DataType::DT_INT64), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(shape_t.shape()) && + (shape_t.dtype() == DataType::DT_INT32 || + shape_t.dtype() == DataType::DT_INT64), errors::InvalidArgument( "shape must be a vector of {int32,int64}, got shape: ", shape_t.DebugString())); @@ -325,7 +326,7 @@ class RandomGammaOp : public OpKernel { // avoid a couple flops which can be done on a per-alpha basis. auto DoWork = [num_samples, num_alphas, &rng, samples_flat, alpha_flat]( - int start_output, int limit_output) { + int start_output, int limit_output) { using Eigen::numext::exp; using Eigen::numext::log; using Eigen::numext::pow; @@ -448,40 +449,40 @@ class RandomGammaOp : public OpKernel { } // namespace -#define REGISTER(TYPE) \ - template struct functor::FillPhiloxRandom< \ - CPUDevice, random::UniformDistribution >; \ - template struct functor::FillPhiloxRandom< \ - CPUDevice, random::NormalDistribution >; \ - template struct functor::FillPhiloxRandom< \ - CPUDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >; \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomUniform") \ - .Device(DEVICE_CPU) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomStandardNormal") \ - .Device(DEVICE_CPU) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("TruncatedNormal") \ - .Device(DEVICE_CPU) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp< \ - CPUDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomGamma").Device(DEVICE_CPU).TypeConstraint("T"), \ +#define REGISTER(TYPE) \ + template struct functor::FillPhiloxRandom< \ + CPUDevice, random::UniformDistribution>; \ + template struct functor::FillPhiloxRandom< \ + CPUDevice, random::NormalDistribution>; \ + template struct functor::FillPhiloxRandom< \ + CPUDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>; \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomUniform") \ + .Device(DEVICE_CPU) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomStandardNormal") \ + .Device(DEVICE_CPU) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("TruncatedNormal") \ + .Device(DEVICE_CPU) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp< \ + CPUDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomGamma").Device(DEVICE_CPU).TypeConstraint("T"), \ RandomGammaOp) #define REGISTER_INT(IntType) \ @@ -504,33 +505,33 @@ TF_CALL_int64(REGISTER_INT); #if GOOGLE_CUDA -#define REGISTER(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomUniform") \ - .Device(DEVICE_GPU) \ - .HostMemory("shape") \ - .TypeConstraint("T") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomStandardNormal") \ - .Device(DEVICE_GPU) \ - .HostMemory("shape") \ - .TypeConstraint("T") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("TruncatedNormal") \ - .Device(DEVICE_GPU) \ - .HostMemory("shape") \ - .TypeConstraint("T") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp< \ - GPUDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >); +#define REGISTER(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomUniform") \ + .Device(DEVICE_GPU) \ + .HostMemory("shape") \ + .TypeConstraint("T") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomStandardNormal") \ + .Device(DEVICE_GPU) \ + .HostMemory("shape") \ + .TypeConstraint("T") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("TruncatedNormal") \ + .Device(DEVICE_GPU) \ + .HostMemory("shape") \ + .TypeConstraint("T") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp< \ + GPUDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>); #define REGISTER_INT(IntType) \ REGISTER_KERNEL_BUILDER(Name("RandomUniformInt") \ @@ -565,13 +566,12 @@ struct FillPhiloxRandomKernel; template struct FillPhiloxRandomKernel { typedef typename Distribution::ResultElementType T; - using write_accessor = sycl::accessor; + using write_accessor = sycl::accessor; - FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, Distribution& dist) - : data_(data), - gen_(gen), - dist_(dist) { - } + FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, + Distribution& dist) + : data_(data), gen_(gen), dist_(dist) {} void operator()(sycl::nd_item<1> item) { const size_t kGroupSize = Distribution::kResultElementCount; @@ -597,7 +597,7 @@ struct FillPhiloxRandomKernel { const typename Distribution::ResultType samples = dist_(&gen_); for (size_t i = 0; i < kGroupSize; ++i) { if (offset >= size) { - return; + return; } data[offset] = samples[i]; ++offset; @@ -610,17 +610,15 @@ struct FillPhiloxRandomKernel { Distribution dist_; }; - template struct FillPhiloxRandomKernel { typedef typename Distribution::ResultElementType T; - using write_accessor = sycl::accessor; + using write_accessor = sycl::accessor; - FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, Distribution& dist) - : data_(data), - gen_(gen), - dist_(dist) { - } + FillPhiloxRandomKernel(write_accessor& data, random::PhiloxRandom& gen, + Distribution& dist) + : data_(data), gen_(gen), dist_(dist) {} void operator()(sycl::nd_item<1> item) { using random::PhiloxRandom; @@ -628,9 +626,9 @@ struct FillPhiloxRandomKernel { const size_t kReservedSamplesPerOutput = 256; const size_t kGroupSize = Distribution::kResultElementCount; - const size_t kGeneratorSkipPerOutputGroup = kGroupSize * - kReservedSamplesPerOutput / - PhiloxRandom::kResultElementCount; + const size_t kGeneratorSkipPerOutputGroup = + kGroupSize * kReservedSamplesPerOutput / + PhiloxRandom::kResultElementCount; const size_t item_id = item.get_global(0); const size_t total_item_count = item.get_global_range(); @@ -674,10 +672,9 @@ class FillRandomKernel; // It splits the work into several tasks and run them in parallel template void FillPhiloxRandom::operator()( - OpKernelContext* context, const SYCLDevice& device, random::PhiloxRandom gen, - typename Distribution::ResultElementType* data, int64 size, - Distribution dist) { - + OpKernelContext* context, const SYCLDevice& device, + random::PhiloxRandom gen, typename Distribution::ResultElementType* data, + int64 size, Distribution dist) { const size_t group_size = device.maxSyclThreadsPerBlock(); const size_t group_count = (size + group_size - 1) / group_size; @@ -686,50 +683,52 @@ void FillPhiloxRandom::operator()( device.sycl_queue().submit([&](sycl::handler& cgh) { auto access = buffer.template get_access(cgh); - FillPhiloxRandomKernel task(access, gen, dist); + FillPhiloxRandomKernel + task(access, gen, dist); cgh.parallel_for>( - sycl::nd_range<1>(sycl::range<1>(group_count * group_size), sycl::range<1>(group_size)), - task - ); + sycl::nd_range<1>(sycl::range<1>(group_count * group_size), + sycl::range<1>(group_size)), + task); }); } -} +} // namespace functor + +#define REGISTER(TYPE) \ + template struct functor::FillPhiloxRandom< \ + SYCLDevice, random::UniformDistribution>; \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomUniform") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("RandomStandardNormal") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp>); \ + REGISTER_KERNEL_BUILDER( \ + Name("TruncatedNormal") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .TypeConstraint("dtype"), \ + PhiloxRandomOp< \ + SYCLDevice, \ + random::TruncatedNormalDistribution< \ + random::SingleSampleAdapter, TYPE>>); -#define REGISTER(TYPE) \ - template struct functor::FillPhiloxRandom< \ - SYCLDevice, random::UniformDistribution >; \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomUniform") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("RandomStandardNormal") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp >); \ - REGISTER_KERNEL_BUILDER( \ - Name("TruncatedNormal") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .TypeConstraint("dtype"), \ - PhiloxRandomOp< \ - SYCLDevice, \ - random::TruncatedNormalDistribution< \ - random::SingleSampleAdapter, TYPE> >); - -#define REGISTER_INT(IntType) \ - REGISTER_KERNEL_BUILDER(Name("RandomUniformInt") \ - .Device(DEVICE_SYCL) \ - .HostMemory("shape") \ - .HostMemory("minval") \ - .HostMemory("maxval") \ - .TypeConstraint("Tout"), \ +#define REGISTER_INT(IntType) \ + REGISTER_KERNEL_BUILDER(Name("RandomUniformInt") \ + .Device(DEVICE_SYCL) \ + .HostMemory("shape") \ + .HostMemory("minval") \ + .HostMemory("maxval") \ + .TypeConstraint("Tout"), \ RandomUniformIntOp); TF_CALL_float(REGISTER); @@ -740,6 +739,6 @@ TF_CALL_int64(REGISTER_INT); #undef REGISTER #undef REGISTER_INT -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow diff --git a/tensorflow/core/kernels/random_op_gpu.cu.cc b/tensorflow/core/kernels/random_op_gpu.cu.cc index 7afa6974c6..3393b39faf 100644 --- a/tensorflow/core/kernels/random_op_gpu.cu.cc +++ b/tensorflow/core/kernels/random_op_gpu.cu.cc @@ -222,9 +222,8 @@ void FillPhiloxRandom::operator()( (d.getNumCudaMultiProcessors() * d.maxCudaThreadsPerMultiProcessor()) / block_size; - FillPhiloxRandomKernelLaunch< - Distribution><<>>(gen, data, size, - dist); + FillPhiloxRandomKernelLaunch + <<>>(gen, data, size, dist); }; // Explicit instantiation of the GPU distributions functors diff --git a/tensorflow/core/kernels/random_poisson_op.cc b/tensorflow/core/kernels/random_poisson_op.cc index bf1d83ec75..64fb4a5c22 100644 --- a/tensorflow/core/kernels/random_poisson_op.cc +++ b/tensorflow/core/kernels/random_poisson_op.cc @@ -103,7 +103,7 @@ struct PoissonFunctor { typedef random::UniformDistribution Uniform; auto DoWork = [num_samples, num_rate, &rng, samples_flat, rate_flat]( - int start_output, int limit_output) { + int start_output, int limit_output) { // Capturing "rng" by value would only make a copy for the _shared_ // lambda. Since we want to let each worker have its own copy, we pass // "rng" by reference and explicitly do a copy assignment. diff --git a/tensorflow/core/kernels/random_shuffle_queue_op.cc b/tensorflow/core/kernels/random_shuffle_queue_op.cc index e9695cfde3..87fc943331 100644 --- a/tensorflow/core/kernels/random_shuffle_queue_op.cc +++ b/tensorflow/core/kernels/random_shuffle_queue_op.cc @@ -334,96 +334,95 @@ void RandomShuffleQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx, // TODO(josh11b): This makes two copies of callback, avoid this if possible. dequeue_attempts_.emplace_back( num_elements, [callback]() { callback(Tuple()); }, ctx, cm, token, - [callback, allow_small_batch, this](Attempt* attempt) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - int32 queue_size = queues_[0].size(); - if (closed_ && queue_size < attempt->elements_requested) { - // If we don't have enough for a full dequeue, we have - // to reset the attempt tuple. - if (!attempt->tuple.empty()) { - // Restore already-dequeued elements to the queue. - for (int64 i = attempt->tuple[0].dim_size(0) - - attempt->elements_requested - 1; - i >= 0; --i) { - for (int j = 0; j < num_components(); ++j) { - PersistentTensor element; - Status s = GetElementComponentFromBatch( - attempt->tuple, i, j, attempt->context, &element); - if (!s.ok()) { - attempt->context->SetStatus( - errors::DataLoss("Failed to restore element from " - "partially-dequeued batch " - "to RandomShuffleQueue: ", - s.error_message())); - } - queues_[j].push_back(element); - } - } - } - if (allow_small_batch && !queues_[0].empty()) { - // Request all remaining elements in the queue. - queue_size = queues_[0].size(); - attempt->tuple.clear(); - attempt->elements_requested = queue_size; - } else { - if (allow_small_batch) { - // There may be some other attempts containing - // values. If so, we'll yield and wait for them - // to add elements to the queue. - if (!enqueue_attempts_.empty()) return kProgress; - } - if (attempt->context->status().ok()) { - attempt->context->SetStatus(errors::OutOfRange( - "RandomShuffleQueue '", name_, "' is closed and has ", - "insufficient elements (requested ", - attempt->elements_requested, ", current size ", - queue_size, ")")); + [callback, allow_small_batch, + this](Attempt* attempt) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + int32 queue_size = queues_[0].size(); + if (closed_ && queue_size < attempt->elements_requested) { + // If we don't have enough for a full dequeue, we have + // to reset the attempt tuple. + if (!attempt->tuple.empty()) { + // Restore already-dequeued elements to the queue. + for (int64 i = attempt->tuple[0].dim_size(0) - + attempt->elements_requested - 1; + i >= 0; --i) { + for (int j = 0; j < num_components(); ++j) { + PersistentTensor element; + Status s = GetElementComponentFromBatch( + attempt->tuple, i, j, attempt->context, &element); + if (!s.ok()) { + attempt->context->SetStatus( + errors::DataLoss("Failed to restore element from " + "partially-dequeued batch " + "to RandomShuffleQueue: ", + s.error_message())); } - return kComplete; + queues_[j].push_back(element); } } + } + if (allow_small_batch && !queues_[0].empty()) { + // Request all remaining elements in the queue. + queue_size = queues_[0].size(); + attempt->tuple.clear(); + attempt->elements_requested = queue_size; + } else { + if (allow_small_batch) { + // There may be some other attempts containing + // values. If so, we'll yield and wait for them + // to add elements to the queue. + if (!enqueue_attempts_.empty()) return kProgress; + } + if (attempt->context->status().ok()) { + attempt->context->SetStatus(errors::OutOfRange( + "RandomShuffleQueue '", name_, "' is closed and has ", + "insufficient elements (requested ", + attempt->elements_requested, ", current size ", + queue_size, ")")); + } + return kComplete; + } + } - RunResult result = kNoProgress; - if (!closed_) queue_size -= min_after_dequeue_; - for (; queue_size > 0; --queue_size) { - if (attempt->tuple.empty()) { - // Only allocate tuple when we have something to dequeue - // so we don't use excessive memory when there are many - // blocked dequeue attempts waiting. - attempt->tuple.reserve(num_components()); - for (int i = 0; i < num_components(); ++i) { - const TensorShape shape = - ManyOutShape(i, attempt->elements_requested); - Tensor element; - attempt->context->SetStatus( - attempt->context->allocate_temp(component_dtypes_[i], - shape, &element)); - if (!attempt->context->status().ok()) return kComplete; - attempt->tuple.emplace_back(element); - } - } - result = kProgress; - Tuple tuple; - DequeueLocked(attempt->context, &tuple); - const int index = attempt->tuple[0].dim_size(0) - - attempt->elements_requested; - for (int i = 0; i < num_components(); ++i) { - attempt->context->SetStatus(batch_util::CopyElementToSlice( - std::move(tuple[i]), &attempt->tuple[i], index)); - if (!attempt->context->status().ok()) return kComplete; - } - tuple.clear(); - --attempt->elements_requested; - if (attempt->elements_requested == 0) { - tuple = attempt->tuple; - attempt->done_callback = [callback, tuple]() { - callback(tuple); - }; - return kComplete; - } + RunResult result = kNoProgress; + if (!closed_) queue_size -= min_after_dequeue_; + for (; queue_size > 0; --queue_size) { + if (attempt->tuple.empty()) { + // Only allocate tuple when we have something to dequeue + // so we don't use excessive memory when there are many + // blocked dequeue attempts waiting. + attempt->tuple.reserve(num_components()); + for (int i = 0; i < num_components(); ++i) { + const TensorShape shape = + ManyOutShape(i, attempt->elements_requested); + Tensor element; + attempt->context->SetStatus(attempt->context->allocate_temp( + component_dtypes_[i], shape, &element)); + if (!attempt->context->status().ok()) return kComplete; + attempt->tuple.emplace_back(element); } - return result; - }); + } + result = kProgress; + Tuple tuple; + DequeueLocked(attempt->context, &tuple); + const int index = + attempt->tuple[0].dim_size(0) - attempt->elements_requested; + for (int i = 0; i < num_components(); ++i) { + attempt->context->SetStatus(batch_util::CopyElementToSlice( + std::move(tuple[i]), &attempt->tuple[i], index)); + if (!attempt->context->status().ok()) return kComplete; + } + tuple.clear(); + --attempt->elements_requested; + if (attempt->elements_requested == 0) { + tuple = attempt->tuple; + attempt->done_callback = [callback, tuple]() { + callback(tuple); + }; + return kComplete; + } + } + return result; + }); } } if (!already_cancelled) { diff --git a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h index 36ca7f834f..15ae4c1fc5 100644 --- a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h +++ b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h @@ -312,8 +312,7 @@ __global__ void ColumnReduceKernel( int col = blockIdx.x * 32 + threadIdx.x; value_type sum = initVal; - if (row < num_rows && col < num_cols) - sum = in[row * num_cols + col]; + if (row < num_rows && col < num_cols) sum = in[row * num_cols + col]; // 1D array necessary due to bug in CUDA 9 compiler. // TODO(nluehr) revert to 2D array when compiler is ready. @@ -366,8 +365,7 @@ __global__ void CleanupSegments( const int tid = threadIdx.x + blockIdx.x * blockDim.x; value_type val = initVal; - if (tid < segment_size * num_cols) - val = partial_sums[tid]; + if (tid < segment_size * num_cols) val = partial_sums[tid]; typedef cub::WarpReduce WarpReduce; diff --git a/tensorflow/core/kernels/relu_op.cc b/tensorflow/core/kernels/relu_op.cc index afad288cc0..d52358737f 100644 --- a/tensorflow/core/kernels/relu_op.cc +++ b/tensorflow/core/kernels/relu_op.cc @@ -31,7 +31,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_RELU_KERNELS(type) \ REGISTER_KERNEL_BUILDER( \ @@ -113,8 +113,7 @@ namespace functor { \ template <> \ void Selu::operator()( \ - const GPUDevice& d, \ - typename TTypes::ConstTensor features, \ + const GPUDevice& d, typename TTypes::ConstTensor features, \ typename TTypes::Tensor activations); \ extern template struct Selu; \ \ @@ -125,8 +124,6 @@ namespace functor { typename TTypes::Tensor backprops); \ extern template struct SeluGrad; - - TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC); } // namespace functor @@ -157,8 +154,6 @@ TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC); Name("SeluGrad").Device(DEVICE_GPU).TypeConstraint("T"), \ SeluGradOp) - - TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); #undef REGISTER_GPU_KERNELS @@ -192,10 +187,8 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); Name("SeluGrad").Device(DEVICE_SYCL).TypeConstraint("T"), \ SeluGradOp) - - TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL_KERNELS); #undef REGISTER_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/relu_op_functor.h b/tensorflow/core/kernels/relu_op_functor.h index 24b789c543..3bc5ba8a50 100644 --- a/tensorflow/core/kernels/relu_op_functor.h +++ b/tensorflow/core/kernels/relu_op_functor.h @@ -85,10 +85,9 @@ struct Relu6Grad { // make sure not to propagate the associated gradient // value. This allows "features" to be either the input or the output of // the relu6. - backprops.device(d) = - gradients * - ((features > static_cast(0)) * (features < static_cast(6))) - .template cast(); + backprops.device(d) = gradients * ((features > static_cast(0)) * + (features < static_cast(6))) + .template cast(); } }; @@ -161,8 +160,8 @@ struct SeluGrad { const auto scale = static_cast(1.0507009873554804934193349852946); const auto scale_alpha = static_cast(1.7580993408473768599402175208123); backprops.device(d) = - (activations < static_cast(0)).select( - gradients * (activations + scale_alpha), gradients * scale); + (activations < static_cast(0)) + .select(gradients * (activations + scale_alpha), gradients * scale); } }; diff --git a/tensorflow/core/kernels/resize_bicubic_op.cc b/tensorflow/core/kernels/resize_bicubic_op.cc index 1a9cf4c640..86e61bbcef 100644 --- a/tensorflow/core/kernels/resize_bicubic_op.cc +++ b/tensorflow/core/kernels/resize_bicubic_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" @@ -28,7 +29,6 @@ limitations under the License. #include "tensorflow/core/kernels/image_resizer_state.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { namespace { diff --git a/tensorflow/core/kernels/resize_bicubic_op_test.cc b/tensorflow/core/kernels/resize_bicubic_op_test.cc index 9e10fec423..25a37d5e1a 100644 --- a/tensorflow/core/kernels/resize_bicubic_op_test.cc +++ b/tensorflow/core/kernels/resize_bicubic_op_test.cc @@ -286,13 +286,14 @@ BM_ResizeBicubicDev(32, 128, 3); BM_ResizeBicubicDev(32, 512, 3); BM_ResizeBicubicDev(32, 1024, 3); -#define BM_ResizeBicubicExpand(BATCH, SIZE, CHANNELS) \ - static void BM_ResizeBicubicExpand##_##BATCH##_##SIZE##_##CHANNELS(int iters) { \ - testing::ItemsProcessed(static_cast(iters) * BATCH * SIZE * SIZE * \ - CHANNELS * 8 * 8); \ - test::Benchmark("cpu", ResizeBicubic(BATCH, SIZE, CHANNELS, 8, 8)) \ - .Run(iters); \ - } \ +#define BM_ResizeBicubicExpand(BATCH, SIZE, CHANNELS) \ + static void BM_ResizeBicubicExpand##_##BATCH##_##SIZE##_##CHANNELS( \ + int iters) { \ + testing::ItemsProcessed(static_cast(iters) * BATCH * SIZE * SIZE * \ + CHANNELS * 8 * 8); \ + test::Benchmark("cpu", ResizeBicubic(BATCH, SIZE, CHANNELS, 8, 8)) \ + .Run(iters); \ + } \ BENCHMARK(BM_ResizeBicubicExpand##_##BATCH##_##SIZE##_##CHANNELS); BM_ResizeBicubicExpand(12, 48, 1); diff --git a/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc b/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc index a7da7a0777..f82c3fcd9f 100644 --- a/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc +++ b/tensorflow/core/kernels/resize_bilinear_op_gpu.cu.cc @@ -164,11 +164,11 @@ struct ResizeBilinear { if (total_count == 0) return; CudaLaunchConfig config = GetCudaLaunchConfig(total_count, d); - ResizeBilinearKernel< - T><<>>( - config.virtual_thread_count, images.data(), height_scale, width_scale, - batch, in_height, in_width, channels, out_height, out_width, - output.data()); + ResizeBilinearKernel + <<>>( + config.virtual_thread_count, images.data(), height_scale, + width_scale, batch, in_height, in_width, channels, out_height, + out_width, output.data()); } }; @@ -200,11 +200,11 @@ struct ResizeBilinearGrad { // Accumulate. total_count = batch * resized_height * resized_width * channels; config = GetCudaLaunchConfig(total_count, d); - ResizeBilinearGradKernel< - T><<>>( - config.virtual_thread_count, input_grad.data(), height_scale, - width_scale, batch, original_height, original_width, channels, - resized_height, resized_width, output_grad.data()); + ResizeBilinearGradKernel + <<>>( + config.virtual_thread_count, input_grad.data(), height_scale, + width_scale, batch, original_height, original_width, channels, + resized_height, resized_width, output_grad.data()); } }; diff --git a/tensorflow/core/kernels/reverse_op.cc b/tensorflow/core/kernels/reverse_op.cc index 8f82784d93..bb96c42f10 100644 --- a/tensorflow/core/kernels/reverse_op.cc +++ b/tensorflow/core/kernels/reverse_op.cc @@ -269,10 +269,10 @@ class ReverseV2Op : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(0, input.shape(), &output)); -// TODO(cwhipkey): we can do dimension folding to reduce, e.g., a reverse of -// a single dimension to the dims=3 or dims=2 case, regardless of the number -// of dimensions in the tensor. This would let some ops use faster -// lower-dimension code (and use optimized versions). + // TODO(cwhipkey): we can do dimension folding to reduce, e.g., a reverse + // of a single dimension to the dims=3 or dims=2 case, regardless of the + // number of dimensions in the tensor. This would let some ops use faster + // lower-dimension code (and use optimized versions). #define HANDLE_REVERSE(NDIMS) \ case NDIMS: \ diff --git a/tensorflow/core/kernels/reverse_op_gpu.cu.cc b/tensorflow/core/kernels/reverse_op_gpu.cu.cc index b05a7c5550..3ee49db669 100644 --- a/tensorflow/core/kernels/reverse_op_gpu.cu.cc +++ b/tensorflow/core/kernels/reverse_op_gpu.cu.cc @@ -28,14 +28,14 @@ typedef Eigen::GpuDevice GPUDevice; #define DEFINE_REVERSE(T, DIM) \ template struct functor::Reverse; #define DEFINE_REVERSE_ALL_DIMS(T) \ - DEFINE_REVERSE(T, 0) \ - DEFINE_REVERSE(T, 1) \ - DEFINE_REVERSE(T, 2) \ - DEFINE_REVERSE(T, 3) \ - DEFINE_REVERSE(T, 4) \ - DEFINE_REVERSE(T, 5) \ - DEFINE_REVERSE(T, 6) \ - DEFINE_REVERSE(T, 7) \ + DEFINE_REVERSE(T, 0) \ + DEFINE_REVERSE(T, 1) \ + DEFINE_REVERSE(T, 2) \ + DEFINE_REVERSE(T, 3) \ + DEFINE_REVERSE(T, 4) \ + DEFINE_REVERSE(T, 5) \ + DEFINE_REVERSE(T, 6) \ + DEFINE_REVERSE(T, 7) \ DEFINE_REVERSE(T, 8) TF_CALL_uint8(DEFINE_REVERSE_ALL_DIMS); diff --git a/tensorflow/core/kernels/reverse_sequence_op.cc b/tensorflow/core/kernels/reverse_sequence_op.cc index d1980d4b65..15a707a9c6 100644 --- a/tensorflow/core/kernels/reverse_sequence_op.cc +++ b/tensorflow/core/kernels/reverse_sequence_op.cc @@ -51,8 +51,7 @@ void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) { // Copy seq_len info down for validity checks context->eigen_device().memcpyDeviceToHost( - seq_lens_vec.data(), seq_lens_t.data(), - sizeof(Tlen) * seq_lens_t.size()); + seq_lens_vec.data(), seq_lens_t.data(), sizeof(Tlen) * seq_lens_t.size()); OP_REQUIRES(context, batch_dim != seq_dim, errors::InvalidArgument("batch_dim == seq_dim == ", seq_dim)); @@ -76,8 +75,7 @@ void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) { } } -void CheckErrorsGPU(OpKernelContext* context, int batch_dim, - int seq_dim) { +void CheckErrorsGPU(OpKernelContext* context, int batch_dim, int seq_dim) { const Tensor& input = context->input(0); const Tensor& seq_lens = context->input(1); @@ -98,13 +96,13 @@ void CheckErrorsGPU(OpKernelContext* context, int batch_dim, template <> void CheckErrors(OpKernelContext* context, int batch_dim, - int seq_dim) { + int seq_dim) { CheckErrorsGPU(context, batch_dim, seq_dim); } template <> void CheckErrors(OpKernelContext* context, int batch_dim, - int seq_dim) { + int seq_dim) { CheckErrorsGPU(context, batch_dim, seq_dim); } @@ -164,14 +162,15 @@ class ReverseSequenceOp : public OpKernel { TF_DISALLOW_COPY_AND_ASSIGN(ReverseSequenceOp); }; -#define REGISTER_REVERSE_SEQUENCE(type, len_type) \ - REGISTER_KERNEL_BUILDER( \ - Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint("T"). \ - TypeConstraint("Tlen"), \ - ReverseSequenceOp); +#define REGISTER_REVERSE_SEQUENCE(type, len_type) \ + REGISTER_KERNEL_BUILDER(Name("ReverseSequence") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tlen"), \ + ReverseSequenceOp); -#define REGISTER_REVERSE_SEQUENCE_LEN(type) \ - REGISTER_REVERSE_SEQUENCE(type, int32); \ +#define REGISTER_REVERSE_SEQUENCE_LEN(type) \ + REGISTER_REVERSE_SEQUENCE(type, int32); \ REGISTER_REVERSE_SEQUENCE(type, int64); TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_LEN); @@ -181,23 +180,23 @@ TF_CALL_bool(REGISTER_REVERSE_SEQUENCE_LEN); // Forward declarations of the functor specializations for GPU. namespace functor { -#define DECLARE_GPU_SPEC(T, Tlen, Dims) \ - template <> \ - void ReverseSequence::Compute( \ - const GPUDevice& d, typename TTypes::ConstTensor input, \ - int32 batch_dim, int32 seq_dim, \ - typename TTypes::ConstVec seq_lens, \ - typename TTypes::Tensor output); \ +#define DECLARE_GPU_SPEC(T, Tlen, Dims) \ + template <> \ + void ReverseSequence::Compute( \ + const GPUDevice& d, typename TTypes::ConstTensor input, \ + int32 batch_dim, int32 seq_dim, \ + typename TTypes::ConstVec seq_lens, \ + typename TTypes::Tensor output); \ extern template struct ReverseSequence; -#define DECLARE_GPU_SPEC_LEN(T, Dims) \ - DECLARE_GPU_SPEC(T, int32, Dims); \ +#define DECLARE_GPU_SPEC_LEN(T, Dims) \ + DECLARE_GPU_SPEC(T, int32, Dims); \ DECLARE_GPU_SPEC(T, int64, Dims); -#define DECLARE_GPU_SPECS(T) \ - DECLARE_GPU_SPEC_LEN(T, 2); \ - DECLARE_GPU_SPEC_LEN(T, 3); \ - DECLARE_GPU_SPEC_LEN(T, 4); \ +#define DECLARE_GPU_SPECS(T) \ + DECLARE_GPU_SPEC_LEN(T, 2); \ + DECLARE_GPU_SPEC_LEN(T, 3); \ + DECLARE_GPU_SPEC_LEN(T, 4); \ DECLARE_GPU_SPEC_LEN(T, 5); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); @@ -206,14 +205,15 @@ TF_CALL_bool(DECLARE_GPU_SPECS); } // namespace functor // Registration of the GPU implementations. -#define REGISTER_REVERSE_SEQUENCE_GPU(type, len_type) \ - REGISTER_KERNEL_BUILDER( \ - Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint("T"). \ - TypeConstraint("Tlen"), \ - ReverseSequenceOp); - -#define REGISTER_REVERSE_SEQUENCE_GPU_LEN(type) \ - REGISTER_REVERSE_SEQUENCE_GPU(type, int32); \ +#define REGISTER_REVERSE_SEQUENCE_GPU(type, len_type) \ + REGISTER_KERNEL_BUILDER(Name("ReverseSequence") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tlen"), \ + ReverseSequenceOp); + +#define REGISTER_REVERSE_SEQUENCE_GPU_LEN(type) \ + REGISTER_REVERSE_SEQUENCE_GPU(type, int32); \ REGISTER_REVERSE_SEQUENCE_GPU(type, int64); TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU_LEN); diff --git a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc index cb49f14525..4a2136a2cd 100644 --- a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc +++ b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc @@ -28,14 +28,14 @@ typedef Eigen::GpuDevice GPUDevice; template class generator::ReverseGenerator; \ template struct functor::ReverseSequence; -#define DEFINE_GPU_SPEC_LEN(T, dims) \ - DEFINE_GPU_SPEC(T, int32, dims); \ +#define DEFINE_GPU_SPEC_LEN(T, dims) \ + DEFINE_GPU_SPEC(T, int32, dims); \ DEFINE_GPU_SPEC(T, int64, dims); -#define DEFINE_GPU_SPECS(T) \ - DEFINE_GPU_SPEC_LEN(T, 2); \ - DEFINE_GPU_SPEC_LEN(T, 3); \ - DEFINE_GPU_SPEC_LEN(T, 4); \ +#define DEFINE_GPU_SPECS(T) \ + DEFINE_GPU_SPEC_LEN(T, 2); \ + DEFINE_GPU_SPEC_LEN(T, 3); \ + DEFINE_GPU_SPEC_LEN(T, 4); \ DEFINE_GPU_SPEC_LEN(T, 5); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/save_restore_tensor.cc b/tensorflow/core/kernels/save_restore_tensor.cc index df60eda759..990bd2bff9 100644 --- a/tensorflow/core/kernels/save_restore_tensor.cc +++ b/tensorflow/core/kernels/save_restore_tensor.cc @@ -106,11 +106,11 @@ void SaveTensors( OP_REQUIRES_OK(context, checkpoint::ParseShapeAndSlice( shape_spec, &shape, &slice, &slice_shape)); OP_REQUIRES(context, slice_shape.IsSameSize(input.shape()), - errors::InvalidArgument("Slice in shape_and_slice " - "specification does not match the " - "shape of the tensor to save: ", - shape_spec, ", tensor: ", - input.shape().DebugString())); + errors::InvalidArgument( + "Slice in shape_and_slice " + "specification does not match the " + "shape of the tensor to save: ", + shape_spec, ", tensor: ", input.shape().DebugString())); } #define WRITER_ADD(T) \ diff --git a/tensorflow/core/kernels/scatter_functor.h b/tensorflow/core/kernels/scatter_functor.h index c6e35fe329..079f15e101 100644 --- a/tensorflow/core/kernels/scatter_functor.h +++ b/tensorflow/core/kernels/scatter_functor.h @@ -29,7 +29,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace scatter_op { @@ -117,7 +117,7 @@ struct AssignSYCL { p.device(d) = p / u; } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace internal } // namespace scatter_op @@ -156,7 +156,7 @@ struct ScatterFunctorBase { #ifdef TENSORFLOW_USE_SYCL template -struct ScatterFunctorBase { +struct ScatterFunctorBase { Index operator()(OpKernelContext* c, const SYCLDevice& d, typename TTypes::Matrix params, typename TTypes::ConstMatrix updates, @@ -171,13 +171,13 @@ struct ScatterFunctorBase { const Index index = ::tensorflow::internal::SubtleMustCopy(indices(i)); if (!FastBoundsCheck(index, limit)) return i; // Copy last Ndim-1 dimensions of updates[i] to params[index] - scatter_op::internal::AssignSYCL::Run(d, params.template chip<0>(index), - updates.template chip<0>(i)); + scatter_op::internal::AssignSYCL::Run( + d, params.template chip<0>(index), updates.template chip<0>(i)); } return -1; } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template struct ScatterFunctorBase { @@ -217,7 +217,7 @@ struct ScatterFunctorBase { template struct ScatterFunctor - : ScatterFunctorBase{}; + : ScatterFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template @@ -239,7 +239,7 @@ struct ScatterFunctorSYCL { return -1; } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/scatter_functor_gpu.cu.h b/tensorflow/core/kernels/scatter_functor_gpu.cu.h index e116077d3c..be18658543 100644 --- a/tensorflow/core/kernels/scatter_functor_gpu.cu.h +++ b/tensorflow/core/kernels/scatter_functor_gpu.cu.h @@ -30,9 +30,10 @@ namespace tensorflow { typedef Eigen::GpuDevice GPUDevice; template -__global__ void ScatterOpCustomKernel( - T* params, const T* updates, const Index* indices, - Index first_dim_size, Index updates_size, Index indices_size) { +__global__ void ScatterOpCustomKernel(T* params, const T* updates, + const Index* indices, + Index first_dim_size, Index updates_size, + Index indices_size) { Index update_block = updates_size / indices_size; CUDA_1D_KERNEL_LOOP(i, updates_size) { int indices_i = i / update_block; @@ -85,8 +86,8 @@ struct ScatterFunctor { CudaLaunchConfig config = GetCudaLaunchConfig(updates_size, d); ScatterOpCustomKernel <<>>( - params.data(), updates.data(), indices.data(), - first_dim_size, updates_size, indices_size); + params.data(), updates.data(), indices.data(), first_dim_size, + updates_size, indices_size); return -1; } }; diff --git a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h index c6c9d4e658..e82660dcc1 100644 --- a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h +++ b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h @@ -40,7 +40,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class OpKernelContext; @@ -251,7 +251,7 @@ REGISTER_SCATTER_ND_MATH_SYCL(int32); #undef REGISTER_SCATTER_ND_INDEX_SYCL #undef REGISTER_SCATTER_ND_FULL_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor diff --git a/tensorflow/core/kernels/scatter_op.cc b/tensorflow/core/kernels/scatter_op.cc index 8607c7f95a..282165349f 100644 --- a/tensorflow/core/kernels/scatter_op.cc +++ b/tensorflow/core/kernels/scatter_op.cc @@ -25,7 +25,7 @@ limitations under the License. #ifdef TENSORFLOW_USE_SYCL #include "tensorflow/core/common_runtime/sycl/sycl_util.h" -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL namespace tensorflow { @@ -33,7 +33,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Check whether updates.shape = indices.shape + params.shape[1:] static bool ValidShapes(const Tensor& params, const Tensor& updates, @@ -102,11 +102,12 @@ class ScatterUpdateOp : public OpKernel { // Check that we have enough index space const int64 N_big = indices.NumElements(); - OP_REQUIRES(c, N_big <= std::numeric_limits::max(), - errors::InvalidArgument( - "indices has too many elements for ", - DataTypeString(DataTypeToEnum::v()), " indexing: ", - N_big, " > ", std::numeric_limits::max())); + OP_REQUIRES( + c, N_big <= std::numeric_limits::max(), + errors::InvalidArgument("indices has too many elements for ", + DataTypeString(DataTypeToEnum::v()), + " indexing: ", N_big, " > ", + std::numeric_limits::max())); const Index N = static_cast(indices.NumElements()); OP_REQUIRES( c, params.dim_size(0) <= std::numeric_limits::max(), @@ -137,7 +138,7 @@ class ScatterUpdateOp : public OpKernel { #ifdef TENSORFLOW_USE_SYCL template -class ScatterUpdateOp : public OpKernel { +class ScatterUpdateOp : public OpKernel { public: explicit ScatterUpdateOp(OpKernelConstruction* c) : OpKernel(c) { OP_REQUIRES_OK(c, c->GetAttr("use_locking", &use_exclusive_lock_)); @@ -165,11 +166,12 @@ class ScatterUpdateOp : public OpKernel { // Check that we have enough index space const int64 N_big = indices.NumElements(); - OP_REQUIRES(c, N_big <= std::numeric_limits::max(), - errors::InvalidArgument( - "indices has too many elements for ", - DataTypeString(DataTypeToEnum::v()), " indexing: ", - N_big, " > ", std::numeric_limits::max())); + OP_REQUIRES( + c, N_big <= std::numeric_limits::max(), + errors::InvalidArgument("indices has too many elements for ", + DataTypeString(DataTypeToEnum::v()), + " indexing: ", N_big, " > ", + std::numeric_limits::max())); const Index N = static_cast(indices.NumElements()); OP_REQUIRES( c, params.dim_size(0) <= std::numeric_limits::max(), @@ -206,7 +208,7 @@ class ScatterUpdateOp : public OpKernel { } } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_SCATTER_KERNEL_INDEX(type, index_type, dev, name, op) \ REGISTER_KERNEL_BUILDER(Name(name) \ diff --git a/tensorflow/core/kernels/sdca_internal.cc b/tensorflow/core/kernels/sdca_internal.cc index 863c123b43..066a4b80a2 100644 --- a/tensorflow/core/kernels/sdca_internal.cc +++ b/tensorflow/core/kernels/sdca_internal.cc @@ -37,9 +37,8 @@ void FeatureWeightsDenseStorage::UpdateDenseDeltaWeights( const size_t num_weight_vectors = normalized_bounded_dual_delta.size(); if (num_weight_vectors == 1) { deltas_.device(device) = - deltas_ + - dense_vector.RowAsMatrix() * - deltas_.constant(normalized_bounded_dual_delta[0]); + deltas_ + dense_vector.RowAsMatrix() * + deltas_.constant(normalized_bounded_dual_delta[0]); } else { // Transform the dual vector into a column matrix. const Eigen::TensorMap> @@ -61,9 +60,8 @@ void FeatureWeightsSparseStorage::UpdateSparseDeltaWeights( const Example::SparseFeatures& sparse_features, const std::vector& normalized_bounded_dual_delta) { for (int64 k = 0; k < sparse_features.indices->size(); ++k) { - const double feature_value = sparse_features.values == nullptr - ? 1.0 - : (*sparse_features.values)(k); + const double feature_value = + sparse_features.values == nullptr ? 1.0 : (*sparse_features.values)(k); auto it = indices_to_id_.find((*sparse_features.indices)(k)); for (size_t l = 0; l < normalized_bounded_dual_delta.size(); ++l) { deltas_(l, it->second) += @@ -122,23 +120,24 @@ Status ModelWeights::Initialize(OpKernelContext* const context) { } // Reads in the weights, and allocates and initializes the delta weights. - const auto initialize_weights = [&]( - const OpInputList& weight_inputs, OpOutputList* const weight_outputs, - std::vector* const feature_weights) { - for (int i = 0; i < weight_inputs.size(); ++i) { - Tensor* delta_t; - TF_RETURN_IF_ERROR( - weight_outputs->allocate(i, weight_inputs[i].shape(), &delta_t)); - // Convert the input vector to a row matrix in internal representation. - auto deltas = delta_t->shaped({1, delta_t->NumElements()}); - deltas.setZero(); - feature_weights->emplace_back( - FeatureWeightsDenseStorage{weight_inputs[i].shaped( - {1, weight_inputs[i].NumElements()}), - deltas}); - } - return Status::OK(); - }; + const auto initialize_weights = + [&](const OpInputList& weight_inputs, OpOutputList* const weight_outputs, + std::vector* const feature_weights) { + for (int i = 0; i < weight_inputs.size(); ++i) { + Tensor* delta_t; + TF_RETURN_IF_ERROR( + weight_outputs->allocate(i, weight_inputs[i].shape(), &delta_t)); + // Convert the input vector to a row matrix in internal + // representation. + auto deltas = delta_t->shaped({1, delta_t->NumElements()}); + deltas.setZero(); + feature_weights->emplace_back(FeatureWeightsDenseStorage{ + weight_inputs[i].shaped( + {1, weight_inputs[i].NumElements()}), + deltas}); + } + return Status::OK(); + }; return initialize_weights(dense_weights_inputs, &dense_weights_outputs, &dense_weights_); diff --git a/tensorflow/core/kernels/sdca_internal.h b/tensorflow/core/kernels/sdca_internal.h index 9f07270075..45915693ac 100644 --- a/tensorflow/core/kernels/sdca_internal.h +++ b/tensorflow/core/kernels/sdca_internal.h @@ -149,7 +149,8 @@ class Example { // 1.0f. struct SparseFeatures { std::unique_ptr::UnalignedConstVec> indices; - std::unique_ptr::UnalignedConstVec> values; // nullptr encodes optional. + std::unique_ptr::UnalignedConstVec> + values; // nullptr encodes optional. }; // A dense vector which is a row-slice of the underlying matrix. diff --git a/tensorflow/core/kernels/sdca_ops.cc b/tensorflow/core/kernels/sdca_ops.cc index 0f5c2424b3..dbe0177dda 100644 --- a/tensorflow/core/kernels/sdca_ops.cc +++ b/tensorflow/core/kernels/sdca_ops.cc @@ -57,11 +57,11 @@ namespace tensorflow { namespace { -using sdca::Regularizations; using sdca::Example; using sdca::Examples; using sdca::ExampleStatistics; using sdca::ModelWeights; +using sdca::Regularizations; struct ComputeOptions { explicit ComputeOptions(OpKernelConstruction* const context) { @@ -76,8 +76,9 @@ struct ComputeOptions { } else if (loss_type == "smooth_hinge_loss") { loss_updater.reset(new SmoothHingeLossUpdater); } else { - OP_REQUIRES(context, false, errors::InvalidArgument( - "Unsupported loss type: ", loss_type)); + OP_REQUIRES( + context, false, + errors::InvalidArgument("Unsupported loss type: ", loss_type)); } OP_REQUIRES_OK(context, context->GetAttr("adaptative", &adaptative)); OP_REQUIRES_OK( @@ -90,9 +91,10 @@ struct ComputeOptions { context, num_sparse_features + num_dense_features > 0, errors::InvalidArgument("Requires at least one feature to train.")); - OP_REQUIRES(context, static_cast(num_sparse_features) + - static_cast(num_dense_features) <= - std::numeric_limits::max(), + OP_REQUIRES(context, + static_cast(num_sparse_features) + + static_cast(num_dense_features) <= + std::numeric_limits::max(), errors::InvalidArgument( strings::Printf("Too many feature groups: %lld > %d", static_cast(num_sparse_features) + diff --git a/tensorflow/core/kernels/segment_reduction_ops.cc b/tensorflow/core/kernels/segment_reduction_ops.cc index 3ef1cd1e06..27b8081eb8 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.cc +++ b/tensorflow/core/kernels/segment_reduction_ops.cc @@ -115,7 +115,7 @@ class SegmentReductionOp : public OpKernel { Eigen::DSizes dims_to_reduce; dims_to_reduce[0] = 0; #else - Eigen::IndexList> dims_to_reduce; + Eigen::IndexList > dims_to_reduce; #endif Index start = 0, end = 1; @@ -359,7 +359,8 @@ TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_SORTED_KERNELS_ALL); namespace functor { // UnsortedSegmentSumFunctor implementation for CPUDevice. -// todo: Remove duplicate code in UnsortedSegmentSumFunctor and UnsortedSegmentMaxFunctor. +// todo: Remove duplicate code in UnsortedSegmentSumFunctor and +// UnsortedSegmentMaxFunctor. template struct UnsortedSegmentSumFunctor : UnsortedSegmentBaseFunctor { @@ -461,9 +462,10 @@ class UnsortedSegmentBaseOp : public OpKernel { auto data_ptr = data.template flat().data(); reduction_functor_(context, context->template eigen_device(), - output_rows, segment_ids.shape(), segment_flat, - data.NumElements(), data_ptr, output_flat); + output_rows, segment_ids.shape(), segment_flat, + data.NumElements(), data_ptr, output_flat); } + private: functor::UnsortedSegmentBaseFunctor& reduction_functor_; }; @@ -472,22 +474,20 @@ template class UnsortedSegmentSumOp : public UnsortedSegmentBaseOp { public: explicit UnsortedSegmentSumOp(OpKernelConstruction* context) - : UnsortedSegmentBaseOp( - context, - sum_functor_) {} + : UnsortedSegmentBaseOp(context, sum_functor_) {} + private: - functor::UnsortedSegmentSumFunctor sum_functor_; + functor::UnsortedSegmentSumFunctor sum_functor_; }; template class UnsortedSegmentMaxOp : public UnsortedSegmentBaseOp { public: explicit UnsortedSegmentMaxOp(OpKernelConstruction* context) - : UnsortedSegmentBaseOp( - context, - max_functor_) {} + : UnsortedSegmentBaseOp(context, max_functor_) {} + private: - functor::UnsortedSegmentMaxFunctor max_functor_; + functor::UnsortedSegmentMaxFunctor max_functor_; }; #define REGISTER_REAL_CPU_UNSORTED_KERNELS(type, index_type) \ @@ -663,9 +663,9 @@ class SparseSegmentReductionOpBase : public OpKernel { Reduce(input_flat, indices_vec, start, end - start, out); OP_REQUIRES(context, bad_offset < 0, errors::InvalidArgument( - "Bad: indices[", start + bad_offset, "] == ", - indices_vec(start + bad_offset), " out of range [0, ", - input_flat.dimension(0), ")")); + "Bad: indices[", start + bad_offset, + "] == ", indices_vec(start + bad_offset), + " out of range [0, ", input_flat.dimension(0), ")")); start = end; ++end; diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h index bcdd42c80c..5c9cfe0906 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.h +++ b/tensorflow/core/kernels/segment_reduction_ops.h @@ -51,13 +51,14 @@ struct SegmentSumFunctor { // BaseFunctor for definition of UnsorteSegmentReductionOp // for usage without templates. template -struct UnsortedSegmentBaseFunctor{ - virtual ~UnsortedSegmentBaseFunctor(){} +struct UnsortedSegmentBaseFunctor { + virtual ~UnsortedSegmentBaseFunctor() {} virtual void operator()(OpKernelContext* ctx, const Device& d, - const Index output_rows, const TensorShape& segment_ids_shape, - typename TTypes::ConstFlat segment_ids, - const Index data_size, const T* data, - typename TTypes::Tensor output){}; + const Index output_rows, + const TensorShape& segment_ids_shape, + typename TTypes::ConstFlat segment_ids, + const Index data_size, const T* data, + typename TTypes::Tensor output){}; }; // Functor for UnsortedSegmentSumOp. @@ -70,7 +71,8 @@ struct UnsortedSegmentBaseFunctor{ // data: input data tensor. // output: output reshaped to {output_rows, output.size/output_rows} template -struct UnsortedSegmentSumFunctor: public UnsortedSegmentBaseFunctor { +struct UnsortedSegmentSumFunctor + : public UnsortedSegmentBaseFunctor { void operator()(OpKernelContext* ctx, const Device& d, const Index output_rows, const TensorShape& segment_ids_shape, typename TTypes::ConstFlat segment_ids, @@ -88,7 +90,8 @@ struct UnsortedSegmentSumFunctor: public UnsortedSegmentBaseFunctor -struct UnsortedSegmentMaxFunctor: public UnsortedSegmentBaseFunctor { +struct UnsortedSegmentMaxFunctor + : public UnsortedSegmentBaseFunctor { void operator()(OpKernelContext* ctx, const Device& d, const Index output_rows, const TensorShape& segment_ids_shape, typename TTypes::ConstFlat segment_ids, diff --git a/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc b/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc index 159fada621..39d520698e 100644 --- a/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc @@ -194,7 +194,8 @@ void SegmentSumFunctor::operator()( // UnsortedSegmentSumFunctor implementation for GPUDevice. template -struct UnsortedSegmentSumFunctor: UnsortedSegmentBaseFunctor { +struct UnsortedSegmentSumFunctor + : UnsortedSegmentBaseFunctor { void operator()(OpKernelContext* ctx, const GPUDevice& d, const Index output_rows, const TensorShape& segment_ids_shape, typename TTypes::ConstFlat segment_ids, @@ -221,11 +222,10 @@ struct UnsortedSegmentSumFunctor: UnsortedSegmentBaseFuncto const Index input_inner_dim_size = input_total_size / input_outer_dim_size; config = GetCudaLaunchConfig(input_total_size, d); - UnsortedSegmentSumCustomKernel< - T, - Index><<>>( - input_outer_dim_size, input_inner_dim_size, output_rows, - segment_ids.data(), data, output.data()); + UnsortedSegmentSumCustomKernel + <<>>( + input_outer_dim_size, input_inner_dim_size, output_rows, + segment_ids.data(), data, output.data()); } }; diff --git a/tensorflow/core/kernels/self_adjoint_eig_op.cc b/tensorflow/core/kernels/self_adjoint_eig_op.cc index 9765780726..bcd8877390 100644 --- a/tensorflow/core/kernels/self_adjoint_eig_op.cc +++ b/tensorflow/core/kernels/self_adjoint_eig_op.cc @@ -25,7 +25,6 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" - namespace tensorflow { template diff --git a/tensorflow/core/kernels/sendrecv_ops.cc b/tensorflow/core/kernels/sendrecv_ops.cc index 206fd40fa6..688e61fcad 100644 --- a/tensorflow/core/kernels/sendrecv_ops.cc +++ b/tensorflow/core/kernels/sendrecv_ops.cc @@ -114,7 +114,7 @@ REGISTER_KERNEL_BUILDER(Name("_Send").Device(DEVICE_GPU), SendOp); REGISTER_KERNEL_BUILDER(Name("_Send").Device(DEVICE_SYCL), SendOp); REGISTER_KERNEL_BUILDER( Name("_HostSend").Device(DEVICE_SYCL).HostMemory("tensor"), SendOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("_HostSend").Device(DEVICE_CPU), SendOp); REGISTER_KERNEL_BUILDER( @@ -198,7 +198,7 @@ REGISTER_KERNEL_BUILDER(Name("_Recv").Device(DEVICE_GPU), RecvOp); #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("_Recv").Device(DEVICE_SYCL), RecvOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("_HostRecv").Device(DEVICE_CPU), RecvOp); REGISTER_KERNEL_BUILDER( @@ -207,6 +207,6 @@ REGISTER_KERNEL_BUILDER( #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER( Name("_HostRecv").Device(DEVICE_SYCL).HostMemory("tensor"), RecvOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace tensorflow diff --git a/tensorflow/core/kernels/sequence_ops.cc b/tensorflow/core/kernels/sequence_ops.cc index e2e3758d87..9db0bd4d98 100644 --- a/tensorflow/core/kernels/sequence_ops.cc +++ b/tensorflow/core/kernels/sequence_ops.cc @@ -53,13 +53,13 @@ class RangeOp : public OpKernel { if (delta > 0) { OP_REQUIRES( context, start <= limit, - errors::InvalidArgument("Requires start <= limit when delta > 0: ", - start, "/", limit)); + errors::InvalidArgument( + "Requires start <= limit when delta > 0: ", start, "/", limit)); } else { OP_REQUIRES( context, start >= limit, - errors::InvalidArgument("Requires start >= limit when delta < 0: ", - start, "/", limit)); + errors::InvalidArgument( + "Requires start >= limit when delta < 0: ", start, "/", limit)); } int64 size = (std::is_integral::value ? ((std::abs(limit - start) + std::abs(delta) - 1) / diff --git a/tensorflow/core/kernels/session_ops.cc b/tensorflow/core/kernels/session_ops.cc index 185c5b248f..f2dd2812b5 100644 --- a/tensorflow/core/kernels/session_ops.cc +++ b/tensorflow/core/kernels/session_ops.cc @@ -144,7 +144,7 @@ REGISTER_GPU_KERNEL(bool); TF_CALL_NUMBER_TYPES(REGISTER_SYCL_KERNEL); REGISTER_SYCL_KERNEL(bool); #undef REGISTER_SYCL_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class DeleteSessionTensorOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/shape_ops.h b/tensorflow/core/kernels/shape_ops.h index 8d9d0ea846..55be308901 100644 --- a/tensorflow/core/kernels/shape_ops.h +++ b/tensorflow/core/kernels/shape_ops.h @@ -235,10 +235,10 @@ class SqueezeOp : public OpKernel { if (!wrapped_squeeze_dims.empty()) { if (wrapped_squeeze_dims.count(i) > 0) { OP_REQUIRES(ctx, existing_dim == 1, - errors::InvalidArgument("Tried to explicitly squeeze " - "dimension ", - i, " but dimension was not 1: ", - existing_dim)); + errors::InvalidArgument( + "Tried to explicitly squeeze " + "dimension ", + i, " but dimension was not 1: ", existing_dim)); } else { // This dimension is not being squeezed. new_shape.push_back(existing_dim); diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc index 82595de779..79369fd4a9 100644 --- a/tensorflow/core/kernels/slice_op.cc +++ b/tensorflow/core/kernels/slice_op.cc @@ -58,7 +58,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Shared code that is not dependent on the type of T. We do this to reduce // code size by not duplicating all this for all T (float, double, int32, etc.) @@ -72,10 +72,11 @@ static void SharedValidation(OpKernelContext* context, const Tensor& size_tensor = context->input(2); OP_REQUIRES( - context, context->op_kernel().IsLegacyVector(begin_tensor.shape()) && - context->op_kernel().IsLegacyVector(size_tensor.shape()) && - begin_tensor.NumElements() == input.dims() && - size_tensor.NumElements() == input.dims(), + context, + context->op_kernel().IsLegacyVector(begin_tensor.shape()) && + context->op_kernel().IsLegacyVector(size_tensor.shape()) && + begin_tensor.NumElements() == input.dims() && + size_tensor.NumElements() == input.dims(), errors::InvalidArgument( "Expected begin and size arguments to be 1-D tensors of size ", input.dims(), ", but got shapes ", begin_tensor.shape().DebugString(), @@ -125,8 +126,7 @@ static void SharedSliceCommonCases(OpKernelContext* context, TensorShape* output_shape, gtl::InlinedVector* begin, gtl::InlinedVector* size, - Tensor** result, - bool* done) { + Tensor** result, bool* done) { bool is_identity = true; bool slice_dim0 = true; *done = false; @@ -142,8 +142,8 @@ static void SharedSliceCommonCases(OpKernelContext* context, return; } - if (slice_dim0 && IsDim0SliceAligned(input.shape(), (*begin)[0], - (*size)[0])) { + if (slice_dim0 && + IsDim0SliceAligned(input.shape(), (*begin)[0], (*size)[0])) { VLOG(1) << "Slice dim 0: " << input.shape().DebugString(); CHECK_GE(input.dims(), 1); // Otherwise, is_identity should be true. context->set_output(0, input.Slice((*begin)[0], (*begin)[0] + (*size)[0])); @@ -154,7 +154,6 @@ static void SharedSliceCommonCases(OpKernelContext* context, OP_REQUIRES_OK(context, context->allocate_output(0, *output_shape, result)); } - template class SliceOp : public OpKernel { public: @@ -206,8 +205,9 @@ class SliceOp : public OpKernel { #undef HANDLE_DIM - OP_REQUIRES(context, false, errors::Unimplemented( - "SliceOp : Unhandled input dimensions")); + OP_REQUIRES( + context, false, + errors::Unimplemented("SliceOp : Unhandled input dimensions")); } } @@ -280,8 +280,9 @@ class MklSliceOp : public OpKernel { #undef HANDLE_DIM - OP_REQUIRES(context, false, errors::Unimplemented( - "SliceOp : Unhandled input dimensions")); + OP_REQUIRES( + context, false, + errors::Unimplemented("SliceOp : Unhandled input dimensions")); } } @@ -292,9 +293,9 @@ class MklSliceOp : public OpKernel { // as the sizes of all the dimensions of the input except slice_dim, then // returns True. Otherwise, returns False. bool DoesSliceShapeDifferInOnly1DHelper(const TensorShape& input_shape, - const gtl::ArraySlice& begin, - const gtl::ArraySlice& size, - int slice_dim) { + const gtl::ArraySlice& begin, + const gtl::ArraySlice& size, + int slice_dim) { for (int dim = 0; dim < 4; dim++) { if (dim != slice_dim && (begin[dim] != 0 || size[dim] != input_shape.dim_size(dim))) { @@ -316,9 +317,9 @@ class MklSliceOp : public OpKernel { // Returns True if Slicing over a single dimension, and sets slice_dim // to the number of the dimension that satisfies criteria. bool DoesSliceShapeDifferInOnly1D(const TensorShape& input_shape, - const gtl::ArraySlice& begin, - const gtl::ArraySlice& size, - int* slice_dim) { + const gtl::ArraySlice& begin, + const gtl::ArraySlice& size, + int* slice_dim) { for (int dim = 0; dim < 4; dim++) { if (DoesSliceShapeDifferInOnly1DHelper(input_shape, begin, size, dim)) { *slice_dim = dim; @@ -329,8 +330,7 @@ class MklSliceOp : public OpKernel { } template - void HandleCase(OpKernelContext* context, - const gtl::ArraySlice& begin, + void HandleCase(OpKernelContext* context, const gtl::ArraySlice& begin, const gtl::ArraySlice& size, Tensor* result) { int slice_dim = -1; TensorShape in_shape = context->input(0).shape(); @@ -340,67 +340,63 @@ class MklSliceOp : public OpKernel { // format over channel dimension. if (NDIM == 4 && DoesSliceShapeDifferInOnly1D(in_shape, begin, size, &slice_dim)) { - size_t in_strides[4] = { (size_t) in_shape.dim_size(1) * - in_shape.dim_size(2) * - in_shape.dim_size(3), - (size_t) in_shape.dim_size(2) * - in_shape.dim_size(3), - (size_t) in_shape.dim_size(3), - (size_t) 1 - }; - - size_t out_strides[4] = { (size_t) size[1] * size[2] * size[3], - (size_t) size[2] * size[3], - (size_t) size[3], - (size_t) 1 }; - - T *in_buf = const_cast(const_cast( - context->input(0).flat().data())); - T *op_buf = result->flat().data(); - - if (slice_dim == 1) { - /* data format = NCHW */ - - #pragma omp parallel for - for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { - T *ip = in_buf + (d0 * in_strides[0]); - T *op = op_buf + ((d0 - begin[0]) * out_strides[0]); - #pragma omp parallel for - for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { - T *ip1 = ip + (d1 * in_strides[1]); - T *op1 = op + ((d1 - begin[1]) * out_strides[1]); - // For NCHW, H and W will be contiguous. So we can copy - // both with one memcpy. - memcpy(static_cast(op1), static_cast(ip1), - sizeof(T) * in_strides[1]); - } + size_t in_strides[4] = { + (size_t)in_shape.dim_size(1) * in_shape.dim_size(2) * + in_shape.dim_size(3), + (size_t)in_shape.dim_size(2) * in_shape.dim_size(3), + (size_t)in_shape.dim_size(3), (size_t)1}; + + size_t out_strides[4] = {(size_t)size[1] * size[2] * size[3], + (size_t)size[2] * size[3], (size_t)size[3], + (size_t)1}; + + T* in_buf = const_cast( + const_cast(context->input(0).flat().data())); + T* op_buf = result->flat().data(); + + if (slice_dim == 1) { + /* data format = NCHW */ + +#pragma omp parallel for + for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { + T* ip = in_buf + (d0 * in_strides[0]); + T* op = op_buf + ((d0 - begin[0]) * out_strides[0]); +#pragma omp parallel for + for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { + T* ip1 = ip + (d1 * in_strides[1]); + T* op1 = op + ((d1 - begin[1]) * out_strides[1]); + // For NCHW, H and W will be contiguous. So we can copy + // both with one memcpy. + memcpy(static_cast(op1), static_cast(ip1), + sizeof(T) * in_strides[1]); } - return; - } else if (slice_dim == 3) { - /* data_format = NHWC */ - - #pragma omp parallel for - for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { - T *ip = in_buf + (d0 * in_strides[0]); - T *op = op_buf + ((d0 - begin[0]) * out_strides[0]); - #pragma omp parallel for - for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { - T *ip1 = ip + (d1 * in_strides[1]); - T *op1 = op + ((d1 - begin[1]) * out_strides[1]); - #pragma omp parallel for - for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) { - T *ip2 = ip1 + (d2 * in_strides[2]); - T *ip3 = ip2 + begin[3]; - T *op2 = op1 + ((d2 - begin[2]) * out_strides[2]); - T *op3 = op2; - memcpy(static_cast(op3), static_cast(ip3), - sizeof(T) * size[3]); - } + } + return; + } else if (slice_dim == 3) { + /* data_format = NHWC */ + +#pragma omp parallel for + for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) { + T* ip = in_buf + (d0 * in_strides[0]); + T* op = op_buf + ((d0 - begin[0]) * out_strides[0]); +#pragma omp parallel for + for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) { + T* ip1 = ip + (d1 * in_strides[1]); + T* op1 = op + ((d1 - begin[1]) * out_strides[1]); +#pragma omp parallel for + for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) { + T* ip2 = ip1 + (d2 * in_strides[2]); + T* ip3 = ip2 + begin[3]; + T* op2 = op1 + ((d2 - begin[2]) * out_strides[2]); + T* op3 = op2; + memcpy(static_cast(op3), static_cast(ip3), + sizeof(T) * size[3]); } } - return; } - // slice_dim is not 1 or 3, then we fallback to Eigen implementation. + return; + } + // slice_dim is not 1 or 3, then we fallback to Eigen implementation. } Eigen::DSizes indices; @@ -535,13 +531,13 @@ REGISTER_KERNEL_BUILDER(Name("Slice") #ifdef TENSORFLOW_USE_SYCL // Forward declarations of the functor specializations for SYCL. namespace functor { -#define DECLARE_SYCL_SPEC(T, NDIM) \ - template <> \ - void Slice::operator()( \ - const SYCLDevice& d, typename TTypes::Tensor output,\ - typename TTypes::ConstTensor input, \ - const Eigen::DSizes& indices, \ - const Eigen::DSizes& sizes); \ +#define DECLARE_SYCL_SPEC(T, NDIM) \ + template <> \ + void Slice::operator()( \ + const SYCLDevice& d, typename TTypes::Tensor output, \ + typename TTypes::ConstTensor input, \ + const Eigen::DSizes& indices, \ + const Eigen::DSizes& sizes); \ extern template struct Slice; #define DECLARE_FOR_N(T) \ diff --git a/tensorflow/core/kernels/slice_op.h b/tensorflow/core/kernels/slice_op.h index 0362a02133..db7eded745 100644 --- a/tensorflow/core/kernels/slice_op.h +++ b/tensorflow/core/kernels/slice_op.h @@ -24,7 +24,6 @@ limitations under the License. namespace tensorflow { namespace functor { - template struct Slice { void operator()(const Device& d, typename TTypes::Tensor output, diff --git a/tensorflow/core/kernels/slice_op_cpu_impl.h b/tensorflow/core/kernels/slice_op_cpu_impl.h index 47f1d5342a..64b6948190 100644 --- a/tensorflow/core/kernels/slice_op_cpu_impl.h +++ b/tensorflow/core/kernels/slice_op_cpu_impl.h @@ -43,7 +43,7 @@ TF_CALL_GPU_NUMBER_TYPES(DEFINE_SYCL_KERNELS); DEFINE_SYCL_KERNELS(int32); #undef DEFINE_SYCL_KERNELS -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/softmax_op.cc b/tensorflow/core/kernels/softmax_op.cc index 590f01c469..e1712ac239 100644 --- a/tensorflow/core/kernels/softmax_op.cc +++ b/tensorflow/core/kernels/softmax_op.cc @@ -30,7 +30,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Partial specialization for a CPUDevice, that uses the Eigen implementation // from SoftmaxEigenImpl. @@ -48,7 +48,7 @@ struct SoftmaxFunctor : SoftmaxFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template struct SoftmaxFunctor : SoftmaxFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor template @@ -100,5 +100,5 @@ REGISTER_KERNEL_BUILDER( REGISTER_KERNEL_BUILDER( Name("Softmax").Device(DEVICE_SYCL).TypeConstraint("T"), SoftmaxOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/spacetobatch_benchmark_test.cc b/tensorflow/core/kernels/spacetobatch_benchmark_test.cc index c25ce2d8bb..92ddf8edbf 100644 --- a/tensorflow/core/kernels/spacetobatch_benchmark_test.cc +++ b/tensorflow/core/kernels/spacetobatch_benchmark_test.cc @@ -70,7 +70,7 @@ static Graph* ConstructSpaceToBatchGraph( } \ BENCHMARK( \ BM_##OP##_##DEVICE##_##DTYPE##_##B##_##H##_##W##_##D##_bs##BS##_pad##P00##_##P01##_##P10##_##P11); -#define BM_SpaceToBatch(OP, ...) \ +#define BM_SpaceToBatch(OP, ...) \ BM_Expand(BM_SpaceToBatchDev(OP, cpu, DT_FLOAT, __VA_ARGS__)); \ BM_Expand(BM_SpaceToBatchDev(OP, gpu, DT_FLOAT, __VA_ARGS__)); \ BM_Expand(BM_SpaceToBatchDev(OP, cpu, DT_HALF, __VA_ARGS__)); \ diff --git a/tensorflow/core/kernels/spacetobatch_functor.cc b/tensorflow/core/kernels/spacetobatch_functor.cc index 23d8a5f9ed..4c374b8d99 100644 --- a/tensorflow/core/kernels/spacetobatch_functor.cc +++ b/tensorflow/core/kernels/spacetobatch_functor.cc @@ -154,7 +154,7 @@ struct SpaceToBatchFunctor { #define INSTANTIATE(NUM_BLOCK_DIMS, T) \ template struct SpaceToBatchFunctor; \ template struct SpaceToBatchFunctor; \ -/**/ + /**/ #define INSTANTIATE_FOR_T(T) \ TF_SPACETOBATCH_FOR_EACH_NUM_BLOCK_DIMS(INSTANTIATE, T) diff --git a/tensorflow/core/kernels/spacetobatch_functor.h b/tensorflow/core/kernels/spacetobatch_functor.h index 06813650c0..f46a84da1e 100644 --- a/tensorflow/core/kernels/spacetobatch_functor.h +++ b/tensorflow/core/kernels/spacetobatch_functor.h @@ -44,7 +44,7 @@ constexpr int kMaxSpaceToBatchBlockDims = 4; MACRO(2 /**/, ##__VA_ARGS__) \ MACRO(3 /**/, ##__VA_ARGS__) \ MACRO(4 /**/, ##__VA_ARGS__) \ -/**/ + /**/ namespace internal { namespace spacetobatch { diff --git a/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc b/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc index db8d419c38..5687141c9e 100644 --- a/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/spacetobatch_functor_gpu.cu.cc @@ -141,10 +141,10 @@ struct SpaceToBatchFunctor { } CudaLaunchConfig config = GetCudaLaunchConfig(static_cast(total_count), d); - S2B<<>>( - config.virtual_thread_count, const_cast(space_tensor.data()), args, - const_cast(batch_tensor.data())); + S2B + <<>>( + config.virtual_thread_count, const_cast(space_tensor.data()), + args, const_cast(batch_tensor.data())); return Status::OK(); } }; @@ -153,7 +153,7 @@ struct SpaceToBatchFunctor { #define INSTANTIATE(NUM_BLOCK_DIMS, T) \ template struct SpaceToBatchFunctor; \ template struct SpaceToBatchFunctor; \ -/**/ + /**/ #define INSTANTIATE_FOR_T(T) \ TF_SPACETOBATCH_FOR_EACH_NUM_BLOCK_DIMS(INSTANTIATE, T) diff --git a/tensorflow/core/kernels/spacetobatch_op.cc b/tensorflow/core/kernels/spacetobatch_op.cc index 95c1f5e7e8..fdc08ec8e3 100644 --- a/tensorflow/core/kernels/spacetobatch_op.cc +++ b/tensorflow/core/kernels/spacetobatch_op.cc @@ -58,9 +58,10 @@ void SpaceToBatchOpCompute(OpKernelContext* context, errors::InvalidArgument("input rank should be >= ", 1 + block_dims, " instead of ", orig_input_tensor.dims())); - OP_REQUIRES(context, TensorShapeUtils::IsMatrix(orig_paddings.shape()) && - block_dims == orig_paddings.dim_size(0) && - 2 == orig_paddings.dim_size(1), + OP_REQUIRES(context, + TensorShapeUtils::IsMatrix(orig_paddings.shape()) && + block_dims == orig_paddings.dim_size(0) && + 2 == orig_paddings.dim_size(1), errors::InvalidArgument("paddings should have shape [", block_dims, ", 2] instead of ", orig_paddings.shape().DebugString())); diff --git a/tensorflow/core/kernels/sparse_add_grad_op.cc b/tensorflow/core/kernels/sparse_add_grad_op.cc index d8ed0c6f0c..8597f3a8f7 100644 --- a/tensorflow/core/kernels/sparse_add_grad_op.cc +++ b/tensorflow/core/kernels/sparse_add_grad_op.cc @@ -35,9 +35,10 @@ class SparseAddGradOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("b_indices", &b_indices)); OP_REQUIRES_OK(ctx, ctx->input("sum_indices", &sum_indices)); - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_indices->shape()) && - TensorShapeUtils::IsMatrix(b_indices->shape()) && - TensorShapeUtils::IsMatrix(sum_indices->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsMatrix(a_indices->shape()) && + TensorShapeUtils::IsMatrix(b_indices->shape()) && + TensorShapeUtils::IsMatrix(sum_indices->shape()), errors::InvalidArgument( "Input indices should be matrices but received shapes: ", a_indices->shape().DebugString(), " and ", @@ -49,8 +50,9 @@ class SparseAddGradOp : public OpKernel { "Input backprop_val_grad should be a vector but received shape: ", backprop_val_grad->shape().DebugString())); OP_REQUIRES( - ctx, a_indices->dim_size(1) == b_indices->dim_size(1) && - b_indices->dim_size(1) == sum_indices->dim_size(1), + ctx, + a_indices->dim_size(1) == b_indices->dim_size(1) && + b_indices->dim_size(1) == sum_indices->dim_size(1), errors::InvalidArgument("The densified operands should have the same " "ndims; for A, B, sum got: ", a_indices->dim_size(1), b_indices->dim_size(1), diff --git a/tensorflow/core/kernels/sparse_add_op.cc b/tensorflow/core/kernels/sparse_add_op.cc index bd91dfdce6..d16317af67 100644 --- a/tensorflow/core/kernels/sparse_add_op.cc +++ b/tensorflow/core/kernels/sparse_add_op.cc @@ -34,8 +34,9 @@ class SparseAddOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("a_indices", &a_indices)); OP_REQUIRES_OK(ctx, ctx->input("b_indices", &b_indices)); - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_indices->shape()) && - TensorShapeUtils::IsMatrix(b_indices->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsMatrix(a_indices->shape()) && + TensorShapeUtils::IsMatrix(b_indices->shape()), errors::InvalidArgument( "Input indices should be matrices but received shapes: ", a_indices->shape().DebugString(), " and ", @@ -46,8 +47,9 @@ class SparseAddOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("a_values", &a_values_t)); OP_REQUIRES_OK(ctx, ctx->input("b_values", &b_values_t)); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_values_t->shape()) && - TensorShapeUtils::IsVector(b_values_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_values_t->shape()) && + TensorShapeUtils::IsVector(b_values_t->shape()), errors::InvalidArgument( "Input values should be vectors but received shapes: ", a_values_t->shape().DebugString(), " and ", @@ -62,8 +64,9 @@ class SparseAddOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("a_shape", &a_shape)); OP_REQUIRES_OK(ctx, ctx->input("b_shape", &b_shape)); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_shape->shape()) && - TensorShapeUtils::IsVector(b_shape->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_shape->shape()) && + TensorShapeUtils::IsVector(b_shape->shape()), errors::InvalidArgument( "Input shapes should be a vector but received shapes ", a_shape->shape().DebugString(), " and ", diff --git a/tensorflow/core/kernels/sparse_add_op_test.cc b/tensorflow/core/kernels/sparse_add_op_test.cc index 4cad02bbee..1f08e6c5ce 100644 --- a/tensorflow/core/kernels/sparse_add_op_test.cc +++ b/tensorflow/core/kernels/sparse_add_op_test.cc @@ -61,9 +61,9 @@ TEST_F(SparseAddOpTest, TwoD_AddSparseTensorWithSelf) { // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); #define ADD_TENSOR_INPUT() \ diff --git a/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc b/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc index c122616cf1..80bc1f1934 100644 --- a/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc +++ b/tensorflow/core/kernels/sparse_conditional_accumulator_op.cc @@ -103,8 +103,9 @@ class SparseAccumulatorTakeGradientOp DoneCallback callback) override { // Check signature OP_REQUIRES_OK_ASYNC( - ctx, ctx->MatchSignature({DT_STRING_REF, DT_INT32}, - {DT_INT64, accumulator->dtype(), DT_INT64}), + ctx, + ctx->MatchSignature({DT_STRING_REF, DT_INT32}, + {DT_INT64, accumulator->dtype(), DT_INT64}), callback); } diff --git a/tensorflow/core/kernels/sparse_cross_op.cc b/tensorflow/core/kernels/sparse_cross_op.cc index 07d935d55f..7cd4532ad6 100644 --- a/tensorflow/core/kernels/sparse_cross_op.cc +++ b/tensorflow/core/kernels/sparse_cross_op.cc @@ -288,8 +288,7 @@ struct CrossTraits { template class SparseCrossOp : public OpKernel { public: - explicit SparseCrossOp(OpKernelConstruction* context) - : OpKernel(context) { + explicit SparseCrossOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_)); // Read signed_hash_key_ as int64 since uint64 attributes are not // supported by REGISTER_OP. @@ -316,8 +315,8 @@ class SparseCrossOp : public OpKernel { GenerateColumnsFromInput(indices_list_in, values_list_in, shapes_list_in, dense_list_in); - typename CrossTraits::Crosser - crosser(columns, num_buckets_, hash_key_); + typename CrossTraits::Crosser crosser( + columns, num_buckets_, hash_key_); Tensor* indices_out; Tensor* values_out; Tensor* shape_out; @@ -326,8 +325,8 @@ class SparseCrossOp : public OpKernel { CreateOutputTensors(columns, batch_size, context, &indices_out, &values_out, &shape_out, &output_start_indices); - typename CrossTraits::Updater - updater(output_start_indices, indices_out, values_out); + typename CrossTraits::Updater updater( + output_start_indices, indices_out, values_out); auto do_work = [this, &columns, crosser, updater](int64 begin, int64 end) { for (int b = begin; b < end; b++) { ProductIterator product_iterator(columns, b); @@ -381,8 +380,9 @@ class SparseCrossOp : public OpKernel { "Input values should be a std::vector but received shape ", values_list_in[i].shape().DebugString(), " at position ", i)); OP_REQUIRES( - context, indices_list_in[i].shape().dim_size(0) == - values_list_in[i].shape().dim_size(0), + context, + indices_list_in[i].shape().dim_size(0) == + values_list_in[i].shape().dim_size(0), errors::InvalidArgument( "Expected size of values to be ", indices_list_in[i].shape().dim_size(0), " got ", diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc index cc0f86ce05..ac48202ada 100644 --- a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc +++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc @@ -70,8 +70,9 @@ class SparseDenseBinaryOpShared : public OpKernel { errors::InvalidArgument( "Input sp_indices should be a matrix but received shape: ", indices_t->shape().DebugString())); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(values_t->shape()) && - TensorShapeUtils::IsVector(shape_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(values_t->shape()) && + TensorShapeUtils::IsVector(shape_t->shape()), errors::InvalidArgument( "Inputs sp_values and sp_shape should be vectors " "but received shapes: ", @@ -150,8 +151,9 @@ class SparseDenseBinaryOpShared : public OpKernel { CASE(4); CASE(5); default: - OP_REQUIRES(ctx, false, errors::InvalidArgument( - "Only tensors with ranks between 1 and 5 " + OP_REQUIRES( + ctx, false, + errors::InvalidArgument("Only tensors with ranks between 1 and 5 " "are currently supported. Tensor rank: ", ndims)); #undef CASE diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc index eaf1884243..fe198af7e6 100644 --- a/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc +++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared_test.cc @@ -96,9 +96,9 @@ TEST_F(SparseDenseCDivTest, SameShape) { // [2 ] cdiv [dense: same shape, all 1's] // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); // Tensor dense(DT_FLOAT, TensorShape({3, 1})); @@ -125,9 +125,9 @@ TEST_F(SparseDenseCDivTest, BroadcastDenseSameDims) { // [2 ] cdiv [dense: shape [3,1], all 1's] // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); Tensor dense(DT_FLOAT, TensorShape({3, 1})); @@ -152,9 +152,9 @@ TEST_F(SparseDenseCDivTest, BroadcastDenseFewerDims) { // [2 ] cdiv [dense: shape [2]] // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); Tensor dense(DT_FLOAT, TensorShape({2})); @@ -184,9 +184,9 @@ TEST_F(SparseDenseCMulTest, BroadcastDense) { // [1 ?] where ? remains implicitly zero. // [1.5 0] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); Tensor dense(DT_FLOAT, TensorShape({2})); diff --git a/tensorflow/core/kernels/sparse_matmul_op.cc b/tensorflow/core/kernels/sparse_matmul_op.cc index 8ab23b64d3..a1f9667b78 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.cc +++ b/tensorflow/core/kernels/sparse_matmul_op.cc @@ -159,8 +159,8 @@ struct SparseSlice { template template -void SparseSlice::Initialize(const typename SparseSlice::ConstMatrixMap& mat, - int col_offset) { +void SparseSlice::Initialize( + const typename SparseSlice::ConstMatrixMap& mat, int col_offset) { const int mat_rows = Transpose ? mat.dimension(1) : mat.dimension(0); const int mat_cols = Transpose ? mat.dimension(0) : mat.dimension(1); DCHECK_LE(num_rows, mat_rows); @@ -278,9 +278,9 @@ ALWAYS_INLINE float ConvertBfloat16ToFloat(const bfloat16* src) { float out = 0; auto tmp = reinterpret_cast(&out); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - tmp[0] = *src; + tmp[0] = *src; #else - tmp[1] = *src; + tmp[1] = *src; #endif return out; } @@ -970,9 +970,9 @@ class SparseMatMulOp : public OpKernel { const int k2 = transpose_b_ ? b.dim_size(1) : b.dim_size(0); OP_REQUIRES(ctx, k == k2, - errors::InvalidArgument("Matrix size incompatible: a: ", - a.shape().DebugString(), ", b: ", - b.shape().DebugString())); + errors::InvalidArgument( + "Matrix size incompatible: a: ", a.shape().DebugString(), + ", b: ", b.shape().DebugString())); Tensor* output = nullptr; OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({m, n}), &output)); @@ -1224,8 +1224,9 @@ ALWAYS_INLINE void CopyAndMayBeInterleave(void* dst, const void* src, template inline BlockingCounter* SparseMatMul::ShuffleMatrix( - const typename SparseMatMul::ConstMatrixMapR& mat, int slice_row_start, - int slice_num_rows, int slice_col_start, int slice_num_cols, const int N, + const typename SparseMatMul::ConstMatrixMapR& mat, + int slice_row_start, int slice_num_rows, int slice_col_start, + int slice_num_cols, const int N, const DeviceBase::CpuWorkerThreads* thread_pool, MatrixR* buffer) { DCHECK_EQ(N % 2, 0); DCHECK_LE(kNumOperands * sizeof(float) / sizeof(TR), N); @@ -1306,8 +1307,9 @@ inline std::unique_ptr SparseMatMul::CreateDenseSlices( template inline void SparseMatMul::ComputeBlockSizes( const typename SparseMatMul::ConstMatrixMapL& left, - const typename SparseMatMul::ConstMatrixMapR& right, bool transpose_left, - int num_threads, int* KR, int* NR, int* KL, int* JB, int* IB) { + const typename SparseMatMul::ConstMatrixMapR& right, + bool transpose_left, int num_threads, int* KR, int* NR, int* KL, int* JB, + int* IB) { // Heuristics for calculating block sizes // Assume two hyperthreads per core. const int est_num_cores = std::max(1, (num_threads + 1) / 2); diff --git a/tensorflow/core/kernels/sparse_matmul_op.h b/tensorflow/core/kernels/sparse_matmul_op.h index cca52558ae..14ef2ed704 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.h +++ b/tensorflow/core/kernels/sparse_matmul_op.h @@ -159,25 +159,25 @@ EIGEN_STRONG_INLINE Packet4f pload2bf16(const float* from) { // Return a packet with the first value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_first(const Packet4f& a) { - return vec_splat (a, 0); + return vec_splat(a, 0); } // Return a packet with the second value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_second(const Packet4f& a) { - return vec_splat (a, 1); + return vec_splat(a, 1); } // Return a packet with the third value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_third(const Packet4f& a) { - return vec_splat (a, 2); + return vec_splat(a, 2); } // Return a packet with the fourth value of the input Packet replicated template <> EIGEN_STRONG_INLINE Packet4f pbroadcast_fourth(const Packet4f& a) { - return vec_splat (a, 3); + return vec_splat(a, 3); } #endif diff --git a/tensorflow/core/kernels/sparse_matmul_op_test.cc b/tensorflow/core/kernels/sparse_matmul_op_test.cc index f815ca9e34..ebc6d8fa4e 100644 --- a/tensorflow/core/kernels/sparse_matmul_op_test.cc +++ b/tensorflow/core/kernels/sparse_matmul_op_test.cc @@ -284,11 +284,11 @@ class SparseMatmulOpTest : public ::testing::Test { uint16_t* data3_bfloat16_p = reinterpret_cast(data3_bfloat16) + i; #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - data3_p[1] = 0; - data3_bfloat16_p[0] = data3_p[0]; + data3_p[1] = 0; + data3_bfloat16_p[0] = data3_p[0]; #else - data3_p[0] = 0; - data3_bfloat16_p[0] = data3_p[1]; + data3_p[0] = 0; + data3_bfloat16_p[0] = data3_p[1]; #endif } } diff --git a/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc b/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc index 110376be42..96246c7a71 100644 --- a/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc +++ b/tensorflow/core/kernels/sparse_reduce_sum_op_test.cc @@ -51,9 +51,9 @@ TEST_F(SparseReduceSumOpTest, SimpleReduce) { // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); AddInputFromArray(indices_shape, indices); @@ -93,9 +93,9 @@ TEST_F(SparseReduceSumSparseOpTest, SimpleReduce) { // [3 4] const auto indices_shape = TensorShape({4, 2}); - std::initializer_list in{ 0, 1, 1, 0, 2, 0, 2, 1 }; + std::initializer_list in{0, 1, 1, 0, 2, 0, 2, 1}; const gtl::ArraySlice indices(in); - std::initializer_list sh{ 3, 2 }; + std::initializer_list sh{3, 2}; const gtl::ArraySlice shape(sh); AddInputFromArray(indices_shape, indices); diff --git a/tensorflow/core/kernels/sparse_softmax_op.cc b/tensorflow/core/kernels/sparse_softmax_op.cc index 327a94b8a1..444a5f657a 100644 --- a/tensorflow/core/kernels/sparse_softmax_op.cc +++ b/tensorflow/core/kernels/sparse_softmax_op.cc @@ -50,8 +50,9 @@ class SparseSoftmaxOp : public OpKernel { errors::InvalidArgument( "Input sp_indices should be a matrix but received shape: ", indices_t->shape().DebugString())); - OP_REQUIRES(context, TensorShapeUtils::IsVector(values_t->shape()) && - TensorShapeUtils::IsVector(shape_t->shape()), + OP_REQUIRES(context, + TensorShapeUtils::IsVector(values_t->shape()) && + TensorShapeUtils::IsVector(shape_t->shape()), errors::InvalidArgument( "Inputs sp_values and sp_shape should be vectors " "but received shapes: ", diff --git a/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc b/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc index b027adba6b..09cb2a6a71 100644 --- a/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc +++ b/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc @@ -132,14 +132,16 @@ class SparseSparseBinaryOpShared : public OpKernel { // Validations. OP_REQUIRES( - ctx, TensorShapeUtils::IsMatrix(a_indices_t->shape()) && - TensorShapeUtils::IsMatrix(b_indices_t->shape()), + ctx, + TensorShapeUtils::IsMatrix(a_indices_t->shape()) && + TensorShapeUtils::IsMatrix(b_indices_t->shape()), errors::InvalidArgument("Inputs a_indices and b_indices should be " "matrices but received shapes: ", a_indices_t->shape().DebugString(), ", ", b_indices_t->shape().DebugString())); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_values_t->shape()) && - TensorShapeUtils::IsVector(b_values_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_values_t->shape()) && + TensorShapeUtils::IsVector(b_values_t->shape()), errors::InvalidArgument( "Inputs a_values and b_values should be vectors " "but received shapes: ", @@ -157,8 +159,9 @@ class SparseSparseBinaryOpShared : public OpKernel { " non-empty input values, got ", a_values.size(), " and ", b_values.size())); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(a_shape_t->shape()) && - TensorShapeUtils::IsVector(b_shape_t->shape()), + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(a_shape_t->shape()) && + TensorShapeUtils::IsVector(b_shape_t->shape()), errors::InvalidArgument( "Input shapes should be a vector but received shapes ", a_shape_t->shape().DebugString(), " and ", diff --git a/tensorflow/core/kernels/sparse_split_op.cc b/tensorflow/core/kernels/sparse_split_op.cc index 6171b532aa..67dcf05a6c 100644 --- a/tensorflow/core/kernels/sparse_split_op.cc +++ b/tensorflow/core/kernels/sparse_split_op.cc @@ -48,18 +48,20 @@ class SparseSplitOp : public OpKernel { "Input shape should be a vector but received shape ", input_shape.shape().DebugString())); - OP_REQUIRES(context, input_shape.dim_size(0) && - split_dim < input_shape.vec().size(), - errors::InvalidArgument( - "Input split_dim should be between 0 and rank (", - input_shape.vec().size(), "), got ", split_dim)); - - OP_REQUIRES(context, num_split_ >= 1 && - num_split_ <= input_shape.vec()(split_dim), - errors::InvalidArgument("Input num_split should be between 1 " - "and the splitting dimension size (", - input_shape.vec()(split_dim), - "), got ", num_split_)); + OP_REQUIRES( + context, + input_shape.dim_size(0) && split_dim < input_shape.vec().size(), + errors::InvalidArgument( + "Input split_dim should be between 0 and rank (", + input_shape.vec().size(), "), got ", split_dim)); + + OP_REQUIRES( + context, + num_split_ >= 1 && num_split_ <= input_shape.vec()(split_dim), + errors::InvalidArgument("Input num_split should be between 1 " + "and the splitting dimension size (", + input_shape.vec()(split_dim), "), got ", + num_split_)); sparse::SparseTensor sparse_tensor(input_indices, input_values, TensorShape(input_shape.vec())); diff --git a/tensorflow/core/kernels/sparse_to_dense_op.cc b/tensorflow/core/kernels/sparse_to_dense_op.cc index 6a6cc3d813..ba3da21a43 100644 --- a/tensorflow/core/kernels/sparse_to_dense_op.cc +++ b/tensorflow/core/kernels/sparse_to_dense_op.cc @@ -73,8 +73,9 @@ class SparseToDense : public OpKernel { // sparse_values const Tensor& sparse_values = c->input(2); const int64 num_values = sparse_values.NumElements(); - OP_REQUIRES(c, sparse_values.dims() == 0 || - (sparse_values.dims() == 1 && num_values == num_elems), + OP_REQUIRES(c, + sparse_values.dims() == 0 || + (sparse_values.dims() == 1 && num_values == num_elems), errors::InvalidArgument("sparse_values has incorrect shape ", sparse_values.shape().DebugString(), ", should be [] or [", num_elems, "]")); diff --git a/tensorflow/core/kernels/sparse_to_dense_op_test.cc b/tensorflow/core/kernels/sparse_to_dense_op_test.cc index f0d19da804..d8b0f93082 100644 --- a/tensorflow/core/kernels/sparse_to_dense_op_test.cc +++ b/tensorflow/core/kernels/sparse_to_dense_op_test.cc @@ -38,7 +38,6 @@ namespace { class SparseToDenseTest : public OpsTestBase { protected: - void MakeOp(int dim, DataType index_type, DataType value_type) { TF_ASSERT_OK(NodeDefBuilder("sparsetodense", "SparseToDense") .Input(FakeInput(index_type)) diff --git a/tensorflow/core/kernels/sparse_xent_op.cc b/tensorflow/core/kernels/sparse_xent_op.cc index c35ba42db2..f84ffd5323 100644 --- a/tensorflow/core/kernels/sparse_xent_op.cc +++ b/tensorflow/core/kernels/sparse_xent_op.cc @@ -39,10 +39,10 @@ Status CheckInvalidLabelIndex(const Tensor& labels, int64 max_index) { if (*min_max_dim_value.first < 0 || *min_max_dim_value.second >= max_index) { bad_index = (*min_max_dim_value.first < 0) ? *min_max_dim_value.first : *min_max_dim_value.second; - return errors::InvalidArgument("Received a label value of ", bad_index, - " which is outside the valid range of [0, ", - max_index, "). Label values: ", - labels.SummarizeValue(labels.NumElements())); + return errors::InvalidArgument( + "Received a label value of ", bad_index, + " which is outside the valid range of [0, ", max_index, + "). Label values: ", labels.SummarizeValue(labels.NumElements())); } return Status::OK(); } diff --git a/tensorflow/core/kernels/sparse_xent_op_test.cc b/tensorflow/core/kernels/sparse_xent_op_test.cc index b8ea0d2d7e..afb0bf7626 100644 --- a/tensorflow/core/kernels/sparse_xent_op_test.cc +++ b/tensorflow/core/kernels/sparse_xent_op_test.cc @@ -41,10 +41,10 @@ static Graph* SparseXent(int batch_size, int num_classes) { return g; } -#define BM_SparseXentDev(BATCH, CLASS, DEVICE) \ - static void BM_SparseXent##_##BATCH##_##CLASS##_##DEVICE(int iters) { \ +#define BM_SparseXentDev(BATCH, CLASS, DEVICE) \ + static void BM_SparseXent##_##BATCH##_##CLASS##_##DEVICE(int iters) { \ testing::ItemsProcessed(static_cast(iters) * BATCH * CLASS); \ - test::Benchmark(#DEVICE, SparseXent(BATCH, CLASS)).Run(iters); \ + test::Benchmark(#DEVICE, SparseXent(BATCH, CLASS)).Run(iters); \ } \ BENCHMARK(BM_SparseXent##_##BATCH##_##CLASS##_##DEVICE); diff --git a/tensorflow/core/kernels/split_lib.h b/tensorflow/core/kernels/split_lib.h index ff92ffeeb3..a08949e626 100644 --- a/tensorflow/core/kernels/split_lib.h +++ b/tensorflow/core/kernels/split_lib.h @@ -57,7 +57,7 @@ struct Split { const Eigen::DSizes& slice_indices, const Eigen::DSizes& slice_sizes); }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/split_lib_cpu.cc b/tensorflow/core/kernels/split_lib_cpu.cc index 25026208d1..771c633b15 100644 --- a/tensorflow/core/kernels/split_lib_cpu.cc +++ b/tensorflow/core/kernels/split_lib_cpu.cc @@ -49,13 +49,13 @@ void Split::operator()( typename TTypes::ConstTensor input, const Eigen::DSizes& slice_indices, const Eigen::DSizes& slice_sizes) { - output.device(d) = input.slice(slice_indices, slice_sizes); + output.device(d) = input.slice(slice_indices, slice_sizes); } #define DEFINE_SYCL_KERNELS(T) template struct Split; TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DEFINE_SYCL_KERNELS); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor } // namespace tensorflow diff --git a/tensorflow/core/kernels/split_op.cc b/tensorflow/core/kernels/split_op.cc index 78badde27e..85f529326d 100644 --- a/tensorflow/core/kernels/split_op.cc +++ b/tensorflow/core/kernels/split_op.cc @@ -39,7 +39,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class SplitOpBase : public OpKernel { @@ -142,8 +142,9 @@ class SplitOpCPU : public SplitOpBase { // Android also uses int32 indexing, so check here also. OP_REQUIRES( - context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), + context, + FastBoundsCheck(input.NumElements(), + std::numeric_limits::max()), errors::InvalidArgument("Split requires input size < ", std::numeric_limits::max())); @@ -245,10 +246,11 @@ class SplitOpGPU : public SplitOpBase { const int32 split_dim = split_dim_orig < 0 ? split_dim_orig + input.dims() : split_dim_orig; const int32 num_split = Base::num_outputs(); - OP_REQUIRES(context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("Split on GPU requires input size " - "< max int32")); + OP_REQUIRES( + context, + FastBoundsCheck(input.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("Split on GPU requires input size " + "< max int32")); int32 prefix_dim_size; int32 split_dim_size; int32 suffix_dim_size; @@ -304,8 +306,9 @@ class SplitOpSYCL : public SplitOpBase { // Android also uses int32 indexing, so check here also. OP_REQUIRES( - context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), + context, + FastBoundsCheck(input.NumElements(), + std::numeric_limits::max()), errors::InvalidArgument("Split requires input size < ", std::numeric_limits::max())); @@ -342,14 +345,14 @@ class SplitOpSYCL : public SplitOpBase { {prefix_dim_size, split_dim_output_size, suffix_dim_size}); functor::Split()(context->eigen_device(), - result_shaped, input_reshaped, - slice_indices, slice_sizes); + result_shaped, input_reshaped, + slice_indices, slice_sizes); } indices[1] += split_dim_output_size; } } }; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #define REGISTER_SPLIT(type) \ REGISTER_KERNEL_BUILDER(Name("Split") \ @@ -381,11 +384,11 @@ REGISTER_GPU(bfloat16); #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL -#define REGISTER_SYCL(type) \ - REGISTER_KERNEL_BUILDER(Name("Split") \ - .Device(DEVICE_SYCL) \ - .TypeConstraint("T") \ - .HostMemory("split_dim"), \ +#define REGISTER_SYCL(type) \ + REGISTER_KERNEL_BUILDER(Name("Split") \ + .Device(DEVICE_SYCL) \ + .TypeConstraint("T") \ + .HostMemory("split_dim"), \ SplitOpSYCL) TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SYCL); diff --git a/tensorflow/core/kernels/split_v_op.cc b/tensorflow/core/kernels/split_v_op.cc index f1078ac349..7ff5df47d7 100644 --- a/tensorflow/core/kernels/split_v_op.cc +++ b/tensorflow/core/kernels/split_v_op.cc @@ -197,8 +197,9 @@ class SplitVOpCPU : public SplitVOpBase { // Android also uses int32 indexing, so check here also. OP_REQUIRES( - context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), + context, + FastBoundsCheck(input.NumElements(), + std::numeric_limits::max()), errors::InvalidArgument("Split requires input size < ", std::numeric_limits::max())); @@ -305,10 +306,11 @@ class SplitVOpGPU : public SplitVOpBase { const int32 split_dim_orig = context->input(2).flat()(0); const int32 split_dim = split_dim_orig < 0 ? split_dim_orig + input.dims() : split_dim_orig; - OP_REQUIRES(context, FastBoundsCheck(input.NumElements(), - std::numeric_limits::max()), - errors::InvalidArgument("Split on GPU requires input size " - "< max int32")); + OP_REQUIRES( + context, + FastBoundsCheck(input.NumElements(), std::numeric_limits::max()), + errors::InvalidArgument("Split on GPU requires input size " + "< max int32")); int32 prefix_dim_size; int32 split_dim_size; diff --git a/tensorflow/core/kernels/stack_ops.cc b/tensorflow/core/kernels/stack_ops.cc index affe81a555..65296f61fd 100644 --- a/tensorflow/core/kernels/stack_ops.cc +++ b/tensorflow/core/kernels/stack_ops.cc @@ -42,7 +42,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class Stack : public ResourceBase { public: @@ -242,7 +242,7 @@ REGISTER_KERNEL_BUILDER(Name("StackV2") .HostMemory("max_size") .HostMemory("handle"), StackOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class StackPushOp : public AsyncOpKernel { @@ -274,11 +274,11 @@ class StackPushOp : public AsyncOpKernel { static constexpr int kCopyThreshold = 2048; static constexpr double kOccupancy = 0.7; if (swap_memory_ && !alloc_attrs.on_host() && - ( std::is_same::value + (std::is_same::value #ifdef TENSORFLOW_USE_SYCL - || std::is_same::value -#endif // TENSORFLOW_USE_SYCL - ) && + || std::is_same::value +#endif // TENSORFLOW_USE_SYCL + ) && tensor.TotalBytes() > kCopyThreshold && stack->IsUsefulToSwap(tensor)) { DeviceContext* device_ctxt = ctx->op_device_context(); auto device = static_cast(ctx->device()); @@ -391,7 +391,7 @@ REGISTER_SYCL_HOST_KERNEL(int32); REGISTER_SYCL_HOST_KERNEL(bool); #undef REGISTER_SYCL_KERNEL #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class StackPopOp : public AsyncOpKernel { public: @@ -498,7 +498,7 @@ REGISTER_SYCL_HOST_KERNEL(bool); #undef REGISTER_SYCL_KERNEL #undef REGISTER_SYCL_HOST_KERNEL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class StackCloseOp : public OpKernel { public: @@ -526,6 +526,6 @@ REGISTER_KERNEL_BUILDER( REGISTER_KERNEL_BUILDER( Name("StackCloseV2").Device(DEVICE_SYCL).HostMemory("handle"), StackCloseOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/stage_op.cc b/tensorflow/core/kernels/stage_op.cc index 0fae46dea6..03fc4467a1 100644 --- a/tensorflow/core/kernels/stage_op.cc +++ b/tensorflow/core/kernels/stage_op.cc @@ -70,12 +70,11 @@ class Buffer : public ResourceBase { return bytes + current_bytes_ > memory_limit_; } - std::size_t GetTupleBytes(const Tuple & tuple) - { + std::size_t GetTupleBytes(const Tuple& tuple) { return std::accumulate(tuple.begin(), tuple.end(), 0, - [](const std::size_t & lhs, const Tensor & rhs) { - return lhs + rhs.TotalBytes(); - }); + [](const std::size_t& lhs, const Tensor& rhs) { + return lhs + rhs.TotalBytes(); + }); } public: @@ -90,19 +89,22 @@ class Buffer : public ResourceBase { std::size_t tuple_bytes = GetTupleBytes(*tuple); // Sanity check so that we don't block for ever below - if(memory_limit_ > 0 && tuple_bytes > memory_limit_) { - return Status(errors::ResourceExhausted("Attempted to insert " - "tensors with combined size of '", tuple_bytes, "' bytes into " - "Staging Area with a memory limit of '", memory_limit_, "'.")); + if (memory_limit_ > 0 && tuple_bytes > memory_limit_) { + return Status( + errors::ResourceExhausted("Attempted to insert " + "tensors with combined size of '", + tuple_bytes, + "' bytes into " + "Staging Area with a memory limit of '", + memory_limit_, "'.")); } - // If buffer capacity is bounded wait until elements have been removed - if(IsBounded()) { + if (IsBounded()) { full_cond_var_.wait(lock, [tuple_bytes, this]() { // If there's a memory limit, check if there's space for insertion - bool memory_limit_valid = memory_limit_ > 0 ? - !WouldExceedMemoryLimit(tuple_bytes) : true; + bool memory_limit_valid = + memory_limit_ > 0 ? !WouldExceedMemoryLimit(tuple_bytes) : true; // If we're configured for capacity check if there's space for insertion bool capacity_valid = capacity_ > 0 ? !IsCapacityFull() : true; @@ -186,8 +188,7 @@ Status GetBuffer(OpKernelContext* ctx, const NodeDef& ndef, Buffer** buf) { ContainerInfo cinfo; // Lambda for creating the Staging Area - auto create_fn = [&ndef](Buffer** ret) -> Status - { + auto create_fn = [&ndef](Buffer** ret) -> Status { int64 capacity; int64 memory_limit; TF_RETURN_IF_ERROR(GetNodeAttr(ndef, "capacity", &capacity)); @@ -196,7 +197,6 @@ Status GetBuffer(OpKernelContext* ctx, const NodeDef& ndef, Buffer** buf) { return Status::OK(); }; - TF_RETURN_IF_ERROR(cinfo.Init(rm, ndef, true /* use name() */)); TF_RETURN_IF_ERROR(rm->LookupOrCreate(cinfo.container(), cinfo.name(), buf, create_fn)); @@ -228,7 +228,7 @@ REGISTER_KERNEL_BUILDER(Name("Stage").Device(DEVICE_GPU), StageOp); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("Stage").Device(DEVICE_SYCL), StageOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class UnstageOp : public OpKernel { public: @@ -244,7 +244,8 @@ class UnstageOp : public OpKernel { buf->Get(&tuple); - OP_REQUIRES(ctx, tuple.size() == (size_t)ctx->num_outputs(), + OP_REQUIRES( + ctx, tuple.size() == (size_t)ctx->num_outputs(), errors::InvalidArgument("Mismatch stage/unstage: ", tuple.size(), " vs. ", ctx->num_outputs())); @@ -260,7 +261,7 @@ REGISTER_KERNEL_BUILDER(Name("Unstage").Device(DEVICE_GPU), UnstageOp); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("Unstage").Device(DEVICE_SYCL), UnstageOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL class StagePeekOp : public OpKernel { public: @@ -278,7 +279,8 @@ class StagePeekOp : public OpKernel { OP_REQUIRES_OK(ctx, buf->Peek(index, &tuple)); - OP_REQUIRES(ctx, tuple.size() == (size_t)ctx->num_outputs(), + OP_REQUIRES( + ctx, tuple.size() == (size_t)ctx->num_outputs(), errors::InvalidArgument("Mismatch stage/unstage: ", tuple.size(), " vs. ", ctx->num_outputs())); @@ -288,17 +290,15 @@ class StagePeekOp : public OpKernel { } }; -REGISTER_KERNEL_BUILDER(Name("StagePeek").Device(DEVICE_CPU), - StagePeekOp); +REGISTER_KERNEL_BUILDER(Name("StagePeek").Device(DEVICE_CPU), StagePeekOp); #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER(Name("StagePeek").HostMemory("index"). - Device(DEVICE_GPU), StagePeekOp); +REGISTER_KERNEL_BUILDER( + Name("StagePeek").HostMemory("index").Device(DEVICE_GPU), StagePeekOp); #endif #ifdef TENSORFLOW_USE_SYCL -REGISTER_KERNEL_BUILDER(Name("StagePeek").HostMemory("index") - .Device(DEVICE_SYCL), StagePeekOp); -#endif // TENSORFLOW_USE_SYCL - +REGISTER_KERNEL_BUILDER( + Name("StagePeek").HostMemory("index").Device(DEVICE_SYCL), StagePeekOp); +#endif // TENSORFLOW_USE_SYCL class StageSizeOp : public OpKernel { public: @@ -312,9 +312,8 @@ class StageSizeOp : public OpKernel { core::ScopedUnref scope(buf); // Allocate size output tensor - Tensor * size = nullptr; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), - &size)); + Tensor* size = nullptr; + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &size)); // Set it to the actual size size->scalar().setConstant(buf->Size()); @@ -323,13 +322,13 @@ class StageSizeOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("StageSize").Device(DEVICE_CPU), StageSizeOp); #if GOOGLE_CUDA -REGISTER_KERNEL_BUILDER(Name("StageSize").HostMemory("size") - .Device(DEVICE_GPU), StageSizeOp); +REGISTER_KERNEL_BUILDER(Name("StageSize").HostMemory("size").Device(DEVICE_GPU), + StageSizeOp); #endif #ifdef TENSORFLOW_USE_SYCL -REGISTER_KERNEL_BUILDER(Name("StageSize").HostMemory("size") - .Device(DEVICE_SYCL), StageSizeOp); -#endif // TENSORFLOW_USE_SYCL +REGISTER_KERNEL_BUILDER( + Name("StageSize").HostMemory("size").Device(DEVICE_SYCL), StageSizeOp); +#endif // TENSORFLOW_USE_SYCL class StageClearOp : public OpKernel { public: @@ -352,7 +351,6 @@ REGISTER_KERNEL_BUILDER(Name("StageClear").Device(DEVICE_GPU), StageClearOp); #endif #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER(Name("StageClear").Device(DEVICE_SYCL), StageClearOp); -#endif // TENSORFLOW_USE_SYCL - +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc index 7c213e14d2..8f7f91c9df 100644 --- a/tensorflow/core/kernels/strided_slice_op.cc +++ b/tensorflow/core/kernels/strided_slice_op.cc @@ -541,5 +541,5 @@ REGISTER_KERNEL_BUILDER(Name("ResourceStridedSliceAssign") .HostMemory("strides"), StridedSliceAssignOp) #undef REGISTER_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/strided_slice_op_impl.h b/tensorflow/core/kernels/strided_slice_op_impl.h index a84ba38ef4..ac1259a9ac 100644 --- a/tensorflow/core/kernels/strided_slice_op_impl.h +++ b/tensorflow/core/kernels/strided_slice_op_impl.h @@ -302,7 +302,7 @@ DECLARE_FOR_N_SYCL(int32); DECLARE_FOR_N_SYCL(int64); #undef DECLARE_FOR_N_SYCL -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL #undef INSTANTIATE #undef DECLARE_FOR_N_CPU diff --git a/tensorflow/core/kernels/string_join_op.cc b/tensorflow/core/kernels/string_join_op.cc index 721702bec6..28cca9f448 100644 --- a/tensorflow/core/kernels/string_join_op.cc +++ b/tensorflow/core/kernels/string_join_op.cc @@ -50,9 +50,9 @@ class StringJoinOp : public OpKernel { } else { OP_REQUIRES( context, input_shape == input.shape(), - errors::InvalidArgument("Input shapes do not match: ", - input_shape.DebugString(), " vs. ", - input.shape().DebugString())); + errors::InvalidArgument( + "Input shapes do not match: ", input_shape.DebugString(), + " vs. ", input.shape().DebugString())); } } } diff --git a/tensorflow/core/kernels/substr_op.cc b/tensorflow/core/kernels/substr_op.cc index 743f113150..e29f67297f 100644 --- a/tensorflow/core/kernels/substr_op.cc +++ b/tensorflow/core/kernels/substr_op.cc @@ -95,9 +95,9 @@ class SubstrOp : public OpKernel { // Create BCast helper with shape of input and pos/len BCast bcast(BCast::FromShape(input_shape), BCast::FromShape(pos_shape)); OP_REQUIRES(context, bcast.IsValid(), - errors::InvalidArgument("Incompatible shapes: ", - input_shape.DebugString(), " vs. ", - pos_shape.DebugString())); + errors::InvalidArgument( + "Incompatible shapes: ", input_shape.DebugString(), + " vs. ", pos_shape.DebugString())); TensorShape output_shape = BCast::ToShape(bcast.result_shape()); int ndims = output_shape.dims(); Tensor* output_tensor = nullptr; diff --git a/tensorflow/core/kernels/summary_image_op.cc b/tensorflow/core/kernels/summary_image_op.cc index 233b824bcc..29b21ee735 100644 --- a/tensorflow/core/kernels/summary_image_op.cc +++ b/tensorflow/core/kernels/summary_image_op.cc @@ -54,18 +54,20 @@ class SummaryImageOp : public OpKernel { const Tensor& tensor = c->input(1); OP_REQUIRES(c, IsLegacyScalar(tags.shape()), errors::InvalidArgument("Tags must be a scalar")); - OP_REQUIRES(c, tensor.dims() == 4 && - (tensor.dim_size(3) == 1 || tensor.dim_size(3) == 3 || - tensor.dim_size(3) == 4), + OP_REQUIRES(c, + tensor.dims() == 4 && + (tensor.dim_size(3) == 1 || tensor.dim_size(3) == 3 || + tensor.dim_size(3) == 4), errors::InvalidArgument( "Tensor must be 4-D with last dim 1, 3, or 4, not ", tensor.shape().DebugString())); const string& base_tag = tags.scalar()(); - OP_REQUIRES(c, tensor.dim_size(0) < (1LL << 31) && - tensor.dim_size(1) < (1LL << 31) && - tensor.dim_size(2) < (1LL << 31) && - (tensor.dim_size(1) * tensor.dim_size(2)) < (1LL << 29), + OP_REQUIRES(c, + tensor.dim_size(0) < (1LL << 31) && + tensor.dim_size(1) < (1LL << 31) && + tensor.dim_size(2) < (1LL << 31) && + (tensor.dim_size(1) * tensor.dim_size(2)) < (1LL << 29), errors::InvalidArgument("Tensor too large for summary ", tensor.shape().DebugString())); diff --git a/tensorflow/core/kernels/summary_op.cc b/tensorflow/core/kernels/summary_op.cc index b818724ec2..1f4e3418f4 100644 --- a/tensorflow/core/kernels/summary_op.cc +++ b/tensorflow/core/kernels/summary_op.cc @@ -41,11 +41,12 @@ class SummaryScalarOp : public OpKernel { const Tensor& values = c->input(1); OP_REQUIRES( - c, tags.IsSameSize(values) || - (IsLegacyScalar(tags.shape()) && IsLegacyScalar(values.shape())), - errors::InvalidArgument("tags and values not the same shape: ", - tags.shape().DebugString(), " != ", - values.shape().DebugString(), SingleTag(tags))); + c, + tags.IsSameSize(values) || + (IsLegacyScalar(tags.shape()) && IsLegacyScalar(values.shape())), + errors::InvalidArgument( + "tags and values not the same shape: ", tags.shape().DebugString(), + " != ", values.shape().DebugString(), SingleTag(tags))); auto Ttags = tags.flat(); auto Tvalues = values.flat(); Summary s; diff --git a/tensorflow/core/kernels/tile_functor_cpu.cc b/tensorflow/core/kernels/tile_functor_cpu.cc index b2fd669541..f814486701 100644 --- a/tensorflow/core/kernels/tile_functor_cpu.cc +++ b/tensorflow/core/kernels/tile_functor_cpu.cc @@ -15,10 +15,10 @@ limitations under the License. #define EIGEN_USE_THREADS -#include "tensorflow/core/kernels/tile_functor.h" #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/kernels/tile_functor.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/tile_ops_cpu_impl.h b/tensorflow/core/kernels/tile_ops_cpu_impl.h index 054b31ef9e..df6a666cd4 100644 --- a/tensorflow/core/kernels/tile_ops_cpu_impl.h +++ b/tensorflow/core/kernels/tile_ops_cpu_impl.h @@ -63,7 +63,7 @@ TF_CALL_int64(DEFINE_TYPE); #undef DEFINE_DIM #undef DEFINE_TYPE -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // end namespace functor } // end namespace tensorflow diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc index 38e77ab60f..07befa27bc 100644 --- a/tensorflow/core/kernels/training_ops.cc +++ b/tensorflow/core/kernels/training_ops.cc @@ -3279,7 +3279,6 @@ REGISTER_KERNELS(double, int64); #undef REGISTER_KERNELS - template class ApplyAddSignOp : public OpKernel { public: @@ -3362,17 +3361,15 @@ TF_CALL_double(REGISTER_CPU_KERNELS); #if GOOGLE_CUDA // Forward declarations of the functor specializations for GPU. namespace functor { -#define DECLARE_GPU_SPEC(T) \ - template <> \ - void ApplyAddSign::operator()( \ - const GPUDevice& d, \ - typename TTypes::Flat var, \ - typename TTypes::Flat m, \ - typename TTypes::ConstScalar lr, \ - typename TTypes::ConstScalar alpha, \ - typename TTypes::ConstScalar sign_decay, \ - typename TTypes::ConstScalar beta, \ - typename TTypes::ConstFlat grad); \ +#define DECLARE_GPU_SPEC(T) \ + template <> \ + void ApplyAddSign::operator()( \ + const GPUDevice& d, typename TTypes::Flat var, \ + typename TTypes::Flat m, typename TTypes::ConstScalar lr, \ + typename TTypes::ConstScalar alpha, \ + typename TTypes::ConstScalar sign_decay, \ + typename TTypes::ConstScalar beta, \ + typename TTypes::ConstFlat grad); \ extern template struct ApplyAddSign; DECLARE_GPU_SPEC(Eigen::half); DECLARE_GPU_SPEC(float); @@ -3387,7 +3384,6 @@ REGISTER_KERNELS(GPU, double); #undef REGISTER_CPU_KERNELS #undef REGISTER_KERNELS - template class ApplyPowerSignOp : public OpKernel { public: @@ -3470,17 +3466,15 @@ TF_CALL_double(REGISTER_CPU_KERNELS); #if GOOGLE_CUDA // Forward declarations of the functor specializations for GPU. namespace functor { -#define DECLARE_GPU_SPEC(T) \ - template <> \ - void ApplyPowerSign::operator()( \ - const GPUDevice& d, \ - typename TTypes::Flat var, \ - typename TTypes::Flat m, \ - typename TTypes::ConstScalar lr, \ - typename TTypes::ConstScalar logbase, \ - typename TTypes::ConstScalar sign_decay, \ - typename TTypes::ConstScalar beta, \ - typename TTypes::ConstFlat grad); \ +#define DECLARE_GPU_SPEC(T) \ + template <> \ + void ApplyPowerSign::operator()( \ + const GPUDevice& d, typename TTypes::Flat var, \ + typename TTypes::Flat m, typename TTypes::ConstScalar lr, \ + typename TTypes::ConstScalar logbase, \ + typename TTypes::ConstScalar sign_decay, \ + typename TTypes::ConstScalar beta, \ + typename TTypes::ConstFlat grad); \ extern template struct ApplyPowerSign; DECLARE_GPU_SPEC(Eigen::half); DECLARE_GPU_SPEC(float); diff --git a/tensorflow/core/kernels/training_ops_gpu.cu.cc b/tensorflow/core/kernels/training_ops_gpu.cu.cc index d443a6b3c1..0376a3b2c6 100644 --- a/tensorflow/core/kernels/training_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/training_ops_gpu.cu.cc @@ -17,8 +17,8 @@ limitations under the License. #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/training_ops.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/training_ops.h" namespace tensorflow { @@ -115,13 +115,11 @@ struct ApplyAdam { Eigen::Sizes<1> single; const auto one = static_cast(1.0); m.device(d) = - m + - (beta1.constant(one) - beta1).reshape(single).broadcast(bcast) * - (grad - m); + m + (beta1.constant(one) - beta1).reshape(single).broadcast(bcast) * + (grad - m); v.device(d) = - v + - (beta2.constant(one) - beta2).reshape(single).broadcast(bcast) * - (grad.square() - v); + v + (beta2.constant(one) - beta2).reshape(single).broadcast(bcast) * + (grad.square() - v); if (use_nesterov) { var.device(d) -= @@ -157,9 +155,9 @@ struct ApplyRMSProp { bcast[0] = grad.dimension(0); Eigen::Sizes<1> single; const auto one = static_cast(1.0); - ms.device(d) = ms + - (rho.constant(one) - rho).reshape(single).broadcast(bcast) * - (grad.square() - ms); + ms.device(d) = + ms + (rho.constant(one) - rho).reshape(single).broadcast(bcast) * + (grad.square() - ms); mom.device(d) = mom * momentum.reshape(single).broadcast(bcast) + lr.reshape(single).broadcast(bcast) * grad / @@ -212,7 +210,7 @@ struct ApplyAddSign { auto beta_bcast = beta.reshape(single).broadcast(bcast); auto one_minus_beta = (beta.constant(one) - beta).reshape(single).broadcast(bcast); - m.device(d) = m * beta_bcast + grad * one_minus_beta; + m.device(d) = m * beta_bcast + grad * one_minus_beta; // The following is the GPU equivalent of the CPU version: // var.device(d) -= lr() * (alpha() + sign_decay() * sign_gm) * grad; @@ -244,7 +242,7 @@ struct ApplyPowerSign { auto beta_bcast = beta.reshape(single).broadcast(bcast); auto one_minus_beta = (beta.constant(one) - beta).reshape(single).broadcast(bcast); - m.device(d) = m * beta_bcast + grad * one_minus_beta; + m.device(d) = m * beta_bcast + grad * one_minus_beta; // The following is the GPU equivalent of the CPU version: // auto grad_scale = (logbase() * sign_decay() * sign_gm).exp(); @@ -253,7 +251,7 @@ struct ApplyPowerSign { auto lr_bcast = lr.reshape(single).broadcast(bcast); auto logbase_bcast = logbase.reshape(single).broadcast(bcast); auto sign_decay_bcast = sign_decay.reshape(single).broadcast(bcast); - auto grad_scale = (logbase_bcast * sign_decay_bcast * sign_gm).exp(); + auto grad_scale = (logbase_bcast * sign_decay_bcast * sign_gm).exp(); var.device(d) -= lr_bcast * grad_scale * grad; } }; diff --git a/tensorflow/core/kernels/training_ops_test.cc b/tensorflow/core/kernels/training_ops_test.cc index ffa7f87c9e..2dcc4a500e 100644 --- a/tensorflow/core/kernels/training_ops_test.cc +++ b/tensorflow/core/kernels/training_ops_test.cc @@ -176,8 +176,9 @@ static void Adam(int32 n, Graph** init_g, Graph** train_g) { auto beta2 = Scalar(g, 0.99); auto epsilon = Scalar(g, 1e-8); auto grad = Random(g, n); - test::graph::Multi(g, "ApplyAdam", {var, m, v, beta1_power, beta2_power, lr, - beta1, beta2, epsilon, grad}); + test::graph::Multi( + g, "ApplyAdam", + {var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad}); *train_g = g; } } diff --git a/tensorflow/core/kernels/transpose_op.cc b/tensorflow/core/kernels/transpose_op.cc index 2e0d18b634..7177ad7888 100644 --- a/tensorflow/core/kernels/transpose_op.cc +++ b/tensorflow/core/kernels/transpose_op.cc @@ -176,9 +176,10 @@ void TransposeOp::Compute(OpKernelContext* ctx) { } } for (int i = 0; i < dims; ++i) { - OP_REQUIRES(ctx, bits[i], errors::InvalidArgument( - i, " is missing from {", - str_util::Join(permutation, ","), "}.")); + OP_REQUIRES( + ctx, bits[i], + errors::InvalidArgument(i, " is missing from {", + str_util::Join(permutation, ","), "}.")); } // 0-D, 1-D, and identity transposes do nothing. diff --git a/tensorflow/core/kernels/typed_queue.h b/tensorflow/core/kernels/typed_queue.h index 0d608d9b87..43dcb4cef7 100644 --- a/tensorflow/core/kernels/typed_queue.h +++ b/tensorflow/core/kernels/typed_queue.h @@ -58,9 +58,9 @@ Status TypedQueue::Initialize() { if (!component_shapes_.empty() && component_dtypes_.size() != component_shapes_.size()) { return errors::InvalidArgument( - "Different number of component types. ", "Types: ", - DataTypeSliceString(component_dtypes_), ", Shapes: ", - ShapeListString(component_shapes_)); + "Different number of component types. ", + "Types: ", DataTypeSliceString(component_dtypes_), + ", Shapes: ", ShapeListString(component_shapes_)); } mutex_lock lock(mu_); diff --git a/tensorflow/core/kernels/unpack_op.cc b/tensorflow/core/kernels/unpack_op.cc index 397bdd5670..764b6a252a 100644 --- a/tensorflow/core/kernels/unpack_op.cc +++ b/tensorflow/core/kernels/unpack_op.cc @@ -34,7 +34,7 @@ typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class UnpackOp : public OpKernel { @@ -65,8 +65,9 @@ class UnpackOp : public OpKernel { output_shape.RemoveDim(axis); const int64 output_size = output_shape.num_elements(); OP_REQUIRES( - context, FastBoundsCheck(output_size, - std::numeric_limits::max()), + context, + FastBoundsCheck(output_size, + std::numeric_limits::max()), errors::InvalidArgument("output size must fit in Eigen DenseIndex")); // This optimization is currently not applicable for SYCL devices diff --git a/tensorflow/core/kernels/word2vec_kernels.cc b/tensorflow/core/kernels/word2vec_kernels.cc index 2d05d72bff..3477445197 100644 --- a/tensorflow/core/kernels/word2vec_kernels.cc +++ b/tensorflow/core/kernels/word2vec_kernels.cc @@ -188,9 +188,9 @@ class SkipgramOp : public OpKernel { ++corpus_size_; } if (corpus_size_ < window_size_ * 10) { - return errors::InvalidArgument("The text file ", filename, - " contains too little data: ", - corpus_size_, " words"); + return errors::InvalidArgument( + "The text file ", filename, + " contains too little data: ", corpus_size_, " words"); } typedef std::pair WordFreq; std::vector ordered; diff --git a/tensorflow/core/kernels/xent_op.cc b/tensorflow/core/kernels/xent_op.cc index 0f8d027caa..a6a71fdfaf 100644 --- a/tensorflow/core/kernels/xent_op.cc +++ b/tensorflow/core/kernels/xent_op.cc @@ -30,7 +30,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL template class SoftmaxXentWithLogitsOp : public OpKernel { @@ -44,8 +44,8 @@ class SoftmaxXentWithLogitsOp : public OpKernel { OP_REQUIRES(context, logits_in.IsSameSize(labels_in), errors::InvalidArgument( "logits and labels must be same size: logits_size=", - logits_in.shape().DebugString(), " labels_size=", - labels_in.shape().DebugString())); + logits_in.shape().DebugString(), + " labels_size=", labels_in.shape().DebugString())); OP_REQUIRES(context, TensorShapeUtils::IsMatrix(logits_in.shape()), errors::InvalidArgument("logits must be 2-dimensional")); // As we already tested that both inputs have the same shape no need to @@ -72,7 +72,7 @@ class SoftmaxXentWithLogitsOp : public OpKernel { functor(context->eigen_device(), logits_in.matrix(), labels_in.matrix(), scratch.matrix(), loss_out->vec(), back_out->matrix()); - } + } } }; @@ -87,7 +87,7 @@ struct XentFunctorBase { typename TTypes::Vec loss, typename TTypes::Matrix backprop) { XentEigenImpl::Compute(d, logits, labels, scratch, loss, - backprop); + backprop); } }; @@ -97,7 +97,7 @@ struct XentFunctor : XentFunctorBase {}; #ifdef TENSORFLOW_USE_SYCL template struct XentFunctor : XentFunctorBase {}; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace functor #define REGISTER_CPU(T) \ @@ -129,6 +129,6 @@ REGISTER_KERNEL_BUILDER(Name("SoftmaxCrossEntropyWithLogits") .Device(DEVICE_SYCL) .TypeConstraint("T"), SoftmaxXentWithLogitsOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/xsmm_conv2d_test.cc b/tensorflow/core/kernels/xsmm_conv2d_test.cc index e294701246..481f3b7ba4 100644 --- a/tensorflow/core/kernels/xsmm_conv2d_test.cc +++ b/tensorflow/core/kernels/xsmm_conv2d_test.cc @@ -13,18 +13,17 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/conv_ops.h" -#include "tensorflow/core/platform/test.h" +#include "include/libxsmm.h" +#include "tensorflow/core/framework/fake_input.h" #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/graph/node_builder.h" +#include "tensorflow/core/kernels/conv_ops.h" #include "tensorflow/core/kernels/ops_testutil.h" -#include "include/libxsmm.h" -#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/platform/test.h" namespace tensorflow { namespace { - typedef struct { int nImg; int nIfm; @@ -49,45 +48,41 @@ typedef struct { int stride_w; } naive_conv_t; - -LIBXSMM_INLINE void naive_copy_NCHW_to_NHWC(const float* nchw, Tensor &nhwc, int N, int H, int W, int C) -{ - LIBXSMM_VLA_DECL(4, const float, input, nchw, C, H, W); +LIBXSMM_INLINE void naive_copy_NCHW_to_NHWC(const float* nchw, Tensor& nhwc, + int N, int H, int W, int C) { + LIBXSMM_VLA_DECL(4, const float, input, nchw, C, H, W); int n, h, w, c; - auto output = nhwc.flat(); - for ( n = 0; n < N; n++ ) { - for ( h = 0; h < H; h++ ) { - for ( w = 0; w < W; w++ ) { - for ( c = 0; c < C; c++ ) { - output(n*H*W*C + h*W*C +w*C + c) = - LIBXSMM_VLA_ACCESS(4, input, n, c, h, w, C, H, W); + auto output = nhwc.flat(); + for (n = 0; n < N; n++) { + for (h = 0; h < H; h++) { + for (w = 0; w < W; w++) { + for (c = 0; c < C; c++) { + output(n * H * W * C + h * W * C + w * C + c) = + LIBXSMM_VLA_ACCESS(4, input, n, c, h, w, C, H, W); } } } } } - -LIBXSMM_INLINE void naive_copy_KCRS_to_RSCK(const float* kcrs, Tensor &rsck, int R, int S, int C, int K) -{ - LIBXSMM_VLA_DECL(4, const float, input, kcrs, C, R, S); +LIBXSMM_INLINE void naive_copy_KCRS_to_RSCK(const float* kcrs, Tensor& rsck, + int R, int S, int C, int K) { + LIBXSMM_VLA_DECL(4, const float, input, kcrs, C, R, S); int r, s, c, k; - auto output = rsck.flat(); - - for ( r = 0; r < R; r++ ) { - for ( s = 0; s < S; s++ ) { - for ( c = 0; c < C; c++ ) { - for ( k = 0; k < K; k++ ) { - output(r*S*C*K + s*C*K + c*K + k) = - LIBXSMM_VLA_ACCESS(4, input, k, c, r, s, C, R, S); + auto output = rsck.flat(); + + for (r = 0; r < R; r++) { + for (s = 0; s < S; s++) { + for (c = 0; c < C; c++) { + for (k = 0; k < K; k++) { + output(r * S * C * K + s * C * K + c * K + k) = + LIBXSMM_VLA_ACCESS(4, input, k, c, r, s, C, R, S); } } } } } - - LIBXSMM_INLINE void zero_buf(float* buf, long size) { int i; for (i = 0; i < size; ++i) { @@ -95,52 +90,53 @@ LIBXSMM_INLINE void zero_buf(float* buf, long size) { } } -LIBXSMM_INLINE void copy_buf(Tensor &dst,float *src,long size) { - long i; - auto output = dst.flat(); - for (i = 0; i < size; ++i) - output(i) = src[i]; +LIBXSMM_INLINE void copy_buf(Tensor& dst, float* src, long size) { + long i; + auto output = dst.flat(); + for (i = 0; i < size; ++i) output(i) = src[i]; } -LIBXSMM_INLINE void init_buf(float* buf, long size, int initPos, int initOne) -{ +LIBXSMM_INLINE void init_buf(float* buf, long size, int initPos, int initOne) { int i; zero_buf(buf, size); for (i = 0; i < size; ++i) { - buf[i] = (float)((initOne != 0) ? 1.0 : ((initPos != 0) ? drand48() : (0.05 - drand48()/10.0))); + buf[i] = + (float)((initOne != 0) + ? 1.0 + : ((initPos != 0) ? drand48() : (0.05 - drand48() / 10.0))); } } - - -LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float* output, const float* filter) -{ - int nImg = param->nImg; - int nIfm = param->nIfm; - int nOfm = param->nOfm; - int ifhp = param->ifhp; - int ifwp = param->ifwp; - int ofhp = param->ofhp; - int ofwp = param->ofwp; - int ifh = param->ifh; - int ifw = param->ifw; - int ofh = param->ofh; - int ofw = param->ofw; - int pad_h = param->pad_h; - int pad_w = param->pad_w; - int pad_h_in = param->pad_h_in; - int pad_w_in = param->pad_w_in; +LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, + float* output, const float* filter) { + int nImg = param->nImg; + int nIfm = param->nIfm; + int nOfm = param->nOfm; + int ifhp = param->ifhp; + int ifwp = param->ifwp; + int ofhp = param->ofhp; + int ofwp = param->ofwp; + int ifh = param->ifh; + int ifw = param->ifw; + int ofh = param->ofh; + int ofw = param->ofw; + int pad_h = param->pad_h; + int pad_w = param->pad_w; + int pad_h_in = param->pad_h_in; + int pad_w_in = param->pad_w_in; int pad_h_out = param->pad_h_out; int pad_w_out = param->pad_w_out; - int kh = param->kh; - int kw = param->kw; - int stride_h = param->stride_h; - int stride_w = param->stride_w; + int kh = param->kh; + int kw = param->kw; + int stride_h = param->stride_h; + int stride_w = param->stride_w; /* loop counters */ int img, ofm, ifm, oj, oi, ij, ii, kj, ki; - LIBXSMM_VLA_DECL(4, float, output_t, output + (pad_w_out * ofwp + pad_h_out), nOfm, ofhp, ofwp); - LIBXSMM_VLA_DECL(4, const float, input_t, input + (pad_w_in * ifwp + pad_h_in), nIfm, ifhp, ifwp); + LIBXSMM_VLA_DECL(4, float, output_t, output + (pad_w_out * ofwp + pad_h_out), + nOfm, ofhp, ofwp); + LIBXSMM_VLA_DECL(4, const float, input_t, + input + (pad_w_in * ifwp + pad_h_in), nIfm, ifhp, ifwp); LIBXSMM_VLA_DECL(4, const float, filter_t, filter, nIfm, kh, kw); for (img = 0; img < nImg; ++img) { @@ -151,12 +147,15 @@ LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float for (oi = 0; oi < ofw; ++oi) { ii = oi * stride_w - pad_w; for (kj = 0; kj < kh; ++kj) { - if(ij+kj < 0 || ij+kj >= ifh) continue; + if (ij + kj < 0 || ij + kj >= ifh) continue; for (ki = 0; ki < kw; ++ki) { - if(ii+ki < 0 || ii+ki >= ifw) continue; - LIBXSMM_VLA_ACCESS( 4, output_t, img, ofm, oj, oi, nOfm, ofhp, ofwp) += - LIBXSMM_VLA_ACCESS(4, input_t, img, ifm, ij + kj, ii + ki, nIfm, ifhp, ifwp) - * LIBXSMM_VLA_ACCESS(4, filter_t, ofm, ifm, kj, ki, nIfm, kh, kw); + if (ii + ki < 0 || ii + ki >= ifw) continue; + LIBXSMM_VLA_ACCESS(4, output_t, img, ofm, oj, oi, nOfm, ofhp, + ofwp) += + LIBXSMM_VLA_ACCESS(4, input_t, img, ifm, ij + kj, ii + ki, + nIfm, ifhp, ifwp) * + LIBXSMM_VLA_ACCESS(4, filter_t, ofm, ifm, kj, ki, nIfm, kh, + kw); } } } @@ -168,134 +167,118 @@ LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float void RunXsmmVsGeneric() {} - class XsmmConv2DTest : public OpsTestBase { protected: void MakeOp(int stride) { - TF_CHECK_OK(NodeDefBuilder("xsmm", "Conv2D") - .Input(FakeInput(DT_FLOAT)) - .Input(FakeInput(DT_FLOAT)) - .Attr("strides", {1, stride,stride, 1}) - .Attr("padding", "VALID" ) - .Finalize(node_def())); - + .Input(FakeInput(DT_FLOAT)) + .Input(FakeInput(DT_FLOAT)) + .Attr("strides", {1, stride, stride, 1}) + .Attr("padding", "VALID") + .Finalize(node_def())); TF_ASSERT_OK(InitOp()); } }; TEST_F(XsmmConv2DTest, Basic) { - MakeOp(1); + MakeOp(1); - // setup scoped allocator, which uses cpu_allocator() for this scope - const libxsmm_tf_allocator tf_allocator; + // setup scoped allocator, which uses cpu_allocator() for this scope + const libxsmm_tf_allocator tf_allocator; - int ifw = 14; /* input width, "W" */ - int ifh = 14; /* input height, "H" */ - int nImg = 32; /* mini-batch size, "N" */ - int nIfm = 64; /* number of input feature maps, "C" */ - int nOfm = 64; /* number of output feature maps, "K" */ - int kh = 3; /* filter height, "R" */ - int kw = 3; /* filter width, "S" */ - int pad = 0; /* padding in output */ - int stride = 1; /* stride when accessing inputs */ + int ifw = 14; /* input width, "W" */ + int ifh = 14; /* input height, "H" */ + int nImg = 32; /* mini-batch size, "N" */ + int nIfm = 64; /* number of input feature maps, "C" */ + int nOfm = 64; /* number of output feature maps, "K" */ + int kh = 3; /* filter height, "R" */ + int kw = 3; /* filter width, "S" */ + int pad = 0; /* padding in output */ + int stride = 1; /* stride when accessing inputs */ + int stride_w = stride; + int stride_h = stride; + int pad_h = pad; + int pad_w = pad; - int stride_w = stride; - int stride_h = stride; - int pad_h = pad; - int pad_w = pad; + int pad_h_in = pad_h; + int pad_w_in = pad_w; - int pad_h_in = pad_h; - int pad_w_in = pad_w; - - int pad_h_out = 0; - int pad_w_out = 0; + int pad_h_out = 0; + int pad_w_out = 0; /* deriving some values for naive code */ - int ofh = (ifh + 2 * pad_h - kh) / stride_h + 1; - int ofw = (ifw + 2 * pad_w - kw) / stride_w + 1; - int ifhp = ifh + 2 * pad_h_in; - int ifwp = ifw + 2 * pad_w_in; - int ofhp = ofh + 2 * pad_h_out; - int ofwp = ofw + 2 * pad_w_out; - - - //Initialization of Filter and Image - - /* allocate data */ - float *naive_input = (float*)libxsmm_aligned_scratch( nImg*nIfm*ifhp*ifwp*sizeof(float), 2097152); - float *naive_output = (float*)libxsmm_aligned_scratch( nImg*nOfm*ofhp*ofwp*sizeof(float), 2097152); - float *naive_filter = (float*)libxsmm_aligned_scratch( nOfm*nIfm*kh*kw* sizeof(float), 2097152); - /* initialize data */ - init_buf(naive_input, nImg*nIfm*ifhp*ifwp, 0, 0); - zero_buf(naive_output, nImg*nOfm*ofhp*ofwp); - init_buf(naive_filter, nOfm*nIfm*kh*kw, 0, 0); - - - Tensor image(DT_FLOAT, - {nImg, ifhp, ifwp, nIfm}); - - - Tensor filter(DT_FLOAT, {kh,kw,nIfm,nOfm}); - - - naive_copy_NCHW_to_NHWC(naive_input, image, nImg, ifhp, ifwp, nIfm); - naive_copy_KCRS_to_RSCK(naive_filter, filter, kh, kw, nIfm, nOfm); - - - //Run naive convolution - - naive_conv_t naive_param; - - naive_param.nImg = nImg; - naive_param.nIfm = nIfm; - naive_param.nOfm = nOfm; - naive_param.ifhp = ifhp; - naive_param.ifwp = ifwp; - naive_param.ofhp = ofhp; - naive_param.ofwp = ofwp; - naive_param.ifh = ifh; - naive_param.ifw = ifw; - naive_param.ofh = ofh; - naive_param.ofw = ofw; - naive_param.pad_h = pad_h; - naive_param.pad_w = pad_w; - naive_param.pad_h_in = pad_h_in; - naive_param.pad_w_in = pad_w_in; - naive_param.pad_h_out = pad_h_out; - naive_param.pad_w_out = pad_w_out; - naive_param.kh = kh; - naive_param.kw = kw; - naive_param.stride_h = stride_h; - naive_param.stride_w = stride_w; - - - naive_conv_fp(&naive_param, naive_input, naive_output, naive_filter); - - - - AddInputFromArray(image.shape(), image.flat()); - AddInputFromArray(filter.shape(), filter.flat()); - - - - //Run Op (TF) - TF_ASSERT_OK(RunOpKernel()); - - // Check the output. - Tensor expected(DT_FLOAT, {nImg,ofhp,ofwp, nOfm}); - naive_copy_NCHW_to_NHWC(naive_output, expected, nImg, ofhp, ofwp, nOfm); - - - test::ExpectTensorNear(expected, *GetOutput(0), 1e-5); - libxsmm_free(naive_input); - libxsmm_free(naive_output); - libxsmm_free(naive_filter); - - - + int ofh = (ifh + 2 * pad_h - kh) / stride_h + 1; + int ofw = (ifw + 2 * pad_w - kw) / stride_w + 1; + int ifhp = ifh + 2 * pad_h_in; + int ifwp = ifw + 2 * pad_w_in; + int ofhp = ofh + 2 * pad_h_out; + int ofwp = ofw + 2 * pad_w_out; + + // Initialization of Filter and Image + + /* allocate data */ + float* naive_input = (float*)libxsmm_aligned_scratch( + nImg * nIfm * ifhp * ifwp * sizeof(float), 2097152); + float* naive_output = (float*)libxsmm_aligned_scratch( + nImg * nOfm * ofhp * ofwp * sizeof(float), 2097152); + float* naive_filter = (float*)libxsmm_aligned_scratch( + nOfm * nIfm * kh * kw * sizeof(float), 2097152); + /* initialize data */ + init_buf(naive_input, nImg * nIfm * ifhp * ifwp, 0, 0); + zero_buf(naive_output, nImg * nOfm * ofhp * ofwp); + init_buf(naive_filter, nOfm * nIfm * kh * kw, 0, 0); + + Tensor image(DT_FLOAT, {nImg, ifhp, ifwp, nIfm}); + + Tensor filter(DT_FLOAT, {kh, kw, nIfm, nOfm}); + + naive_copy_NCHW_to_NHWC(naive_input, image, nImg, ifhp, ifwp, nIfm); + naive_copy_KCRS_to_RSCK(naive_filter, filter, kh, kw, nIfm, nOfm); + + // Run naive convolution + + naive_conv_t naive_param; + + naive_param.nImg = nImg; + naive_param.nIfm = nIfm; + naive_param.nOfm = nOfm; + naive_param.ifhp = ifhp; + naive_param.ifwp = ifwp; + naive_param.ofhp = ofhp; + naive_param.ofwp = ofwp; + naive_param.ifh = ifh; + naive_param.ifw = ifw; + naive_param.ofh = ofh; + naive_param.ofw = ofw; + naive_param.pad_h = pad_h; + naive_param.pad_w = pad_w; + naive_param.pad_h_in = pad_h_in; + naive_param.pad_w_in = pad_w_in; + naive_param.pad_h_out = pad_h_out; + naive_param.pad_w_out = pad_w_out; + naive_param.kh = kh; + naive_param.kw = kw; + naive_param.stride_h = stride_h; + naive_param.stride_w = stride_w; + + naive_conv_fp(&naive_param, naive_input, naive_output, naive_filter); + + AddInputFromArray(image.shape(), image.flat()); + AddInputFromArray(filter.shape(), filter.flat()); + + // Run Op (TF) + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(DT_FLOAT, {nImg, ofhp, ofwp, nOfm}); + naive_copy_NCHW_to_NHWC(naive_output, expected, nImg, ofhp, ofwp, nOfm); + + test::ExpectTensorNear(expected, *GetOutput(0), 1e-5); + libxsmm_free(naive_input); + libxsmm_free(naive_output); + libxsmm_free(naive_filter); } /* @@ -325,7 +308,8 @@ TEST(XsmmConv2DTest, Basic) { desc.threads = num_threads; desc.algo = LIBXSMM_DNN_CONV_ALGO_DIRECT; desc.buffer_format = LIBXSMM_DNN_TENSOR_FORMAT_NHWC; - desc.filter_format = LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;//LIBXSMM_DNN_TENSOR_FORMAT_RSCK; + desc.filter_format = +LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;//LIBXSMM_DNN_TENSOR_FORMAT_RSCK; desc.fuse_ops = LIBXSMM_DNN_CONV_FUSE_NONE; desc.options = LIBXSMM_DNN_CONV_OPTION_NONE; desc.datatype = LIBXSMM_DNN_DATATYPE_F32; diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index 279a5876f9..fb9e8ad50c 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -977,8 +977,8 @@ REGISTER_OP("GatherNd") if (c->Value(r_dim) > c->Rank(params)) { return errors::InvalidArgument( "indices.shape[-1] must be <= params.rank, but saw indices shape: ", - c->DebugString(indices), " and params shape: ", - c->DebugString(params)); + c->DebugString(indices), + " and params shape: ", c->DebugString(params)); } // Remove r_dim from indices to get output. @@ -1252,12 +1252,12 @@ REGISTER_OP("ReverseSequence") // Validate batch_dim and seq_dim against input. const int32 input_rank = c->Rank(input); if (batch_dim >= input_rank) { - return errors::InvalidArgument("batch_dim must be < input rank: ", - batch_dim, " vs. ", input_rank); + return errors::InvalidArgument( + "batch_dim must be < input rank: ", batch_dim, " vs. ", input_rank); } if (seq_dim >= input_rank) { - return errors::InvalidArgument("seq_dim must be < input rank: ", - seq_dim, " vs. ", input_rank); + return errors::InvalidArgument( + "seq_dim must be < input rank: ", seq_dim, " vs. ", input_rank); } DimensionHandle batch_dim_dim = c->Dim(input, batch_dim); @@ -2638,8 +2638,9 @@ Status ScatterNdShape(InferenceContext* c) { Status s = c->Merge(prefix_indices, prefix_updates, &unused); if (!s.ok()) { return errors::InvalidArgument( - "The outer ", outer_dims, " dimensions of indices.shape=", - c->DebugString(indices_shape), " must match the outer ", outer_dims, + "The outer ", outer_dims, + " dimensions of indices.shape=", c->DebugString(indices_shape), + " must match the outer ", outer_dims, " dimensions of updates.shape=", c->DebugString(updates_shape), ": ", s.error_message()); } diff --git a/tensorflow/core/ops/array_ops_test.cc b/tensorflow/core/ops/array_ops_test.cc index a182fd1c47..86d64635f4 100644 --- a/tensorflow/core/ops/array_ops_test.cc +++ b/tensorflow/core/ops/array_ops_test.cc @@ -142,8 +142,13 @@ TEST(ArrayOpsTest, Const_ShapeFn) { TEST(ArrayOpsTest, UnchangedShapes_ShapeFn) { for (const char* op_name : { - "CheckNumerics", "Identity", "RefIdentity", "QuantizeAndDequantize", - "StopGradient", "ZerosLike", "OnesLike", + "CheckNumerics", + "Identity", + "RefIdentity", + "QuantizeAndDequantize", + "StopGradient", + "ZerosLike", + "OnesLike", }) { ShapeInferenceTestOp op(op_name); INFER_OK(op, "?", "in0"); diff --git a/tensorflow/core/ops/candidate_sampling_ops_test.cc b/tensorflow/core/ops/candidate_sampling_ops_test.cc index c79b443914..f367371604 100644 --- a/tensorflow/core/ops/candidate_sampling_ops_test.cc +++ b/tensorflow/core/ops/candidate_sampling_ops_test.cc @@ -23,9 +23,12 @@ namespace tensorflow { TEST(CandidateSamplerOpsTest, CandidateSampler_ShapeFn) { for (const char* op_name : { - "AllCandidateSampler", "FixedUnigramCandidateSampler", - "LearnedUnigramCandidateSampler", "LogUniformCandidateSampler", - "ThreadUnsafeUnigramCandidateSampler", "UniformCandidateSampler", + "AllCandidateSampler", + "FixedUnigramCandidateSampler", + "LearnedUnigramCandidateSampler", + "LogUniformCandidateSampler", + "ThreadUnsafeUnigramCandidateSampler", + "UniformCandidateSampler", }) { ShapeInferenceTestOp op(op_name); TF_ASSERT_OK(NodeDefBuilder("test", op.name) diff --git a/tensorflow/core/ops/functional_grad.cc b/tensorflow/core/ops/functional_grad.cc index 6df3536795..eeccb72da6 100644 --- a/tensorflow/core/ops/functional_grad.cc +++ b/tensorflow/core/ops/functional_grad.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/framework/function.h" #include +#include "tensorflow/core/framework/function.h" #include "tensorflow/core/lib/core/errors.h" namespace tensorflow { diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index dd484c3ee7..872ebe98c1 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -1172,12 +1172,12 @@ Status RangeSize(const Tensor* start_t, const Tensor* limit_t, T limit = limit_t->scalar()(); T delta = delta_t->scalar()(); if (start > limit && delta > 0) { - return errors::InvalidArgument("Requires start <= limit when delta > 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start <= limit when delta > 0: ", start, "/", limit); } if (start < limit && delta < 0) { - return errors::InvalidArgument("Requires start >= limit when delta < 0: ", - start, "/", limit); + return errors::InvalidArgument( + "Requires start >= limit when delta < 0: ", start, "/", limit); } if (delta == 0) { return errors::InvalidArgument("Requires delta != 0"); diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index 3f72b41569..62661fe4bd 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1155,9 +1155,9 @@ Status TopKShapeFn(InferenceContext* c) { DimensionHandle last_dim = c->Dim(input, -1); if (c->ValueKnown(last_dim) && c->ValueKnown(k_dim) && c->Value(last_dim) < c->Value(k_dim)) { - return errors::InvalidArgument("input must have last dimension >= k = ", - c->Value(k_dim), " but is ", - c->Value(last_dim)); + return errors::InvalidArgument( + "input must have last dimension >= k = ", c->Value(k_dim), " but is ", + c->Value(last_dim)); } // Replace last_dim with k_dim. @@ -1211,9 +1211,9 @@ REGISTER_OP("NthElement") DimensionHandle last_dim = c->Dim(input, -1); if (c->ValueKnown(last_dim) && c->ValueKnown(n_dim) && c->Value(last_dim) <= c->Value(n_dim)) { - return errors::InvalidArgument("Input must have last dimension > n = ", - c->Value(n_dim), " but is ", - c->Value(last_dim)); + return errors::InvalidArgument( + "Input must have last dimension > n = ", c->Value(n_dim), + " but is ", c->Value(last_dim)); } // Reduce last_dim for output tensor diff --git a/tensorflow/core/ops/sdca_ops.cc b/tensorflow/core/ops/sdca_ops.cc index e67d95fa8c..4025070adb 100644 --- a/tensorflow/core/ops/sdca_ops.cc +++ b/tensorflow/core/ops/sdca_ops.cc @@ -19,8 +19,8 @@ limitations under the License. namespace tensorflow { -using shape_inference::ShapeHandle; using shape_inference::InferenceContext; +using shape_inference::ShapeHandle; // -------------------------------------------------------------------------- static Status ApplySdcaOptimizerShapeFn(InferenceContext* c) { diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc index 8beb28de0a..e4c5bcfb54 100644 --- a/tensorflow/core/ops/string_ops.cc +++ b/tensorflow/core/ops/string_ops.cc @@ -137,9 +137,9 @@ REGISTER_OP("Substr") DimensionHandle pos_dim = c->Dim(pos_shape, i); DimensionHandle len_dim = c->Dim(len_shape, i); if (c->Value(pos_dim) != c->Value(len_dim)) { - return errors::InvalidArgument("pos and len shapes must match: ", - c->DebugString(pos_shape), " vs. ", - c->DebugString(len_shape)); + return errors::InvalidArgument( + "pos and len shapes must match: ", c->DebugString(pos_shape), + " vs. ", c->DebugString(len_shape)); } } // c->input(0) is the ShapeHandle to input strings diff --git a/tensorflow/core/ops/training_ops_test.cc b/tensorflow/core/ops/training_ops_test.cc index de4e3cd9e7..0f309c1f4e 100644 --- a/tensorflow/core/ops/training_ops_test.cc +++ b/tensorflow/core/ops/training_ops_test.cc @@ -24,7 +24,7 @@ static void TestGradAndIndicesErrorHandling(const ShapeInferenceTestOp& op, string shape_spec_middle, const string& shape_spec_end = "") { auto shape_spec = [&shape_spec_middle, shape_spec_end]( - const char* var_spec, const char* grad_indices_spec) { + const char* var_spec, const char* grad_indices_spec) { return strings::StrCat(var_spec, ";", shape_spec_middle, ";", grad_indices_spec, shape_spec_end); }; diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 01b3e92d2d..a323d5bc39 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2668,6 +2668,7 @@ cuda_py_test( ":nn_ops_gen", "//third_party/py/numpy", ], + shard_count = 4, tags = ["no_windows"], ) diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index b107670275..e3a52141a0 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -2103,6 +2103,10 @@ class Operation(object): logging.warning("Operation._control_inputs is private, use " "Operation.control_inputs instead. " "Operation._control_inputs will eventually be removed.") + # Copy value because it may be self._control_inputs_val (in particular if + # this is called from self._control_inputs += ...), and we don't want to + # clear value below. + value = copy.copy(value) self._remove_all_control_inputs() self._add_control_inputs(value) diff --git a/tensorflow/python/kernel_tests/softmax_op_test.py b/tensorflow/python/kernel_tests/softmax_op_test.py index be72c19407..bb3f6970e4 100644 --- a/tensorflow/python/kernel_tests/softmax_op_test.py +++ b/tensorflow/python/kernel_tests/softmax_op_test.py @@ -25,11 +25,13 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn_ops from tensorflow.python.platform import test +@test_util.with_c_api class SoftmaxTest(test.TestCase): def _npSoftmax(self, features, dim=-1, log=False): @@ -174,8 +176,11 @@ class SoftmaxTest(test.TestCase): def testDimTooLarge(self): with self.test_session(): + # Use placeholder to make sure we get runtime error instead of shape + # inference error. + dim = array_ops.placeholder_with_default(100, shape=[]) with self.assertRaises(errors_impl.InvalidArgumentError): - nn_ops.softmax([1., 2., 3., 4.], dim=100).eval() + nn_ops.softmax([1., 2., 3., 4.], dim=dim).eval() def testLargeDims(self): # Make sure that we properly handle large inputs. See diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py index e5b93a54f7..7bf62d45b8 100644 --- a/tensorflow/python/layers/core.py +++ b/tensorflow/python/layers/core.py @@ -49,9 +49,6 @@ class Dense(base.Layer): and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). - Note: if the input to the layer has a rank greater than 2, then it is - flattened prior to the initial matrix multiply by `kernel`. - Arguments: units: Integer or Long, dimensionality of the output space. activation: Activation function (callable). Set it to None to maintain a @@ -199,9 +196,6 @@ def dense( and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). - Note: if the `inputs` tensor has a rank greater than 2, then it is - flattened prior to the initial matrix multiply by `kernel`. - Arguments: inputs: Tensor input. units: Integer or Long, dimensionality of the output space. @@ -230,7 +224,8 @@ def dense( by the same name. Returns: - Output tensor. + Output tensor the same shape as `inputs` except the last dimension is of + size `units`. Raises: ValueError: if eager execution is enabled. diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index b8e8207bb2..9a8ac93de9 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -1841,12 +1841,11 @@ def reduce_logsumexp(input_tensor, reduce_sum( gen_math_ops.exp(input_tensor - my_max), axis, - keepdims=True, - reduction_indices=reduction_indices)) + my_max + keepdims=keepdims, + reduction_indices=reduction_indices)) if not keepdims: - if isinstance(axis, int): - axis = [axis] - result = array_ops.squeeze(result, axis) + my_max = array_ops.reshape(my_max, array_ops.shape(result)) + result += my_max return _may_reduce_to_scalar(keepdims, axis, reduction_indices, result) diff --git a/tensorflow/stream_executor/executor_cache.cc b/tensorflow/stream_executor/executor_cache.cc index a23d6a70ba..d1a8aae167 100644 --- a/tensorflow/stream_executor/executor_cache.cc +++ b/tensorflow/stream_executor/executor_cache.cc @@ -23,6 +23,14 @@ namespace gputools { port::StatusOr ExecutorCache::GetOrCreate( const StreamExecutorConfig& config, const std::function& factory) { + // In the fast path case, the cache already has an entry and we can just + // return after Get() which only takes a shared lock and not a unique lock. + // If we need to create, we take a unique lock on cache_. + auto fast_result = Get(config); + if (fast_result.ok()) { + return fast_result; + } + Entry* entry = nullptr; { mutex_lock lock{mutex_}; @@ -59,12 +67,17 @@ port::StatusOr ExecutorCache::Get( const StreamExecutorConfig& config) { Entry* entry = nullptr; { - mutex_lock lock{mutex_}; - entry = &cache_[config.ordinal]; - // Release the map lock; the address of 'entry' is stable because - // std::map guarantees reference stability. + tf_shared_lock lock{mutex_}; + auto it = cache_.find(config.ordinal); + if (it != cache_.end()) { + entry = &it->second; + } else { + return port::Status(port::error::NOT_FOUND, + port::Printf("No executors registered for ordinal %d", + config.ordinal)); + } } - mutex_lock lock{entry->configurations_mutex}; + tf_shared_lock lock{entry->configurations_mutex}; if (entry->configurations.empty()) { return port::Status( port::error::NOT_FOUND, diff --git a/tensorflow/stream_executor/multi_platform_manager.cc b/tensorflow/stream_executor/multi_platform_manager.cc index cc32a6beaa..f23224ae77 100644 --- a/tensorflow/stream_executor/multi_platform_manager.cc +++ b/tensorflow/stream_executor/multi_platform_manager.cc @@ -45,7 +45,7 @@ namespace gputools { /* static */ port::StatusOr MultiPlatformManager::PlatformWithName( const string& target) { - mutex_lock lock(GetPlatformsMutex()); + tf_shared_lock lock(GetPlatformsMutex()); auto it = GetPlatformMap()->find(port::Lowercase(target)); if (it == GetPlatformMap()->end()) { @@ -59,7 +59,7 @@ namespace gputools { /* static */ port::StatusOr MultiPlatformManager::PlatformWithId( const Platform::Id& id) { - mutex_lock lock(GetPlatformsMutex()); + tf_shared_lock lock(GetPlatformsMutex()); auto it = GetPlatformByIdMap()->find(id); if (it == GetPlatformByIdMap()->end()) { return port::Status( diff --git a/tensorflow/tools/graph_transforms/sparsify_gather.cc b/tensorflow/tools/graph_transforms/sparsify_gather.cc index 96324d0dea..593c654f9f 100644 --- a/tensorflow/tools/graph_transforms/sparsify_gather.cc +++ b/tensorflow/tools/graph_transforms/sparsify_gather.cc @@ -15,6 +15,7 @@ limitations under the License. #include #include +#include #include "tensorflow/c/checkpoint_reader.h" #include "tensorflow/core/framework/tensor.h" @@ -28,9 +29,10 @@ limitations under the License. #include "tensorflow/tools/graph_transforms/transform_utils.h" namespace tensorflow { -using strings::StrCat; using str_util::Join; using str_util::Split; +using str_util::StringReplace; +using strings::StrCat; namespace graph_transforms { @@ -89,7 +91,7 @@ Status ObtainTensorSlice(const GraphDef& input_graph_def, string* shape_slice_string) { string restore_node_name; for (const auto& node : input_graph_def.node()) { - std::vector node_name_parts = str_util::Split(node.name(), "/"); + std::vector node_name_parts = Split(node.name(), "/"); if (node_name_parts.size() == 2 && StringPiece(node_name_parts[0]).starts_with("save") && StringPiece(node_name_parts[1]).starts_with("Assign") && @@ -119,13 +121,13 @@ Status ObtainTensorSlice(const GraphDef& input_graph_def, } string GetMonolithicTensorKey(const string& tensor_slice_name) { - std::vector names = str_util::Split(tensor_slice_name, "/"); + std::vector names = Split(tensor_slice_name, "/"); CHECK_GE(names.size(), 2); CHECK(StringPiece(names[names.size() - 1]).starts_with("part_")); // Remove the "part_x" suffix names.pop_back(); - return str_util::Join(names, "/"); + return Join(names, "/"); } Status ReadTensorFromCheckpoint( @@ -193,6 +195,15 @@ Status SparsifyGatherInternal( GraphDef current_graph_def = input_graph_def; bool any_match_found = false; + // Populate references. + std::unordered_map refs; + for (const auto& node : current_graph_def.node()) { + for (const auto& input : node.input()) { + auto parsed_input = StringReplace(input, "^", "", true); + refs[parsed_input] += 1; + } + } + // The subgraphs may have overlapping components, therefore GraphMatcher // doesn't return all subgraphs in one round -- this has to be multi-round // update. @@ -200,15 +211,15 @@ Status SparsifyGatherInternal( any_match_found = false; GraphDef replaced_graph_def = current_graph_def; std::vector init_table_node_names; - std::vector removed_variable_names; + std::vector removed_node_names; TF_RETURN_IF_ERROR(ReplaceMatchingOpTypes( current_graph_def, pattern, [&ckpt_reader, &any_match_found, &init_table_node_names, - &shapes_and_slices, &removed_variable_names]( - const NodeMatch& match, const std::set& input_nodes, - const std::set& output_nodes, - std::vector* new_nodes) { + &shapes_and_slices, &removed_node_names, + &refs](const NodeMatch& match, const std::set& input_nodes, + const std::set& output_nodes, + std::vector* new_nodes) { any_match_found = true; // The captured subgraph should be of the following pattern: @@ -291,8 +302,12 @@ Status SparsifyGatherInternal( weights_node.name(), ckpt_reader, (*shapes_and_slices)[weights_node.name()], &weight)); // Add both both weight and identity node names. - removed_variable_names.push_back(weights_node.name()); - removed_variable_names.push_back(match.inputs[0].node.name()); + removed_node_names.push_back(weights_node.name()); + removed_node_names.push_back(match.inputs[0].node.name()); + for (auto input_node : match.inputs[0].node.input()) { + auto parsed_input = StringReplace(input_node, "^", "", true); + refs[parsed_input]--; + } } Tensor indices_tensor; Tensor values_tensor; @@ -362,15 +377,23 @@ Status SparsifyGatherInternal( // Connect nodes AddNodeInput(hashtable_node.name(), &init_table_node); + refs[hashtable_node.name()]++; AddNodeInput(indices_node.name(), &init_table_node); + refs[indices_node.name()]++; AddNodeInput(values_node.name(), &init_table_node); + refs[values_node.name()]++; AddNodeInput(hashtable_node.name(), &lookup_node); + refs[hashtable_node.name()]++; AddNodeInput(gather_node.input(1), &lookup_node); + refs[gather_node.input(1)]++; AddNodeInput(default_value_node.name(), &lookup_node); + refs[default_value_node.name()]++; AddNodeInput(lookup_node.name(), &expand_dims_node); + refs[lookup_node.name()]++; AddNodeInput(dim_idx_node.name(), &expand_dims_node); + refs[dim_idx_node.name()]++; // Copy 'ids' input of original 'Gather' new_nodes->push_back(match.inputs[1].node); @@ -404,22 +427,44 @@ Status SparsifyGatherInternal( for (const string& name : init_table_node_names) { // Add control dependence from init_table_node to group_deps_node AddNodeInput(StrCat("^", name), init_op); + refs[name]++; + } + + // Erase inputs and outputs as they are not considered for deletion. + for (const auto& output : context.output_names) { + refs.erase(output); + } + + for (const auto& input : context.input_names) { + refs.erase(input); } - // Remove all dependencies associated with removed variables. - while (!removed_variable_names.empty()) { - auto name = removed_variable_names.back(); - removed_variable_names.pop_back(); + // Add nodes with a reference count of 0 for deletion. + for (auto entry : refs) { + if (entry.second == 0) { + removed_node_names.push_back(entry.first); + } + } + + while (!removed_node_names.empty()) { + auto name = removed_node_names.back(); + removed_node_names.pop_back(); + int i = 0; while (i < replaced_graph_def.node_size()) { - if (!replaced_graph_def.node(i).input_size()) { - if (replaced_graph_def.node(i).name() == name) { - replaced_graph_def.mutable_node()->SwapElements( - i, replaced_graph_def.node_size() - 1); - replaced_graph_def.mutable_node()->RemoveLast(); - continue; + // Revisit this to see if we can safely remove RestoreV2 nodes. + if ((replaced_graph_def.node(i).name() == name) && + (replaced_graph_def.node(i).op() != "RestoreV2")) { + for (const auto& input : replaced_graph_def.node(i).input()) { + auto parsed_input = StringReplace(input, "^", "", true); + refs[parsed_input] -= 1; + if (refs[parsed_input] == 0) { + removed_node_names.push_back(parsed_input); + } } - i++; + replaced_graph_def.mutable_node()->SwapElements( + i, replaced_graph_def.node_size() - 1); + replaced_graph_def.mutable_node()->RemoveLast(); continue; } int j = 0; @@ -433,18 +478,16 @@ Status SparsifyGatherInternal( } j++; } - if ((replaced_graph_def.node(i).input_size() == 0) || - (replaced_graph_def.node(i).op() == "Assign" && - replaced_graph_def.node(i).input_size() == 1)) { - removed_variable_names.push_back(replaced_graph_def.node(i).name()); - if (replaced_graph_def.node(i).input_size() == 1) { - removed_variable_names.push_back( - replaced_graph_def.node(i).input(0)); + if (!replaced_graph_def.node(i).input_size()) { + if ((refs.find(replaced_graph_def.node(i).name()) != refs.end()) && + (refs[replaced_graph_def.node(i).name()] == 0)) { + removed_node_names.push_back(replaced_graph_def.node(i).name()); } - replaced_graph_def.mutable_node()->SwapElements( - i, replaced_graph_def.node_size() - 1); - replaced_graph_def.mutable_node()->RemoveLast(); - continue; + } + + if (replaced_graph_def.node(i).op() == "Assign" && + replaced_graph_def.node(i).input_size() == 1) { + removed_node_names.push_back(replaced_graph_def.node(i).name()); } i++; } diff --git a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc index 000568a0cc..6627df1331 100644 --- a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc +++ b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc @@ -80,6 +80,8 @@ class SparsifyGatherTest : public ::testing::Test { // Build the graph. NodeDef* input_node = CreateNode("ids", "Const", {}, &graph_def); NodeDef* w_node; + NodeDef* zeros_const; + NodeDef* zeros_shape; NodeDef* zeros_node; NodeDef* assign_node; @@ -92,8 +94,12 @@ class SparsifyGatherTest : public ::testing::Test { } else { w_node = CreateNode("w/part_1", "VariableV2", {}, &graph_def); - zeros_node = - CreateNode("w/part_1/Initializer/zeros", "Const", {}, &graph_def); + zeros_shape = CreateNode("w/part_1/Initializer/zeros/shape_as_tensor", + "Const", {}, &graph_def); + zeros_const = CreateNode("w/part_1/Initializer/zeros/Const", "Const", {}, + &graph_def); + zeros_node = CreateNode("w/part_1/Initializer/zeros", "Fill", + {zeros_shape, zeros_const}, &graph_def); assign_node = CreateNode("w/part_1/Assign", "Assign", {w_node, zeros_node}, &graph_def); @@ -151,6 +157,9 @@ class SparsifyGatherTest : public ::testing::Test { MapNamesToNodes(result, &node_lookup); // Check nodes. + EXPECT_EQ(0, + node_lookup.count("w/part_1/Initializer/zeros/shape_as_tensor")); + EXPECT_EQ(0, node_lookup.count("w/part_1/Initializer/zeros/Const")); EXPECT_EQ(0, node_lookup.count("w/part_1/Initializer/zeros")); EXPECT_EQ(0, node_lookup.count("w/part_1/Assign")); @@ -247,7 +256,11 @@ class SparsifyGatherTest : public ::testing::Test { // Two partitions NodeDef* w_node1; NodeDef* w_node2; + NodeDef* zeros_const1; + NodeDef* zeros_shape1; NodeDef* zeros_node1; + NodeDef* zeros_const2; + NodeDef* zeros_shape2; NodeDef* zeros_node2; NodeDef* assign_node1; NodeDef* assign_node2; @@ -261,8 +274,13 @@ class SparsifyGatherTest : public ::testing::Test { SetNodeTensorAttr("value", weights, w_node2); } else { w_node1 = CreateNode("w1/part_1", "VariableV2", {}, &graph_def); - zeros_node1 = - CreateNode("w1/part_1/Initializer/zeros", "Const", {}, &graph_def); + + zeros_shape1 = CreateNode("w1/part_1/Initializer/zeros/shape_as_tensor", + "Const", {}, &graph_def); + zeros_const1 = CreateNode("w1/part_1/Initializer/zeros/Const", "Const", + {}, &graph_def); + zeros_node1 = CreateNode("w1/part_1/Initializer/zeros", "Fill", + {zeros_shape1, zeros_const1}, &graph_def); assign_node1 = CreateNode("w1/part_1/Assign", "Assign", {w_node1, zeros_node1}, &graph_def); @@ -285,8 +303,12 @@ class SparsifyGatherTest : public ::testing::Test { CreateNode("save/Assign", "Assign", {w_node1, restore_node1}, &graph_def); w_node2 = CreateNode("w2/part_1", "VariableV2", {}, &graph_def); - zeros_node2 = - CreateNode("w2/part_1/Initializer/zeros", "Const", {}, &graph_def); + zeros_shape2 = CreateNode("w2/part_1/Initializer/zeros/shape_as_tensor", + "Const", {}, &graph_def); + zeros_const2 = CreateNode("w2/part_1/Initializer/zeros/Const", "Const", + {}, &graph_def); + zeros_node2 = CreateNode("w2/part_1/Initializer/zeros", "Fill", + {zeros_shape2, zeros_const2}, &graph_def); assign_node2 = CreateNode("w2/part_1/Assign", "Assign", {w_node2, zeros_node2}, &graph_def); @@ -350,8 +372,14 @@ class SparsifyGatherTest : public ::testing::Test { MapNamesToNodes(result, &node_lookup); // Check nodes. + EXPECT_EQ(0, + node_lookup.count("w1/part_1/Initializer/zeros/shape_as_tensor")); + EXPECT_EQ(0, node_lookup.count("w1/part_1/Initializer/zeros/Const")); EXPECT_EQ(0, node_lookup.count("w1/part_1/Initializer/zeros")); EXPECT_EQ(0, node_lookup.count("w1/part_1/Assign")); + EXPECT_EQ(0, + node_lookup.count("w2/part_1/Initializer/zeros/shape_as_tensor")); + EXPECT_EQ(0, node_lookup.count("w2/part_1/Initializer/zeros/Const")); EXPECT_EQ(0, node_lookup.count("w2/part_1/Initializer/zeros")); EXPECT_EQ(0, node_lookup.count("w2/part_1/Assign")); EXPECT_EQ(1, node_lookup.count("ids")); -- GitLab From 1bd5b2e6fada5334b04e2db87cf246d1a83fa533 Mon Sep 17 00:00:00 2001 From: Adam Roberts Date: Fri, 26 Jan 2018 13:43:47 -0800 Subject: [PATCH 150/423] Automated g4 rollback of changelist 183321394 PiperOrigin-RevId: 183438398 --- .../contrib/seq2seq/python/ops/helper.py | 36 ------------------- 1 file changed, 36 deletions(-) diff --git a/tensorflow/contrib/seq2seq/python/ops/helper.py b/tensorflow/contrib/seq2seq/python/ops/helper.py index 6d8f786223..ef3722ee41 100644 --- a/tensorflow/contrib/seq2seq/python/ops/helper.py +++ b/tensorflow/contrib/seq2seq/python/ops/helper.py @@ -72,14 +72,6 @@ class Helper(object): """ raise NotImplementedError("batch_size has not been implemented") - @abc.abstractproperty - def input_shape(self): - """Shape of each input element in batch. - - Returns a `TensorShape`. - """ - raise NotImplementedError("input_shape has not been implemented") - @abc.abstractproperty def sample_ids_shape(self): """Shape of tensor returned by `sample`, excluding the batch dimension. @@ -135,7 +127,6 @@ class CustomHelper(Helper): self._sample_fn = sample_fn self._next_inputs_fn = next_inputs_fn self._batch_size = None - self._input_shape = None self._sample_ids_shape = tensor_shape.TensorShape(sample_ids_shape or []) self._sample_ids_dtype = sample_ids_dtype or dtypes.int32 @@ -158,8 +149,6 @@ class CustomHelper(Helper): (finished, next_inputs) = self._initialize_fn() if self._batch_size is None: self._batch_size = array_ops.size(finished) - if self._input_shape is None: - self._input_shape = next_inputs.shape[1:] return (finished, next_inputs) def sample(self, time, outputs, state, name=None): @@ -195,7 +184,6 @@ class TrainingHelper(Helper): """ with ops.name_scope(name, "TrainingHelper", [inputs, sequence_length]): inputs = ops.convert_to_tensor(inputs, name="inputs") - self._inputs = inputs if not time_major: inputs = nest.map_structure(_transpose_batch_time, inputs) @@ -211,16 +199,11 @@ class TrainingHelper(Helper): lambda inp: array_ops.zeros_like(inp[0, :]), inputs) self._batch_size = array_ops.size(sequence_length) - self._input_shape = inputs.shape[2:] @property def batch_size(self): return self._batch_size - @property - def input_shape(self): - return self._input_shape - @property def sample_ids_shape(self): return tensor_shape.TensorShape([]) @@ -229,14 +212,6 @@ class TrainingHelper(Helper): def sample_ids_dtype(self): return dtypes.int32 - @property - def inputs(self): - return self._inputs - - @property - def sequence_length(self): - return self._sequence_length - def initialize(self, name=None): with ops.name_scope(name, "TrainingHelperInitialize"): finished = math_ops.equal(0, self._sequence_length) @@ -541,16 +516,11 @@ class GreedyEmbeddingHelper(Helper): if self._end_token.get_shape().ndims != 0: raise ValueError("end_token must be a scalar") self._start_inputs = self._embedding_fn(self._start_tokens) - self._input_shape = self._start_inputs.shape[1:] @property def batch_size(self): return self._batch_size - @property - def input_shape(self): - return self._input_shape - @property def sample_ids_shape(self): return tensor_shape.TensorShape([]) @@ -662,8 +632,6 @@ class InferenceHelper(Helper): self._sample_dtype = sample_dtype self._next_inputs_fn = next_inputs_fn self._batch_size = array_ops.shape(start_inputs)[0] - self._input_shape = start_inputs.shape[1:] - self._start_inputs = ops.convert_to_tensor( start_inputs, name="start_inputs") @@ -671,10 +639,6 @@ class InferenceHelper(Helper): def batch_size(self): return self._batch_size - @property - def input_shape(self): - return self._input_shape - @property def sample_ids_shape(self): return self._sample_shape -- GitLab From ea25bf9558a79747d82220e493d4347901853976 Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Fri, 26 Jan 2018 14:02:33 -0800 Subject: [PATCH 151/423] Add op level memory usage estimation to the op_level_cost_estimator PiperOrigin-RevId: 183441321 --- .../costs/analytical_cost_estimator_test.cc | 2 +- .../core/grappler/costs/cost_estimator.h | 6 ++ .../grappler/costs/op_level_cost_estimator.cc | 58 ++++++++++++++----- .../grappler/costs/op_level_cost_estimator.h | 2 + 4 files changed, 52 insertions(+), 16 deletions(-) diff --git a/tensorflow/core/grappler/costs/analytical_cost_estimator_test.cc b/tensorflow/core/grappler/costs/analytical_cost_estimator_test.cc index 1c2c171383..f241922471 100644 --- a/tensorflow/core/grappler/costs/analytical_cost_estimator_test.cc +++ b/tensorflow/core/grappler/costs/analytical_cost_estimator_test.cc @@ -102,7 +102,7 @@ TEST_F(AnalyticalCostEstimatorTest, SimpleTest) { Costs summary; TF_ASSERT_OK(estimator.PredictCosts(item.graph, &cost_graph, &summary)); - EXPECT_EQ(Costs::NanoSeconds(9150), summary.execution_time); + EXPECT_EQ(Costs::NanoSeconds(9151), summary.execution_time); // Make this estimate accurate: // TODO(http://b/70031255): Accurate estimator for RandomUniform op needed diff --git a/tensorflow/core/grappler/costs/cost_estimator.h b/tensorflow/core/grappler/costs/cost_estimator.h index d442861339..9e01ec5ff5 100644 --- a/tensorflow/core/grappler/costs/cost_estimator.h +++ b/tensorflow/core/grappler/costs/cost_estimator.h @@ -100,6 +100,8 @@ struct Costs { // requirements of a graph. For example, it might assume that all activations // are live for all of a graph's execution. int64 max_memory; // Maximum main memory requirement in bytes over all ops. + int64 persistent_memory; + int64 temporary_memory; // These fields are used for TPU-related estimations. They are per-op // maximums, so each op is evaluated independently, but we want the maximum of @@ -132,6 +134,8 @@ Costs::Costs() { compute_time = Duration::zero(); memory_time = Duration::zero(); max_memory = kMemoryUnknown; + persistent_memory = kMemoryUnknown; + temporary_memory = kMemoryUnknown; max_per_op_buffers = kMemoryUnknown; max_per_op_streaming = kMemoryUnknown; } @@ -142,6 +146,8 @@ Costs Costs::ZeroCosts() { costs.compute_time = Duration::zero(); costs.memory_time = Duration::zero(); costs.max_memory = kZeroMemory; + costs.persistent_memory = kZeroMemory; + costs.temporary_memory = kZeroMemory; costs.max_per_op_buffers = kZeroMemory; costs.max_per_op_streaming = kZeroMemory; return costs; diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc index 6bc136a3f8..cf317374cf 100644 --- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc +++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc @@ -47,6 +47,8 @@ constexpr char kSize[] = "Size"; constexpr char kStopGradient[] = "StopGradient"; constexpr char kPreventGradient[] = "PreventGradient"; +static const Costs::Duration kMinComputeTime(1); + namespace { string GetDataFormat(const OpInfo& op_features) { @@ -163,18 +165,20 @@ OpLevelCostEstimator::OpLevelCostEstimator() { {kSparseMatMul, wrap(&OpLevelCostEstimator::PredictMatMul)}, {kBatchMatMul, wrap(&OpLevelCostEstimator::PredictBatchMatMul)}, - {kPlaceholder, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kIdentity, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kRefIdentity, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kStopGradient, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kPreventGradient, wrap(&OpLevelCostEstimator::PredictNoOp)}, {kNoOp, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kReshape, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kRecv, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kSend, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kConst, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kVariable, wrap(&OpLevelCostEstimator::PredictNoOp)}, - {kVariableV2, wrap(&OpLevelCostEstimator::PredictNoOp)}, + + {kPlaceholder, wrap(&OpLevelCostEstimator::PredictIdentity)}, + {kIdentity, wrap(&OpLevelCostEstimator::PredictIdentity)}, + {kRefIdentity, wrap(&OpLevelCostEstimator::PredictIdentity)}, + {kStopGradient, wrap(&OpLevelCostEstimator::PredictIdentity)}, + {kPreventGradient, wrap(&OpLevelCostEstimator::PredictIdentity)}, + {kReshape, wrap(&OpLevelCostEstimator::PredictIdentity)}, + {kRecv, wrap(&OpLevelCostEstimator::PredictIdentity)}, + {kSend, wrap(&OpLevelCostEstimator::PredictIdentity)}, + + {kConst, wrap(&OpLevelCostEstimator::PredictVariable)}, + {kVariable, wrap(&OpLevelCostEstimator::PredictVariable)}, + {kVariableV2, wrap(&OpLevelCostEstimator::PredictVariable)}, {kRank, wrap(&OpLevelCostEstimator::PredictMetadata)}, {kShape, wrap(&OpLevelCostEstimator::PredictMetadata)}, @@ -429,6 +433,7 @@ Costs OpLevelCostEstimator::PredictOpCountBasedCost( costs.execution_time = compute_cost + memory_cost; } costs.inaccurate = found_unknown_shapes; + costs.max_memory = total_output_size; return costs; } @@ -885,6 +890,30 @@ Costs OpLevelCostEstimator::PredictNoOp(const OpContext& op_context) const { return Costs::ZeroCosts(); } +Costs OpLevelCostEstimator::PredictIdentity(const OpContext& op_context) const { + const auto& op_features = op_context.op_info; + VLOG(1) << "Op:" << op_features.op() << " Execution Time 0 (ns)"; + Costs result = Costs::ZeroCosts(); + result.max_memory = CalculateOutputSize(op_features, &result.inaccurate); + // Assign the minimum amount of time we can represent to the identity op since + // it tends to be really cheap. + result.compute_time = kMinComputeTime; + result.execution_time = result.compute_time; + return result; +} + +Costs OpLevelCostEstimator::PredictVariable(const OpContext& op_context) const { + const auto& op_features = op_context.op_info; + VLOG(1) << "Op:" << op_features.op() << " Execution Time 0 (ns)"; + Costs result = Costs::ZeroCosts(); + result.persistent_memory = + CalculateOutputSize(op_features, &result.inaccurate); + + result.compute_time = kMinComputeTime; + result.execution_time = result.execution_time; + return result; +} + Costs OpLevelCostEstimator::PredictBatchMatMul( const OpContext& op_context) const { const auto& op_features = op_context.op_info; @@ -898,13 +927,12 @@ Costs OpLevelCostEstimator::PredictBatchMatMul( Costs OpLevelCostEstimator::PredictMetadata(const OpContext& op_context) const { const auto& op_features = op_context.op_info; - Costs costs; + Costs costs = Costs::ZeroCosts(); costs.max_memory = CalculateOutputSize(op_features, &costs.inaccurate); // Metadata operations are so cheap we assume they take the minimum amount of // time we can represent (1 ns). - costs.execution_time = 1; - costs.compute_time = 1; - costs.memory_time = 0; + costs.compute_time = kMinComputeTime; + costs.execution_time = costs.compute_time; return costs; } diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.h b/tensorflow/core/grappler/costs/op_level_cost_estimator.h index 5f541ccf04..a292e5e97f 100644 --- a/tensorflow/core/grappler/costs/op_level_cost_estimator.h +++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.h @@ -132,6 +132,8 @@ class OpLevelCostEstimator { Costs PredictConv2DBackpropFilter(const OpContext& op_context) const; Costs PredictMatMul(const OpContext& op_context) const; Costs PredictNoOp(const OpContext& op_context) const; + Costs PredictIdentity(const OpContext& op_context) const; + Costs PredictVariable(const OpContext& op_context) const; Costs PredictBatchMatMul(const OpContext& op_context) const; Costs PredictMetadata(const OpContext& op_context) const; -- GitLab From 7c41a89e22b6bd895082a30e6f2847ae56c5db31 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 26 Jan 2018 14:14:34 -0800 Subject: [PATCH 152/423] Fix build error with GCC 7.2.1 on AWS Linux 2 (#16470) This fix fixes a build failure when compiling with GCC 7.2.1 on AWS Linux 2: ``` gcc version 7.2.1 20170915 (Red Hat 7.2.1-2) (GCC) ``` The eror output was: ``` ... ./tensorflow/contrib/lite/toco/model.h:1567:25: error: 'std::function' has not been declared void EraseArrays(std::function discardable) { ..... ``` This fix is related to 16046. Signed-off-by: Yong Tang --- tensorflow/contrib/lite/toco/model.h | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index d1af371fd4..6fba8f2629 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -15,6 +15,7 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_TOCO_MODEL_H_ #define TENSORFLOW_CONTRIB_LITE_TOCO_MODEL_H_ +#include #include #include #include -- GitLab From 1cbd7bcd4caf0925efa0fad62c1167400ba4ab36 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 14:14:30 -0800 Subject: [PATCH 153/423] Delete mkl_tfconv_op.cc which seem to be a duplicate of mkl_tfconv_op.h, and did not exist in the external github TF repository. PiperOrigin-RevId: 183443347 --- tensorflow/core/kernels/mkl_tfconv_op.cc | 124 ----------------------- 1 file changed, 124 deletions(-) delete mode 100644 tensorflow/core/kernels/mkl_tfconv_op.cc diff --git a/tensorflow/core/kernels/mkl_tfconv_op.cc b/tensorflow/core/kernels/mkl_tfconv_op.cc deleted file mode 100644 index c35f857cfe..0000000000 --- a/tensorflow/core/kernels/mkl_tfconv_op.cc +++ /dev/null @@ -1,124 +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. -==============================================================================*/ - -#ifdef INTEL_MKL - -#include -#include -#include "tensorflow/core/framework/numeric_op.h" -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/framework/register_types.h" -#include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/framework/tensor_shape.h" -#include "tensorflow/core/kernels/ops_util.h" -#include "tensorflow/core/platform/cpu_info.h" -#include "tensorflow/core/platform/macros.h" -#include "tensorflow/core/util/tensor_format.h" - -#include "mkl_dnn.h" -#include "mkl_dnn_types.h" -#include "tensorflow/core/util/mkl_util.h" - -namespace tensorflow { -typedef Eigen::ThreadPoolDevice CPUDevice; - -/////////////////////////////////////////////////////////// -// Op kernel -/////////////////////////////////////////////////////////// - -template -class MklToTfOp : public OpKernel { - public: - explicit MklToTfOp(OpKernelConstruction* context) : OpKernel(context) { - OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format_str)); - OP_REQUIRES_OK(context, context->GetAttr("T", &op_data_type)); - has_avx512f_ = port::TestCPUFeature(port::CPUFeature::AVX512F); - } - - void Compute(OpKernelContext* context) override { - // Check that input tensor is in MKL format. - const Tensor& input_tensor = MklGetInput(context, 0); - MklShape input_shape; - GetMklShape(context, 0, &input_shape); - - // if input is already in Tf format, then just copy input tensor to output. - if (!input_shape.IsMklTensor()) { - context->set_output(0, input_tensor); - VLOG(1) << "MKLToTFConversion: No conversion needed, " - << "copying input to output"; - return; - } - - // Check that input data type is same as operator data type and that it is - // same as output data type. - DataType input_data_type = input_type(0); - DataType output_data_type = output_type(0); - CHECK_EQ(op_data_type, input_data_type); - CHECK_EQ(op_data_type, output_data_type); - - TensorShape output_shape; - size_t ndims = input_shape.GetDimension(); - size_t* in_sizes = new size_t[ndims]; - for (size_t i = 0; i < ndims; i++) { - // Outermost to innermost dimension - output_shape.AddDim(input_shape.GetSizes()[input_shape.tf_dim_idx(i)]); - in_sizes[i] = input_shape.GetSizes()[i]; - } - - // Allocate output tensor. - Tensor* output_tensor = NULL; - OP_REQUIRES_OK(context, - context->allocate_output(0, output_shape, &output_tensor)); - - dnnLayout_t output_layout = - static_cast(input_shape.GetTfLayout()); - // Execute DNNConversion. - void* input_buffer = - static_cast(const_cast(input_tensor.flat().data())); - delete[] in_sizes; - void* output_buffer = - static_cast(const_cast(output_tensor->flat().data())); - input_shape.GetConvertedFlatData(output_layout, input_buffer, - output_buffer); - VLOG(1) << "MKLToTFConversion complete successfully."; - } - - private: - /// Data format of the operation - string data_format_str; - - /// Data type of the operation - DataType op_data_type; - - /// CPUIDInfo - bool has_avx512f_ = false; -}; - -/////////////////////////////////////////////////////////// -// Register kernel -/////////////////////////////////////////////////////////// - -#define REGISTER_CPU(T) \ - REGISTER_KERNEL_BUILDER(Name("_MklToTf") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T") \ - .Label(mkl_op_registry::kMklOpLabel), \ - MklToTfOp); - -TF_CALL_float(REGISTER_CPU); -#undef REGISTER_CPU -} // namespace tensorflow -#endif /* INTEL_MKL */ -- GitLab From 84d7f94efd1489b939a4672b0f47d6aa66d9eb91 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 14:16:19 -0800 Subject: [PATCH 154/423] To add __init__.py to some paths that are imported by other modules. PiperOrigin-RevId: 183443656 --- .../boosted_trees/python/training/__init__.py | 18 ++++++++++++++++++ .../python/training/functions/__init__.py | 18 ++++++++++++++++++ .../boosted_trees/python/utils/__init__.py | 18 ++++++++++++++++++ 3 files changed, 54 insertions(+) create mode 100644 tensorflow/contrib/boosted_trees/python/training/__init__.py create mode 100644 tensorflow/contrib/boosted_trees/python/training/functions/__init__.py create mode 100644 tensorflow/contrib/boosted_trees/python/utils/__init__.py diff --git a/tensorflow/contrib/boosted_trees/python/training/__init__.py b/tensorflow/contrib/boosted_trees/python/training/__init__.py new file mode 100644 index 0000000000..b569ac5fdb --- /dev/null +++ b/tensorflow/contrib/boosted_trees/python/training/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""training module under boosted_trees.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/__init__.py b/tensorflow/contrib/boosted_trees/python/training/functions/__init__.py new file mode 100644 index 0000000000..c1750117cd --- /dev/null +++ b/tensorflow/contrib/boosted_trees/python/training/functions/__init__.py @@ -0,0 +1,18 @@ +# 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 module under boosted_trees.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function diff --git a/tensorflow/contrib/boosted_trees/python/utils/__init__.py b/tensorflow/contrib/boosted_trees/python/utils/__init__.py new file mode 100644 index 0000000000..6ceb150c26 --- /dev/null +++ b/tensorflow/contrib/boosted_trees/python/utils/__init__.py @@ -0,0 +1,18 @@ +# 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. +# ============================================================================== +"""utils module under boosted_trees.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function -- GitLab From 887509ffe25387efd4c869ffbe46b47ba6049860 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Fri, 26 Jan 2018 14:32:45 -0800 Subject: [PATCH 155/423] tfe.metrics.{Mean,Accuracy} return their inputs. This makes chaining them easier. Control dependencies to ensure updates happen are implicitly added by the function code. PiperOrigin-RevId: 183446211 --- tensorflow/contrib/eager/python/metrics_impl.py | 12 ++++++++++++ tensorflow/contrib/eager/python/metrics_test.py | 13 +++++++++++++ 2 files changed, 25 insertions(+) diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py index bf029ca5f9..ea8dbf2b46 100644 --- a/tensorflow/contrib/eager/python/metrics_impl.py +++ b/tensorflow/contrib/eager/python/metrics_impl.py @@ -291,6 +291,9 @@ class Mean(Metric): Args: values: Tensor with the per-example value. weights: Optional weighting of each example. Defaults to 1. + + Returns: + The arguments, for easy chaining. """ if weights is None: self.denom.assign_add( @@ -302,6 +305,9 @@ class Mean(Metric): self.denom.assign_add(math_ops.reduce_sum(weights)) values = math_ops.cast(values, self.dtype) * weights self.numer.assign_add(math_ops.reduce_sum(values)) + if weights is None: + return values + return values, weights def result(self): t = self.numer / self.denom @@ -329,7 +335,13 @@ class Accuracy(Mean): per element of the Tensor. predictions: Tensor with the predicted label for each example. weights: Optional weighting of each example. Defaults to 1. + + Returns: + The arguments, for easy chaining. """ matches = math_ops.equal(labels, predictions) matches = math_ops.cast(matches, dtypes.float64) super(Accuracy, self).call(matches, weights=weights) + if weights is None: + return labels, predictions + return labels, predictions, weights diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py index 9cf34fd9b2..a9ecaa3f8b 100644 --- a/tensorflow/contrib/eager/python/metrics_test.py +++ b/tensorflow/contrib/eager/python/metrics_test.py @@ -180,6 +180,19 @@ class MetricsTest(test.TestCase): m2 = metrics.Mean() m2(2) + def testMetricsChain(self): + with context.graph_mode(), self.test_session(): + m1 = metrics.Mean() + m2 = metrics.Mean(name="m2") + update_m2 = m2(3.0) + update_m2_2 = m2(m1(1.0)) + m1.init_variables().run() + m2.init_variables().run() + update_m2.eval() + update_m2_2.eval() + self.assertAllEqual(m2.result().eval(), 2.0) + self.assertAllEqual(m1.result().eval(), 1.0) + if __name__ == "__main__": test.main() -- GitLab From b45c09ce715fb1d4d0a0f7e09586bd532031049e Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Fri, 26 Jan 2018 14:35:04 -0800 Subject: [PATCH 156/423] Improvements to eager linear regression benchmark: 1. Using _shape_tuple 2. Bypassing * over math_ops.mul etc 3. Flatmaps in the tape code 4. Cache for ones similar to for zeros 5. Fast path for _SubGrad 6. Fast global_step += 1 for resource variables 7. Bypassing deprecated args decorator in eager mode PiperOrigin-RevId: 183446593 --- tensorflow/c/eager/tape.h | 51 ++++++++++++------------- tensorflow/python/eager/backprop.py | 8 ++-- tensorflow/python/ops/math_grad.py | 26 ++++++++----- tensorflow/python/training/optimizer.py | 10 ++++- tensorflow/python/util/deprecation.py | 5 ++- 5 files changed, 60 insertions(+), 40 deletions(-) diff --git a/tensorflow/c/eager/tape.h b/tensorflow/c/eager/tape.h index 2b65e38f54..bdb0815d6b 100644 --- a/tensorflow/c/eager/tape.h +++ b/tensorflow/c/eager/tape.h @@ -18,12 +18,12 @@ limitations under the License. // Language-agnostic gradient tape. Does not perform backpropagation, just // maintains the data structures required to do so. -#include -#include #include #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/platform/types.h" namespace tensorflow { @@ -54,11 +54,11 @@ struct OpTapeEntry { // Map from tensor_id to internally-defined operation-id of the operation which // produced this tensor. A value of -1 means that the tensor was directly // watched and not the result of any operation in the tape. -using TensorTape = std::unordered_map; +using TensorTape = gtl::FlatMap; // Map from operation-id to tape entry. template -using OpTape = std::unordered_map>; +using OpTape = gtl::FlatMap>; // Operations the tape needs to perform on tensors to do backpropagation. Named // "vspace" because a subset of these are related to a vector space, such as @@ -159,7 +159,7 @@ class GradientTape { // Map from tensor id to number of remaining usages (i.e. how many entries in // the tape refer to it); to aid in tape garbage collection. - std::unordered_map tensor_usage_; + gtl::FlatMap tensor_usage_; // If false, all activations are deleted in the first call to ComputeGradient. // Else, only when this is destructed. @@ -286,11 +286,11 @@ struct BackpropInitialState { // Map from tensor ID to how many references still exist for this tensor in // the tape. - std::unordered_map tensor_usage_counts; + gtl::FlatMap tensor_usage_counts; // Maps from op ID to how many output tensors of this op still need to have // their gradients computed. - std::unordered_map op_missing_tensor; + gtl::FlatMap op_missing_tensor; }; // If `persistent_tape` is true, op_tape is not changed and none of the @@ -301,8 +301,8 @@ struct BackpropInitialState { template BackpropInitialState PrepareBackprop( gtl::ArraySlice target, const TensorTape& tensor_tape, - OpTape* op_tape, - const std::unordered_set& sources_set, bool persistent_tape) { + OpTape* op_tape, const gtl::FlatSet& sources_set, + bool persistent_tape) { std::vector tensor_stack; tensor_stack.reserve(target.size()); for (auto t : target) { @@ -362,7 +362,7 @@ BackpropInitialState PrepareBackprop( template std::vector InitialStack( const OpTape& op_tape, - const std::unordered_map& op_missing_tensor) { + const gtl::FlatMap& op_missing_tensor) { std::vector result; for (auto& op_entry : op_tape) { if (op_missing_tensor.find(op_entry.first) == op_missing_tensor.end()) { @@ -373,13 +373,13 @@ std::vector InitialStack( } template -Status InitialGradients( - const VSpace& vspace, - gtl::ArraySlice target_tensor_ids, - gtl::ArraySlice output_gradients, const TensorTape& tensor_tape, - const OpTape& op_tape, - const std::unordered_map& tensor_usage_counts, - std::unordered_map>* result) { +Status InitialGradients(const VSpace& vspace, + gtl::ArraySlice target_tensor_ids, + gtl::ArraySlice output_gradients, + const TensorTape& tensor_tape, + const OpTape& op_tape, + const gtl::FlatMap& tensor_usage_counts, + gtl::FlatMap>* result) { for (int i = 0; i < target_tensor_ids.size(); ++i) { const int64 id = target_tensor_ids[i]; if (tensor_usage_counts.find(id) != tensor_usage_counts.end()) { @@ -441,13 +441,13 @@ Status GradientTape::ComputeGradient( gtl::ArraySlice source_tensor_ids, gtl::ArraySlice output_gradients, std::vector* result) { - std::unordered_set sources_set(source_tensor_ids.begin(), - source_tensor_ids.end()); + gtl::FlatSet sources_set(source_tensor_ids.begin(), + source_tensor_ids.end()); BackpropInitialState state = PrepareBackprop( target_tensor_ids, tensor_tape_, &op_tape_, sources_set, persistent_); std::vector op_stack = InitialStack(state.op_tape, state.op_missing_tensor); - std::unordered_map> gradients; + gtl::FlatMap> gradients; Status s = InitialGradients(vspace, target_tensor_ids, output_gradients, tensor_tape_, state.op_tape, state.tensor_usage_counts, &gradients); @@ -463,7 +463,7 @@ Status GradientTape::ComputeGradient( cleanup(); return s; } - std::unordered_map gradients_size; + gtl::FlatMap gradients_size; // TODO(apassos) multiple threads could be dequeuing from op_stack at the same // time, for better CPU backprop performance. VLOG(1) << "Initial stack:"; @@ -472,11 +472,10 @@ Status GradientTape::ComputeGradient( VLOG(1) << " " << t; } } - std::unordered_map> - functions_accept_none_for_indices({ - {"SoftmaxCrossEntropyWithLogits", {1}}, - {"FusedBatchNorm", {1, 2, 3, 4}}, - }); + gtl::FlatMap> functions_accept_none_for_indices({ + {"SoftmaxCrossEntropyWithLogits", {1}}, + {"FusedBatchNorm", {1, 2, 3, 4}}, + }); while (!op_stack.empty()) { const int64 op = op_stack.back(); VLOG(1) << "Popped " << op; diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index a2a3e230bb..d79d1fc0a6 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -734,7 +734,7 @@ def _num_elements(grad): raise ValueError("`grad` not a Tensor or IndexedSlices.") -_last_shape_dtype = [None, None] +_last_zero_shape_dtype = [None, None] _last_zero = [None] @@ -748,13 +748,15 @@ def _zeros(shape, dtype): # TODO(apassos): need to save enough information about variant tensors to do # a zeros return None - if [shape, dtype] != _last_shape_dtype: - _last_shape_dtype[:] = [shape, dtype] + if [shape, dtype] != _last_zero_shape_dtype: + _last_zero_shape_dtype[:] = [shape, dtype] _last_zero[0] = _fast_fill(0, shape, dtype) return _last_zero[0] def _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/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index bca4c665d2..3cb71eba8c 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -40,15 +40,16 @@ def _SumGrad(op, grad): """Gradient for Sum.""" # Fast path for when reducing to a scalar and ndims is known: adds only # Reshape and Tile ops (and possibly a Shape). - if op.inputs[0].get_shape().ndims is not None: + input_0_shape = op.inputs[0]._shape_tuple() # pylint: disable=protected-access + if input_0_shape is not None: axes = tensor_util.constant_value(op.inputs[1]) if axes is not None: - rank = op.inputs[0].get_shape().ndims + rank = len(input_0_shape) if np.array_equal(axes, np.arange(rank)): # Reduce all dims. grad = array_ops.reshape(grad, [1] * rank) # If shape is not fully defined (but rank is), we use Shape. - if op.inputs[0].get_shape().is_fully_defined(): - input_shape = op.inputs[0].get_shape().as_list() + if None not in input_0_shape: + input_shape = input_0_shape else: input_shape = array_ops.shape(op.inputs[0]) return [array_ops.tile(grad, input_shape), None] @@ -96,9 +97,12 @@ def _MinGrad(op, grad): def _MeanGrad(op, grad): """Gradient for Mean.""" sum_grad = _SumGrad(op, grad)[0] - input_size = op.inputs[0].get_shape().num_elements() - output_size = op.outputs[0].get_shape().num_elements() - if input_size is not None and output_size is not None: + input_shape = op.inputs[0]._shape_tuple() # pylint: disable=protected-access + output_shape = op.outputs[0]._shape_tuple() # pylint: disable=protected-access + if (input_shape is not None and output_shape is not None and + None not in input_shape and None not in output_shape): + input_size = np.prod(input_shape) + output_size = np.prod(output_shape) factor = input_size // max(output_size, 1) factor = constant_op.constant(factor, dtype=sum_grad.dtype) else: @@ -106,7 +110,7 @@ def _MeanGrad(op, grad): output_shape = array_ops.shape(op.outputs[0]) factor = _safe_shape_div( math_ops.reduce_prod(input_shape), math_ops.reduce_prod(output_shape)) - return sum_grad / math_ops.cast(factor, sum_grad.dtype), None + return math_ops.truediv(sum_grad, math_ops.cast(factor, sum_grad.dtype)), None @ops.RegisterGradient("Prod") @@ -330,7 +334,7 @@ def _SquareGrad(op, grad): # Added control dependencies to prevent 2*x from being computed too early. with ops.control_dependencies([grad]): x = math_ops.conj(x) - return grad * (2.0 * x) + return math_ops.multiply(grad, math_ops.multiply(x, 2.0)) @ops.RegisterGradient("Sqrt") @@ -756,8 +760,12 @@ def _AddGrad(op, grad): @ops.RegisterGradient("Sub") def _SubGrad(op, grad): + """Gradient for Sub.""" x = op.inputs[0] y = op.inputs[1] + if (isinstance(grad, ops.Tensor) and + _ShapesFullySpecifiedAndEqual(x, y, grad)): + return grad, -grad sx = array_ops.shape(x) sy = array_ops.shape(y) # pylint: disable=protected-access diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index 719b83e5ca..a06b3eada6 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -533,7 +533,15 @@ class Optimizer(object): else: with ops.control_dependencies([self._finish(update_ops, "update")]): with ops.colocate_with(global_step): - apply_updates = state_ops.assign_add(global_step, 1, name=name) + 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) + else: + apply_updates = state_ops.assign_add(global_step, 1, name=name) if context.in_graph_mode(): if isinstance(apply_updates, ops.Tensor): diff --git a/tensorflow/python/util/deprecation.py b/tensorflow/python/util/deprecation.py index 8a66f0435a..2110fc64cf 100644 --- a/tensorflow/python/util/deprecation.py +++ b/tensorflow/python/util/deprecation.py @@ -22,6 +22,7 @@ import collections import functools import re +from tensorflow.python.eager import context from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import decorator_utils from tensorflow.python.util import tf_contextlib @@ -284,7 +285,9 @@ def deprecated_args(date, instructions, *deprecated_arg_names_or_tuples, @functools.wraps(func) def new_func(*args, **kwargs): """Deprecation wrapper.""" - if _PRINT_DEPRECATION_WARNINGS: + # TODO(apassos) figure out a way to have reasonable performance with + # deprecation warnings and eager mode. + if context.in_graph_mode() and _PRINT_DEPRECATION_WARNINGS: invalid_args = [] named_args = tf_inspect.getcallargs(func, *args, **kwargs) for arg_name, spec in iter(deprecated_positions.items()): -- GitLab From 201d957baf20c3adabcae3f6a616430ae81b94ae Mon Sep 17 00:00:00 2001 From: Martin Wicke Date: Fri, 26 Jan 2018 14:46:01 -0800 Subject: [PATCH 157/423] Add a security document discussing high level best practices and explain vulnerability reporting process. PiperOrigin-RevId: 183448435 --- tensorflow/SECURITY.md | 239 +++++++++++++++++++++++ tensorflow/docs_src/community/welcome.md | 4 +- 2 files changed, 241 insertions(+), 2 deletions(-) create mode 100644 tensorflow/SECURITY.md diff --git a/tensorflow/SECURITY.md b/tensorflow/SECURITY.md new file mode 100644 index 0000000000..074eed2951 --- /dev/null +++ b/tensorflow/SECURITY.md @@ -0,0 +1,239 @@ +# Using TensorFlow Securely + +This document discusses how to safely deal with untrusted programs (models or +model parameters), and input data. Below, we also provide guidelines on how to +report vulnerabilities in TensorFlow. + +## TensorFlow models are programs + +TensorFlow's runtime system interprets and executes programs. What machine +learning practitioners term +[**models**](https://developers.google.com/machine-learning/glossary/#model) are +expressed as programs that TensorFlow executes. TensorFlow programs are encoded +as computation +[**graphs**](https://developers.google.com/machine-learning/glossary/#graph). +The model's parameters are often stored separately in **checkpoints**. + +At runtime, TensorFlow executes the computation graph using the parameters +provided. Note that the behavior of the computation graph may change +depending on the parameters provided. TensorFlow itself is not a sandbox. When +executing the computation graph, TensorFlow may read and write files, send and +receive data over the network, and even spawn additional processes. All these +tasks are performed with the permissions of the TensorFlow process. Allowing +for this flexibility makes for a powerful machine learning platform, +but it has implications for security. + +The computation graph may also accept **inputs**. Those inputs are the +data you supply to TensorFlow to train a model, or to use a model to run +inference on the data. + +**TensorFlow models are programs, and need to be treated as such from a security +perspective.** + +## Running untrusted models + +As a general rule: **Always** execute untrusted models inside a sandbox (e.g., +[nsjail](https://github.com/google/nsjail)). + +There are several ways in which a model could become untrusted. Obviously, if an +untrusted party supplies TensorFlow kernels, arbitrary code may be executed. +The same is true if the untrusted party provides Python code, such as the +Python code that generates TensorFlow graphs. + +Even if the untrusted party only supplies the serialized computation +graph (in form of a `GraphDef`, `SavedModel`, or equivalent on-disk format), the +set of computation primitives available to TensorFlow is powerful enough that +you should assume that the TensorFlow process effectively executes arbitrary +code. One common solution is to whitelist only a few safe Ops. While this is +possible in theory, we still recommend you sandbox the execution. + +It depends on the computation graph whether a user provided checkpoint is safe. +It is easily possible to create computation graphs in which malicious +checkpoints can trigger unsafe behavior. For example, consider a graph that +contains a `tf.cond` depending on the value of a `tf.Variable`. One branch of +the `tf.cond` is harmless, but the other is unsafe. Since the `tf.Variable` is +stored in the checkpoint, whoever provides the checkpoint now has the ability to +trigger unsafe behavior, even though the graph is not under their control. + +In other words, graphs can contain vulnerabilities of their own. To allow users +to provide checkpoints to a model you run on their behalf (e.g., in order to +compare model quality for a fixed model architecture), you must carefully audit +your model, and we recommend you run the TensorFlow process in a sandbox. + +## Accepting untrusted Inputs + +It is possible to write models that are secure in a sense that they can safely +process untrusted inputs assuming there are no bugs. There are two main reasons +to not rely on this: first, it is easy to write models which must not be exposed +to untrusted inputs, and second, there are bugs in any software system of +sufficient complexity. Letting users control inputs could allow them to trigger +bugs either in TensorFlow or in dependent libraries. + +In general, it is good practice to isolate parts of any system which is exposed +to untrusted (e.g., user-provided) inputs in a sandbox. + +A useful analogy to how any TensorFlow graph is executed is any interpreted +programming language, such as Python. While it is possible to write secure +Python code which can be exposed to user supplied inputs (by, e.g., carefully +quoting and sanitizing input strings, size-checking input blobs, etc.), it is +very easy to write Python programs which are insecure. Even secure Python code +could be rendered insecure by a bug in the Python interpreter, or in a bug in a +Python library used (e.g., +[this one](https://www.cvedetails.com/cve/CVE-2017-12852/)). + +## Running a TensorFlow server + +TensorFlow is a platform for distributed computing, and as such there is a +TensorFlow server (`tf.train.Server`). **The TensorFlow server is meant for +internal communication only. It is not built for use in an untrusted network.** + +For performance reasons, the default TensorFlow server does not include any +authorization protocol and sends messages unencrypted. It accepts connections +from anywhere, and executes the graphs it is sent without performing any checks. +Therefore, if you run a `tf.train.Server` in your network, anybody with +access to the network can execute what you should consider arbitrary code with +the privileges of the process running the `tf.train.Server`. + +When running distributed TensorFlow, you must isolate the network in which the +cluster lives. Cloud providers provide instructions for setting up isolated +networks, which are sometimes branded as "virtual private cloud." Refer to the +instructions for +[GCP](https://cloud.google.com/compute/docs/networks-and-firewalls) and +[AWS](https://aws.amazon.com/vpc/)) for details. + +Note that `tf.train.Server` is different from the server created by +`tensorflow/serving` (the default binary for which is called `ModelServer`). +By default, `ModelServer` also has no built-in mechanism for authentication. +Connecting it to an untrusted network allows anyone on this network to run the +graphs known to the `ModelServer`. This means that an attacker may run +graphs using untrusted inputs as described above, but they would not be able to +execute arbitrary graphs. It is possible to safely expose a `ModelServer` +directly to an untrusted network, **but only if the graphs it is configured to +use have been carefully audited to be safe**. + +Similar to best practices for other servers, we recommend running any +`ModelServer` with appropriate privileges (i.e., using a separate user with +reduced permisisons). In the spirit of defense in depth, we recommend +authenticating requests to any TensorFlow server connected to an untrusted +network, as well as sandboxing the server to minimize the adverse effects of +any breach. + +## Vulnerabilities in TensorFlow + +TensorFlow is a large and complex system. It also depends on a large set of +third party libraries (e.g., `numpy`, `libjpeg-turbo`, PNG parsers, `protobuf`). +It is possible that TensorFlow or its dependent libraries contain +vulnerabilities that would allow triggering unexpected or dangerous behavior +with specially crafted inputs. + +### What is a vulnerability? + +Given TensorFlow's flexibility, it is possible to specify computation graphs +which exhibit unexpected or unwanted behaviors. The fact that TensorFlow models +can perform arbitrary computations means that they may read and write files, +communicate via the network, produce deadlocks and infinite loops, or run out +of memory. It is only when these behaviors are outside the specifications of the +operations involved that such behavior is a vulnerability. + +A `FileWriter` writing a file is not unexpected behavior and therefore is not a +vulnerability in TensorFlow. A `MatMul` allowing arbitrary binary code execution +**is** a vulnerability. + +This is more subtle from a system perspective. For example, it is easy to cause +a TensorFlow process to try to allocate more memory than available by specifying +a computation graph containing an ill-considered `tf.tile` operation. TensorFlow +should exit cleanly in this case (it would raise an exception in Python, or +return an error `Status` in C++). However, if the surrounding system is not +expecting the possibility, such behavior could be used in a denial of service +attack (or worse). Because TensorFlow behaves correctly, this is not a +vulnerability in TensorFlow (although it would be a vulnerability of this +hypothetical system). + +As a general rule, it is incorrect behavior for Tensorflow to access memory it +does not own, or to terminate in an unclean way. Bugs in TensorFlow that lead to +such behaviors constitute a vulnerability. + +One of the most critical parts of any system is input handling. If malicious +input can trigger side effects or incorrect behavior, this is a bug, and likely +a vulnerability. + +### Reporting vulnerabilities + +Please email reports about any security related issues you find to +`security@tensorflow.org`. This mail is delivered to a small security team. Your +email will be acknowledged within one business day, and you'll receive a more +detailed response to your email within 7 days indicating the next steps in +handling your report. For critical problems, you may encrypt your report (see +below). + +Please use a descriptive subject line for your report email. After the initial +reply to your report, the security team will endeavor to keep you informed of +the progress being made towards a fix and announcement. + +If you believe that an existing (public) issue is security-related, please send +an email to `security@tensorflow.org`. The email should include the issue ID and +a short description of why it should be handled according to this security +policy. + +Once an issue is reported, TensorFlow uses the following disclosure process: + +* When a report is received, we confirm the issue and determine its severity. +* If we know of specific third-party services or software based on TensorFlow + that require mitigation before publication, those projects will be notified. +* An advisory is prepared (but not published) which details the problem and + steps for mitigation. +* Wherever possible, fixes are prepared for the last minor release of the two + latest major releases, as well as the master branch. We will attempt to + commit these fixes as soon as possible, and as close together as + possible. +* Patch releases are published for all fixed released versions, a + notification is sent to discuss@tensorflow.org, and the advisory is published. + +Past security advisories are listed below. We credit reporters for identifying +security issues, although we keep your name confidential if you request it. + +#### Encryption key for `security@tensorflow.org` + +If your disclosure is extremely sensitive, you may choose to encrypt your +report using the key below. Please only use this for critical security +reports. + +``` +-----BEGIN PGP PUBLIC KEY BLOCK----- + +mQENBFpqdzwBCADTeAHLNEe9Vm77AxhmGP+CdjlY84O6DouOCDSq00zFYdIU/7aI +LjYwhEmDEvLnRCYeFGdIHVtW9YrVktqYE9HXVQC7nULU6U6cvkQbwHCdrjaDaylP +aJUXkNrrxibhx9YYdy465CfusAaZ0aM+T9DpcZg98SmsSml/HAiiY4mbg/yNVdPs +SEp/Ui4zdIBNNs6at2gGZrd4qWhdM0MqGJlehqdeUKRICE/mdedXwsWLM8AfEA0e +OeTVhZ+EtYCypiF4fVl/NsqJ/zhBJpCx/1FBI1Uf/lu2TE4eOS1FgmIqb2j4T+jY +e+4C8kGB405PAC0n50YpOrOs6k7fiQDjYmbNABEBAAG0LVRlbnNvckZsb3cgU2Vj +dXJpdHkgPHNlY3VyaXR5QHRlbnNvcmZsb3cub3JnPokBTgQTAQgAOBYhBEkvXzHm +gOJBnwP4Wxnef3wVoM2yBQJaanc8AhsDBQsJCAcCBhUKCQgLAgQWAgMBAh4BAheA +AAoJEBnef3wVoM2yNlkIAICqetv33MD9W6mPAXH3eon+KJoeHQHYOuwWfYkUF6CC +o+X2dlPqBSqMG3bFuTrrcwjr9w1V8HkNuzzOJvCm1CJVKaxMzPuXhBq5+DeT67+a +T/wK1L2R1bF0gs7Pp40W3np8iAFEh8sgqtxXvLGJLGDZ1Lnfdprg3HciqaVAiTum +HBFwszszZZ1wAnKJs5KVteFN7GSSng3qBcj0E0ql2nPGEqCVh+6RG/TU5C8gEsEf +3DX768M4okmFDKTzLNBm+l08kkBFt+P43rNK8dyC4PXk7yJa93SmS/dlK6DZ16Yw +2FS1StiZSVqygTW59rM5XNwdhKVXy2mf/RtNSr84gSi5AQ0EWmp3PAEIALInfBLR +N6fAUGPFj+K3za3PeD0fWDijlC9f4Ety/icwWPkOBdYVBn0atzI21thPRbfuUxfe +zr76xNNrtRRlbDSAChA1J5T86EflowcQor8dNC6fS+oHFCGeUjfEAm16P6mGTo0p +osdG2XnnTHOOEFbEUeWOwR/zT0QRaGGknoy2pc4doWcJptqJIdTl1K8xyBieik/b +nSoClqQdZJa4XA3H9G+F4NmoZGEguC5GGb2P9NHYAJ3MLHBHywZip8g9oojIwda+ +OCLL4UPEZ89cl0EyhXM0nIAmGn3Chdjfu3ebF0SeuToGN8E1goUs3qSE77ZdzIsR +BzZSDFrgmZH+uP0AEQEAAYkBNgQYAQgAIBYhBEkvXzHmgOJBnwP4Wxnef3wVoM2y +BQJaanc8AhsMAAoJEBnef3wVoM2yX4wIALcYZbQhSEzCsTl56UHofze6C3QuFQIH +J4MIKrkTfwiHlCujv7GASGU2Vtis5YEyOoMidUVLlwnebE388MmaJYRm0fhYq6lP +A3vnOCcczy1tbo846bRdv012zdUA+wY+mOITdOoUjAhYulUR0kiA2UdLSfYzbWwy +7Obq96Jb/cPRxk8jKUu2rqC/KDrkFDtAtjdIHh6nbbQhFuaRuWntISZgpIJxd8Bt +Gwi0imUVd9m9wZGuTbDGi6YTNk0GPpX5OMF5hjtM/objzTihSw9UN+65Y/oSQM81 +v//Fw6ZeY+HmRDFdirjD7wXtIuER4vqCryIqR6Xe9X8oJXz9L/Jhslc= +=CDME +-----END PGP PUBLIC KEY BLOCK----- +``` + +### Known vulnerabilities + +| Type | Versions affected | Reported by | Additional Information | +|------|:-----------------:|---------------------------------------| +| out of bounds read| <=1.4 | @zhangbo5891001 | [issue report](https://github.com/tensorflow/tensorflow/issues/14959) | + diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md index a3abf25507..d2d3f9edae 100644 --- a/tensorflow/docs_src/community/welcome.md +++ b/tensorflow/docs_src/community/welcome.md @@ -12,7 +12,6 @@ The source code for TensorFlow is on Before contributing to TensorFlow source code, please review the [Contribution guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md). - ### Projects developed by the TensorFlow community The TensorFlow community has created many great projects around TensorFlow, including: @@ -65,5 +64,6 @@ please read the following list carefully: [TensorFlow issues tracker](https://github.com/tensorflow/tensorflow/issues) on GitHub. For example, use the issue tracker to request a new operation in TensorFlow. - + * To report vulnerabilities, please follow our + [vulnerability disclosure guidelines](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md). -- GitLab From 8ce4986d26f904ccc6e7d5291883931571dc2713 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 14:54:42 -0800 Subject: [PATCH 158/423] Raise shard count to 10 for tensorflow/python/kernel_tests:metrics_test The test was sometimes taking over six minutes to run in asan mode, causing it to hit the 5 minute timeout. Setting the shard count to 6 was inufficient, but setting it to 10 brought the runtime down to about 3:30 in the worst case over 100 runs. PiperOrigin-RevId: 183449941 --- tensorflow/python/kernel_tests/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index 8c1d16c2a8..a49d6fb44a 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -2821,7 +2821,7 @@ tf_py_test( "//tensorflow/python:random_ops", "//tensorflow/python:variables", ], - shard_count = 3, + shard_count = 10, tags = ["no_windows_gpu"], ) -- GitLab From b9494ce8990cab65a20f3d5110f4c2c4402342be Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 14:57:03 -0800 Subject: [PATCH 159/423] Remove dead code PiperOrigin-RevId: 183450369 --- .../boosted_trees/kernels/quantile_ops.cc | 3 -- tensorflow/core/debug/debug_io_utils.cc | 2 -- .../grappler/optimizers/layout_optimizer.cc | 11 ------- .../data/group_by_window_dataset_op.cc | 6 ---- .../core/platform/cloud/gcs_file_system.cc | 4 --- tensorflow/python/eager/pywrap_tfe_src.cc | 29 ++----------------- .../tools/graph_transforms/quantize_nodes.cc | 16 ---------- 7 files changed, 3 insertions(+), 68 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc index 88f3006407..e91232bf10 100644 --- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc @@ -42,7 +42,6 @@ using boosted_trees::QuantileStreamResource; namespace { const char* const kExampleWeightsName = "example_weights"; const char* const kMaxElementsName = "max_elements"; -const char* const kHandleName = "handle"; const char* const kNextStampTokenName = "next_stamp_token"; const char* const kStampTokenName = "stamp_token"; const char* const kAreBucketsReadyName = "are_buckets_ready"; @@ -52,7 +51,6 @@ const char* const kNumSparseFeaturesName = "num_sparse_features"; const char* const kSparseBucketsName = "sparse_buckets"; const char* const kSparseValuesName = "sparse_values"; const char* const kSparseIndicesName = "sparse_indices"; -const char* const kSparseStreamsStateName = "sparse_streams_state"; const char* const kSparseSummariesName = "sparse_summaries"; const char* const kSparseConfigName = "sparse_config"; const char* const kSparseOutputTensorName = "sparse_quantiles"; @@ -60,7 +58,6 @@ const char* const kSparseOutputTensorName = "sparse_quantiles"; const char* const kDenseBucketsName = "dense_buckets"; const char* const kDenseConfigName = "dense_config"; const char* const kDenseOutputTensorName = "dense_quantiles"; -const char* const kDenseStreamsStateName = "dense_streams_state"; const char* const kDenseSummariesName = "dense_summaries"; const char* const kDenseValuesName = "dense_values"; const char* const kNumDenseFeaturesName = "num_dense_features"; diff --git a/tensorflow/core/debug/debug_io_utils.cc b/tensorflow/core/debug/debug_io_utils.cc index f81445c20b..baa8c08fdf 100644 --- a/tensorflow/core/debug/debug_io_utils.cc +++ b/tensorflow/core/debug/debug_io_utils.cc @@ -574,8 +574,6 @@ Status DebugIO::CloseDebugURL(const string& debug_url) { } } -static Status CloseDebugURL(const string& debug_url) { return Status::OK(); } - Status DebugFileIO::DumpTensorToDir(const DebugNodeKey& debug_node_key, const Tensor& tensor, const uint64 wall_time_us, diff --git a/tensorflow/core/grappler/optimizers/layout_optimizer.cc b/tensorflow/core/grappler/optimizers/layout_optimizer.cc index 735d78e7ee..433b3564fe 100644 --- a/tensorflow/core/grappler/optimizers/layout_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/layout_optimizer.cc @@ -2041,17 +2041,6 @@ class DataLayoutOptimizer : GraphProcessor { const LayoutOptimizer::TuningConfig& config_; }; -int GetNumTranspose(const GraphDef& graph) { - int number = 0; - for (const auto& node : graph.node()) { - if (IsTranspose(node)) { - number++; - } - } - VLOG(1) << "Number of Transpose nodes: " << number; - return number; -} - int GetNumGPUs(const Cluster& cluster) { auto devices = cluster.GetDevices(); int num_gpus = 0; diff --git a/tensorflow/core/kernels/data/group_by_window_dataset_op.cc b/tensorflow/core/kernels/data/group_by_window_dataset_op.cc index eb047e10ec..834c06bb93 100644 --- a/tensorflow/core/kernels/data/group_by_window_dataset_op.cc +++ b/tensorflow/core/kernels/data/group_by_window_dataset_op.cc @@ -23,7 +23,6 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -510,10 +509,6 @@ class GroupByWindowDatasetOp : public UnaryDatasetOpKernel { return Status::OK(); } - // A resource name for the temporary window dataset that is - // created as the input to the reduce function. - static constexpr const char* kWindowResourceName = "__window_dataset"; - const DatasetBase* const input_; const NameAttrList key_func_; const NameAttrList reduce_func_; @@ -537,5 +532,4 @@ REGISTER_KERNEL_BUILDER(Name("GroupByWindowDataset").Device(DEVICE_CPU), GroupByWindowDatasetOp); } // namespace - } // namespace tensorflow diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc index 520720372d..91d381bd6f 100644 --- a/tensorflow/core/platform/cloud/gcs_file_system.cc +++ b/tensorflow/core/platform/cloud/gcs_file_system.cc @@ -50,7 +50,6 @@ limitations under the License. #endif namespace tensorflow { - namespace { constexpr char kGcsUriBase[] = "https://www.googleapis.com/storage/v1/"; @@ -59,9 +58,6 @@ constexpr char kGcsUploadUriBase[] = constexpr char kStorageHost[] = "storage.googleapis.com"; constexpr size_t kReadAppendableFileBufferSize = 1024 * 1024; // In bytes. constexpr int kGetChildrenDefaultPageSize = 1000; -// Initial delay before retrying a GCS upload. -// Subsequent delays can be larger due to exponential back-off. -constexpr uint64 kUploadRetryDelayMicros = 1000000L; // The HTTP response code "308 Resume Incomplete". constexpr uint64 HTTP_CODE_RESUME_INCOMPLETE = 308; // The environment variable that overrides the size of the readahead buffer. diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 647f03351d..836998cfdc 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -86,30 +86,6 @@ bool ParseBoolValue(const string& key, PyObject* py_value, TF_Status* status, return true; } -const char* ParseProtoValue(const string& key, const char* proto_name, - PyObject* py_value, size_t* size, - TF_Status* status) { - char* output = nullptr; - Py_ssize_t py_size; - if (PyBytes_Check(py_value) && - PyBytes_AsStringAndSize(py_value, &output, &py_size) >= 0) { - *size = static_cast(py_size); - return output; - } -#if PY_MAJOR_VERSION >= 3 - if (PyUnicode_Check(py_value) && - (output = PyUnicode_AsUTF8AndSize(py_value, &py_size)) != nullptr) { - *size = static_cast(py_size); - return output; - } -#endif - TF_SetStatus(status, TF_INVALID_ARGUMENT, - tensorflow::strings::StrCat("Expecting a string (serialized ", - proto_name, ") value for attr ", key) - .c_str()); - return nullptr; -} - bool SetOpAttrList(TFE_Op* op, const char* key, PyObject* py_list, TF_AttrType type, TF_Status* status) { if (!PySequence_Check(py_list)) { @@ -329,8 +305,9 @@ void SetOpAttrs(TFE_Context* ctx, TFE_Op* op, PyObject* attrs, int start_index, tensorflow::mutex exception_class_mutex(tensorflow::LINKER_INITIALIZED); PyObject* exception_class GUARDED_BY(exception_class_mutex) = nullptr; -static tensorflow::mutex _uid_mutex(tensorflow::LINKER_INITIALIZED); -static tensorflow::int64 _uid GUARDED_BY(_uid_mutex) = 0; +tensorflow::mutex _uid_mutex(tensorflow::LINKER_INITIALIZED); +tensorflow::int64 _uid GUARDED_BY(_uid_mutex) = 0; + } // namespace void TFE_Py_Execute(TFE_Context* ctx, const char* device_name, diff --git a/tensorflow/tools/graph_transforms/quantize_nodes.cc b/tensorflow/tools/graph_transforms/quantize_nodes.cc index 5ccd88cfa1..a022f57926 100644 --- a/tensorflow/tools/graph_transforms/quantize_nodes.cc +++ b/tensorflow/tools/graph_transforms/quantize_nodes.cc @@ -183,22 +183,6 @@ Status ExtractRangeFromParams(const TransformFuncContext& context, return Status::OK(); } -bool AreAttrsEqual(const NodeDef* current_node, const NodeDef* other_node) { - if (current_node->attr_size() != other_node->attr_size()) { - return false; - } - string current_serialized; - string other_serialized; - for (const auto& attr : other_node->attr()) { - auto iter = current_node->attr().find(attr.first); - if (iter == current_node->attr().end()) return false; - iter->second.SerializeToString(¤t_serialized); - attr.second.SerializeToString(&other_serialized); - if (current_serialized != other_serialized) return false; - } - return true; -} - } // namespace // Analyzes all the nodes in the graph to figure out which ones are duplicates -- GitLab From 3e9bf0874ed19b1f96f835c444a4b80167de4663 Mon Sep 17 00:00:00 2001 From: Nick Desaulniers Date: Fri, 26 Jan 2018 15:01:40 -0800 Subject: [PATCH 160/423] [XLA] optimize NearComparator#ExpectLiteralsNear() While tracking down the issue of timeouts when running THE ISOLATOR, it was observed that NearComparator#ExpectLiteralsNear() could be optimized in the case of matching layouts to not compute multi indexes. In the process of tracking down timeouts in THE ISOLATOR, I had assumed that time spent was dominated by either generating input data, executing the input data on various backends, or comparing the data. Never assume you know where the time is spent in a program; the profiler may surprise you. After making that optimization and then profiling the code before and after, I was surprised by the profile. Image the shock, horror, and disgust I experienced when discovering that runs of THE ISOLATOR were dominated (45%) by calls to Literal#ToString() in NearComparator#ExpectLiteralsNear() for huge (>120 million elements) literals that failed comparisons. No wonder passing shards of THE ISOLATOR were fast, and failing shards were slow. Further, computing multi indexes many times is expensive enough (18%) to show up in profiles, so avoid calculating it until it is necessary. The optimizations in this patch: * Don't call Literal#ToString() on huge literals that are going to get written to disk anyways. The utility of printing said literal to stdout is suspect. * Initialize NearComparator#miscompares_ to false, only update miscompares_ and other stats when miscompare occurs. * Split NearComparator#ExpectLiteralsNear() into two, since we only need to log and update stats if an actual miscompare occurs. * Add fast path in NearComparator#ExpectLiteralsNear() for case of matching layouts, being careful not to compute multi index unless mismatch actually occurs. This optimized NearComparator#ExpectLiteralsNear() for the case of many element literals, with few miscompares. For many miscompares, we cannot avoid calculating multi indexes, but can fast path for equal layouts. For zero miscompares, we can at least fast path in the case of matching layouts. Before this CL, a run of THE ISOLATOR for a single literal with >120 million elements and a few miscompares took 379s (6.3m). With this CL, the same test case now takes 44s. Beautiful flame graphs omitted from public commit message, regrettably. PiperOrigin-RevId: 183451138 --- tensorflow/compiler/xla/literal_util.h | 1 + .../compiler/xla/tests/literal_test_util.cc | 186 +++++++++++++----- 2 files changed, 138 insertions(+), 49 deletions(-) diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index e0196509a7..2b68b8f177 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -486,6 +486,7 @@ class Literal { std::vector> elements); // Returns a string representation of the literal value. + // Warning: this function can take minutes for multi-million element Literals. string ToString(bool print_layout = false) const; // Invokes the "per cell" callback for each element in the provided diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc index f8205de702..39c07297d6 100644 --- a/tensorflow/compiler/xla/tests/literal_test_util.cc +++ b/tensorflow/compiler/xla/tests/literal_test_util.cc @@ -355,9 +355,9 @@ class NearComparator { // temporary files on failure. Returns true if literals match. bool ExpectNear(const Literal& expected, const Literal& actual) { VLOG(1) << "expected:"; - XLA_VLOG_LINES(1, expected.ToString()); + XLA_VLOG_LINES(1, TruncateHugeLiteral(expected)); VLOG(1) << "actual:"; - XLA_VLOG_LINES(1, actual.ToString()); + XLA_VLOG_LINES(1, TruncateHugeLiteral(actual)); // If the shapes mismatch, we simply fail the expectation instead of // printing out data, as it's a type error rather than a value error. @@ -377,6 +377,7 @@ class NearComparator { max_rel_err_ = 0.0; max_abs_err_ = 0.0; miscompares_ = Literal(ShapeUtil::ChangeElementType(actual.shape(), PRED)); + miscompares_.PopulateWithValue(false); multi_index_.resize(expected.shape().dimensions_size(), 0); switch (expected.shape().element_type()) { @@ -404,21 +405,33 @@ class NearComparator { if (num_miscompares_ > 0) { if (!VLOG_IS_ON(1)) { LOG(INFO) << "expected: " << ShapeUtil::HumanString(expected.shape()) - << " " << expected.ToString(); + << " " << TruncateHugeLiteral(expected); LOG(INFO) << "actual: " << ShapeUtil::HumanString(actual.shape()) - << " " << actual.ToString(); + << " " << TruncateHugeLiteral(actual); + LOG(INFO) << "Dumping literals to temp files..."; + WriteLiteralToTempFile(expected, "expected"); + WriteLiteralToTempFile(actual, "actual"); + WriteLiteralToTempFile(miscompares_, "miscompares"); } EXPECT_TRUE(num_miscompares_ == 0) << "\nmax relative mismatch at index " - << LiteralTestUtil::MultiIndexAsString(max_rel_multi_index_) + << LiteralTestUtil::MultiIndexAsString( + IndexUtil::LinearIndexToMultidimensionalIndex( + actual.shape(), max_rel_linear_index_)) << "\nmaximum relative error " << max_rel_err_ << "\nmax absolute mismatch at index " - << LiteralTestUtil::MultiIndexAsString(max_abs_multi_index_) + << LiteralTestUtil::MultiIndexAsString( + IndexUtil::LinearIndexToMultidimensionalIndex( + actual.shape(), max_abs_linear_index_)) << "\nmaximum absolute error " << max_abs_err_ << "\nfirst mismatch at index " - << LiteralTestUtil::MultiIndexAsString(first_multi_index_) + << LiteralTestUtil::MultiIndexAsString( + IndexUtil::LinearIndexToMultidimensionalIndex( + actual.shape(), first_linear_index_)) << "\nlast mismatch at index " - << LiteralTestUtil::MultiIndexAsString(last_multi_index_) + << LiteralTestUtil::MultiIndexAsString( + IndexUtil::LinearIndexToMultidimensionalIndex( + actual.shape(), last_linear_index_)) << "\ntotal absolute error " << abs_diff_sum_ << "\ntotal absolute error of miscompares " << abs_diff_miscompare_sum_ << "\ntotal relative error " @@ -426,10 +439,6 @@ class NearComparator { << "\ntotal relative error of miscompares " << (abs_diff_miscompare_sum_ / abs_expected_miscompare_sum_) << "\nfailure count " << num_miscompares_; - - WriteLiteralToTempFile(expected, "expected"); - WriteLiteralToTempFile(actual, "actual"); - WriteLiteralToTempFile(miscompares_, "miscompares"); } return num_miscompares_ == 0; } @@ -457,57 +466,93 @@ class NearComparator { return true; } - float abs_diff = std::abs(actual - expected); - float rel_err = abs_diff / std::abs(expected); + const float abs_diff = std::abs(actual - expected); + const float rel_err = abs_diff / std::abs(expected); + const bool nan_mismatch = NanMismatch(expected, actual); + const bool mismatch = + (nan_mismatch || (abs_diff >= error_.abs && rel_err >= error_.rel)); + return !mismatch; + } + + // Assumes that expected vs actual fail ExpectValuesNear. + template + void UpdateAndLogMiscompares(const NativeT expected, const NativeT actual, + const Shape& shape, const int64 linear_index) { + const float abs_diff = std::abs(actual - expected); + const float rel_err = abs_diff / std::abs(expected); abs_diff_sum_ += abs_diff; abs_expected_sum_ += std::abs(expected); if (rel_err > max_rel_err_) { max_rel_err_ = rel_err; - max_rel_multi_index_ = multi_index_; + max_rel_linear_index_ = linear_index; } if (abs_diff > max_abs_err_) { max_abs_err_ = abs_diff; - max_abs_multi_index_ = multi_index_; + max_abs_linear_index_ = linear_index; } - VLOG(10) << tensorflow::strings::Printf( - "index %s abs_diff %f rel_err %f", - LiteralTestUtil::MultiIndexAsString(multi_index_).c_str(), abs_diff, - rel_err); - bool nan_mismatch = NanMismatch(expected, actual); - bool mismatch = - (nan_mismatch || (abs_diff >= error_.abs && rel_err >= error_.rel)); - if (mismatch) { - abs_diff_miscompare_sum_ += abs_diff; - abs_expected_miscompare_sum_ += std::abs(expected); - const int64 kMaxFailures = 2; - if (num_miscompares_ < kMaxFailures) { - ::testing::Message msg; - msg << "mismatch at index " - << LiteralTestUtil::MultiIndexAsString(multi_index_) << " abs diff " - << abs_diff << " rel err " << rel_err << " failure #" - << num_miscompares_; - ExpectNear(expected, actual, msg); - } else if (num_miscompares_ == kMaxFailures) { - LOG(ERROR) - << "reached max 'loud' failure count; silently proceeding..."; - } - if (num_miscompares_ == 0) { - first_multi_index_ = multi_index_; - } - num_miscompares_++; - last_multi_index_ = multi_index_; + if (VLOG_IS_ON(10)) { + VLOG(10) << tensorflow::strings::Printf( + "index %s abs_diff %f rel_err %f", + LiteralTestUtil::MultiIndexAsString( + IndexUtil::LinearIndexToMultidimensionalIndex(shape, + linear_index)) + .c_str(), + abs_diff, rel_err); } - return !mismatch; + abs_diff_miscompare_sum_ += abs_diff; + abs_expected_miscompare_sum_ += std::abs(expected); + const int64 kMaxFailures = 2; + if (num_miscompares_ < kMaxFailures) { + const auto multi_index = + IndexUtil::LinearIndexToMultidimensionalIndex(shape, linear_index); + ::testing::Message msg; + msg << "mismatch at index " + << LiteralTestUtil::MultiIndexAsString(multi_index) << " abs diff " + << abs_diff << " rel err " << rel_err << " failure #" + << num_miscompares_; + ExpectNear(expected, actual, msg); + } else if (num_miscompares_ == kMaxFailures) { + LOG(ERROR) << "reached max 'loud' failure count; silently proceeding..."; + } + if (num_miscompares_ == 0) { + first_linear_index_ = linear_index; + } + num_miscompares_++; + last_linear_index_ = linear_index; + miscompares_.data()[linear_index] = true; } // Recursive function which compares the two given literals elementwise. template void ExpectLiteralsNear(const Literal& expected, const Literal& actual, int64 dimension) { + // Fast path optimization for the case were layouts match. + if (LayoutUtil::Equal(actual.shape().layout(), expected.shape().layout())) { + tensorflow::gtl::ArraySlice expected_data = + expected.data(); + tensorflow::gtl::ArraySlice actual_data = + actual.data(); + const int64 len = expected_data.size(); + for (int64 i = 0; i < len; ++i) { + const bool near = ExpectValuesNear(expected_data[i], actual_data[i]); + if (!near) { + UpdateAndLogMiscompares(expected_data[i], actual_data[i], + actual.shape(), i); + } + } + return; + } + if (dimension == expected.shape().dimensions_size()) { bool near = ExpectValuesNear(expected.Get(multi_index_), actual.Get(multi_index_)); - miscompares_.Set(multi_index_, !near); + if (!near) { + UpdateAndLogMiscompares( + expected.Get(multi_index_), + actual.Get(multi_index_), actual.shape(), + IndexUtil::MultidimensionalIndexToLinearIndex(actual.shape(), + multi_index_)); + } } else { for (int64 i = 0; i < expected.shape().dimensions(dimension); ++i) { multi_index_[dimension] = i; @@ -528,6 +573,32 @@ class NearComparator { LOG(ERROR) << "wrote to " << name << " file: " << filename; } + // Gets the total element count. For tuples, this is not the count of tuple + // elements, but the sum of elements of each tuple element. + int64 RecursiveElementCount(const Shape& shape) { + if (ShapeUtil::IsTuple(shape)) { + const int64 tuple_elements = ShapeUtil::TupleElementCount(shape); + int64 total = 0; + for (int64 i = 0; i < tuple_elements; ++i) { + total += + RecursiveElementCount(ShapeUtil::GetTupleElementShape(shape, i)); + } + return total; + } else { + return ShapeUtil::ElementsIn(shape); + } + } + + // Calling ToString on a literal with over 100 million elements takes around + // 3 minutes. The utility of printing a literal with >1000 elements is + // questionable, especially when writing the Literal proto to disk is orders + // of magnitude faster. + string TruncateHugeLiteral(const Literal& literal) { + return RecursiveElementCount(literal.shape()) < 1000 + ? literal.ToString() + : "[TRUNCATED, Literal with more than 1000 values]"; + } + ErrorSpec error_; // Number of element miscomparisons encountered so far. @@ -548,10 +619,10 @@ class NearComparator { double abs_expected_miscompare_sum_; float max_rel_err_; float max_abs_err_; - std::vector first_multi_index_; - std::vector last_multi_index_; - std::vector max_rel_multi_index_; - std::vector max_abs_multi_index_; + int64 first_linear_index_; + int64 last_linear_index_; + int64 max_rel_linear_index_; + int64 max_abs_linear_index_; }; template <> @@ -584,6 +655,23 @@ bool NearComparator::ExpectValuesNear(half expected, half actual) { static_cast(std::move(actual))); } +template <> +void NearComparator::UpdateAndLogMiscompares( + const bfloat16 expected, const bfloat16 actual, const Shape& shape, + const int64 linear_index) { + UpdateAndLogMiscompares(static_cast(expected), + static_cast(actual), shape, linear_index); +} + +template <> +void NearComparator::UpdateAndLogMiscompares(half expected, half actual, + const Shape& shape, + const int64 linear_index) { + UpdateAndLogMiscompares(static_cast(std::move(expected)), + static_cast(std::move(actual)), shape, + linear_index); +} + } // namespace /* static */ ::testing::AssertionResult LiteralTestUtil::Near( -- GitLab From e4a628adf84a2373d773103cdeabc96cbffd7b47 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Fri, 26 Jan 2018 16:43:30 -0700 Subject: [PATCH 161/423] Making MKL-DNN default build choice (#16474) --- tensorflow/core/graph/mkl_layout_pass.cc | 6 ++-- tensorflow/core/graph/mkl_layout_pass_test.cc | 6 ++-- tensorflow/core/kernels/mkl_aggregate_ops.cc | 6 ++-- tensorflow/core/kernels/mkl_avgpooling_op.cc | 16 +++++++--- tensorflow/core/kernels/mkl_concat_op.cc | 6 ++-- .../core/kernels/mkl_conv_grad_filter_ops.cc | 6 ++-- .../core/kernels/mkl_conv_grad_input_ops.cc | 6 ++-- tensorflow/core/kernels/mkl_conv_ops.cc | 9 ++++-- tensorflow/core/kernels/mkl_conv_ops.h | 6 ++-- .../core/kernels/mkl_fused_batch_norm_op.cc | 6 ++-- tensorflow/core/kernels/mkl_identity_op.cc | 4 +-- .../core/kernels/mkl_input_conversion_op.cc | 4 +-- tensorflow/core/kernels/mkl_lrn_op.cc | 6 ++-- tensorflow/core/kernels/mkl_maxpooling_op.cc | 10 +++--- .../core/kernels/mkl_pooling_ops_common.cc | 6 ++-- .../core/kernels/mkl_pooling_ops_common.h | 8 ++--- tensorflow/core/kernels/mkl_relu_op.cc | 11 ++++--- tensorflow/core/kernels/mkl_reshape_op.cc | 6 ++-- tensorflow/core/kernels/mkl_softmax_op.cc | 4 +-- tensorflow/core/kernels/mkl_tfconv_op.h | 4 +-- tensorflow/core/ops/nn_ops.cc | 8 ++--- tensorflow/core/util/mkl_util.h | 32 +++++++++---------- tensorflow/core/util/mkl_util_test.cc | 4 +-- 23 files changed, 96 insertions(+), 84 deletions(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 55bc401b9d..911d931a52 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -42,7 +42,7 @@ limitations under the License. namespace tensorflow { -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // This pass implements rewriting of graph to support following scenarios: // (A) Merging nodes in the graph @@ -2215,7 +2215,7 @@ Status MklLayoutRewritePass::Run( return Status::OK(); } -#else // INTEL_MKL_DNN +#else // INTEL_MKL_ML // This pass implements rewriting of graph to support following scenarios: // (A) Merging nodes in the graph @@ -4325,7 +4325,7 @@ Status MklLayoutRewritePass::Run( return Status::OK(); } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow #endif diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 75f7ca2d4d..7b8c3cccc5 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -38,7 +38,7 @@ limitations under the License. namespace tensorflow { -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML namespace { @@ -1885,7 +1885,7 @@ BENCHMARK(BM_MklLayoutRewritePass)->Arg(1000)->Arg(10000); } // namespace -#else // INTEL_MKL_DNN +#else // INTEL_MKL_ML namespace { @@ -3503,7 +3503,7 @@ BENCHMARK(BM_MklLayoutRewritePass)->Arg(1000)->Arg(10000); } // namespace -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow diff --git a/tensorflow/core/kernels/mkl_aggregate_ops.cc b/tensorflow/core/kernels/mkl_aggregate_ops.cc index 89d37d2f87..49c34fed02 100644 --- a/tensorflow/core/kernels/mkl_aggregate_ops.cc +++ b/tensorflow/core/kernels/mkl_aggregate_ops.cc @@ -28,7 +28,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::stream; using mkldnn::sum; @@ -37,7 +37,7 @@ using mkldnn::sum; namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklAddNOp : public OpKernel { @@ -285,7 +285,7 @@ class MklAddNOp : public OpKernel { } MklAddNOpContext; }; -#else // INTEL_MKL_DNN +#else // INTEL_MKL_ML template class MklAddNOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/mkl_avgpooling_op.cc b/tensorflow/core/kernels/mkl_avgpooling_op.cc index a7c569ee05..ebaa0f4e2a 100644 --- a/tensorflow/core/kernels/mkl_avgpooling_op.cc +++ b/tensorflow/core/kernels/mkl_avgpooling_op.cc @@ -24,7 +24,7 @@ #include "tensorflow/core/kernels/mkl_pooling_ops_common.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::algorithm; using mkldnn::engine; @@ -40,8 +40,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -// For now, MKL-ML is default. So making MKL-DNN not a default choice. -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklAvgPoolingOp : public OpKernel { @@ -429,7 +428,10 @@ class MklAvgPoolingGradOp : public OpKernel { TensorFormat data_format_; }; // MklAvgPoolingGradOp -#else // INTEL_MKL_DNN is defined + + +#else + template class MklAvgPoolingOp : public MklPoolingForwardOpBase { @@ -678,7 +680,11 @@ class MklAvgPoolingGradOp : public MklPoolingBackwardOpBase { } }; // MklAvgPoolingGradOp -#endif // INTEL_MKL_DNN + + + +#endif // INTEL_MKL_ML + REGISTER_KERNEL_BUILDER(Name("_MklAvgPool") .Device(DEVICE_CPU) diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index 7da63604d2..f1f267e849 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -30,7 +30,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::concat; @@ -62,7 +62,7 @@ class EigenConcatBaseOp : public OpKernel { // we need to have empty Compute because Compute is pure virtual function. void Compute(OpKernelContext* c) {} -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML void Compute(OpKernelContext* c, const std::vector& values) { const Tensor* concat_dim_tensor; @@ -230,7 +230,7 @@ class EigenConcatBaseOp : public OpKernel { #endif }; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // -------------------------------------------------------------------------- // Mkl Concat Op diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc index ef3f8cfec1..1401bc65a4 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc @@ -42,7 +42,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::convolution_backward_weights; @@ -55,7 +55,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklConv2DCustomBackpropFilterOp : public OpKernel { @@ -655,7 +655,7 @@ class MklConv2DCustomBackpropFilterOp TF_CALL_float(REGISTER_MKL_FILTER_KERNELS); #undef REGISTER_MKL_FILTER_KERNELS -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow diff --git a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc index a6745489f4..eeed009531 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc @@ -44,7 +44,7 @@ limitations under the License. #include "tensorflow/core/util/use_cudnn.h" #include "tensorflow/core/util/work_sharder.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::convolution_backward_data; @@ -56,7 +56,7 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklConv2DCustomBackpropInputOp : public OpKernel { @@ -493,7 +493,7 @@ class MklConv2DCustomBackpropInputOp } }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML #define REGISTER_MKL_CPU_KERNELS(T) \ REGISTER_KERNEL_BUILDER(Name("_MklConv2DBackpropInput") \ diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index e44fba754b..cbda12689f 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -41,7 +41,10 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN + + +#ifndef INTEL_MKL_ML + #include "mkldnn.hpp" using mkldnn::prop_kind; @@ -58,8 +61,8 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -// For now, MKL-ML is default. So making MKL-DNN not a default choice. -#ifndef INTEL_MKL_DNN +// MKL-DNN is now default. MKL-ML must be specified explicitly. +#ifdef INTEL_MKL_ML template class MklConv2DOp : public OpKernel { diff --git a/tensorflow/core/kernels/mkl_conv_ops.h b/tensorflow/core/kernels/mkl_conv_ops.h index 8b65eaea0d..9dd88221a8 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.h +++ b/tensorflow/core/kernels/mkl_conv_ops.h @@ -40,7 +40,7 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::prop_kind; @@ -52,7 +52,7 @@ using mkldnn::convolution_forward; namespace tensorflow { -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML class MklDnnConvUtil { protected: @@ -553,7 +553,7 @@ class MklConv2DBackpropCommonOp : public OpKernel { Padding padding_; TensorFormat data_format_; }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML ///////////////////////////////////////////////////////////////////// /// Dummy Mkl op that is just used for operators that are intermediate diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc index 0b6d838e09..8313224d7f 100644 --- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc +++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc @@ -25,7 +25,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::batch_normalization_backward; @@ -41,7 +41,7 @@ using mkldnn::use_scale_shift; namespace tensorflow { using CPUDevice = Eigen::ThreadPoolDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklFusedBatchNormOp : public OpKernel { @@ -683,7 +683,7 @@ class MklFusedBatchNormGradOp : public OpKernel { }; #endif -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML template class MklFusedBatchNormOp : public OpKernel { diff --git a/tensorflow/core/kernels/mkl_identity_op.cc b/tensorflow/core/kernels/mkl_identity_op.cc index 9ee27ee21c..6c027f8e72 100644 --- a/tensorflow/core/kernels/mkl_identity_op.cc +++ b/tensorflow/core/kernels/mkl_identity_op.cc @@ -28,14 +28,14 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" #endif namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklIdentityOp : public OpKernel { diff --git a/tensorflow/core/kernels/mkl_input_conversion_op.cc b/tensorflow/core/kernels/mkl_input_conversion_op.cc index 73d41efce1..4337e4b49e 100644 --- a/tensorflow/core/kernels/mkl_input_conversion_op.cc +++ b/tensorflow/core/kernels/mkl_input_conversion_op.cc @@ -31,7 +31,7 @@ limitations under the License. #include "tensorflow/core/kernels/mkl_tfconv_op.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::stream; @@ -59,7 +59,7 @@ typedef Eigen::ThreadPoolDevice CPUDevice; // convert the TF format input to MKL format /////////////////////////////////////////////////////////// -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklInputConversionOp : public OpKernel { public: diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc index a8b45004b7..5f0a12a1fb 100644 --- a/tensorflow/core/kernels/mkl_lrn_op.cc +++ b/tensorflow/core/kernels/mkl_lrn_op.cc @@ -38,7 +38,7 @@ limitations under the License. #include "tensorflow/core/util/work_sharder.h" #endif -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::lrn_across_channels; using mkldnn::lrn_backward; @@ -67,7 +67,7 @@ void GetBandMatrix(int depth, int depth_radius, } // namespace -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklLRNOp : public OpKernel { @@ -1343,7 +1343,7 @@ class MklLRNGradOp : public OpKernel { float beta_; }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML #define REGISTER_MKL_LRN_CPU(T) \ REGISTER_KERNEL_BUILDER(Name("_MklLRN") \ diff --git a/tensorflow/core/kernels/mkl_maxpooling_op.cc b/tensorflow/core/kernels/mkl_maxpooling_op.cc index 0de27ccd60..14607f26e0 100644 --- a/tensorflow/core/kernels/mkl_maxpooling_op.cc +++ b/tensorflow/core/kernels/mkl_maxpooling_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/padding.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include #include "mkldnn.hpp" using mkldnn::algorithm; @@ -39,8 +39,8 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; -// For now, MKL-ML is default. So making MKL-DNN not a default choice. -#ifndef INTEL_MKL_DNN +// MKL-DNN is now default. MKL-ML must be specified explicitly. +#ifdef INTEL_MKL_ML // An implementation of MaxPooling (forward). template @@ -494,7 +494,7 @@ class MklMaxPoolingGradOp : public OpKernel { bool workspace_enabled_; }; // MklMaxPoolingGradOp -#else // INTEL_MKL_DNN is defined +#else // An implementation of MaxPooling (forward). template @@ -793,7 +793,7 @@ class MklMaxPoolingGradOp : public MklPoolingBackwardOpBase { } }; // MklMaxPoolingGradOp -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML REGISTER_KERNEL_BUILDER(Name("_MklMaxPool") .Device(DEVICE_CPU) diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.cc b/tensorflow/core/kernels/mkl_pooling_ops_common.cc index ef8597b057..5ef6ce2a57 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.cc +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.cc @@ -42,7 +42,7 @@ void MklPoolParameters::Init(OpKernelContext* context, Init(context, ksize, stride, padding, data_format); } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // Initialization for MKL format void MklPoolParameters::Init(OpKernelContext* context, const std::vector& ksize, @@ -72,7 +72,7 @@ void MklPoolParameters::Init(OpKernelContext* context, Init(context, ksize, stride, padding, data_format); } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML // Common Initialization for TensorFlow and MKL formats void MklPoolParameters::Init(OpKernelContext* context, const std::vector& ksize, @@ -107,7 +107,7 @@ void MklPoolParameters::Init(OpKernelContext* context, OP_REQUIRES_OK(context, GetWindowedOutputSizeVerbose( tensor_in_cols, window_cols, col_stride, padding, &out_width, &pad_left, &pad_right)); -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // TF can work with int64, but mkldnn only supports int32 // Fail if the height or width are greater than MAX_INT diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index 880e45ab1e..279167aba2 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/padding.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::memory; using mkldnn::pooling_backward; @@ -85,7 +85,7 @@ struct MklPoolParameters { void Init(OpKernelContext* context, const std::vector& ksize, const std::vector& stride, Padding padding, TensorFormat data_format, const TensorShape& tensor_in_shape); -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML void Init(OpKernelContext* context, const std::vector& ksize, const std::vector& stride, Padding padding, TensorFormat data_format, const MklShape* mkl_in_shape); @@ -102,7 +102,7 @@ struct MklPoolParameters { TensorFormat data_format); }; -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML template class MklPoolingOpBase : public OpKernel { @@ -395,7 +395,7 @@ class MklPoolingBackwardOpBase : public MklPoolingOpBase { return grad_reorder_needed ? target_diff_dst_md : original_input_grad_md; } }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML //------------------------------------------------------------------- // Utility functions diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc index 873aca30ca..0be8355afa 100644 --- a/tensorflow/core/kernels/mkl_relu_op.cc +++ b/tensorflow/core/kernels/mkl_relu_op.cc @@ -28,7 +28,7 @@ limitations under the License. #include "tensorflow/core/platform/default/logging.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::algorithm; @@ -58,7 +58,7 @@ struct MklReluHelpers { } }; -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template class MklReluOp : public OpKernel { @@ -368,7 +368,10 @@ void MklReluGradOp::Compute(OpKernelContext* context) { mkl_context.MklCleanup(); } -#else // INTEL_MKL_DNN + + +#else // INTEL_MKL_ML + template class MklReluOpBase : public OpKernel { @@ -849,7 +852,7 @@ class MklTanhGradOp : public MklReluGradOpBase { MklReluGradOp); TF_CALL_float(REGISTER_RELU_MKL_SUPPORTED_KERNELS_TYPES); -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // register dnn kernels for supported operations and supported types #define REGISTER_ELU_MKL_SUPPORTED_KERNELS_TYPES(type) \ diff --git a/tensorflow/core/kernels/mkl_reshape_op.cc b/tensorflow/core/kernels/mkl_reshape_op.cc index 7d471e1e4c..5dbc4a2709 100644 --- a/tensorflow/core/kernels/mkl_reshape_op.cc +++ b/tensorflow/core/kernels/mkl_reshape_op.cc @@ -28,7 +28,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::stream; #endif @@ -40,7 +40,7 @@ class MklReshapeOp : public OpKernel { public: explicit MklReshapeOp(OpKernelConstruction* context) : OpKernel(context) {} -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML void Compute(OpKernelContext* context) override { const Tensor& input = MklGetInput(context, 0); const Tensor& sizes = MklGetInput(context, 1); @@ -312,7 +312,7 @@ class MklReshapeOp : public OpKernel { } } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML private: const int kInputSlotIdx = 0; diff --git a/tensorflow/core/kernels/mkl_softmax_op.cc b/tensorflow/core/kernels/mkl_softmax_op.cc index c46eabdde1..aceef1e234 100644 --- a/tensorflow/core/kernels/mkl_softmax_op.cc +++ b/tensorflow/core/kernels/mkl_softmax_op.cc @@ -15,7 +15,7 @@ limitations under the License. // See docs in ../ops/nn_ops.cc. #ifdef INTEL_MKL -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/numeric_op.h" @@ -156,5 +156,5 @@ TF_CALL_float(REGISTER_SOFTMAX_MKL_SUPPORTED_KERNELS_TYPES); } // namespace tensorflow -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML #endif // INTEL_MKL diff --git a/tensorflow/core/kernels/mkl_tfconv_op.h b/tensorflow/core/kernels/mkl_tfconv_op.h index c4d5a45d3c..5fafa14b5d 100644 --- a/tensorflow/core/kernels/mkl_tfconv_op.h +++ b/tensorflow/core/kernels/mkl_tfconv_op.h @@ -35,7 +35,7 @@ limitations under the License. #include "mkl_dnn_types.h" #include "tensorflow/core/util/mkl_util.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML using mkldnn::stream; #endif @@ -61,7 +61,7 @@ class MklToTfOp : public OpKernel { VLOG(1) << "MKLToTFConversion complete successfully."; } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML static void ConvertMklToTf(OpKernel* op_kernel, OpKernelContext* context, string data_format_str, DataType op_data_type, bool has_avx512f, uint input_number) { diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index 62661fe4bd..67481fd202 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1818,7 +1818,7 @@ REGISTER_OP("_MklMaxPool") .Input("input: T") .Input("mkl_input: uint8") .Output("output: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Output("workspace: T") #else .Output("workspace: uint8") @@ -1844,7 +1844,7 @@ REGISTER_OP("_MklMaxPoolGrad") .Input("orig_input: T") .Input("orig_output: T") .Input("grad: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Input("workspace: T") #else .Input("workspace: uint8") @@ -1916,7 +1916,7 @@ REGISTER_OP("_MklLRN") .Input("input: T") .Input("mkl_input: uint8") .Output("output: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Output("workspace: T") #else .Output("workspace: uint8") @@ -1944,7 +1944,7 @@ REGISTER_OP("_MklLRNGrad") .Input("input_grads: T") .Input("input_image: T") .Input("output_image: T") -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML .Input("workspace: T") #else .Input("workspace: uint8") diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index 2caf5fc56d..34ef7ba21b 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -35,7 +35,7 @@ limitations under the License. #include "tensorflow/core/util/padding.h" #include "tensorflow/core/util/tensor_format.h" -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML #include "mkldnn.hpp" using mkldnn::engine; @@ -324,7 +324,7 @@ class MklShape { nullptr; // TF dimension corresponding to this MKL dimension }; -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // Forward decl TensorFormat MklDnnDataFormatToTFDataFormat(memory::format format); @@ -659,7 +659,7 @@ class MklDnnShape { typedef std::vector MklShapeList; -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML typedef std::vector MklDnnShapeList; #endif @@ -673,7 +673,7 @@ inline bool AreAllMklTensors(const MklShapeList& shapes) { return true; } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML template inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, const MklShape& mkl_shape) { @@ -724,7 +724,7 @@ inline void GetMklShape(OpKernelContext* ctext, int n, MklShape* mklshape) { sizeof(uint8)); } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML inline void GetMklShape(OpKernelContext* ctext, int n, MklDnnShape* mklshape) { mklshape->DeSerializeMklDnnShape( ctext->input(GetTensorMetaDataIndex(n, ctext->num_inputs())) @@ -749,7 +749,7 @@ inline void GetMklInputList(OpKernelContext* ctext, StringPiece name, } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML inline void GetMklShapeList(OpKernelContext* ctext, StringPiece name, MklShapeList* mkl_shapes) { @@ -779,7 +779,7 @@ inline void GetMklShapeList(OpKernelContext* ctext, StringPiece name, #endif -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML /// Get shape of input tensor pointed by 'input_idx' in TensorShape format. /// If the input tensor is in MKL layout, then obtains TensorShape from /// MklShape. @@ -814,7 +814,7 @@ inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, second_tensor->flat().size() * sizeof(uint8)); } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // Allocate the second output tensor that will contain // the MKL shape serialized inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, @@ -851,7 +851,7 @@ inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, second_tensor->flat().size() * sizeof(uint8)); } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML // Allocate the output tensor, create a second output tensor that will contain // the MKL shape serialized inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, @@ -875,7 +875,7 @@ inline void AllocateOutputSetMklShape(OpKernelContext* ctext, int n, // Allocates a temp tensor and returns the data buffer for temporary storage. // Currently -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML template inline void AllocTmpBuffer(OpKernelContext* context, Tensor* tensor_out, const memory::primitive_desc& pd, void** buf_out) { @@ -994,7 +994,7 @@ inline void CopyMklTensorInToOut(OpKernelContext* context, context->set_output(idx_meta_out, meta_output); } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, int idx_in, int idx_out, const TensorShape& shape) { @@ -1032,7 +1032,7 @@ inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, } #endif -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML inline void ForwardTfTensorInToOut(OpKernelContext* context, int idx_in, int idx_out) { @@ -1090,7 +1090,7 @@ inline void ForwardMklTensorInToOut(OpKernelContext* context, } } -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML inline void ForwardMklTensorInToOutWithMklShape(OpKernelContext* context, int idx_in, int idx_out, const MklDnnShape& mkl_shape) { @@ -1132,7 +1132,7 @@ inline void SetDummyMklShapeOutput(OpKernelContext* context, AllocateOutputSetMklShape(context, idx_data_out, mkl_shape_output); } -#ifndef INTEL_MKL_DNN +#ifdef INTEL_MKL_ML // We don't need these functions in MKLDNN. We have defined equality operator // on MklDnnShape class directly. @@ -1242,7 +1242,7 @@ inline void MklNCHWToNHWC(const Tensor& input, Tensor** output) { // ------------------------------------------------------------------- -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML /// Return MKL-DNN data type (memory::data_type) for input type T /// @@ -1753,7 +1753,7 @@ class MklDnnData { } }; -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace tensorflow #endif // INTEL_MKL diff --git a/tensorflow/core/util/mkl_util_test.cc b/tensorflow/core/util/mkl_util_test.cc index 8b73eadb40..cd1d0713ad 100644 --- a/tensorflow/core/util/mkl_util_test.cc +++ b/tensorflow/core/util/mkl_util_test.cc @@ -22,7 +22,7 @@ limitations under the License. namespace tensorflow { namespace { -#ifdef INTEL_MKL_DNN +#ifndef INTEL_MKL_ML TEST(MklUtilTest, MklDnnTfShape) { auto cpu_engine = engine(engine::cpu, 0); @@ -84,7 +84,7 @@ TEST(MklUtilTest, MklDnnBlockedFormatTest) { EXPECT_EQ(b_md2.data.format, mkldnn_blocked); } -#endif // INTEL_MKL_DNN +#endif // INTEL_MKL_ML } // namespace } // namespace tensorflow -- GitLab From 90033a0c87bb3d289144cbd429a9067a4a4881d6 Mon Sep 17 00:00:00 2001 From: cclauss Date: Sat, 27 Jan 2018 00:43:48 +0100 Subject: [PATCH 162/423] Placate pylint on jupyter_notebook_config.py (#16449) --- tensorflow/tools/docker/jupyter_notebook_config.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/tools/docker/jupyter_notebook_config.py b/tensorflow/tools/docker/jupyter_notebook_config.py index 0acbf6fcee..05dcefb099 100644 --- a/tensorflow/tools/docker/jupyter_notebook_config.py +++ b/tensorflow/tools/docker/jupyter_notebook_config.py @@ -15,6 +15,7 @@ import os from IPython.lib import passwd +c = c # pylint:disable=undefined-variable c.NotebookApp.ip = '*' c.NotebookApp.port = int(os.getenv('PORT', 8888)) c.NotebookApp.open_browser = False -- GitLab From 1d1a50e3a5f0e297e6d4d480cf28ca5be51d7c73 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 26 Jan 2018 15:54:38 -0800 Subject: [PATCH 163/423] [XLA] (Re-land) Add HLO matcher for CustomCall that accepts a call target. Now with less build breakage! PiperOrigin-RevId: 183458987 --- .../compiler/xla/service/hlo_matchers.cc | 30 +++++++++++ .../compiler/xla/service/hlo_matchers.h | 53 +++++++++++++++++-- .../compiler/xla/service/hlo_matchers_test.cc | 33 ++++++++++++ 3 files changed, 113 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_matchers.cc b/tensorflow/compiler/xla/service/hlo_matchers.cc index 4255d60866..bc74c4bc10 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers.cc @@ -102,6 +102,36 @@ bool HloGetTupleElementMatcher::MatchAndExplain( return true; } +void HloCustomCallMatcher::DescribeTo(std::ostream* os) const { + HloMatcher::DescribeTo(os); + *os << " with call target that "; + call_target_matcher_.DescribeTo(os); +} + +bool HloCustomCallMatcher::MatchAndExplain( + const HloInstruction* instruction, + ::testing::MatchResultListener* listener) const { + if (!HloMatcher::MatchAndExplain(instruction, listener)) { + return false; + } + ::testing::StringMatchResultListener sub_listener; + bool result = ExplainMatchResult( + call_target_matcher_, instruction->custom_call_target(), &sub_listener); + if (sub_listener.str().empty()) { + sub_listener << " that "; + + std::stringstream desc_stream; + if (result) { + call_target_matcher_.DescribeTo(&desc_stream); + } else { + call_target_matcher_.DescribeNegationTo(&desc_stream); + } + sub_listener << desc_stream.str(); + } + *listener << "custom-call with call target" << sub_listener.str(); + return result; +} + } // namespace testing void PrintTo(const HloInstruction* inst, ::std::ostream* os) { diff --git a/tensorflow/compiler/xla/service/hlo_matchers.h b/tensorflow/compiler/xla/service/hlo_matchers.h index 9206cdac05..103f04a2cb 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers.h +++ b/tensorflow/compiler/xla/service/hlo_matchers.h @@ -56,8 +56,8 @@ class HloParameterMatcher : public HloMatcher { // index to match. class HloGetTupleElementMatcher : public HloMatcher { public: - explicit HloGetTupleElementMatcher( - ::testing::Matcher operand, int64 tuple_index) + HloGetTupleElementMatcher(::testing::Matcher operand, + int64 tuple_index) : HloMatcher(HloOpcode::kGetTupleElement, /*operands=*/{operand}), tuple_index_(tuple_index) {} @@ -68,6 +68,24 @@ class HloGetTupleElementMatcher : public HloMatcher { int64 tuple_index_; }; +// Custom matcher for custom-call instructions, which accepts a matcher for its +// call target. +class HloCustomCallMatcher : public HloMatcher { + public: + HloCustomCallMatcher( + ::testing::Matcher call_target_matcher, + std::vector<::testing::Matcher> operands) + : HloMatcher(HloOpcode::kCustomCall, operands), + call_target_matcher_(call_target_matcher) {} + + bool MatchAndExplain(const HloInstruction* instruction, + ::testing::MatchResultListener* listener) const override; + void DescribeTo(std::ostream* os) const override; + + private: + ::testing::Matcher call_target_matcher_; +}; + // HloInstruction* matchers for opcode and operands. Example: // namespace op = xla::opcode_matchers; // EXPECT_THAT(instruction, @@ -94,7 +112,6 @@ HLO_MATCHER(Convert); HLO_MATCHER(Convolution); HLO_MATCHER(Copy); HLO_MATCHER(CrossReplicaSum); -HLO_MATCHER(CustomCall); HLO_MATCHER(Divide); HLO_MATCHER(Dot); HLO_MATCHER(DynamicSlice); @@ -184,6 +201,36 @@ inline ::testing::Matcher GetTupleElement() { new ::xla::testing::HloMatcher(HloOpcode::kGetTupleElement, {})); } +// - CustomCall(T, operand1, ..., operandN) matches a CustomCall with call +// target T and the given operands. +// +// - CustomCall(operand1, ..., operandN) matches any CustomCall HLO with the +// given operands. +// +// - CustomCall() matches any CustomCall HLO at all. +template +inline ::testing::Matcher CustomCall( + ::testing::Matcher call_target_matcher, M... operands) { + return ::testing::MakeMatcher(new ::xla::testing::HloCustomCallMatcher( + call_target_matcher, {operands...})); +} +// This overload of CustomCall(A, B, C, ...) exists iff A is not convertible to +// ::testing::Matcher. In that case, we want to prefer the overload +// above. +template >::value, + void>::type*> +inline ::testing::Matcher CustomCall( + FirstM operands_first, M... operands_rest) { + return ::testing::MakeMatcher(new ::xla::testing::HloMatcher( + HloOpcode::kCustomCall, {operands_first, operands_rest...})); +} +inline ::testing::Matcher CustomCall() { + return ::testing::MakeMatcher( + new ::xla::testing::HloMatcher(HloOpcode::kCustomCall, {})); +} + #undef HLO_MATCHER } // namespace opcode_matchers diff --git a/tensorflow/compiler/xla/service/hlo_matchers_test.cc b/tensorflow/compiler/xla/service/hlo_matchers_test.cc index 1465d1cacd..1c21703a45 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers_test.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers_test.cc @@ -23,6 +23,12 @@ using ::testing::Eq; namespace xla { namespace { +string DescribeHloMatcher(const ::testing::Matcher& m) { + std::stringstream ss; + m.DescribeTo(&ss); + return ss.str(); +} + template string Explain(const T& t, const M& m) { ::testing::StringMatchResultListener listener; @@ -67,5 +73,32 @@ TEST(HloMatchersTest, Test) { "add")); } +TEST(HloMatchersTest, CustomCallMatcher) { + auto c1 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); + auto c2 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); + auto call = HloInstruction::CreateCustomCall( + ShapeUtil::MakeShape(F32, {1}), {c1.get(), c2.get()}, "foo_target"); + + EXPECT_THAT(call.get(), op::CustomCall()); + EXPECT_THAT(call.get(), op::CustomCall(c1.get(), c2.get())); + EXPECT_THAT(call.get(), op::CustomCall("foo_target")); + EXPECT_THAT(call.get(), op::CustomCall("foo_target", c1.get(), c2.get())); + EXPECT_THAT(call.get(), op::CustomCall(::testing::StartsWith("foo"))); + EXPECT_THAT(call.get(), + op::CustomCall(::testing::Not(::testing::StartsWith("bar")))); + + // Wrong number of operands. + EXPECT_THAT(call.get(), ::testing::Not(op::CustomCall(c1.get()))); + + // Call target does not match. + EXPECT_THAT(call.get(), + ::testing::Not(op::CustomCall(::testing::StartsWith("bar")))); + + EXPECT_THAT(Explain(call.get(), op::CustomCall("bar")), + R"(custom-call with call target that isn't equal to "bar")"); + EXPECT_THAT(DescribeHloMatcher(op::CustomCall("foo_target")), + R"(custom-call with call target that is equal to "foo_target")"); +} + } // namespace } // namespace xla -- GitLab From e9dc418b4bafa359654fd66c72b5f4ba371b68db Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 16:23:40 -0800 Subject: [PATCH 164/423] [XLA] Don't print "{no layout}" if there is no layout. PiperOrigin-RevId: 183463264 --- tensorflow/compiler/xla/shape_util.cc | 2 -- 1 file changed, 2 deletions(-) diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index cba73322fa..d63e16ce2b 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -475,8 +475,6 @@ StatusOr StringToPrimitiveType(const string& name) { if (LayoutUtil::HasLayout(shape)) { tensorflow::strings::StrAppend(&result, LayoutUtil::HumanString(shape.layout())); - } else { - tensorflow::strings::StrAppend(&result, "{no layout}"); } } return result; -- GitLab From a977a77299f292e556ace48c75251a5a11d118ff Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 16:36:55 -0800 Subject: [PATCH 165/423] Add bidirectional sequence RNN to TFLite Ops. PiperOrigin-RevId: 183465032 --- tensorflow/contrib/lite/kernels/BUILD | 13 + .../kernels/bidirectional_sequence_rnn.cc | 249 +++++ .../bidirectional_sequence_rnn_test.cc | 931 ++++++++++++++++++ tensorflow/contrib/lite/kernels/register.cc | 3 + tensorflow/contrib/lite/model.cc | 1 + tensorflow/contrib/lite/nnapi_delegate.cc | 1 + tensorflow/contrib/lite/schema/schema.fbs | 7 + 7 files changed, 1205 insertions(+) create mode 100644 tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc create mode 100644 tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index b5428d3246..d9051f3516 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -104,6 +104,7 @@ cc_library( "add.cc", "basic_rnn.cc", "batch_to_space_nd.cc", + "bidirectional_sequence_rnn.cc", "concatenation.cc", "conv.cc", "depthwise_conv.cc", @@ -288,6 +289,18 @@ tf_cc_test( ], ) +tf_cc_test( + name = "bidirectional_sequence_rnn_test", + size = "small", + srcs = ["bidirectional_sequence_rnn_test.cc"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + tf_cc_test( name = "unidirectional_sequence_rnn_test", size = "small", diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc new file mode 100644 index 0000000000..f540816235 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc @@ -0,0 +1,249 @@ +/* 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 +#include +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/activation_functor.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace bidirectional_sequence_rnn { + +constexpr int kInputTensor = 0; +// Forward and backward cell tensors. +constexpr int kFwWeightsTensor = 1; +constexpr int kFwRecurrentWeightsTensor = 2; +constexpr int kFwBiasTensor = 3; +constexpr int kBwWeightsTensor = 4; +constexpr int kBwRecurrentWeightsTensor = 5; +constexpr int kBwBiasTensor = 6; +// State and output tensors. +constexpr int kFwHiddenStateTensor = 0; +constexpr int kFwOutputTensor = 1; +constexpr int kBwHiddenStateTensor = 2; +constexpr int kBwOutputTensor = 3; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + // Check we have all the inputs and outputs we need. + TF_LITE_ENSURE_EQ(context, node->inputs->size, 7); + TF_LITE_ENSURE_EQ(context, node->outputs->size, 4); + + TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]]; + TfLiteTensor* fw_input_weights = + &context->tensors[node->inputs->data[kFwWeightsTensor]]; + TfLiteTensor* fw_recurrent_weights = + &context->tensors[node->inputs->data[kFwRecurrentWeightsTensor]]; + TfLiteTensor* fw_bias = &context->tensors[node->inputs->data[kFwBiasTensor]]; + TfLiteTensor* bw_input_weights = + &context->tensors[node->inputs->data[kBwWeightsTensor]]; + TfLiteTensor* bw_recurrent_weights = + &context->tensors[node->inputs->data[kBwRecurrentWeightsTensor]]; + TfLiteTensor* bw_bias = &context->tensors[node->inputs->data[kBwBiasTensor]]; + + // Check all the parameters of tensor match within themselves and match the + // input configuration. + const int batch_size = input->dims->data[0]; + const int max_time = input->dims->data[1]; + const int fw_num_units = fw_input_weights->dims->data[0]; + const int bw_num_units = bw_input_weights->dims->data[0]; + TF_LITE_ASSERT_EQ(input->dims->data[2], fw_input_weights->dims->data[1]); + TF_LITE_ASSERT_EQ(input->dims->data[2], bw_input_weights->dims->data[1]); + TF_LITE_ASSERT_EQ(fw_input_weights->dims->data[0], fw_bias->dims->data[0]); + TF_LITE_ASSERT_EQ(bw_input_weights->dims->data[0], bw_bias->dims->data[0]); + TF_LITE_ASSERT_EQ(fw_recurrent_weights->dims->data[0], + fw_bias->dims->data[0]); + TF_LITE_ASSERT_EQ(bw_recurrent_weights->dims->data[1], + bw_bias->dims->data[0]); + + TfLiteTensor* fw_output = + &context->tensors[node->outputs->data[kFwOutputTensor]]; + TfLiteTensor* bw_output = + &context->tensors[node->outputs->data[kBwOutputTensor]]; + + // Resize hidden states. + TfLiteIntArray* fw_hidden_state_size_array = TfLiteIntArrayCreate(2); + fw_hidden_state_size_array->data[0] = batch_size; + fw_hidden_state_size_array->data[1] = fw_num_units; + TfLiteTensor* fw_hidden_state = + &context->tensors[node->outputs->data[kFwHiddenStateTensor]]; + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_hidden_state, + fw_hidden_state_size_array)); + + TfLiteIntArray* bw_hidden_state_size_array = TfLiteIntArrayCreate(2); + bw_hidden_state_size_array->data[0] = batch_size; + bw_hidden_state_size_array->data[1] = fw_num_units; + TfLiteTensor* bw_hidden_state = + &context->tensors[node->outputs->data[kBwHiddenStateTensor]]; + TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_hidden_state, + bw_hidden_state_size_array)); + + // Mark hidden states as a persistent tensor. + fw_hidden_state->allocation_type = kTfLiteArenaRwPersistent; + bw_hidden_state->allocation_type = kTfLiteArenaRwPersistent; + + // Resize outputs. + TfLiteIntArray* fw_output_size_array = TfLiteIntArrayCreate(3); + fw_output_size_array->data[0] = batch_size; + fw_output_size_array->data[1] = max_time; + fw_output_size_array->data[2] = fw_num_units; + TF_LITE_ENSURE_OK( + context, context->ResizeTensor(context, fw_output, fw_output_size_array)); + TfLiteIntArray* bw_output_size_array = TfLiteIntArrayCreate(3); + bw_output_size_array->data[0] = batch_size; + bw_output_size_array->data[1] = max_time; + bw_output_size_array->data[2] = bw_num_units; + TF_LITE_ENSURE_OK( + context, context->ResizeTensor(context, bw_output, bw_output_size_array)); + + return kTfLiteOk; +} + +namespace { +// Performs one RNN computation step for the input specified by input_ptr_batch. +// The RNN cell is specified by the pointers to its weights and biases, along +// with the input size, number of units, strides, activation. +// The pointers to the hidden state and the output are updated as a result. +// TODO(mirkov): factor out this function to a shared library. +void RnnStep(const float* input_ptr_batch, const float* input_weights_ptr, + const float* recurrent_weights_ptr, const float* bias_ptr, + int input_size, int num_units, int input_weights_stride, + int recurrent_weights_stride, TfLiteFusedActivation activation, + float* hidden_state_ptr_batch, float* output_ptr_batch) { + // Output = bias + for (int o = 0; o < num_units; o++) { + output_ptr_batch[o] = bias_ptr[o]; + } + + // Output += input * input_weights + for (int o = 0; o < num_units; o++) { + for (int i = 0; i < input_size; i++) { + output_ptr_batch[o] += input_ptr_batch[i] * input_weights_ptr[i]; + } + input_weights_ptr += input_weights_stride; + } + + // Output += recurrent_weights * hidden_state + for (int o = 0; o < num_units; o++) { + for (int h = 0; h < num_units; h++) { + output_ptr_batch[o] += + hidden_state_ptr_batch[h] * recurrent_weights_ptr[h]; + } + recurrent_weights_ptr += recurrent_weights_stride; + } + + // Output = activation(Output) and update hidden_state + for (int o = 0; o < num_units; o++) { + output_ptr_batch[o] = (ActivationFunctor(activation))(output_ptr_batch[o]); + hidden_state_ptr_batch[o] = output_ptr_batch[o]; + } +} +} // namespace + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); + + TfLiteTensor* input = &context->tensors[node->inputs->data[kInputTensor]]; + TfLiteTensor* fw_input_weights = + &context->tensors[node->inputs->data[kFwWeightsTensor]]; + TfLiteTensor* fw_recurrent_weights = + &context->tensors[node->inputs->data[kFwRecurrentWeightsTensor]]; + TfLiteTensor* fw_bias = &context->tensors[node->inputs->data[kFwBiasTensor]]; + TfLiteTensor* fw_hidden_state = + &context->tensors[node->outputs->data[kFwHiddenStateTensor]]; + TfLiteTensor* fw_output = + &context->tensors[node->outputs->data[kFwOutputTensor]]; + + TfLiteTensor* bw_input_weights = + &context->tensors[node->inputs->data[kBwWeightsTensor]]; + TfLiteTensor* bw_recurrent_weights = + &context->tensors[node->inputs->data[kBwRecurrentWeightsTensor]]; + TfLiteTensor* bw_bias = &context->tensors[node->inputs->data[kBwBiasTensor]]; + TfLiteTensor* bw_hidden_state = + &context->tensors[node->outputs->data[kBwHiddenStateTensor]]; + TfLiteTensor* bw_output = + &context->tensors[node->outputs->data[kBwOutputTensor]]; + + const int batch_size = input->dims->data[0]; + const int max_time = input->dims->data[1]; + const int input_size = input->dims->data[2]; + + const int fw_num_units = fw_input_weights->dims->data[0]; + const int fw_input_weights_stride = fw_input_weights->dims->data[1]; + const int fw_recurrent_weights_stride = fw_recurrent_weights->dims->data[1]; + const float* fw_bias_ptr = fw_bias->data.f; + const float* fw_input_weights_ptr = fw_input_weights->data.f; + const float* fw_recurrent_weights_ptr = fw_recurrent_weights->data.f; + + const int bw_num_units = bw_input_weights->dims->data[0]; + const int bw_input_weights_stride = bw_input_weights->dims->data[1]; + const int bw_recurrent_weights_stride = bw_recurrent_weights->dims->data[1]; + const float* bw_bias_ptr = bw_bias->data.f; + const float* bw_input_weights_ptr = bw_input_weights->data.f; + const float* bw_recurrent_weights_ptr = bw_recurrent_weights->data.f; + + for (int b = 0; b < batch_size; b++) { + // Forward cell. + float* fw_hidden_state_ptr_batch = + fw_hidden_state->data.f + b * fw_num_units; + for (int s = 0; s < max_time; s++) { + const float* input_ptr_batch = + input->data.f + b * input_size * max_time + s * input_size; + float* output_ptr_batch = + fw_output->data.f + b * fw_num_units * max_time + s * fw_num_units; + + RnnStep(input_ptr_batch, fw_input_weights_ptr, fw_recurrent_weights_ptr, + fw_bias_ptr, input_size, fw_num_units, fw_input_weights_stride, + fw_recurrent_weights_stride, params->activation, + fw_hidden_state_ptr_batch, output_ptr_batch); + } + // Backward cell. + float* bw_hidden_state_ptr_batch = + bw_hidden_state->data.f + b * bw_num_units; + for (int s = max_time - 1; s >= 0; s--) { + const float* input_ptr_batch = + input->data.f + b * input_size * max_time + s * input_size; + float* output_ptr_batch = + bw_output->data.f + b * bw_num_units * max_time + s * bw_num_units; + + RnnStep(input_ptr_batch, bw_input_weights_ptr, bw_recurrent_weights_ptr, + bw_bias_ptr, input_size, bw_num_units, bw_input_weights_stride, + bw_recurrent_weights_stride, params->activation, + bw_hidden_state_ptr_batch, output_ptr_batch); + } + } + return kTfLiteOk; +} + +} // namespace bidirectional_sequence_rnn + +TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_RNN() { + static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr, + bidirectional_sequence_rnn::Prepare, + bidirectional_sequence_rnn::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc new file mode 100644 index 0000000000..12f4ff97cf --- /dev/null +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn_test.cc @@ -0,0 +1,931 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +// Unit test for TFLite Bidirectional RNN op. + +#include +#include + +#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; + +static float rnn_input[] = { + 0.23689353, 0.285385, 0.037029743, -0.19858193, -0.27569133, + 0.43773448, 0.60379338, 0.35562468, -0.69424844, -0.93421471, + -0.87287879, 0.37144363, -0.62476718, 0.23791671, 0.40060222, + 0.1356622, -0.99774903, -0.98858172, -0.38952237, -0.47685933, + 0.31073618, 0.71511042, -0.63767755, -0.31729108, 0.33468103, + 0.75801885, 0.30660987, -0.37354088, 0.77002847, -0.62747043, + -0.68572164, 0.0069220066, 0.65791464, 0.35130811, 0.80834007, + -0.61777675, -0.21095741, 0.41213346, 0.73784804, 0.094794154, + 0.47791874, 0.86496925, -0.53376222, 0.85315156, 0.10288584, + 0.86684, -0.011186242, 0.10513687, 0.87825835, 0.59929144, + 0.62827742, 0.18899453, 0.31440187, 0.99059987, 0.87170351, + -0.35091716, 0.74861872, 0.17831337, 0.2755419, 0.51864719, + 0.55084288, 0.58982027, -0.47443086, 0.20875752, -0.058871567, + -0.66609079, 0.59098077, 0.73017097, 0.74604273, 0.32882881, + -0.17503482, 0.22396147, 0.19379807, 0.29120302, 0.077113032, + -0.70331609, 0.15804303, -0.93407321, 0.40182066, 0.036301374, + 0.66521823, 0.0300982, -0.7747041, -0.02038002, 0.020698071, + -0.90300065, 0.62870288, -0.23068321, 0.27531278, -0.095755219, + -0.712036, -0.17384434, -0.50593495, -0.18646687, -0.96508682, + 0.43519354, 0.14744234, 0.62589407, 0.1653645, -0.10651493, + -0.045277178, 0.99032974, -0.88255352, -0.85147917, 0.28153265, + 0.19455957, -0.55479527, -0.56042433, 0.26048636, 0.84702539, + 0.47587705, -0.074295521, -0.12287641, 0.70117295, 0.90532446, + 0.89782166, 0.79817224, 0.53402734, -0.33286154, 0.073485017, + -0.56172788, -0.044897556, 0.89964068, -0.067662835, 0.76863563, + 0.93455386, -0.6324693, -0.083922029}; + +static float rnn_golden_fw_output[] = { + 0.496726, 0, 0.965996, 0, 0.0584254, 0, + 0, 0.12315, 0, 0, 0.612266, 0.456601, + 0, 0.52286, 1.16099, 0.0291232, + + 0, 0, 0.524901, 0, 0, 0, + 0, 1.02116, 0, 1.35762, 0, 0.356909, + 0.436415, 0.0355727, 0, 0, + + 0, 0, 0, 0.262335, 0, 0, + 0, 1.33992, 0, 2.9739, 0, 0, + 1.31914, 2.66147, 0, 0, + + 0.942568, 0, 0, 0, 0.025507, 0, + 0, 0, 0.321429, 0.569141, 1.25274, 1.57719, + 0.8158, 1.21805, 0.586239, 0.25427, + + 1.04436, 0, 0.630725, 0, 0.133801, 0.210693, + 0.363026, 0, 0.533426, 0, 1.25926, 0.722707, + 0, 1.22031, 1.30117, 0.495867, + + 0.222187, 0, 0.72725, 0, 0.767003, 0, + 0, 0.147835, 0, 0, 0, 0.608758, + 0.469394, 0.00720298, 0.927537, 0, + + 0.856974, 0.424257, 0, 0, 0.937329, 0, + 0, 0, 0.476425, 0, 0.566017, 0.418462, + 0.141911, 0.996214, 1.13063, 0, + + 0.967899, 0, 0, 0, 0.0831304, 0, + 0, 1.00378, 0, 0, 0, 1.44818, + 1.01768, 0.943891, 0.502745, 0, + + 0.940135, 0, 0, 0, 0, 0, + 0, 2.13243, 0, 0.71208, 0.123918, 1.53907, + 1.30225, 1.59644, 0.70222, 0, + + 0.804329, 0, 0.430576, 0, 0.505872, 0.509603, + 0.343448, 0, 0.107756, 0.614544, 1.44549, 1.52311, + 0.0454298, 0.300267, 0.562784, 0.395095, + + 0.228154, 0, 0.675323, 0, 1.70536, 0.766217, + 0, 0, 0, 0.735363, 0.0759267, 1.91017, + 0.941888, 0, 0, 0, + + 0, 0, 1.5909, 0, 0, 0, + 0, 0.5755, 0, 0.184687, 0, 1.56296, + 0.625285, 0, 0, 0, + + 0, 0, 0.0857888, 0, 0, 0, + 0, 0.488383, 0.252786, 0, 0, 0, + 1.02817, 1.85665, 0, 0, + + 0.00981836, 0, 1.06371, 0, 0, 0, + 0, 0, 0, 0.290445, 0.316406, 0, + 0.304161, 1.25079, 0.0707152, 0, + + 0.986264, 0.309201, 0, 0, 0, 0, + 0, 1.64896, 0.346248, 0, 0.918175, 0.78884, + 0.524981, 1.92076, 2.07013, 0.333244, + + 0.415153, 0.210318, 0, 0, 0, 0, + 0, 2.02616, 0, 0.728256, 0.84183, 0.0907453, + 0.628881, 3.58099, 1.49974, 0}; + +static float rnn_golden_bw_output[] = { + 0.496726, 0, 1.00883, 0, 0.0584256, 0, 0, + 0.236412, 0, 0, 0.612267, 0.487726, 0, 0.54883, + 1.16099, 0.0291233, 0, 0, 0.428302, 0, 0, + 0, 0, 1.13262, 0, 1.64415, 0, 0.311249, + 0.570804, 0.259696, 0, 0, 0, 0, 0, + 0.262334, 0, 0, 0, 1.23781, 0, 2.86532, + 0, 0, 1.34389, 2.76409, 0, 0, 1.03969, + 0, 0.00410865, 0, 0.0470295, 0, 0, 0, + 0.371556, 0.27175, 1.36614, 1.63956, 0.683887, 1.06176, 0.719552, + 0.301314, 0.971195, 0, 0.697143, 0, 0.215219, 0.210693, + 0.363027, 0, 0.501283, 0, 1.13399, 0.623774, 0, + 1.09851, 1.33313, 0.470441, 0.210965, 0, 0.664178, 0, + 0.839686, 0, 0, 0.147834, 0, 0, 0, + 0.58786, 0.490128, 0, 0.905806, 0, 0.932134, 0.424257, + 0, 0, 0.860629, 0, 0, 0, 0.476425, + 0, 0.566017, 0.513721, 0.207341, 1.09508, 1.08385, 0, + 0.973787, 0, 0, 0, 0, 0, 0, + 1.20698, 0, 0, 0, 1.56135, 1.12369, 0.99588, + 0.459803, 0, 0.915854, 0, 0, 0, 0, + 0, 0, 2.03206, 0, 0.773264, 0.267228, 1.55012, + 1.202, 1.51611, 0.701202, 0, 0.725088, 0, 0.509069, + 0, 0.671349, 0.581129, 0.343447, 0, 0.107755, 0.611838, + 1.4331, 1.55871, 0.015242, 0.140624, 0.492562, 0.395095, 0.147722, + 0, 0.784925, 0, 1.65477, 0.715257, 0, 0, + 0, 0.685024, 0, 1.89505, 1.00037, 0, 0, + 0, 0, 0, 1.52659, 0, 0, 0, + 0, 0.618583, 0, 0.11115, 0, 1.37194, 0.630225, + 0, 0, 0, 0, 0, 0.0322124, 0, + 0, 0, 0, 0.430834, 0.252786, 0, 0, + 0, 0.991297, 1.98451, 0, 0, 0.111511, 0, + 1.05513, 0, 0, 0, 0, 0, 0, + 0.290445, 0.412559, 0.0429958, 0.256564, 1.27858, 0.289948, 0, + 1.01693, 0.327141, 0, 0, 0, 0, 0, + 1.83508, 0.346248, 0, 0.961535, 0.790026, 0.552203, 2.13457, + 2.19233, 0.333244, 0.316526, 0.179398, 0, 0, 0, + 0, 0, 1.86126, 0, 0.728256, 0.750013, 0.011861, + 0.576383, 3.38891, 1.29273, 0}; + +constexpr std::initializer_list weights = { + 0.461459, 0.153381, 0.529743, -0.00371218, 0.676267, -0.211346, + 0.317493, 0.969689, -0.343251, 0.186423, 0.398151, 0.152399, + 0.448504, 0.317662, 0.523556, -0.323514, 0.480877, 0.333113, + -0.757714, -0.674487, -0.643585, 0.217766, -0.0251462, 0.79512, + -0.595574, -0.422444, 0.371572, -0.452178, -0.556069, -0.482188, + -0.685456, -0.727851, 0.841829, 0.551535, -0.232336, 0.729158, + -0.00294906, -0.69754, 0.766073, -0.178424, 0.369513, -0.423241, + 0.548547, -0.0152023, -0.757482, -0.85491, 0.251331, -0.989183, + 0.306261, -0.340716, 0.886103, -0.0726757, -0.723523, -0.784303, + 0.0354295, 0.566564, -0.485469, -0.620498, 0.832546, 0.697884, + -0.279115, 0.294415, -0.584313, 0.548772, 0.0648819, 0.968726, + 0.723834, -0.0080452, -0.350386, -0.272803, 0.115121, -0.412644, + -0.824713, -0.992843, -0.592904, -0.417893, 0.863791, -0.423461, + -0.147601, -0.770664, -0.479006, 0.654782, 0.587314, -0.639158, + 0.816969, -0.337228, 0.659878, 0.73107, 0.754768, -0.337042, + 0.0960841, 0.368357, 0.244191, -0.817703, -0.211223, 0.442012, + 0.37225, -0.623598, -0.405423, 0.455101, 0.673656, -0.145345, + -0.511346, -0.901675, -0.81252, -0.127006, 0.809865, -0.721884, + 0.636255, 0.868989, -0.347973, -0.10179, -0.777449, 0.917274, + 0.819286, 0.206218, -0.00785118, 0.167141, 0.45872, 0.972934, + -0.276798, 0.837861, 0.747958, -0.0151566, -0.330057, -0.469077, + 0.277308, 0.415818}; + +static float endtoend_input[] = { + 0.996808, 0.060710, 0.981855, 0.570017, 0.525164, 0.796859, 0.696547, + 0.505925, 0.991844, 0.461208, 0.949371, 0.027624, 0.539236, 0.841854, + 0.915222, 0.538569, 0.069375, 0.237905, 0.903700, 0.441703, 0.536196, + 0.402724, 0.761635, 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-0.196606, -1.923437, 0.604962, -2.088319, 1.406834, -5.227296, + 2.247351, -4.421744, 1.729791, -5.007922, 1.264769, -0.897019, 0.922902, + -3.887108, 2.087432, -1.310226, -0.101938, -3.359082, -0.079662, -0.514988, + -0.963179, -4.038209, 2.223278, -0.590083, -2.310458, -1.748338, 0.363406, + -0.540731, -0.885913, -4.179595, 2.216781, -3.044339, -0.447100, -2.446098, + 0.931101, -1.676190, 2.096175, -4.980755, 2.262151, -1.095047, 1.897516, + -5.996138, 2.191038, 0.297128, -0.780974, -2.884299, 1.195408, -0.521065, + -1.955837, -3.091064, -0.404183, -1.961519, 4.076096, -7.521851, 2.242064, + -1.988043, 0.303300, -2.422585, 0.322230, -3.377634, 3.499955, -7.084434, + 2.375587, -0.718851, 2.150076, -5.412241, 2.374280, -2.006088, 2.229828, + -5.848188, 2.543077, -2.171042, 2.096026, -5.300007, 0.141405, -1.187745, + 0.105340, -4.003816, 1.034281, -3.980804, 1.856709, -5.103042, 0.623737, + -2.080307, 0.896140, -3.104050, 0.983158, -0.424898, -1.154270, -3.805728, + 1.978917, -1.314387, 1.235096, -3.148906, 1.113173, 0.111713, 2.055213, + -7.565283, 2.100342}; +constexpr std::initializer_list biases = { + 0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068, -0.23566568, + -0.389184, 0.47481549, -0.4791103, 0.29931796, 0.10463274, 0.83918178, + 0.37197268, 0.61957061, 0.3956964, -0.37609905}; + +constexpr std::initializer_list recurrent_weights = { + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1}; + +class BidirectionalRNNOpModel : public SingleOpModel { + public: + BidirectionalRNNOpModel(int batches, int sequence_len, int fw_units, + int bw_units, int input_size) + : batches_(batches), + sequence_len_(sequence_len), + fw_units_(fw_units), + bw_units_(bw_units), + input_size_(input_size) { + input_ = AddInput(TensorType_FLOAT32); + fw_weights_ = AddInput(TensorType_FLOAT32); + fw_recurrent_weights_ = AddInput(TensorType_FLOAT32); + fw_bias_ = AddInput(TensorType_FLOAT32); + fw_hidden_state_ = AddOutput(TensorType_FLOAT32); + fw_output_ = AddOutput(TensorType_FLOAT32); + bw_weights_ = AddInput(TensorType_FLOAT32); + bw_recurrent_weights_ = AddInput(TensorType_FLOAT32); + bw_bias_ = AddInput(TensorType_FLOAT32); + bw_hidden_state_ = AddOutput(TensorType_FLOAT32); + bw_output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, + BuiltinOptions_SequenceRNNOptions, + CreateSequenceRNNOptions(builder_, /*time_major=*/false, + ActivationFunctionType_RELU) + .Union()); + BuildInterpreter({ + {batches_, sequence_len_, input_size_}, // input + {fw_units_, input_size_}, // fw_weights + {fw_units_, fw_units_}, // fw_recurrent_weights + {fw_units_}, // fw_bias + {bw_units_, input_size_}, // bw_weights + {bw_units_, bw_units_}, // bw_recurrent_weights + {bw_units_} // bw_bias + }); + } + + void SetFwBias(std::initializer_list f) { + PopulateTensor(fw_bias_, f); + } + + void SetBwBias(std::initializer_list f) { + PopulateTensor(bw_bias_, f); + } + + void SetFwWeights(std::initializer_list f) { + PopulateTensor(fw_weights_, f); + } + + void SetBwWeights(std::initializer_list f) { + PopulateTensor(bw_weights_, f); + } + + void SetFwRecurrentWeights(std::initializer_list f) { + PopulateTensor(fw_recurrent_weights_, f); + } + + void SetBwRecurrentWeights(std::initializer_list f) { + PopulateTensor(bw_recurrent_weights_, f); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + void ResetHiddenStates() { + const int fw_zero_buffer_size = fw_units_ * batches_; + std::unique_ptr fw_zero_buffer(new float[fw_zero_buffer_size]); + memset(fw_zero_buffer.get(), 0, fw_zero_buffer_size * sizeof(float)); + PopulateTensor(fw_hidden_state_, 0, fw_zero_buffer.get(), + fw_zero_buffer.get() + fw_zero_buffer_size); + const int bw_zero_buffer_size = bw_units_ * batches_; + std::unique_ptr bw_zero_buffer(new float[bw_zero_buffer_size]); + memset(bw_zero_buffer.get(), 0, bw_zero_buffer_size * sizeof(float)); + PopulateTensor(bw_hidden_state_, 0, bw_zero_buffer.get(), + bw_zero_buffer.get() + bw_zero_buffer_size); + } + + std::vector GetFwOutput() { return ExtractVector(fw_output_); } + std::vector GetBwOutput() { return ExtractVector(bw_output_); } + + int input_size() { return input_size_; } + int num_fw_units() { return fw_units_; } + int num_bw_units() { return bw_units_; } + int num_batches() { return batches_; } + int sequence_len() { return sequence_len_; } + + private: + int input_; + int fw_weights_; + int fw_recurrent_weights_; + int fw_bias_; + int fw_hidden_state_; + int fw_output_; + int bw_weights_; + int bw_recurrent_weights_; + int bw_bias_; + int bw_hidden_state_; + int bw_output_; + + int batches_; + int sequence_len_; + int fw_units_; + int bw_units_; + int input_size_; +}; + +// TODO(mirkov): add another test which directly compares to TF once TOCO +// supports the conversion from dynamic_rnn with BasicRNNCell. +TEST(BidirectionalRNNOpTest, BlackBoxTest) { + BidirectionalRNNOpModel rnn(/*batches=*/2, /*sequence_len=*/16, + /*fw_units=*/16, /*bw_units=*/16, + /*input_size=*/8); + rnn.SetFwWeights(weights); + rnn.SetBwWeights(weights); + rnn.SetFwBias(biases); + rnn.SetBwBias(biases); + rnn.SetFwRecurrentWeights(recurrent_weights); + rnn.SetBwRecurrentWeights(recurrent_weights); + + rnn.ResetHiddenStates(); + const int input_sequence_size = rnn.input_size() * rnn.sequence_len(); + float* batch_start = rnn_input; + float* batch_end = batch_start + input_sequence_size; + rnn.SetInput(0, batch_start, batch_end); + rnn.SetInput(input_sequence_size, batch_start, batch_end); + + rnn.Invoke(); + + float* golden_fw_start = rnn_golden_fw_output; + float* golden_fw_end = + golden_fw_start + rnn.num_fw_units() * rnn.sequence_len(); + std::vector fw_expected; + fw_expected.insert(fw_expected.end(), golden_fw_start, golden_fw_end); + fw_expected.insert(fw_expected.end(), golden_fw_start, golden_fw_end); + EXPECT_THAT(rnn.GetFwOutput(), ElementsAreArray(ArrayFloatNear(fw_expected))); + + float* golden_bw_start = rnn_golden_bw_output; + float* golden_bw_end = + golden_bw_start + rnn.num_bw_units() * rnn.sequence_len(); + std::vector bw_expected; + bw_expected.insert(bw_expected.end(), golden_bw_start, golden_bw_end); + bw_expected.insert(bw_expected.end(), golden_bw_start, golden_bw_end); + EXPECT_THAT(rnn.GetBwOutput(), ElementsAreArray(ArrayFloatNear(bw_expected))); +} + +// Check that if the input sequence is reversed the outputs are the same just +// forward and backward are swapped (and reversed). +TEST(BidirectionalRNNOpTest, BlackBoxTestReverseInputs) { + BidirectionalRNNOpModel rnn(/*batches=*/2, /*sequence_len=*/16, + /*fw_units=*/16, /*bw_units=*/16, + /*input_size=*/8); + rnn.SetFwWeights(weights); + rnn.SetBwWeights(weights); + rnn.SetFwBias(biases); + rnn.SetBwBias(biases); + rnn.SetFwRecurrentWeights(recurrent_weights); + rnn.SetBwRecurrentWeights(recurrent_weights); + + rnn.ResetHiddenStates(); + + // Reverse inputs in each batch: in_1, in_2,..., in_k is inserted in the + // following order: [in_k,..., in_2, in_1, in_k,...,in_2, in_1]. + for (int i = 0; i < rnn.sequence_len(); i++) { + float* batch_start = rnn_input + i * rnn.input_size(); + float* batch_end = batch_start + rnn.input_size(); + const int reverse_idx = rnn.sequence_len() - i - 1; + rnn.SetInput(reverse_idx * rnn.input_size(), batch_start, batch_end); + rnn.SetInput((rnn.sequence_len() + reverse_idx) * rnn.input_size(), + batch_start, batch_end); + } + + rnn.Invoke(); + + // The forward and backward outputs are swapped. + std::vector fw_expected; // consider using std::deque instead. + for (int i = 0; i < rnn.sequence_len(); i++) { + float* golden_fw_start = rnn_golden_bw_output + i * rnn.num_fw_units(); + float* golden_fw_end = golden_fw_start + rnn.num_fw_units(); + fw_expected.insert(fw_expected.begin(), golden_fw_start, golden_fw_end); + } + fw_expected.insert(fw_expected.end(), fw_expected.begin(), fw_expected.end()); + EXPECT_THAT(rnn.GetFwOutput(), ElementsAreArray(ArrayFloatNear(fw_expected))); + + std::vector bw_expected; + for (int i = 0; i < rnn.sequence_len(); i++) { + float* golden_bw_start = rnn_golden_fw_output + i * rnn.num_bw_units(); + float* golden_bw_end = golden_bw_start + rnn.num_bw_units(); + bw_expected.insert(bw_expected.begin(), golden_bw_start, golden_bw_end); + } + bw_expected.insert(bw_expected.end(), bw_expected.begin(), bw_expected.end()); + EXPECT_THAT(rnn.GetBwOutput(), ElementsAreArray(ArrayFloatNear(bw_expected))); +} + +// Tests an end-to-end neural network with a Bidirectional RNN followed by a +// DNN that aggregates the outputs from the two sequences. +TEST(BidirectionalRNNOpTest, EndToEndTest) { + BidirectionalRNNOpModel rnn(/*batches=*/1, /*sequence_len=*/4, + /*fw_units=*/16, /*bw_units=*/16, + /*input_size=*/8); + const int output_size = 4; + float dnn_weights[] = { + -0.5782342, -0.052212059, 0.73036242, -0.81216097, -0.80088139, + -0.23420811, -0.39647382, 0.31423986, 0.61819065, -0.73659575, + -0.89698344, -0.8931554, -0.0845688, 0.5617367, 0.38415289, + -0.11487955, -0.7617774, 0.17927337, 0.15726972, 0.059798479, + 0.19009054, -0.27616632, -0.39142907, 0.77744663, -0.046830714, + -0.6603595, 0.21945822, 0.051494241, 0.23785079, 0.19239247, + -0.53268754, 0.65961659, -0.85981959, -0.80232513, 0.84745562, + -0.66070104, -0.036533296, -0.54901814, 0.65353882, -0.41834265, + -0.28561389, 0.75655544, -0.31149811, 0.62981737, 0.31829214, + -0.92734522, -0.48506218, 0.55651462, 0.25192821, 0.67220747, + -0.3836869, -0.55798125, -0.60395885, 0.22488403, -0.78053463, + 0.3492105, 0.56452453, 0.4389236, -0.59929526, -0.19762468, + -0.36868393, -0.13198286, -0.53800809, -0.22850353}; + + std::initializer_list dnn_biases = { + 0.29177809, -0.98799044, 0.065919638, 0.68781924}; + + rnn.SetFwWeights(weights); + rnn.SetBwWeights(weights); + rnn.SetFwBias(biases); + rnn.SetBwBias(biases); + rnn.SetFwRecurrentWeights(recurrent_weights); + rnn.SetBwRecurrentWeights(recurrent_weights); + + rnn.ResetHiddenStates(); + + const int input_sequence_size = rnn.input_size() * rnn.sequence_len(); + const int output_sequence_size = output_size * rnn.sequence_len(); + const int num_examples = 64; + for (int k = 0; k < num_examples; k++) { + float* batch_start = endtoend_input + k * input_sequence_size; + float* batch_end = batch_start + input_sequence_size; + rnn.SetInput(0, batch_start, batch_end); + + rnn.Invoke(); + + std::vector fw_output = rnn.GetFwOutput(); + std::vector bw_output = rnn.GetBwOutput(); + EXPECT_EQ(fw_output.size(), bw_output.size()); + + std::transform(fw_output.begin(), fw_output.end(), bw_output.begin(), + fw_output.begin(), std::plus()); + + std::vector sequence_result; + for (int s = 0; s < rnn.sequence_len(); s++) { + const float* rnn_output = fw_output.data() + s * rnn.num_fw_units(); + std::vector results(dnn_biases); + for (int i = 0; i < output_size; i++) { + for (int j = 0; j < rnn.num_fw_units(); j++) { + results[i] += *(rnn_output + j) * dnn_weights[output_size * j + i]; + } + } + sequence_result.insert(sequence_result.end(), results.begin(), + results.end()); + } + + float* golden_start = golden_endtoend_output + k * output_sequence_size; + float* golden_end = golden_start + output_sequence_size; + + std::vector expected; + expected.insert(expected.end(), golden_start, golden_end); + EXPECT_THAT(sequence_result, ElementsAreArray(ArrayFloatNear(expected))); + } +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + // On Linux, add: 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 f605deaa5b..1fb779fd51 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -31,6 +31,7 @@ TfLiteRegistration* Register_CONV_2D(); TfLiteRegistration* Register_DEPTHWISE_CONV_2D(); TfLiteRegistration* Register_SVDF(); TfLiteRegistration* Register_RNN(); +TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_RNN(); TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_RNN(); TfLiteRegistration* Register_EMBEDDING_LOOKUP(); TfLiteRegistration* Register_EMBEDDING_LOOKUP_SPARSE(); @@ -73,6 +74,8 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, Register_DEPTHWISE_CONV_2D()); AddBuiltin(BuiltinOperator_SVDF, Register_SVDF()); AddBuiltin(BuiltinOperator_RNN, Register_RNN()); + AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, + Register_BIDIRECTIONAL_SEQUENCE_RNN()); AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, Register_UNIDIRECTIONAL_SEQUENCE_RNN()); AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, Register_EMBEDDING_LOOKUP()); diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 415d984ad8..3f53a1abe7 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -328,6 +328,7 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, builtin_data = reinterpret_cast(params); break; } + case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: { TfLiteSequenceRNNParams* params = MallocPOD(); if (auto* sequence_rnn_params = diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index d5b9319407..da9ceec2f1 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -319,6 +319,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter, case tflite::BuiltinOperator_SVDF: case tflite::BuiltinOperator_HASHTABLE_LOOKUP: case tflite::BuiltinOperator_RNN: + case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: case tflite::BuiltinOperator_EMBEDDING_LOOKUP: case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index ec202cd407..4c82cb9549 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -119,6 +119,7 @@ enum BuiltinOperator : byte { SQUEEZE = 43, UNIDIRECTIONAL_SEQUENCE_LSTM = 44, STRIDED_SLICE = 45, + BIDIRECTIONAL_SEQUENCE_RNN = 46, } // Options for the builtin operators. @@ -224,6 +225,12 @@ table SequenceRNNOptions { fused_activation_function:ActivationFunctionType; } +// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell. +table BidirectionalSequenceRNNOptions { + time_major:bool; + fused_activation_function:ActivationFunctionType; +} + // An implementation of TensorFlow fully_connected (a.k.a Dense) layer. table FullyConnectedOptions { fused_activation_function:ActivationFunctionType; -- GitLab From e95537708f070a98607393a8f60bc61f1611a77b Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Fri, 26 Jan 2018 16:51:25 -0800 Subject: [PATCH 166/423] [tf.data] Support for initializing all the tables of the given graph. PiperOrigin-RevId: 183466905 --- .../contrib/data/python/kernel_tests/BUILD | 1 + .../dataset_serialization_test_base.py | 19 +++++++++++-------- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 1cf0202fd8..04a21f2b0f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -126,6 +126,7 @@ py_library( "//tensorflow/python:client_testlib", "//tensorflow/python:errors", "//tensorflow/python:framework_ops", + "//tensorflow/python:lookup_ops", "//tensorflow/python:platform", "//tensorflow/python:sparse_tensor", "//tensorflow/python:training", diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py index 7cde6e05b2..701fc8247e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py +++ b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py @@ -27,6 +27,7 @@ from tensorflow.python.data.ops import iterator_ops from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import test @@ -235,8 +236,7 @@ class DatasetSerializationTestBase(test.TestCase): ds_fn, sparse_tensors=sparse_tensors) with self.test_session(graph=g) as sess: self._restore(saver, sess) - sess.run(variables.global_variables_initializer()) - sess.run(init_op) + self._initialize(init_op, sess) for _ in range(num_outputs): actual.append(sess.run(get_next_op)) if verify_exhausted: @@ -390,8 +390,7 @@ class DatasetSerializationTestBase(test.TestCase): init_op, get_next_op, saver = self._build_graph( ds_fn, sparse_tensors=sparse_tensors) with self.test_session(graph=g) as sess: - sess.run(variables.global_variables_initializer()) - sess.run(init_op) + self._initialize(init_op, sess) for _ in range(break_point): sess.run(get_next_op) with self.assertRaises(error): @@ -493,12 +492,10 @@ class DatasetSerializationTestBase(test.TestCase): with self.test_session(graph=g) as sess: if ckpt_saved: if init_before_restore: - sess.run(variables.global_variables_initializer()) - sess.run(init_op) + self._initialize(init_op, sess) self._restore(saver, sess) else: - sess.run(variables.global_variables_initializer()) - sess.run(init_op) + self._initialize(init_op, sess) start = break_points[i - 1] if i > 0 else 0 end = break_points[i] if i < len(break_points) else num_outputs num_iters = end - start @@ -621,8 +618,14 @@ class DatasetSerializationTestBase(test.TestCase): saver.save(sess, self._ckpt_path()) def _restore(self, saver, sess): + sess.run(lookup_ops.tables_initializer()) saver.restore(sess, self._latest_ckpt()) + def _initialize(self, init_op, sess): + sess.run(variables.global_variables_initializer()) + sess.run(lookup_ops.tables_initializer()) + sess.run(init_op) + def _import_meta_graph(self): meta_file_path = self._ckpt_path() + ".meta" return saver_lib.import_meta_graph(meta_file_path) -- GitLab From aee7f95a027accc94f1f9130f0cfaecd9399bc1d Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Fri, 26 Jan 2018 16:53:59 -0800 Subject: [PATCH 167/423] Add C0301 line-too-long error to pylint sanity check. PiperOrigin-RevId: 183467186 --- .../bayesflow/python/ops/custom_grad_impl.py | 6 +- .../python/kernel_tests/bucketing_test.py | 33 +- tensorflow/contrib/eager/python/datasets.py | 7 +- .../contrib/framework/python/ops/arg_scope.py | 14 +- .../fused_conv2d_bias_activation_benchmark.py | 10 +- .../single_image_random_dot_stereograms.py | 33 +- .../kernel_methods/python/losses_test.py | 17 +- .../layers/python/layers/layers_test.py | 221 ++++---- .../learn/python/learn/datasets/mnist.py | 15 +- .../python/learn/datasets/synthetic_test.py | 56 +- .../python/learn/estimators/debug_test.py | 112 ++-- .../learn/estimators/estimator_input_test.py | 56 +- .../estimators/logistic_regressor_test.py | 5 +- .../contrib/learn/python/learn/evaluable.py | 10 +- .../contrib/learn/python/learn/experiment.py | 126 ++--- .../python/learn/learn_io/data_feeder.py | 55 +- .../contrib/learn/python/learn/trainable.py | 32 +- .../contrib/losses/python/losses/loss_ops.py | 130 ++--- .../examples/cifar10/cifar10_pruning.py | 2 +- .../mpi_collectives/python/ops/mpi_ops.py | 14 +- .../rnn/python/kernel_tests/core_rnn_test.py | 389 ++++++++------ tensorflow/contrib/session_bundle/exporter.py | 21 +- .../contrib/slim/python/slim/learning_test.py | 28 +- .../examples/tutorials/mnist/mnist_softmax.py | 16 +- tensorflow/python/client/device_lib_test.py | 3 +- tensorflow/python/debug/wrappers/hooks.py | 21 +- tensorflow/python/framework/dtypes.py | 209 +++++--- tensorflow/python/framework/importer.py | 49 +- tensorflow/python/framework/tensor_util.py | 171 +++--- tensorflow/python/framework/test_util.py | 99 ++-- .../kernel_tests/atrous_convolution_test.py | 46 +- .../candidate_sampler_ops_test.py | 2 +- .../kernel_tests/control_flow_ops_py_test.py | 198 +++---- .../python/kernel_tests/cwise_ops_test.py | 111 ++-- .../kernel_tests/dynamic_stitch_op_test.py | 33 +- .../partitioned_variables_test.py | 12 +- .../kernel_tests/random/random_ops_test.py | 21 +- .../python/kernel_tests/xent_op_test.py | 73 +-- tensorflow/python/ops/image_ops_test.py | 469 +++++++---------- tensorflow/python/ops/math_grad.py | 81 +-- tensorflow/python/ops/matmul_benchmark.py | 11 +- .../python/ops/matmul_benchmark_test.py | 75 +-- .../python/ops/nn_fused_batchnorm_test.py | 3 +- tensorflow/python/ops/nn_ops.py | 487 ++++++++++-------- .../python/ops/quantized_conv_ops_test.py | 3 +- tensorflow/python/ops/quantized_ops_test.py | 5 +- tensorflow/python/ops/sparse_ops.py | 239 +++++---- tensorflow/python/ops/special_math_ops.py | 4 +- tensorflow/python/saved_model/simple_save.py | 9 +- .../python/training/learning_rate_decay.py | 145 ++++-- tensorflow/python/training/rmsprop.py | 15 +- tensorflow/tools/ci_build/ci_sanity.sh | 3 +- tensorflow/tools/ci_build/pylintrc | 12 +- .../tools/dist_test/python/mnist_replica.py | 32 +- 54 files changed, 2184 insertions(+), 1865 deletions(-) diff --git a/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py b/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py index fdc12e3b21..d44fe6529a 100644 --- a/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py +++ b/tensorflow/contrib/bayesflow/python/ops/custom_grad_impl.py @@ -31,8 +31,7 @@ __all__ = [ ] -def custom_gradient(fx, gx, x, axis=(), - fx_gx_manually_stopped=False, +def custom_gradient(fx, gx, x, axis=(), fx_gx_manually_stopped=False, name=None): """Enables specifying a custom gradient. @@ -43,7 +42,8 @@ def custom_gradient(fx, gx, x, axis=(), h(x) = x * stop_gradient(g(x)) + stop_gradient(f(x) - x * g(x)) ``` - is such that `h(x) = stop_gradient(f(x))` and `grad[h(x), x] = stop_gradient(g(x)).` + is such that `h(x) = stop_gradient(f(x))` and `grad[h(x), x] = + stop_gradient(g(x)).` In addition to scalar-domain/scalar-range functions, this function also supports tensor-domain/scalar-range functions. However, in the latter case it diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py index 4d984bb4d7..6de93059d8 100644 --- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py @@ -41,8 +41,7 @@ class GroupByWindowTest(test.TestCase): dataset_ops.Dataset.from_tensor_slices(components).map(lambda x: x * x) .apply( grouping.group_by_window(lambda x: x % 2, lambda _, xs: xs.batch(4), - 4)) - .make_initializable_iterator()) + 4)).make_initializable_iterator()) init_op = iterator.initializer get_next = iterator.get_next() @@ -53,7 +52,8 @@ class GroupByWindowTest(test.TestCase): while True: result = sess.run(get_next) self.assertTrue( - all(x % 2 == 0 for x in result) or all(x % 2 == 1) + all(x % 2 == 0 + for x in result) or all(x % 2 == 1) for x in result) counts.append(result.shape[0]) @@ -116,8 +116,8 @@ class GroupByWindowTest(test.TestCase): iterator = ( dataset_ops.Dataset.from_tensor_slices(components) .map(lambda x: (x, ops.convert_to_tensor([x * x]))).apply( - grouping.group_by_window(lambda x, _: x % 2, reduce_func, 32)) - .make_initializable_iterator()) + grouping.group_by_window(lambda x, _: x % 2, reduce_func, + 32)).make_initializable_iterator()) init_op = iterator.initializer get_next = iterator.get_next() @@ -136,7 +136,8 @@ class GroupByWindowTest(test.TestCase): window.padded_batch( 4, padded_shapes=tensor_shape.TensorShape([None])), window.padded_batch( - 4, padded_shapes=ops.convert_to_tensor([(key + 1) * 10])),)) + 4, padded_shapes=ops.convert_to_tensor([(key + 1) * 10])), + )) iterator = ( dataset_ops.Dataset.from_tensor_slices(components) @@ -200,9 +201,10 @@ class BucketTest(test.TestCase): # dynamically and does not rely on static shape information about # the arguments. return dataset_ops.Dataset.zip( - (dataset_ops.Dataset.from_tensors(bucket), window.padded_batch( - 32, (tensor_shape.TensorShape([]), tensor_shape.TensorShape([None]), - tensor_shape.TensorShape([3]))))) + (dataset_ops.Dataset.from_tensors(bucket), + window.padded_batch( + 32, (tensor_shape.TensorShape([]), tensor_shape.TensorShape( + [None]), tensor_shape.TensorShape([3]))))) def testSingleBucket(self): @@ -307,12 +309,13 @@ class BucketTest(test.TestCase): def _dynamic_pad_fn(bucket, window, _): return dataset_ops.Dataset.zip( - (dataset_ops.Dataset.from_tensors(bucket), window.padded_batch( - 32, { - "x": tensor_shape.TensorShape([]), - "y": tensor_shape.TensorShape([None]), - "z": tensor_shape.TensorShape([3]) - }))) + (dataset_ops.Dataset.from_tensors(bucket), + window.padded_batch( + 32, { + "x": tensor_shape.TensorShape([]), + "y": tensor_shape.TensorShape([None]), + "z": tensor_shape.TensorShape([3]) + }))) input_dataset = ( dataset_ops.Dataset.from_tensor_slices(math_ops.range(128)).map(_map_fn) diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index 544a3eafc0..d177bfeab2 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -112,7 +112,7 @@ class Iterator(object): 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( + self._buffer_resource_handle = prefetching_ops.function_buffering_resource( # pylint: disable=line-too-long string_arg=iter_string_handle, f=remote_fn, target_device=target, @@ -120,8 +120,9 @@ class Iterator(object): thread_pool_size=1, container="", shared_name=_generate_shared_name("function_buffer_resource")) - self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( - handle=self._buffer_resource_handle, handle_device=self._device) + self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long + handle=self._buffer_resource_handle, + handle_device=self._device) def __iter__(self): return self diff --git a/tensorflow/contrib/framework/python/ops/arg_scope.py b/tensorflow/contrib/framework/python/ops/arg_scope.py index 2bce00fde2..409657fe1d 100644 --- a/tensorflow/contrib/framework/python/ops/arg_scope.py +++ b/tensorflow/contrib/framework/python/ops/arg_scope.py @@ -53,7 +53,8 @@ net = layers.conv2d(net, 256, [5, 5], scope='conv2') ``` - Example of how to use tf.contrib.framework.add_arg_scope to enable your function to be called within an arg_scope later: + Example of how to use tf.contrib.framework.add_arg_scope to enable your + function to be called within an arg_scope later: @tf.contrib.framework.add_arg_scope def conv2d(*args, **kwargs) @@ -65,11 +66,10 @@ from __future__ import print_function from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator -__all__ = ['arg_scope', - 'add_arg_scope', - 'current_arg_scope', - 'has_arg_scope', - 'arg_scoped_arguments'] +__all__ = [ + 'arg_scope', 'add_arg_scope', 'current_arg_scope', 'has_arg_scope', + 'arg_scoped_arguments' +] _ARGSTACK = [{}] @@ -172,6 +172,7 @@ def add_arg_scope(func): Returns: A tuple with the decorated function func_with_args(). """ + def func_with_args(*args, **kwargs): current_scope = current_arg_scope() current_args = kwargs @@ -180,6 +181,7 @@ def add_arg_scope(func): current_args = current_scope[key_func].copy() current_args.update(kwargs) return func(*args, **current_args) + _add_op(func) setattr(func_with_args, '_key_op', _key_op(func)) return tf_decorator.make_decorator(func, func_with_args) diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py index a65d4bc50f..96cdd8b1ca 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_benchmark.py @@ -116,7 +116,7 @@ def build_fused_conv_bias_relu_graph(device, input_shape, filter_shape, strides, for _ in range(1, num_iters): with ops.control_dependencies([fused_out]): # pylint: disable=g-line-too-long - fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation( + fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation( # pylint: disable=line-too-long inp, filt, bias, @@ -166,10 +166,10 @@ class FusedConv2DBiasActivationBenchmark(test.Benchmark): duration = (time.time() - start_time) / num_iters print("%s inputshape:%s filtershape:%s strides:%s padding:%s " - "%d iters: %.8f sec" % - (device, str(input_shape).replace(" ", ""), - str(filter_shape).replace(" ", ""), - str(strides).replace(" ", ""), padding, num_iters, duration)) + "%d iters: %.8f sec" % (device, str(input_shape).replace(" ", ""), + str(filter_shape).replace(" ", ""), + str(strides).replace(" ", ""), padding, + num_iters, duration)) name_template = ( "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_" "strides_{strides}_padding_{padding}") diff --git a/tensorflow/contrib/image/python/ops/single_image_random_dot_stereograms.py b/tensorflow/contrib/image/python/ops/single_image_random_dot_stereograms.py index bb766e59d2..d4a6a5bcbb 100755 --- a/tensorflow/contrib/image/python/ops/single_image_random_dot_stereograms.py +++ b/tensorflow/contrib/image/python/ops/single_image_random_dot_stereograms.py @@ -26,18 +26,20 @@ _sirds_ops = loader.load_op_library( resource_loader.get_path_to_datafile( "_single_image_random_dot_stereograms.so")) -def single_image_random_dot_stereograms( - depth_values, - hidden_surface_removal=None, - convergence_dots_size=None, - dots_per_inch=None, - eye_separation=None, mu=None, - normalize=None, normalize_max=None, - normalize_min=None, - border_level=None, - number_colors=None, - output_image_shape=None, - output_data_window=None): + +def single_image_random_dot_stereograms(depth_values, + hidden_surface_removal=None, + convergence_dots_size=None, + dots_per_inch=None, + eye_separation=None, + mu=None, + normalize=None, + normalize_max=None, + normalize_min=None, + border_level=None, + number_colors=None, + output_image_shape=None, + output_data_window=None): """Output a RandomDotStereogram Tensor for export via encode_PNG/JPG OP. Given the 2-D tensor 'depth_values' with encoded Z values, this operation @@ -45,7 +47,8 @@ def single_image_random_dot_stereograms( for the encode_PNG/JPG ops. Be careful with image compression as this may corrupt the encode 3-D data witin the image. - Based upon [this paper](http://www.learningace.com/doc/4331582/b6ab058d1e206d68ab60e4e1ead2fe6e/sirds-paper). + Based upon [this + paper](http://www.learningace.com/doc/4331582/b6ab058d1e206d68ab60e4e1ead2fe6e/sirds-paper). This outputs a SIRDS image as picture_out.png: @@ -113,7 +116,8 @@ def single_image_random_dot_stereograms( hidden_surface_removal=hidden_surface_removal, convergence_dots_size=convergence_dots_size, dots_per_inch=dots_per_inch, - eye_separation=eye_separation, mu=mu, + eye_separation=eye_separation, + mu=mu, normalize=normalize, normalize_max=normalize_max, normalize_min=normalize_min, @@ -123,4 +127,5 @@ def single_image_random_dot_stereograms( output_data_window=output_data_window) return result + ops.NotDifferentiable("SingleImageRandomDotStereograms") diff --git a/tensorflow/contrib/kernel_methods/python/losses_test.py b/tensorflow/contrib/kernel_methods/python/losses_test.py index d38d8041ce..72507539f8 100644 --- a/tensorflow/contrib/kernel_methods/python/losses_test.py +++ b/tensorflow/contrib/kernel_methods/python/losses_test.py @@ -119,19 +119,20 @@ class SparseMulticlassHingeLossTest(test.TestCase): def testUnknownShape(self): """Result keeps same with `testZeroLossInt32Labels`""" - logits_np = np.array([[1.2, -1.4, -1.0], - [1.4, 1.8, 4.0], - [0.5, 1.8, -1.0]]) + logits_np = np.array([[1.2, -1.4, -1.0], [1.4, 1.8, 4.0], [0.5, 1.8, -1.0]]) labels_np = np.array([0, 2, 1], dtype=np.int32) - logits_shapes = [[3, 3], # batch_size, num_classes - [None, 3], - [3, None], - [None, None]] + logits_shapes = [ + [3, 3], # batch_size, num_classes + [None, 3], + [3, None], + [None, None] + ] for batch_size, num_classes in logits_shapes: with self.test_session(): - logits = array_ops.placeholder(dtypes.float32, shape=(batch_size, num_classes)) + logits = array_ops.placeholder( + dtypes.float32, shape=(batch_size, num_classes)) labels = array_ops.placeholder(dtypes.int32, shape=(batch_size,)) loss = losses.sparse_multiclass_hinge_loss(labels, logits) result = loss.eval(feed_dict={logits: logits_np, labels: labels_np}) diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index a9bdbe0138..49b23ce8fa 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -126,8 +126,8 @@ class AvgPool3DTest(test.TestCase): def testInvalidDataFormat(self): depth, height, width = 3, 6, 9 images = np.random.uniform(size=(5, depth, height, width, 3)) - with self.assertRaisesRegexp(ValueError, - 'data_format has to be either NCDHW or NDHWC.'): + with self.assertRaisesRegexp( + ValueError, 'data_format has to be either NCDHW or NDHWC.'): _layers.avg_pool3d(images, [3, 3, 3], data_format='CDHWN') def testCreateAvgPool(self): @@ -147,7 +147,8 @@ class AvgPool3DTest(test.TestCase): def testCollectOutputs(self): depth, height, width = 3, 6, 9 images = random_ops.random_uniform((5, depth, height, width, 3), seed=1) - output = _layers.avg_pool3d(images, [3, 3, 3], outputs_collections='outputs') + output = _layers.avg_pool3d( + images, [3, 3, 3], outputs_collections='outputs') output_collected = ops.get_collection('outputs')[0] self.assertEqual(output_collected.aliases, ['AvgPool3D']) self.assertEqual(output_collected, output) @@ -182,7 +183,8 @@ class AvgPool3DTest(test.TestCase): depth, height, width = 3, 6, 9 images = random_ops.random_uniform((5, depth, height, width, 3), seed=1) output = _layers.avg_pool3d(images, [3, 3, 3], stride=1, padding='SAME') - self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3]) + self.assertListEqual(output.get_shape().as_list(), + [5, depth, height, width, 3]) def testGlobalAvgPool(self): depth, height, width = 3, 6, 9 @@ -514,7 +516,9 @@ class ConvolutionTest(test.TestCase): with arg_scope( [layers_lib.convolution2d], normalizer_fn=_layers.batch_norm, - normalizer_params={'decay': 0.9}): + normalizer_params={ + 'decay': 0.9 + }): net = layers_lib.convolution2d(images, 32, [3, 3]) net = layers_lib.convolution2d(net, 32, [3, 3]) self.assertEqual(len(variables.get_variables()), 8) @@ -528,7 +532,9 @@ class ConvolutionTest(test.TestCase): with arg_scope( [layers_lib.convolution2d], normalizer_fn=_layers.batch_norm, - normalizer_params={'decay': 0.9}): + normalizer_params={ + 'decay': 0.9 + }): net = layers_lib.convolution2d(images, 32, [3, 3], scope='Conv') net = layers_lib.convolution2d( net, 32, [3, 3], scope='Conv', reuse=True) @@ -1030,7 +1036,8 @@ class Convolution2dTransposeTests(test.TestCase): for _ in range(10): num_filters = 1 input_size = [ - 1, np.random.randint(1, max_image_size), + 1, + np.random.randint(1, max_image_size), np.random.randint(1, max_image_size), 1 ] filter_size = [ @@ -1184,8 +1191,10 @@ class ConvolutionInPlaneTest(test.TestCase): with self.test_session() as sess: sess.run(init_op) - result = sess.run(horz_gradients, - feed_dict={image: np.ones((1, 10, 10, 1))}) + result = sess.run( + horz_gradients, feed_dict={ + image: np.ones((1, 10, 10, 1)) + }) expected = np.zeros((1, 10, 9, 1)) self.assertAllEqual(result, expected) @@ -1406,8 +1415,7 @@ class FlattenTest(test.TestCase): with ops.Graph().as_default() as g, self.test_session(g): inputs = array_ops.placeholder(dtype=dtypes.float32) inputs.set_shape(tensor_shape.TensorShape((5,))) - with self.assertRaisesRegexp(ValueError, - 'incompatible with the layer'): + with self.assertRaisesRegexp(ValueError, 'incompatible with the layer'): _layers.flatten(inputs) def testUnknownLastDim(self): @@ -1717,7 +1725,9 @@ class FCTest(test.TestCase): with arg_scope( [_layers.fully_connected], normalizer_fn=_layers.batch_norm, - normalizer_params={'decay': 0.9}): + normalizer_params={ + 'decay': 0.9 + }): net = _layers.fully_connected(images, 27) net = _layers.fully_connected(net, 27) self.assertEqual(len(variables.get_variables()), 8) @@ -1733,7 +1743,9 @@ class FCTest(test.TestCase): with arg_scope( [_layers.fully_connected], normalizer_fn=_layers.batch_norm, - normalizer_params={'decay': 0.9}): + normalizer_params={ + 'decay': 0.9 + }): net = _layers.fully_connected(images, 27, scope='fc1') net = _layers.fully_connected(net, 27, scope='fc1', reuse=True) self.assertEqual(len(variables.get_variables()), 4) @@ -1750,8 +1762,8 @@ class BatchNormTest(test.TestCase): def testBatchNormCenterFalse(self): a = array_ops.placeholder(dtype=dtypes.float32, shape=(10, 10, 10, 10)) # Test that center=False builds a valid graph. - _layers.batch_norm(a, center=False, data_format='NCHW', - zero_debias_moving_mean=True) + _layers.batch_norm( + a, center=False, data_format='NCHW', zero_debias_moving_mean=True) def testUnknownShape(self): with ops.Graph().as_default() as g, self.test_session(g): @@ -1788,8 +1800,8 @@ class BatchNormTest(test.TestCase): images = np.random.uniform(size=(5, height, width, 3)).astype( dtype.as_numpy_dtype) output = _layers.batch_norm(images, fused=fused) - expected_name = ('BatchNorm/FusedBatchNorm' if fused else - 'BatchNorm/batchnorm') + expected_name = ('BatchNorm/FusedBatchNorm' + if fused else 'BatchNorm/batchnorm') self.assertTrue(output.op.name.startswith(expected_name)) self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3]) self.assertEqual( @@ -2008,8 +2020,8 @@ class BatchNormTest(test.TestCase): expected_var = np.var(image_values, axis=axis) if fused: # Add Bessel's correction - expected_var, _ = self._addBesselsCorrection(batch_size * height * - width, expected_var) + expected_var, _ = self._addBesselsCorrection( + batch_size * height * width, expected_var) images = constant_op.constant( image_values, shape=image_shape, dtype=dtypes.float32) output = _layers.batch_norm( @@ -2528,8 +2540,8 @@ class BatchNormTest(test.TestCase): expected_var = np.var(image_values, axis=axis) if fused: # Add Bessel's correction - expected_var, _ = self._addBesselsCorrection(batch_size * height * - width, expected_var) + expected_var, _ = self._addBesselsCorrection( + batch_size * height * width, expected_var) images = constant_op.constant( image_values, shape=image_shape, dtype=dtypes.float32) output = _layers.batch_norm( @@ -2559,8 +2571,9 @@ class BatchNormTest(test.TestCase): np_output, new_images_gradients = sess.run([output, images_gradients]) # The outputs should be close to 0.0 mean and 1.0 variance self.assertAllClose( - np.mean( - np_output, axis=axis), [0] * channels, rtol=0.001, atol=0.001) + np.mean(np_output, axis=axis), [0] * channels, + rtol=0.001, + atol=0.001) self.assertAllClose( np.var(np_output, axis=axis), [1] * channels, rtol=0.01, atol=0.01) # The gradients should change slowly while updating moving_mean. @@ -2588,14 +2601,14 @@ class BatchNormTest(test.TestCase): channels = 3 with self.test_session() as sess: images = (np.ones((5, height, width, channels)) * 9.0).astype('f') - beta = init_ops.constant_initializer((np.ones(channels) * 5.0).astype( - 'f')) - gamma = init_ops.constant_initializer((np.ones(channels) * 2.0).astype( - 'f')) - mean = init_ops.constant_initializer((np.ones(channels) * 5.0).astype( - 'f')) - variance = init_ops.constant_initializer((np.ones(channels) * 4.0).astype( - 'f')) + beta = init_ops.constant_initializer( + (np.ones(channels) * 5.0).astype('f')) + gamma = init_ops.constant_initializer( + (np.ones(channels) * 2.0).astype('f')) + mean = init_ops.constant_initializer( + (np.ones(channels) * 5.0).astype('f')) + variance = init_ops.constant_initializer( + (np.ones(channels) * 4.0).astype('f')) output = _layers.batch_norm( images, is_training=False, @@ -2616,21 +2629,18 @@ class BatchNormTest(test.TestCase): with self.test_session(use_gpu=True) as sess: images = np.arange(np.product(shape), dtype=np.float32).reshape(shape) beta = init_ops.constant_initializer( - np.arange( - 2, channels + 2, dtype=np.float32)) + np.arange(2, channels + 2, dtype=np.float32)) gamma = init_ops.constant_initializer( - np.arange( - 10, channels + 10, dtype=np.float32) * 2.0) + np.arange(10, channels + 10, dtype=np.float32) * 2.0) mean = init_ops.constant_initializer( - np.arange( - 3, channels + 3, dtype=np.float32) * 5.0) + np.arange(3, channels + 3, dtype=np.float32) * 5.0) variance = init_ops.constant_initializer( - np.arange( - 1, channels + 1, dtype=np.float32) * 4.0) + np.arange(1, channels + 1, dtype=np.float32) * 4.0) if data_format == 'NCHW': # Reshape inputs from NHWC to NCHW format. images = array_ops.transpose( - images, [0, len(shape) - 1] + list(range(1, len(shape) - 1))) + images, [0, len(shape) - 1] + list(range(1, + len(shape) - 1))) output = _layers.batch_norm( images, is_training=is_training, @@ -2733,16 +2743,16 @@ class BatchNormTest(test.TestCase): # Tests that the adjustment is appropriately passed to and used by the core # BN layer. all_adjustments = [] + def _create_adjustment(shape): adjustments = [array_ops.ones(shape[-1:]), array_ops.zeros(shape[-1:])] all_adjustments.extend(adjustments) return adjustments + depth = 8 images = array_ops.zeros([10, 5, 5, depth]) output = _layers.batch_norm( - images, - is_training=True, - adjustment=_create_adjustment) + images, is_training=True, adjustment=_create_adjustment) self.assertListEqual(output.shape.as_list(), images.shape.as_list()) self.assertEqual(len(all_adjustments), 2) self.assertListEqual(all_adjustments[0].shape.as_list(), [depth]) @@ -2807,7 +2817,10 @@ class LayerNormTest(test.TestCase): # output_train and output_eval should be the same. self.assertAllClose(sess.run([output_train]), sess.run([output_eval])) - def doOutputTest(self, input_shape, tol=1e-5, begin_norm_axis=1, + def doOutputTest(self, + input_shape, + tol=1e-5, + begin_norm_axis=1, dtype=dtypes.float64): expected_mean = np.zeros(input_shape[:begin_norm_axis]) expected_var = np.ones(input_shape[:begin_norm_axis]) @@ -2838,13 +2851,10 @@ class LayerNormTest(test.TestCase): # Layer-norm implemented in numpy eps = 1e-12 expected_out = ( - (gamma * ( - input_values - - np.mean(input_values, axis=moments_axis, keepdims=True)) - / np.sqrt( - eps - + np.var(input_values, axis=moments_axis, keepdims=True))) - + beta) + (gamma * (input_values - np.mean( + input_values, axis=moments_axis, keepdims=True)) / + np.sqrt(eps + np.var( + input_values, axis=moments_axis, keepdims=True))) + beta) self.assertAllClose(expected_mean, mean, atol=tol, rtol=tol) self.assertAllClose(expected_var, var, atol=tol) # The full computation gets a bigger tolerance @@ -2862,10 +2872,10 @@ class LayerNormTest(test.TestCase): def testOutput4DInputNormOnInnermostAxis(self): # Equivalent tests - self.doOutputTest((100, 10, 10, 3), begin_norm_axis=3, tol=1e-4, - dtype=dtypes.float64) - self.doOutputTest((100, 10, 10, 3), begin_norm_axis=-1, tol=1e-4, - dtype=dtypes.float64) + self.doOutputTest( + (100, 10, 10, 3), begin_norm_axis=3, tol=1e-4, dtype=dtypes.float64) + self.doOutputTest( + (100, 10, 10, 3), begin_norm_axis=-1, tol=1e-4, dtype=dtypes.float64) def testOutputSmallInput(self): self.doOutputTest((10, 10, 10, 30)) @@ -2902,7 +2912,7 @@ class GDNTest(test.TestCase): x = np.random.uniform(size=(1, 2, 3, 4)[:ndim]) y = self._runGDN(x, x.shape, False, 'channels_last') self.assertEqual(x.shape, y.shape) - self.assertAllClose(y, x / np.sqrt(1 + .1 * (x ** 2)), rtol=0, atol=1e-6) + self.assertAllClose(y, x / np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6) def testChannelsFirst(self): # `bias_add` doesn't support NCHW on CPU. @@ -2911,8 +2921,7 @@ class GDNTest(test.TestCase): x = np.random.uniform(size=(4, 3, 2, 1)[:ndim]) y = self._runGDN(x, x.shape, False, 'channels_first') self.assertEqual(x.shape, y.shape) - self.assertAllClose( - y, x / np.sqrt(1 + .1 * (x ** 2)), rtol=0, atol=1e-6) + self.assertAllClose(y, x / np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6) def testWrongDims(self): for ndim in [1, 2, 6]: @@ -2924,7 +2933,7 @@ class GDNTest(test.TestCase): x = np.random.uniform(size=(1, 2, 3, 4)) y = self._runGDN(x, x.shape, True, 'channels_last') self.assertEqual(x.shape, y.shape) - self.assertAllClose(y, x * np.sqrt(1 + .1 * (x ** 2)), rtol=0, atol=1e-6) + self.assertAllClose(y, x * np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6) class MaxPool2DTest(test.TestCase): @@ -3001,20 +3010,22 @@ class MaxPool3DTest(test.TestCase): def testInvalidDataFormat(self): depth, height, width = 3, 6, 9 images = np.random.uniform(size=(5, depth, height, width, 3)) - with self.assertRaisesRegexp(ValueError, - 'data_format has to be either NCDHW or NDHWC.'): + with self.assertRaisesRegexp( + ValueError, 'data_format has to be either NCDHW or NDHWC.'): _layers.max_pool3d(images, [3, 3, 3], data_format='CDHWN') def testCreateMaxPool(self): depth, height, width = 3, 6, 9 - images = np.random.uniform(size=(5, depth, height, width, 3)).astype(np.float32) + images = np.random.uniform(size=(5, depth, height, width, 3)).astype( + np.float32) output = _layers.max_pool3d(images, [3, 3, 3]) self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D') self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3]) def testCreateMaxPoolNCDHW(self): depth, height, width = 3, 6, 9 - images = np.random.uniform(size=(5, 3, depth, height, width)).astype(np.float32) + images = np.random.uniform(size=(5, 3, depth, height, width)).astype( + np.float32) output = _layers.max_pool3d(images, [3, 3, 3], data_format='NCDHW') self.assertEquals(output.op.name, 'MaxPool3D/transpose_1') self.assertListEqual(output.get_shape().as_list(), [5, 3, 1, 2, 4]) @@ -3022,7 +3033,8 @@ class MaxPool3DTest(test.TestCase): def testCollectOutputs(self): depth, height, width = 3, 6, 9 images = random_ops.random_uniform((5, depth, height, width, 3), seed=1) - output = _layers.max_pool3d(images, [3, 3, 3], outputs_collections='outputs') + output = _layers.max_pool3d( + images, [3, 3, 3], outputs_collections='outputs') output_collected = ops.get_collection('outputs')[0] self.assertEqual(output_collected.aliases, ['MaxPool3D']) self.assertEqual(output_collected, output) @@ -3057,7 +3069,8 @@ class MaxPool3DTest(test.TestCase): depth, height, width = 3, 6, 9 images = random_ops.random_uniform((5, depth, height, width, 3), seed=1) output = _layers.max_pool3d(images, [3, 3, 3], stride=1, padding='SAME') - self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3]) + self.assertListEqual(output.get_shape().as_list(), + [5, depth, height, width, 3]) def testGlobalMaxPool(self): depth, height, width = 3, 6, 9 @@ -3469,8 +3482,7 @@ class SpatialSoftmaxTests(test.TestCase): sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) - self.assertAllEqual(keypoints.shape, - (batch_shape[0], batch_shape[3] * 2)) + self.assertAllEqual(keypoints.shape, (batch_shape[0], batch_shape[3] * 2)) def testSpatialSoftmaxShapeNCHW(self): batch_shape = (2, 2, 35, 35) @@ -3481,8 +3493,7 @@ class SpatialSoftmaxTests(test.TestCase): sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) - self.assertAllEqual(keypoints.shape, - (batch_shape[0], batch_shape[1] * 2)) + self.assertAllEqual(keypoints.shape, (batch_shape[0], batch_shape[1] * 2)) def testTwoMaxActivationsSameChannel(self): batch_size, height, width, nchannels = (2, 35, 35, 1) @@ -3501,8 +3512,8 @@ class SpatialSoftmaxTests(test.TestCase): x_loc = [avg_x] y_loc = [avg_y] - np_keypoints = self._SpatialSoftmax( - x_loc, y_loc, height, width, batch_size, nchannels) + np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size, + nchannels) # Make sure expected location keypoints matches actual location keypoints. with self.test_session() as sess: @@ -3520,13 +3531,13 @@ class SpatialSoftmaxTests(test.TestCase): spatial_softmax = _layers.spatial_softmax(features) np_features = np.zeros(batch_shape, dtype=np.float32) - edges = [(0, 0), (0, width-1), (height-1, 0), (height-1, width-1)] + edges = [(0, 0), (0, width - 1), (height - 1, 0), (height - 1, width - 1)] x_loc, y_loc = zip(*edges) for c in range(nchannels): np_features[:, x_loc[c], y_loc[c], c] = 100. - np_keypoints = self._SpatialSoftmax( - x_loc, y_loc, height, width, batch_size, nchannels) + np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size, + nchannels) # Make sure expected location keypoints matches actual location keypoints. with self.test_session() as sess: @@ -3555,10 +3566,10 @@ class SpatialSoftmaxTests(test.TestCase): np_features1[:, x_loc[c], y_loc[c], c] = 100. np_features2[:, x_loc[c], y_loc[c], c] = 100. - np_keypoints1 = self._SpatialSoftmax( - x_loc, y_loc, height1, width1, batch_size, nchannels) - np_keypoints2 = self._SpatialSoftmax( - x_loc, y_loc, height2, width2, batch_size, nchannels) + np_keypoints1 = self._SpatialSoftmax(x_loc, y_loc, height1, width1, + batch_size, nchannels) + np_keypoints2 = self._SpatialSoftmax(x_loc, y_loc, height2, width2, + batch_size, nchannels) # Make sure expected location keypoints matches actual location keypoints. with self.test_session() as sess: @@ -3584,8 +3595,8 @@ class SpatialSoftmaxTests(test.TestCase): for c in range(nchannels): np_features[:, x_loc[c], y_loc[c], c] = 100. - np_keypoints = self._SpatialSoftmax( - x_loc, y_loc, height, width, batch_size, nchannels) + np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size, + nchannels) # Make sure expected location keypoints matches actual location keypoints. with self.test_session() as sess: @@ -3607,8 +3618,8 @@ class SpatialSoftmaxTests(test.TestCase): for c in range(nchannels): np_features[:, c, x_loc[c], y_loc[c]] = 100. - np_keypoints = self._SpatialSoftmax( - x_loc, y_loc, height, width, batch_size, nchannels) + np_keypoints = self._SpatialSoftmax(x_loc, y_loc, height, width, batch_size, + nchannels) # Make sure expected location keypoints matches actual location keypoints. with self.test_session() as sess: @@ -3703,8 +3714,7 @@ class UnitNormTests(test.TestCase): image = random_ops.random_uniform((height, width, 3)) output = _layers.unit_norm(image, dim=dim, epsilon=1e-6) norms = math_ops.sqrt( - math_ops.reduce_sum( - math_ops.square(output), reduction_indices=dim)) + math_ops.reduce_sum(math_ops.square(output), reduction_indices=dim)) shape = [height, width, 3] del shape[dim] @@ -3740,8 +3750,7 @@ class UnitNormTests(test.TestCase): image = array_ops.placeholder(dtypes.float32, (None, None, 3)) output = _layers.unit_norm(image, dim=dim, epsilon=1e-6) norms = math_ops.sqrt( - math_ops.reduce_sum( - math_ops.square(output), reduction_indices=dim)) + math_ops.reduce_sum(math_ops.square(output), reduction_indices=dim)) with self.test_session(): actual = norms.eval({image: placeholder_value}) @@ -3805,8 +3814,8 @@ class PoincareNormalizeTest(test.TestCase): with self.test_session(): x_tf = constant_op.constant(x_np, name='x') y_tf = _layers.poincare_normalize(x_tf, dim) - err = gradient_checker.compute_gradient_error(x_tf, x_shape, - y_tf, x_shape) + err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf, + x_shape) print('PoinCareNormalize gradient err = %g ' % err) self.assertLess(err, 1e-4) @@ -3818,14 +3827,9 @@ class LegacyFullyConnectedTest(test.TestCase): test.TestCase.setUp(self) random_seed.set_random_seed(1234) self.input = constant_op.constant([[1., 2., 3.], [-4., 15., -6.]]) - self.input_3_dim_arr = [[[1., 1.1, 1.2], - [2., 2.1, 2.2], - [3., 3.1, 3.2], - [4., 4.1, 4.2]], - [[5., 5.1, 5.2], - [6., 6.1, 6.2], - [7., 7.1, 7.2], - [8., 8.1, 8.2]]] + self.input_3_dim_arr = [[[1., 1.1, 1.2], [2., 2.1, 2.2], [3., 3.1, 3.2], + [4., 4.1, 4.2]], [[5., 5.1, 5.2], [6., 6.1, 6.2], + [7., 7.1, 7.2], [8., 8.1, 8.2]]] self.input_3_dim = constant_op.constant(self.input_3_dim_arr) assert not ops.get_collection(ops.GraphKeys.SUMMARIES) @@ -3920,15 +3924,10 @@ class LegacyFullyConnectedTest(test.TestCase): self._custom_initializers(self.input, 2, [[13.0, 13.0], [11.0, 11.0]]) def test_custom_initializers_multi_dim(self): - self._custom_initializers(self.input_3_dim, 2, - [[[7.6, 7.6], - [13.6, 13.6], - [19.6, 19.6], - [25.6, 25.6]], - [[31.6, 31.6], - [37.6, 37.6], - [43.6, 43.6], - [49.6, 49.6]]]) + self._custom_initializers( + self.input_3_dim, 2, + [[[7.6, 7.6], [13.6, 13.6], [19.6, 19.6], [25.6, 25.6]], + [[31.6, 31.6], [37.6, 37.6], [43.6, 43.6], [49.6, 49.6]]]) def test_custom_collections(self): layers_lib.legacy_relu( @@ -4038,12 +4037,16 @@ class LegacyFullyConnectedTest(test.TestCase): with self.test_session() as sess: variables_lib.global_variables_initializer().run() # we can feed in input with first dimension 2 - shape_value = sess.run(array_ops.shape(y), - feed_dict={x: self.input_3_dim_arr}) + shape_value = sess.run( + array_ops.shape(y), feed_dict={ + x: self.input_3_dim_arr + }) self.assertAllClose(shape_value, [2, 4, 1]) # we can feed in input with first dimension 1 - shape_value = sess.run(array_ops.shape(y), - feed_dict={x: [self.input_3_dim_arr[0]]}) + shape_value = sess.run( + array_ops.shape(y), feed_dict={ + x: [self.input_3_dim_arr[0]] + }) self.assertAllClose(shape_value, [1, 4, 1]) # we cannot feed in input with inconsistent dimensions with self.assertRaises(ValueError): diff --git a/tensorflow/contrib/learn/python/learn/datasets/mnist.py b/tensorflow/contrib/learn/python/learn/datasets/mnist.py index 1f3295747e..37f9175015 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/mnist.py +++ b/tensorflow/contrib/learn/python/learn/datasets/mnist.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Functions for downloading and reading MNIST data.""" from __future__ import absolute_import @@ -123,8 +122,8 @@ class DataSet(object): numpy.random.seed(seed1 if seed is None else seed2) dtype = dtypes.as_dtype(dtype).base_dtype if dtype not in (dtypes.uint8, dtypes.float32): - raise TypeError('Invalid image dtype %r, expected uint8 or float32' % - dtype) + raise TypeError( + 'Invalid image dtype %r, expected uint8 or float32' % dtype) if fake_data: self._num_examples = 10000 self.one_hot = one_hot @@ -202,7 +201,9 @@ class DataSet(object): end = self._index_in_epoch images_new_part = self._images[start:end] labels_new_part = self._labels[start:end] - return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0) + return numpy.concatenate( + (images_rest_part, images_new_part), axis=0), numpy.concatenate( + (labels_rest_part, labels_new_part), axis=0) else: self._index_in_epoch += batch_size end = self._index_in_epoch @@ -257,16 +258,14 @@ def read_data_sets(train_dir, test_labels = extract_labels(f, one_hot=one_hot) if not 0 <= validation_size <= len(train_images): - raise ValueError( - 'Validation size should be between 0 and {}. Received: {}.' - .format(len(train_images), validation_size)) + raise ValueError('Validation size should be between 0 and {}. Received: {}.' + .format(len(train_images), validation_size)) validation_images = train_images[:validation_size] validation_labels = train_labels[:validation_size] train_images = train_images[validation_size:] train_labels = train_labels[validation_size:] - options = dict(dtype=dtype, reshape=reshape, seed=seed) train = DataSet(train_images, train_labels, **options) diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py index 5340afab46..613d8d39a3 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py @@ -24,12 +24,14 @@ from tensorflow.python.platform import test from tensorflow.contrib.learn.python.learn import datasets from tensorflow.contrib.learn.python.learn.datasets import synthetic + class SyntheticTest(test.TestCase): """Test synthetic dataset generation""" def test_make_dataset(self): """Test if the synthetic routine wrapper complains about the name""" - self.assertRaises(ValueError, datasets.make_dataset, name='_non_existing_name') + self.assertRaises( + ValueError, datasets.make_dataset, name='_non_existing_name') def test_all_datasets_callable(self): """Test if all methods inside the `SYNTHETIC` are callable""" @@ -52,9 +54,10 @@ class SyntheticTest(test.TestCase): """ n_samples = 100 n_classes = 2 - circ = synthetic.circles(n_samples = n_samples, noise = None, n_classes = n_classes) + circ = synthetic.circles( + n_samples=n_samples, noise=None, n_classes=n_classes) self.assertIsInstance(circ, datasets.base.Dataset) - self.assertTupleEqual(circ.data.shape, (n_samples,2)) + self.assertTupleEqual(circ.data.shape, (n_samples, 2)) self.assertTupleEqual(circ.target.shape, (n_samples,)) self.assertSetEqual(set(circ.target), set(range(n_classes))) @@ -67,17 +70,24 @@ class SyntheticTest(test.TestCase): """ seed = 42 noise = 0.1 - circ0 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed) - circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed) + circ0 = synthetic.circles( + n_samples=100, noise=noise, n_classes=2, seed=seed) + circ1 = synthetic.circles( + n_samples=100, noise=noise, n_classes=2, seed=seed) np.testing.assert_array_equal(circ0.data, circ1.data) np.testing.assert_array_equal(circ0.target, circ1.target) - circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed+1) - self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data) - self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.target, circ1.target) + circ1 = synthetic.circles( + n_samples=100, noise=noise, n_classes=2, seed=seed + 1) + self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, + circ1.data) + self.assertRaises(AssertionError, np.testing.assert_array_equal, + circ0.target, circ1.target) - circ1 = synthetic.circles(n_samples = 100, noise = noise/2., n_classes = 2, seed = seed) - self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data) + circ1 = synthetic.circles( + n_samples=100, noise=noise / 2., n_classes=2, seed=seed) + self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, + circ1.data) def test_spirals(self): """Test if the circles are generated correctly @@ -89,13 +99,14 @@ class SyntheticTest(test.TestCase): - returned `target` shape is (n_samples,) - set of unique classes range is [0, n_classes) """ - self.assertRaises(ValueError, synthetic.spirals, mode='_unknown_mode_spiral_') + self.assertRaises( + ValueError, synthetic.spirals, mode='_unknown_mode_spiral_') n_samples = 100 modes = ('archimedes', 'bernoulli', 'fermat') for mode in modes: - spir = synthetic.spirals(n_samples = n_samples, noise = None, mode = mode) + spir = synthetic.spirals(n_samples=n_samples, noise=None, mode=mode) self.assertIsInstance(spir, datasets.base.Dataset) - self.assertTupleEqual(spir.data.shape, (n_samples,2)) + self.assertTupleEqual(spir.data.shape, (n_samples, 2)) self.assertTupleEqual(spir.target.shape, (n_samples,)) self.assertSetEqual(set(spir.target), set(range(2))) @@ -110,18 +121,21 @@ class SyntheticTest(test.TestCase): noise = 0.1 modes = ('archimedes', 'bernoulli', 'fermat') for mode in modes: - spir0 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed) - spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed) + spir0 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed) + spir1 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed) np.testing.assert_array_equal(spir0.data, spir1.data) np.testing.assert_array_equal(spir0.target, spir1.target) - spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed+1) - self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data) - self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.target, spir1.target) + spir1 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed + 1) + self.assertRaises(AssertionError, np.testing.assert_array_equal, + spir0.data, spir1.data) + self.assertRaises(AssertionError, np.testing.assert_array_equal, + spir0.target, spir1.target) - spir1 = synthetic.spirals(n_samples = 1000, noise = noise/2., seed = seed) - self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data) + spir1 = synthetic.spirals(n_samples=1000, noise=noise / 2., seed=seed) + self.assertRaises(AssertionError, np.testing.assert_array_equal, + spir0.data, spir1.data) -if __name__ == "__main__": +if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py index 6b125534a4..b968aeed1b 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/debug_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/debug_test.py @@ -44,7 +44,6 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import test from tensorflow.python.training import input as input_lib - NUM_EXAMPLES = 100 N_CLASSES = 5 # Cardinality of multiclass labels. LABEL_DIMENSION = 3 # Dimensionality of regression labels. @@ -52,8 +51,10 @@ LABEL_DIMENSION = 3 # Dimensionality of regression labels. def _train_test_split(features_and_labels): features, labels = features_and_labels - train_set = (features[:int(len(features) / 2)], labels[:int(len(features) / 2)]) - test_set = (features[int(len(features) / 2):], labels[int(len(features) / 2):]) + train_set = (features[:int(len(features) / 2)], + labels[:int(len(features) / 2)]) + test_set = (features[int(len(features) / 2):], + labels[int(len(features) / 2):]) return train_set, test_set @@ -86,17 +87,17 @@ class DebugClassifierTest(test.TestCase): (train_features, train_labels), (test_features, test_labels) = _train_test_split( [self.features, self.labels]) - majority_class, _ = max(collections.Counter(train_labels).items(), - key=operator.itemgetter(1)) + majority_class, _ = max( + collections.Counter(train_labels).items(), key=operator.itemgetter(1)) expected_prediction = np.vstack( [[majority_class] for _ in range(test_labels.shape[0])]) classifier = debug.DebugClassifier(n_classes=N_CLASSES) - classifier.fit(input_fn=_input_fn_builder(train_features, train_labels), - steps=50) + classifier.fit( + input_fn=_input_fn_builder(train_features, train_labels), steps=50) - pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features, - None)) + pred = classifier.predict_classes( + input_fn=_input_fn_builder(test_features, None)) self.assertAllEqual(expected_prediction, np.vstack(pred)) def testPredictBinary(self): @@ -105,34 +106,34 @@ class DebugClassifierTest(test.TestCase): test_labels) = _train_test_split( [self.features, self.binary_labels]) - majority_class, _ = max(collections.Counter(train_labels).items(), - key=operator.itemgetter(1)) + majority_class, _ = max( + collections.Counter(train_labels).items(), key=operator.itemgetter(1)) expected_prediction = np.vstack( [[majority_class] for _ in range(test_labels.shape[0])]) classifier = debug.DebugClassifier(n_classes=2) - classifier.fit(input_fn=_input_fn_builder(train_features, train_labels), - steps=50) + classifier.fit( + input_fn=_input_fn_builder(train_features, train_labels), steps=50) - pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features, - None)) + pred = classifier.predict_classes( + input_fn=_input_fn_builder(test_features, None)) self.assertAllEqual(expected_prediction, np.vstack(pred)) - (train_features, train_labels), ( - test_features, test_labels) = _train_test_split( - [self.features, self.binary_float_labels]) + (train_features, + train_labels), (test_features, test_labels) = _train_test_split( + [self.features, self.binary_float_labels]) - majority_class, _ = max(collections.Counter(train_labels).items(), - key=operator.itemgetter(1)) + majority_class, _ = max( + collections.Counter(train_labels).items(), key=operator.itemgetter(1)) expected_prediction = np.vstack( [[majority_class] for _ in range(test_labels.shape[0])]) classifier = debug.DebugClassifier(n_classes=2) - classifier.fit(input_fn=_input_fn_builder(train_features, train_labels), - steps=50) + classifier.fit( + input_fn=_input_fn_builder(train_features, train_labels), steps=50) - pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features, - None)) + pred = classifier.predict_classes( + input_fn=_input_fn_builder(test_features, None)) self.assertAllEqual(expected_prediction, np.vstack(pred)) def testPredictProba(self): @@ -150,8 +151,8 @@ class DebugClassifierTest(test.TestCase): [class_distribution for _ in range(test_labels.shape[0])]) classifier = debug.DebugClassifier(n_classes=N_CLASSES) - classifier.fit(input_fn=_input_fn_builder(train_features, train_labels), - steps=50) + classifier.fit( + input_fn=_input_fn_builder(train_features, train_labels), steps=50) pred = classifier.predict_proba( input_fn=_input_fn_builder(test_features, None)) @@ -173,17 +174,17 @@ class DebugClassifierTest(test.TestCase): [class_distribution for _ in range(test_labels.shape[0])]) classifier = debug.DebugClassifier(n_classes=2) - classifier.fit(input_fn=_input_fn_builder(train_features, train_labels), - steps=50) + classifier.fit( + input_fn=_input_fn_builder(train_features, train_labels), steps=50) pred = classifier.predict_proba( input_fn=_input_fn_builder(test_features, None)) self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1) - (train_features, train_labels), ( - test_features, test_labels) = _train_test_split( - [self.features, self.binary_float_labels]) + (train_features, + train_labels), (test_features, test_labels) = _train_test_split( + [self.features, self.binary_float_labels]) class_distribution = np.zeros((1, 2)) for label in train_labels: @@ -194,8 +195,8 @@ class DebugClassifierTest(test.TestCase): [class_distribution for _ in range(test_labels.shape[0])]) classifier = debug.DebugClassifier(n_classes=2) - classifier.fit(input_fn=_input_fn_builder(train_features, train_labels), - steps=50) + classifier.fit( + input_fn=_input_fn_builder(train_features, train_labels), steps=50) pred = classifier.predict_proba( input_fn=_input_fn_builder(test_features, None)) @@ -232,13 +233,12 @@ class DebugClassifierTest(test.TestCase): def _input_fn(): iris = test_data.prepare_iris_data_for_logistic_regression() return { - 'feature': constant_op.constant( - iris.data, dtype=dtypes.float32) + 'feature': constant_op.constant(iris.data, dtype=dtypes.float32) }, constant_op.constant( iris.target, shape=[100], dtype=dtypes.int32) - classifier = debug.DebugClassifier(config=run_config.RunConfig( - tf_random_seed=1)) + classifier = debug.DebugClassifier( + config=run_config.RunConfig(tf_random_seed=1)) classifier.fit(input_fn=_input_fn, steps=5) scores = classifier.evaluate(input_fn=_input_fn, steps=1) self.assertIn('loss', scores) @@ -342,8 +342,7 @@ class DebugClassifierTest(test.TestCase): def _input_fn(): iris = base.load_iris() return { - 'feature': constant_op.constant( - iris.data, dtype=dtypes.float32) + 'feature': constant_op.constant(iris.data, dtype=dtypes.float32) }, constant_op.constant( iris.target, shape=[150], dtype=dtypes.int32) @@ -387,7 +386,9 @@ class DebugClassifierTest(test.TestCase): # Create 4 rows, one of them (y = x), three of them (y=Not(x)) # The logistic prediction should be (y = 0.25). labels = constant_op.constant([[1], [0], [0], [0]]) - features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),} + features = { + 'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32), + } return features, labels classifier = debug.DebugClassifier(n_classes=2) @@ -404,8 +405,7 @@ class DebugClassifierTest(test.TestCase): # The logistic prediction should be (y = 0.25). labels = constant_op.constant([[1.], [0.], [0.], [0.]]) features = { - 'x': array_ops.ones( - shape=[4, 1], dtype=dtypes.float32), + 'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32), 'w': constant_op.constant([[1.], [1.], [1.], [1.]]) } return features, labels @@ -414,8 +414,7 @@ class DebugClassifierTest(test.TestCase): # 4 rows, with different weights. labels = constant_op.constant([[1.], [0.], [0.], [0.]]) features = { - 'x': array_ops.ones( - shape=[4, 1], dtype=dtypes.float32), + 'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32), 'w': constant_op.constant([[7.], [1.], [1.], [1.]]) } return features, labels @@ -438,8 +437,7 @@ class DebugClassifierTest(test.TestCase): # than (y=Not(x)) due to the relative higher weight of the first row. labels = constant_op.constant([[1], [0], [0], [0]]) features = { - 'x': array_ops.ones( - shape=[4, 1], dtype=dtypes.float32), + 'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32), 'w': constant_op.constant([[100.], [3.], [2.], [2.]]) } return features, labels @@ -448,8 +446,7 @@ class DebugClassifierTest(test.TestCase): # Create 4 rows (y = x) labels = constant_op.constant([[1], [1], [1], [1]]) features = { - 'x': array_ops.ones( - shape=[4, 1], dtype=dtypes.float32), + 'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32), 'w': constant_op.constant([[1.], [1.], [1.], [1.]]) } return features, labels @@ -469,8 +466,7 @@ class DebugClassifierTest(test.TestCase): features = { 'x': input_lib.limit_epochs( - array_ops.ones( - shape=[4, 1], dtype=dtypes.float32), + array_ops.ones(shape=[4, 1], dtype=dtypes.float32), num_epochs=num_epochs), } return features, labels @@ -578,12 +574,11 @@ class DebugClassifierTest(test.TestCase): language = feature_column.sparse_column_with_hash_bucket('language', 100) feature_columns = [ feature_column.real_valued_column('age'), - feature_column.embedding_column( - language, dimension=1) + feature_column.embedding_column(language, dimension=1) ] - classifier = debug.DebugClassifier(config=run_config.RunConfig( - tf_random_seed=1)) + classifier = debug.DebugClassifier( + config=run_config.RunConfig(tf_random_seed=1)) classifier.fit(input_fn=input_fn, steps=5) def default_input_fn(unused_estimator, examples): @@ -614,8 +609,8 @@ class DebugRegressorTest(test.TestCase): classifier.fit( input_fn=_input_fn_builder(train_features, train_labels), steps=50) - pred = classifier.predict_scores(input_fn=_input_fn_builder(test_features, - None)) + pred = classifier.predict_scores( + input_fn=_input_fn_builder(test_features, None)) self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1) def testExperimentIntegration(self): @@ -698,7 +693,9 @@ class DebugRegressorTest(test.TestCase): # Create 4 rows, one of them (y = x), three of them (y=Not(x)) # The algorithm should learn (y = 0.25). labels = constant_op.constant([[1.], [0.], [0.], [0.]]) - features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),} + features = { + 'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32), + } return features, labels regressor = debug.DebugRegressor( @@ -853,5 +850,6 @@ class DebugRegressorTest(test.TestCase): predictions2 = list(regressor2.predict_scores(input_fn=predict_input_fn)) self.assertAllClose(predictions, predictions2) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py b/tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py index 9d7c1a099a..d4a46b41d0 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py @@ -41,7 +41,6 @@ from tensorflow.python.platform import test from tensorflow.python.training import input as input_lib from tensorflow.python.training import queue_runner_impl - _BOSTON_INPUT_DIM = 13 _IRIS_INPUT_DIM = 4 @@ -93,8 +92,8 @@ def boston_eval_fn(): constant_op.constant(boston.data), [n_examples, _BOSTON_INPUT_DIM]) labels = array_ops.reshape( constant_op.constant(boston.target), [n_examples, 1]) - return array_ops.concat([features, features], 0), array_ops.concat( - [labels, labels], 0) + return array_ops.concat([features, features], + 0), array_ops.concat([labels, labels], 0) def extract(data, key): @@ -129,7 +128,10 @@ def linear_model_fn(features, labels, mode): (_, features), = features.items() prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( - loss, training_util.get_global_step(), optimizer='Adagrad', learning_rate=0.1) + loss, + training_util.get_global_step(), + optimizer='Adagrad', + learning_rate=0.1) return prediction, loss, train_op @@ -139,7 +141,10 @@ def linear_model_fn_with_model_fn_ops(features, labels, mode): model_fn.ModeKeys.INFER) prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( - loss, training_util.get_global_step(), optimizer='Adagrad', learning_rate=0.1) + loss, + training_util.get_global_step(), + optimizer='Adagrad', + learning_rate=0.1) return model_fn.ModelFnOps( mode=mode, predictions=prediction, loss=loss, train_op=train_op) @@ -150,7 +155,10 @@ def logistic_model_no_mode_fn(features, labels): labels = array_ops.one_hot(labels, 3, 1, 0) prediction, loss = (models.logistic_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( - loss, training_util.get_global_step(), optimizer='Adagrad', learning_rate=0.1) + loss, + training_util.get_global_step(), + optimizer='Adagrad', + learning_rate=0.1) return { 'class': math_ops.argmax(prediction, 1), 'prob': prediction @@ -173,7 +181,9 @@ class EstimatorInputTest(test.TestCase): scores = est.evaluate( x=boston_input, y=float64_target, - metrics={'MSE': metric_ops.streaming_mean_squared_error}) + metrics={ + 'MSE': metric_ops.streaming_mean_squared_error + }) del est # Create another estimator object with the same output dir. est2 = estimator.Estimator(model_fn=linear_model_fn, model_dir=output_dir) @@ -182,7 +192,9 @@ class EstimatorInputTest(test.TestCase): scores2 = est2.evaluate( x=boston_input, y=float64_target, - metrics={'MSE': metric_ops.streaming_mean_squared_error}) + metrics={ + 'MSE': metric_ops.streaming_mean_squared_error + }) self.assertAllClose(scores2['MSE'], scores['MSE']) predictions = np.array(list(est2.predict(x=boston_input))) other_score = _sklearn.mean_squared_error(predictions, @@ -197,7 +209,9 @@ class EstimatorInputTest(test.TestCase): scores = est.score( x=boston.data, y=float64_labels, - metrics={'MSE': metric_ops.streaming_mean_squared_error}) + metrics={ + 'MSE': metric_ops.streaming_mean_squared_error + }) predictions = np.array(list(est.predict(x=boston.data))) other_score = _sklearn.mean_squared_error(predictions, boston.target) self.assertAllClose(scores['MSE'], other_score) @@ -213,7 +227,9 @@ class EstimatorInputTest(test.TestCase): scores = est.evaluate( x=boston_input, y=float64_target, - metrics={'MSE': metric_ops.streaming_mean_squared_error}) + metrics={ + 'MSE': metric_ops.streaming_mean_squared_error + }) predictions = np.array(list(est.predict(x=boston_input))) other_score = _sklearn.mean_squared_error(predictions, boston.target) self.assertAllClose(other_score, scores['MSE']) @@ -228,14 +244,15 @@ class EstimatorInputTest(test.TestCase): scores = est.score( x=iris.data, y=iris.target, - metrics={('accuracy', 'class'): metric_ops.streaming_accuracy}) + metrics={ + ('accuracy', 'class'): metric_ops.streaming_accuracy + }) predictions = est.predict(x=iris.data) predictions_class = est.predict(x=iris.data, outputs=['class'])['class'] self.assertEqual(predictions['prob'].shape[0], iris.target.shape[0]) self.assertAllClose(predictions['class'], predictions_class) - self.assertAllClose( - predictions['class'], np.argmax( - predictions['prob'], axis=1)) + self.assertAllClose(predictions['class'], + np.argmax(predictions['prob'], axis=1)) other_score = _sklearn.accuracy_score(iris.target, predictions['class']) self.assertAllClose(scores['accuracy'], other_score) self.assertTrue('global_step' in scores) @@ -250,17 +267,18 @@ class EstimatorInputTest(test.TestCase): scores = est.evaluate( x=iris_data, y=iris_target, - metrics={('accuracy', 'class'): metric_ops.streaming_accuracy}) + metrics={ + ('accuracy', 'class'): metric_ops.streaming_accuracy + }) predictions = list(est.predict(x=iris_data)) predictions_class = list(est.predict(x=iris_data, outputs=['class'])) self.assertEqual(len(predictions), iris.target.shape[0]) classes_batch = np.array([p['class'] for p in predictions]) self.assertAllClose(classes_batch, np.array([p['class'] for p in predictions_class])) - self.assertAllClose( - classes_batch, - np.argmax( - np.array([p['prob'] for p in predictions]), axis=1)) + self.assertAllClose(classes_batch, + np.argmax( + np.array([p['prob'] for p in predictions]), axis=1)) other_score = _sklearn.accuracy_score(iris.target, classes_batch) self.assertAllClose(other_score, scores['accuracy']) self.assertTrue('global_step' in scores) diff --git a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py index 656d68b768..ac2d10011e 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor_test.py @@ -57,7 +57,10 @@ def _logistic_regression_model_fn(features, labels, mode): predictions = math_ops.sigmoid(logits) loss = losses.sigmoid_cross_entropy(labels, logits) train_op = optimizers.optimize_loss( - loss, training_util.get_global_step(), optimizer='Adagrad', learning_rate=0.1) + loss, + training_util.get_global_step(), + optimizer='Adagrad', + learning_rate=0.1) return predictions, loss, train_op diff --git a/tensorflow/contrib/learn/python/learn/evaluable.py b/tensorflow/contrib/learn/python/learn/evaluable.py index 66e1526517..8f6cd39864 100644 --- a/tensorflow/contrib/learn/python/learn/evaluable.py +++ b/tensorflow/contrib/learn/python/learn/evaluable.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """`Evaluable` interface.""" from __future__ import absolute_import @@ -59,9 +58,12 @@ class Evaluable(object): for which this evaluation was performed. Args: - x: Matrix of shape [n_samples, n_features...] or dictionary of many matrices - containing the input samples for fitting the model. Can be iterator that returns - arrays of features or dictionary of array of features. If set, `input_fn` must + x: Matrix of shape [n_samples, n_features...] or dictionary of many + matrices + containing the input samples for fitting the model. Can be iterator that + returns + arrays of features or dictionary of array of features. If set, + `input_fn` must be `None`. y: Vector or matrix [n_samples] or [n_samples, n_outputs] containing the label values (class labels in classification, real numbers in diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py index 9576ff21c2..bec976afd2 100644 --- a/tensorflow/contrib/learn/python/learn/experiment.py +++ b/tensorflow/contrib/learn/python/learn/experiment.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Experiment class collecting information needed for a single training run.""" from __future__ import absolute_import @@ -43,7 +42,6 @@ from tensorflow.python.training import saver from tensorflow.python.training import server_lib from tensorflow.python.util import compat - __all__ = ["Experiment"] @@ -278,8 +276,7 @@ class Experiment(object): self._train_steps_per_iteration = train_steps_per_iteration if (self._train_steps_per_iteration is not None and not isinstance(self._train_steps_per_iteration, int)): - raise ValueError( - "`train_steps_per_iteration` must be an integer.") + raise ValueError("`train_steps_per_iteration` must be an integer.") @property def estimator(self): @@ -359,9 +356,10 @@ class Experiment(object): config.cluster_spec and config.master): self._start_server() elif config.cluster_spec and config.master: - raise ValueError('For distributed runtime, Experiment class only works with' - 'tf.contrib.learn.RunConfig for now, but provided {}' - .format(type(config))) + raise ValueError( + "For distributed runtime, Experiment class only works with" + "tf.contrib.learn.RunConfig for now, but provided {}".format( + type(config))) extra_hooks = [] if delay_secs is None: @@ -414,11 +412,12 @@ class Experiment(object): logging.info("Waiting %d secs before starting eval.", delay_secs) time.sleep(delay_secs) - return self._call_evaluate(input_fn=self._eval_input_fn, - steps=self._eval_steps, - metrics=self._eval_metrics, - name=(name or "one_pass"), - hooks=self._eval_hooks) + return self._call_evaluate( + input_fn=self._eval_input_fn, + steps=self._eval_steps, + metrics=self._eval_metrics, + name=(name or "one_pass"), + hooks=self._eval_hooks) @deprecated( "2016-10-23", @@ -499,15 +498,12 @@ class Experiment(object): previous_path = None eval_result = None last_warning_time = 0 - while (not predicate_fn or - predicate_fn( - eval_result, - checkpoint_path=previous_path if eval_result else None)): + while (not predicate_fn or predicate_fn( + eval_result, checkpoint_path=previous_path if eval_result else None)): # Exit if we have already reached number of steps to train. if self._has_training_stopped(eval_result): logging.info("Exiting continuous eval, global_step=%s >= " - "train_step=%s", - eval_result[ops.GraphKeys.GLOBAL_STEP], + "train_step=%s", eval_result[ops.GraphKeys.GLOBAL_STEP], self._train_steps) return @@ -528,12 +524,13 @@ class Experiment(object): logging.warning(error_msg) last_warning_time = time.time() else: - eval_result = self._call_evaluate(input_fn=input_fn, - steps=self._eval_steps, - metrics=self._eval_metrics, - name=name, - checkpoint_path=latest_path, - hooks=self._eval_hooks) + eval_result = self._call_evaluate( + input_fn=input_fn, + steps=self._eval_steps, + metrics=self._eval_metrics, + name=name, + checkpoint_path=latest_path, + hooks=self._eval_hooks) # Ensure eval result is not None for next round of evaluation. if not eval_result: eval_result = {} @@ -558,8 +555,8 @@ class Experiment(object): return False global_step = eval_result.get(ops.GraphKeys.GLOBAL_STEP) - return global_step and self._train_steps and ( - global_step >= self._train_steps) + return global_step and self._train_steps and (global_step >= + self._train_steps) def continuous_eval(self, delay_secs=None, @@ -678,8 +675,7 @@ class Experiment(object): return eval_result, export_results @experimental - def continuous_train_and_eval(self, - continuous_eval_predicate_fn=None): + def continuous_train_and_eval(self, continuous_eval_predicate_fn=None): """Interleaves training and evaluation. The frequency of evaluation is controlled by the `train_steps_per_iteration` @@ -752,10 +748,9 @@ class Experiment(object): elif self._train_steps is not None: train_steps_per_iteration = int(self._train_steps / 10) - while (not predicate_fn or - predicate_fn( - eval_result, - checkpoint_path=latest_checkpoint if eval_result else None)): + while (not predicate_fn or predicate_fn( + eval_result, checkpoint_path=latest_checkpoint + if eval_result else None)): if self._has_training_stopped(eval_result): # Exits once max steps of training is satisfied. @@ -785,8 +780,7 @@ class Experiment(object): def _maybe_export(self, eval_result, checkpoint_path=None): """Export the Estimator using export_fn, if defined.""" export_dir_base = os.path.join( - compat.as_bytes(self._estimator.model_dir), - compat.as_bytes("export")) + compat.as_bytes(self._estimator.model_dir), compat.as_bytes("export")) export_results = [] for strategy in self._export_strategies: @@ -824,10 +818,11 @@ class Experiment(object): hooks=self._train_monitors, saving_listeners=self._saving_listeners) - eval_result = self._call_evaluate(input_fn=self._eval_input_fn, - steps=1, - metrics=self._eval_metrics, - name="one_pass") + eval_result = self._call_evaluate( + input_fn=self._eval_input_fn, + steps=1, + metrics=self._eval_metrics, + name="one_pass") _ = self._maybe_export(eval_result) return eval_result @@ -849,9 +844,14 @@ class Experiment(object): server.start() return server - def _call_train(self, _sentinel=None, # pylint: disable=invalid-name, - input_fn=None, steps=None, hooks=None, max_steps=None, - saving_listeners=None): + def _call_train( + self, + _sentinel=None, # pylint: disable=invalid-name, + input_fn=None, + steps=None, + hooks=None, + max_steps=None, + saving_listeners=None): if _sentinel is not None: raise ValueError("_call_train should be called with keyword args only") @@ -867,14 +867,18 @@ class Experiment(object): hooks=hooks, saving_listeners=saving_listeners) else: - return self._estimator.fit(input_fn=input_fn, - steps=steps, - max_steps=max_steps, - monitors=hooks) - - def _call_evaluate(self, _sentinel=None, # pylint: disable=invalid-name, - input_fn=None, steps=None, metrics=None, name=None, - checkpoint_path=None, hooks=None): + return self._estimator.fit( + input_fn=input_fn, steps=steps, max_steps=max_steps, monitors=hooks) + + def _call_evaluate( + self, + _sentinel=None, # pylint: disable=invalid-name, + input_fn=None, + steps=None, + metrics=None, + name=None, + checkpoint_path=None, + hooks=None): if _sentinel is not None: raise ValueError("_call_evaluate should be called with keyword args only") @@ -882,18 +886,20 @@ class Experiment(object): if metrics is not None: raise ValueError( "`eval_metrics` must be `None` with `tf.estimator.Estimator`") - return self._estimator.evaluate(input_fn=input_fn, - steps=steps, - name=name, - checkpoint_path=checkpoint_path, - hooks=hooks) + return self._estimator.evaluate( + input_fn=input_fn, + steps=steps, + name=name, + checkpoint_path=checkpoint_path, + hooks=hooks) else: - return self._estimator.evaluate(input_fn=input_fn, - steps=steps, - metrics=metrics, - name=name, - checkpoint_path=checkpoint_path, - hooks=hooks) + return self._estimator.evaluate( + input_fn=input_fn, + steps=steps, + metrics=metrics, + name=name, + checkpoint_path=checkpoint_path, + hooks=hooks) @contextlib.contextmanager diff --git a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py index f36a778b52..96be8b1bc4 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py @@ -35,6 +35,7 @@ from tensorflow.python.platform import tf_logging as logging # pylint: disable=g-multiple-import,g-bad-import-order from .pandas_io import HAS_PANDAS, extract_pandas_data, extract_pandas_matrix, extract_pandas_labels from .dask_io import HAS_DASK, extract_dask_data, extract_dask_labels + # pylint: enable=g-multiple-import,g-bad-import-order @@ -74,11 +75,11 @@ def _get_in_out_shape(x_shape, y_shape, n_classes, batch_size=None): if not y_is_dict: output_shape = out_el_shape(y_shape, n_classes) else: - output_shape = dict([ - (k, out_el_shape(v, n_classes[k] - if n_classes is not None and k in n_classes else None)) - for k, v in list(y_shape.items()) - ]) + output_shape = dict([(k, + out_el_shape(v, n_classes[k] + if n_classes is not None and + k in n_classes else None)) + for k, v in list(y_shape.items())]) return input_shape, output_shape, batch_size @@ -314,23 +315,23 @@ class DataFeeder(object): input_dtype: DType of input (or dictionary of shapes). output_dtype: DType of output (or dictionary of shapes. """ - x_is_dict, y_is_dict = isinstance(x, dict), y is not None and isinstance( - y, dict) + x_is_dict, y_is_dict = isinstance( + x, dict), y is not None and isinstance(y, dict) if isinstance(y, list): y = np.array(y) self._x = dict([(k, check_array(v, v.dtype)) for k, v in list(x.items()) ]) if x_is_dict else check_array(x, x.dtype) - self._y = None if y is None else ( - dict([(k, check_array(v, v.dtype)) for k, v in list(y.items())]) - if y_is_dict else check_array(y, y.dtype)) + self._y = None if y is None else (dict( + [(k, check_array(v, v.dtype)) for k, v in list(y.items())]) + if y_is_dict else check_array(y, y.dtype)) # self.n_classes is not None means we're converting raw target indices # to one-hot. if n_classes is not None: if not y_is_dict: - y_dtype = (np.int64 - if n_classes is not None and n_classes > 1 else np.float32) + y_dtype = ( + np.int64 if n_classes is not None and n_classes > 1 else np.float32) self._y = (None if y is None else check_array(y, dtype=y_dtype)) self.n_classes = n_classes @@ -352,8 +353,8 @@ class DataFeeder(object): # self._output_dtype == np.float32 when y is None self._output_dtype = ( dict([(k, _check_dtype(v.dtype)) for k, v in list(self._y.items())]) - if y_is_dict else ( - _check_dtype(self._y.dtype) if y is not None else np.float32)) + if y_is_dict else (_check_dtype(self._y.dtype) + if y is not None else np.float32)) # self.n_classes is None means we're passing in raw target indices if n_classes is not None and y_is_dict: @@ -478,8 +479,8 @@ class DataFeeder(object): # Assign input features from random indices. def extract(data, indices): - return (np.array(_access(data, indices)).reshape((indices.shape[0], 1)) if - len(data.shape) == 1 else _access(data, indices)) + return (np.array(_access(data, indices)).reshape((indices.shape[0], 1)) + if len(data.shape) == 1 else _access(data, indices)) # assign labels from random indices def assign_label(data, shape, dtype, n_classes, indices): @@ -511,16 +512,18 @@ class DataFeeder(object): feed_dict[self._epoch_placeholder.name] = [self.epoch] # Take next batch of indices. - x_len = list(self._x.values())[0].shape[ - 0] if x_is_dict else self._x.shape[0] + x_len = list( + self._x.values())[0].shape[0] if x_is_dict else self._x.shape[0] end = min(x_len, self.offset + self._batch_size) batch_indices = self.indices[self.offset:end] # adding input placeholder feed_dict.update( dict([(self._input_placeholder[k].name, extract(v, batch_indices)) - for k, v in list(self._x.items())]) if x_is_dict else - {self._input_placeholder.name: extract(self._x, batch_indices)}) + for k, v in list(self._x.items())]) if x_is_dict else { + self._input_placeholder.name: + extract(self._x, batch_indices) + }) # move offset and reset it if necessary self.offset += self._batch_size @@ -545,7 +548,8 @@ class DataFeeder(object): assign_label(v, shape, dtype, n_classes, batch_indices) }) else: - shape, dtype, n_classes = self.output_shape, self._output_dtype, self.n_classes + shape, dtype, n_classes = (self.output_shape, self._output_dtype, + self.n_classes) feed_dict.update({ self._output_placeholder.name: assign_label(self._y, shape, dtype, n_classes, batch_indices) @@ -621,8 +625,9 @@ class StreamingDataFeeder(DataFeeder): elif y is None: y_first_el_shape = None else: - y_first_el_shape = ([1] + list(y_first_el[0].shape if isinstance( - y_first_el, list) else y_first_el.shape)) + y_first_el_shape = ( + [1] + list(y_first_el[0].shape + if isinstance(y_first_el, list) else y_first_el.shape)) self.input_shape, self.output_shape, self._batch_size = _get_in_out_shape( x_first_el_shape, y_first_el_shape, n_classes, batch_size) @@ -683,8 +688,8 @@ class StreamingDataFeeder(DataFeeder): if shape is None: return None elif isinstance(shape, dict): - return dict([(k, np.zeros(shape[k], dtype[k])) - for k in list(shape.keys())]) + return dict( + [(k, np.zeros(shape[k], dtype[k])) for k in list(shape.keys())]) else: return np.zeros(shape, dtype=dtype) diff --git a/tensorflow/contrib/learn/python/learn/trainable.py b/tensorflow/contrib/learn/python/learn/trainable.py index 972fec026f..429b6040be 100644 --- a/tensorflow/contrib/learn/python/learn/trainable.py +++ b/tensorflow/contrib/learn/python/learn/trainable.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """`Trainable` interface.""" from __future__ import absolute_import @@ -28,18 +27,31 @@ class Trainable(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod - def fit(self, x=None, y=None, input_fn=None, steps=None, batch_size=None, - monitors=None, max_steps=None): + def fit(self, + x=None, + y=None, + input_fn=None, + steps=None, + batch_size=None, + monitors=None, + max_steps=None): """Trains a model given training data `x` predictions and `y` labels. Args: - x: Matrix of shape [n_samples, n_features...] or the dictionary of Matrices. - Can be iterator that returns arrays of features or dictionary of arrays of features. - The training input samples for fitting the model. If set, `input_fn` must be `None`. - y: Vector or matrix [n_samples] or [n_samples, n_outputs] or the dictionary of same. - Can be iterator that returns array of labels or dictionary of array of labels. - The training label values (class labels in classification, real numbers in regression). - If set, `input_fn` must be `None`. Note: For classification, label values must + x: Matrix of shape [n_samples, n_features...] or the dictionary of + Matrices. + Can be iterator that returns arrays of features or dictionary of arrays + of features. + The training input samples for fitting the model. If set, `input_fn` + must be `None`. + y: Vector or matrix [n_samples] or [n_samples, n_outputs] or the + dictionary of same. + Can be iterator that returns array of labels or dictionary of array of + labels. + The training label values (class labels in classification, real numbers + in regression). + If set, `input_fn` must be `None`. Note: For classification, label + values must be integers representing the class index (i.e. values from 0 to n_classes-1). input_fn: Input function returning a tuple of: diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py index 7c523ad492..8c3a8afe7a 100644 --- a/tensorflow/contrib/losses/python/losses/loss_ops.py +++ b/tensorflow/contrib/losses/python/losses/loss_ops.py @@ -30,20 +30,13 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.util.deprecation import deprecated from tensorflow.python.util.deprecation import deprecated_args -__all__ = ["absolute_difference", - "add_loss", - "cosine_distance", - "compute_weighted_loss", - "get_losses", - "get_regularization_losses", - "get_total_loss", - "hinge_loss", - "log_loss", - "mean_pairwise_squared_error", - "mean_squared_error", - "sigmoid_cross_entropy", - "softmax_cross_entropy", - "sparse_softmax_cross_entropy"] +__all__ = [ + "absolute_difference", "add_loss", "cosine_distance", + "compute_weighted_loss", "get_losses", "get_regularization_losses", + "get_total_loss", "hinge_loss", "log_loss", "mean_pairwise_squared_error", + "mean_squared_error", "sigmoid_cross_entropy", "softmax_cross_entropy", + "sparse_softmax_cross_entropy" +] def _scale_losses(losses, weights): @@ -66,8 +59,8 @@ def _scale_losses(losses, weights): # First, compute the sum of the losses over all elements: start_index = max(0, weights.get_shape().ndims) reduction_indices = list(range(start_index, losses.get_shape().ndims)) - reduced_losses = math_ops.reduce_sum(losses, - reduction_indices=reduction_indices) + reduced_losses = math_ops.reduce_sum( + losses, reduction_indices=reduction_indices) reduced_losses = math_ops.multiply(reduced_losses, weights) return math_ops.reduce_sum(reduced_losses) @@ -90,9 +83,10 @@ def _safe_div(numerator, denominator, name="value"): """ return array_ops.where( math_ops.greater(denominator, 0), - math_ops.div(numerator, array_ops.where( - math_ops.equal(denominator, 0), - array_ops.ones_like(denominator), denominator)), + math_ops.div(numerator, + array_ops.where( + math_ops.equal(denominator, 0), + array_ops.ones_like(denominator), denominator)), array_ops.zeros_like(numerator), name=name) @@ -176,14 +170,15 @@ def _num_present(losses, weights, per_batch=False): """ # If weights is a scalar, its easy to compute: if weights.get_shape().ndims == 0: - batch_size = array_ops.reshape(array_ops.slice(array_ops.shape(losses), - [0], [1]), []) - num_per_batch = math_ops.div(math_ops.to_float(array_ops.size(losses)), - math_ops.to_float(batch_size)) - num_per_batch = array_ops.where(math_ops.equal(weights, 0), - 0.0, num_per_batch) - num_per_batch = math_ops.multiply(array_ops.ones( - array_ops.reshape(batch_size, [1])), num_per_batch) + batch_size = array_ops.reshape( + array_ops.slice(array_ops.shape(losses), [0], [1]), []) + num_per_batch = math_ops.div( + math_ops.to_float(array_ops.size(losses)), + math_ops.to_float(batch_size)) + num_per_batch = array_ops.where( + math_ops.equal(weights, 0), 0.0, num_per_batch) + num_per_batch = math_ops.multiply( + array_ops.ones(array_ops.reshape(batch_size, [1])), num_per_batch) return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch) # First, count the number of nonzero weights: @@ -194,8 +189,8 @@ def _num_present(losses, weights, per_batch=False): reduction_indices=reduction_indices) # Next, determine the number of elements that weights would broadcast to: - broadcast_dims = array_ops.slice(array_ops.shape(losses), - [weights.get_shape().ndims], [-1]) + broadcast_dims = array_ops.slice( + array_ops.shape(losses), [weights.get_shape().ndims], [-1]) num_to_broadcast = math_ops.to_float(math_ops.reduce_prod(broadcast_dims)) num_per_batch = math_ops.multiply(num_nonzero_per_batch, num_to_broadcast) @@ -303,8 +298,11 @@ def absolute_difference(predictions, labels=None, weights=1.0, scope=None): @deprecated("2016-12-30", "Use tf.losses.sigmoid_cross_entropy instead. Note that the order " "of the predictions and labels arguments has been changed.") -def sigmoid_cross_entropy( - logits, multi_class_labels, weights=1.0, label_smoothing=0, scope=None): +def sigmoid_cross_entropy(logits, + multi_class_labels, + weights=1.0, + label_smoothing=0, + scope=None): """Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. `weights` acts as a coefficient for the loss. If a scalar is provided, @@ -340,20 +338,22 @@ def sigmoid_cross_entropy( multi_class_labels = math_ops.cast(multi_class_labels, logits.dtype) if label_smoothing > 0: - multi_class_labels = (multi_class_labels * (1 - label_smoothing) + - 0.5 * label_smoothing) + multi_class_labels = ( + multi_class_labels * (1 - label_smoothing) + 0.5 * label_smoothing) - losses = nn.sigmoid_cross_entropy_with_logits(labels=multi_class_labels, - logits=logits, - name="xentropy") + losses = nn.sigmoid_cross_entropy_with_logits( + labels=multi_class_labels, logits=logits, name="xentropy") return compute_weighted_loss(losses, weights, scope=scope) @deprecated("2016-12-30", "Use tf.losses.softmax_cross_entropy instead. Note that the order " "of the logits and labels arguments has been changed.") -def softmax_cross_entropy( - logits, onehot_labels, weights=1.0, label_smoothing=0, scope=None): +def softmax_cross_entropy(logits, + onehot_labels, + weights=1.0, + label_smoothing=0, + scope=None): """Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits. `weights` acts as a coefficient for the loss. If a scalar is provided, @@ -393,9 +393,8 @@ def softmax_cross_entropy( smooth_negatives = label_smoothing / num_classes onehot_labels = onehot_labels * smooth_positives + smooth_negatives - losses = nn.softmax_cross_entropy_with_logits(labels=onehot_labels, - logits=logits, - name="xentropy") + losses = nn.softmax_cross_entropy_with_logits( + labels=onehot_labels, logits=logits, name="xentropy") return compute_weighted_loss(losses, weights, scope=scope) @@ -429,9 +428,8 @@ def sparse_softmax_cross_entropy(logits, labels, weights=1.0, scope=None): [logits, labels, weights]) as scope: labels = array_ops.reshape(labels, shape=[array_ops.shape(labels)[0]]) - losses = nn.sparse_softmax_cross_entropy_with_logits(labels=labels, - logits=logits, - name="xentropy") + losses = nn.sparse_softmax_cross_entropy_with_logits( + labels=labels, logits=logits, name="xentropy") return compute_weighted_loss(losses, weights, scope=scope) @@ -470,8 +468,7 @@ def log_loss(predictions, labels=None, weights=1.0, epsilon=1e-7, scope=None): predictions = math_ops.to_float(predictions) labels = math_ops.to_float(labels) losses = -math_ops.multiply( - labels, - math_ops.log(predictions + epsilon)) - math_ops.multiply( + labels, math_ops.log(predictions + epsilon)) - math_ops.multiply( (1 - labels), math_ops.log(1 - predictions + epsilon)) return compute_weighted_loss(losses, weights, scope=scope) @@ -490,7 +487,8 @@ def hinge_loss(logits, labels=None, scope=None): scope: The scope for the operations performed in computing the loss. Returns: - An unweighted `Tensor` of same shape as `logits` and `labels` representing the + An unweighted `Tensor` of same shape as `logits` and `labels` representing + the loss values across the batch. Raises: @@ -544,8 +542,10 @@ def mean_squared_error(predictions, labels=None, weights=1.0, scope=None): @deprecated("2016-12-30", "Use tf.losses.mean_pairwise_squared_error instead. Note that the " "order of the predictions and labels arguments has been changed.") -def mean_pairwise_squared_error( - predictions, labels=None, weights=1.0, scope=None): +def mean_pairwise_squared_error(predictions, + labels=None, + weights=1.0, + scope=None): """Adds a pairwise-errors-squared loss to the training procedure. Unlike `mean_squared_error`, which is a measure of the differences between @@ -602,31 +602,34 @@ def mean_pairwise_squared_error( reduction_indices = list(range(1, diffs.get_shape().ndims)) sum_squares_diff_per_batch = math_ops.reduce_sum( - math_ops.square(diffs), - reduction_indices=reduction_indices) + math_ops.square(diffs), reduction_indices=reduction_indices) num_present_per_batch = _num_present(diffs, weights, per_batch=True) - term1 = 2.0 * _safe_div(sum_squares_diff_per_batch, - num_present_per_batch) + term1 = 2.0 * _safe_div(sum_squares_diff_per_batch, num_present_per_batch) sum_diff = math_ops.reduce_sum(diffs, reduction_indices=reduction_indices) - term2 = 2.0 * _safe_div(math_ops.square(sum_diff), - math_ops.square(num_present_per_batch)) + term2 = 2.0 * _safe_div( + math_ops.square(sum_diff), math_ops.square(num_present_per_batch)) loss = _scale_losses(term1 - term2, weights) - mean_loss = array_ops.where(math_ops.reduce_sum(num_present_per_batch) > 0, - loss, - array_ops.zeros_like(loss), - name="value") + mean_loss = array_ops.where( + math_ops.reduce_sum(num_present_per_batch) > 0, + loss, + array_ops.zeros_like(loss), + name="value") add_loss(mean_loss) return mean_loss @deprecated("2016-12-30", "Use tf.losses.cosine_distance instead.") @deprecated_args(None, "dim is deprecated, use axis instead", "dim") -def cosine_distance( - predictions, labels=None, axis=None, weights=1.0, scope=None, dim=None): +def cosine_distance(predictions, + labels=None, + axis=None, + weights=1.0, + scope=None, + dim=None): """Adds a cosine-distance loss to the training procedure. Note that the function assumes that `predictions` and `labels` are already @@ -662,5 +665,8 @@ def cosine_distance( labels = math_ops.to_float(labels) radial_diffs = math_ops.multiply(predictions, labels) - losses = 1 - math_ops.reduce_sum(radial_diffs, reduction_indices=[axis,]) + losses = 1 - math_ops.reduce_sum( + radial_diffs, reduction_indices=[ + axis, + ]) return compute_weighted_loss(losses, weights, scope=scope) diff --git a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py index 73dd56398c..660f0168b1 100644 --- a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py +++ b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_pruning.py @@ -48,7 +48,7 @@ from tensorflow.contrib.model_pruning.python import pruning # Global constants describing the CIFAR-10 data set. IMAGE_SIZE = cifar10_input.IMAGE_SIZE NUM_CLASSES = cifar10_input.NUM_CLASSES -NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN +NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN # pylint: disable=line-too-long NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL BATCH_SIZE = 128 DATA_DIR = '/tmp/cifar10_data' diff --git a/tensorflow/contrib/mpi_collectives/python/ops/mpi_ops.py b/tensorflow/contrib/mpi_collectives/python/ops/mpi_ops.py index f0a116239d..2fbefef0d3 100644 --- a/tensorflow/contrib/mpi_collectives/python/ops/mpi_ops.py +++ b/tensorflow/contrib/mpi_collectives/python/ops/mpi_ops.py @@ -26,7 +26,8 @@ from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader _mpi_ops_so = loader.load_op_library( - resource_loader.get_path_to_datafile("_mpi_ops.so")) + resource_loader.get_path_to_datafile('_mpi_ops.so')) + def size(name=None): """An op which returns the number of MPI processes. @@ -120,15 +121,14 @@ def allgather(tensor, name=None): """ # Specify that first allgather is to collect the tensor gather sizes, # indicated by passing in a scalar (0-D tensor) of value 0 - sizes_flag = tf.constant(0, dtype=tf.int64, name="size_flag_const") - my_size = tf.slice(tf.shape(tensor, out_type=tf.int64), [0], [1], name="size_slice") + sizes_flag = tf.constant(0, dtype=tf.int64, name='size_flag_const') + my_size = tf.slice( + tf.shape(tensor, out_type=tf.int64), [0], [1], name='size_slice') if name is None: - name = "allgather" - sizing_name = "{}_sizing".format(name) + name = 'allgather' + sizing_name = '{}_sizing'.format(name) sizes = gen_mpi_ops.mpi_allgather(my_size, sizes_flag, name=sizing_name) return gen_mpi_ops.mpi_allgather(tensor, sizes, name=name) ops.NotDifferentiable('MPIAllgather') - - diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index 0258d7202d..57521c6a9b 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -45,6 +45,7 @@ from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging from tensorflow.python.util import nest + class Plus1RNNCell(rnn_lib.RNNCell): """RNN Cell generating (output, new_state) = (input + 1, state + 1).""" @@ -160,8 +161,7 @@ class RNNTest(test.TestCase): input_size = 5 max_length = 8 # unrolled up to this length inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32) self.assertEqual(len(outputs), len(inputs)) @@ -178,10 +178,9 @@ class RNNTest(test.TestCase): self.assertAllClose(v, input_value + 1.0) # Final state - self.assertAllClose( - values[-1], - max_length * np.ones( - (batch_size, input_size), dtype=np.float32)) + self.assertAllClose(values[-1], + max_length * np.ones( + (batch_size, input_size), dtype=np.float32)) def testDropout(self): cell = Plus1RNNCell() @@ -191,8 +190,7 @@ class RNNTest(test.TestCase): input_size = 5 max_length = 8 inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] with variable_scope.variable_scope("share_scope"): outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32) @@ -207,8 +205,10 @@ class RNNTest(test.TestCase): with self.test_session(use_gpu=True) as sess: input_value = np.random.randn(batch_size, input_size) values = sess.run(outputs + [state], feed_dict={inputs[0]: input_value}) - full_dropout_values = sess.run(dropped_outputs, - feed_dict={inputs[0]: input_value}) + full_dropout_values = sess.run( + dropped_outputs, feed_dict={ + inputs[0]: input_value + }) for v in values[:-1]: self.assertAllClose(v, input_value + 1.0) @@ -222,8 +222,7 @@ class RNNTest(test.TestCase): input_size = 5 max_length = 8 inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] with variable_scope.variable_scope("drop_scope"): dynamic_outputs, dynamic_state = rnn.static_rnn( @@ -234,12 +233,16 @@ class RNNTest(test.TestCase): input_value = np.random.randn(batch_size, input_size) dynamic_values = sess.run( dynamic_outputs, - feed_dict={inputs[0]: input_value, - sequence_length: [2, 3]}) + feed_dict={ + inputs[0]: input_value, + sequence_length: [2, 3] + }) dynamic_state_value = sess.run( [dynamic_state], - feed_dict={inputs[0]: input_value, - sequence_length: [2, 3]}) + feed_dict={ + inputs[0]: input_value, + sequence_length: [2, 3] + }) # outputs are fully calculated for t = 0, 1 for v in dynamic_values[:2]: @@ -289,8 +292,7 @@ class RNNTest(test.TestCase): input_size = 5 max_length = 8 # unrolled up to this length inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] return rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope=scope) @@ -316,8 +318,7 @@ class LSTMTest(test.TestCase): cell = rnn_cell.LSTMCell( num_units, initializer=initializer, state_is_tuple=False) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32) self.assertEqual(len(outputs), len(inputs)) @@ -343,8 +344,7 @@ class LSTMTest(test.TestCase): initializer=initializer, state_is_tuple=False) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32) self.assertEqual(len(outputs), len(inputs)) @@ -374,8 +374,7 @@ class LSTMTest(test.TestCase): initializer=initializer, state_is_tuple=False) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] with variable_scope.variable_scope("share_scope"): outputs, state = rnn.static_state_saving_rnn( @@ -388,7 +387,9 @@ class LSTMTest(test.TestCase): input_value = np.random.randn(batch_size, input_size) (last_state_value, saved_state_value) = sess.run( [state, state_saver.saved_state["save_lstm"]], - feed_dict={inputs[0]: input_value}) + feed_dict={ + inputs[0]: input_value + }) self.assertAllEqual(last_state_value, saved_state_value) def testNoProjNoShardingTupleStateSaver(self): @@ -406,8 +407,7 @@ class LSTMTest(test.TestCase): initializer=initializer, state_is_tuple=True) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] with variable_scope.variable_scope("share_scope"): outputs, state = rnn.static_state_saving_rnn( @@ -420,7 +420,9 @@ class LSTMTest(test.TestCase): input_value = np.random.randn(batch_size, input_size) last_and_saved_states = sess.run( state + (state_saver.saved_state["c"], state_saver.saved_state["m"]), - feed_dict={inputs[0]: input_value}) + feed_dict={ + inputs[0]: input_value + }) self.assertEqual(4, len(last_and_saved_states)) self.assertAllEqual(last_and_saved_states[:2], last_and_saved_states[2:]) @@ -432,16 +434,17 @@ class LSTMTest(test.TestCase): with self.test_session(graph=ops_lib.Graph()) as sess: initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) - state_saver = TestStateSaver(batch_size, { - "c0": num_units, - "m0": num_units, - "c1": num_units + 1, - "m1": num_units + 1, - "c2": num_units + 2, - "m2": num_units + 2, - "c3": num_units + 3, - "m3": num_units + 3 - }) + state_saver = TestStateSaver( + batch_size, { + "c0": num_units, + "m0": num_units, + "c1": num_units + 1, + "m1": num_units + 1, + "c2": num_units + 2, + "m2": num_units + 2, + "c3": num_units + 3, + "m3": num_units + 3 + }) def _cell(i): return rnn_cell.LSTMCell( @@ -459,8 +462,7 @@ class LSTMTest(test.TestCase): self.assertEqual(len(cell.state_size[i]), 2) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] state_names = (("c0", "m0"), ("c1", "m1"), ("c2", "m2"), ("c3", "m3")) @@ -475,10 +477,15 @@ class LSTMTest(test.TestCase): variables_lib.global_variables_initializer().run() input_value = np.random.randn(batch_size, input_size) - last_states = sess.run(list(nest.flatten(state)), - feed_dict={inputs[0]: input_value}) - saved_states = sess.run(list(state_saver.saved_state.values()), - feed_dict={inputs[0]: input_value}) + last_states = sess.run( + list(nest.flatten(state)), feed_dict={ + inputs[0]: input_value + }) + saved_states = sess.run( + list(state_saver.saved_state.values()), + feed_dict={ + inputs[0]: input_value + }) self.assertEqual(8, len(last_states)) self.assertEqual(8, len(saved_states)) flat_state_names = nest.flatten(state_names) @@ -499,8 +506,7 @@ class LSTMTest(test.TestCase): initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] cell = rnn_cell.LSTMCell( num_units, @@ -526,8 +532,7 @@ class LSTMTest(test.TestCase): initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] cell_notuple = rnn_cell.LSTMCell( num_units, @@ -569,14 +574,20 @@ class LSTMTest(test.TestCase): variables_lib.global_variables_initializer().run() input_value = np.random.randn(batch_size, input_size) - outputs_notuple_v = sess.run(outputs_notuple, - feed_dict={inputs[0]: input_value}) - outputs_tuple_v = sess.run(outputs_tuple, - feed_dict={inputs[0]: input_value}) + outputs_notuple_v = sess.run( + outputs_notuple, feed_dict={ + inputs[0]: input_value + }) + outputs_tuple_v = sess.run( + outputs_tuple, feed_dict={ + inputs[0]: input_value + }) self.assertAllEqual(outputs_notuple_v, outputs_tuple_v) - (state_notuple_v,) = sess.run((state_notuple,), - feed_dict={inputs[0]: input_value}) + (state_notuple_v,) = sess.run( + (state_notuple,), feed_dict={ + inputs[0]: input_value + }) state_tuple_v = sess.run(state_tuple, feed_dict={inputs[0]: input_value}) self.assertAllEqual(state_notuple_v, np.hstack(state_tuple_v)) @@ -593,8 +604,7 @@ class LSTMTest(test.TestCase): -0.01, 0.01, seed=self._seed) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] cell = rnn_cell.LSTMCell( @@ -625,8 +635,7 @@ class LSTMTest(test.TestCase): with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess: initializer = init_ops.random_uniform_initializer(-1, 1, seed=self._seed) inputs = max_length * [ - array_ops.placeholder( - dtypes.float64, shape=(None, input_size)) + array_ops.placeholder(dtypes.float64, shape=(None, input_size)) ] cell = rnn_cell.LSTMCell( @@ -661,8 +670,7 @@ class LSTMTest(test.TestCase): max_length = 8 with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess: inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] initializer = init_ops.constant_initializer(0.001) @@ -721,8 +729,7 @@ class LSTMTest(test.TestCase): initializer = init_ops.random_uniform_initializer( -0.01, 0.01, seed=self._seed) inputs = max_length * [ - array_ops.placeholder( - dtypes.float64, shape=(None, input_size)) + array_ops.placeholder(dtypes.float64, shape=(None, input_size)) ] cell = rnn_cell.LSTMCell( @@ -743,16 +750,21 @@ class LSTMTest(test.TestCase): self.assertEqual(len(outputs), len(inputs)) - variables_lib.global_variables_initializer().run( - feed_dict={sequence_length: [2, 3]}) + variables_lib.global_variables_initializer().run(feed_dict={ + sequence_length: [2, 3] + }) input_value = np.asarray( np.random.randn(batch_size, input_size), dtype=np.float64) values = sess.run( - outputs, feed_dict={inputs[0]: input_value, - sequence_length: [2, 3]}) + outputs, feed_dict={ + inputs[0]: input_value, + sequence_length: [2, 3] + }) state_value = sess.run( - [state], feed_dict={inputs[0]: input_value, - sequence_length: [2, 3]}) + [state], feed_dict={ + inputs[0]: input_value, + sequence_length: [2, 3] + }) self.assertEqual(values[0].dtype, input_value.dtype) self.assertEqual(state_value[0].dtype, input_value.dtype) @@ -767,8 +779,7 @@ class LSTMTest(test.TestCase): initializer_d = init_ops.random_uniform_initializer( -1, 1, seed=self._seed + 1) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] cell = rnn_cell.LSTMCell( num_units, @@ -792,8 +803,10 @@ class LSTMTest(test.TestCase): variables_lib.global_variables_initializer().run() input_value = np.random.randn(batch_size, input_size) - output_values = sess.run(outputs0 + outputs1 + outputs2, - feed_dict={inputs[0]: input_value}) + output_values = sess.run( + outputs0 + outputs1 + outputs2, feed_dict={ + inputs[0]: input_value + }) outputs0_values = output_values[:max_length] outputs1_values = output_values[max_length:2 * max_length] outputs2_values = output_values[2 * max_length:] @@ -814,8 +827,7 @@ class LSTMTest(test.TestCase): with self.test_session(graph=ops_lib.Graph()) as sess: initializer = init_ops.random_uniform_initializer(-1, 1, seed=self._seed) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] cell = rnn_cell.LSTMCell( num_units, @@ -833,8 +845,10 @@ class LSTMTest(test.TestCase): variables_lib.global_variables_initializer().run() input_value = np.random.randn(batch_size, input_size) - output_values = sess.run(outputs0 + outputs1, - feed_dict={inputs[0]: input_value}) + output_values = sess.run( + outputs0 + outputs1, feed_dict={ + inputs[0]: input_value + }) outputs0_values = output_values[:max_length] outputs1_values = output_values[max_length:] self.assertEqual(len(outputs0_values), len(outputs1_values)) @@ -861,8 +875,7 @@ class LSTMTest(test.TestCase): -0.01, 0.01, seed=self._seed) if in_graph_mode: inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] else: inputs = max_length * [ @@ -939,8 +952,7 @@ class LSTMTest(test.TestCase): -0.01, 0.01, seed=self._seed) if in_graph_mode: inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None, input_size)) + array_ops.placeholder(dtypes.float32, shape=(None, input_size)) ] else: inputs = max_length * [ @@ -1100,8 +1112,8 @@ class LSTMTest(test.TestCase): # Test gradients to inputs and variables w.r.t. outputs & final state static_grad_values = sess.run(static_gradients, feed_dict=feeds) - static_individual_grad_values = sess.run(static_individual_gradients, - feed_dict=feeds) + static_individual_grad_values = sess.run( + static_individual_gradients, feed_dict=feeds) static_individual_var_grad_values = sess.run( static_individual_variable_gradients, feed_dict=feeds) @@ -1148,8 +1160,10 @@ class LSTMTest(test.TestCase): # Generate gradients of several individual outputs w.r.t. inputs dynamic_individual_gradients = nest.flatten([ gradients_impl.gradients(y, [concat_inputs]) - for y in - [split_outputs_dynamic[0], split_outputs_dynamic[-1], state_dynamic] + for y in [ + split_outputs_dynamic[0], split_outputs_dynamic[-1], + state_dynamic + ] ]) # Generate gradients of individual variables w.r.t. inputs @@ -1159,8 +1173,10 @@ class LSTMTest(test.TestCase): "Count of trainable variables: %d" % len(trainable_variables)) dynamic_individual_variable_gradients = nest.flatten([ gradients_impl.gradients(y, trainable_variables) - for y in - [split_outputs_dynamic[0], split_outputs_dynamic[-1], state_dynamic] + for y in [ + split_outputs_dynamic[0], split_outputs_dynamic[-1], + state_dynamic + ] ]) # Test forward pass @@ -1170,8 +1186,8 @@ class LSTMTest(test.TestCase): # Test gradients to inputs and variables w.r.t. outputs & final state dynamic_grad_values = sess.run(dynamic_gradients, feed_dict=feeds) - dynamic_individual_grad_values = sess.run(dynamic_individual_gradients, - feed_dict=feeds) + dynamic_individual_grad_values = sess.run( + dynamic_individual_gradients, feed_dict=feeds) dynamic_individual_var_grad_values = sess.run( dynamic_individual_variable_gradients, feed_dict=feeds) @@ -1207,8 +1223,8 @@ class LSTMTest(test.TestCase): for i, (a, b) in enumerate( zip(static_individual_var_grad_values, dynamic_individual_var_grad_values)): - tf_logging.info("Comparing individual variable gradients iteration %d" % - i) + tf_logging.info( + "Comparing individual variable gradients iteration %d" % i) self.assertAllEqual(a, b) @test_util.run_in_graph_and_eager_modes() @@ -1223,10 +1239,7 @@ class BidirectionalRNNTest(test.TestCase): self._seed = 23489 np.random.seed(self._seed) - def _createBidirectionalRNN(self, - use_shape, - use_sequence_length, - scope=None): + def _createBidirectionalRNN(self, use_shape, use_sequence_length, scope=None): num_units = 3 input_size = 5 batch_size = 2 @@ -1270,8 +1283,10 @@ class BidirectionalRNNTest(test.TestCase): # Run with pre-specified sequence length of 2, 3 out, s_fw, s_bw = sess.run( [outputs, state_fw, state_bw], - feed_dict={inputs[0]: input_value, - sequence_length: [2, 3]}) + feed_dict={ + inputs[0]: input_value, + sequence_length: [2, 3] + }) # Since the forward and backward LSTM cells were initialized with the # same parameters, the forward and backward output has to be the same, @@ -1312,8 +1327,10 @@ class BidirectionalRNNTest(test.TestCase): input_value, inputs, outputs, state_fw, state_bw, _ = ( self._createBidirectionalRNN(use_shape, False)) variables_lib.global_variables_initializer().run() - out, s_fw, s_bw = sess.run([outputs, state_fw, state_bw], - feed_dict={inputs[0]: input_value}) + out, s_fw, s_bw = sess.run( + [outputs, state_fw, state_bw], feed_dict={ + inputs[0]: input_value + }) # Since the forward and backward LSTM cells were initialized with the # same parameters, the forward and backward output has to be the same, @@ -1396,13 +1413,11 @@ class BidirectionalRNNTest(test.TestCase): use_time_major, use_sequence_length): with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess: input_value, inputs, outputs, state_fw, state_bw, sequence_length = ( - self._createBidirectionalDynamicRNN(use_shape, - use_state_tuple, use_time_major, - use_sequence_length)) + self._createBidirectionalDynamicRNN( + use_shape, use_state_tuple, use_time_major, use_sequence_length)) variables_lib.global_variables_initializer().run() # Run with pre-specified sequence length of 2, 3 - feed_dict = ( - {sequence_length: [2, 3]} if use_sequence_length else {}) + feed_dict = ({sequence_length: [2, 3]} if use_sequence_length else {}) feed_dict.update({inputs[0]: input_value}) if use_state_tuple: out, c_fw, m_fw, c_bw, m_bw = sess.run( @@ -1538,8 +1553,7 @@ class MultiDimensionalLSTMTest(test.TestCase): sequence_length = [4, 6] with self.test_session(graph=ops_lib.Graph()) as sess: inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(None,) + input_size) + array_ops.placeholder(dtypes.float32, shape=(None,) + input_size) ] inputs_using_dim = max_length * [ array_ops.placeholder( @@ -1585,14 +1599,22 @@ class MultiDimensionalLSTMTest(test.TestCase): input_total_size = (batch_size,) + input_size input_value = np.random.randn(*input_total_size) - outputs_static_v = sess.run(outputs_static, - feed_dict={inputs[0]: input_value}) - outputs_dynamic_v = sess.run(outputs_dynamic, - feed_dict={inputs[0]: input_value}) - outputs_bid_v = sess.run(outputs_bid, - feed_dict={inputs_using_dim[0]: input_value}) - outputs_sav_v = sess.run(outputs_sav, - feed_dict={inputs_using_dim[0]: input_value}) + outputs_static_v = sess.run( + outputs_static, feed_dict={ + inputs[0]: input_value + }) + outputs_dynamic_v = sess.run( + outputs_dynamic, feed_dict={ + inputs[0]: input_value + }) + outputs_bid_v = sess.run( + outputs_bid, feed_dict={ + inputs_using_dim[0]: input_value + }) + outputs_sav_v = sess.run( + outputs_sav, feed_dict={ + inputs_using_dim[0]: input_value + }) self.assertAllEqual(outputs_static_v, outputs_dynamic_v) self.assertAllEqual(outputs_static_v, outputs_sav_v) @@ -1602,16 +1624,26 @@ class MultiDimensionalLSTMTest(test.TestCase): outputs_bid_array = np.array(outputs_bid_v) self.assertAllEqual(outputs_static_array_double, outputs_bid_array) - state_static_v = sess.run(state_static, - feed_dict={inputs[0]: input_value}) - state_dynamic_v = sess.run(state_dynamic, - feed_dict={inputs[0]: input_value}) - state_bid_fw_v = sess.run(state_fw, - feed_dict={inputs_using_dim[0]: input_value}) - state_bid_bw_v = sess.run(state_bw, - feed_dict={inputs_using_dim[0]: input_value}) - state_sav_v = sess.run(state_sav, - feed_dict={inputs_using_dim[0]: input_value}) + state_static_v = sess.run( + state_static, feed_dict={ + inputs[0]: input_value + }) + state_dynamic_v = sess.run( + state_dynamic, feed_dict={ + inputs[0]: input_value + }) + state_bid_fw_v = sess.run( + state_fw, feed_dict={ + inputs_using_dim[0]: input_value + }) + state_bid_bw_v = sess.run( + state_bw, feed_dict={ + inputs_using_dim[0]: input_value + }) + state_sav_v = sess.run( + state_sav, feed_dict={ + inputs_using_dim[0]: input_value + }) self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v)) self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v)) self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v)) @@ -1633,16 +1665,17 @@ class NestedLSTMTest(test.TestCase): with self.test_session(graph=ops_lib.Graph()) as sess: state_saver = TestStateSaver(batch_size, state_size) single_input = (array_ops.placeholder( - dtypes.float32, shape=(None, input_size)), array_ops.placeholder( - dtypes.float32, shape=(None, input_size))) + dtypes.float32, shape=(None, input_size)), + array_ops.placeholder( + dtypes.float32, shape=(None, input_size))) inputs = max_length * [single_input] inputs_c = (array_ops.stack([input_[0] for input_ in inputs]), array_ops.stack([input_[1] for input_ in inputs])) - single_input_using_dim = ( - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)), - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size))) + single_input_using_dim = (array_ops.placeholder( + dtypes.float32, shape=(batch_size, input_size)), + array_ops.placeholder( + dtypes.float32, + shape=(batch_size, input_size))) inputs_using_dim = max_length * [single_input_using_dim] # Create a cell for the whole test. This is fine because the cell has no @@ -1688,14 +1721,22 @@ class NestedLSTMTest(test.TestCase): input_total_size = (batch_size, input_size) input_value = (np.random.randn(*input_total_size), np.random.randn(*input_total_size)) - outputs_dynamic_v = sess.run(outputs_dynamic, - feed_dict={single_input: input_value}) - outputs_static_v = sess.run(outputs_static, - feed_dict={single_input: input_value}) - outputs_sav_v = sess.run(outputs_sav, - feed_dict={single_input_using_dim: input_value}) - outputs_bid_v = sess.run(outputs_bid, - feed_dict={single_input_using_dim: input_value}) + outputs_dynamic_v = sess.run( + outputs_dynamic, feed_dict={ + single_input: input_value + }) + outputs_static_v = sess.run( + outputs_static, feed_dict={ + single_input: input_value + }) + outputs_sav_v = sess.run( + outputs_sav, feed_dict={ + single_input_using_dim: input_value + }) + outputs_bid_v = sess.run( + outputs_bid, feed_dict={ + single_input_using_dim: input_value + }) self.assertAllEqual(outputs_static_v, np.transpose(outputs_dynamic_v, (1, 0, 2, 3))) @@ -1706,16 +1747,26 @@ class NestedLSTMTest(test.TestCase): outputs_bid_array = np.array(outputs_bid_v) self.assertAllEqual(outputs_static_array_double, outputs_bid_array) - state_dynamic_v = sess.run(state_dynamic, - feed_dict={single_input: input_value}) - state_static_v = sess.run(state_static, - feed_dict={single_input: input_value}) - state_bid_fw_v = sess.run(state_fw, - feed_dict={single_input_using_dim: input_value}) - state_bid_bw_v = sess.run(state_bw, - feed_dict={single_input_using_dim: input_value}) - state_sav_v = sess.run(state_sav, - feed_dict={single_input_using_dim: input_value}) + state_dynamic_v = sess.run( + state_dynamic, feed_dict={ + single_input: input_value + }) + state_static_v = sess.run( + state_static, feed_dict={ + single_input: input_value + }) + state_bid_fw_v = sess.run( + state_fw, feed_dict={ + single_input_using_dim: input_value + }) + state_bid_bw_v = sess.run( + state_bw, feed_dict={ + single_input_using_dim: input_value + }) + state_sav_v = sess.run( + state_sav, feed_dict={ + single_input_using_dim: input_value + }) self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v)) self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v)) self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v)) @@ -1764,8 +1815,7 @@ class StateSaverRNNTest(test.TestCase): initializer=initializer, state_is_tuple=False) inputs = max_length * [ - array_ops.placeholder( - dtypes.float32, shape=(batch_size, input_size)) + array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size)) ] return rnn.static_state_saving_rnn( cell, @@ -1931,8 +1981,10 @@ class RawRNNTest(test.TestCase): (outputs_val, outputs_dynamic_rnn_val, final_state_val, final_state_dynamic_rnn_val) = sess.run( [outputs, outputs_dynamic_rnn, final_state, final_state_dynamic_rnn], - feed_dict={inputs: rand_input, - sequence_length: rand_seq_len}) + feed_dict={ + inputs: rand_input, + sequence_length: rand_seq_len + }) self.assertAllClose(outputs_dynamic_rnn_val, outputs_val) self.assertAllClose(final_state_dynamic_rnn_val, final_state_val) @@ -1945,12 +1997,16 @@ class RawRNNTest(test.TestCase): self.assertEqual(len(gradients), len(gradients_dynamic_rnn)) gradients_val = sess.run( gradients, - feed_dict={inputs: rand_input, - sequence_length: rand_seq_len}) + feed_dict={ + inputs: rand_input, + sequence_length: rand_seq_len + }) gradients_dynamic_rnn_val = sess.run( gradients_dynamic_rnn, - feed_dict={inputs: rand_input, - sequence_length: rand_seq_len}) + feed_dict={ + inputs: rand_input, + sequence_length: rand_seq_len + }) self.assertEqual(len(gradients_val), len(gradients_dynamic_rnn_val)) input_gradients_val = gradients_val[0] input_gradients_dynamic_rnn_val = gradients_dynamic_rnn_val[0] @@ -2067,14 +2123,13 @@ class RawRNNTest(test.TestCase): def loop_fn(time_, cell_output, cell_state, _): if cell_output is None: - emit_output = (array_ops.zeros( - [2, 3], dtype=dtypes.int32), array_ops.zeros( - [unknown_dim], dtype=dtypes.int64)) + emit_output = (array_ops.zeros([2, 3], dtype=dtypes.int32), + array_ops.zeros([unknown_dim], dtype=dtypes.int64)) next_state = cell.zero_state(batch_size, dtypes.float32) else: - emit_output = (array_ops.ones( - [batch_size, 2, 3], dtype=dtypes.int32), array_ops.ones( - [batch_size, unknown_dim], dtype=dtypes.int64)) + emit_output = (array_ops.ones([batch_size, 2, 3], dtype=dtypes.int32), + array_ops.ones( + [batch_size, unknown_dim], dtype=dtypes.int64)) next_state = cell_state elements_finished = array_ops.tile([time_ >= max_time], [batch_size]) finished = math_ops.reduce_all(elements_finished) @@ -2193,8 +2248,8 @@ class TensorArrayOnCorrectDeviceTest(test.TestCase): cell = rnn_cell.LSTMCell(num_units, use_peepholes=True) gpu_cell = DeviceWrapperCell(cell, cell_device) - inputs = np.random.randn(batch_size, time_steps, - input_size).astype(np.float32) + inputs = np.random.randn(batch_size, time_steps, input_size).astype( + np.float32) sequence_length = np.random.randint(0, time_steps, size=batch_size) if input_device is not None: @@ -2262,8 +2317,7 @@ class TensorArrayOnCorrectDeviceTest(test.TestCase): gpu_dev = test.gpu_device_name() run_metadata = self._execute_rnn_on( - rnn_device="/cpu:0", cell_device="/cpu:0", - input_device=gpu_dev) + rnn_device="/cpu:0", cell_device="/cpu:0", input_device=gpu_dev) cpu_stats, gpu_stats = self._retrieve_cpu_gpu_stats(run_metadata) def _assert_in(op_str, in_stats, out_stats): @@ -2278,8 +2332,7 @@ class TensorArrayOnCorrectDeviceTest(test.TestCase): return # Test requires access to a GPU gpu_dev = test.gpu_device_name() - run_metadata = self._execute_rnn_on( - input_device=gpu_dev) + run_metadata = self._execute_rnn_on(input_device=gpu_dev) cpu_stats, gpu_stats = self._retrieve_cpu_gpu_stats(run_metadata) def _assert_in(op_str, in_stats, out_stats): diff --git a/tensorflow/contrib/session_bundle/exporter.py b/tensorflow/contrib/session_bundle/exporter.py index f6f663aae7..08983337fc 100644 --- a/tensorflow/contrib/session_bundle/exporter.py +++ b/tensorflow/contrib/session_bundle/exporter.py @@ -281,11 +281,12 @@ class Exporter(object): tmp_export_dir = compat.as_text(export_dir) + "-tmp" gfile.MakeDirs(tmp_export_dir) - self._saver.save(sess, - os.path.join( - compat.as_text(tmp_export_dir), - compat.as_text(constants.EXPORT_BASE_NAME)), - meta_graph_suffix=constants.EXPORT_SUFFIX_NAME) + self._saver.save( + sess, + os.path.join( + compat.as_text(tmp_export_dir), + compat.as_text(constants.EXPORT_BASE_NAME)), + meta_graph_suffix=constants.EXPORT_SUFFIX_NAME) # Run the asset callback. if self._assets_callback and self._assets_to_copy: @@ -301,12 +302,12 @@ class Exporter(object): if exports_to_keep: # create a simple parser that pulls the export_version from the directory. def parser(path): - if os.name == 'nt': - match = re.match("^" + export_dir_base.replace('\\','/') + "/(\\d{8})$", - path.path.replace('\\','/')) + if os.name == "nt": + match = re.match( + "^" + export_dir_base.replace("\\", "/") + "/(\\d{8})$", + path.path.replace("\\", "/")) else: - match = re.match("^" + export_dir_base + "/(\\d{8})$", - path.path) + match = re.match("^" + export_dir_base + "/(\\d{8})$", path.path) if not match: return None return path._replace(export_version=int(match.group(1))) diff --git a/tensorflow/contrib/slim/python/slim/learning_test.py b/tensorflow/contrib/slim/python/slim/learning_test.py index 4e816f9b11..831c6e427a 100644 --- a/tensorflow/contrib/slim/python/slim/learning_test.py +++ b/tensorflow/contrib/slim/python/slim/learning_test.py @@ -197,9 +197,7 @@ class MultiplyGradientsTest(test.TestCase): gradient = constant_op.constant(self._grad_vec, dtype=dtypes.float32) variable = variables_lib.Variable(array_ops.zeros_like(gradient)) multiplier_flag = variables_lib.Variable(True) - tensor_multiplier = array_ops.where(multiplier_flag, - self._multiplier, - 1.0) + tensor_multiplier = array_ops.where(multiplier_flag, self._multiplier, 1.0) grad_to_var = (gradient, variable) gradient_multipliers = {variable: tensor_multiplier} @@ -212,11 +210,8 @@ class MultiplyGradientsTest(test.TestCase): sess.run(multiplier_flag.assign(False)) gradient_false_flag = sess.run(grad_to_var[0]) np_testing.assert_almost_equal(gradient_true_flag, - self._multiplied_grad_vec, - 5) - np_testing.assert_almost_equal(gradient_false_flag, - self._grad_vec, - 5) + self._multiplied_grad_vec, 5) + np_testing.assert_almost_equal(gradient_false_flag, self._grad_vec, 5) def LogisticClassifier(inputs): @@ -502,6 +497,7 @@ class TrainTest(test.TestCase): purpose. """ dump_root = tempfile.mkdtemp() + def dumping_wrapper(sess): # pylint: disable=invalid-name return dumping_wrapper_lib.DumpingDebugWrapperSession(sess, dump_root) @@ -519,16 +515,13 @@ class TrainTest(test.TestCase): train_op = learning.create_train_op(total_loss, optimizer) loss = learning.train( - train_op, - None, - number_of_steps=1, - session_wrapper=dumping_wrapper) + train_op, None, number_of_steps=1, session_wrapper=dumping_wrapper) self.assertIsNotNone(loss) run_root = glob.glob(os.path.join(dump_root, 'run_*'))[-1] dump = debug_data.DebugDumpDir(run_root) - self.assertAllEqual( - 0, dump.get_tensors('global_step', 0, 'DebugIdentity')[0]) + self.assertAllEqual(0, + dump.get_tensors('global_step', 0, 'DebugIdentity')[0]) def testTrainWithTrace(self): logdir = os.path.join( @@ -961,8 +954,8 @@ class TrainTest(test.TestCase): self.assertGreater(losses[0], losses[1]) def testTrainWithEpochLimit(self): - logdir = os.path.join(tempfile.mkdtemp(prefix=self.get_temp_dir()), - 'tmp_logs') + logdir = os.path.join( + tempfile.mkdtemp(prefix=self.get_temp_dir()), 'tmp_logs') with ops.Graph().as_default(): random_seed.set_random_seed(0) tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32) @@ -982,7 +975,8 @@ class TrainTest(test.TestCase): self.assertIsNotNone(loss) self.assertLess(loss, .015) self.assertTrue(os.path.isfile('{}/model.ckpt-300.index'.format(logdir))) - self.assertTrue(os.path.isfile('{}/model.ckpt-300.data-00000-of-00001'.format(logdir))) + self.assertTrue( + os.path.isfile('{}/model.ckpt-300.data-00000-of-00001'.format(logdir))) if __name__ == '__main__': diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax.py b/tensorflow/examples/tutorials/mnist/mnist_softmax.py index fb3ac94203..47dd6a1947 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_softmax.py +++ b/tensorflow/examples/tutorials/mnist/mnist_softmax.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """A very simple MNIST classifier. See extensive documentation at @@ -67,12 +66,19 @@ def main(_): # Test trained model correct_prediction = tf.equal(tf.argmax(y, 1), y_) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print(sess.run(accuracy, feed_dict={x: mnist.test.images, - y_: mnist.test.labels})) + print(sess.run( + accuracy, feed_dict={ + x: mnist.test.images, + y_: mnist.test.labels + })) + if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') + parser.add_argument( + '--data_dir', + type=str, + default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/client/device_lib_test.py b/tensorflow/python/client/device_lib_test.py index 7bba10efac..aaf41626ab 100644 --- a/tensorflow/python/client/device_lib_test.py +++ b/tensorflow/python/client/device_lib_test.py @@ -34,7 +34,8 @@ class DeviceLibTest(test_util.TensorFlowTestCase): # GPU test if test.is_gpu_available(): self.assertGreater(len(devices), 1) - self.assertTrue("GPU" in [d.device_type for d in devices] or "SYCL" in [d.device_type for d in devices]) + self.assertTrue("GPU" in [d.device_type for d in devices] or + "SYCL" in [d.device_type for d in devices]) if __name__ == "__main__": diff --git a/tensorflow/python/debug/wrappers/hooks.py b/tensorflow/python/debug/wrappers/hooks.py index 989ad801e5..0204254cca 100644 --- a/tensorflow/python/debug/wrappers/hooks.py +++ b/tensorflow/python/debug/wrappers/hooks.py @@ -35,10 +35,7 @@ class LocalCLIDebugHook(session_run_hook.SessionRunHook): `tf.contrib.learn`'s `Estimator`s and `Experiment`s. """ - def __init__(self, - ui_type="curses", - dump_root=None, - thread_name_filter=None): + def __init__(self, ui_type="curses", dump_root=None, thread_name_filter=None): """Create a local debugger command-line interface (CLI) hook. Args: @@ -62,7 +59,8 @@ class LocalCLIDebugHook(session_run_hook.SessionRunHook): """Add a tensor filter. See doc of `LocalCLIDebugWrapperSession.add_tensor_filter()` for details. - Override default behavior to accommodate the possibility of this method being + Override default behavior to accommodate the possibility of this method + being called prior to the initialization of the underlying `LocalCLIDebugWrapperSession` object. @@ -137,9 +135,7 @@ class LocalCLIDebugHook(session_run_hook.SessionRunHook): # pylint: enable=protected-access with stepper.NodeStepper( - run_context.session, - run_context.original_args. - fetches, + run_context.session, run_context.original_args.fetches, run_context.original_args.feed_dict) as node_stepper: self._session_wrapper.invoke_node_stepper( node_stepper, restore_variable_values_on_exit=True) @@ -149,8 +145,8 @@ class LocalCLIDebugHook(session_run_hook.SessionRunHook): def after_run(self, run_context, run_values): # Adapt run_context and run_values to OnRunEndRequest and invoke superclass # on_run_end() - on_run_end_request = framework.OnRunEndRequest( - self._performed_action, run_values.run_metadata) + on_run_end_request = framework.OnRunEndRequest(self._performed_action, + run_values.run_metadata) self._session_wrapper.on_run_end(on_run_end_request) @@ -260,8 +256,8 @@ class GrpcDebugHook(session_run_hook.SessionRunHook): self._thread_name_filter = thread_name_filter self._grpc_debug_server_addresses = ( grpc_debug_server_addresses - if isinstance(grpc_debug_server_addresses, list) - else [grpc_debug_server_addresses]) + if isinstance(grpc_debug_server_addresses, list) else + [grpc_debug_server_addresses]) self._watch_fn = watch_fn self._log_usage = log_usage @@ -334,6 +330,7 @@ class TensorBoardDebugHook(GrpcDebugHook): log_usage: Whether the usage of this class is to be logged (if applicable). """ + def _gated_grpc_watch_fn(fetches, feeds): del fetches, feeds # Unused. return framework.WatchOptions( diff --git a/tensorflow/python/framework/dtypes.py b/tensorflow/python/framework/dtypes.py index 67ccf990d6..c825114483 100644 --- a/tensorflow/python/framework/dtypes.py +++ b/tensorflow/python/framework/dtypes.py @@ -12,20 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Library of dtypes (Tensor element types).""" from __future__ import absolute_import from __future__ import division from __future__ import print_function - import numpy as np from tensorflow.core.framework import types_pb2 from tensorflow.python import pywrap_tensorflow from tensorflow.python.util.tf_export import tf_export - _np_bfloat16 = pywrap_tensorflow.TF_bfloat16_type() @@ -83,8 +80,8 @@ class DType(object): # TODO(mrry): Make the necessary changes (using __new__) to ensure # that calling this returns one of the interned values. type_enum = int(type_enum) - if (type_enum not in types_pb2.DataType.values() - or type_enum == types_pb2.DT_INVALID): + if (type_enum not in types_pb2.DataType.values() or + type_enum == types_pb2.DT_INVALID): raise TypeError( "type_enum is not a valid types_pb2.DataType: %s" % type_enum) self._type_enum = type_enum @@ -123,10 +120,10 @@ class DType(object): @property def is_numpy_compatible(self): - numpy_incompatible = [types_pb2.DT_VARIANT, - types_pb2.DT_VARIANT_REF, - types_pb2.DT_RESOURCE, - types_pb2.DT_RESOURCE_REF] + numpy_incompatible = [ + types_pb2.DT_VARIANT, types_pb2.DT_VARIANT_REF, types_pb2.DT_RESOURCE, + types_pb2.DT_RESOURCE_REF + ] return self._type_enum not in numpy_incompatible @property @@ -153,9 +150,9 @@ class DType(object): @property def is_floating(self): """Returns whether this is a (non-quantized, real) floating point type.""" - return ((self.is_numpy_compatible and np.issubdtype(self.as_numpy_dtype, - np.floating)) - or self.base_dtype == bfloat16) + return ((self.is_numpy_compatible and + np.issubdtype(self.as_numpy_dtype, np.floating)) or + self.base_dtype == bfloat16) @property def is_complex(self): @@ -190,8 +187,8 @@ class DType(object): TypeError: if this is a non-numeric, unordered, or quantized type. """ - if (self.is_quantized or self.base_dtype in - (bool, string, complex64, complex128)): + if (self.is_quantized or + self.base_dtype in (bool, string, complex64, complex128)): raise TypeError("Cannot find minimum value of %s." % self) # there is no simple way to get the min value of a dtype, we have to check @@ -214,8 +211,8 @@ class DType(object): TypeError: if this is a non-numeric, unordered, or quantized type. """ - if (self.is_quantized or self.base_dtype in - (bool, string, complex64, complex128)): + if (self.is_quantized or + self.base_dtype in (bool, string, complex64, complex128)): raise TypeError("Cannot find maximum value of %s." % self) # there is no simple way to get the max value of a dtype, we have to check @@ -266,8 +263,8 @@ class DType(object): this `DType`. """ other = as_dtype(other) - return self._type_enum in ( - other.as_datatype_enum, other.base_dtype.as_datatype_enum) + return self._type_enum in (other.as_datatype_enum, + other.base_dtype.as_datatype_enum) def __eq__(self, other): """Returns True iff this DType refers to the same type as `other`.""" @@ -307,19 +304,22 @@ class DType(object): return 1 return np.dtype(self.as_numpy_dtype).itemsize + # Define data type range of numpy dtype -dtype_range = {np.bool_: (False, True), - np.bool8: (False, True), - np.uint8: (0, 255), - np.uint16: (0, 65535), - np.int8: (-128, 127), - np.int16: (-32768, 32767), - np.int64: (-2**63, 2**63 - 1), - np.uint64: (0, 2**64 - 1), - np.int32: (-2**31, 2**31 - 1), - np.uint32: (0, 2**32 - 1), - np.float32: (-1, 1), - np.float64: (-1, 1)} +dtype_range = { + np.bool_: (False, True), + np.bool8: (False, True), + np.uint8: (0, 255), + np.uint16: (0, 65535), + np.int8: (-128, 127), + np.int16: (-32768, 32767), + np.int64: (-2**63, 2**63 - 1), + np.uint64: (0, 2**64 - 1), + np.int32: (-2**31, 2**31 - 1), + np.uint32: (0, 2**32 - 1), + np.float32: (-1, 1), + np.float64: (-1, 1) +} # Define standard wrappers for the types_pb2.DataType enum. resource = DType(types_pb2.DT_RESOURCE) @@ -396,7 +396,6 @@ quint16_ref = DType(types_pb2.DT_QUINT16_REF) qint32_ref = DType(types_pb2.DT_QINT32_REF) bfloat16_ref = DType(types_pb2.DT_BFLOAT16_REF) - # Maintain an intern table so that we don't have to create a large # number of small objects. _INTERN_TABLE = { @@ -448,7 +447,6 @@ _INTERN_TABLE = { types_pb2.DT_VARIANT_REF: variant_ref, } - # Standard mappings between types_pb2.DataType values and string names. _TYPE_TO_STRING = { types_pb2.DT_HALF: "float16", @@ -498,8 +496,10 @@ _TYPE_TO_STRING = { types_pb2.DT_RESOURCE_REF: "resource_ref", types_pb2.DT_VARIANT_REF: "variant_ref", } -_STRING_TO_TF = {value: _INTERN_TABLE[key] - for key, value in _TYPE_TO_STRING.items()} +_STRING_TO_TF = { + value: _INTERN_TABLE[key] + for key, value in _TYPE_TO_STRING.items() +} # Add non-canonical aliases. _STRING_TO_TF["half"] = float16 _STRING_TO_TF["half_ref"] = float16_ref @@ -508,7 +508,6 @@ _STRING_TO_TF["float_ref"] = float32_ref _STRING_TO_TF["double"] = float64 _STRING_TO_TF["double_ref"] = float64_ref - # Numpy representation for quantized dtypes. # # These are magic strings that are used in the swig wrapper to identify @@ -551,58 +550,100 @@ _NP_TO_TF = frozenset([ (_np_bfloat16, bfloat16), ]) _TF_TO_NP = { - types_pb2.DT_HALF: np.float16, - types_pb2.DT_FLOAT: np.float32, - types_pb2.DT_DOUBLE: np.float64, - types_pb2.DT_INT32: np.int32, - types_pb2.DT_UINT8: np.uint8, - types_pb2.DT_UINT16: np.uint16, - types_pb2.DT_UINT32: np.uint32, - types_pb2.DT_UINT64: np.uint64, - types_pb2.DT_INT16: np.int16, - types_pb2.DT_INT8: np.int8, + types_pb2.DT_HALF: + np.float16, + types_pb2.DT_FLOAT: + np.float32, + types_pb2.DT_DOUBLE: + np.float64, + types_pb2.DT_INT32: + np.int32, + types_pb2.DT_UINT8: + np.uint8, + types_pb2.DT_UINT16: + np.uint16, + types_pb2.DT_UINT32: + np.uint32, + types_pb2.DT_UINT64: + np.uint64, + types_pb2.DT_INT16: + np.int16, + types_pb2.DT_INT8: + np.int8, # NOTE(touts): For strings we use np.object as it supports variable length # strings. - types_pb2.DT_STRING: np.object, - types_pb2.DT_COMPLEX64: np.complex64, - types_pb2.DT_COMPLEX128: np.complex128, - types_pb2.DT_INT64: np.int64, - types_pb2.DT_BOOL: np.bool, - types_pb2.DT_QINT8: _np_qint8, - types_pb2.DT_QUINT8: _np_quint8, - types_pb2.DT_QINT16: _np_qint16, - types_pb2.DT_QUINT16: _np_quint16, - types_pb2.DT_QINT32: _np_qint32, - types_pb2.DT_BFLOAT16: _np_bfloat16, + types_pb2.DT_STRING: + np.object, + types_pb2.DT_COMPLEX64: + np.complex64, + types_pb2.DT_COMPLEX128: + np.complex128, + types_pb2.DT_INT64: + np.int64, + types_pb2.DT_BOOL: + np.bool, + types_pb2.DT_QINT8: + _np_qint8, + types_pb2.DT_QUINT8: + _np_quint8, + types_pb2.DT_QINT16: + _np_qint16, + types_pb2.DT_QUINT16: + _np_quint16, + types_pb2.DT_QINT32: + _np_qint32, + types_pb2.DT_BFLOAT16: + _np_bfloat16, # Ref types - types_pb2.DT_HALF_REF: np.float16, - types_pb2.DT_FLOAT_REF: np.float32, - types_pb2.DT_DOUBLE_REF: np.float64, - types_pb2.DT_INT32_REF: np.int32, - types_pb2.DT_UINT32_REF: np.uint32, - types_pb2.DT_UINT8_REF: np.uint8, - types_pb2.DT_UINT16_REF: np.uint16, - types_pb2.DT_INT16_REF: np.int16, - types_pb2.DT_INT8_REF: np.int8, - types_pb2.DT_STRING_REF: np.object, - types_pb2.DT_COMPLEX64_REF: np.complex64, - types_pb2.DT_COMPLEX128_REF: np.complex128, - types_pb2.DT_INT64_REF: np.int64, - types_pb2.DT_UINT64_REF: np.uint64, - types_pb2.DT_BOOL_REF: np.bool, - types_pb2.DT_QINT8_REF: _np_qint8, - types_pb2.DT_QUINT8_REF: _np_quint8, - types_pb2.DT_QINT16_REF: _np_qint16, - types_pb2.DT_QUINT16_REF: _np_quint16, - types_pb2.DT_QINT32_REF: _np_qint32, - types_pb2.DT_BFLOAT16_REF: _np_bfloat16, + types_pb2.DT_HALF_REF: + np.float16, + types_pb2.DT_FLOAT_REF: + np.float32, + types_pb2.DT_DOUBLE_REF: + np.float64, + types_pb2.DT_INT32_REF: + np.int32, + types_pb2.DT_UINT32_REF: + np.uint32, + types_pb2.DT_UINT8_REF: + np.uint8, + types_pb2.DT_UINT16_REF: + np.uint16, + types_pb2.DT_INT16_REF: + np.int16, + types_pb2.DT_INT8_REF: + np.int8, + types_pb2.DT_STRING_REF: + np.object, + types_pb2.DT_COMPLEX64_REF: + np.complex64, + types_pb2.DT_COMPLEX128_REF: + np.complex128, + types_pb2.DT_INT64_REF: + np.int64, + types_pb2.DT_UINT64_REF: + np.uint64, + types_pb2.DT_BOOL_REF: + np.bool, + types_pb2.DT_QINT8_REF: + _np_qint8, + types_pb2.DT_QUINT8_REF: + _np_quint8, + types_pb2.DT_QINT16_REF: + _np_qint16, + types_pb2.DT_QUINT16_REF: + _np_quint16, + types_pb2.DT_QINT32_REF: + _np_qint32, + types_pb2.DT_BFLOAT16_REF: + _np_bfloat16, } - -QUANTIZED_DTYPES = frozenset( - [qint8, quint8, qint16, quint16, qint32, qint8_ref, quint8_ref, qint16_ref, - quint16_ref, qint32_ref]) +QUANTIZED_DTYPES = frozenset([ + qint8, quint8, qint16, quint16, qint32, qint8_ref, quint8_ref, qint16_ref, + quint16_ref, qint32_ref +]) tf_export("QUANTIZED_DTYPES").export_constant(__name__, "QUANTIZED_DTYPES") @@ -613,7 +654,8 @@ def as_dtype(type_value): Args: type_value: A value that can be converted to a `tf.DType` object. This may currently be a `tf.DType` object, a - [`DataType` enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto), + [`DataType` + enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto), a string type name, or a `numpy.dtype`. Returns: @@ -650,5 +692,4 @@ def as_dtype(type_value): except TypeError as e: raise TypeError("Cannot convert {} to a dtype. {}".format(type_value, e)) - raise TypeError( - "Cannot convert value %r to a TensorFlow DType." % type_value) + raise TypeError("Cannot convert value %r to a TensorFlow DType." % type_value) diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 00fff8d040..c26644362c 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """A utility function for importing TensorFlow graphs.""" from __future__ import absolute_import from __future__ import division @@ -43,8 +42,8 @@ from tensorflow.python.util.tf_export import tf_export # the logic here. def _GetNodeAttr(node_def, attr_name): if attr_name not in node_def.attr: - raise ValueError('Expected one attr with name %r in %s.' - % (attr_name, str(node_def))) + raise ValueError('Expected one attr with name %r in %s.' % (attr_name, + str(node_def))) return node_def.attr[attr_name] @@ -170,9 +169,8 @@ def _ProcessInputMapParam(input_map): if input_map is None: input_map = {} else: - if not (isinstance(input_map, dict) - and all(isinstance(k, compat.bytes_or_text_types) - for k in input_map.keys())): + if not (isinstance(input_map, dict) and all( + isinstance(k, compat.bytes_or_text_types) for k in input_map.keys())): raise TypeError('input_map must be a dictionary mapping strings to ' 'Tensor objects.') return input_map @@ -180,9 +178,10 @@ def _ProcessInputMapParam(input_map): def _ProcessReturnElementsParam(return_elements): """Type-checks and possibly canonicalizes `return_elements`.""" - if return_elements is None: return None - if not all(isinstance(x, compat.bytes_or_text_types) - for x in return_elements): + if return_elements is None: + return None + if not all( + isinstance(x, compat.bytes_or_text_types) for x in return_elements): raise TypeError('return_elements must be a list of strings.') return tuple(compat.as_str(x) for x in return_elements) @@ -262,14 +261,14 @@ def _PopulateTFImportGraphDefOptions(options, prefix, input_map, if input_src.startswith('^'): 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) + c_api.TF_ImportGraphDefOptionsRemapControlDependency( + options, src_name, dst_op) else: src_name, src_idx = _ParseTensorName(input_src) src_name = compat.as_str(src_name) dst_output = input_dst._as_tf_output() # pylint: disable=protected-access - c_api.TF_ImportGraphDefOptionsAddInputMapping(options, src_name, - src_idx, dst_output) + c_api.TF_ImportGraphDefOptionsAddInputMapping(options, src_name, src_idx, + dst_output) for name in return_elements or []: if ':' in name: op_name, index = _ParseTensorName(name) @@ -315,8 +314,8 @@ def _ProcessNewOps(graph): coloc_op = graph._get_operation_by_name_unsafe(coloc_op_name) # pylint: disable=protected-access except KeyError: raise ValueError('Specified colocation to an op that ' - 'does not exist during import: %s in %s' % ( - coloc_op_name, op.name)) + 'does not exist during import: %s in %s' % + (coloc_op_name, op.name)) if coloc_op.device: coloc_device = pydev.DeviceSpec.from_string(coloc_op.device) break @@ -373,10 +372,13 @@ def _GatherReturnElements(requested_return_elements, graph, results): @tf_export('import_graph_def') @deprecated_args(None, 'Please file an issue at ' 'https://github.com/tensorflow/tensorflow/issues if you depend' - ' on this feature.', - 'op_dict') -def import_graph_def(graph_def, input_map=None, return_elements=None, - name=None, op_dict=None, producer_op_list=None): + ' on this feature.', 'op_dict') +def import_graph_def(graph_def, + input_map=None, + return_elements=None, + name=None, + op_dict=None, + producer_op_list=None): """Imports the graph from `graph_def` into the current default `Graph`. This function provides a way to import a serialized TensorFlow @@ -480,11 +482,12 @@ def import_graph_def(graph_def, input_map=None, return_elements=None, c_api.TF_ImportGraphDefResultsMissingUnusedInputMappings_wrapper( results)) if missing_unused_input_keys: - missing_unused_input_keys = [compat.as_str(s) - for s in missing_unused_input_keys] + missing_unused_input_keys = [ + compat.as_str(s) for s in missing_unused_input_keys + ] raise ValueError( - 'Attempted to map inputs that were not found in graph_def: [%s]' - % ', '.join(missing_unused_input_keys)) + 'Attempted to map inputs that were not found in graph_def: [%s]' % + ', '.join(missing_unused_input_keys)) if return_elements is None: return None diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py index d2b8e80305..0e5f696111 100644 --- a/tensorflow/python/framework/tensor_util.py +++ b/tensorflow/python/framework/tensor_util.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Utilities to create TensorProtos.""" from __future__ import absolute_import from __future__ import division @@ -39,6 +38,7 @@ except ImportError: from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.util.tf_export import tf_export + # pylint: enable=g-import-not-at-top @@ -47,8 +47,8 @@ def ExtractBitsFromFloat16(x): def SlowAppendFloat16ArrayToTensorProto(tensor_proto, proto_values): - tensor_proto.half_val.extend([ - ExtractBitsFromFloat16(x) for x in proto_values]) + tensor_proto.half_val.extend( + [ExtractBitsFromFloat16(x) for x in proto_values]) def ExtractBitsFromBFloat16(x): @@ -57,31 +57,47 @@ def ExtractBitsFromBFloat16(x): def SlowAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values): - tensor_proto.half_val.extend([ - ExtractBitsFromBFloat16(x) for x in proto_values]) + tensor_proto.half_val.extend( + [ExtractBitsFromBFloat16(x) for x in proto_values]) if _FAST_TENSOR_UTIL_AVAILABLE: _NP_TO_APPEND_FN = { - dtypes.bfloat16.as_numpy_dtype: SlowAppendBFloat16ArrayToTensorProto, + dtypes.bfloat16.as_numpy_dtype: + SlowAppendBFloat16ArrayToTensorProto, # TODO(sesse): We should have a # fast_tensor_util.AppendFloat16ArrayToTensorProto, # but it seems np.float16_t doesn't exist? - np.float16: SlowAppendFloat16ArrayToTensorProto, - np.float32: fast_tensor_util.AppendFloat32ArrayToTensorProto, - np.float64: fast_tensor_util.AppendFloat64ArrayToTensorProto, - np.int32: fast_tensor_util.AppendInt32ArrayToTensorProto, - np.int64: fast_tensor_util.AppendInt64ArrayToTensorProto, - np.uint8: fast_tensor_util.AppendUInt8ArrayToTensorProto, - np.uint16: fast_tensor_util.AppendUInt16ArrayToTensorProto, - np.uint32: fast_tensor_util.AppendUInt32ArrayToTensorProto, - np.uint64: fast_tensor_util.AppendUInt64ArrayToTensorProto, - np.int8: fast_tensor_util.AppendInt8ArrayToTensorProto, - np.int16: fast_tensor_util.AppendInt16ArrayToTensorProto, - np.complex64: fast_tensor_util.AppendComplex64ArrayToTensorProto, - np.complex128: fast_tensor_util.AppendComplex128ArrayToTensorProto, - np.object: fast_tensor_util.AppendObjectArrayToTensorProto, - np.bool: fast_tensor_util.AppendBoolArrayToTensorProto, + np.float16: + SlowAppendFloat16ArrayToTensorProto, + np.float32: + fast_tensor_util.AppendFloat32ArrayToTensorProto, + np.float64: + fast_tensor_util.AppendFloat64ArrayToTensorProto, + np.int32: + fast_tensor_util.AppendInt32ArrayToTensorProto, + np.int64: + fast_tensor_util.AppendInt64ArrayToTensorProto, + np.uint8: + fast_tensor_util.AppendUInt8ArrayToTensorProto, + np.uint16: + fast_tensor_util.AppendUInt16ArrayToTensorProto, + np.uint32: + fast_tensor_util.AppendUInt32ArrayToTensorProto, + np.uint64: + fast_tensor_util.AppendUInt64ArrayToTensorProto, + np.int8: + fast_tensor_util.AppendInt8ArrayToTensorProto, + np.int16: + fast_tensor_util.AppendInt16ArrayToTensorProto, + np.complex64: + fast_tensor_util.AppendComplex64ArrayToTensorProto, + np.complex128: + fast_tensor_util.AppendComplex128ArrayToTensorProto, + np.object: + fast_tensor_util.AppendObjectArrayToTensorProto, + np.bool: + fast_tensor_util.AppendBoolArrayToTensorProto, dtypes.qint8.as_numpy_dtype: fast_tensor_util.AppendInt8ArrayToTensorProto, dtypes.quint8.as_numpy_dtype: @@ -118,14 +134,12 @@ else: tensor_proto.uint64_val.extend([np.asscalar(x) for x in proto_values]) def SlowAppendComplex64ArrayToTensorProto(tensor_proto, proto_values): - tensor_proto.scomplex_val.extend([np.asscalar(v) - for x in proto_values - for v in [x.real, x.imag]]) + tensor_proto.scomplex_val.extend( + [np.asscalar(v) for x in proto_values for v in [x.real, x.imag]]) def SlowAppendComplex128ArrayToTensorProto(tensor_proto, proto_values): - tensor_proto.dcomplex_val.extend([np.asscalar(v) - for x in proto_values - for v in [x.real, x.imag]]) + tensor_proto.dcomplex_val.extend( + [np.asscalar(v) for x in proto_values for v in [x.real, x.imag]]) def SlowAppendObjectArrayToTensorProto(tensor_proto, proto_values): tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values]) @@ -252,15 +266,16 @@ def _FilterTuple(v): return None if isinstance(v, list): if not any(isinstance(x, (list, tuple)) for x in v): - return _FirstNotNone([None if isinstance(x, (list, tuple)) else x for x in v]) + return _FirstNotNone( + [None if isinstance(x, (list, tuple)) else x for x in v]) return _FirstNotNone([_FilterTuple(x) for x in v]) def _FilterInt(v): if isinstance(v, (list, tuple)): return _FirstNotNone([_FilterInt(x) for x in v]) - return None if isinstance(v, (compat.integral_types, - tensor_shape.Dimension)) else _NotNone(v) + return None if isinstance( + v, (compat.integral_types, tensor_shape.Dimension)) else _NotNone(v) def _FilterFloat(v): @@ -380,8 +395,11 @@ def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False): if dtype: dtype = dtypes.as_dtype(dtype) - is_quantized = (dtype in [dtypes.qint8, dtypes.quint8, dtypes.qint16, - dtypes.quint16, dtypes.qint32]) + is_quantized = ( + dtype in [ + dtypes.qint8, dtypes.quint8, dtypes.qint16, dtypes.quint16, + dtypes.qint32 + ]) # We first convert value to a numpy array or scalar. if isinstance(values, (np.ndarray, np.generic)): @@ -419,9 +437,9 @@ def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False): if (list(nparray.shape) != _GetDenseDimensions(values) and not is_quantized): raise ValueError("""Argument must be a dense tensor: %s""" - """ - got shape %s, but wanted %s.""" % ( - values, list(nparray.shape), - _GetDenseDimensions(values))) + """ - got shape %s, but wanted %s.""" % + (values, list(nparray.shape), + _GetDenseDimensions(values))) # python/numpy default float type is float64. We prefer float32 instead. if (nparray.dtype == np.float64) and dtype is None: @@ -446,8 +464,8 @@ def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False): if dtype is not None and (not hasattr(dtype, "base_dtype") or dtype.base_dtype != numpy_dtype.base_dtype): - raise TypeError("Incompatible types: %s vs. %s. Value is %s" - % (dtype, nparray.dtype, values)) + raise TypeError("Incompatible types: %s vs. %s. Value is %s" % + (dtype, nparray.dtype, values)) # If shape is not given, get the shape from the numpy array. if shape is None: @@ -510,8 +528,8 @@ def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False): append_fn = GetNumpyAppendFn(proto_values.dtype) if append_fn is None: - raise TypeError("Element type not supported in TensorProto: %s" % - numpy_dtype.name) + raise TypeError( + "Element type not supported in TensorProto: %s" % numpy_dtype.name) append_fn(tensor_proto, proto_values) return tensor_proto @@ -553,19 +571,23 @@ def MakeNdarray(tensor): return tmp.reshape(shape) elif tensor_dtype == dtypes.float32: if len(tensor.float_val) == 1: - return np.repeat(np.array(tensor.float_val[0], dtype=dtype), - num_elements).reshape(shape) + return np.repeat( + np.array(tensor.float_val[0], dtype=dtype), + num_elements).reshape(shape) else: return np.fromiter(tensor.float_val, dtype=dtype).reshape(shape) elif tensor_dtype == dtypes.float64: if len(tensor.double_val) == 1: - return np.repeat(np.array(tensor.double_val[0], dtype=dtype), - num_elements).reshape(shape) + return np.repeat( + np.array(tensor.double_val[0], dtype=dtype), + num_elements).reshape(shape) else: return np.fromiter(tensor.double_val, dtype=dtype).reshape(shape) - elif tensor_dtype in [dtypes.int32, dtypes.uint8, dtypes.uint16, dtypes.int16, - dtypes.int8, dtypes.qint32, dtypes.quint8, dtypes.qint8, - dtypes.qint16, dtypes.quint16, dtypes.bfloat16]: + elif tensor_dtype in [ + dtypes.int32, dtypes.uint8, dtypes.uint16, dtypes.int16, dtypes.int8, + dtypes.qint32, dtypes.quint8, dtypes.qint8, dtypes.qint16, dtypes.quint16, + dtypes.bfloat16 + ]: if len(tensor.int_val) == 1: return np.repeat(np.array(tensor.int_val[0], dtype=dtype), num_elements).reshape(shape) @@ -573,35 +595,41 @@ def MakeNdarray(tensor): return np.fromiter(tensor.int_val, dtype=dtype).reshape(shape) elif tensor_dtype == dtypes.int64: if len(tensor.int64_val) == 1: - return np.repeat(np.array(tensor.int64_val[0], dtype=dtype), - num_elements).reshape(shape) + return np.repeat( + np.array(tensor.int64_val[0], dtype=dtype), + num_elements).reshape(shape) else: return np.fromiter(tensor.int64_val, dtype=dtype).reshape(shape) elif tensor_dtype == dtypes.string: if len(tensor.string_val) == 1: - return np.repeat(np.array(tensor.string_val[0], dtype=dtype), - num_elements).reshape(shape) + return np.repeat( + np.array(tensor.string_val[0], dtype=dtype), + num_elements).reshape(shape) else: - return np.array([x for x in tensor.string_val], - dtype=dtype).reshape(shape) + return np.array( + [x for x in tensor.string_val], dtype=dtype).reshape(shape) elif tensor_dtype == dtypes.complex64: it = iter(tensor.scomplex_val) if len(tensor.scomplex_val) == 2: - return np.repeat(np.array(complex(tensor.scomplex_val[0], - tensor.scomplex_val[1]), dtype=dtype), - num_elements).reshape(shape) + return np.repeat( + np.array( + complex(tensor.scomplex_val[0], tensor.scomplex_val[1]), + dtype=dtype), num_elements).reshape(shape) else: - return np.array([complex(x[0], x[1]) for x in zip(it, it)], - dtype=dtype).reshape(shape) + return np.array( + [complex(x[0], x[1]) for x in zip(it, it)], + dtype=dtype).reshape(shape) elif tensor_dtype == dtypes.complex128: it = iter(tensor.dcomplex_val) if len(tensor.dcomplex_val) == 2: - return np.repeat(np.array(complex(tensor.dcomplex_val[0], - tensor.dcomplex_val[1]), dtype=dtype), - num_elements).reshape(shape) + return np.repeat( + np.array( + complex(tensor.dcomplex_val[0], tensor.dcomplex_val[1]), + dtype=dtype), num_elements).reshape(shape) else: - return np.array([complex(x[0], x[1]) for x in zip(it, it)], - dtype=dtype).reshape(shape) + return np.array( + [complex(x[0], x[1]) for x in zip(it, it)], + dtype=dtype).reshape(shape) elif tensor_dtype == dtypes.bool: if len(tensor.bool_val) == 1: return np.repeat(np.array(tensor.bool_val[0], dtype=dtype), @@ -645,8 +673,9 @@ def _ConstantValue(tensor, partial): elif tensor.op.type == "Shape": input_shape = tensor.op.inputs[0].get_shape() if input_shape.is_fully_defined(): - return np.array([dim.value for dim in input_shape.dims], - dtype=tensor.dtype.as_numpy_dtype) + return np.array( + [dim.value for dim in input_shape.dims], + dtype=tensor.dtype.as_numpy_dtype) else: return None elif tensor.op.type == "Size": @@ -658,8 +687,10 @@ def _ConstantValue(tensor, partial): elif tensor.op.type == "Rank": input_shape = tensor.op.inputs[0].get_shape() if input_shape.ndims is not None: - return np.ndarray(shape=(), buffer=np.array([input_shape.ndims], dtype=np.int32), - dtype=np.int32) + return np.ndarray( + shape=(), + buffer=np.array([input_shape.ndims], dtype=np.int32), + dtype=np.int32) else: return None elif tensor.op.type == "Range": @@ -861,8 +892,8 @@ def constant_value_as_shape(tensor): # pylint: disable=invalid-name new_axis_mask = tensor.op.get_attr("new_axis_mask") shrink_axis_mask = tensor.op.get_attr("shrink_axis_mask") valid_attributes = (not ellipsis_mask and not new_axis_mask and - not shrink_axis_mask and - (not begin_mask or (begin_mask == 1)) and + not shrink_axis_mask and (not begin_mask or + (begin_mask == 1)) and (not end_mask or (end_mask == 1))) if valid_attributes: # additional inputs not supported prev = constant_value_as_shape(tensor.op.inputs[0]) @@ -878,8 +909,8 @@ def constant_value_as_shape(tensor): # pylint: disable=invalid-name ret = tensor_shape.unknown_shape(shape[0].value) value = constant_value(tensor) if value is not None: - ret = ret.merge_with(tensor_shape.TensorShape( - [d if d >= 0 else None for d in value])) + ret = ret.merge_with( + tensor_shape.TensorShape([d if d >= 0 else None for d in value])) return ret diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 6a7e1d0c89..4a8aa2e258 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -123,11 +123,11 @@ def assert_equal_graph_def(actual, expected, checkpoint_v2=False): TypeError: If either argument is not a `GraphDef`. """ if not isinstance(actual, graph_pb2.GraphDef): - raise TypeError("Expected tf.GraphDef for actual, got %s" % - type(actual).__name__) + raise TypeError( + "Expected tf.GraphDef for actual, got %s" % type(actual).__name__) if not isinstance(expected, graph_pb2.GraphDef): - raise TypeError("Expected tf.GraphDef for expected, got %s" % - type(expected).__name__) + raise TypeError( + "Expected tf.GraphDef for expected, got %s" % type(expected).__name__) if checkpoint_v2: _strip_checkpoint_v2_randomized(actual) @@ -152,11 +152,10 @@ def assert_meta_graph_protos_equal(tester, a, b): a_proto = proto_type() b_proto = proto_type() # Number of entries in the collections is the same - tester.assertEqual(len(a_value.bytes_list.value), - len(b_value.bytes_list.value)) - for (a_value_item, b_value_item) in zip( - a_value.bytes_list.value, - b_value.bytes_list.value): + tester.assertEqual( + len(a_value.bytes_list.value), len(b_value.bytes_list.value)) + for (a_value_item, b_value_item) in zip(a_value.bytes_list.value, + b_value.bytes_list.value): a_proto.ParseFromString(a_value_item) b_proto.ParseFromString(b_value_item) tester.assertProtoEquals(a_proto, b_proto) @@ -220,10 +219,7 @@ def NHWCToNCHW(input_tensor): converted tensor or shape array """ # tensor dim -> new axis order - new_axes = { - 4: [0, 3, 1, 2], - 5: [0, 4, 1, 2, 3] - } + new_axes = {4: [0, 3, 1, 2], 5: [0, 4, 1, 2, 3]} if isinstance(input_tensor, ops.Tensor): ndims = input_tensor.shape.ndims return array_ops.transpose(input_tensor, new_axes[ndims]) @@ -250,8 +246,9 @@ def NHWCToNCHW_VECT_C(input_shape_or_tensor): """ permutations = {5: [0, 3, 1, 2, 4], 6: [0, 4, 1, 2, 3, 5]} is_tensor = isinstance(input_shape_or_tensor, ops.Tensor) - temp_shape = (input_shape_or_tensor.shape.as_list() - if is_tensor else input_shape_or_tensor) + temp_shape = ( + input_shape_or_tensor.shape.as_list() + if is_tensor else input_shape_or_tensor) if temp_shape[-1] % 4 != 0: raise ValueError( "Last dimension of input must be evenly divisible by 4 to convert to " @@ -283,8 +280,9 @@ def NCHW_VECT_CToNHWC(input_shape_or_tensor): """ permutations = {5: [0, 2, 3, 1, 4], 6: [0, 2, 3, 4, 1, 5]} is_tensor = isinstance(input_shape_or_tensor, ops.Tensor) - input_shape = (input_shape_or_tensor.shape.as_list() - if is_tensor else input_shape_or_tensor) + input_shape = ( + input_shape_or_tensor.shape.as_list() + if is_tensor else input_shape_or_tensor) if input_shape[-1] != 4: raise ValueError("Last dimension of NCHW_VECT_C must be 4.") permutation = permutations[len(input_shape)] @@ -307,10 +305,7 @@ def NCHWToNHWC(input_tensor): converted tensor or shape array """ # tensor dim -> new axis order - new_axes = { - 4: [0, 2, 3, 1], - 5: [0, 2, 3, 4, 1] - } + new_axes = {4: [0, 2, 3, 1], 5: [0, 2, 3, 4, 1]} if isinstance(input_tensor, ops.Tensor): ndims = input_tensor.shape.ndims return array_ops.transpose(input_tensor, new_axes[ndims]) @@ -329,6 +324,8 @@ def _use_c_api_wrapper(fn, use_c_api, *args, **kwargs): fn(*args, **kwargs) finally: ops._USE_C_API = prev_value + + # pylint: disable=protected-access @@ -345,7 +342,9 @@ def skip_if(condition): Returns: The wrapped function """ + def real_skip_if(fn): + def wrapper(*args, **kwargs): if callable(condition): skip = condition() @@ -353,7 +352,9 @@ def skip_if(condition): skip = condition if not skip: fn(*args, **kwargs) + return wrapper + return real_skip_if @@ -370,8 +371,10 @@ def disable_c_api(fn): Returns: The wrapped function """ + def wrapper(*args, **kwargs): _use_c_api_wrapper(fn, False, *args, **kwargs) + return wrapper @@ -388,8 +391,10 @@ def enable_c_api(fn): Returns: The wrapped function """ + def wrapper(*args, **kwargs): _use_c_api_wrapper(fn, True, *args, **kwargs) + return wrapper @@ -561,13 +566,17 @@ def assert_no_garbage_created(f): # not hold on to every object in other tests. gc.set_debug(previous_debug_flags) gc.enable() + return decorator -def run_in_graph_and_eager_modes( - __unused__=None, graph=None, config=None, - use_gpu=False, force_gpu=False, - reset_test=True, assert_no_eager_garbage=False): +def run_in_graph_and_eager_modes(__unused__=None, + graph=None, + config=None, + use_gpu=False, + force_gpu=False, + reset_test=True, + assert_no_eager_garbage=False): """Runs the test in both graph and eager modes. Args: @@ -596,6 +605,7 @@ def run_in_graph_and_eager_modes( def decorator(f): """Test method decorator.""" + def decorated(self, **kwargs): """Decorated the test method.""" with context.graph_mode(): @@ -631,6 +641,7 @@ def run_in_graph_and_eager_modes( run_eager_mode(self, **kwargs) return decorated + return decorator @@ -767,8 +778,10 @@ class TensorFlowTestCase(googletest.TestCase): self._AssertProtoEquals(expected_message, message) elif isinstance(expected_message_maybe_ascii, str): expected_message = type(message)() - text_format.Merge(expected_message_maybe_ascii, expected_message, - descriptor_pool=descriptor_pool.Default()) + text_format.Merge( + expected_message_maybe_ascii, + expected_message, + descriptor_pool=descriptor_pool.Default()) self._AssertProtoEquals(expected_message, message) else: assert False, ("Can't compare protos of type %s and %s" % @@ -852,7 +865,8 @@ class TensorFlowTestCase(googletest.TestCase): trigger the creation of a new session. Use the `use_gpu` and `force_gpu` options to control where ops are run. If - `force_gpu` is True, all ops are pinned to `/device:GPU:0`. Otherwise, if `use_gpu` + `force_gpu` is True, all ops are pinned to `/device:GPU:0`. Otherwise, if + `use_gpu` is True, TensorFlow tries to run as many ops on the GPU as possible. If both `force_gpu and `use_gpu` are False, all ops are pinned to the CPU. @@ -1051,6 +1065,7 @@ class TensorFlowTestCase(googletest.TestCase): self._threads.append(ret) return ret + # pylint: enable=invalid-name def assertNear(self, f1, f2, err, msg=None): @@ -1118,7 +1133,8 @@ class TensorFlowTestCase(googletest.TestCase): # the absolute difference between a and b. Here, we want to # print out which elements violate such conditions. cond = np.logical_or( - np.abs(a - b) > atol + rtol * np.abs(b), np.isnan(a) != np.isnan(b)) + np.abs(a - b) > atol + rtol * np.abs(b), + np.isnan(a) != np.isnan(b)) if a.ndim: x = a[np.where(cond)] y = b[np.where(cond)] @@ -1380,8 +1396,11 @@ class TensorFlowTestCase(googletest.TestCase): @tf_export("test.create_local_cluster") -def create_local_cluster(num_workers, num_ps, protocol="grpc", - worker_config=None, ps_config=None): +def create_local_cluster(num_workers, + num_ps, + protocol="grpc", + worker_config=None, + ps_config=None): """Create and start local servers and return the associated `Server` objects. Example: @@ -1431,15 +1450,21 @@ def create_local_cluster(num_workers, num_ps, protocol="grpc", workers = [ server_lib.Server( - cs, job_name="worker", protocol=protocol, task_index=ix, - config=worker_config, start=True) - for ix in range(num_workers) + cs, + job_name="worker", + protocol=protocol, + task_index=ix, + config=worker_config, + start=True) for ix in range(num_workers) ] ps_servers = [ server_lib.Server( - cs, job_name="ps", protocol=protocol, task_index=ix, - config=ps_config, start=True) - for ix in range(num_ps) + cs, + job_name="ps", + protocol=protocol, + task_index=ix, + config=ps_config, + start=True) for ix in range(num_ps) ] return workers, ps_servers diff --git a/tensorflow/python/kernel_tests/atrous_convolution_test.py b/tensorflow/python/kernel_tests/atrous_convolution_test.py index 04248fb2ba..2d1b3d9b7e 100644 --- a/tensorflow/python/kernel_tests/atrous_convolution_test.py +++ b/tensorflow/python/kernel_tests/atrous_convolution_test.py @@ -81,6 +81,7 @@ class AtrousConvolutionTest(test.TestCase): otherwise, it's delayed after the context. """ checks = [] + def add_check(check, *args, **kwargs): if context.in_eager_mode(): args_val, kwargs_val = self.evaluate([args, kwargs]) @@ -96,12 +97,12 @@ class AtrousConvolutionTest(test.TestCase): def _test_atrous_convolution(self, add_check, input_shape, filter_shape, dilation_rate, **kwargs): - filters = np.arange(np.prod(filter_shape), - dtype=np.float32).reshape(filter_shape) + filters = np.arange( + np.prod(filter_shape), dtype=np.float32).reshape(filter_shape) filters_upsampled = upsample_filters(filters, dilation_rate) x = np.arange(np.prod(input_shape), dtype=np.float32).reshape(input_shape) - y1 = nn_ops.convolution(input=x, filter=filters, - dilation_rate=dilation_rate, **kwargs) + y1 = nn_ops.convolution( + input=x, filter=filters, dilation_rate=dilation_rate, **kwargs) y2 = nn_ops.convolution(input=x, filter=filters_upsampled, **kwargs) def check(y1_eval, y2_eval): @@ -112,13 +113,15 @@ class AtrousConvolutionTest(test.TestCase): def test_unknown_spatial_dims_for_channel_last_format(self): x = array_ops.placeholder(dtypes.float32, [1, None, None, 10]) w = array_ops.zeros([3, 3, 10, 20]) - y = nn_ops.convolution(x, w, "VALID", dilation_rate=[2, 2], data_format="NHWC") + y = nn_ops.convolution( + x, w, "VALID", dilation_rate=[2, 2], data_format="NHWC") self.assertEqual(y.shape.as_list(), [1, None, None, 20]) def test_unknown_spatial_dims_for_channel_first_format(self): x = array_ops.placeholder(dtypes.float32, [1, 10, None, None]) w = array_ops.zeros([3, 3, 10, 20]) - y = nn_ops.convolution(x, w, "VALID", dilation_rate=[2, 2], data_format="NCHW") + y = nn_ops.convolution( + x, w, "VALID", dilation_rate=[2, 2], data_format="NCHW") self.assertEqual(y.shape.as_list(), [1, 20, None, None]) @test_util.run_in_graph_and_eager_modes() @@ -215,28 +218,35 @@ class AtrousConvolutionTest(test.TestCase): def combined_op(converted_input, num_spatial_dims, padding_arg): # pylint: disable=unused-argument # pylint: disable=cell-var-from-loop - result = nn_ops.convolution(input=converted_input, filter=f1, - padding=padding) - result = nn_ops.convolution(input=result, filter=f2, - padding=padding) + result = nn_ops.convolution( + input=converted_input, filter=f1, padding=padding) + result = nn_ops.convolution( + input=result, filter=f2, padding=padding) # pylint: enable=cell-var-from-loop return result for rate_height in range(2, 4): for rate_width in range(2, 4): dilation_rate = [rate_height, rate_width] - y1 = nn_ops.convolution(input=x, filter=f1, padding=padding, - dilation_rate=dilation_rate) - y1 = nn_ops.convolution(input=y1, filter=f2, - padding=padding, - dilation_rate=dilation_rate) + y1 = nn_ops.convolution( + input=x, + filter=f1, + padding=padding, + dilation_rate=dilation_rate) + y1 = nn_ops.convolution( + input=y1, + filter=f2, + padding=padding, + dilation_rate=dilation_rate) y2 = nn_ops.with_space_to_batch( - input=x, dilation_rate=dilation_rate, op=combined_op, + input=x, + dilation_rate=dilation_rate, + op=combined_op, padding="VALID") def check(y1_eval, y2_eval): - self.assertAllClose(y1_eval, y2_eval, rtol=1e-2, - atol=1e-2) + self.assertAllClose(y1_eval, y2_eval, rtol=1e-2, atol=1e-2) + add_check(check, y1, y2) def _test_gradient(self, x_shape, f_shape, dilation_rate, padding): diff --git a/tensorflow/python/kernel_tests/candidate_sampler_ops_test.py b/tensorflow/python/kernel_tests/candidate_sampler_ops_test.py index 88b3f20469..28b3dc45e9 100644 --- a/tensorflow/python/kernel_tests/candidate_sampler_ops_test.py +++ b/tensorflow/python/kernel_tests/candidate_sampler_ops_test.py @@ -80,7 +80,7 @@ class RangeSamplerOpsTest(test.TestCase): with self.test_session(): true_classes = constant_op.constant( [[1, 2], [0, 4], [3, 3]], dtype=dtypes.int64) - _, _, sampled_expected_count = candidate_sampling_ops.all_candidate_sampler( + _, _, sampled_expected_count = candidate_sampling_ops.all_candidate_sampler( # pylint: disable=line-too-long true_classes, self.NUM_TRUE, self.NUM_SAMPLED, True) sampled_log_expected_count = math_ops.log(sampled_expected_count) result = sampled_log_expected_count.eval() diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index 6e18ed132c..5d648bb235 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -181,8 +181,8 @@ class ControlFlowTest(test.TestCase): self.assertEqual(enter_v_constant.shape, [2]) # Otherwise, the shape should be unknown. - enter_v_non_constant = control_flow_ops.enter(v, "frame2", - is_constant=False) + enter_v_non_constant = control_flow_ops.enter( + v, "frame2", is_constant=False) self.assertEqual(enter_v_non_constant.shape, None) def testSwitchMergeIndexedSlices(self): @@ -736,24 +736,21 @@ class ControlFlowTest(test.TestCase): with self.test_session(): s = constant_op.constant([1, 2, 3, 4, 5]) r = isum(s, maximum_iterations=3) - self.assertAllEqual([1+3, 2+3, 3+3, 4+3, 5+3], r.eval()) + self.assertAllEqual([1 + 3, 2 + 3, 3 + 3, 4 + 3, 5 + 3], r.eval()) def testWhileWithMaximumIterationsAndSingleArgument(self): with self.test_session(): r = control_flow_ops.while_loop( - lambda i: i < 3, - lambda i: i + 1, - [0], - maximum_iterations=1) + lambda i: i < 3, lambda i: i + 1, [0], maximum_iterations=1) self.assertEqual(1, r.eval()) def testSingleNestedMaximumIterationsWhileLoopGradientInXLAContext(self): v = constant_op.constant(1.0) + def training_loop_with_gradient(i): out = control_flow_ops.while_loop( lambda i_, _: i_ < 3, - lambda i_, j: [i_ + 1, j * v], - [0, 1.0], + lambda i_, j: [i_ + 1, j * v], [0, 1.0], maximum_iterations=i) g = gradients_impl.gradients(out, v) with ops.control_dependencies(g): @@ -763,8 +760,8 @@ class ControlFlowTest(test.TestCase): xla_context.Enter() # Create training loop, ensure we can call gradient() of # while_loop inside the training loop. - loop = control_flow_ops.while_loop( - lambda i: i < 3, training_loop_with_gradient, [0]) + loop = control_flow_ops.while_loop(lambda i: i < 3, + training_loop_with_gradient, [0]) xla_context.Exit() loop_execute = array_ops.identity(loop) # Because loop is not fetchable. @@ -774,17 +771,18 @@ class ControlFlowTest(test.TestCase): def testInvalidMaximumIterationsWhileLoopGradientInXLAContext(self): v = constant_op.constant(1.0) + def inner_body(i, x): out = control_flow_ops.while_loop( lambda i, _: i < 3, - lambda i, j: [i + 1, j * v], - [0, x], + lambda i, j: [i + 1, j * v], [0, x], maximum_iterations=i) return out def create_while_loop(maximum_iterations=None): return control_flow_ops.while_loop( - lambda i, _: i < 3, inner_body, [0, 1.0], + lambda i, _: i < 3, + inner_body, [0, 1.0], maximum_iterations=maximum_iterations) loop_no_xla = create_while_loop(maximum_iterations=5) @@ -819,14 +817,17 @@ class ControlFlowTest(test.TestCase): def create_while_loop(): max_iter_holder = [] + def create_mi(): max_iter_holder.append(array_ops.placeholder(dtypes.int32, shape=())) return 1.0 - _ = control_flow_ops.cond(constant_op.constant(True), - create_mi, create_mi) + + _ = control_flow_ops.cond( + constant_op.constant(True), create_mi, create_mi) return control_flow_ops.while_loop( - lambda i, _: i < 3, lambda i, x: (i + 1, v * x), (0, 1.0), + lambda i, _: i < 3, + lambda i, x: (i + 1, v * x), (0, 1.0), maximum_iterations=max_iter_holder[0]) xla_context = control_flow_ops.XLAControlFlowContext() @@ -849,28 +850,32 @@ class ControlFlowTest(test.TestCase): p = array_ops.placeholder(dtype=dtypes.int32) def mid_body_builder(iterations): + def mid_body(i, x): r = control_flow_ops.while_loop( lambda *_: True, - lambda i, x: (i + 1, v * x), - (0, x), - maximum_iterations=iterations, name="inner") + lambda i, x: (i + 1, v * x), (0, x), + maximum_iterations=iterations, + name="inner") return (i + 1, gradients_impl.gradients(x + r[1], v)[0]) + return mid_body def outer_body(i, x): iterations = array_ops.size(p, name="iterations") - return ( - i + 1, - x + control_flow_ops.while_loop( - lambda *_: True, mid_body_builder(iterations), (0, x), - maximum_iterations=iterations, name="mid")[1]) + return (i + 1, x + control_flow_ops.while_loop( + lambda *_: True, + mid_body_builder(iterations), (0, x), + maximum_iterations=iterations, + name="mid")[1]) def create_while_loop(): with ops.device("/cpu:0"): r = control_flow_ops.while_loop( - lambda *_: True, outer_body, (0, 1.0), - maximum_iterations=5, name="outer") + lambda *_: True, + outer_body, (0, 1.0), + maximum_iterations=5, + name="outer") return array_ops.identity(r[1]) xla_context = control_flow_ops.XLAControlFlowContext() @@ -881,18 +886,19 @@ class ControlFlowTest(test.TestCase): final_without_xla_context = create_while_loop() with self.test_session(use_gpu=False) as sess: - opts = config_pb2.RunOptions( - trace_level=config_pb2.RunOptions.FULL_TRACE) + opts = config_pb2.RunOptions(trace_level=config_pb2.RunOptions.FULL_TRACE) run_metadata = config_pb2.RunMetadata() final_value_without_xla_context = sess.run( - final_without_xla_context, - feed_dict={p: [0, 0, 0]}) + final_without_xla_context, feed_dict={ + p: [0, 0, 0] + }) final_value_with_xla_context = sess.run( final_with_xla_context, feed_dict={p: [0, 0, 0]}, - options=opts, run_metadata=run_metadata) + options=opts, + run_metadata=run_metadata) node_stats = run_metadata.step_stats.dev_stats[0].node_stats stack_push_count = len( @@ -901,8 +907,8 @@ class ControlFlowTest(test.TestCase): # the last two "3"s comes from size(p), when p == [0, 0, 0]. self.assertEqual(stack_push_count, 5 * 3 * 3) - self.assertAllClose( - final_value_with_xla_context, final_value_without_xla_context) + self.assertAllClose(final_value_with_xla_context, + final_value_without_xla_context) # Have more than 10 parallel iterations and hence exercise k-bound # most of the time. @@ -951,8 +957,7 @@ class ControlFlowTest(test.TestCase): with self.test_session(): def compute(i, c, o): - c = array_ops.strided_slice(x, - array_ops.expand_dims(i, 0), + c = array_ops.strided_slice(x, array_ops.expand_dims(i, 0), [1] + array_ops.expand_dims(i, 0)) o = array_ops.concat([o, c], 0) i = math_ops.add(i, 1) @@ -963,11 +968,12 @@ class ControlFlowTest(test.TestCase): o = ops.convert_to_tensor([0]) x = ops.convert_to_tensor([1, 2, 3, 4, 5, 6]) s = array_ops.size(x) - r = control_flow_ops.while_loop( - lambda i, c, o: math_ops.less(i, s), compute, [i, c, o], [ - i.get_shape(), tensor_shape.unknown_shape(), - tensor_shape.unknown_shape() - ]) + r = control_flow_ops.while_loop(lambda i, c, o: math_ops.less(i, s), + compute, [i, c, o], [ + i.get_shape(), + tensor_shape.unknown_shape(), + tensor_shape.unknown_shape() + ]) result = r[2].eval() self.assertAllEqual(np.array([0, 1, 2, 3, 4, 5, 6]), result) @@ -1033,7 +1039,8 @@ class ControlFlowTest(test.TestCase): return [new_i, new_j] r = control_flow_ops.while_loop( - c, _b, [i, m], [i.get_shape(), tensor_shape.unknown_shape()]) + c, _b, [i, m], + [i.get_shape(), tensor_shape.unknown_shape()]) r = r[1] * array_ops.ones([8, 8]) self.assertAllEqual(np.ones((8, 8)), r.eval()) @@ -1065,7 +1072,8 @@ class ControlFlowTest(test.TestCase): return [new_i, new_j] r = control_flow_ops.while_loop( - c, b, [i, m], [i.get_shape(), tensor_shape.TensorShape([None, 2])]) + c, b, [i, m], + [i.get_shape(), tensor_shape.TensorShape([None, 2])]) self.assertTrue(r[1].get_shape()[0].value is None) self.assertEqual(r[1].get_shape()[1], tensor_shape.Dimension(2)) @@ -1092,20 +1100,22 @@ class ControlFlowTest(test.TestCase): def b(i, x): return [ - i + 1, sparse_tensor.SparseTensor(x.indices, x.values * 2.0, - x.dense_shape) + i + 1, + sparse_tensor.SparseTensor(x.indices, x.values * 2.0, x.dense_shape) ] _, r = control_flow_ops.while_loop(c, b, [i, x]) self.assertEqual(r.dense_shape.get_shape()[0].value, 1) _, r = control_flow_ops.while_loop( - c, b, [i, x], [i.get_shape(), tensor_shape.TensorShape([None])]) + c, b, [i, x], + [i.get_shape(), tensor_shape.TensorShape([None])]) self.assertTrue(r.dense_shape.get_shape()[0].value is None) with self.assertRaisesRegexp(ValueError, "is not compatible with"): _, r = control_flow_ops.while_loop( - c, b, [i, x], [i.get_shape(), tensor_shape.TensorShape([5])]) + c, b, [i, x], + [i.get_shape(), tensor_shape.TensorShape([5])]) def testWhileShapeInferenceIndexedSlices(self): with self.test_session(): @@ -1120,7 +1130,8 @@ class ControlFlowTest(test.TestCase): def b(i, x): return [ - i + 1, ops.IndexedSlices(x.values * 2.0, x.indices, x.dense_shape) + i + 1, + ops.IndexedSlices(x.values * 2.0, x.indices, x.dense_shape) ] _, r = control_flow_ops.while_loop(c, b, [i, x]) @@ -1128,14 +1139,16 @@ class ControlFlowTest(test.TestCase): self.assertEqual(r.values.get_shape(), tensor_shape.TensorShape([2, 2])) _, r = control_flow_ops.while_loop( - c, b, [i, x], [i.get_shape(), tensor_shape.TensorShape([None, 2])]) + c, b, [i, x], + [i.get_shape(), tensor_shape.TensorShape([None, 2])]) self.assertEqual(r.dense_shape.get_shape()[0].value, 2) self.assertTrue(r.values.get_shape()[0].value is None) self.assertEqual(r.values.get_shape()[1].value, 2) with self.assertRaisesRegexp(ValueError, "is not compatible with"): _, r = control_flow_ops.while_loop( - c, b, [i, x], [i.get_shape(), tensor_shape.TensorShape([None, 5])]) + c, b, [i, x], + [i.get_shape(), tensor_shape.TensorShape([None, 5])]) def _testNestedWhile_1(self, use_gpu): with self.test_session(use_gpu=use_gpu): @@ -1276,16 +1289,17 @@ class ControlFlowTest(test.TestCase): "v", [], initializer=init_ops.constant_initializer(2)) i0 = constant_op.constant(0) with ops.control_dependencies([i0]): + def loop_condition(i): return i < 4 def loop_body(i): some_cond = control_flow_ops.cond( constant_op.constant(True), - lambda: state_ops.assign(v, math_ops.square(v)), - lambda: v) + lambda: state_ops.assign(v, math_ops.square(v)), lambda: v) with ops.control_dependencies([some_cond]): return i + 1 + r = control_flow_ops.while_loop(loop_condition, loop_body, (i0,)) variables.global_variables_initializer().run() self.assertEqual(4, r.eval()) @@ -1600,7 +1614,8 @@ class ControlFlowTest(test.TestCase): _, rx = control_flow_ops.while_loop( c1, - b1, [r, x], [r.get_shape(), tensor_shape.unknown_shape()], + b1, [r, x], + [r.get_shape(), tensor_shape.unknown_shape()], parallel_iterations=1) self.assertEqual(45, rx.eval()) @@ -1663,7 +1678,8 @@ class ControlFlowTest(test.TestCase): b = lambda i, v: [i + 1, math_ops.multiply(x, v)] r = control_flow_ops.while_loop( c, - b, [n, v], [n.get_shape(), tensor_shape.unknown_shape()], + b, [n, v], + [n.get_shape(), tensor_shape.unknown_shape()], parallel_iterations=1) r = gradients_impl.gradients(r[1], x)[0] @@ -1797,8 +1813,8 @@ class ControlFlowTest(test.TestCase): named = collections.namedtuple("named", ("a", "b")) loop_vars = [ named(a=constant_op.constant(0.0), b=constant_op.constant(1.0)), - (constant_op.constant(2.0), - constant_op.constant(3.0)), constant_op.constant(4.0) + (constant_op.constant(2.0), constant_op.constant(3.0)), + constant_op.constant(4.0) ] c = lambda lv0, _1, _2: lv0.a < 100.0 @@ -1824,8 +1840,8 @@ class ControlFlowTest(test.TestCase): named = collections.namedtuple("named", ("a", "b")) loop_vars = [ named(a=constant_op.constant(0.0), b=constant_op.constant(1.0)), - (constant_op.constant(2.0), - constant_op.constant(3.0)), constant_op.constant(4.0) + (constant_op.constant(2.0), constant_op.constant(3.0)), + constant_op.constant(4.0) ] c = lambda lv0, _1, _2: lv0.a < 100.0 @@ -2176,7 +2192,8 @@ class ControlFlowTest(test.TestCase): def b(i, x): return [ - i + 1, ops.IndexedSlices(x.values * 2.0, x.indices, x.dense_shape) + i + 1, + ops.IndexedSlices(x.values * 2.0, x.indices, x.dense_shape) ] _, r = control_flow_ops.while_loop(c, b, [i, x]) @@ -2197,8 +2214,8 @@ class ControlFlowTest(test.TestCase): def b(i, x): return [ - i + 1, sparse_tensor.SparseTensor(x.indices, x.values * 2.0, - x.dense_shape) + i + 1, + sparse_tensor.SparseTensor(x.indices, x.values * 2.0, x.dense_shape) ] _, r = control_flow_ops.while_loop(c, b, [i, x]) @@ -2220,8 +2237,8 @@ class ControlFlowTest(test.TestCase): x1 = x + gradients_impl.gradients(data, params)[0] return i + 1, x1 - output_grad = control_flow_ops.while_loop(c, b, - [i0, constant_op.constant(0.0)]) + output_grad = control_flow_ops.while_loop( + c, b, [i0, constant_op.constant(0.0)]) self.assertAllClose(600.0, sess.run(output_grad)[1]) def testWhileAndTensorArray(self): @@ -2359,9 +2376,12 @@ class ControlFlowTest(test.TestCase): def testStopGradMultiFlows(self): with self.test_session(): + def body(i, y, r): x = variable_scope.get_variable( - "x", shape=(), dtype=dtypes.float32, + "x", + shape=(), + dtype=dtypes.float32, initializer=init_ops.ones_initializer()) y *= x return [i + 1, y, r + math_ops.reduce_sum(y)] @@ -2773,7 +2793,8 @@ class ControlFlowTest(test.TestCase): r = control_flow_ops.while_loop( lambda i, v: i < 2, lambda i, v: [i + 1, func(v)], [constant_op.constant(0), x], - [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()]) + [tensor_shape.unknown_shape(), + tensor_shape.unknown_shape()]) self.assertEqual(r[1].eval(), 65536.0) r = gradients_impl.gradients(r, x)[0] @@ -2800,12 +2821,14 @@ class ControlFlowContextCheckTest(test.TestCase): def _getCondTensor(self): cond_tensor = [] + def true_fn(): if not cond_tensor: cond_tensor.append(constant_op.constant(1)) return cond_tensor[0] - control_flow_ops.cond(math_ops.less(1, 2), true_fn, - lambda: constant_op.constant(0)) + + control_flow_ops.cond( + math_ops.less(1, 2), true_fn, lambda: constant_op.constant(0)) return cond_tensor[0] def testInvalidContext(self): @@ -2821,14 +2844,13 @@ class ControlFlowContextCheckTest(test.TestCase): # Accessing a while loop tensor in cond is illegal. while_tensor = self._getWhileTensor() with self.assertRaisesRegexp( - ValueError, - "Cannot use 'while/Const_1' as input to 'cond/Add' because " + ValueError, "Cannot use 'while/Const_1' as input to 'cond/Add' because " "'while/Const_1' is in a while loop. See info log for more details."): # TODO(skyewm): this passes if we return while_tensor directly instead # of using it as input to another op. - control_flow_ops.cond(math_ops.less(1, 2), - lambda: math_ops.add(1, while_tensor), - lambda: constant_op.constant(0)) + control_flow_ops.cond( + math_ops.less(1, 2), lambda: math_ops.add(1, while_tensor), + lambda: constant_op.constant(0)) def testInvalidContextInWhile(self): # Accessing a while loop tensor in a different while loop is illegal. @@ -2856,6 +2878,7 @@ class ControlFlowContextCheckTest(test.TestCase): # Accessing a tensor from a cond context from the other branch's cond # context is OK (although dangerous). cond_tensor = [] + def branch_fn(): if not cond_tensor: cond_tensor.append(constant_op.constant(1)) @@ -2892,12 +2915,13 @@ class ControlFlowContextCheckTest(test.TestCase): while_tensor = self._getWhileTensor() return control_flow_ops.while_loop(lambda i: i < 3, lambda i: i + while_tensor, [0]) + with self.assertRaisesRegexp( ValueError, "Cannot use 'cond/while_1/add' as input to 'cond/while/Const_1' because" " they are in different while loops. See info log for more details."): - control_flow_ops.cond(math_ops.less(1, 2), true_fn, - lambda: constant_op.constant(0)) + control_flow_ops.cond( + math_ops.less(1, 2), true_fn, lambda: constant_op.constant(0)) @test_util.with_c_api @@ -3005,11 +3029,13 @@ class AssertTest(test.TestCase): sess.run(unguarded_assert, options=opts, run_metadata=unguarded_metadata) guarded_nodestat_names = [ n.node_name - for d in guarded_metadata.step_stats.dev_stats for n in d.node_stats + for d in guarded_metadata.step_stats.dev_stats + for n in d.node_stats ] unguarded_nodestat_names = [ n.node_name - for d in unguarded_metadata.step_stats.dev_stats for n in d.node_stats + for d in unguarded_metadata.step_stats.dev_stats + for n in d.node_stats ] guarded_memcpy_nodestat_names = [ n for n in guarded_nodestat_names if "MEMCPYDtoH" in n @@ -3066,6 +3092,7 @@ class WhileOpBenchmark(test.Benchmark): Returns: The duration of the run in seconds. """ + def loop_body(i, x): with ops.device("/gpu:0"): # Always put loop body on GPU. @@ -3107,7 +3134,7 @@ class WhileOpBenchmark(test.Benchmark): start_time = time.time() for _ in xrange(num_iters): sess.run(r) - return (time.time() - start_time)/num_iters + return (time.time() - start_time) / num_iters def benchmarkWhileOpCrossDevicePlacement(self): iters = 10 @@ -3154,23 +3181,20 @@ class EagerTest(test.TestCase): def testWhileLoop(self): with context.eager_mode(): tensor = constant_op.constant([1, 2, 3, 4, 5]) - self.assertAllEqual(isum(tensor).numpy(), - [46, 47, 48, 49, 50]) + self.assertAllEqual(isum(tensor).numpy(), [46, 47, 48, 49, 50]) def testWhileLoopWithMaxIterations(self): with context.eager_mode(): tensor = constant_op.constant([1, 2, 3, 4, 5]) - self.assertAllEqual(isum(tensor, maximum_iterations=3).numpy(), - [1+3, 2+3, 3+3, 4+3, 5+3]) + self.assertAllEqual( + isum(tensor, maximum_iterations=3).numpy(), + [1 + 3, 2 + 3, 3 + 3, 4 + 3, 5 + 3]) def testWhileWithMaximumIterationsAndSingleArgument(self): with context.eager_mode(): tensor = constant_op.constant(0) r = control_flow_ops.while_loop( - lambda i: i < 3, - lambda i: i + 1, - [tensor], - maximum_iterations=1) + lambda i: i < 3, lambda i: i + 1, [tensor], maximum_iterations=1) self.assertEqual(1, r.numpy()) def testWithDependencies(self): @@ -3197,8 +3221,8 @@ class EagerTest(test.TestCase): f2 = lambda: constant_op.constant(23) f3 = lambda: constant_op.constant(-1) - r1 = control_flow_ops.case([(x < y, f1), (x > z, f2)], - default=f3, exclusive=True) + r1 = control_flow_ops.case( + [(x < y, f1), (x > z, f2)], default=f3, exclusive=True) self.assertAllEqual(r1.numpy(), 17) diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index a91917b27f..0d9b46c30d 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -71,6 +71,7 @@ def _sparsify(x, thresh=0.5, index_dtype=np.int64): return sparse_tensor.SparseTensor( indices=x_indices, values=x_values, dense_shape=x_shape), x_values + def _default_tolerance(dtype): """Returns a sensible default tolerance for comparing results of a given type""" @@ -81,7 +82,7 @@ def _default_tolerance(dtype): elif dtype in (np.float64, np.complex128): return 1e-5 else: - return None # Fail fast for unexpected types + return None # Fail fast for unexpected types class UnaryOpTest(test.TestCase): @@ -233,10 +234,10 @@ class UnaryOpTest(test.TestCase): self._compareBoth(k, np.arccos, math_ops.acos) self._compareBoth(x, np.arctan, math_ops.atan) self._compareBoth(x, np.tan, math_ops.tan) - self._compareBoth( - y, - np.vectorize(self._replace_domain_error_with_inf(math.lgamma)), - math_ops.lgamma) + self._compareBoth(y, + np.vectorize( + self._replace_domain_error_with_inf(math.lgamma)), + math_ops.lgamma) self._compareBoth(x, np.vectorize(math.erf), math_ops.erf) self._compareBoth(x, np.vectorize(math.erfc), math_ops.erfc) @@ -298,8 +299,8 @@ class UnaryOpTest(test.TestCase): w = x - x.min() + 1.02 # all greater than 1 y = (x + .5).astype(np.float64) # no zero z = (x + 15.5).astype(np.float64) # all positive - k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype( - np.float64) # between -1 and 1 + k = np.arange(-0.90, 0.90, + 0.35).reshape(1, 3, 2).astype(np.float64) # between -1 and 1 self._compareBoth(x, np.abs, math_ops.abs) self._compareBoth(x, np.abs, _ABS) self._compareBoth(x, np.negative, math_ops.negative) @@ -322,10 +323,10 @@ class UnaryOpTest(test.TestCase): self._compareBoth(y, np.sign, math_ops.sign) self._compareBoth(x, np.sin, math_ops.sin) self._compareBoth(x, np.cos, math_ops.cos) - self._compareBoth( - y, - np.vectorize(self._replace_domain_error_with_inf(math.lgamma)), - math_ops.lgamma) + self._compareBoth(y, + np.vectorize( + self._replace_domain_error_with_inf(math.lgamma)), + math_ops.lgamma) self._compareBoth(x, np.vectorize(math.erf), math_ops.erf) self._compareBoth(x, np.vectorize(math.erfc), math_ops.erfc) self._compareBoth(x, np.arctan, math_ops.atan) @@ -362,10 +363,10 @@ class UnaryOpTest(test.TestCase): self._compareBoth(y, np.sign, math_ops.sign) self._compareBoth(x, np.sin, math_ops.sin) self._compareBoth(x, np.cos, math_ops.cos) - self._compareBoth( - y, - np.vectorize(self._replace_domain_error_with_inf(math.lgamma)), - math_ops.lgamma) + self._compareBoth(y, + np.vectorize( + self._replace_domain_error_with_inf(math.lgamma)), + math_ops.lgamma) self._compareBoth(x, np.vectorize(math.erf), math_ops.erf) self._compareBoth(x, np.vectorize(math.erfc), math_ops.erfc) @@ -406,8 +407,8 @@ class UnaryOpTest(test.TestCase): self._compareBothSparse(x, np.sign, math_ops.sign) def testComplex64Basic(self): - x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, - 2).astype(np.complex64) + x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype( + np.complex64) y = x + np.complex(0.5, 0.5) # no zeros self._compareBoth(x, np.abs, math_ops.abs) self._compareBoth(x, np.abs, _ABS) @@ -450,8 +451,8 @@ class UnaryOpTest(test.TestCase): self._compareBothSparse(y, complex_sign, math_ops.sign) def testComplex128Basic(self): - x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, - 2).astype(np.complex128) + x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype( + np.complex128) y = x + np.complex(0.5, 0.5) # no zeros self._compareBoth(x, np.abs, math_ops.abs) self._compareBoth(x, np.abs, _ABS) @@ -805,10 +806,10 @@ class BinaryOpTest(test.TestCase): self._compareBoth(x, y, np.mod, _MOD) def testComplex64Basic(self): - x = np.complex(1, 1) * np.linspace(-10, 10, 6).reshape( - 1, 3, 2).astype(np.complex64) - y = np.complex(1, 1) * np.linspace(20, -20, 6).reshape( - 1, 3, 2).astype(np.complex64) + x = np.complex(1, 1) * np.linspace(-10, 10, 6).reshape(1, 3, 2).astype( + np.complex64) + y = np.complex(1, 1) * np.linspace(20, -20, 6).reshape(1, 3, 2).astype( + np.complex64) self._compareBoth(x, y, np.add, math_ops.add) self._compareBoth(x, y, np.subtract, math_ops.subtract) self._compareBoth(x, y, np.multiply, math_ops.multiply) @@ -819,10 +820,10 @@ class BinaryOpTest(test.TestCase): self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV) def testComplex128Basic(self): - x = np.complex(1, 1) * np.linspace(-10, 10, 6).reshape( - 1, 3, 2).astype(np.complex128) - y = np.complex(1, 1) * np.linspace(20, -20, 6).reshape( - 1, 3, 2).astype(np.complex128) + x = np.complex(1, 1) * np.linspace(-10, 10, 6).reshape(1, 3, 2).astype( + np.complex128) + y = np.complex(1, 1) * np.linspace(20, -20, 6).reshape(1, 3, 2).astype( + np.complex128) self._compareBoth(x, y, np.add, math_ops.add) self._compareBoth(x, y, np.subtract, math_ops.subtract) self._compareBoth(x, y, np.multiply, math_ops.multiply) @@ -1127,8 +1128,8 @@ class BinaryOpTest(test.TestCase): def testMismatchedDimensions(self): for func in [ - math_ops.add, math_ops.subtract, math_ops.multiply, math_ops.div, - _ADD, _SUB, _MUL, _TRUEDIV, _FLOORDIV + math_ops.add, math_ops.subtract, math_ops.multiply, math_ops.div, _ADD, + _SUB, _MUL, _TRUEDIV, _FLOORDIV ]: with self.assertRaisesWithPredicateMatch( ValueError, lambda e: "Dimensions must" in str(e)): @@ -1161,8 +1162,8 @@ class BinaryOpTest(test.TestCase): (1.2345, float("inf")), (1.2345, -float("inf")), (-4.321, float("inf")), (-4.125, -float("inf")), (float("inf"), float("inf")), (float("inf"), -float("inf")), - (-float("inf"), float("inf")), (-float("inf"), - -float("inf"))) + (-float("inf"), float("inf")), + (-float("inf"), -float("inf"))) for dtype in np.float32, np.float64: x1 = np.array(x1l).astype(dtype) x2 = np.array(x2l).astype(dtype) @@ -1213,22 +1214,22 @@ class ComparisonOpTest(test.TestCase): for x in data: for y in data: self.assertEqual(self._compareScalar(math_ops.less, x, y, t), x < y) - self.assertEqual(self._compareScalar(math_ops.less_equal, x, y, t), - x <= y) - self.assertEqual(self._compareScalar(math_ops.greater, x, y, t), - x > y) + self.assertEqual( + self._compareScalar(math_ops.less_equal, x, y, t), x <= y) + self.assertEqual( + self._compareScalar(math_ops.greater, x, y, t), x > y) self.assertEqual( self._compareScalar(math_ops.greater_equal, x, y, t), x >= y) self.assertEqual(self._compareScalar(math_ops.equal, x, y, t), x == y) - self.assertEqual(self._compareScalar(math_ops.not_equal, x, y, t), - x != y) + self.assertEqual( + self._compareScalar(math_ops.not_equal, x, y, t), x != y) data = [-1, 0, 1, -1j, 1j, 1 + 1j, 1 - 1j] for t in [np.complex64, np.complex128]: for x in data: for y in data: self.assertEqual(self._compareScalar(math_ops.equal, x, y, t), x == y) - self.assertEqual(self._compareScalar(math_ops.not_equal, x, y, t), - x != y) + self.assertEqual( + self._compareScalar(math_ops.not_equal, x, y, t), x != y) def _compare(self, x, y, np_func, tf_func): np_ans = np_func(x, y) @@ -1311,8 +1312,8 @@ class ComparisonOpTest(test.TestCase): self._testBCastByFunc(np.equal, math_ops.equal, include_complex=True) def testBCastNotEqual(self): - self._testBCastByFunc(np.not_equal, math_ops.not_equal, - include_complex=True) + self._testBCastByFunc( + np.not_equal, math_ops.not_equal, include_complex=True) def testShapeMismatch(self): dtypes = [np.float16, np.float32, np.float64, np.int32, np.int64] @@ -1771,9 +1772,8 @@ class MathOpsOverloadTest(test.TestCase): def _compareUnary(self, x, dtype, np_func, tf_func): np_ans = np_func(x).astype(dtype.as_numpy_dtype) with self.test_session(use_gpu=False): - self.assertAllClose( - np_ans, tf_func(ops.convert_to_tensor( - x, dtype=dtype)).eval()) + self.assertAllClose(np_ans, + tf_func(ops.convert_to_tensor(x, dtype=dtype)).eval()) def testOverload(self): dtypes = [ @@ -1795,8 +1795,8 @@ class MathOpsOverloadTest(test.TestCase): ] for dtype in dtypes: for np_func, tf_func in funcs: - if dtype in (dtypes_lib.complex64, dtypes_lib.complex128 - ) and tf_func == _FLOORDIV: + if dtype in (dtypes_lib.complex64, + dtypes_lib.complex128) and tf_func == _FLOORDIV: continue # floordiv makes no sense for complex self._compareBinary(10, 5, dtype, np_func, tf_func) # Mod only works for int32 and int64. @@ -2008,7 +2008,8 @@ class ComplexMakeRealImagTest(test.TestCase): # self._compareAngle(cplx, use_gpu=True) def testRealReal(self): - for dtype in dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.float32, dtypes_lib.float64: + for dtype in (dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.float32, + dtypes_lib.float64): x = array_ops.placeholder(dtype) y = math_ops.real(x) self.assertEqual(x, y) @@ -2037,15 +2038,16 @@ class ComplexMakeRealImagTest(test.TestCase): self._compareConj(cplx, use_gpu=True) def testConjReal(self): - for dtype in dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.float16, dtypes_lib.float32, dtypes_lib.float64: + for dtype in (dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.float16, + dtypes_lib.float32, dtypes_lib.float64): x = array_ops.placeholder(dtype) y = math_ops.conj(x) self.assertEqual(x, y) def testConjString(self): x = array_ops.placeholder(dtypes_lib.string) - with self.assertRaisesRegexp( - TypeError, r"Expected numeric or variant tensor"): + with self.assertRaisesRegexp(TypeError, + r"Expected numeric or variant tensor"): math_ops.conj(x) def _compareGradient(self, x): @@ -2060,8 +2062,9 @@ class ComplexMakeRealImagTest(test.TestCase): real, imag = array_ops.reshape(real, [-1]), array_ops.reshape(imag, [-1]) cplx = math_ops.complex(real, imag) cplx = math_ops.conj(cplx) - loss = math_ops.reduce_sum(math_ops.square(math_ops.real( - cplx))) + math_ops.reduce_sum(math_ops.square(math_ops.imag(cplx))) + loss = math_ops.reduce_sum(math_ops.square( + math_ops.real(cplx))) + math_ops.reduce_sum( + math_ops.square(math_ops.imag(cplx))) epsilon = 1e-3 jacob_t, jacob_n = gradient_checker.compute_gradient( inx, list(x.shape), loss, [1], x_init_value=x, delta=epsilon) @@ -2125,8 +2128,8 @@ class AccumulateTest(test.TestCase): np.random.rand(16, 16, 16, 16).astype(np.float32) for _ in range(20) ] random_tensors = [ - ops.convert_to_tensor( - x, dtype=dtypes_lib.float32) for x in random_arrays + ops.convert_to_tensor(x, dtype=dtypes_lib.float32) + for x in random_arrays ] tf_val = math_ops.accumulate_n(random_tensors) np_val = random_arrays[0] diff --git a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py index cf723f5eec..a4b30e4319 100644 --- a/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py +++ b/tensorflow/python/kernel_tests/dynamic_stitch_op_test.py @@ -48,8 +48,10 @@ class DynamicStitchTestBase(object): def testShapeInferenceForScalarWithNonConstantIndices(self): with self.test_session(use_gpu=True): - indices = [array_ops.placeholder(dtype=dtypes.int32), - constant_op.constant(1)] + indices = [ + array_ops.placeholder(dtype=dtypes.int32), + constant_op.constant(1) + ] data = [constant_op.constant(40), constant_op.constant(60)] for step in -1, 1: stitched_t = self.stitch_op(indices[::step], data) @@ -61,7 +63,8 @@ class DynamicStitchTestBase(object): def testSimpleOneDimensional(self): with self.test_session(use_gpu=True): indices = [ - constant_op.constant([0, 4, 7]), constant_op.constant([1, 6, 2, 3, 5]) + constant_op.constant([0, 4, 7]), + constant_op.constant([1, 6, 2, 3, 5]) ] data = [ constant_op.constant([0, 40, 70]), @@ -86,7 +89,8 @@ class DynamicStitchTestBase(object): def testSimpleTwoDimensional(self): with self.test_session(use_gpu=True): indices = [ - constant_op.constant([0, 4, 7]), constant_op.constant([1, 6]), + constant_op.constant([0, 4, 7]), + constant_op.constant([1, 6]), constant_op.constant([2, 3, 5]) ] data = [ @@ -104,7 +108,8 @@ class DynamicStitchTestBase(object): def testHigherRank(self): with self.test_session(use_gpu=True) as sess: indices = [ - constant_op.constant(6), constant_op.constant([4, 1]), + constant_op.constant(6), + constant_op.constant([4, 1]), constant_op.constant([[5, 2], [0, 3]]) ] data = [ @@ -127,7 +132,8 @@ class DynamicStitchTestBase(object): def testErrorIndicesMultiDimensional(self): indices = [ - constant_op.constant([0, 4, 7]), constant_op.constant([[1, 6, 2, 3, 5]]) + constant_op.constant([0, 4, 7]), + constant_op.constant([[1, 6, 2, 3, 5]]) ] data = [ constant_op.constant([[0, 40, 70]]), @@ -138,7 +144,8 @@ class DynamicStitchTestBase(object): def testErrorDataNumDimsMismatch(self): indices = [ - constant_op.constant([0, 4, 7]), constant_op.constant([1, 6, 2, 3, 5]) + constant_op.constant([0, 4, 7]), + constant_op.constant([1, 6, 2, 3, 5]) ] data = [ constant_op.constant([0, 40, 70]), @@ -149,7 +156,8 @@ class DynamicStitchTestBase(object): def testErrorDataDimSizeMismatch(self): indices = [ - constant_op.constant([0, 4, 5]), constant_op.constant([1, 6, 2, 3]) + constant_op.constant([0, 4, 5]), + constant_op.constant([1, 6, 2, 3]) ] data = [ constant_op.constant([[0], [40], [70]]), @@ -160,7 +168,8 @@ class DynamicStitchTestBase(object): def testErrorDataAndIndicesSizeMismatch(self): indices = [ - constant_op.constant([0, 4, 7]), constant_op.constant([1, 6, 2, 3, 5]) + constant_op.constant([0, 4, 7]), + constant_op.constant([1, 6, 2, 3, 5]) ] data = [ constant_op.constant([0, 40, 70]), @@ -235,13 +244,15 @@ class ParallelDynamicStitchTest(DynamicStitchTestBase, test.TestCase): def testHigherRankGPU(self): with self.test_session() as sess: indices = [ - constant_op.constant(6), constant_op.constant([4, 1]), + constant_op.constant(6), + constant_op.constant([4, 1]), constant_op.constant([[5, 2], [0, 3]]) ] data = [ constant_op.constant([61, 62], dtype=dtypes.float32), constant_op.constant([[41, 42], [11, 12]], dtype=dtypes.float32), - constant_op.constant([[[51, 52], [21, 22]], [[1, 2], [31, 32]]], dtype=dtypes.float32) + constant_op.constant( + [[[51, 52], [21, 22]], [[1, 2], [31, 32]]], dtype=dtypes.float32) ] stitched_t = data_flow_ops.dynamic_stitch(indices, data) stitched_val = stitched_t.eval() diff --git a/tensorflow/python/kernel_tests/partitioned_variables_test.py b/tensorflow/python/kernel_tests/partitioned_variables_test.py index 56a07cb012..f5c6255c34 100644 --- a/tensorflow/python/kernel_tests/partitioned_variables_test.py +++ b/tensorflow/python/kernel_tests/partitioned_variables_test.py @@ -50,8 +50,7 @@ class PartitionerCreatorsTest(test.TestCase): with self.test_session(): partitioner = partitioned_variables.fixed_size_partitioner(4, axis=0) with variable_scope.variable_scope("root", partitioner=partitioner): - v0 = variable_scope.get_variable( - "v0", dtype=dtypes.int64, shape=[20]) + v0 = variable_scope.get_variable("v0", dtype=dtypes.int64, shape=[20]) v0_list = v0._get_variable_list() self.assertEqual(len(v0_list), 4) @@ -169,8 +168,10 @@ class PartitionerCreatorsTest(test.TestCase): max_shards=2) # Use the partitioner with strings - partitioner_axis3_str = partitioned_variables.variable_axis_size_partitioner( - axis=3, max_shard_bytes=32768, bytes_per_string_element=8) + partitioner_axis3_str = partitioned_variables.variable_axis_size_partitioner( # pylint: disable=line-too-long + axis=3, + max_shard_bytes=32768, + bytes_per_string_element=8) with variable_scope.variable_scope( "root", partitioner=partitioner_axis3_str): @@ -423,8 +424,7 @@ class PartitionedVariablesTestCase(test.TestCase): def testRandomInitUnevenPartitions(self): with self.test_session(): rnd = variables.Variable( - random_ops.random_uniform( - [20, 43], dtype=dtypes.float64)) + random_ops.random_uniform([20, 43], dtype=dtypes.float64)) var_lists = [ partitioned_variables.create_partitioned_variables( rnd.get_shape(), [1, i], rnd.initialized_value()) diff --git a/tensorflow/python/kernel_tests/random/random_ops_test.py b/tensorflow/python/kernel_tests/random/random_ops_test.py index 5a2903a423..df37dd98ec 100644 --- a/tensorflow/python/kernel_tests/random/random_ops_test.py +++ b/tensorflow/python/kernel_tests/random/random_ops_test.py @@ -203,7 +203,8 @@ class RandomUniformTest(test.TestCase): return func def testRange(self): - for dt in dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64: + for dt in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, + dtypes.int64): sampler = self._Sampler(1000, minv=-2, maxv=8, dtype=dt, use_gpu=True) x = sampler() self.assertTrue(-2 <= np.min(x)) @@ -213,7 +214,8 @@ class RandomUniformTest(test.TestCase): # to see the same sequence of values. Will catch buggy # implementations which uses the same random number seed. def testDistinct(self): - for dt in dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64: + for dt in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, + dtypes.int64): maxv = 1.0 if dt.is_floating else 1 << 30 sampler = self._Sampler(1000, minv=0, maxv=maxv, dtype=dt, use_gpu=True) x = sampler() @@ -251,7 +253,8 @@ class RandomUniformTest(test.TestCase): # Checks that the CPU and GPU implementation returns the same results, # given the same random seed def testCPUGPUMatch(self): - for dt in dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64: + for dt in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, + dtypes.int64): maxv = 1.0 if dt.is_floating else 17 results = {} for use_gpu in False, True: @@ -261,7 +264,8 @@ class RandomUniformTest(test.TestCase): self.assertAllEqual(results[False], results[True]) def testSeed(self): - for dt in dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64: + for dt in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, + dtypes.int64): for seed in [345, 2**100, -2**100]: sx = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=True, seed=seed) sy = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=True, seed=seed) @@ -285,8 +289,7 @@ class RandomShapeTest(test.TestCase): self.assertEqual([1, 2, 3], rnd1.get_shape()) # Partially known shape. rnd2 = random_ops.truncated_normal( - array_ops.placeholder( - dtypes.int32, shape=(3,))) + array_ops.placeholder(dtypes.int32, shape=(3,))) self.assertEqual([None, None, None], rnd2.get_shape().as_list()) # Unknown shape. rnd3 = random_ops.truncated_normal(array_ops.placeholder(dtypes.int32)) @@ -298,8 +301,7 @@ class RandomShapeTest(test.TestCase): self.assertEqual([1, 2, 3], rnd1.get_shape()) # Partially known shape. rnd2 = random_ops.random_normal( - array_ops.placeholder( - dtypes.int32, shape=(3,))) + array_ops.placeholder(dtypes.int32, shape=(3,))) self.assertEqual([None, None, None], rnd2.get_shape().as_list()) # Unknown shape. rnd3 = random_ops.random_normal(array_ops.placeholder(dtypes.int32)) @@ -311,8 +313,7 @@ class RandomShapeTest(test.TestCase): self.assertEqual([1, 2, 3], rnd1.get_shape()) # Partially known shape. rnd2 = random_ops.random_uniform( - array_ops.placeholder( - dtypes.int32, shape=(3,))) + array_ops.placeholder(dtypes.int32, shape=(3,))) self.assertEqual([None, None, None], rnd2.get_shape().as_list()) # Unknown shape. rnd3 = random_ops.random_uniform(array_ops.placeholder(dtypes.int32)) diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py index c6c7c4e26c..e152f02d8e 100644 --- a/tensorflow/python/kernel_tests/xent_op_test.py +++ b/tensorflow/python/kernel_tests/xent_op_test.py @@ -38,9 +38,8 @@ class XentTest(test.TestCase): dim = len(features.shape) - 1 one_only_on_dim = list(features.shape) one_only_on_dim[dim] = 1 - e = np.exp(features - np.reshape( - np.amax( - features, axis=dim), one_only_on_dim)) + e = np.exp( + features - np.reshape(np.amax(features, axis=dim), one_only_on_dim)) probs = e / np.reshape(np.sum(e, axis=dim), one_only_on_dim) bp = (probs - labels) l = -np.sum(labels * np.log(probs + 1.0e-20), axis=dim) @@ -85,10 +84,10 @@ class XentTest(test.TestCase): def testRankTooLarge(self): for dtype in np.float16, np.float32: - np_features = np.array( - [[[1., 1., 1., 1.]], [[1., 2., 3., 4.]]]).astype(dtype) - np_labels = np.array( - [[[0., 0., 0., 1.]], [[0., .5, .5, 0.]]]).astype(dtype) + np_features = np.array([[[1., 1., 1., 1.]], [[1., 2., 3., + 4.]]]).astype(dtype) + np_labels = np.array([[[0., 0., 0., 1.]], [[0., .5, .5, + 0.]]]).astype(dtype) self.assertRaisesRegexp(ValueError, "must be rank 2", gen_nn_ops._softmax_cross_entropy_with_logits, np_features, np_labels) @@ -121,8 +120,8 @@ class XentTest(test.TestCase): # = [1.3862, 1.9401] np_loss, np_backprop = self._npXent(np.array(features), np.array(labels)) self.assertAllClose( - np.array([[0.25, 0.25, 0.25, -0.75], - [0.0321, -0.4129, -0.2632, 0.6439]]), + np.array([[0.25, 0.25, 0.25, -0.75], [0.0321, -0.4129, -0.2632, + 0.6439]]), np_backprop, rtol=1.e-3, atol=1.e-3) @@ -168,15 +167,17 @@ class XentTest(test.TestCase): shape=[3, 4], dtype=dtypes.float64, name="f") - x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=f, - name="xent") + x = nn_ops.softmax_cross_entropy_with_logits( + labels=l, logits=f, name="xent") err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3]) # Check that no extra computation performed. When only first derivative is requested, # second derivative must not be computed. So when there is no second derivative, # there is no `BatchMatMul` op in the graph. - op_names = [op.op_def.name for op in sess.graph.get_operations() if op.op_def] - self.assertNotIn('BatchMatMul', op_names) + op_names = [ + op.op_def.name for op in sess.graph.get_operations() if op.op_def + ] + self.assertNotIn("BatchMatMul", op_names) print("cross entropy gradient err = ", err) self.assertLess(err, 5e-8) @@ -193,24 +194,29 @@ class XentTest(test.TestCase): shape=[3, 4], dtype=dtypes.float64, name="f") - x = nn_ops.softmax_cross_entropy_with_logits_v2(labels=l, logits=f, - name="xent") + x = nn_ops.softmax_cross_entropy_with_logits_v2( + labels=l, logits=f, name="xent") err = gradient_checker.compute_gradient_error(l, [3, 4], x, [3]) self.assertLess(err, 5e-8) def testSecondGradient(self): with self.test_session() as sess: - l = constant_op.constant([0.0, 0.0, 1.0/3, 0.0, - 1.0/3, 0.0, 0.0, 0.0, - 0.0, 0.5/3, 0.0, 0.5/3], shape=[12], - dtype=dtypes.float64, name="l") - f = constant_op.constant([0.1, 0.2, 0.3, 0.4, - 0.1, 0.4, 0.9, 1.6, - 0.1, 0.8, 2.7, 6.4], shape=[12], - dtype=dtypes.float64, name="f") - x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=f, - name="xent") + l = constant_op.constant( + [ + 0.0, 0.0, 1.0 / 3, 0.0, 1.0 / 3, 0.0, 0.0, 0.0, 0.0, 0.5 / 3, 0.0, + 0.5 / 3 + ], + shape=[12], + dtype=dtypes.float64, + name="l") + f = constant_op.constant( + [0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4], + shape=[12], + dtype=dtypes.float64, + name="f") + x = nn_ops.softmax_cross_entropy_with_logits( + labels=l, logits=f, name="xent") loss = math_ops.reduce_sum(x) gradients = gradients_impl.gradients(loss, [f])[0] @@ -219,20 +225,23 @@ class XentTest(test.TestCase): # Check that second derivative is calculated. # (it is equivalent to being `BatchMatMul` op in the graph because of implementation of xentropy grad) - op_names = [op.op_def.name for op in sess.graph.get_operations() if op.op_def] - self.assertIn('BatchMatMul', op_names) + op_names = [ + op.op_def.name for op in sess.graph.get_operations() if op.op_def + ] + self.assertIn("BatchMatMul", op_names) print("cross entropy hessian err = ", err) self.assertLess(err, 5e-8) def testWrapper(self): - features = np.array( - [[[1., 1., 1., 1.], [1., 2., 3., 4.]], - [[2., 3., 4., 5.], [6., 7., 8., 9.]], - [[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype(np.float32) + features = np.array([[[1., 1., 1., 1.], [1., 2., 3., 4.]], + [[2., 3., 4., 5.], [6., 7., 8., 9.]], + [[5., 4., 3., 2.], [1., 2., 3., 4.]]]).astype( + np.float32) labels = np.array([[[0., 0., 0., 1.], [0., 1., 0., 0.]], [[0., 0.5, 0.5, 0.], [0.5, 0.5, 0., 0.]], - [[0., 1., 0., 0.], [0., 0., 1., 0.]]]).astype(np.float32) + [[0., 1., 0., 0.], [0., 0., 1., 0.]]]).astype( + np.float32) self._testXentWrapper(features, labels, dim=0, use_gpu=False) self._testXentWrapper(features, labels, dim=0, use_gpu=True) self._testXentWrapper(features, labels, dim=1, use_gpu=False) diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 80911ffe07..47dd8231c0 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -194,11 +194,11 @@ class AdjustGamma(test_util.TensorFlowTestCase): with self.test_session(): x_data = np.random.uniform(0, 255, (8, 8)) x_np = np.array(x_data, dtype=np.float32) - + x = constant_op.constant(x_np, shape=x_np.shape) - err_msg = 'Gamma should be a non-negative real number.' - + err_msg = "Gamma should be a non-negative real number." + try: image_ops.adjust_gamma(x, gamma=-1) except Exception as e: @@ -212,13 +212,13 @@ class AdjustGamma(test_util.TensorFlowTestCase): with self.test_session(): x_data = np.random.uniform(0, 255, (8, 8)) x_np = np.array(x_data, dtype=np.float32) - + x = constant_op.constant(x_np, shape=x_np.shape) y = constant_op.constant(-1.0, dtype=dtypes.float32) - + image = image_ops.adjust_gamma(x, gamma=y) - - err_msg = 'Gamma should be a non-negative real number.' + + err_msg = "Gamma should be a non-negative real number." try: image.eval() except Exception as e: @@ -226,7 +226,7 @@ class AdjustGamma(test_util.TensorFlowTestCase): raise else: raise AssertionError("Exception not raised: %s" % err_msg) - + def test_adjust_gamma_zero(self): """White image should be returned for gamma equal to zero""" with self.test_session(): @@ -253,13 +253,13 @@ class AdjustGamma(test_util.TensorFlowTestCase): y_tf = np.trunc(y.eval()) y_np = np.array( - [[0, 31, 45, 55, 63, 71, 78, 84], - [90, 95, 100, 105, 110, 115, 119, 123], - [127, 131, 135, 139, 142, 146, 149, 153], - [156, 159, 162, 165, 168, 171, 174, 177], - [180, 183, 186, 188, 191, 194, 196, 199], - [201, 204, 206, 209, 211, 214, 216, 218], - [221, 223, 225, 228, 230, 232, 234, 236], + [[0, 31, 45, 55, 63, 71, 78, 84], [ + 90, 95, 100, 105, 110, 115, 119, 123 + ], [127, 131, 135, 139, 142, 146, 149, 153], [ + 156, 159, 162, 165, 168, 171, 174, 177 + ], [180, 183, 186, 188, 191, 194, 196, 199], [ + 201, 204, 206, 209, 211, 214, 216, 218 + ], [221, 223, 225, 228, 230, 232, 234, 236], [238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.float32) @@ -274,14 +274,12 @@ class AdjustGamma(test_util.TensorFlowTestCase): y_tf = np.trunc(y.eval()) y_np = np.array( - [[0, 0, 0, 0, 1, 1, 2, 3], - [4, 5, 6, 7, 9, 10, 12, 14], - [16, 18, 20, 22, 25, 27, 30, 33], - [36, 39, 42, 45, 49, 52, 56, 60], - [64, 68, 72, 76, 81, 85, 90, 95], - [100, 105, 110, 116, 121, 127, 132, 138], - [144, 150, 156, 163, 169, 176, 182, 189], - [196, 203, 211, 218, 225, 233, 241, 249]], + [[0, 0, 0, 0, 1, 1, 2, 3], [4, 5, 6, 7, 9, 10, 12, 14], [ + 16, 18, 20, 22, 25, 27, 30, 33 + ], [36, 39, 42, 45, 49, 52, 56, 60], [64, 68, 72, 76, 81, 85, 90, 95], + [100, 105, 110, 116, 121, 127, 132, 138], [ + 144, 150, 156, 163, 169, 176, 182, 189 + ], [196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.float32) self.assertAllClose(y_tf, y_np, 1e-6) @@ -425,8 +423,7 @@ class FlipImageBenchmark(test.Benchmark): with session.Session("", graph=ops.Graph(), config=config) as sess: with ops.device(device): inputs = variables.Variable( - random_ops.random_uniform( - image_shape, dtype=dtypes.float32) * 255, + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, trainable=False, dtype=dtypes.float32) run_op = image_ops.flip_left_right(inputs) @@ -456,8 +453,7 @@ class FlipImageBenchmark(test.Benchmark): with session.Session("", graph=ops.Graph(), config=config) as sess: with ops.device(device): inputs = variables.Variable( - random_ops.random_uniform( - image_shape, dtype=dtypes.float32) * 255, + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, trainable=False, dtype=dtypes.float32) run_op = image_ops.random_flip_left_right(inputs) @@ -508,8 +504,7 @@ class AdjustHueBenchmark(test.Benchmark): with session.Session("", graph=ops.Graph(), config=config) as sess: with ops.device(device): inputs = variables.Variable( - random_ops.random_uniform( - image_shape, dtype=dtypes.float32) * 255, + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, trainable=False, dtype=dtypes.float32) delta = constant_op.constant(0.1, dtype=dtypes.float32) @@ -553,8 +548,7 @@ class AdjustSaturationBenchmark(test.Benchmark): with session.Session("", graph=ops.Graph(), config=config) as sess: with ops.device(device): inputs = variables.Variable( - random_ops.random_uniform( - image_shape, dtype=dtypes.float32) * 255, + random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, trainable=False, dtype=dtypes.float32) delta = constant_op.constant(0.1, dtype=dtypes.float32) @@ -609,10 +603,11 @@ class ResizeBilinearBenchmark(test.Benchmark): results = self.run_op_benchmark( sess, benchmark_op, - name=("resize_bilinear_%s_%s_%s" % - (image_size[0], image_size[1], num_channels))) - print("%s : %.2f ms/img" % (results["name"], 1000 * results["wall_time"] - / (batch_size * num_ops))) + name=("resize_bilinear_%s_%s_%s" % (image_size[0], image_size[1], + num_channels))) + print("%s : %.2f ms/img" % + (results["name"], + 1000 * results["wall_time"] / (batch_size * num_ops))) def benchmarkSimilar3Channel(self): self._benchmarkResize((183, 229), 3) @@ -659,8 +654,9 @@ class ResizeBicubicBenchmark(test.Benchmark): min_iters=20, name=("resize_bicubic_%s_%s_%s" % (image_size[0], image_size[1], num_channels))) - print("%s : %.2f ms/img" % (results["name"], 1000 * results["wall_time"] - / (batch_size * num_ops))) + print("%s : %.2f ms/img" % + (results["name"], + 1000 * results["wall_time"] / (batch_size * num_ops))) def benchmarkSimilar3Channel(self): self._benchmarkResize((183, 229), 3) @@ -696,8 +692,8 @@ class ResizeAreaBenchmark(test.Benchmark): batch_size = 1 num_ops = 1000 img = variables.Variable( - random_ops.random_normal([batch_size, image_size[0], - image_size[1], num_channels]), + random_ops.random_normal( + [batch_size, image_size[0], image_size[1], num_channels]), name="img") deps = [] @@ -710,12 +706,13 @@ class ResizeAreaBenchmark(test.Benchmark): with session.Session() as sess: sess.run(variables.global_variables_initializer()) results = self.run_op_benchmark( - sess, benchmark_op, - name=("resize_area_%s_%s_%s" % - (image_size[0], image_size[1], num_channels))) - print("%s : %.2f ms/img" % ( - results["name"], - 1000*results["wall_time"] / (batch_size * num_ops))) + sess, + benchmark_op, + name=("resize_area_%s_%s_%s" % (image_size[0], image_size[1], + num_channels))) + print("%s : %.2f ms/img" % + (results["name"], + 1000 * results["wall_time"] / (batch_size * num_ops))) def benchmarkSimilar3Channel(self): self._benchmarkResize((183, 229), 3) @@ -789,8 +786,7 @@ class AdjustSaturationTest(test_util.TensorFlowTestCase): flt_image = image_ops.convert_image_dtype(image, dtypes.float32) saturation_adjusted_image = gen_image_ops.adjust_saturation( flt_image, saturation_factor) - return image_ops.convert_image_dtype(saturation_adjusted_image, - orig_dtype) + return image_ops.convert_image_dtype(saturation_adjusted_image, orig_dtype) def testHalfSaturationFused(self): x_shape = [2, 2, 3] @@ -895,7 +891,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) y = image_ops.flip_left_right(x_tf) - self.assertTrue(y.op.name.startswith('flip_left_right')) + self.assertTrue(y.op.name.startswith("flip_left_right")) y_tf = y.eval() self.assertAllEqual(y_tf, y_np) @@ -906,7 +902,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) y = image_ops.random_flip_left_right(x_tf) - self.assertTrue(y.op.name.startswith('random_flip_left_right')) + self.assertTrue(y.op.name.startswith("random_flip_left_right")) count_flipped = 0 count_unflipped = 0 @@ -937,7 +933,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) y = image_ops.flip_up_down(x_tf) - self.assertTrue(y.op.name.startswith('flip_up_down')) + self.assertTrue(y.op.name.startswith("flip_up_down")) y_tf = y.eval() self.assertAllEqual(y_tf, y_np) @@ -948,7 +944,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) y = image_ops.random_flip_up_down(x_tf) - self.assertTrue(y.op.name.startswith('random_flip_up_down')) + self.assertTrue(y.op.name.startswith("random_flip_up_down")) count_flipped = 0 count_unflipped = 0 for _ in range(50): @@ -978,7 +974,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) y = image_ops.transpose_image(x_tf) - self.assertTrue(y.op.name.startswith('transpose_image')) + self.assertTrue(y.op.name.startswith("transpose_image")) y_tf = y.eval() self.assertAllEqual(y_tf, y_np) @@ -1203,7 +1199,7 @@ class PerImageWhiteningTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x = constant_op.constant(x_np, shape=x_shape) y = image_ops.per_image_standardization(x) - self.assertTrue(y.op.name.startswith('per_image_standardization')) + self.assertTrue(y.op.name.startswith("per_image_standardization")) y_tf = y.eval() self.assertAllClose(y_tf, y_np, atol=1e-4) @@ -1375,9 +1371,10 @@ class CropToBoundingBoxTest(test_util.TensorFlowTestCase): # Each line is a test configuration: # (offset_height, offset_width, target_height, target_width), err_msg - test_config = (([-1, 0, 3, 3], "offset_height must be >= 0"), - ([0, -1, 3, 3], "offset_width must be >= 0"), - ([0, 0, 0, 3], "target_height must be > 0"), + test_config = (([-1, 0, 3, 3], "offset_height must be >= 0"), ([ + 0, -1, 3, 3 + ], "offset_width must be >= 0"), ([0, 0, 0, 3], + "target_height must be > 0"), ([0, 0, 3, 0], "target_width must be > 0"), ([2, 0, 3, 3], "height must be >= target + offset"), ([0, 2, 3, 3], "width must be >= target + offset")) @@ -1388,7 +1385,7 @@ class CropToBoundingBoxTest(test_util.TensorFlowTestCase): def testNameScope(self): image = array_ops.placeholder(dtypes.float32, shape=[55, 66, 3]) y = image_ops.crop_to_bounding_box(image, 0, 0, 55, 66) - self.assertTrue(y.name.startswith('crop_to_bounding_box')) + self.assertTrue(y.name.startswith("crop_to_bounding_box")) class CentralCropTest(test_util.TensorFlowTestCase): @@ -1413,9 +1410,10 @@ class CentralCropTest(test_util.TensorFlowTestCase): def testCropping(self): x_shape = [4, 8, 1] - x_np = np.array([[1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8], - [1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8]], - dtype=np.int32).reshape(x_shape) + x_np = np.array( + [[1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8], + [1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8]], + dtype=np.int32).reshape(x_shape) y_np = np.array([[3, 4, 5, 6], [3, 4, 5, 6]]).reshape([2, 4, 1]) with self.test_session(use_gpu=True): x = constant_op.constant(x_np, shape=x_shape) @@ -1432,7 +1430,7 @@ class CentralCropTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x = array_ops.placeholder(shape=x_shape, dtype=dtypes.int32) y = image_ops.central_crop(x, 0.33) - y_tf = y.eval(feed_dict={x:x_np}) + y_tf = y.eval(feed_dict={x: x_np}) self.assertAllEqual(y_tf, y_np) self.assertAllEqual(y_tf.shape, y_np.shape) @@ -1471,7 +1469,7 @@ class CentralCropTest(test_util.TensorFlowTestCase): x_np = np.ones(x_shape, dtype=np.float32) with self.test_session(use_gpu=True): y = image_ops.central_crop(x_np, 1.0) - self.assertTrue(y.op.name.startswith('central_crop')) + self.assertTrue(y.op.name.startswith("central_crop")) class PadToBoundingBoxTest(test_util.TensorFlowTestCase): @@ -1544,15 +1542,10 @@ class PadToBoundingBoxTest(test_util.TensorFlowTestCase): self.assertEqual(y.get_shape().as_list(), post_shape) def testInt64(self): - x = [1, 2, 3, - 4, 5, 6, - 7, 8, 9] + x = [1, 2, 3, 4, 5, 6, 7, 8, 9] x_shape = [3, 3, 1] - y = [0, 0, 0, - 1, 2, 3, - 4, 5, 6, - 7, 8, 9] + y = [0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9] y_shape = [4, 3, 1] x = np.array(x).reshape(x_shape) y = np.array(y).reshape(y_shape) @@ -1569,38 +1562,26 @@ class PadToBoundingBoxTest(test_util.TensorFlowTestCase): self._assertReturns(x, x_shape, offset_height, offset_width, x, x_shape) def testPadding(self): - x = [1, 2, 3, - 4, 5, 6, - 7, 8, 9] + x = [1, 2, 3, 4, 5, 6, 7, 8, 9] x_shape = [3, 3, 1] offset_height, offset_width = [1, 0] - y = [0, 0, 0, - 1, 2, 3, - 4, 5, 6, - 7, 8, 9] + y = [0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9] y_shape = [4, 3, 1] self._assertReturns(x, x_shape, offset_height, offset_width, y, y_shape) offset_height, offset_width = [0, 1] - y = [0, 1, 2, 3, - 0, 4, 5, 6, - 0, 7, 8, 9] + y = [0, 1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9] y_shape = [3, 4, 1] self._assertReturns(x, x_shape, offset_height, offset_width, y, y_shape) offset_height, offset_width = [0, 0] - y = [1, 2, 3, - 4, 5, 6, - 7, 8, 9, - 0, 0, 0] + y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0, 0] y_shape = [4, 3, 1] self._assertReturns(x, x_shape, offset_height, offset_width, y, y_shape) offset_height, offset_width = [0, 0] - y = [1, 2, 3, 0, - 4, 5, 6, 0, - 7, 8, 9, 0] + y = [1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9, 0] y_shape = [3, 4, 1] self._assertReturns(x, x_shape, offset_height, offset_width, y, y_shape) @@ -1632,9 +1613,7 @@ class PadToBoundingBoxTest(test_util.TensorFlowTestCase): # Input image has 0-length dimension(s). # Each line is a test configuration: # x_shape, target_height, target_width - test_config = (([0, 2, 2], 2, 2), - ([2, 0, 2], 2, 2), - ([2, 2, 0], 2, 2)) + test_config = (([0, 2, 2], 2, 2), ([2, 0, 2], 2, 2), ([2, 2, 0], 2, 2)) offset_height, offset_width = [0, 0] x = [] @@ -1679,7 +1658,7 @@ class PadToBoundingBoxTest(test_util.TensorFlowTestCase): def testNameScope(self): image = array_ops.placeholder(dtypes.float32, shape=[55, 66, 3]) y = image_ops.pad_to_bounding_box(image, 0, 0, 55, 66) - self.assertTrue(y.op.name.startswith('pad_to_bounding_box')) + self.assertTrue(y.op.name.startswith("pad_to_bounding_box")) class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): @@ -1692,8 +1671,8 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): (bounding_box[2] - bounding_box[0])) image_size_np = np.array(image.shape, dtype=np.int32) - bounding_box_np = (np.array( - bounding_box, dtype=np.float32).reshape([1, 1, 4])) + bounding_box_np = ( + np.array(bounding_box, dtype=np.float32).reshape([1, 1, 4])) aspect_ratios = [] area_ratios = [] @@ -1738,7 +1717,9 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): y = array_ops.strided_slice(image_tf, begin, begin + size) for _ in xrange(num_iter): - y_tf = y.eval(feed_dict={min_object_covered_placeholder: min_object_covered}) + y_tf = y.eval(feed_dict={ + min_object_covered_placeholder: min_object_covered + }) crop_height = y_tf.shape[0] crop_width = y_tf.shape[1] aspect_ratio = float(crop_width) / float(crop_height) @@ -1832,7 +1813,8 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): bounding_box = constant_op.constant( [0.0, 0.0, 1.0, 1.0], shape=[4], - dtype=dtypes.float32,) + dtype=dtypes.float32, + ) begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box( image_size=image_size, bounding_boxes=bounding_box, @@ -1860,13 +1842,15 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase): class ResizeImagesTest(test_util.TensorFlowTestCase): - OPTIONS = [image_ops.ResizeMethod.BILINEAR, - image_ops.ResizeMethod.NEAREST_NEIGHBOR, - image_ops.ResizeMethod.BICUBIC, - image_ops.ResizeMethod.AREA] + OPTIONS = [ + image_ops.ResizeMethod.BILINEAR, image_ops.ResizeMethod.NEAREST_NEIGHBOR, + image_ops.ResizeMethod.BICUBIC, image_ops.ResizeMethod.AREA + ] - TYPES = [np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, - np.float16, np.float32, np.float64] + TYPES = [ + np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, np.float16, + np.float32, np.float64 + ] def _assertShapeInference(self, pre_shape, size, post_shape): # Try single image resize @@ -1894,12 +1878,10 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): single_shape = [6, 4, 1] # This test is also conducted with int8, so 127 is the maximum # value that can be used. - data = [127, 127, 64, 64, - 127, 127, 64, 64, - 64, 64, 127, 127, - 64, 64, 127, 127, - 50, 50, 100, 100, - 50, 50, 100, 100] + data = [ + 127, 127, 64, 64, 127, 127, 64, 64, 64, 64, 127, 127, 64, 64, 127, 127, + 50, 50, 100, 100, 50, 50, 100, 100 + ] target_height = 6 target_width = 4 @@ -1930,12 +1912,10 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): single_shape = [6, 4, 1] # This test is also conducted with int8, so 127 is the maximum # value that can be used. - data = [127, 127, 64, 64, - 127, 127, 64, 64, - 64, 64, 127, 127, - 64, 64, 127, 127, - 50, 50, 100, 100, - 50, 50, 100, 100] + data = [ + 127, 127, 64, 64, 127, 127, 64, 64, 64, 64, 127, 127, 64, 64, 127, 127, + 50, 50, 100, 100, 50, 50, 100, 100 + ] new_size = array_ops.placeholder(dtypes.int32, shape=(2)) img_np = np.array(data, dtype=np.uint8).reshape(img_shape) @@ -1989,8 +1969,10 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): image_ops.ResizeMethod.BILINEAR) def testReturnDtype(self): - target_shapes = [[6, 4], [3, 2], [array_ops.placeholder(dtypes.int32), - array_ops.placeholder(dtypes.int32)]] + target_shapes = [[6, 4], [3, 2], [ + array_ops.placeholder(dtypes.int32), + array_ops.placeholder(dtypes.int32) + ]] for nptype in self.TYPES: image = array_ops.placeholder(nptype, shape=[1, 6, 4, 1]) for opt in self.OPTIONS: @@ -2007,12 +1989,10 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): img_shape = [1, 6, 4, 1] # This test is also conducted with int8, so 127 is the maximum # value that can be used. - data = [127, 127, 64, 64, - 127, 127, 64, 64, - 64, 64, 127, 127, - 64, 64, 127, 127, - 50, 50, 100, 100, - 50, 50, 100, 100] + data = [ + 127, 127, 64, 64, 127, 127, 64, 64, 64, 64, 127, 127, 64, 64, 127, 127, + 50, 50, 100, 100, 50, 50, 100, 100 + ] # Test size where width is specified as a tensor which is a sum # of two tensors. width_1 = constant_op.constant(1) @@ -2034,15 +2014,11 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): def testResizeDown(self): # This test is also conducted with int8, so 127 is the maximum # value that can be used. - data = [127, 127, 64, 64, - 127, 127, 64, 64, - 64, 64, 127, 127, - 64, 64, 127, 127, - 50, 50, 100, 100, - 50, 50, 100, 100] - expected_data = [127, 64, - 64, 127, - 50, 100] + data = [ + 127, 127, 64, 64, 127, 127, 64, 64, 64, 64, 127, 127, 64, 64, 127, 127, + 50, 50, 100, 100, 50, 50, 100, 100 + ] + expected_data = [127, 64, 64, 127, 50, 100] target_height = 3 target_width = 2 @@ -2068,39 +2044,31 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): def testResizeUpAlignCornersFalse(self): img_shape = [1, 3, 2, 1] - data = [64, 32, - 32, 64, - 50, 100] + data = [64, 32, 32, 64, 50, 100] target_height = 6 target_width = 4 expected_data = {} expected_data[image_ops.ResizeMethod.BILINEAR] = [ - 64.0, 48.0, 32.0, 32.0, - 48.0, 48.0, 48.0, 48.0, - 32.0, 48.0, 64.0, 64.0, - 41.0, 61.5, 82.0, 82.0, - 50.0, 75.0, 100.0, 100.0, - 50.0, 75.0, 100.0, 100.0] + 64.0, 48.0, 32.0, 32.0, 48.0, 48.0, 48.0, 48.0, 32.0, 48.0, 64.0, 64.0, + 41.0, 61.5, 82.0, 82.0, 50.0, 75.0, 100.0, 100.0, 50.0, 75.0, 100.0, + 100.0 + ] expected_data[image_ops.ResizeMethod.NEAREST_NEIGHBOR] = [ - 64.0, 64.0, 32.0, 32.0, - 64.0, 64.0, 32.0, 32.0, - 32.0, 32.0, 64.0, 64.0, - 32.0, 32.0, 64.0, 64.0, - 50.0, 50.0, 100.0, 100.0, - 50.0, 50.0, 100.0, 100.0] + 64.0, 64.0, 32.0, 32.0, 64.0, 64.0, 32.0, 32.0, 32.0, 32.0, 64.0, 64.0, + 32.0, 32.0, 64.0, 64.0, 50.0, 50.0, 100.0, 100.0, 50.0, 50.0, 100.0, + 100.0 + ] expected_data[image_ops.ResizeMethod.AREA] = [ - 64.0, 64.0, 32.0, 32.0, - 64.0, 64.0, 32.0, 32.0, - 32.0, 32.0, 64.0, 64.0, - 32.0, 32.0, 64.0, 64.0, - 50.0, 50.0, 100.0, 100.0, - 50.0, 50.0, 100.0, 100.0] + 64.0, 64.0, 32.0, 32.0, 64.0, 64.0, 32.0, 32.0, 32.0, 32.0, 64.0, 64.0, + 32.0, 32.0, 64.0, 64.0, 50.0, 50.0, 100.0, 100.0, 50.0, 50.0, 100.0, + 100.0 + ] for nptype in self.TYPES: for opt in [ image_ops.ResizeMethod.BILINEAR, - image_ops.ResizeMethod.NEAREST_NEIGHBOR, - image_ops.ResizeMethod.AREA]: + image_ops.ResizeMethod.NEAREST_NEIGHBOR, image_ops.ResizeMethod.AREA + ]: with self.test_session(use_gpu=True): img_np = np.array(data, dtype=nptype).reshape(img_shape) image = constant_op.constant(img_np, shape=img_shape) @@ -2113,41 +2081,29 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): def testResizeUpAlignCornersTrue(self): img_shape = [1, 3, 2, 1] - data = [6, 3, - 3, 6, - 6, 9] + data = [6, 3, 3, 6, 6, 9] target_height = 5 target_width = 4 expected_data = {} expected_data[image_ops.ResizeMethod.BILINEAR] = [ - 6.0, 5.0, 4.0, 3.0, - 4.5, 4.5, 4.5, 4.5, - 3.0, 4.0, 5.0, 6.0, - 4.5, 5.5, 6.5, 7.5, - 6.0, 7.0, 8.0, 9.0 + 6.0, 5.0, 4.0, 3.0, 4.5, 4.5, 4.5, 4.5, 3.0, 4.0, 5.0, 6.0, 4.5, 5.5, + 6.5, 7.5, 6.0, 7.0, 8.0, 9.0 ] expected_data[image_ops.ResizeMethod.NEAREST_NEIGHBOR] = [ - 6.0, 6.0, 3.0, 3.0, - 3.0, 3.0, 6.0, 6.0, - 3.0, 3.0, 6.0, 6.0, - 6.0, 6.0, 9.0, 9.0, - 6.0, 6.0, 9.0, 9.0 + 6.0, 6.0, 3.0, 3.0, 3.0, 3.0, 6.0, 6.0, 3.0, 3.0, 6.0, 6.0, 6.0, 6.0, + 9.0, 9.0, 6.0, 6.0, 9.0, 9.0 ] # TODO(b/37749740): Improve alignment of ResizeMethod.AREA when # align_corners=True. expected_data[image_ops.ResizeMethod.AREA] = [ - 6.0, 6.0, 6.0, 3.0, - 6.0, 6.0, 6.0, 3.0, - 3.0, 3.0, 3.0, 6.0, - 3.0, 3.0, 3.0, 6.0, - 6.0, 6.0, 6.0, 9.0 + 6.0, 6.0, 6.0, 3.0, 6.0, 6.0, 6.0, 3.0, 3.0, 3.0, 3.0, 6.0, 3.0, 3.0, + 3.0, 6.0, 6.0, 6.0, 6.0, 9.0 ] for nptype in self.TYPES: for opt in [ image_ops.ResizeMethod.BILINEAR, - image_ops.ResizeMethod.NEAREST_NEIGHBOR, - image_ops.ResizeMethod.AREA + image_ops.ResizeMethod.NEAREST_NEIGHBOR, image_ops.ResizeMethod.AREA ]: with self.test_session(use_gpu=True): img_np = np.array(data, dtype=nptype).reshape(img_shape) @@ -2161,23 +2117,21 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): def testResizeUpBicubic(self): img_shape = [1, 6, 6, 1] - data = [128, 128, 64, 64, 128, 128, 64, 64, - 64, 64, 128, 128, 64, 64, 128, 128, - 50, 50, 100, 100, 50, 50, 100, 100, - 50, 50, 100, 100, 50, 50, 100, 100, - 50, 50, 100, 100] + data = [ + 128, 128, 64, 64, 128, 128, 64, 64, 64, 64, 128, 128, 64, 64, 128, 128, + 50, 50, 100, 100, 50, 50, 100, 100, 50, 50, 100, 100, 50, 50, 100, 100, + 50, 50, 100, 100 + ] img_np = np.array(data, dtype=np.uint8).reshape(img_shape) target_height = 8 target_width = 8 - expected_data = [128, 135, 96, 55, 64, 114, 134, 128, - 78, 81, 68, 52, 57, 118, 144, 136, - 55, 49, 79, 109, 103, 89, 83, 84, - 74, 70, 95, 122, 115, 69, 49, 55, - 100, 105, 75, 43, 50, 89, 105, 100, - 57, 54, 74, 96, 91, 65, 55, 58, - 70, 69, 75, 81, 80, 72, 69, 70, - 105, 112, 75, 36, 45, 92, 111, 105] + expected_data = [ + 128, 135, 96, 55, 64, 114, 134, 128, 78, 81, 68, 52, 57, 118, 144, 136, + 55, 49, 79, 109, 103, 89, 83, 84, 74, 70, 95, 122, 115, 69, 49, 55, 100, + 105, 75, 43, 50, 89, 105, 100, 57, 54, 74, 96, 91, 65, 55, 58, 70, 69, + 75, 81, 80, 72, 69, 70, 105, 112, 75, 36, 45, 92, 111, 105 + ] with self.test_session(use_gpu=True): image = constant_op.constant(img_np, shape=img_shape) @@ -2190,20 +2144,17 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): def testResizeDownArea(self): img_shape = [1, 6, 6, 1] - data = [128, 64, 32, 16, 8, 4, - 4, 8, 16, 32, 64, 128, - 128, 64, 32, 16, 8, 4, - 5, 10, 15, 20, 25, 30, - 30, 25, 20, 15, 10, 5, - 5, 10, 15, 20, 25, 30] + data = [ + 128, 64, 32, 16, 8, 4, 4, 8, 16, 32, 64, 128, 128, 64, 32, 16, 8, 4, 5, + 10, 15, 20, 25, 30, 30, 25, 20, 15, 10, 5, 5, 10, 15, 20, 25, 30 + ] img_np = np.array(data, dtype=np.uint8).reshape(img_shape) target_height = 4 target_width = 4 - expected_data = [73, 33, 23, 39, - 73, 33, 23, 39, - 14, 16, 19, 21, - 14, 16, 19, 21] + expected_data = [ + 73, 33, 23, 39, 73, 33, 23, 39, 14, 16, 19, 21, 14, 16, 19, 21 + ] with self.test_session(use_gpu=True): image = constant_op.constant(img_np, shape=img_shape) @@ -2290,7 +2241,7 @@ class ResizeImagesTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): single_image = array_ops.placeholder(dtypes.float32, shape=[50, 60, 3]) y = image_ops.resize_images(single_image, [55, 66]) - self.assertTrue(y.op.name.startswith('resize_images')) + self.assertTrue(y.op.name.startswith("resize_images")) class ResizeImageWithCropOrPadTest(test_util.TensorFlowTestCase): @@ -2363,133 +2314,93 @@ class ResizeImageWithCropOrPadTest(test_util.TensorFlowTestCase): def testPad(self): # Pad even along col. - x = [1, 2, 3, 4, - 5, 6, 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [2, 4, 1] - y = [0, 1, 2, 3, 4, 0, - 0, 5, 6, 7, 8, 0] + y = [0, 1, 2, 3, 4, 0, 0, 5, 6, 7, 8, 0] y_shape = [2, 6, 1] self._assertReturns(x, x_shape, y, y_shape) # Pad odd along col. - x = [1, 2, 3, 4, - 5, 6, 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [2, 4, 1] - y = [0, 1, 2, 3, 4, 0, 0, - 0, 5, 6, 7, 8, 0, 0] + y = [0, 1, 2, 3, 4, 0, 0, 0, 5, 6, 7, 8, 0, 0] y_shape = [2, 7, 1] self._assertReturns(x, x_shape, y, y_shape) # Pad even along row. - x = [1, 2, 3, 4, - 5, 6, 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [2, 4, 1] - y = [0, 0, 0, 0, - 1, 2, 3, 4, - 5, 6, 7, 8, - 0, 0, 0, 0] + y = [0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0] y_shape = [4, 4, 1] self._assertReturns(x, x_shape, y, y_shape) # Pad odd along row. - x = [1, 2, 3, 4, - 5, 6, 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [2, 4, 1] - y = [0, 0, 0, 0, - 1, 2, 3, 4, - 5, 6, 7, 8, - 0, 0, 0, 0, - 0, 0, 0, 0] + y = [0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0] y_shape = [5, 4, 1] self._assertReturns(x, x_shape, y, y_shape) def testCrop(self): # Crop even along col. - x = [1, 2, 3, 4, - 5, 6, 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [2, 4, 1] - y = [2, 3, - 6, 7] + y = [2, 3, 6, 7] y_shape = [2, 2, 1] self._assertReturns(x, x_shape, y, y_shape) # Crop odd along col. - x = [1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12] + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] x_shape = [2, 6, 1] - y = [2, 3, 4, - 8, 9, 10] + y = [2, 3, 4, 8, 9, 10] y_shape = [2, 3, 1] self._assertReturns(x, x_shape, y, y_shape) # Crop even along row. - x = [1, 2, - 3, 4, - 5, 6, - 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [4, 2, 1] - y = [3, 4, - 5, 6] + y = [3, 4, 5, 6] y_shape = [2, 2, 1] self._assertReturns(x, x_shape, y, y_shape) # Crop odd along row. - x = [1, 2, - 3, 4, - 5, 6, - 7, 8, - 9, 10, - 11, 12, - 13, 14, - 15, 16] + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] x_shape = [8, 2, 1] - y = [3, 4, - 5, 6, - 7, 8, - 9, 10, - 11, 12] + y = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12] y_shape = [5, 2, 1] self._assertReturns(x, x_shape, y, y_shape) def testCropAndPad(self): # Pad along row but crop along col. - x = [1, 2, 3, 4, - 5, 6, 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [2, 4, 1] - y = [0, 0, - 2, 3, - 6, 7, - 0, 0] + y = [0, 0, 2, 3, 6, 7, 0, 0] y_shape = [4, 2, 1] self._assertReturns(x, x_shape, y, y_shape) # Crop along row but pad along col. - x = [1, 2, - 3, 4, - 5, 6, - 7, 8] + x = [1, 2, 3, 4, 5, 6, 7, 8] x_shape = [4, 2, 1] - y = [0, 3, 4, 0, - 0, 5, 6, 0] + y = [0, 3, 4, 0, 0, 5, 6, 0] y_shape = [2, 4, 1] self._assertReturns(x, x_shape, y, y_shape) @@ -2570,7 +2481,7 @@ class ResizeImageWithCropOrPadTest(test_util.TensorFlowTestCase): def testNameScope(self): image = array_ops.placeholder(dtypes.float32, shape=[50, 60, 3]) y = image_ops.resize_image_with_crop_or_pad(image, 55, 66) - self.assertTrue(y.op.name.startswith('resize_image_with_crop_or_pad')) + self.assertTrue(y.op.name.startswith("resize_image_with_crop_or_pad")) def _SimpleColorRamp(): @@ -2839,8 +2750,8 @@ class GifTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True) as sess: gif = io_ops.read_file(filename) image = image_ops.decode_gif(gif) - with self.assertRaisesRegexp( - errors.InvalidArgumentError, "can't process optimized gif"): + with self.assertRaisesRegexp(errors.InvalidArgumentError, + "can't process optimized gif"): gif, image = sess.run([gif, image]) def testValid(self): @@ -2902,8 +2813,9 @@ class ConvertImageTest(test_util.TensorFlowTestCase): y = image_ops.convert_image_dtype(image, output_dtype) self.assertTrue(y.dtype == output_dtype) self.assertAllClose(y.eval(), y_np, atol=1e-5) - if output_dtype in [dtypes.float32, dtypes.float64, - dtypes.int32, dtypes.int64]: + if output_dtype in [ + dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64 + ]: y_saturate = image_ops.convert_image_dtype( image, output_dtype, saturate=True) self.assertTrue(y_saturate.dtype == output_dtype) @@ -2923,8 +2835,8 @@ class ConvertImageTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): self._convert([0, 255], dtypes.uint8, dtypes.int16, [0, 255 * 128]) self._convert([0, 32767], dtypes.int16, dtypes.uint8, [0, 255]) - self._convert([0, 2 ** 32], dtypes.int64, dtypes.int32, [0, 1]) - self._convert([0, 1], dtypes.int32, dtypes.int64, [0, 2 ** 32]) + self._convert([0, 2**32], dtypes.int64, dtypes.int32, [0, 1]) + self._convert([0, 1], dtypes.int32, dtypes.int64, [0, 2**32]) def testConvertBetweenFloat(self): # Make sure converting to between float types does nothing interesting @@ -2945,20 +2857,14 @@ class ConvertImageTest(test_util.TensorFlowTestCase): def testConvertBetweenInt16AndInt8(self): with self.test_session(use_gpu=True): # uint8, uint16 - self._convert([0, 255 * 256], dtypes.uint16, dtypes.uint8, - [0, 255]) - self._convert([0, 255], dtypes.uint8, dtypes.uint16, - [0, 255 * 256]) + self._convert([0, 255 * 256], dtypes.uint16, dtypes.uint8, [0, 255]) + self._convert([0, 255], dtypes.uint8, dtypes.uint16, [0, 255 * 256]) # int8, uint16 - self._convert([0, 127 * 2 * 256], dtypes.uint16, dtypes.int8, - [0, 127]) - self._convert([0, 127], dtypes.int8, dtypes.uint16, - [0, 127 * 2 * 256]) + self._convert([0, 127 * 2 * 256], dtypes.uint16, dtypes.int8, [0, 127]) + self._convert([0, 127], dtypes.int8, dtypes.uint16, [0, 127 * 2 * 256]) # int16, uint16 - self._convert([0, 255 * 256], dtypes.uint16, dtypes.int16, - [0, 255 * 128]) - self._convert([0, 255 * 128], dtypes.int16, dtypes.uint16, - [0, 255 * 256]) + self._convert([0, 255 * 256], dtypes.uint16, dtypes.int16, [0, 255 * 128]) + self._convert([0, 255 * 128], dtypes.int16, dtypes.uint16, [0, 255 * 256]) class TotalVariationTest(test_util.TensorFlowTestCase): @@ -3091,20 +2997,17 @@ class TotalVariationTest(test_util.TensorFlowTestCase): # The following are the sum of absolute differences between the pixels. # sum row dif = (4-1) + (7-2) = 3 + 5 = 8 # sum col dif = (2-1) + (7-4) = 1 + 3 = 4 - r = [[1, 2], - [4, 7]] + r = [[1, 2], [4, 7]] # Blue color channel. # sum row dif = 18 + 29 = 47 # sum col dif = 7 + 18 = 25 - g = [[11, 18], - [29, 47]] + g = [[11, 18], [29, 47]] # Green color channel. # sum row dif = 120 + 193 = 313 # sum col dif = 47 + 120 = 167 - b = [[73, 120], - [193, 313]] + b = [[73, 120], [193, 313]] # Combine the 3 color channels into a single 3-dim array. # The shape is (2, 2, 3) corresponding to (height, width and color). @@ -3133,9 +3036,7 @@ class TotalVariationTest(test_util.TensorFlowTestCase): # Combine these 3 images into a single array of shape (3, 2, 2, 3) # where the first dimension is for the image-number. - multi = np.vstack((a[np.newaxis, :], - b[np.newaxis, :], - c[np.newaxis, :])) + multi = np.vstack((a[np.newaxis, :], b[np.newaxis, :], c[np.newaxis, :])) # Check that TensorFlow correctly calculates the total variation # for each image individually and returns the correct array. diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index 3cb71eba8c..53308484c4 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -173,8 +173,7 @@ def _SegmentMeanGrad(op, grad): array_ops.shape(op.inputs[1]), array_ops.fill(array_ops.expand_dims(input_rank - 1, 0), 1) ], 0) - ones = array_ops.fill(ones_shape, - constant_op.constant(1, dtype=grad.dtype)) + ones = array_ops.fill(ones_shape, constant_op.constant(1, dtype=grad.dtype)) scaled_grad = math_ops.div(grad, math_ops.segment_sum(ones, op.inputs[1])) return array_ops.gather(scaled_grad, op.inputs[1]), None @@ -230,16 +229,19 @@ def _SparseSegmentSqrtNWithNumSegmentsGrad(op, grad): def _SegmentMinOrMaxGrad(op, grad, is_sorted): - """Gradient for SegmentMin and (unsorted) SegmentMax. They share similar code.""" - zeros = array_ops.zeros(array_ops.shape(op.inputs[0]), - dtype=op.inputs[0].dtype) + """Gradient for SegmentMin and (unsorted) SegmentMax. + + They share similar code. + """ + zeros = array_ops.zeros( + array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype) # Get the number of selected (minimum or maximum) elements in each segment. gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1]) is_selected = math_ops.equal(op.inputs[0], gathered_outputs) if is_sorted: - num_selected = math_ops.segment_sum(math_ops.cast(is_selected, grad.dtype), - op.inputs[1]) + num_selected = math_ops.segment_sum( + math_ops.cast(is_selected, grad.dtype), op.inputs[1]) else: num_selected = math_ops.unsorted_segment_sum( math_ops.cast(is_selected, grad.dtype), op.inputs[1], op.inputs[2]) @@ -536,8 +538,8 @@ def _IgammaGrad(op, grad): # and Gamma'(a) can grow large. partial_x = math_ops.exp(-x + (a - 1) * math_ops.log(x) - math_ops.lgamma(a)) # TODO(b/36815900): Mark None return values as NotImplemented - return (None, - array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx)) + return (None, array_ops.reshape( + math_ops.reduce_sum(partial_x * grad, rx), sx)) @ops.RegisterGradient("Igammac") @@ -563,15 +565,17 @@ def _BetaincGrad(op, grad): # Perform operations in log space before summing, because terms # can grow large. - log_beta = (gen_math_ops.lgamma(a) + gen_math_ops.lgamma(b) - - gen_math_ops.lgamma(a + b)) - partial_x = math_ops.exp( - (b - 1) * math_ops.log(1 - x) + (a - 1) * math_ops.log(x) - log_beta) + log_beta = ( + gen_math_ops.lgamma(a) + gen_math_ops.lgamma(b) - + gen_math_ops.lgamma(a + b)) + partial_x = math_ops.exp((b - 1) * math_ops.log(1 - x) + + (a - 1) * math_ops.log(x) - log_beta) # TODO(b/36815900): Mark None return values as NotImplemented - return (None, # da - None, # db - array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx)) + return ( + None, # da + None, # db + array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx)) @ops.RegisterGradient("Zeta") @@ -735,10 +739,8 @@ def _ShapesFullySpecifiedAndEqual(x, y, grad): y_shape = y._shape_tuple() grad_shape = grad._shape_tuple() # pylint: enable=protected-access - return (x_shape == y_shape and - x_shape == grad_shape and - x_shape is not None and - None not in x_shape) + return (x_shape == y_shape and x_shape == grad_shape and + x_shape is not None and None not in x_shape) @ops.RegisterGradient("Add") @@ -856,10 +858,10 @@ def _RealDivGrad(op, grad): x = math_ops.conj(x) y = math_ops.conj(y) return (array_ops.reshape( - math_ops.reduce_sum(math_ops.realdiv(grad, y), rx), - sx), array_ops.reshape( - math_ops.reduce_sum(grad * math_ops.realdiv(math_ops.realdiv(-x, y), y), - ry), sy)) + math_ops.reduce_sum(math_ops.realdiv(grad, y), rx), sx), + array_ops.reshape( + math_ops.reduce_sum( + grad * math_ops.realdiv(math_ops.realdiv(-x, y), y), ry), sy)) @ops.RegisterGradient("Pow") @@ -954,8 +956,8 @@ def _SelectGrad(op, grad): c = op.inputs[0] x = op.inputs[1] zeros = array_ops.zeros_like(x) - return (None, array_ops.where(c, grad, zeros), - array_ops.where(c, zeros, grad)) + return (None, array_ops.where(c, grad, zeros), array_ops.where( + c, zeros, grad)) @ops.RegisterGradient("MatMul") @@ -1017,21 +1019,20 @@ def _SparseMatMulGrad(op, grad): dtype_a = op.inputs[0].dtype dtype_b = op.inputs[1].dtype if not t_a and not t_b: - return (_SparseMatMul( - grad, op.inputs[1], dtype_a, transpose_b=True), _SparseMatMul( - op.inputs[0], grad, dtype_b, transpose_a=True)) + return (_SparseMatMul(grad, op.inputs[1], dtype_a, transpose_b=True), + _SparseMatMul(op.inputs[0], grad, dtype_b, transpose_a=True)) elif not t_a and t_b: - return (_SparseMatMul(grad, op.inputs[1], dtype_a), _SparseMatMul( - grad, op.inputs[0], dtype_b, transpose_a=True)) + return (_SparseMatMul(grad, op.inputs[1], dtype_a), + _SparseMatMul(grad, op.inputs[0], dtype_b, transpose_a=True)) elif t_a and not t_b: - return (_SparseMatMul( - op.inputs[1], grad, dtype_a, transpose_b=True), + return (_SparseMatMul(op.inputs[1], grad, dtype_a, transpose_b=True), _SparseMatMul(op.inputs[0], grad, dtype_b)) elif t_a and t_b: return (_SparseMatMul( - op.inputs[1], grad, dtype_a, transpose_a=True, - transpose_b=True), _SparseMatMul( - grad, op.inputs[0], dtype_b, transpose_a=True, transpose_b=True)) + op.inputs[1], grad, dtype_a, transpose_a=True, transpose_b=True), + _SparseMatMul( + grad, op.inputs[0], dtype_b, transpose_a=True, + transpose_b=True)) @ops.RegisterGradient("Floor") @@ -1135,8 +1136,8 @@ def _ComplexAbsGrad(op, grad): """Returns the gradient of ComplexAbs.""" # TODO(b/27786104): The cast to complex could be removed once arithmetic # supports mixtures of complex64 and real values. - return (math_ops.complex(grad, array_ops.zeros_like(grad)) * - math_ops.sign(op.inputs[0])) + return (math_ops.complex(grad, array_ops.zeros_like(grad)) * math_ops.sign( + op.inputs[0])) @ops.RegisterGradient("Cast") @@ -1166,8 +1167,8 @@ def _CumsumGrad(op, grad): exclusive = op.get_attr("exclusive") reverse = op.get_attr("reverse") return [ - math_ops.cumsum( - grad, axis, exclusive=exclusive, reverse=not reverse), None + math_ops.cumsum(grad, axis, exclusive=exclusive, reverse=not reverse), + None ] diff --git a/tensorflow/python/ops/matmul_benchmark.py b/tensorflow/python/ops/matmul_benchmark.py index f95cf08de1..6e5fe74290 100644 --- a/tensorflow/python/ops/matmul_benchmark.py +++ b/tensorflow/python/ops/matmul_benchmark.py @@ -95,8 +95,8 @@ class MatmulBenchmark(test.Benchmark): num_items = n * m * k * 2 throughput = num_items * num_iters / duration / 1e9 print('%s %s input_info:%s %d %.4fsec, %.4fGitems/s.' % - (device, str(dtype), str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:' - + str(transpose_a) + '.tb:' + str(transpose_b), num_iters, + (device, str(dtype), str(n) + 'x' + str(m) + 'x' + str(k) + + ',ta:' + str(transpose_a) + '.tb:' + str(transpose_b), num_iters, duration, throughput)) name_template = ('matmul_{device}_{dtype}_input_info_{inputinfo}') @@ -112,7 +112,8 @@ class MatmulBenchmark(test.Benchmark): return duration def run_test_gpu(self, n, m, k, transpose_a, transpose_b, dtype, num_iters): - self.run_graph(test.gpu_device_name(), n, m, k, transpose_a, transpose_b, num_iters, dtype) + self.run_graph(test.gpu_device_name(), n, m, k, transpose_a, transpose_b, + num_iters, dtype) def test_round(self, num_iters): dtypes = [np.float32, np.float64] @@ -124,8 +125,8 @@ class MatmulBenchmark(test.Benchmark): self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters) for n, m, k, (transpose_a, transpose_b) in itertools.product( - [200], [1, 8, 20], [10000], [(False, False), (True, False), (False, - True)]): + [200], [1, 8, 20], [10000], [(False, False), (True, False), + (False, True)]): self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters) for (n, m, k), (transpose_a, transpose_b) in itertools.product( diff --git a/tensorflow/python/ops/matmul_benchmark_test.py b/tensorflow/python/ops/matmul_benchmark_test.py index 5a9c0a7a49..3df0c66ef9 100644 --- a/tensorflow/python/ops/matmul_benchmark_test.py +++ b/tensorflow/python/ops/matmul_benchmark_test.py @@ -33,11 +33,11 @@ def BuildGraphTest(n, m, k, transpose_a, transpose_b, dtype): def Test(self): if not googletest.is_gpu_available(): - tf_logging.info("Skipping BuildGraphTest %s", (n, m, k, transpose_a, - transpose_b)) + tf_logging.info("Skipping BuildGraphTest %s", + (n, m, k, transpose_a, transpose_b)) return - tf_logging.info("Testing BuildGraphTest %s", (n, m, k, transpose_a, - transpose_b)) + tf_logging.info("Testing BuildGraphTest %s", + (n, m, k, transpose_a, transpose_b)) self._VerifyBuildGraph(n, m, k, transpose_a, transpose_b, dtype) return Test @@ -47,11 +47,11 @@ def RunGraphTest(n, m, k, transpose_a, transpose_b, dtype): def Test(self): if not googletest.is_gpu_available(): - tf_logging.info("Skipping RunGraphTest %s", (n, m, k, transpose_a, - transpose_b)) + tf_logging.info("Skipping RunGraphTest %s", + (n, m, k, transpose_a, transpose_b)) return - tf_logging.info("Testing RunGraphTest %s", (n, m, k, transpose_a, - transpose_b)) + tf_logging.info("Testing RunGraphTest %s", + (n, m, k, transpose_a, transpose_b)) self._VerifyRunGraph(n, m, k, transpose_a, transpose_b, dtype) return Test @@ -71,40 +71,41 @@ class MatmulBenchmarkTest(googletest.TestCase): def _VerifyBuildGraph(self, n, m, k, transpose_a, transpose_b, dtype): graph = ops.Graph() with graph.as_default(): - matmul_benchmark.build_graph(googletest.gpu_device_name(), n, m, k, transpose_a, transpose_b, - dtype) + matmul_benchmark.build_graph(googletest.gpu_device_name(), n, m, k, + transpose_a, transpose_b, dtype) gd = graph.as_graph_def() - dev=googletest.gpu_device_name() + dev = googletest.gpu_device_name() proto_expected = """ - node { name: "random_uniform/shape" op: "Const" device: \""""+ dev +"""\" } - node { name: "random_uniform/min" op: "Const" device: \""""+ dev +"""\" } - node { name: "random_uniform/max" op: "Const" device: \""""+ dev +"""\" } - node { name: "random_uniform/RandomUniform" op: "RandomUniform" input: "random_uniform/shape" device: \""""+ dev +"""\" } - node { name: "random_uniform/sub" op: "Sub" input: "random_uniform/max" input: "random_uniform/min" device: \""""+ dev +"""\" } - node { name: "random_uniform/mul" op: "Mul" input: "random_uniform/RandomUniform" input: "random_uniform/sub" device: \""""+ dev +"""\" } - node { name: "random_uniform" op: "Add" input: "random_uniform/mul" input: "random_uniform/min" device: \""""+ dev +"""\" } - node { name: "Variable" op: "VariableV2" device: \""""+ dev +"""\" } - node { name: "Variable/Assign" op: "Assign" input: "Variable" input: "random_uniform" device: \""""+ dev +"""\" } - node { name: "Variable/read" op: "Identity" input: "Variable" device: \""""+ dev +"""\" } - node { name: "random_uniform_1/shape" op: "Const" device: \""""+ dev +"""\" } - node { name: "random_uniform_1/min" op: "Const" device: \""""+ dev +"""\" } - node { name: "random_uniform_1/max" op: "Const" device: \""""+ dev +"""\" } - node { name: "random_uniform_1/RandomUniform" op: "RandomUniform" input: "random_uniform_1/shape" device: \""""+ dev +"""\" } - node { name: "random_uniform_1/sub" op: "Sub" input: "random_uniform_1/max" input: "random_uniform_1/min" device: \""""+ dev +"""\" } - node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: \""""+ dev +"""\" } - node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: \""""+ dev +"""\" } - node { name: "Variable_1" op: "VariableV2" device: \""""+ dev +"""\" } - node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: \""""+ dev +"""\" } - node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: \""""+ dev +"""\" } - node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: \""""+ dev +"""\" } - node { name: "group_deps" op: "NoOp" input: "^MatMul" device: \""""+ dev +"""\" } + node { name: "random_uniform/shape" op: "Const" device: \"""" + dev + """\" } + node { name: "random_uniform/min" op: "Const" device: \"""" + dev + """\" } + node { name: "random_uniform/max" op: "Const" device: \"""" + dev + """\" } + node { name: "random_uniform/RandomUniform" op: "RandomUniform" input: "random_uniform/shape" device: \"""" + dev + """\" } + node { name: "random_uniform/sub" op: "Sub" input: "random_uniform/max" input: "random_uniform/min" device: \"""" + dev + """\" } + node { name: "random_uniform/mul" op: "Mul" input: "random_uniform/RandomUniform" input: "random_uniform/sub" device: \"""" + dev + """\" } + node { name: "random_uniform" op: "Add" input: "random_uniform/mul" input: "random_uniform/min" device: \"""" + dev + """\" } + node { name: "Variable" op: "VariableV2" device: \"""" + dev + """\" } + node { name: "Variable/Assign" op: "Assign" input: "Variable" input: "random_uniform" device: \"""" + dev + """\" } + node { name: "Variable/read" op: "Identity" input: "Variable" device: \"""" + dev + """\" } + node { name: "random_uniform_1/shape" op: "Const" device: \"""" + dev + """\" } + node { name: "random_uniform_1/min" op: "Const" device: \"""" + dev + """\" } + node { name: "random_uniform_1/max" op: "Const" device: \"""" + dev + """\" } + node { name: "random_uniform_1/RandomUniform" op: "RandomUniform" input: "random_uniform_1/shape" device: \"""" + dev + """\" } + node { name: "random_uniform_1/sub" op: "Sub" input: "random_uniform_1/max" input: "random_uniform_1/min" device: \"""" + dev + """\" } + node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: \"""" + dev + """\" } + node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: \"""" + dev + """\" } + node { name: "Variable_1" op: "VariableV2" device: \"""" + dev + """\" } + node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: \"""" + dev + """\" } + node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: \"""" + dev + """\" } + node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: \"""" + dev + """\" } + node { name: "group_deps" op: "NoOp" input: "^MatMul" device: \"""" + dev + """\" } """ self.assertProtoEquals(str(proto_expected), self._StripGraph(gd)) def _VerifyRunGraph(self, n, m, k, transpose_a, transpose_b, dtype): benchmark_instance = matmul_benchmark.MatmulBenchmark() - duration = benchmark_instance.run_graph(googletest.gpu_device_name(), n, m, k, transpose_a, - transpose_b, 1, dtype) + duration = benchmark_instance.run_graph(googletest.gpu_device_name(), n, m, + k, transpose_a, transpose_b, 1, + dtype) self.assertTrue(duration > 1e-6) @@ -113,8 +114,8 @@ if __name__ == "__main__": index = 0 for _dtype in dtypes: for _n, _m, (_transpose_a, _transpose_b) in itertools.product( - [512, 1024], [1, 8, 16, 128], [(False, False), (True, False), (False, - True)]): + [512, 1024], [1, 8, 16, 128], [(False, False), (True, False), + (False, True)]): _k = _n setattr(MatmulBenchmarkTest, "testBuildGraph_" + str(index), BuildGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype)) diff --git a/tensorflow/python/ops/nn_fused_batchnorm_test.py b/tensorflow/python/ops/nn_fused_batchnorm_test.py index 0593ed2cfa..a08b836025 100644 --- a/tensorflow/python/ops/nn_fused_batchnorm_test.py +++ b/tensorflow/python/ops/nn_fused_batchnorm_test.py @@ -278,7 +278,8 @@ class BatchNormalizationTest(test.TestCase): epsilon = y.op.get_attr('epsilon') data_format = y.op.get_attr('data_format') grad_vals = sess.run([grad_x, grad_scale, grad_offset]) - grad_internal = nn_grad._BatchNormGrad(grad_y, x, scale, pop_mean, pop_var, epsilon, data_format) + grad_internal = nn_grad._BatchNormGrad(grad_y, x, scale, pop_mean, + pop_var, epsilon, data_format) grad_internal_vals = sess.run(list(grad_internal)) for grad_val, grad_internal_val in zip(grad_vals, grad_internal_vals): self.assertAllClose(grad_val, grad_internal_val, atol=err_tolerance) diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 32b14f86b5..9c875b4bcb 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -41,15 +41,19 @@ from tensorflow.python.ops.gen_nn_ops import * from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export - # Aliases for some automatically-generated names. local_response_normalization = gen_nn_ops.lrn # pylint: disable=protected-access -def _non_atrous_convolution(input, filter, padding, data_format=None, # pylint: disable=redefined-builtin - strides=None, name=None): +def _non_atrous_convolution( + input, + filter, + padding, + data_format=None, # pylint: disable=redefined-builtin + strides=None, + name=None): """Computes sums of N-D convolutions (actually cross correlation). It is required that 1 <= N <= 3. @@ -94,12 +98,13 @@ def _non_atrous_convolution(input, filter, padding, data_format=None, # pylint: input_shape = input.get_shape() filter = ops.convert_to_tensor(filter, name="filter") filter_shape = filter.get_shape() - op = _NonAtrousConvolution(input_shape, - filter_shape=filter_shape, - padding=padding, - data_format=data_format, - strides=strides, - name=scope) + op = _NonAtrousConvolution( + input_shape, + filter_shape=filter_shape, + padding=padding, + data_format=data_format, + strides=strides, + name=scope) return op(input, filter) @@ -119,11 +124,14 @@ class _NonAtrousConvolution(object): name: see _non_atrous_convolution. """ - def __init__(self, - input_shape, - filter_shape, # pylint: disable=redefined-builtin - padding, data_format=None, - strides=None, name=None): + def __init__( + self, + input_shape, + filter_shape, # pylint: disable=redefined-builtin + padding, + data_format=None, + strides=None, + name=None): filter_shape = filter_shape.with_rank(input_shape.ndims) self.padding = padding self.name = name @@ -137,8 +145,8 @@ class _NonAtrousConvolution(object): if strides is None: strides = [1] * conv_dims elif len(strides) != conv_dims: - raise ValueError("len(strides)=%d, but should be %d" % - (len(strides), conv_dims)) + raise ValueError("len(strides)=%d, but should be %d" % (len(strides), + conv_dims)) if conv_dims == 1: # conv1d uses the 2-d data format names if data_format is None or data_format == "NWC": @@ -177,8 +185,14 @@ class _NonAtrousConvolution(object): # those for gen_nn_ops.conv2d and gen_nn_ops.conv3d. # pylint: disable=redefined-builtin def _conv1d(self, input, filter, strides, padding, data_format, name): - return conv1d(value=input, filters=filter, stride=strides, padding=padding, - data_format=data_format, name=name) + return conv1d( + value=input, + filters=filter, + stride=strides, + padding=padding, + data_format=data_format, + name=name) + # pylint: enable=redefined-builtin def __call__(self, inp, filter): # pylint: disable=redefined-builtin @@ -340,13 +354,14 @@ def with_space_to_batch( def build_op(num_spatial_dims, padding): return lambda inp, _: op(inp, num_spatial_dims, padding) - new_op = _WithSpaceToBatch(input_shape, - dilation_rate, - padding, - build_op, - filter_shape=filter_shape, - spatial_dims=spatial_dims, - data_format=data_format) + new_op = _WithSpaceToBatch( + input_shape, + dilation_rate, + padding, + build_op, + filter_shape=filter_shape, + spatial_dims=spatial_dims, + data_format=data_format) return new_op(input, None) @@ -377,9 +392,8 @@ class _WithSpaceToBatch(object): spatial_dims=None, data_format=None): """Helper class for _with_space_to_batch.""" - dilation_rate = ops.convert_to_tensor(dilation_rate, - dtypes.int32, - name="dilation_rate") + dilation_rate = ops.convert_to_tensor( + dilation_rate, dtypes.int32, name="dilation_rate") try: rate_shape = dilation_rate.get_shape().with_rank(1) except ValueError: @@ -439,9 +453,7 @@ class _WithSpaceToBatch(object): if const_filter_shape is not None: filter_shape = const_filter_shape self.base_paddings = _with_space_to_batch_base_paddings( - const_filter_shape, - num_spatial_dims, - rate_or_const_rate) + const_filter_shape, num_spatial_dims, rate_or_const_rate) else: self.num_spatial_dims = num_spatial_dims self.rate_or_const_rate = rate_or_const_rate @@ -478,9 +490,7 @@ class _WithSpaceToBatch(object): # shape was not fully defined. filter_shape = array_ops.shape(filter) base_paddings = _with_space_to_batch_base_paddings( - filter_shape, - self.num_spatial_dims, - self.rate_or_const_rate) + filter_shape, self.num_spatial_dims, self.rate_or_const_rate) paddings, crops = array_ops.required_space_to_batch_paddings( input_shape=input_spatial_shape, base_paddings=base_paddings, @@ -491,9 +501,7 @@ class _WithSpaceToBatch(object): paddings = _with_space_to_batch_adjust(paddings, 0, spatial_dims) crops = _with_space_to_batch_adjust(crops, 0, spatial_dims) input_converted = array_ops.space_to_batch_nd( - input=inp, - block_shape=dilation_rate, - paddings=paddings) + input=inp, block_shape=dilation_rate, paddings=paddings) result = self.op(input_converted, filter) @@ -519,17 +527,17 @@ def _with_space_to_batch_base_paddings(filter_shape, num_spatial_dims, # Spatial dimensions of the filters and the upsampled filters in which we # introduce (rate - 1) zeros between consecutive filter values. filter_spatial_shape = filter_shape[:num_spatial_dims] - dilated_filter_spatial_shape = (filter_spatial_shape + - (filter_spatial_shape - 1) * - (rate_or_const_rate - 1)) + dilated_filter_spatial_shape = ( + filter_spatial_shape + (filter_spatial_shape - 1) * + (rate_or_const_rate - 1)) pad_extra_shape = dilated_filter_spatial_shape - 1 # When full_padding_shape is odd, we pad more at end, following the same # convention as conv2d. pad_extra_start = pad_extra_shape // 2 pad_extra_end = pad_extra_shape - pad_extra_start - base_paddings = array_ops.stack([[pad_extra_start[i], pad_extra_end[i]] - for i in range(num_spatial_dims)]) + base_paddings = array_ops.stack( + [[pad_extra_start[i], pad_extra_end[i]] for i in range(num_spatial_dims)]) return base_paddings @@ -623,8 +631,8 @@ def _get_strides_and_dilation_rate(num_spatial_dims, strides, dilation_rate): if strides is None: strides = [1] * num_spatial_dims elif len(strides) != num_spatial_dims: - raise ValueError("len(strides)=%d but should be %d" % - (len(strides), num_spatial_dims)) + raise ValueError("len(strides)=%d but should be %d" % (len(strides), + num_spatial_dims)) strides = np.array(strides, dtype=np.int32) if np.any(strides < 1): raise ValueError("all values of strides must be positive") @@ -636,9 +644,14 @@ def _get_strides_and_dilation_rate(num_spatial_dims, strides, dilation_rate): @tf_export("nn.convolution") -def convolution(input, filter, # pylint: disable=redefined-builtin - padding, strides=None, dilation_rate=None, - name=None, data_format=None): +def convolution( + input, + filter, # pylint: disable=redefined-builtin + padding, + strides=None, + dilation_rate=None, + name=None, + data_format=None): # pylint: disable=line-too-long """Computes sums of N-D convolutions (actually cross-correlation). @@ -757,12 +770,14 @@ def convolution(input, filter, # pylint: disable=redefined-builtin input_shape = input.get_shape() filter = ops.convert_to_tensor(filter, name="filter") filter_shape = filter.get_shape() - op = Convolution(input_shape, - filter_shape, - padding, - strides=strides, - dilation_rate=dilation_rate, - name=name, data_format=data_format) + op = Convolution( + input_shape, + filter_shape, + padding, + strides=strides, + dilation_rate=dilation_rate, + name=name, + data_format=data_format) return op(input, filter) @@ -786,8 +801,11 @@ class Convolution(object): def __init__(self, input_shape, filter_shape, - padding, strides=None, dilation_rate=None, - name=None, data_format=None): + padding, + strides=None, + dilation_rate=None, + name=None, + data_format=None): """Helper function for convolution.""" num_total_dims = filter_shape.ndims if num_total_dims is None: @@ -809,17 +827,17 @@ class Convolution(object): if data_format is None or not data_format.startswith("NC"): input_channels_dim = input_shape[num_spatial_dims + 1] - spatial_dims = range(1, num_spatial_dims+1) + spatial_dims = range(1, num_spatial_dims + 1) else: input_channels_dim = input_shape[1] - spatial_dims = range(2, num_spatial_dims+2) + spatial_dims = range(2, num_spatial_dims + 2) - if not input_channels_dim.is_compatible_with(filter_shape[ - num_spatial_dims]): + if not input_channels_dim.is_compatible_with( + filter_shape[num_spatial_dims]): raise ValueError( "number of input channels does not match corresponding dimension of " - "filter, {} != {}".format(input_channels_dim, filter_shape[ - num_spatial_dims])) + "filter, {} != {}".format(input_channels_dim, + filter_shape[num_spatial_dims])) strides, dilation_rate = _get_strides_and_dilation_rate( num_spatial_dims, strides, dilation_rate) @@ -852,14 +870,15 @@ class Convolution(object): @tf_export("nn.pool") -def pool(input, # pylint: disable=redefined-builtin - window_shape, - pooling_type, - padding, - dilation_rate=None, - strides=None, - name=None, - data_format=None): +def pool( + input, # pylint: disable=redefined-builtin + window_shape, + pooling_type, + padding, + dilation_rate=None, + strides=None, + name=None, + data_format=None): # pylint: disable=line-too-long """Performs an N-D pooling operation. @@ -941,8 +960,8 @@ def pool(input, # pylint: disable=redefined-builtin """ # pylint: enable=line-too-long - with ops.name_scope(name, "%s_pool" % - (pooling_type.lower()), [input]) as scope: + with ops.name_scope(name, "%s_pool" % (pooling_type.lower()), + [input]) as scope: input = ops.convert_to_tensor(input, name="input") num_spatial_dims = len(window_shape) @@ -963,17 +982,18 @@ def pool(input, # pylint: disable=redefined-builtin "strides > window_shape not supported due to inconsistency between " "CPU and GPU implementations") - pooling_ops = {("MAX", 1): max_pool, - ("MAX", 2): max_pool, - ("MAX", 3): max_pool3d, # pylint: disable=undefined-variable - ("AVG", 1): avg_pool, - ("AVG", 2): avg_pool, - ("AVG", 3): avg_pool3d, # pylint: disable=undefined-variable - } + pooling_ops = { + ("MAX", 1): max_pool, + ("MAX", 2): max_pool, + ("MAX", 3): max_pool3d, # pylint: disable=undefined-variable + ("AVG", 1): avg_pool, + ("AVG", 2): avg_pool, + ("AVG", 3): avg_pool3d, # pylint: disable=undefined-variable + } op_key = (pooling_type, num_spatial_dims) if op_key not in pooling_ops: - raise ValueError("%d-D %s pooling is not supported." % - (op_key[1], op_key[0])) + raise ValueError("%d-D %s pooling is not supported." % (op_key[1], + op_key[0])) if data_format is None or not data_format.startswith("NC"): adjusted_window_shape = [1] + list(window_shape) + [1] @@ -1000,12 +1020,13 @@ def pool(input, # pylint: disable=redefined-builtin if num_spatial_dims == 1: converted_input = array_ops.expand_dims(converted_input, spatial_dims[0]) - result = pooling_ops[op_key](converted_input, - adjusted_window_shape, - adjusted_strides, - converted_padding, - name=scope, - **data_format_kwargs) + result = pooling_ops[op_key]( + converted_input, + adjusted_window_shape, + adjusted_strides, + converted_padding, + name=scope, + **data_format_kwargs) if num_spatial_dims == 1: result = array_ops.squeeze(result, [spatial_dims[0]]) return result @@ -1021,7 +1042,9 @@ def pool(input, # pylint: disable=redefined-builtin @tf_export("nn.atrous_conv2d") def atrous_conv2d(value, filters, rate, padding, name=None): - """Atrous convolution (a.k.a. convolution with holes or dilated convolution). + """Atrous convolution (a.k.a. + + convolution with holes or dilated convolution). This function is a simpler wrapper around the more general @{tf.nn.convolution}, and exists only for backwards compatibility. You can @@ -1065,7 +1088,8 @@ def atrous_conv2d(value, filters, rate, padding, name=None): that effectively use atrous convolution in different ways are, among others, [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](http://arxiv.org/abs/1312.6229) and [Fast Image - Scanning with Deep Max-Pooling Convolutional Neural Networks](http://arxiv.org/abs/1302.1700). + Scanning with Deep Max-Pooling Convolutional Neural + Networks](http://arxiv.org/abs/1302.1700). Atrous convolution is also closely related to the so-called noble identities in multi-rate signal processing. @@ -1156,13 +1180,14 @@ def atrous_conv2d(value, filters, rate, padding, name=None): @tf_export("nn.conv2d_transpose") -def conv2d_transpose(value, - filter, # pylint: disable=redefined-builtin - output_shape, - strides, - padding="SAME", - data_format="NHWC", - name=None): +def conv2d_transpose( + value, + filter, # pylint: disable=redefined-builtin + output_shape, + strides, + padding="SAME", + data_format="NHWC", + name=None): """The transpose of `conv2d`. This operation is sometimes called "deconvolution" after [Deconvolutional @@ -1207,15 +1232,16 @@ def conv2d_transpose(value, output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(4)): - raise ValueError("output_shape must have shape (4,), got {}" - .format(output_shape_.get_shape())) + raise ValueError("output_shape must have shape (4,), got {}".format( + output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [4] if reached this point. if not filter.get_shape()[2].is_compatible_with(output_shape[axis]): raise ValueError( "output_shape does not match filter's output channels, " - "{} != {}".format(output_shape[axis], filter.get_shape()[2])) + "{} != {}".format(output_shape[axis], + filter.get_shape()[2])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" @@ -1281,29 +1307,32 @@ def atrous_conv2d_transpose(value, if not value.get_shape()[3].is_compatible_with(filters.get_shape()[3]): raise ValueError( "value's input channels does not match filters' input channels, " - "{} != {}".format(value.get_shape()[3], filters.get_shape()[3])) + "{} != {}".format(value.get_shape()[3], + filters.get_shape()[3])) if rate < 1: raise ValueError("rate {} cannot be less than one".format(rate)) if rate == 1: - return conv2d_transpose(value, - filters, - output_shape, - strides=[1, 1, 1, 1], - padding=padding, - data_format="NHWC") + return conv2d_transpose( + value, + filters, + output_shape, + strides=[1, 1, 1, 1], + padding=padding, + data_format="NHWC") output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(4)): - raise ValueError("output_shape must have shape (4,), got {}" - .format(output_shape_.get_shape())) + raise ValueError("output_shape must have shape (4,), got {}".format( + output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [4] if reached this point. if not filters.get_shape()[2].is_compatible_with(output_shape[3]): raise ValueError( "output_shape does not match filter's output channels, " - "{} != {}".format(output_shape[3], filters.get_shape()[2])) + "{} != {}".format(output_shape[3], + filters.get_shape()[2])) # We have two padding contributions. The first is used for converting "SAME" # to "VALID". The second is required so that the height and width of the @@ -1352,14 +1381,13 @@ def atrous_conv2d_transpose(value, # component. space_to_batch_pad = [[0, pad_bottom_extra], [0, pad_right_extra]] - value = array_ops.space_to_batch(input=value, - paddings=space_to_batch_pad, - block_size=rate) + value = array_ops.space_to_batch( + input=value, paddings=space_to_batch_pad, block_size=rate) - input_sizes = [rate * rate * output_shape[0], - (in_height + pad_bottom_extra) // rate, - (in_width + pad_right_extra) // rate, - output_shape[3]] + input_sizes = [ + rate * rate * output_shape[0], (in_height + pad_bottom_extra) // rate, + (in_width + pad_right_extra) // rate, output_shape[3] + ] value = gen_nn_ops.conv2d_backprop_input( input_sizes=input_sizes, @@ -1373,19 +1401,19 @@ def atrous_conv2d_transpose(value, batch_to_space_crop = [[pad_top, pad_bottom + pad_bottom_extra], [pad_left, pad_right + pad_right_extra]] - return array_ops.batch_to_space(input=value, - crops=batch_to_space_crop, - block_size=rate) + return array_ops.batch_to_space( + input=value, crops=batch_to_space_crop, block_size=rate) @tf_export("nn.conv3d_transpose") -def conv3d_transpose(value, - filter, # pylint: disable=redefined-builtin - output_shape, - strides, - padding="SAME", - data_format="NDHWC", - name=None): +def conv3d_transpose( + value, + filter, # pylint: disable=redefined-builtin + output_shape, + strides, + padding="SAME", + data_format="NDHWC", + name=None): """The transpose of `conv3d`. This operation is sometimes called "deconvolution" after [Deconvolutional @@ -1428,27 +1456,29 @@ def conv3d_transpose(value, output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(5)): - raise ValueError("output_shape must have shape (5,), got {}" - .format(output_shape_.get_shape())) + raise ValueError("output_shape must have shape (5,), got {}".format( + output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [5] if reached this point. if not filter.get_shape()[3].is_compatible_with(output_shape[4]): raise ValueError( "output_shape does not match filter's output channels, " - "{} != {}".format(output_shape[4], filter.get_shape()[3])) + "{} != {}".format(output_shape[4], + filter.get_shape()[3])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) - return gen_nn_ops.conv3d_backprop_input_v2(input_sizes=output_shape_, - filter=filter, - out_backprop=value, - strides=strides, - padding=padding, - data_format=data_format, - name=name) + return gen_nn_ops.conv3d_backprop_input_v2( + input_sizes=output_shape_, + filter=filter, + out_backprop=value, + strides=strides, + padding=padding, + data_format=data_format, + name=name) # pylint: disable=protected-access @@ -1514,7 +1544,9 @@ def crelu(features, name=None, axis=-1): Concatenates a ReLU which selects only the positive part of the activation with a ReLU which selects only the *negative* part of the activation. Note that as a result this non-linearity doubles the depth of the activations. - Source: [Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units. W. Shang, et al.](https://arxiv.org/abs/1603.05201) + Source: [Understanding and Improving Convolutional Neural Networks via + Concatenated Rectified Linear Units. W. Shang, et + al.](https://arxiv.org/abs/1603.05201) Args: features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, @@ -1534,7 +1566,9 @@ def crelu(features, name=None, axis=-1): @tf_export("nn.relu6") def relu6(features, name=None): """Computes Rectified Linear 6: `min(max(features, 0), 6)`. - Source: [Convolutional Deep Belief Networks on CIFAR-10. A. Krizhevsky](http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf) + + Source: [Convolutional Deep Belief Networks on CIFAR-10. A. + Krizhevsky](http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf) Args: features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, @@ -1622,14 +1656,16 @@ def _softmax(logits, compute_op, dim=-1, name=None): InvalidArgumentError: if `logits` is empty or `dim` is beyond the last dimension of `logits`. """ + def _swap_axis(logits, dim_index, last_index, name=None): """Swaps logits's dim_index and last_index.""" - return array_ops.transpose(logits, - array_ops.concat([ - math_ops.range(dim_index), [last_index], - math_ops.range(dim_index + 1, last_index), - [dim_index] - ], 0), name=name) + return array_ops.transpose( + logits, + array_ops.concat([ + math_ops.range(dim_index), [last_index], + math_ops.range(dim_index + 1, last_index), [dim_index] + ], 0), + name=name) logits = ops.convert_to_tensor(logits) @@ -1746,9 +1782,12 @@ def _ensure_xent_args(name, sentinel, labels, logits): @tf_export("nn.softmax_cross_entropy_with_logits_v2") -def softmax_cross_entropy_with_logits_v2(_sentinel=None, # pylint: disable=invalid-name - labels=None, logits=None, - dim=-1, name=None): +def softmax_cross_entropy_with_logits_v2( + _sentinel=None, # pylint: disable=invalid-name + labels=None, + logits=None, + dim=-1, + name=None): """Computes softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the @@ -1790,19 +1829,19 @@ def softmax_cross_entropy_with_logits_v2(_sentinel=None, # pylint: disable=inva A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the softmax cross entropy loss. """ - _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, - labels, logits) + _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, labels, + logits) # TODO(pcmurray) Raise an error when the labels do not sum to 1. Note: This # could break users who call this with bad labels, but disregard the bad # results. - with ops.name_scope( - name, "softmax_cross_entropy_with_logits", [logits, labels]) as name: + with ops.name_scope(name, "softmax_cross_entropy_with_logits", + [logits, labels]) as name: logits = ops.convert_to_tensor(logits, name="logits") labels = ops.convert_to_tensor(labels, name="labels") - precise_logits = math_ops.cast(logits, dtypes.float32) if ( - logits.dtype == dtypes.float16) else logits + precise_logits = math_ops.cast( + logits, dtypes.float32) if (logits.dtype == dtypes.float16) else logits # labels and logits must be of the same type labels = math_ops.cast(labels, precise_logits.dtype) input_rank = array_ops.rank(precise_logits) @@ -1811,13 +1850,14 @@ def softmax_cross_entropy_with_logits_v2(_sentinel=None, # pylint: disable=inva # Move the dim to the end if dim is not the last dimension. if dim is not -1: + def _move_dim_to_end(tensor, dim_index, rank): - return array_ops.transpose(tensor, - array_ops.concat([ - math_ops.range(dim_index), - math_ops.range(dim_index + 1, rank), - [dim_index] - ], 0)) + return array_ops.transpose( + tensor, + array_ops.concat([ + math_ops.range(dim_index), + math_ops.range(dim_index + 1, rank), [dim_index] + ], 0)) precise_logits = _move_dim_to_end(precise_logits, dim, input_rank) labels = _move_dim_to_end(labels, dim, input_rank) @@ -1862,9 +1902,12 @@ See tf.nn.softmax_cross_entropy_with_logits_v2. @tf_export("nn.softmax_cross_entropy_with_logits") @deprecation.deprecated(date=None, instructions=_XENT_DEPRECATION) -def softmax_cross_entropy_with_logits(_sentinel=None, # pylint: disable=invalid-name - labels=None, logits=None, - dim=-1, name=None): +def softmax_cross_entropy_with_logits( + _sentinel=None, # pylint: disable=invalid-name + labels=None, + logits=None, + dim=-1, + name=None): """Computes softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the @@ -1906,11 +1949,11 @@ def softmax_cross_entropy_with_logits(_sentinel=None, # pylint: disable=invalid A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the softmax cross entropy loss. """ - _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, - labels, logits) + _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, labels, + logits) - with ops.name_scope( - name, "softmax_cross_entropy_with_logits_sg", [logits, labels]) as name: + with ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", + [logits, labels]) as name: labels = array_ops.stop_gradient(labels, name="labels_stop_gradient") return softmax_cross_entropy_with_logits_v2( @@ -1918,9 +1961,11 @@ def softmax_cross_entropy_with_logits(_sentinel=None, # pylint: disable=invalid @tf_export("nn.sparse_softmax_cross_entropy_with_logits") -def sparse_softmax_cross_entropy_with_logits(_sentinel=None, # pylint: disable=invalid-name - labels=None, logits=None, - name=None): +def sparse_softmax_cross_entropy_with_logits( + _sentinel=None, # pylint: disable=invalid-name + labels=None, + logits=None, + name=None): """Computes sparse softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the @@ -1976,15 +2021,15 @@ def sparse_softmax_cross_entropy_with_logits(_sentinel=None, # pylint: disable= [labels, logits]): labels = ops.convert_to_tensor(labels) logits = ops.convert_to_tensor(logits) - precise_logits = math_ops.cast(logits, dtypes.float32) if ( - dtypes.as_dtype(logits.dtype) == dtypes.float16) else logits + precise_logits = math_ops.cast(logits, dtypes.float32) if (dtypes.as_dtype( + logits.dtype) == dtypes.float16) else logits # Store label shape for result later. labels_static_shape = labels.get_shape() labels_shape = array_ops.shape(labels) if logits.get_shape().ndims is not None and logits.get_shape().ndims == 0: - raise ValueError("Logits cannot be scalars - received shape %s." % - logits.get_shape()) + raise ValueError( + "Logits cannot be scalars - received shape %s." % logits.get_shape()) if logits.get_shape().ndims is not None and ( labels_static_shape.ndims is not None and labels_static_shape.ndims != logits.get_shape().ndims - 1): @@ -2041,12 +2086,13 @@ def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "AvgPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._avg_pool(value, - ksize=ksize, - strides=strides, - padding=padding, - data_format=data_format, - name=name) + return gen_nn_ops._avg_pool( + value, + ksize=ksize, + strides=strides, + padding=padding, + data_format=data_format, + name=name) @tf_export("nn.max_pool") @@ -2070,12 +2116,13 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._max_pool(value, - ksize=ksize, - strides=strides, - padding=padding, - data_format=data_format, - name=name) + return gen_nn_ops._max_pool( + value, + ksize=ksize, + strides=strides, + padding=padding, + data_format=data_format, + name=name) @ops.RegisterStatistics("Conv2D", "flops") @@ -2083,8 +2130,8 @@ def _calc_conv_flops(graph, node): """Calculates the compute resources needed for Conv2D.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() - filter_shape = graph_util.tensor_shape_from_node_def_name(graph, - node.input[1]) + filter_shape = graph_util.tensor_shape_from_node_def_name( + graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() @@ -2092,8 +2139,9 @@ def _calc_conv_flops(graph, node): filter_width = int(filter_shape[1]) filter_in_depth = int(filter_shape[2]) output_count = np.prod(output_shape.as_list()) - return ops.OpStats("flops", (output_count * filter_in_depth * filter_height * - filter_width * 2)) + return ops.OpStats( + "flops", + (output_count * filter_in_depth * filter_height * filter_width * 2)) @ops.RegisterStatistics("DepthwiseConv2dNative", "flops") @@ -2101,8 +2149,8 @@ def _calc_depthwise_conv_flops(graph, node): """Calculates the compute resources needed for DepthwiseConv2dNative.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() - filter_shape = graph_util.tensor_shape_from_node_def_name(graph, - node.input[1]) + filter_shape = graph_util.tensor_shape_from_node_def_name( + graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() @@ -2210,9 +2258,8 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: di if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1: raise ValueError("keep_prob must be a scalar tensor or a float in the " "range (0, 1], got %g" % keep_prob) - keep_prob = ops.convert_to_tensor(keep_prob, - dtype=x.dtype, - name="keep_prob") + keep_prob = ops.convert_to_tensor( + keep_prob, dtype=x.dtype, name="keep_prob") keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar()) # Do nothing if we know keep_prob == 1 @@ -2222,9 +2269,8 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: di noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x) # uniform [keep_prob, 1.0 + keep_prob) random_tensor = keep_prob - random_tensor += random_ops.random_uniform(noise_shape, - seed=seed, - dtype=x.dtype) + random_tensor += random_ops.random_uniform( + noise_shape, seed=seed, dtype=x.dtype) # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = math_ops.floor(random_tensor) ret = math_ops.div(x, keep_prob) * binary_tensor @@ -2293,13 +2339,21 @@ def nth_element(input, n, reverse=False, name=None): @tf_export("nn.conv1d") @deprecation.deprecated_arg_values( - None, "`NCHW` for data_format is deprecated, use `NCW` instead", - warn_once=True, data_format="NCHW") + None, + "`NCHW` for data_format is deprecated, use `NCW` instead", + warn_once=True, + data_format="NCHW") @deprecation.deprecated_arg_values( - None, "`NHWC` for data_format is deprecated, use `NWC` instead", - warn_once=True, data_format="NHWC") -def conv1d(value, filters, stride, padding, - use_cudnn_on_gpu=None, data_format=None, + None, + "`NHWC` for data_format is deprecated, use `NWC` instead", + warn_once=True, + data_format="NHWC") +def conv1d(value, + filters, + stride, + padding, + use_cudnn_on_gpu=None, + data_format=None, name=None): r"""Computes a 1-D convolution given 3-D input and filter tensors. @@ -2358,9 +2412,13 @@ def conv1d(value, filters, stride, padding, raise ValueError("data_format must be \"NWC\" or \"NCW\".") value = array_ops.expand_dims(value, spatial_start_dim) filters = array_ops.expand_dims(filters, 0) - result = gen_nn_ops.conv2d(value, filters, strides, padding, - use_cudnn_on_gpu=use_cudnn_on_gpu, - data_format=data_format) + result = gen_nn_ops.conv2d( + value, + filters, + strides, + padding, + use_cudnn_on_gpu=use_cudnn_on_gpu, + data_format=data_format) return array_ops.squeeze(result, [spatial_start_dim]) @@ -2466,8 +2524,8 @@ def _calc_dilation2d_flops(graph, node): """Calculates the compute resources needed for Dilation2D.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() - filter_shape = graph_util.tensor_shape_from_node_def_name(graph, - node.input[1]) + filter_shape = graph_util.tensor_shape_from_node_def_name( + graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() @@ -2527,12 +2585,13 @@ def erosion2d(value, kernel, strides, rates, padding, name=None): with ops.name_scope(name, "erosion2d", [value, kernel]) as name: # Reduce erosion to dilation by duality. return math_ops.negative( - gen_nn_ops.dilation2d(input=math_ops.negative(value), - filter=array_ops.reverse_v2(kernel, [0, 1]), - strides=strides, - rates=rates, - padding=padding, - name=name)) + gen_nn_ops.dilation2d( + input=math_ops.negative(value), + filter=array_ops.reverse_v2(kernel, [0, 1]), + strides=strides, + rates=rates, + padding=padding, + name=name)) @tf_export("nn.in_top_k") @@ -2565,5 +2624,5 @@ def in_top_k(predictions, targets, k, name=None): Returns: A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`. """ - with ops.name_scope(name, 'in_top_k'): + with ops.name_scope(name, "in_top_k"): return gen_nn_ops._in_top_kv2(predictions, targets, k, name=name) diff --git a/tensorflow/python/ops/quantized_conv_ops_test.py b/tensorflow/python/ops/quantized_conv_ops_test.py index 5e9e710027..4ac2a8f634 100644 --- a/tensorflow/python/ops/quantized_conv_ops_test.py +++ b/tensorflow/python/ops/quantized_conv_ops_test.py @@ -93,7 +93,8 @@ class Conv2DTest(test.TestCase): quantized_range = ((quantized_max - quantized_min) * range_adjust) range_scale = (quantized_range / number_of_steps) lowest_quantized = -(1 << (number_of_bits - 1)) - result = np.array([(quantized_min + ((float(x) - lowest_quantized) * range_scale)) + result = np.array([(quantized_min + + ((float(x) - lowest_quantized) * range_scale)) for x in quantized.flatten()]) return result diff --git a/tensorflow/python/ops/quantized_ops_test.py b/tensorflow/python/ops/quantized_ops_test.py index 4bf3b35e13..d590bc4be6 100644 --- a/tensorflow/python/ops/quantized_ops_test.py +++ b/tensorflow/python/ops/quantized_ops_test.py @@ -34,7 +34,10 @@ class QuantizedOpsTest(test.TestCase): def testQuantizeOp(self): expected_output = [1, 1, 2, 127, 255, 255] with self.test_session(use_gpu=False) as sess: - x = constant_op.constant([1.0, 1.25, 1.75, 127.0, 255.0, 500.0], shape=[6], dtype=dtypes.float32) + x = constant_op.constant( + [1.0, 1.25, 1.75, 127.0, 255.0, 500.0], + shape=[6], + dtype=dtypes.float32) x_min = 0.0 x_max = 255.0 op = array_ops.quantize(x, x_min, x_max, dtypes.quint8, mode="MIN_FIRST") diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index 3224856d7b..0fbbf5a805 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -227,13 +227,14 @@ def sparse_concat(axis, [array_ops.reshape(shape, [1, -1]) for shape in shapes], 0), 0) shapes = [ array_ops.concat([ - max_shape[:axis], shape[-1:] if axis == -1 else - shape[axis:axis + 1], [] if axis == -1 else max_shape[axis + 1:] + max_shape[:axis], shape[-1:] + if axis == -1 else shape[axis:axis + 1], [] + if axis == -1 else max_shape[axis + 1:] ], 0) for shape in shapes ] - output_ind, output_val, output_shape = (gen_sparse_ops._sparse_concat( - inds, vals, shapes, axis, name=name)) + output_ind, output_val, output_shape = ( + gen_sparse_ops._sparse_concat(inds, vals, shapes, axis, name=name)) return sparse_tensor.SparseTensor(output_ind, output_val, output_shape) @@ -300,15 +301,14 @@ def sparse_add(a, b, thresh=0): b = _convert_to_sparse_tensor(b) thresh = ops.convert_to_tensor( thresh, dtype=a.values.dtype.real_dtype.base_dtype, name="thresh") - output_ind, output_val, output_shape = (gen_sparse_ops._sparse_add( - a.indices, a.values, a.dense_shape, - b.indices, b.values, b.dense_shape, - thresh)) + output_ind, output_val, output_shape = ( + gen_sparse_ops._sparse_add(a.indices, a.values, a.dense_shape, + b.indices, b.values, b.dense_shape, thresh)) # Attempt to get output_shape statically. a.get_shape().assert_is_compatible_with(b.get_shape()) - static_shape = array_ops.broadcast_static_shape( - a.get_shape(), b.get_shape()) + static_shape = array_ops.broadcast_static_shape(a.get_shape(), + b.get_shape()) if static_shape.is_fully_defined(): output_shape = static_shape.as_list() @@ -317,8 +317,8 @@ def sparse_add(a, b, thresh=0): # swap to make `a` the SparseTensor. if isinstance(b, sparse_classes): a, b = b, a - return gen_sparse_ops._sparse_tensor_dense_add( - a.indices, a.values, a.dense_shape, b) + return gen_sparse_ops._sparse_tensor_dense_add(a.indices, a.values, + a.dense_shape, b) def _sparse_cross(inputs, name=None): @@ -397,19 +397,25 @@ def _sparse_cross_hashed(inputs, num_buckets=0, hash_key=None, name=None): _DEFAULT_HASH_KEY = 0xDECAFCAFFE -def _sparse_cross_internal( - inputs, hashed_output=False, num_buckets=0, hash_key=None, name=None): +def _sparse_cross_internal(inputs, + hashed_output=False, + num_buckets=0, + hash_key=None, + name=None): """See gen_sparse_ops._sparse_cross.""" if not isinstance(inputs, list): raise TypeError("Inputs must be a list") - if not all(isinstance(i, sparse_tensor.SparseTensor) or - isinstance(i, ops.Tensor) for i in inputs): + if not all( + isinstance(i, sparse_tensor.SparseTensor) or isinstance(i, ops.Tensor) + for i in inputs): raise TypeError("All inputs must be SparseTensors") - sparse_inputs = [i for i in inputs - if isinstance(i, sparse_tensor.SparseTensor)] - dense_inputs = [i for i in inputs - if not isinstance(i, sparse_tensor.SparseTensor)] + sparse_inputs = [ + i for i in inputs if isinstance(i, sparse_tensor.SparseTensor) + ] + dense_inputs = [ + i for i in inputs if not isinstance(i, sparse_tensor.SparseTensor) + ] indices = [sp_input.indices for sp_input in sparse_inputs] values = [sp_input.values for sp_input in sparse_inputs] @@ -504,8 +510,9 @@ def sparse_reorder(sp_input, name=None): """ sp_input = _convert_to_sparse_tensor(sp_input) - reordered_ind, reordered_val = (gen_sparse_ops._sparse_reorder( - sp_input.indices, sp_input.values, sp_input.dense_shape, name=name)) + reordered_ind, reordered_val = ( + gen_sparse_ops._sparse_reorder( + sp_input.indices, sp_input.values, sp_input.dense_shape, name=name)) if sp_input.get_shape().is_fully_defined(): dense_shape = sp_input.get_shape().as_list() @@ -572,8 +579,8 @@ def sparse_reshape(sp_input, shape, name=None): sp_input.indices, sp_input.dense_shape, shape, name=name) reshaped_shape_const = tensor_util.constant_value(shape) - if (reshaped_shape_const is not None - and sp_input.get_shape().is_fully_defined()): + if (reshaped_shape_const is not None and + sp_input.get_shape().is_fully_defined()): num_implied = sum((dim == -1) for dim in reshaped_shape_const) if num_implied > 1: raise ValueError("At most one dimension can be inferred (-1). Found: %s" @@ -589,15 +596,15 @@ def sparse_reshape(sp_input, shape, name=None): in_shape_size // np.prod(non_implied_idx)) reshaped_size = np.prod(reshaped_shape_const) if reshaped_size != in_shape_size: - raise ValueError( - "Cannot reshape a tensor with %d elements to shape %s " - "(%d elements)." - % (in_shape_size, original_reshaped_shape, reshaped_size)) + raise ValueError("Cannot reshape a tensor with %d elements to shape %s " + "(%d elements)." % + (in_shape_size, original_reshaped_shape, + reshaped_size)) reshaped_shape = reshaped_shape_const - return sparse_tensor.SparseTensor( - reshaped_ind, array_ops.identity(sp_input.values), - reshaped_shape) + return sparse_tensor.SparseTensor(reshaped_ind, + array_ops.identity(sp_input.values), + reshaped_shape) # TODO(aselle): Remove keyword required once for 1.0 final @@ -610,8 +617,11 @@ class KeywordRequired(object): @tf_export("sparse_split") def sparse_split(keyword_required=KeywordRequired(), - sp_input=None, num_split=None, axis=None, - name=None, split_dim=None): + sp_input=None, + num_split=None, + axis=None, + name=None, + split_dim=None): """Split a `SparseTensor` into `num_split` tensors along `axis`. If the `sp_input.dense_shape[axis]` is not an integer multiple of `num_split` @@ -660,18 +670,19 @@ def sparse_split(keyword_required=KeywordRequired(), split_dim) sp_input = _convert_to_sparse_tensor(sp_input) - output_inds, output_vals, output_shapes = (gen_sparse_ops._sparse_split( - axis, - sp_input.indices, - sp_input.values, - sp_input.dense_shape, - num_split, - name=name)) + output_inds, output_vals, output_shapes = ( + gen_sparse_ops._sparse_split( + axis, + sp_input.indices, + sp_input.values, + sp_input.dense_shape, + num_split, + name=name)) sparse_tensors = [] for i in range(0, num_split): sparse_tensors.append( - sparse_tensor.SparseTensor( - output_inds[i], output_vals[i], output_shapes[i])) + sparse_tensor.SparseTensor(output_inds[i], output_vals[i], + output_shapes[i])) return sparse_tensors @@ -713,12 +724,15 @@ def sparse_slice(sp_input, start, size, name=None): with ops.name_scope(name, "SparseSlice", [sp_input]) as name: output_indices, output_values, output_shape = gen_sparse_ops.sparse_slice( - sp_input.indices, sp_input.values, sp_input.dense_shape, start, size, name=name) + sp_input.indices, + sp_input.values, + sp_input.dense_shape, + start, + size, + name=name) - return sparse_tensor.SparseTensor( - output_indices, - output_values, - output_shape) + return sparse_tensor.SparseTensor(output_indices, output_values, + output_shape) @tf_export("sparse_to_dense") @@ -819,14 +833,14 @@ def sparse_reduce_max(sp_input, axis=None, keep_dims=False, The reduced Tensor. """ return gen_sparse_ops.sparse_reduce_max( - sp_input.indices, sp_input.values, - sp_input.dense_shape, - math_ops._ReductionDims(sp_input, axis, reduction_axes), - keep_dims) + sp_input.indices, sp_input.values, sp_input.dense_shape, + math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims) @tf_export("sparse_reduce_max_sparse") -def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False, +def sparse_reduce_max_sparse(sp_input, + axis=None, + keep_dims=False, reduction_axes=None): """Computes the max of elements across dimensions of a SparseTensor. @@ -855,10 +869,8 @@ def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False, """ output_ind, output_val, output_shape = ( gen_sparse_ops.sparse_reduce_max_sparse( - sp_input.indices, sp_input.values, - sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis, - reduction_axes), - keep_dims)) + sp_input.indices, sp_input.values, sp_input.dense_shape, + math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims)) return sparse_tensor.SparseTensor(output_ind, output_val, output_shape) @@ -905,14 +917,14 @@ def sparse_reduce_sum(sp_input, axis=None, keep_dims=False, The reduced Tensor. """ return gen_sparse_ops.sparse_reduce_sum( - sp_input.indices, sp_input.values, - sp_input.dense_shape, - math_ops._ReductionDims(sp_input, axis, reduction_axes), - keep_dims) + sp_input.indices, sp_input.values, sp_input.dense_shape, + math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims) @tf_export("sparse_reduce_sum_sparse") -def sparse_reduce_sum_sparse(sp_input, axis=None, keep_dims=False, +def sparse_reduce_sum_sparse(sp_input, + axis=None, + keep_dims=False, reduction_axes=None): """Computes the sum of elements across dimensions of a SparseTensor. @@ -941,10 +953,8 @@ def sparse_reduce_sum_sparse(sp_input, axis=None, keep_dims=False, """ output_ind, output_val, output_shape = ( gen_sparse_ops.sparse_reduce_sum_sparse( - sp_input.indices, sp_input.values, - sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis, - reduction_axes), - keep_dims)) + sp_input.indices, sp_input.values, sp_input.dense_shape, + math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims)) return sparse_tensor.SparseTensor(output_ind, output_val, output_shape) @@ -1053,8 +1063,8 @@ def sparse_to_indicator(sp_input, vocab_size, name=None): with ops.name_scope(name, "SparseToIndicator", [sp_input]) as name: num_entries = array_ops.shape(sp_input.indices)[0] new_values = array_ops.fill(array_ops.expand_dims(num_entries, 0), True) - sp_values = sparse_tensor.SparseTensor( - sp_input.indices, new_values, sp_input.dense_shape) + sp_values = sparse_tensor.SparseTensor(sp_input.indices, new_values, + sp_input.dense_shape) sp_new = sparse_merge(sp_input, sp_values, vocab_size, name) @@ -1174,8 +1184,7 @@ def sparse_merge(sp_ids, sp_values, vocab_size, name=None, raise TypeError("vocab_size has to be a list of Tensors or Python ints. " "Found %s" % type(vocab_size)) for dim in vocab_size: - if not (isinstance(dim, ops.Tensor) or - isinstance(dim, numbers.Integral)): + if not (isinstance(dim, ops.Tensor) or isinstance(dim, numbers.Integral)): raise TypeError( "vocab_size has to be a list of Tensors or Python ints. Found %s" % type(dim)) @@ -1326,24 +1335,23 @@ def sparse_reset_shape(sp_input, new_shape=None): # error before the sparse_tensor.SparseTensor catches it. output_shape_tensor.get_shape()[0].merge_with(in_shape.get_shape()[0]) - output_shape_tensor_const = tensor_util.constant_value( - output_shape_tensor) + output_shape_tensor_const = tensor_util.constant_value(output_shape_tensor) # For cases where all shapes are known during graph construction - if (output_shape_tensor_const is not None - and sp_input.get_shape().is_fully_defined()): + if (output_shape_tensor_const is not None and + sp_input.get_shape().is_fully_defined()): in_shape_const = np.array(sp_input.get_shape().as_list()) if not np.all(in_shape_const <= output_shape_tensor_const): raise ValueError( "Requested new_shape should have dimension sizes >= sp_input.shape." - " Found new_shape (%s), sp_input.shape (%s)." - % (in_shape_const, output_shape_tensor_const)) + " Found new_shape (%s), sp_input.shape (%s)." % + (in_shape_const, output_shape_tensor_const)) output_shape_tensor = output_shape_tensor_const else: # For cases where shape is not known during graph construction. - output_shape_tensor = control_flow_ops.with_dependencies( - [check_ops.assert_equal( - array_ops.shape(in_shape), array_ops.shape(output_shape_tensor))], - output_shape_tensor) + output_shape_tensor = control_flow_ops.with_dependencies([ + check_ops.assert_equal( + array_ops.shape(in_shape), array_ops.shape(output_shape_tensor)) + ], output_shape_tensor) output_shape_tensor = control_flow_ops.with_dependencies( [check_ops.assert_less_equal(in_shape, output_shape_tensor)], output_shape_tensor) @@ -1409,10 +1417,10 @@ def sparse_fill_empty_rows(sp_input, default_value, name=None): values=sp_input.values, dense_shape=sp_input.dense_shape, default_value=default_value) - return (sparse_tensor.SparseTensor(indices=output_indices, - values=output_values, - dense_shape=sp_input.dense_shape), - empty_row_indicator) + return (sparse_tensor.SparseTensor( + indices=output_indices, + values=output_values, + dense_shape=sp_input.dense_shape), empty_row_indicator) @tf_export("serialize_sparse") @@ -1880,8 +1888,8 @@ def sparse_softmax(sp_input, name=None): [sp_input.indices, sp_input.values]) as name: out_vals = gen_sparse_ops.sparse_softmax(sp_input.indices, sp_input.values, sp_input.dense_shape) - return sparse_tensor.SparseTensor( - sp_input.indices, out_vals, sp_input.dense_shape) + return sparse_tensor.SparseTensor(sp_input.indices, out_vals, + sp_input.dense_shape) @tf_export("sparse_maximum") @@ -1907,9 +1915,9 @@ def sparse_maximum(sp_a, sp_b, name=None): Returns: output: the output SparseTensor. """ - with ops.name_scope(name, "SparseSparseMaximum", [sp_a.indices, sp_a.values, - sp_b.indices, - sp_b.values]) as name: + with ops.name_scope( + name, "SparseSparseMaximum", + [sp_a.indices, sp_a.values, sp_b.indices, sp_b.values]) as name: out_indices, out_values = gen_sparse_ops.sparse_sparse_maximum( sp_a.indices, sp_a.values, @@ -1944,9 +1952,9 @@ def sparse_minimum(sp_a, sp_b, name=None): Returns: output: the output SparseTensor. """ - with ops.name_scope(name, "SparseSparseMinimum", [sp_a.indices, sp_a.values, - sp_b.indices, - sp_b.values]) as name: + with ops.name_scope( + name, "SparseSparseMinimum", + [sp_a.indices, sp_a.values, sp_b.indices, sp_b.values]) as name: out_indices, out_values = gen_sparse_ops.sparse_sparse_minimum( sp_a.indices, sp_a.values, @@ -2010,14 +2018,15 @@ def sparse_transpose(sp_input, perm=None, name=None): dense_shape = sp_input.dense_shape transposed_dense_shape = array_ops.gather(dense_shape, perm) transposed_st = sparse_tensor.SparseTensor( - transposed_indices, sp_input.values, - transposed_dense_shape) + transposed_indices, sp_input.values, transposed_dense_shape) transposed_st = sparse_reorder(transposed_st) return transposed_st -def _add_sparse_to_tensors_map(sp_input, container=None, - shared_name=None, name=None): +def _add_sparse_to_tensors_map(sp_input, + container=None, + shared_name=None, + name=None): """Add a `SparseTensor` to a `SparseTensorsMap` and return its handle. Args: @@ -2038,12 +2047,18 @@ def _add_sparse_to_tensors_map(sp_input, container=None, sp_input = _convert_to_sparse_tensor(sp_input) return gen_sparse_ops._add_sparse_to_tensors_map( - sp_input.indices, sp_input.values, sp_input.dense_shape, - container=container, shared_name=shared_name, name=name) + sp_input.indices, + sp_input.values, + sp_input.dense_shape, + container=container, + shared_name=shared_name, + name=name) -def _add_many_sparse_to_tensors_map(sp_input, container=None, - shared_name=None, name=None): +def _add_many_sparse_to_tensors_map(sp_input, + container=None, + shared_name=None, + name=None): """Add a minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles. The `SparseTensor` must have rank `R` greater than 1, and the first dimension @@ -2072,12 +2087,18 @@ def _add_many_sparse_to_tensors_map(sp_input, container=None, sp_input = _convert_to_sparse_tensor(sp_input) return gen_sparse_ops._add_many_sparse_to_tensors_map( - sp_input.indices, sp_input.values, sp_input.dense_shape, - container=container, shared_name=shared_name, name=name) + sp_input.indices, + sp_input.values, + sp_input.dense_shape, + container=container, + shared_name=shared_name, + name=name) -def _take_many_sparse_from_tensors_map( - sparse_map_op, sparse_handles, rank=None, name=None): +def _take_many_sparse_from_tensors_map(sparse_map_op, + sparse_handles, + rank=None, + name=None): """Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. The input `sparse_handles` must be a string matrix of shape `[N, 1]` where @@ -2140,16 +2161,18 @@ def _take_many_sparse_from_tensors_map( raise TypeError("sparse_map_op be an Operation") if sparse_map_op.type not in ("AddSparseToTensorsMap", "AddManySparseToTensorsMap"): - raise TypeError("sparse_map_op must be one of AddSparseToTensorsMap or " - "AddSparseToTensorsMap. Instead, found `%s`." % - sparse_map_op.type) + raise TypeError( + "sparse_map_op must be one of AddSparseToTensorsMap or " + "AddSparseToTensorsMap. Instead, found `%s`." % sparse_map_op.type) with ops.colocate_with(sparse_map_op): shared_name = sparse_map_op.get_attr("shared_name") or sparse_map_op.name output_indices, output_values, output_shape = ( gen_sparse_ops._take_many_sparse_from_tensors_map( - sparse_handles, dtype=sparse_map_op.get_attr("T"), + sparse_handles, + dtype=sparse_map_op.get_attr("T"), container=sparse_map_op.get_attr("container"), - shared_name=shared_name, name=name)) + shared_name=shared_name, + name=name)) # Feed rank data back in, if available output_indices.set_shape([None, rank]) diff --git a/tensorflow/python/ops/special_math_ops.py b/tensorflow/python/ops/special_math_ops.py index 15127862a4..6d7eaababc 100644 --- a/tensorflow/python/ops/special_math_ops.py +++ b/tensorflow/python/ops/special_math_ops.py @@ -192,8 +192,8 @@ def einsum(equation, *inputs, **kwargs): input_count = sum(1 for s in input_axis_labels if a in s) if input_count > 2 and a not in output_axis_labels: logging.warn( - 'Falling back to exponential-space implementation of einsum() because' - ' index "%s" is summed over more than two inputs.', a) + 'Falling back to exponential-space implementation of einsum()' + ' because index "%s" is summed over more than two inputs.', a) return _exponential_space_einsum(equation, *inputs) temp = inputs[0] diff --git a/tensorflow/python/saved_model/simple_save.py b/tensorflow/python/saved_model/simple_save.py index 9a81e5cd80..1e4cc73370 100644 --- a/tensorflow/python/saved_model/simple_save.py +++ b/tensorflow/python/saved_model/simple_save.py @@ -40,17 +40,20 @@ def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None): - It will be treated as a graph for inference / serving (i.e. uses the tag `tag_constants.SERVING`) - The SavedModel will load in TensorFlow Serving and supports the - [Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto). + [Predict + API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto). To use the Classify, Regress, or MultiInference APIs, please use either [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator) or the lower level - [SavedModel APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md). + [SavedModel + APIs](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md). - Some TensorFlow ops depend on information on disk or other information called "assets". These are generally handled automatically by adding the assets to the `GraphKeys.ASSET_FILEPATHS` collection. Only assets in that collection are exported; if you need more custom behavior, you'll need to - use the [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py). + use the + [SavedModelBuilder](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/builder.py). More information about SavedModel and signatures can be found here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md. diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index 3ee49650e0..343a49cded 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Various learning rate decay functions.""" from __future__ import absolute_import from __future__ import division @@ -28,8 +27,12 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -def exponential_decay(learning_rate, global_step, decay_steps, decay_rate, - staircase=False, name=None): +def exponential_decay(learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=False, + name=None): """Applies exponential decay to the learning rate. When training a model, it is often recommended to lower the learning rate as @@ -85,9 +88,9 @@ def exponential_decay(learning_rate, global_step, decay_steps, decay_rate, """ if global_step is None: raise ValueError("global_step is required for exponential_decay.") - with ops.name_scope(name, "ExponentialDecay", - [learning_rate, global_step, - decay_steps, decay_rate]) as name: + with ops.name_scope( + name, "ExponentialDecay", + [learning_rate, global_step, decay_steps, decay_rate]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype global_step = math_ops.cast(global_step, dtype) @@ -96,8 +99,8 @@ def exponential_decay(learning_rate, global_step, decay_steps, decay_rate, p = global_step / decay_steps if staircase: p = math_ops.floor(p) - return math_ops.multiply(learning_rate, math_ops.pow(decay_rate, p), - name=name) + return math_ops.multiply( + learning_rate, math_ops.pow(decay_rate, p), name=name) def piecewise_constant(x, boundaries, values, name=None): @@ -156,15 +159,15 @@ def piecewise_constant(x, boundaries, values, name=None): boundaries[i] = b else: raise ValueError( - "Boundaries (%s) must have the same dtype as x (%s)." % ( - b.dtype.base_dtype, x.dtype.base_dtype)) + "Boundaries (%s) must have the same dtype as x (%s)." % + (b.dtype.base_dtype, x.dtype.base_dtype)) # TODO(rdipietro): Ensure that boundaries' elements are strictly increasing. values = ops.convert_n_to_tensor(values) for v in values[1:]: if v.dtype.base_dtype != values[0].dtype.base_dtype: raise ValueError( - "Values must have elements all with the same dtype (%s vs %s)." % ( - values[0].dtype.base_dtype, v.dtype.base_dtype)) + "Values must have elements all with the same dtype (%s vs %s)." % + (values[0].dtype.base_dtype, v.dtype.base_dtype)) pred_fn_pairs = [] pred_fn_pairs.append((x <= boundaries[0], lambda: values[0])) pred_fn_pairs.append((x > boundaries[-1], lambda: values[-1])) @@ -179,9 +182,13 @@ def piecewise_constant(x, boundaries, values, name=None): return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) -def polynomial_decay(learning_rate, global_step, decay_steps, - end_learning_rate=0.0001, power=1.0, - cycle=False, name=None): +def polynomial_decay(learning_rate, + global_step, + decay_steps, + end_learning_rate=0.0001, + power=1.0, + cycle=False, + name=None): """Applies a polynomial decay to the learning rate. It is commonly observed that a monotonically decreasing learning rate, whose @@ -255,9 +262,10 @@ def polynomial_decay(learning_rate, global_step, decay_steps, """ if global_step is None: raise ValueError("global_step is required for polynomial_decay.") - with ops.name_scope(name, "PolynomialDecay", - [learning_rate, global_step, - decay_steps, end_learning_rate, power]) as name: + with ops.name_scope( + name, "PolynomialDecay", + [learning_rate, global_step, decay_steps, end_learning_rate, power + ]) as name: learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") dtype = learning_rate.dtype global_step = math_ops.cast(global_step, dtype) @@ -267,23 +275,28 @@ def polynomial_decay(learning_rate, global_step, decay_steps, if cycle: # Find the first multiple of decay_steps that is bigger than global_step. # If global_step is zero set the multiplier to 1 - multiplier = control_flow_ops.cond(math_ops.equal(global_step, 0), - lambda: 1.0, - lambda: math_ops.ceil( - global_step / decay_steps)) + multiplier = control_flow_ops.cond( + math_ops.equal(global_step, 0), lambda: 1.0, + lambda: math_ops.ceil(global_step / decay_steps)) decay_steps = math_ops.multiply(decay_steps, multiplier) else: # Make sure that the global_step used is not bigger than decay_steps. global_step = math_ops.minimum(global_step, decay_steps) p = math_ops.div(global_step, decay_steps) - return math_ops.add(math_ops.multiply(learning_rate - end_learning_rate, - math_ops.pow(1 - p, power)), - end_learning_rate, name=name) - - -def natural_exp_decay(learning_rate, global_step, decay_steps, decay_rate, - staircase=False, name=None): + return math_ops.add( + math_ops.multiply(learning_rate - end_learning_rate, + math_ops.pow(1 - p, power)), + end_learning_rate, + name=name) + + +def natural_exp_decay(learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=False, + name=None): """Applies natural exponential decay to the initial learning rate. When training a model, it is often recommended to lower the learning rate as @@ -349,8 +362,12 @@ def natural_exp_decay(learning_rate, global_step, decay_steps, decay_rate, return math_ops.multiply(learning_rate, exponent, name=name) -def inverse_time_decay(learning_rate, global_step, decay_steps, decay_rate, - staircase=False, name=None): +def inverse_time_decay(learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=False, + name=None): """Applies inverse time decay to the initial learning rate. When training a model, it is often recommended to lower the learning rate as @@ -362,13 +379,15 @@ def inverse_time_decay(learning_rate, global_step, decay_steps, decay_rate, The function returns the decayed learning rate. It is computed as: ```python - decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step) + decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / + decay_step) ``` or, if `staircase` is `True`, as: ```python - decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step)) + decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / + decay_step)) ``` Example: decay 1/t with a rate of 0.5: @@ -379,7 +398,8 @@ def inverse_time_decay(learning_rate, global_step, decay_steps, decay_rate, learning_rate = 0.1 decay_steps = 1.0 decay_rate = 0.5 - learning_rate = tf.train.inverse_time_decay(learning_rate, global_step, decay_steps, decay_rate) + learning_rate = tf.train.inverse_time_decay(learning_rate, global_step, + decay_steps, decay_rate) # Passing global_step to minimize() will increment it at each step. learning_step = ( @@ -424,8 +444,7 @@ def inverse_time_decay(learning_rate, global_step, decay_steps, decay_rate, return math_ops.div(learning_rate, denom, name=name) -def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, - name=None): +def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): """Applies cosine decay to the learning rate. See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent @@ -484,8 +503,13 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, return math_ops.multiply(learning_rate, decayed) -def cosine_decay_restarts(learning_rate, global_step, first_decay_steps, - t_mul=2.0, m_mul=1.0, alpha=0.0, name=None): +def cosine_decay_restarts(learning_rate, + global_step, + first_decay_steps, + t_mul=2.0, + m_mul=1.0, + alpha=0.0, + name=None): """Applies cosine decay with restarts to the learning rate. See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent @@ -532,10 +556,9 @@ def cosine_decay_restarts(learning_rate, global_step, first_decay_steps, """ if global_step is None: raise ValueError("cosine decay restarts requires global_step") - with ops.name_scope(name, "SGDRDecay", - [learning_rate, global_step]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, - name="initial_learning_rate") + with ops.name_scope(name, "SGDRDecay", [learning_rate, global_step]) as name: + learning_rate = ops.convert_to_tensor( + learning_rate, name="initial_learning_rate") dtype = learning_rate.dtype global_step = math_ops.cast(global_step, dtype) first_decay_steps = math_ops.cast(first_decay_steps, dtype) @@ -547,11 +570,12 @@ def cosine_decay_restarts(learning_rate, global_step, first_decay_steps, def compute_step(completed_fraction, geometric=False): if geometric: - i_restart = math_ops.floor(math_ops.log(1.0 - completed_fraction * ( - 1.0 - t_mul)) / math_ops.log(t_mul)) + i_restart = math_ops.floor( + math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / + math_ops.log(t_mul)) - sum_r = (1.0 - t_mul ** i_restart) / (1.0 - t_mul) - completed_fraction = (completed_fraction - sum_r) / t_mul ** i_restart + sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) + completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart else: i_restart = math_ops.floor(completed_fraction) @@ -564,16 +588,20 @@ def cosine_decay_restarts(learning_rate, global_step, first_decay_steps, lambda: compute_step(completed_fraction, geometric=False), lambda: compute_step(completed_fraction, geometric=True)) - m_fac = m_mul ** i_restart - cosine_decayed = 0.5 * m_fac * (1.0 + math_ops.cos( - constant_op.constant(math.pi) * completed_fraction)) + m_fac = m_mul**i_restart + cosine_decayed = 0.5 * m_fac * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * completed_fraction)) decayed = (1 - alpha) * cosine_decayed + alpha return math_ops.multiply(learning_rate, decayed, name=name) -def linear_cosine_decay(learning_rate, global_step, decay_steps, - num_periods=0.5, alpha=0.0, beta=0.001, +def linear_cosine_decay(learning_rate, + global_step, + decay_steps, + num_periods=0.5, + alpha=0.0, + beta=0.001, name=None): """Applies linear cosine decay to the learning rate. @@ -651,9 +679,14 @@ def linear_cosine_decay(learning_rate, global_step, decay_steps, return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) -def noisy_linear_cosine_decay(learning_rate, global_step, decay_steps, - initial_variance=1.0, variance_decay=0.55, - num_periods=0.5, alpha=0.0, beta=0.001, +def noisy_linear_cosine_decay(learning_rate, + global_step, + decay_steps, + initial_variance=1.0, + variance_decay=0.55, + num_periods=0.5, + alpha=0.0, + beta=0.001, name=None): """Applies noisy linear cosine decay to the learning rate. @@ -734,8 +767,8 @@ def noisy_linear_cosine_decay(learning_rate, global_step, decay_steps, math_ops.pow(1.0 + global_step, variance_decay)) std = math_ops.sqrt(variance) noisy_linear_decayed = ( - linear_decayed + random_ops.random_normal( - linear_decayed.shape, stddev=std)) + linear_decayed + + random_ops.random_normal(linear_decayed.shape, stddev=std)) completed_fraction = global_step / decay_steps fraction = 2.0 * num_periods * completed_fraction diff --git a/tensorflow/python/training/rmsprop.py b/tensorflow/python/training/rmsprop.py index ebec725b7b..745e612018 100644 --- a/tensorflow/python/training/rmsprop.py +++ b/tensorflow/python/training/rmsprop.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """One-line documentation for rmsprop module. rmsprop algorithm [tieleman2012rmsprop] @@ -52,7 +51,8 @@ from tensorflow.python.training import training_ops class RMSPropOptimizer(optimizer.Optimizer): """Optimizer that implements the RMSProp algorithm. - See the [paper](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf). + See the + [paper](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf). """ def __init__(self, @@ -113,13 +113,12 @@ class RMSPropOptimizer(optimizer.Optimizer): self._zeros_slot(v, "momentum", self._name) def _prepare(self): - self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate, - name="learning_rate") + self._learning_rate_tensor = ops.convert_to_tensor( + self._learning_rate, name="learning_rate") self._decay_tensor = ops.convert_to_tensor(self._decay, name="decay") - self._momentum_tensor = ops.convert_to_tensor(self._momentum, - name="momentum") - self._epsilon_tensor = ops.convert_to_tensor(self._epsilon, - name="epsilon") + self._momentum_tensor = ops.convert_to_tensor( + self._momentum, name="momentum") + self._epsilon_tensor = ops.convert_to_tensor(self._epsilon, name="epsilon") def _apply_dense(self, grad, var): rms = self.get_slot(var, "rms") diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 27fa1b89ce..6f4f0f9859 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -183,7 +183,8 @@ do_pylint() { # W0311 bad-indentation # W0312 mixed-indentation # C0330 bad-continuation - grep -E '(\[E|\[W0311|\[W0312|\[C0330)' ${OUTPUT_FILE} > ${ERRORS_FILE} + # C0301 line-too-long + grep -E '(\[E|\[W0311|\[W0312|\[C0330|\[C0301)' ${OUTPUT_FILE} > ${ERRORS_FILE} N_ERRORS=0 while read -r LINE; do diff --git a/tensorflow/tools/ci_build/pylintrc b/tensorflow/tools/ci_build/pylintrc index e71017e621..68fdb61716 100644 --- a/tensorflow/tools/ci_build/pylintrc +++ b/tensorflow/tools/ci_build/pylintrc @@ -180,7 +180,17 @@ docstring-min-length=10 max-line-length=80 # Regexp for a line that is allowed to be longer than the limit. -ignore-long-lines=^\s*(# )??$ +ignore-long-lines=(?x) + (^\s*(import|from)\s + |\$Id:\s\/\/depot\/.+#\d+\s\$ + |^[a-zA-Z_][a-zA-Z0-9_]*\s*=\s*("[^"]\S+"|'[^']\S+') + |^\s*\#\ LINT\.ThenChange + |^[^#]*\#\ type:\ [a-zA-Z_][a-zA-Z0-9_.,[\] ]*$ + |pylint + |""" + |\# + |lambda + |(https?|ftp):) # Allow the body of an if to be on the same line as the test if there is no # else. diff --git a/tensorflow/tools/dist_test/python/mnist_replica.py b/tensorflow/tools/dist_test/python/mnist_replica.py index e40ecb43f9..a2d12442c4 100644 --- a/tensorflow/tools/dist_test/python/mnist_replica.py +++ b/tensorflow/tools/dist_test/python/mnist_replica.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Distributed MNIST training and validation, with model replicas. A simple softmax model with one hidden layer is defined. The parameters @@ -32,7 +31,6 @@ perform forward computation and gradient calculation in parallel, which should lead to increased training speed for the simple model. """ - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -45,7 +43,6 @@ import time import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data - flags = tf.app.flags flags.DEFINE_string("data_dir", "/tmp/mnist-data", "Directory for storing mnist data") @@ -56,8 +53,7 @@ flags.DEFINE_integer("task_index", None, "Worker task index, should be >= 0. task_index=0 is " "the master worker task the performs the variable " "initialization ") -flags.DEFINE_integer("num_gpus", 1, - "Total number of gpus for each machine." +flags.DEFINE_integer("num_gpus", 1, "Total number of gpus for each machine." "If you don't use GPU, please set it to '0'") flags.DEFINE_integer("replicas_to_aggregate", None, "Number of replicas to aggregate before parameter update" @@ -69,24 +65,24 @@ flags.DEFINE_integer("train_steps", 200, "Number of (global) training steps to perform") flags.DEFINE_integer("batch_size", 100, "Training batch size") flags.DEFINE_float("learning_rate", 0.01, "Learning rate") -flags.DEFINE_boolean("sync_replicas", False, - "Use the sync_replicas (synchronized replicas) mode, " - "wherein the parameter updates from workers are aggregated " - "before applied to avoid stale gradients") +flags.DEFINE_boolean( + "sync_replicas", False, + "Use the sync_replicas (synchronized replicas) mode, " + "wherein the parameter updates from workers are aggregated " + "before applied to avoid stale gradients") flags.DEFINE_boolean( "existing_servers", False, "Whether servers already exists. If True, " "will use the worker hosts via their GRPC URLs (one client process " "per worker host). Otherwise, will create an in-process TensorFlow " "server.") -flags.DEFINE_string("ps_hosts","localhost:2222", +flags.DEFINE_string("ps_hosts", "localhost:2222", "Comma-separated list of hostname:port pairs") flags.DEFINE_string("worker_hosts", "localhost:2223,localhost:2224", "Comma-separated list of hostname:port pairs") -flags.DEFINE_string("job_name", None,"job name: worker or ps") +flags.DEFINE_string("job_name", None, "job name: worker or ps") FLAGS = flags.FLAGS - IMAGE_PIXELS = 28 @@ -97,7 +93,7 @@ def main(unused_argv): if FLAGS.job_name is None or FLAGS.job_name == "": raise ValueError("Must specify an explicit `job_name`") - if FLAGS.task_index is None or FLAGS.task_index =="": + if FLAGS.task_index is None or FLAGS.task_index == "": raise ValueError("Must specify an explicit `task_index`") print("job name = %s" % FLAGS.job_name) @@ -110,9 +106,7 @@ def main(unused_argv): # Get the number of workers. num_workers = len(worker_spec) - cluster = tf.train.ClusterSpec({ - "ps": ps_spec, - "worker": worker_spec}) + cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec}) if not FLAGS.existing_servers: # Not using existing servers. Create an in-process server. @@ -217,7 +211,8 @@ def main(unused_argv): sess_config = tf.ConfigProto( allow_soft_placement=True, log_device_placement=False, - device_filters=["/job:ps", "/job:worker/task:%d" % FLAGS.task_index]) + device_filters=["/job:ps", + "/job:worker/task:%d" % FLAGS.task_index]) # The chief worker (task_index==0) session will prepare the session, # while the remaining workers will wait for the preparation to complete. @@ -231,8 +226,7 @@ def main(unused_argv): server_grpc_url = "grpc://" + worker_spec[FLAGS.task_index] print("Using existing server at: %s" % server_grpc_url) - sess = sv.prepare_or_wait_for_session(server_grpc_url, - config=sess_config) + sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config) else: sess = sv.prepare_or_wait_for_session(server.target, config=sess_config) -- GitLab From f8da6cc63ae1fd71de1ab5d9e91884872b249e55 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Fri, 26 Jan 2018 16:58:00 -0800 Subject: [PATCH 168/423] Add reduce-precision to evaluator and add implicit broadcast remover pass. The reduce precision support is cribbed from the CPU/GPU LLVM-emitted implementation. The implicit broadcast pass removes any implicit broadcasts in the module replacing them with the equivalent explicit broadcast and reshape instructions. PiperOrigin-RevId: 183467648 --- tensorflow/compiler/xla/service/BUILD | 28 +++ .../compiler/xla/service/hlo_evaluator.cc | 110 +++++++++++ .../xla/service/implicit_broadcast_remover.cc | 124 ++++++++++++ .../xla/service/implicit_broadcast_remover.h | 42 +++++ .../implicit_broadcast_remover_test.cc | 176 ++++++++++++++++++ tensorflow/compiler/xla/tests/BUILD | 1 + .../xla/tests/reduce_precision_test.cc | 20 +- 7 files changed, 496 insertions(+), 5 deletions(-) create mode 100644 tensorflow/compiler/xla/service/implicit_broadcast_remover.cc create mode 100644 tensorflow/compiler/xla/service/implicit_broadcast_remover.h create mode 100644 tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 031d077c6a..987367fc68 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -1158,6 +1158,34 @@ tf_cc_test( ], ) +cc_library( + name = "implicit_broadcast_remover", + srcs = ["implicit_broadcast_remover.cc"], + hdrs = ["implicit_broadcast_remover.h"], + deps = [ + ":hlo", + ":hlo_dce", + ":hlo_pass", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "implicit_broadcast_remover_test", + srcs = ["implicit_broadcast_remover_test.cc"], + deps = [ + ":hlo_matchers", + ":implicit_broadcast_remover", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/tests:hlo_verified_test_base", + ], +) + cc_library( name = "dot_decomposer", srcs = ["dot_decomposer.cc"], diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index e3f5c17e35..ab604064d5 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -40,6 +40,7 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/window_util.h" #include "tensorflow/core/lib/core/bitmap.h" +#include "tensorflow/core/lib/core/casts.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" @@ -1706,6 +1707,115 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault { return HandleCos(cos); } + template ::value>::type* = nullptr> + Status HandleReducePrecision(HloInstruction* reduce_precision) { + TF_ASSIGN_OR_RETURN( + parent_->evaluated_[reduce_precision], + ElementWiseUnaryOp(reduce_precision, [reduce_precision]( + ElementwiseT elem) { + uint32_t value_as_int = tensorflow::bit_cast(elem); + const uint32_t mantissa_bits = reduce_precision->mantissa_bits(); + const uint32_t exponent_bits = reduce_precision->exponent_bits(); + + // Code is based on the CPU/GPU implementation in LLVM-emitting code. + // + // Bits in float type: + // mantissa : bits [0:22] + // exponent : bits [23:30] + // sign : bits [31] + if (mantissa_bits < 23) { + const uint32_t last_mantissa_bit_mask = 1u << (23 - mantissa_bits); + + // Compute rounding bias for round-to-nearest with ties to even. + // This is 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; + const uint32_t x_last_mantissa_bit = + (value_as_int & last_mantissa_bit_mask) >> (23 - mantissa_bits); + const uint32_t x_rounding_bias = + x_last_mantissa_bit + 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); + value_as_int = value_as_int + x_rounding_bias; + value_as_int = value_as_int & truncation_mask; + } + if (exponent_bits < 8) { + // Masks for f32 values. + const uint32_t f32_sign_bit_mask = 1u << 31; + const uint32_t f32_exp_bits_mask = 0xffu << 23; + + // An exponent of 2^(n-1)-1 -- that is, 0111... with the zero in the + // most- significant bit -- is equal to 1.0f for all exponent sizes. + // Adding 2^(n-1)-1 to this gives us the highest non-infinite + // exponent for a bit- size of n, and subtracting 2^(n-1)-1 from + // this gives us the lowest' exponent (corresponding to 0.0f). + // + // Thus, the f32 exponent corresponding to the highest non-infinite + // exponent for a bit size of n is (2^7-1) + 2^(n-1)-1, and the f32 + // exponent corresponding to the lowest exponent for a bit size of n + // is (2^7-1) - 2^(n-1)-1. + // + // Note that we have already checked that exponents_bits >= 1. + const uint32_t f32_exponent_bias = (1 << 7) - 1; + const uint32_t reduced_exponent_bias = + (1 << (exponent_bits - 1)) - 1; + const uint32_t reduced_max_exponent = + f32_exponent_bias + reduced_exponent_bias; + const uint32_t reduced_min_exponent = + f32_exponent_bias - reduced_exponent_bias; + + // Do we overflow or underflow? + const uint32_t x_exponent = value_as_int & f32_exp_bits_mask; + const bool x_overflows = x_exponent > (reduced_max_exponent << 23); + const bool x_underflows = + x_exponent <= (reduced_min_exponent << 23); + + // Compute appropriately-signed values of zero and infinity. + const uint32_t x_signed_zero = value_as_int & f32_sign_bit_mask; + const uint32_t x_signed_inf = x_signed_zero | 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.) + value_as_int = x_overflows ? x_signed_inf : value_as_int; + value_as_int = x_underflows ? x_signed_zero : value_as_int; + } + + float reduced_result = tensorflow::bit_cast(value_as_int); + if (std::isnan(elem)) { + reduced_result = mantissa_bits > 0 + ? elem + : std::numeric_limits::infinity(); + } + return reduced_result; + })); + return Status::OK(); + } + + template ::value>::type* = nullptr> + Status HandleReducePrecision(HloInstruction* reduce_precision) { + return InvalidArgument("Double not supported for reduce precision"); + } + + template < + typename NativeT, + typename std::enable_if::value || + is_complex_t::value>::type* = nullptr> + Status HandleReducePrecision(HloInstruction* reduce_precision) { + return InvalidArgument("Unsupported type for reduce precision"); + } + + Status HandleReducePrecision(HloInstruction* reduce_precision) override { + return HandleReducePrecision(reduce_precision); + } + private: template StatusOr> DynamicSlice( diff --git a/tensorflow/compiler/xla/service/implicit_broadcast_remover.cc b/tensorflow/compiler/xla/service/implicit_broadcast_remover.cc new file mode 100644 index 0000000000..ada2134501 --- /dev/null +++ b/tensorflow/compiler/xla/service/implicit_broadcast_remover.cc @@ -0,0 +1,124 @@ +/* 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/implicit_broadcast_remover.h" + +#include +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_dce.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +namespace { + +// Visitor for removing implicit broadcasts. +class ImplicitBroadcastVisitor : public DfsHloVisitorWithDefault { + public: + Status DefaultAction(HloInstruction* hlo_instruction) override { + return Status::OK(); + } + + Status HandleElementwiseBinary(HloInstruction* hlo) override { + return ReplaceImplicitBroadcastOperands(hlo); + } + + Status HandleClamp(HloInstruction* hlo) override { + // Clamp is the only element-wise ternary operation. + return ReplaceImplicitBroadcastOperands(hlo); + } + + // Returns whether any modification has been made to any visited instruction. + bool changed() const { return changed_; } + + private: + // Iterates through the operands of 'hlo' and replace any operands which are + // implicitly broadcast with the equivalent sequence of broadcast and reshape + // instructions. An operand is considered to be implicitly broadcast if the + // operand shape does have the same dimensions as the shape of 'hlo'. + Status ReplaceImplicitBroadcastOperands(HloInstruction* hlo) { + auto fadd = [hlo](std::unique_ptr x) { + return hlo->parent()->AddInstruction(std::move(x)); + }; + std::vector operands; + bool operands_changed = false; + for (int i = 0; i < hlo->operand_count(); ++i) { + HloInstruction* operand = hlo->mutable_operand(i); + if (!ShapeUtil::SameDimensions(hlo->shape(), operand->shape())) { + HloInstruction* new_operand = hlo->parent()->AddInstruction( + HloInstruction::CreateBroadcastSequence(hlo->shape(), operand, + fadd)); + operands.push_back(new_operand); + operands_changed = true; + } else { + operands.push_back(operand); + } + } + if (operands_changed) { + // Create a new HLO instruction because the HloInstruction::Replace* + // methods check that the shape does not change with the replacement. + HloInstruction* new_hlo = hlo->parent()->AddInstruction( + hlo->CloneWithNewOperands(hlo->shape(), operands)); + TF_RETURN_IF_ERROR(hlo->ReplaceAllUsesWith(new_hlo)); + changed_ = true; + } + return Status::OK(); + } + + bool changed_ = false; +}; + +} // namespace + +StatusOr ImplicitBroadcastRemover::Run(HloModule* module) { + VLOG(1) << "Removing implicit broadcast from module " << module->name(); + XLA_VLOG_LINES(2, + "Before removing implicit broadcasts:\n" + module->ToString()); + + ImplicitBroadcastVisitor visitor; + for (HloComputation* computation : module->computations()) { + TF_RETURN_IF_ERROR(computation->Accept(&visitor)); + } + + if (visitor.changed()) { + // HLO instructions with implicitly broadcast operands are cloned and left + // for dead. Remove them. + HloDCE dce; + TF_RETURN_IF_ERROR(dce.Run(module).status()); + } + + XLA_VLOG_LINES(2, + "After removing implicit broadcasts:\n" + module->ToString()); + + return visitor.changed(); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/implicit_broadcast_remover.h b/tensorflow/compiler/xla/service/implicit_broadcast_remover.h new file mode 100644 index 0000000000..aa325dc8a3 --- /dev/null +++ b/tensorflow/compiler/xla/service/implicit_broadcast_remover.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_IMPLICIT_BROADCAST_REMOVER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_IMPLICIT_BROADCAST_REMOVER_H_ + +#include + +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" + +namespace xla { + +// Pass which replaces all implicit broadcasts with their equivalent sequence of +// explicit broadcast and reshape instructions. +class ImplicitBroadcastRemover : public HloPassInterface { + public: + ImplicitBroadcastRemover() {} + ~ImplicitBroadcastRemover() override {} + + tensorflow::StringPiece name() const override { + return "implicit-broadcast-remover"; + } + + StatusOr Run(HloModule* module) override; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_IMPLICIT_BROADCAST_REMOVER_H_ diff --git a/tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc b/tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc new file mode 100644 index 0000000000..8c7b38dd1b --- /dev/null +++ b/tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/implicit_broadcast_remover.h" + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/hlo_matchers.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" + +namespace op = xla::testing::opcode_matchers; + +namespace xla { +namespace { + +class ImplicitBroadcastRemoverTest : public HloVerifiedTestBase { + protected: + ImplicitBroadcastRemover remover_; +}; + +TEST_F(ImplicitBroadcastRemoverTest, NoImplicitBroadcast) { + auto builder = HloComputation::Builder(TestName()); + + const Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + auto param0 = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "p0")); + auto param1 = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "p1")); + builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + + HloComputation* computation = module().AddEntryComputation(builder.Build()); + + EXPECT_FALSE(remover_.Run(&module()).ValueOrDie()); + + EXPECT_THAT(computation->root_instruction(), + op::Add(op::Parameter(), op::Parameter())); +} + +TEST_F(ImplicitBroadcastRemoverTest, ScalarBroadcast) { + auto builder = HloComputation::Builder(TestName()); + + const Shape shape = ShapeUtil::MakeShape(F32, {2, 4}); + auto param0 = builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {}), "scalar_param")); + auto param1 = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "p1")); + builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kPower, param0, param1)); + + HloComputation* computation = module().AddEntryComputation(builder.Build()); + HloInstruction* root = computation->root_instruction(); + + EXPECT_FALSE(ShapeUtil::Compatible(root->shape(), root->operand(0)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(1)->shape())); + + EXPECT_TRUE(remover_.Run(&module()).ValueOrDie()); + root = computation->root_instruction(); + + EXPECT_THAT(root, op::Power(op::Broadcast(op::Parameter()), op::Parameter())); + + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(0)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(1)->shape())); +} + +TEST_F(ImplicitBroadcastRemoverTest, DegenerateDimensionBroadcast) { + auto builder = HloComputation::Builder(TestName()); + + const Shape shape = ShapeUtil::MakeShape(F32, {2, 4, 6}); + auto param0 = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "p0")); + auto param1 = builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(F32, {1, 4, 1}), "p1")); + builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kSubtract, param0, param1)); + + HloComputation* computation = module().AddEntryComputation(builder.Build()); + + EXPECT_TRUE(remover_.Run(&module()).ValueOrDie()); + + HloInstruction* root = computation->root_instruction(); + EXPECT_THAT(root, op::Subtract(op::Parameter(), + op::Broadcast(op::Reshape(op::Parameter())))); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(0)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(1)->shape())); +} + +TEST_F(ImplicitBroadcastRemoverTest, ScalarBroadcastToDegenerateDimensions) { + auto builder = HloComputation::Builder(TestName()); + + const Shape shape = ShapeUtil::MakeShape(F32, {1, 4, 1}); + auto param0 = builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {}), "scalar_param")); + auto param1 = + builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "p1")); + builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kSubtract, param0, param1)); + + HloComputation* computation = module().AddEntryComputation(builder.Build()); + + EXPECT_TRUE(remover_.Run(&module()).ValueOrDie()); + + HloInstruction* root = computation->root_instruction(); + EXPECT_THAT(root, + op::Subtract(op::Broadcast(op::Parameter()), op::Parameter())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(0)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(1)->shape())); +} + +TEST_F(ImplicitBroadcastRemoverTest, TernaryDegenerateDimensionBroadcast) { + auto builder = HloComputation::Builder(TestName()); + + const Shape shape = ShapeUtil::MakeShape(F32, {2, 4, 6, 8}); + auto param0 = builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {1, 4, 1, 8}), "p0")); + auto param1 = builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(F32, {1, 1, 6, 8}), "p1")); + auto param2 = builder.AddInstruction(HloInstruction::CreateParameter( + 2, ShapeUtil::MakeShape(F32, {2, 1, 6, 8}), "p2")); + builder.AddInstruction(HloInstruction::CreateTernary(shape, HloOpcode::kClamp, + param0, param1, param2)); + + HloComputation* computation = module().AddEntryComputation(builder.Build()); + + EXPECT_TRUE(remover_.Run(&module()).ValueOrDie()); + + HloInstruction* root = computation->root_instruction(); + EXPECT_THAT(root, op::Clamp(op::Broadcast(op::Reshape(op::Parameter())), + op::Broadcast(op::Reshape(op::Parameter())), + op::Broadcast(op::Reshape(op::Parameter())))); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(0)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(1)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(2)->shape())); +} + +TEST_F(ImplicitBroadcastRemoverTest, + TernaryScalarAndDegenerateDimensionBroadcast) { + auto builder = HloComputation::Builder(TestName()); + + const Shape shape = ShapeUtil::MakeShape(F32, {2, 4, 6}); + auto param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(F32, {}), "p0")); + auto param1 = builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(F32, {1, 4, 6}), "p1")); + auto param2 = + builder.AddInstruction(HloInstruction::CreateParameter(2, shape, "p2")); + builder.AddInstruction(HloInstruction::CreateTernary(shape, HloOpcode::kClamp, + param0, param1, param2)); + + HloComputation* computation = module().AddEntryComputation(builder.Build()); + + EXPECT_TRUE(remover_.Run(&module()).ValueOrDie()); + + HloInstruction* root = computation->root_instruction(); + EXPECT_THAT(root, op::Clamp(op::Broadcast(op::Parameter()), + op::Broadcast(op::Reshape(op::Parameter())), + op::Parameter())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(0)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(1)->shape())); + EXPECT_TRUE(ShapeUtil::Compatible(root->shape(), root->operand(2)->shape())); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 4410647f84..d4820d1b6d 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -578,6 +578,7 @@ xla_test( xla_test( name = "reduce_precision_test", srcs = ["reduce_precision_test.cc"], + tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:literal_util", diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc index 4756ba0968..dc7ce3253c 100644 --- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc @@ -249,7 +249,9 @@ INSTANTIATE_TEST_CASE_P(ReducePrecisionAccuracyTest, // ReducePrecisionInsertion passes. class ReducePrecisionInsertionTest : public ClientLibraryTestBase {}; -XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionBeforeFusion) { +// The interpreter has no fusion pass, so skip this test. +XLA_TEST_F(ReducePrecisionInsertionTest, + DISABLED_ON_INTERPRETER(ReducePrecisionBeforeFusion)) { ComputationBuilder builder(client_, TestName()); std::unique_ptr a_literal = Literal::CreateR1({1.00001}); @@ -276,7 +278,9 @@ XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionBeforeFusion) { ComputeAndCompareR1(&builder, {0.0f}, {a_data.get()}); } -XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionSkippedAfterFusion) { +// The interpreter has no fusion pass, so skip this test. +XLA_TEST_F(ReducePrecisionInsertionTest, + DISABLED_ON_INTERPRETER(ReducePrecisionSkippedAfterFusion)) { ComputationBuilder builder(client_, TestName()); std::unique_ptr a_literal = Literal::CreateR1({1.00001}); @@ -300,7 +304,9 @@ XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionSkippedAfterFusion) { ComputeAndCompareR1(&builder, {-1.00001f}, {a_data.get()}); } -XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionAddedAfterFusion) { +// The interpreter has no fusion pass, so skip this test. +XLA_TEST_F(ReducePrecisionInsertionTest, + DISABLED_ON_INTERPRETER(ReducePrecisionAddedAfterFusion)) { ComputationBuilder builder(client_, TestName()); std::unique_ptr a_literal = Literal::CreateR1({1.00001}); @@ -322,7 +328,9 @@ XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionAddedAfterFusion) { ComputeAndCompareR1(&builder, {-1.0f}, {a_data.get()}); } -XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionSkippedFusionContains) { +// The interpreter has no fusion pass, so skip this test. +XLA_TEST_F(ReducePrecisionInsertionTest, + DISABLED_ON_INTERPRETER(ReducePrecisionSkippedFusionContains)) { ComputationBuilder builder(client_, TestName()); std::unique_ptr a_literal = Literal::CreateR1({1.00001}); @@ -345,7 +353,9 @@ XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionSkippedFusionContains) { ComputeAndCompareR1(&builder, {-1.00001f}, {a_data.get()}); } -XLA_TEST_F(ReducePrecisionInsertionTest, ReducePrecisionAddedFusionContains) { +// The interpreter has no fusion pass, so skip this test. +XLA_TEST_F(ReducePrecisionInsertionTest, + DISABLED_ON_INTERPRETER(ReducePrecisionAddedFusionContains)) { ComputationBuilder builder(client_, TestName()); std::unique_ptr a_literal = Literal::CreateR1({1.00001}); -- GitLab From 86c10063c8521bd5482df13676bac3575540d9e2 Mon Sep 17 00:00:00 2001 From: Andrew Harp Date: Fri, 26 Jan 2018 20:13:45 -0500 Subject: [PATCH 169/423] Simplify Android/Tegra GPU makefile file lists (#16471) * updating CUDA srcs for Makefile build to fix unsatisfied link error * more makefile refactoring --- tensorflow/contrib/makefile/Makefile | 73 ++++++++-------------------- 1 file changed, 21 insertions(+), 52 deletions(-) diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile index c50f8ceec0..c573cf15da 100644 --- a/tensorflow/contrib/makefile/Makefile +++ b/tensorflow/contrib/makefile/Makefile @@ -606,7 +606,8 @@ $(wildcard tensorflow/core/util/*/*.cc) \ tensorflow/core/util/version_info.cc # Remove duplicates (for version_info.cc) CORE_CC_ALL_SRCS := $(sort $(CORE_CC_ALL_SRCS)) -CORE_CC_EXCLUDE_SRCS := \ + +CORE_CC_EXCLUDE_SRCS_NON_GPU := \ $(wildcard tensorflow/core/*/*test.cc) \ $(wildcard tensorflow/core/*/*testutil*) \ $(wildcard tensorflow/core/*/*testlib*) \ @@ -626,49 +627,31 @@ $(wildcard tensorflow/core/lib/jpeg/*) \ $(wildcard tensorflow/core/lib/png/*) \ $(wildcard tensorflow/core/util/events_writer.*) \ $(wildcard tensorflow/core/util/reporter.*) \ -$(wildcard tensorflow/core/platform/default/cuda_libdevice_path.*) \ -$(wildcard tensorflow/core/platform/default/stream_executor.*) \ $(wildcard tensorflow/core/platform/default/test_benchmark.*) \ -$(wildcard tensorflow/core/platform/cuda.h) \ -$(wildcard tensorflow/core/platform/cuda_libdevice_path.*) \ $(wildcard tensorflow/core/platform/cloud/*) \ $(wildcard tensorflow/core/platform/google/*) \ $(wildcard tensorflow/core/platform/google/*/*) \ $(wildcard tensorflow/core/platform/jpeg.*) \ $(wildcard tensorflow/core/platform/png.*) \ $(wildcard tensorflow/core/platform/s3/*) \ -$(wildcard tensorflow/core/platform/stream_executor.*) \ $(wildcard tensorflow/core/platform/windows/*) \ -$(wildcard tensorflow/core/user_ops/*.cu.cc) \ -$(wildcard tensorflow/core/common_runtime/gpu/*) \ -$(wildcard tensorflow/core/common_runtime/gpu_device_factory.*) \ $(wildcard tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.*) \ $(wildcard tensorflow/core/grappler/inputs/file_input_yielder.*) \ -$(wildcard tensorflow/core/grappler/clusters/single_machine.*) +$(wildcard tensorflow/core/grappler/clusters/single_machine.*) \ +tensorflow/core/util/cuda_kernel_helper_test.cu.cc + +CORE_CC_EXCLUDE_SRCS := \ +$(CORE_CC_EXCLUDE_SRCS_NON_GPU) \ +$(wildcard tensorflow/core/platform/stream_executor.*) \ +$(wildcard tensorflow/core/platform/default/cuda_libdevice_path.*) \ +$(wildcard tensorflow/core/platform/cuda.h) \ +$(wildcard tensorflow/core/platform/cuda_libdevice_path.*) \ +$(wildcard tensorflow/core/user_ops/*.cu.cc) \ +$(wildcard tensorflow/core/common_runtime/gpu/*) \ +$(wildcard tensorflow/core/common_runtime/gpu_device_factory.*) ifeq ($(BUILD_FOR_TEGRA),1) -CORE_CC_ALL_SRCS := \ -$(wildcard tensorflow/core/*.cc) \ -$(wildcard tensorflow/core/common_runtime/*.cc) \ -$(wildcard tensorflow/core/common_runtime/gpu/*.cc) \ -$(wildcard tensorflow/core/framework/*.cc) \ -$(wildcard tensorflow/core/graph/*.cc) \ -$(wildcard tensorflow/core/platform/*.cc) \ -$(wildcard tensorflow/core/platform/*/*.cc) \ -$(wildcard tensorflow/core/platform/*/*/*.cc) \ -$(wildcard tensorflow/core/util/*.cc) \ -$(wildcard tensorflow/core/util/*/*.cc) \ -$(wildcard tensorflow/cc/training/*.cc) \ -$(wildcard tensorflow/stream_executor/*.cc) \ -$(wildcard tensorflow/stream_executor/*/*.cc) \ -$(wildcard tensorflow/core/grappler/optimizers/*.cc) \ -$(wildcard tensorflow/core/grappler/*.cc) \ -$(wildcard tensorflow/core/grappler/costs/*.cc) \ -$(wildcard tensorflow/core/grappler/clusters/*.cc) \ -$(wildcard tensorflow/core/grappler/utils/*.cc) \ -$(wildcard tensorflow/core/lib/core/*.cc) \ -$(wildcard tensorflow/core/lib/*/*.cc) \ -tensorflow/core/grappler/inputs/utils.cc \ +CORE_CC_ALL_SRCS := $(CORE_CC_ALL_SRCS) \ tensorflow/core/kernels/concat_lib_gpu.cc \ tensorflow/core/kernels/cuda_solvers.cc \ tensorflow/core/kernels/cudnn_pooling_gpu.cc \ @@ -677,28 +660,14 @@ tensorflow/core/kernels/fractional_avg_pool_op.cc \ tensorflow/core/kernels/fractional_max_pool_op.cc \ tensorflow/core/kernels/fractional_pool_common.cc \ tensorflow/core/kernels/pooling_ops_3d.cc \ -tensorflow/core/kernels/sparse_fill_empty_rows_op.cc +tensorflow/core/kernels/sparse_fill_empty_rows_op.cc \ +tensorflow/core/kernels/list_kernels.cc \ +$(wildcard tensorflow/core/common_runtime/gpu/*.cc) \ +$(wildcard tensorflow/stream_executor/*.cc) \ +$(wildcard tensorflow/stream_executor/*/*.cc) CORE_CC_EXCLUDE_SRCS := \ -$(wildcard tensorflow/core/*/*test.cc) \ -$(wildcard tensorflow/core/*/*testutil*) \ -$(wildcard tensorflow/core/*/*testlib*) \ -$(wildcard tensorflow/core/*/*/*test.cc) \ -$(wildcard tensorflow/core/*/*/*testutil*) \ -$(wildcard tensorflow/core/framework/op_gen_lib.cc) \ -$(wildcard tensorflow/core/lib/gif/*) \ -$(wildcard tensorflow/core/lib/jpeg/*) \ -$(wildcard tensorflow/core/lib/png/*) \ -$(wildcard tensorflow/core/lib/db/*) \ -$(wildcard tensorflow/core/platform/jpeg.*) \ -$(wildcard tensorflow/core/platform/png.*) \ -$(wildcard tensorflow/core/platform/cloud/*) \ -$(wildcard tensorflow/core/platform/s3/*) \ -$(wildcard tensorflow/core/platform/windows/*) \ -$(wildcard tensorflow/core/*/*/*testlib*) \ -$(wildcard tensorflow/cc/training/*test.cc) \ -tensorflow/core/lib/io/record_reader.cc \ -tensorflow/core/util/cuda_kernel_helper_test.cu.cc +$(CORE_CC_EXCLUDE_SRCS_NON_GPU) CUDA_CC_SRCS := $(wildcard tensorflow/core/kernels/*.cu.cc) CUDA_CC_OBJS := $(addprefix $(OBJDIR), $(CUDA_CC_SRCS:.cc=.o)) -- GitLab From 0b164dd43bbf76547836a9ae6ae424b9cda65968 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 26 Jan 2018 17:12:23 -0800 Subject: [PATCH 170/423] [XLA] Add a DeviceAllocator* argument to compilation. In a later change, the GPU backend will use this allocator to reserve scratch memory when trying out different convolution algorithms during compilation. PiperOrigin-RevId: 183469579 --- .../compiler/jit/kernels/xla_launch_op.cc | 7 ++-- .../compiler/jit/xla_compilation_cache.cc | 1 + tensorflow/compiler/tf2xla/xla_compiler.h | 13 ++++++++ .../compiler/xla/client/local_client.cc | 19 ++++++++--- tensorflow/compiler/xla/client/local_client.h | 12 +++++++ tensorflow/compiler/xla/service/compiler.h | 29 ++++++++++++++-- .../compiler/xla/service/cpu/cpu_compiler.cc | 6 ++-- .../compiler/xla/service/cpu/cpu_compiler.h | 6 ++-- .../compiler/xla/service/gpu/gpu_compiler.cc | 13 +++++--- .../compiler/xla/service/gpu/gpu_compiler.h | 6 ++-- tensorflow/compiler/xla/service/hlo_runner.cc | 6 ++-- .../xla/service/interpreter/compiler.cc | 10 +++--- .../xla/service/interpreter/compiler.h | 9 +++-- .../compiler/xla/service/llvm_compiler.cc | 12 ++++--- .../compiler/xla/service/llvm_compiler.h | 9 +++-- .../compiler/xla/service/local_service.cc | 5 +-- .../compiler/xla/service/local_service.h | 7 ++-- tensorflow/compiler/xla/service/service.cc | 33 +++++++++++-------- tensorflow/compiler/xla/service/service.h | 18 ++++++---- .../compiler/xla/tests/codegen_test_base.cc | 6 ++-- .../compiler/xla/tests/llvm_compiler_test.cc | 6 ++-- .../dumped_computation_to_operation_list.cc | 6 ++-- .../xla/tools/dumped_computation_to_text.cc | 6 ++-- 23 files changed, 175 insertions(+), 70 deletions(-) diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc index 4842877d9a..1d7bd22e60 100644 --- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc +++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc @@ -248,12 +248,16 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) { xla::LocalClient* client = static_cast(cache->client()); + // Builds an XLA allocator for the device. + XlaAllocator xla_allocator(client->platform(), ctx); + XlaCompiler::Options options; options.client = client; options.device_type = &cache->device_type(); options.flib_def = ctx->function_library()->GetFunctionLibraryDefinition(); options.graph_def_version = ctx->function_library()->graph_def_version(); options.allow_cpu_custom_calls = (platform_id_ == gpu::host::kHostPlatformId); + options.device_allocator = &xla_allocator; const XlaCompiler::CompilationResult* kernel; xla::LocalExecutable* executable; @@ -264,9 +268,6 @@ void XlaLocalLaunchOp::Compute(OpKernelContext* ctx) { VLOG(1) << "Executing XLA Computation..."; - // Builds an XLA allocator for the device. - XlaAllocator xla_allocator(client->platform(), ctx); - std::unique_ptr output; // Build xla::ShapedBuffers that point directly to the Tensor buffers. std::vector> arg_buffers; diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc index bfff52c55a..21d3a54f1b 100644 --- a/tensorflow/compiler/jit/xla_compilation_cache.cc +++ b/tensorflow/compiler/jit/xla_compilation_cache.cc @@ -223,6 +223,7 @@ Status XlaCompilationCache::BuildExecutable( xla::ExecutableBuildOptions build_options; build_options.set_device_ordinal(client_->default_device_ordinal()); build_options.set_result_layout(result.xla_output_shape); + build_options.set_device_allocator(options.device_allocator); auto compile_result = client_->Compile(*result.computation, argument_layouts, build_options); diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index 6a46e54f61..30d3c05ee9 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -235,6 +235,19 @@ class XlaCompiler { // device is created, and can be used to create metadata objects // that can be accessed by XLA op kernels. std::function* populate_resource_manager = nullptr; + + // If not nullptr, this memory allocator can be used by the compiler for + // temporary allocations it might want to make during compilation. + // + // For example, the compiler may want to try out different algorithms and + // choose the fastest one, and it might run those algorithms over buffers + // created using this allocator. + // + // The compiler can function correctly without an explicit allocator given + // here, but on some devices (notably, GPUs), TensorFlow tends to eagerly + // allocate most or all available memory on the device, leaving none for the + // compiler to access, unless it can use TensorFlow's allocator. + xla::DeviceMemoryAllocator* device_allocator = nullptr; }; explicit XlaCompiler(Options options); diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index fbeedfcecd..e45787fca6 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -49,6 +49,16 @@ const Shape* ExecutableBuildOptions::result_layout() const { return result_layout_set_ ? &result_layout_ : nullptr; } +ExecutableBuildOptions& ExecutableBuildOptions::set_device_allocator( + DeviceMemoryAllocator* allocator) { + device_allocator_ = allocator; + return *this; +} + +DeviceMemoryAllocator* ExecutableBuildOptions::device_allocator() const { + return device_allocator_; +} + namespace { StatusOr BorrowStreamForDevice(int device_ordinal, Backend* backend) { @@ -270,10 +280,11 @@ StatusOr> LocalClient::Compile( int device_ordinal = options.device_ordinal() == -1 ? default_device_ordinal() : options.device_ordinal(); - TF_ASSIGN_OR_RETURN(std::unique_ptr executable, - local_service_->CompileExecutable( - computation.handle(), argument_layouts, - options.result_layout(), device_ordinal)); + TF_ASSIGN_OR_RETURN( + std::unique_ptr executable, + local_service_->CompileExecutable(computation.handle(), argument_layouts, + options.result_layout(), device_ordinal, + options.device_allocator())); return WrapUnique(new LocalExecutable(std::move(executable), local_service_->mutable_backend(), device_ordinal, options)); diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h index 19fd14f76b..843ad7aa85 100644 --- a/tensorflow/compiler/xla/client/local_client.h +++ b/tensorflow/compiler/xla/client/local_client.h @@ -53,10 +53,22 @@ class ExecutableBuildOptions { ExecutableBuildOptions& set_result_layout(const Shape& shape_with_layout); const Shape* result_layout() const; + // If set, this specifies an allocator that can be used to allocate temporary + // space on the device during compilation. For example, the compiler might + // want to run various algorithms on the device and pick the fastest one -- it + // might allocate buffers for use by these algorithms using this allocator. + // + // This does not need to be the same as the DeviceMemoryAllocator passed when + // running the executable. + ExecutableBuildOptions& set_device_allocator( + DeviceMemoryAllocator* allocator); + DeviceMemoryAllocator* device_allocator() const; + private: int device_ordinal_ = -1; Shape result_layout_; bool result_layout_set_ = false; + DeviceMemoryAllocator* device_allocator_ = nullptr; }; class LocalExecutable { diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h index fc67330f5c..74fd24edf8 100644 --- a/tensorflow/compiler/xla/service/compiler.h +++ b/tensorflow/compiler/xla/service/compiler.h @@ -72,8 +72,18 @@ class AotCompilationOptions { // Returns the ID of the platform to which these options apply. virtual perftools::gputools::Platform::Id PlatformId() const = 0; + // Optional allocator that may be used for allocating temp space on the device + // during compilation. + DeviceMemoryAllocator* device_allocator() const { return device_allocator_; } + void set_device_allocator(DeviceMemoryAllocator* device_allocator) { + device_allocator_ = device_allocator; + } + protected: AotCompilationOptions() = default; + + private: + DeviceMemoryAllocator* device_allocator_ = nullptr; }; // Abstract compiler interface that is subclassed for compilation on a @@ -99,9 +109,16 @@ class Compiler { // Runs Hlo passes to optimize the given Hlo module, returns the optimized // module. + // + // If device_allocator is not null, the compiler may use it to allocate temp + // space on the device for use during compilation. For example, the compiler + // may allocate buffers on the device and then run variants of a given + // algorithm over those buffers, to see which variant is fastest. Any space + // allocated should be deallocated before this function returns. virtual StatusOr> RunHloPasses( std::unique_ptr module, - perftools::gputools::StreamExecutor* executor) = 0; + perftools::gputools::StreamExecutor* executor, + DeviceMemoryAllocator* device_allocator) = 0; // Compiles the HLO module for execution on a device given by the executor, // and returns an executable object or an error status. No HLO passes are @@ -112,21 +129,27 @@ class Compiler { // The compiler may optionally specialize to the individual device // (not just type of device) indicated by the executor. // + // device_allocator is optional; see RunHloPasses. + // // Use the overload below to compile computations that run in parallel. virtual StatusOr> RunBackend( std::unique_ptr module, - perftools::gputools::StreamExecutor* executor) = 0; + perftools::gputools::StreamExecutor* executor, + DeviceMemoryAllocator* device_allocator) = 0; // Compiles a set of HLO modules that can run in parallel, potentially // communicating data between the modules, and returns a corresponding // sequence of executable objects. // + // device_allocator is optional; see RunHloPasses. + // // TODO(b/68666782): Remove this method after adding support for multiple // modules to RunHloPasses and RunBackends. virtual StatusOr>> Compile( std::vector> modules, std::vector> - stream_exec) = 0; + stream_exec, + DeviceMemoryAllocator* device_allocator) = 0; // Compiles the HLO module for ahead-of-time execution. This is intended for // use in static compilation. diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 33af77e1a8..3fdb3d5ca6 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -437,7 +437,8 @@ Status VerifyLlvmModule(const llvm::Module& llvm_module) { StatusOr> CpuCompiler::RunHloPasses( std::unique_ptr module, - perftools::gputools::StreamExecutor* /*stream_exec*/) { + perftools::gputools::StreamExecutor* /*stream_exec*/, + DeviceMemoryAllocator* /*device_allocator*/) { VLOG(2) << "Before optimization:"; XLA_VLOG_LINES(2, module->ToString()); @@ -450,7 +451,8 @@ StatusOr> CpuCompiler::RunHloPasses( StatusOr> CpuCompiler::RunBackend( std::unique_ptr module, - perftools::gputools::StreamExecutor* stream_exec) { + perftools::gputools::StreamExecutor* stream_exec, + DeviceMemoryAllocator* /*device_allocator*/) { const string timer_message = "Compiling [" + module->name() + "] for CPU using JIT"; XLA_SCOPED_LOGGING_TIMER(timer_message); diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h index ebed7058d8..3498139ab9 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h @@ -118,11 +118,13 @@ class CpuCompiler : public LLVMCompiler { StatusOr> RunHloPasses( std::unique_ptr module, - perftools::gputools::StreamExecutor* stream_exec) override; + perftools::gputools::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) override; StatusOr> RunBackend( std::unique_ptr module, - perftools::gputools::StreamExecutor* stream_exec) override; + perftools::gputools::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) override; StatusOr>> CompileAheadOfTime(std::vector> modules, diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index 0cca3ca092..495ae1710f 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -212,7 +212,9 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module) { // Modifies the given HLO module so that it will be accepted by IrEmitter. // Unlike optimization passes, the passes are necessary for correctness. -tensorflow::Status PrepareHloModuleForIrEmitting(HloModule* hlo_module) { +tensorflow::Status PrepareHloModuleForIrEmitting( + HloModule* hlo_module, se::StreamExecutor* stream_exec, + DeviceMemoryAllocator* /*device_allocator*/) { // In some cases, we have to place the result of an instruction in a temporary // buffer. For instance, the buffer that holds an external parameter is // assumed immutable at this point, and should not be reused for output @@ -410,7 +412,8 @@ GpuCompiler::GpuCompiler() .getPointerSize(0 /* default address space */)) {} StatusOr> GpuCompiler::RunHloPasses( - std::unique_ptr module, se::StreamExecutor* /*stream_exec*/) { + std::unique_ptr module, se::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) { XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunHloPasses"); Tracing::TraceMe annotation("HLO Transforms", module->name(), /*is_expensive=*/true); @@ -419,12 +422,14 @@ StatusOr> GpuCompiler::RunHloPasses( } StatusOr> GpuCompiler::RunBackend( - std::unique_ptr module, se::StreamExecutor* stream_exec) { + std::unique_ptr module, se::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) { XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunBackend"); TF_RET_CHECK(stream_exec != nullptr); - TF_RETURN_IF_ERROR(PrepareHloModuleForIrEmitting(module.get())); + TF_RETURN_IF_ERROR(PrepareHloModuleForIrEmitting(module.get(), stream_exec, + device_allocator)); llvm::LLVMContext llvm_context; std::string buffer; diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h index 18e3434020..c352d4d846 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.h @@ -51,11 +51,13 @@ class GpuCompiler : public LLVMCompiler { StatusOr> RunHloPasses( std::unique_ptr module, - perftools::gputools::StreamExecutor* stream_exec) override; + perftools::gputools::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) override; StatusOr> RunBackend( std::unique_ptr module, - perftools::gputools::StreamExecutor* stream_exec) override; + perftools::gputools::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) override; StatusOr>> CompileAheadOfTime(std::vector> module, diff --git a/tensorflow/compiler/xla/service/hlo_runner.cc b/tensorflow/compiler/xla/service/hlo_runner.cc index 204a8bf748..e281538848 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.cc +++ b/tensorflow/compiler/xla/service/hlo_runner.cc @@ -121,12 +121,14 @@ StatusOr> HloRunner::ExecuteInternal( if (run_hlo_passes) { TF_ASSIGN_OR_RETURN( module, backend().compiler()->RunHloPasses( - std::move(module), backend().default_stream_executor())); + std::move(module), backend().default_stream_executor(), + /*device_allocator=*/nullptr)); } TF_ASSIGN_OR_RETURN( std::unique_ptr executable, backend().compiler()->RunBackend(std::move(module), - backend().default_stream_executor())); + backend().default_stream_executor(), + /*device_allocator=*/nullptr)); se::Stream stream(backend().default_stream_executor()); stream.Init(); diff --git a/tensorflow/compiler/xla/service/interpreter/compiler.cc b/tensorflow/compiler/xla/service/interpreter/compiler.cc index dc63a2224d..c83880e030 100644 --- a/tensorflow/compiler/xla/service/interpreter/compiler.cc +++ b/tensorflow/compiler/xla/service/interpreter/compiler.cc @@ -70,15 +70,16 @@ Status InterpreterCompiler::RunHloOptimization(HloModule* hlo_module) { } StatusOr> InterpreterCompiler::RunHloPasses( - std::unique_ptr hlo_module, - se::StreamExecutor* /*stream_exec*/) { + std::unique_ptr hlo_module, se::StreamExecutor* /*stream_exec*/, + DeviceMemoryAllocator* /*device_allocator*/) { VLOG(1) << "Run hlo passes on graph " << hlo_module->name(); TF_RETURN_IF_ERROR(RunHloOptimization(hlo_module.get())); return std::move(hlo_module); } StatusOr> InterpreterCompiler::RunBackend( - std::unique_ptr hlo_module, se::StreamExecutor* stream_exec) { + std::unique_ptr hlo_module, se::StreamExecutor* stream_exec, + DeviceMemoryAllocator* /*device_allocator*/) { TF_RET_CHECK(stream_exec != nullptr); VLOG(1) << "Run backend " << hlo_module->name(); @@ -96,7 +97,8 @@ StatusOr> InterpreterCompiler::RunBackend( StatusOr>> InterpreterCompiler::Compile( std::vector> /*hlo_modules*/, - std::vector> /*stream_execs*/) { + std::vector> /*stream_execs*/, + DeviceMemoryAllocator* /*device_allocator*/) { return tensorflow::errors::Unimplemented( "Compilation of multiple HLO modules is not supported on Interpreter."); } diff --git a/tensorflow/compiler/xla/service/interpreter/compiler.h b/tensorflow/compiler/xla/service/interpreter/compiler.h index 278cf51842..c8660c04d8 100644 --- a/tensorflow/compiler/xla/service/interpreter/compiler.h +++ b/tensorflow/compiler/xla/service/interpreter/compiler.h @@ -45,16 +45,19 @@ class InterpreterCompiler : public Compiler { StatusOr> RunHloPasses( std::unique_ptr hlo_module, - perftools::gputools::StreamExecutor* stream_exec) override; + perftools::gputools::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) override; StatusOr> RunBackend( std::unique_ptr hlo_module, - perftools::gputools::StreamExecutor* stream_exec) override; + perftools::gputools::StreamExecutor* stream_exec, + DeviceMemoryAllocator* device_allocator) override; StatusOr>> Compile( std::vector> hlo_modules, std::vector> - stream_exec) override; + stream_exec, + DeviceMemoryAllocator* device_allocator) override; StatusOr>> CompileAheadOfTime(std::vector> hlo_modules, diff --git a/tensorflow/compiler/xla/service/llvm_compiler.cc b/tensorflow/compiler/xla/service/llvm_compiler.cc index 34f3419269..f98fc0400a 100644 --- a/tensorflow/compiler/xla/service/llvm_compiler.cc +++ b/tensorflow/compiler/xla/service/llvm_compiler.cc @@ -18,8 +18,8 @@ limitations under the License. namespace xla { StatusOr>> LLVMCompiler::Compile( std::vector> modules, - std::vector> - stream_execs) { + std::vector> stream_execs, + DeviceMemoryAllocator* device_allocator) { std::vector> result; for (size_t i = 0; i < modules.size(); i++) { if (stream_execs[i].size() != 1) { @@ -27,10 +27,12 @@ StatusOr>> LLVMCompiler::Compile( "Model partitioning not implemented for the CPU/GPU compilers!"); } - TF_ASSIGN_OR_RETURN( - modules[i], RunHloPasses(std::move(modules[i]), stream_execs[i][0])); + TF_ASSIGN_OR_RETURN(modules[i], + RunHloPasses(std::move(modules[i]), stream_execs[i][0], + device_allocator)); TF_ASSIGN_OR_RETURN(std::unique_ptr executable, - RunBackend(std::move(modules[i]), stream_execs[i][0])); + RunBackend(std::move(modules[i]), stream_execs[i][0], + device_allocator)); result.push_back(std::move(executable)); } diff --git a/tensorflow/compiler/xla/service/llvm_compiler.h b/tensorflow/compiler/xla/service/llvm_compiler.h index c5393cef4f..d74e81bb7f 100644 --- a/tensorflow/compiler/xla/service/llvm_compiler.h +++ b/tensorflow/compiler/xla/service/llvm_compiler.h @@ -60,17 +60,20 @@ class LLVMCompiler : public Compiler { // Bring in // StatusOr> RunBackend( // std::unique_ptr module, - // perftools::gputools::StreamExecutor* stream_exec) + // perftools::gputools::StreamExecutor* stream_exec, + // DeviceMemoryAllocator* device_allocator) // StatusOr> RunHloPasses( // std::unique_ptr module, - // perftools::gputools::StreamExecutor* stream_exec) + // perftools::gputools::StreamExecutor* stream_exec, + // DeviceMemoryAllocator* device_allocator) using Compiler::RunBackend; using Compiler::RunHloPasses; StatusOr>> Compile( std::vector> modules, std::vector> - stream_execs) override; + stream_execs, + DeviceMemoryAllocator* device_allocator) override; protected: ModuleHook user_pre_optimization_hook_; diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index f30530db08..bb9fd447d9 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -71,7 +71,8 @@ LocalService::LocalService(const ServiceOptions& options, StatusOr> LocalService::CompileExecutable( const ComputationHandle& computation, const tensorflow::gtl::ArraySlice argument_layouts, - const Shape* result_layout, int device_ordinal) { + const Shape* result_layout, int device_ordinal, + DeviceMemoryAllocator* device_allocator) { TF_ASSIGN_OR_RETURN(UserComputation * user_computation, computation_tracker_.Resolve(computation)); VersionedComputationHandle versioned_handle = @@ -135,7 +136,7 @@ StatusOr> LocalService::CompileExecutable( execute_backend_->stream_executor(device_ordinal)); return BuildExecutable(versioned_handle, std::move(module_config), - execute_backend_.get(), executor); + execute_backend_.get(), executor, device_allocator); } StatusOr LocalService::ReplicaNumberToDeviceOrdinal(int replica_number) { diff --git a/tensorflow/compiler/xla/service/local_service.h b/tensorflow/compiler/xla/service/local_service.h index acbc726825..16c71b25c4 100644 --- a/tensorflow/compiler/xla/service/local_service.h +++ b/tensorflow/compiler/xla/service/local_service.h @@ -41,11 +41,14 @@ class LocalService : public Service { // Builds an Executable with the given argument layouts and options. If // result_layout is non-null, then the executable is compiled to produce a - // result of the given layout. + // result of the given layout. If device_allocator is non-null, then the + // compiler may use it to allocate temp space on the device. The compiler is + // responsible for freeing any memory it allocates this way. StatusOr> CompileExecutable( const ComputationHandle& computation, const tensorflow::gtl::ArraySlice argument_layouts, - const Shape* result_layout, int device_ordinal); + const Shape* result_layout, int device_ordinal, + DeviceMemoryAllocator* device_allocator); // Returns the device ordinal that corresponds to the given replica number. // diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 849df1d8e6..fea6956345 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -337,7 +337,8 @@ StatusOr>> Service::BuildExecutables( std::vector versioned_handles, std::vector> module_configs, Backend* backend, - std::vector> executors) { + std::vector> executors, + DeviceMemoryAllocator* device_allocator) { VLOG(1) << Printf("BuildExecutable on service %p", this); // Dump computation proto state if flag is set. @@ -383,7 +384,8 @@ StatusOr>> Service::BuildExecutables( TF_ASSIGN_OR_RETURN( std::vector> executables, - backend->compiler()->Compile(std::move(modules), std::move(executors))); + backend->compiler()->Compile(std::move(modules), std::move(executors), + device_allocator)); for (size_t i = 0; i < versioned_handles.size(); ++i) { if (!module_configs[i]->debug_options().xla_dump_executions_to().empty()) { @@ -396,8 +398,8 @@ StatusOr>> Service::BuildExecutables( StatusOr> Service::BuildExecutable( const VersionedComputationHandle& versioned_handle, - std::unique_ptr module_config, - Backend* backend, se::StreamExecutor* executor) { + std::unique_ptr module_config, Backend* backend, + se::StreamExecutor* executor, DeviceMemoryAllocator* device_allocator) { VLOG(1) << Printf("BuildExecutable on service %p with handle %s", this, versioned_handle.ToString().c_str()); @@ -430,11 +432,12 @@ StatusOr> Service::BuildExecutable( TF_RETURN_IF_ERROR(MaybeDumpHloModule(*module)); TF_ASSIGN_OR_RETURN( - module, backend->compiler()->RunHloPasses(std::move(module), executor)); + module, backend->compiler()->RunHloPasses(std::move(module), executor, + device_allocator)); - TF_ASSIGN_OR_RETURN( - std::unique_ptr executable, - backend->compiler()->RunBackend(std::move(module), executor)); + TF_ASSIGN_OR_RETURN(std::unique_ptr executable, + backend->compiler()->RunBackend( + std::move(module), executor, device_allocator)); if (!other_directory_path.empty()) { executable->set_session_module(std::move(session_module)); @@ -445,9 +448,9 @@ StatusOr> Service::BuildExecutable( StatusOr> Service::BuildAndCacheExecutable( const VersionedComputationHandle& versioned_handle, - std::unique_ptr module_config, - Backend* backend, perftools::gputools::StreamExecutor* executor, - ExecutionProfile* profile) { + std::unique_ptr module_config, Backend* backend, + perftools::gputools::StreamExecutor* executor, ExecutionProfile* profile, + DeviceMemoryAllocator* device_allocator) { std::shared_ptr executable = compilation_cache_.LookUp(versioned_handle, *module_config); @@ -469,7 +472,7 @@ StatusOr> Service::BuildAndCacheExecutable( TF_ASSIGN_OR_RETURN( std::unique_ptr executable_unique_ptr, BuildExecutable(versioned_handle, std::move(module_config), backend, - executor)); + executor, device_allocator)); if (profile != nullptr) { uint64 end_micros = tensorflow::Env::Default()->NowMicros(); @@ -771,10 +774,14 @@ tensorflow::Status Service::ExecuteParallel(const ExecuteParallelRequest* arg, // Build the user computations into HloModules and compile to generate the // executables. + // + // TODO(jlebar): There's currently no way to pass a device allocator to + // ExecuteParallel, so we have to pass a null device_allocator below. TF_ASSIGN_OR_RETURN( std::vector> executables, BuildExecutables(versioned_handles, std::move(module_configs), - execute_backend_.get(), all_executors)); + execute_backend_.get(), all_executors, + /*device_allocator=*/nullptr)); std::vector executable_ptrs; executable_ptrs.reserve(executables.size()); for (const auto& executable : executables) { diff --git a/tensorflow/compiler/xla/service/service.h b/tensorflow/compiler/xla/service/service.h index ca77e8fe3a..6ce2419711 100644 --- a/tensorflow/compiler/xla/service/service.h +++ b/tensorflow/compiler/xla/service/service.h @@ -280,10 +280,15 @@ class Service : public ServiceInterface { const UserComputation& user_computation); // Builds an Executable for the given parameters. + // + // If device_allocator is not null, the compiler may use it to allocate temp + // buffers, which the compiler is responsible for freeing. The allocator + // given here need not match the allocator used when running the executable. StatusOr> BuildExecutable( const VersionedComputationHandle& versioned_handle, - std::unique_ptr module_config, - Backend* backend, perftools::gputools::StreamExecutor* executor); + std::unique_ptr module_config, Backend* backend, + perftools::gputools::StreamExecutor* executor, + DeviceMemoryAllocator* device_allocator = nullptr); // Same as BuildExecutable() above, but builds a list of Executables for the // given computations that may interact with each other. @@ -291,16 +296,17 @@ class Service : public ServiceInterface { std::vector versioned_handles, std::vector> module_configs, Backend* backend, - std::vector> executors); + std::vector> executors, + DeviceMemoryAllocator* device_allocator); // Similar to BuildExecutable, but look in the compilation cache for the // executable first. If the executable is not in the cache, it is built and // inserted into the cache. StatusOr> BuildAndCacheExecutable( const VersionedComputationHandle& versioned_handle, - std::unique_ptr module_config, - Backend* backend, perftools::gputools::StreamExecutor* executor, - ExecutionProfile* profile); + std::unique_ptr module_config, Backend* backend, + perftools::gputools::StreamExecutor* executor, ExecutionProfile* profile, + DeviceMemoryAllocator* device_allocator = nullptr); // Runs the given executable with the given arguments and register the result // in the allocation tracker. The handle of the result from the tracker is diff --git a/tensorflow/compiler/xla/tests/codegen_test_base.cc b/tensorflow/compiler/xla/tests/codegen_test_base.cc index e472408dcf..022641394f 100644 --- a/tensorflow/compiler/xla/tests/codegen_test_base.cc +++ b/tensorflow/compiler/xla/tests/codegen_test_base.cc @@ -21,9 +21,11 @@ StatusOr> CodegenTestBase::CompileToExecutable( std::unique_ptr hlo_module) { TF_ASSIGN_OR_RETURN(hlo_module, backend().compiler()->RunHloPasses( std::move(hlo_module), - backend().default_stream_executor())); + backend().default_stream_executor(), + /*device_allocator=*/nullptr)); return backend().compiler()->RunBackend(std::move(hlo_module), - backend().default_stream_executor()); + backend().default_stream_executor(), + /*device_allocator=*/nullptr); } StatusOr> diff --git a/tensorflow/compiler/xla/tests/llvm_compiler_test.cc b/tensorflow/compiler/xla/tests/llvm_compiler_test.cc index b5b95967ff..7e92439c49 100644 --- a/tensorflow/compiler/xla/tests/llvm_compiler_test.cc +++ b/tensorflow/compiler/xla/tests/llvm_compiler_test.cc @@ -74,7 +74,8 @@ class LLVMCompilerTest : public ::testing::Test { ASSERT_TRUE(compiler ->RunBackend(std::move(hlo_module), - backend_->default_stream_executor()) + backend_->default_stream_executor(), + /*device_allocator=*/nullptr) .ok()); // Test that hooks were called. @@ -98,7 +99,8 @@ class LLVMCompilerTest : public ::testing::Test { executors.push_back({backend_->default_stream_executor()}); executors.push_back({backend_->default_stream_executor()}); - EXPECT_IS_OK(compiler->Compile(std::move(modules), std::move(executors))); + EXPECT_IS_OK(compiler->Compile(std::move(modules), std::move(executors), + /*device_allocator=*/nullptr)); } private: 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 5ede37b873..4ad356d045 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc @@ -86,9 +86,9 @@ void RealMain(tensorflow::gtl::ArraySlice args) { layouts.push_back(&program_shape->parameters(i)); } StatusOr> executable = - local_service->CompileExecutable(computation.handle(), layouts, - &program_shape->result(), - /*device_ordinal=*/0); + local_service->CompileExecutable( + computation.handle(), layouts, &program_shape->result(), + /*device_ordinal=*/0, /*device_allocator=*/nullptr); const HloModule& module = executable.ValueOrDie()->module(); diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc index 24417a0cb8..5ebb75a31c 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc @@ -61,9 +61,9 @@ void RealMain(tensorflow::gtl::ArraySlice args, bool compile) { layouts.push_back(&program_shape->parameters(i)); } StatusOr> executable = - local_service->CompileExecutable(computation.handle(), layouts, - &program_shape->result(), - /*device_ordinal=*/0); + local_service->CompileExecutable( + computation.handle(), layouts, &program_shape->result(), + /*device_ordinal=*/0, /*device_allocator=*/nullptr); const HloModule& module = executable.ValueOrDie()->module(); -- GitLab From 8fc47fa3af0e7bc1652e7180c699a270bcc71bbd Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 17:18:16 -0800 Subject: [PATCH 171/423] Raise to 4 the shard counts of //third_party/tensorflow/contrib/{factorization:kmeans_test,linear_optimizer:sdca_estimator_test} These tests were getting flaky timeouts when run under asan, sometimes taking longer than the 5 minute timeout. Increasing the shard count to 4 seems to be sufficient to cause them not to time out. PiperOrigin-RevId: 183470183 --- tensorflow/contrib/factorization/BUILD | 1 + tensorflow/contrib/linear_optimizer/BUILD | 1 + 2 files changed, 2 insertions(+) diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD index fe86a20ab1..180f1b68f3 100644 --- a/tensorflow/contrib/factorization/BUILD +++ b/tensorflow/contrib/factorization/BUILD @@ -221,6 +221,7 @@ py_test( name = "kmeans_test", size = "medium", srcs = ["python/ops/kmeans_test.py"], + shard_count = 4, srcs_version = "PY2AND3", tags = ["notsan"], # b/67512932 deps = [ diff --git a/tensorflow/contrib/linear_optimizer/BUILD b/tensorflow/contrib/linear_optimizer/BUILD index fe2f183ac9..cea3627ed5 100644 --- a/tensorflow/contrib/linear_optimizer/BUILD +++ b/tensorflow/contrib/linear_optimizer/BUILD @@ -126,6 +126,7 @@ py_library( py_test( name = "sdca_estimator_test", srcs = ["python/sdca_estimator_test.py"], + shard_count = 4, srcs_version = "PY2AND3", deps = [ ":sdca_estimator_py", -- GitLab From ca3ac2a464b92f4c0498dfde875f99102a0d410c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 17:38:06 -0800 Subject: [PATCH 172/423] Fixed bug: inconsistency with how damping normalization was applied to ConvDiagonalFB blocks. PiperOrigin-RevId: 183472440 --- tensorflow/contrib/kfac/python/ops/fisher_blocks.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py index 9436caf961..0d2fa706f5 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_blocks.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_blocks.py @@ -457,7 +457,9 @@ class ConvDiagonalFB(FisherBlock): self._num_locations = ( inputs_shape[1] * inputs_shape[2] // (self._strides[1] * self._strides[2])) - self._damping = normalize_damping(damping, self._num_locations) + + self._damping = (self._num_locations + * normalize_damping(damping, self._num_locations)) self._factor = self._layer_collection.make_or_get_factor( fisher_factors.ConvDiagonalFactor, -- GitLab From 704361ad3650ebc891167adc41c459ca93392060 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Fri, 26 Jan 2018 17:50:03 -0800 Subject: [PATCH 173/423] Create different data for each Literal when creating fake data. Thread a generator through the functions for creating fake arguments so the same generator can be reused which avoids repeating the same data patterns for each argument generated. Also tweak the position-dependent biasing heuristic to create both positive and negative numbers for small literals. PiperOrigin-RevId: 183473588 --- tensorflow/compiler/xla/tests/test_utils.cc | 188 +++++++++++--------- 1 file changed, 105 insertions(+), 83 deletions(-) diff --git a/tensorflow/compiler/xla/tests/test_utils.cc b/tensorflow/compiler/xla/tests/test_utils.cc index 8b10aef5b8..b060fb13b1 100644 --- a/tensorflow/compiler/xla/tests/test_utils.cc +++ b/tensorflow/compiler/xla/tests/test_utils.cc @@ -24,51 +24,127 @@ namespace xla { namespace { template -void PopulateWithRandomFloatingPointData(Literal* literal) { +void PopulateWithRandomFloatingPointData(Literal* literal, + std::minstd_rand0* engine) { CHECK_EQ(literal->shape().element_type(), primitive_util::NativeToPrimitiveType()); - std::minstd_rand0 engine; - // Create uniform numbers between 1 and 1.125 ot avoid creating denormal + // Create uniform numbers between 1 and 1.125 to avoid creating denormal // numbers. std::uniform_real_distribution generator(1.0f, 1.125f); + const bool should_index_bias = ShapeUtil::ElementsIn(literal->shape()) > 1000; TF_CHECK_OK(literal->Populate( [&](tensorflow::gtl::ArraySlice indices) { - // Generate a random uniforma number from -0.0625 and 0.0625 and bias it - // with a position dependent nubmer with mean 0.037109375. These number + // Generate a random uniform number from -0.0625 and 0.0625 and bias it + // with a position dependent number with mean 0.037109375. These number // should allow for long chains of accumulation without being too close - // to zero or to large to accumulate all numbers accurately. - return (generator(engine) - 1.0625) + - static_cast(Product(indices) % 113 - 47) / - static_cast(256.0f); + // to zero or too large to accumulate all numbers accurately. Only do + // this for large literals where the number of elements is much greater + // than 47 otherwise only negative values are produced. + // + // The value is positionally biased using a product of the indices. Add + // one to each index value to avoid collapsing to zero if any of the + // indices are zero. + int64 index_product = 1; + for (int64 i : indices) { + index_product *= (1 + i); + } + const int64 negative_bias = should_index_bias ? 47 : 0; + FloatT index_bias = + static_cast(index_product % 113 - negative_bias) / + static_cast(256.0f); + return (generator(*engine) - 1.0625) + index_bias; })); } // The standard library does not have a case for bfloat16, unsurprisingly, so we // handle that one specially. template <> -void PopulateWithRandomFloatingPointData(Literal* literal) { +void PopulateWithRandomFloatingPointData(Literal* literal, + std::minstd_rand0* engine) { CHECK_EQ(literal->shape().element_type(), BF16); - std::minstd_rand0 engine; std::uniform_real_distribution generator(-0.9f, 1.0f); TF_CHECK_OK(literal->Populate( [&](tensorflow::gtl::ArraySlice /*indices*/) { - return static_cast(generator(engine)); + return static_cast(generator(*engine)); })); } template -void PopulateWithRandomIntegralData(Literal* literal) { +void PopulateWithRandomIntegralData(Literal* literal, + std::minstd_rand0* engine) { CHECK_EQ(literal->shape().element_type(), primitive_util::NativeToPrimitiveType()); - std::minstd_rand0 engine; std::uniform_int_distribution generator( std::numeric_limits::lowest(), std::numeric_limits::max()); TF_CHECK_OK(literal->Populate( [&](tensorflow::gtl::ArraySlice /*indices*/) { - return generator(engine); + return generator(*engine); })); } +// Similar to MakeFakeLiteral but takes a random number generator engine to +// enable reusing the engine across randomly generated literals. +StatusOr> MakeFakeLiteralInternal( + const Shape& shape, std::minstd_rand0* engine) { + if (ShapeUtil::IsTuple(shape)) { + std::vector> elements; + for (const Shape& element_shape : shape.tuple_shapes()) { + TF_ASSIGN_OR_RETURN(std::unique_ptr element, + MakeFakeLiteralInternal(element_shape, engine)); + elements.push_back(std::move(element)); + } + return Literal::MakeTupleOwned(std::move(elements)); + } + std::unique_ptr literal = Literal::CreateFromShape(shape); + switch (shape.element_type()) { + case BF16: + PopulateWithRandomFloatingPointData(literal.get(), engine); + break; + case F32: + PopulateWithRandomFloatingPointData(literal.get(), engine); + break; + case F64: + PopulateWithRandomFloatingPointData(literal.get(), engine); + break; + case S8: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case U8: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case S16: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case U16: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case S32: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case U32: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case S64: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case U64: + PopulateWithRandomIntegralData(literal.get(), engine); + break; + case PRED: { + std::uniform_int_distribution generator(0, 1); + TF_CHECK_OK(literal->Populate( + [&](tensorflow::gtl::ArraySlice /*indices*/) { + return generator(*engine); + })); + break; + } + default: + return Unimplemented("Unsupported type for fake literal generation: %s", + ShapeUtil::HumanString(shape).c_str()); + } + return std::move(literal); +} + // Matches binary addition computations. bool LooksLikeSum(const HloComputation& computation) { const HloInstruction* const root = computation.root_instruction(); @@ -95,15 +171,15 @@ bool NeedsZeroInitValue(const HloUse& use) { // Generate random values that are constrained to the input_shape minus the // output_shape so as not to produce wrapping slices, for instance. std::unique_ptr MakeRandomNonwrappingSliceIndex( - const Shape& input_shape, const Shape& slice_shape) { + const Shape& input_shape, const Shape& slice_shape, + std::minstd_rand0* engine) { const int64 rank = ShapeUtil::Rank(input_shape); std::vector start_indices(rank); - std::minstd_rand0 engine; for (int i = 0; i < rank; ++i) { const int32 upper_bound = ShapeUtil::GetDimension(input_shape, i) - ShapeUtil::GetDimension(slice_shape, i); std::uniform_int_distribution generator(0, upper_bound); - start_indices[i] = generator(engine); + start_indices[i] = generator(*engine); } return Literal::CreateR1(start_indices); } @@ -150,7 +226,7 @@ std::vector FindConstrainedUses( // zero in the case of init_values for reductions). StatusOr> CreateLiteralForConstrainedUses( const tensorflow::gtl::ArraySlice constrained_uses, - const HloInstruction& param) { + const HloInstruction& param, std::minstd_rand0* engine) { HloInstruction* needs_index = nullptr; HloInstruction* needs_zero = nullptr; for (HloInstruction* use : constrained_uses) { @@ -185,93 +261,39 @@ StatusOr> CreateLiteralForConstrainedUses( } if (needs_index != nullptr) { return MakeRandomNonwrappingSliceIndex(needs_index->operand(0)->shape(), - needs_index->shape()); + needs_index->shape(), engine); } else if (needs_zero != nullptr) { return Literal::CreateFromShape(param.shape()); } else { - return MakeFakeLiteral(param.shape()); + return MakeFakeLiteralInternal(param.shape(), engine); } } // Given a module entry parameter, use the dataflow analysis to see if a // special case literal must be created, or if we can generate fake data. StatusOr> MakeConstrainedArgument( - const HloDataflowAnalysis& dataflow, const HloInstruction& param) { + const HloDataflowAnalysis& dataflow, const HloInstruction& param, + std::minstd_rand0* engine) { const auto constrained_uses = FindConstrainedUses(dataflow, param); - return CreateLiteralForConstrainedUses(constrained_uses, param); + return CreateLiteralForConstrainedUses(constrained_uses, param, engine); } } // namespace StatusOr> MakeFakeLiteral(const Shape& shape) { - if (ShapeUtil::IsTuple(shape)) { - std::vector> elements; - for (const Shape& element_shape : shape.tuple_shapes()) { - TF_ASSIGN_OR_RETURN(std::unique_ptr element, - MakeFakeLiteral(element_shape)); - elements.push_back(std::move(element)); - } - return Literal::MakeTupleOwned(std::move(elements)); - } - std::unique_ptr literal = Literal::CreateFromShape(shape); - switch (shape.element_type()) { - case BF16: - PopulateWithRandomFloatingPointData(literal.get()); - break; - case F32: - PopulateWithRandomFloatingPointData(literal.get()); - break; - case F64: - PopulateWithRandomFloatingPointData(literal.get()); - break; - case S8: - PopulateWithRandomIntegralData(literal.get()); - break; - case U8: - PopulateWithRandomIntegralData(literal.get()); - break; - case S16: - PopulateWithRandomIntegralData(literal.get()); - break; - case U16: - PopulateWithRandomIntegralData(literal.get()); - break; - case S32: - PopulateWithRandomIntegralData(literal.get()); - break; - case U32: - PopulateWithRandomIntegralData(literal.get()); - break; - case S64: - PopulateWithRandomIntegralData(literal.get()); - break; - case U64: - PopulateWithRandomIntegralData(literal.get()); - break; - case PRED: { - std::uniform_int_distribution generator(0, 1); - std::minstd_rand0 engine; - TF_CHECK_OK(literal->Populate( - [&](tensorflow::gtl::ArraySlice /*indices*/) { - return generator(engine); - })); - break; - } - default: - return Unimplemented("Unsupported type for fake literal generation: %s", - ShapeUtil::HumanString(shape).c_str()); - } - return std::move(literal); + std::minstd_rand0 engine; + return MakeFakeLiteralInternal(shape, &engine); } StatusOr>> MakeFakeArguments( HloModule* const module) { TF_ASSIGN_OR_RETURN(auto dataflow, HloDataflowAnalysis::Run(module)); const auto params = module->entry_computation()->parameter_instructions(); + std::minstd_rand0 engine; std::vector> arguments(params.size()); for (int i = 0; i < params.size(); ++i) { - TF_ASSIGN_OR_RETURN(arguments[i], - MakeConstrainedArgument(*dataflow, *params[i])); + TF_ASSIGN_OR_RETURN( + arguments[i], MakeConstrainedArgument(*dataflow, *params[i], &engine)); } return std::move(arguments); } -- GitLab From 620c36be9a7b07501374eb1ee5f298ca9e0a560d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 17:57:49 -0800 Subject: [PATCH 174/423] Remove protobuf patch that was installed to resolve #8394. It appears to not be necessary any longer. PiperOrigin-RevId: 183474194 --- tensorflow/workspace.bzl | 5 ---- third_party/protobuf/add_noinlines.patch | 30 ------------------------ 2 files changed, 35 deletions(-) delete mode 100644 third_party/protobuf/add_noinlines.patch diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index f7d9075032..26abebe2de 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -357,11 +357,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""): ], sha256 = "e178a25c52efcb6b05988bdbeace4c0d3f2d2fe5b46696d1d9898875c3803d6a", strip_prefix = "protobuf-b04e5cba356212e4e8c66c61bbe0c3a20537c5b9", - # TODO: remove patching when tensorflow stops linking same protos into - # multiple shared libraries loaded in runtime by python. - # This patch fixes a runtime crash when tensorflow is compiled - # with clang -O2 on Linux (see https://github.com/tensorflow/tensorflow/issues/8394) - patch_file = str(Label("//third_party/protobuf:add_noinlines.patch")), ) # We need to import the protobuf library under the names com_google_protobuf diff --git a/third_party/protobuf/add_noinlines.patch b/third_party/protobuf/add_noinlines.patch deleted file mode 100644 index af74798f06..0000000000 --- a/third_party/protobuf/add_noinlines.patch +++ /dev/null @@ -1,30 +0,0 @@ -diff -u -r a/src/google/protobuf/compiler/cpp/cpp_file.cc b/src/google/protobuf/compiler/cpp/cpp_file.cc ---- a/src/google/protobuf/compiler/cpp/cpp_file.cc 2017-02-10 23:55:34.000000000 +0100 -+++ b/src/google/protobuf/compiler/cpp/cpp_file.cc 2017-03-21 13:41:46.931979154 +0100 -@@ -557,7 +557,7 @@ - " $metadata$, $enum_descriptors$, $service_descriptors$);\n" - "}\n" - "\n" -- "void protobuf_AssignDescriptorsOnce() {\n" -+ "GOOGLE_ATTRIBUTE_NOINLINE void protobuf_AssignDescriptorsOnce() {\n" - " static GOOGLE_PROTOBUF_DECLARE_ONCE(once);\n" - " ::google::protobuf::GoogleOnceInit(&once, &protobuf_AssignDescriptors);\n" - "}\n" -@@ -656,7 +656,7 @@ - printer->Print( - "}\n" - "\n" -- "void InitDefaults() {\n" -+ "GOOGLE_ATTRIBUTE_NOINLINE void InitDefaults() {\n" - " static GOOGLE_PROTOBUF_DECLARE_ONCE(once);\n" - " ::google::protobuf::GoogleOnceInit(&once, &TableStruct::InitDefaultsImpl);\n" - "}\n"); -@@ -737,7 +737,7 @@ - printer->Print( - "}\n" - "\n" -- "void AddDescriptors() {\n" -+ "GOOGLE_ATTRIBUTE_NOINLINE void AddDescriptors() {\n" - " static GOOGLE_PROTOBUF_DECLARE_ONCE(once);\n" - " ::google::protobuf::GoogleOnceInit(&once, &AddDescriptorsImpl);\n" - "}\n"); -- GitLab From fc21dc489ba8f66938a13615ee899da824eafeb1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 18:00:08 -0800 Subject: [PATCH 175/423] Add a feature to automatically recapture the traces when no trace event is collected. PiperOrigin-RevId: 183474367 --- .../tpu/profiler/capture_tpu_profile.cc | 34 ++++++++++++++++--- .../contrib/tpu/profiler/dump_tpu_profile.cc | 4 +-- .../contrib/tpu/profiler/dump_tpu_profile.h | 3 +- tensorflow/contrib/tpu/profiler/version.h | 2 +- 4 files changed, 34 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc index 7373d0e17c..cca0a37a89 100644 --- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc @@ -78,14 +78,19 @@ int main(int argc, char** argv) { tensorflow::string FLAGS_service_addr; tensorflow::string FLAGS_logdir; int FLAGS_duration_ms = 2000; + int FLAGS_num_tracing_attempts = 3; bool FLAGS_include_dataset_ops = true; std::vector flag_list = { tensorflow::Flag("service_addr", &FLAGS_service_addr, "Address of TPU profiler service e.g. localhost:8466"), tensorflow::Flag("logdir", &FLAGS_logdir, - "Path of TensorBoard log directory e.g. /tmp/tb_log"), + "Path of TensorBoard log directory e.g. /tmp/tb_log, " + "gs://tb_bucket"), tensorflow::Flag("duration_ms", &FLAGS_duration_ms, "Duration of tracing in ms. Default is 2000ms."), + tensorflow::Flag("num_tracing_attempts", &FLAGS_num_tracing_attempts, + "Automatically retry N times when no trace event " + "is collected. Default is 3."), tensorflow::Flag("include_dataset_ops", &FLAGS_include_dataset_ops, "Set to false to profile longer TPU device traces."), }; @@ -101,11 +106,32 @@ int main(int argc, char** argv) { } tensorflow::port::InitMain(argv[0], &argc, &argv); - int duration_ms = FLAGS_duration_ms; + // Sets the minimum duration_ms and tracing attempts to one. + int duration_ms = max(FLAGS_duration_ms, 1); + int remaining_attempts = max(FLAGS_num_tracing_attempts, 1); tensorflow::ProfileOptions opts; opts.set_include_dataset_ops(FLAGS_include_dataset_ops); - tensorflow::ProfileResponse response = - tensorflow::tpu::Profile(FLAGS_service_addr, duration_ms, opts); + tensorflow::ProfileResponse response; + + while (true) { + std::cout << "Starting to profile TPU traces for " << duration_ms << " ms. " + << "Remaining attempt(s): " << remaining_attempts-- << std::endl; + response = tensorflow::tpu::Profile(FLAGS_service_addr, duration_ms, opts); + if (remaining_attempts <= 0 || !response.encoded_trace().empty()) break; + std::cout << "No trace event is collected. Automatically retrying." + << std::endl + << std::endl; + } + + if (response.encoded_trace().empty()) { + std::cout << "No trace event is collected after " + << FLAGS_num_tracing_attempts << " attempt(s). " + << "Perhaps, you want to try again (with more attempts?)." + << std::endl + << "Tip: increase number of attempts with --num_tracing_attempts." + << std::endl; + } + // Use the current timestamp as the run name. tensorflow::string run = tensorflow::tpu::GetCurrentTimeStampAsString(); TF_CHECK_OK(tensorflow::tpu::WriteTensorboardTPUProfile( diff --git a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc index b842951eb2..64e4e6275d 100644 --- a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc @@ -152,9 +152,7 @@ Status WriteTensorboardTPUProfile(const string& logdir, const string& run, // Ignore computation_graph for now. const bool empty_trace = response.encoded_trace().empty(); - if (empty_trace) { - *os << "No trace event is collected." << std::endl; - } else { + if (!empty_trace) { LOG(INFO) << "Converting trace events to TraceViewer JSON."; TF_RETURN_IF_ERROR( DumpTraceToLogDirectory(profile_run_dir, response.encoded_trace(), os)); diff --git a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h index 25b958bcfe..2f8656a37b 100644 --- a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h +++ b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h @@ -27,7 +27,8 @@ namespace tpu { // The following tools are supported: // - Trace viewer // - Op profile -// - HLO computation graph +// - Input pipeline analyzer +// - Overview page Status WriteTensorboardTPUProfile(const string& logdir, const string& run, const ProfileResponse& response, std::ostream* os); diff --git a/tensorflow/contrib/tpu/profiler/version.h b/tensorflow/contrib/tpu/profiler/version.h index 0f645a5492..dc6a934891 100644 --- a/tensorflow/contrib/tpu/profiler/version.h +++ b/tensorflow/contrib/tpu/profiler/version.h @@ -16,6 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ #define TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ -#define TPU_PROFILER_VERSION "1.4.3" +#define TPU_PROFILER_VERSION "1.5.0" #endif // TENSORFLOW_CONTRIB_TPU_PROFILER_VERSION_H_ -- GitLab From 1464b97c2fb1908eb3425af9f87febcbeb9bcc8f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 18:59:15 -0800 Subject: [PATCH 176/423] Internal change. PiperOrigin-RevId: 183479688 --- .../contrib/lite/schema/schema_generated.h | 176 ++++++++++++++---- 1 file changed, 142 insertions(+), 34 deletions(-) diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index c04a73a2bf..bafd28b626 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -51,6 +51,9 @@ struct RNNOptionsT; struct SequenceRNNOptions; struct SequenceRNNOptionsT; +struct BidirectionalSequenceRNNOptions; +struct BidirectionalSequenceRNNOptionsT; + struct FullyConnectedOptions; struct FullyConnectedOptionsT; @@ -211,11 +214,12 @@ enum BuiltinOperator { BuiltinOperator_SQUEEZE = 43, BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, BuiltinOperator_STRIDED_SLICE = 45, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_STRIDED_SLICE + BuiltinOperator_MAX = BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[43] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[44] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -259,7 +263,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[43] { BuiltinOperator_DIV, BuiltinOperator_SQUEEZE, BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_STRIDED_SLICE}; + BuiltinOperator_STRIDED_SLICE, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN}; return values; } @@ -310,6 +315,7 @@ inline const char **EnumNamesBuiltinOperator() { "SQUEEZE", "UNIDIRECTIONAL_SEQUENCE_LSTM", "STRIDED_SLICE", + "BIDIRECTIONAL_SEQUENCE_RNN", nullptr}; return names; } @@ -2005,6 +2011,85 @@ flatbuffers::Offset CreateSequenceRNNOptions( flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable { + typedef BidirectionalSequenceRNNOptions TableType; + bool time_major; + ActivationFunctionType fused_activation_function; + BidirectionalSequenceRNNOptionsT() + : time_major(false), + fused_activation_function(ActivationFunctionType_NONE) {} +}; + +struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS + : private flatbuffers::Table { + typedef BidirectionalSequenceRNNOptionsT NativeTableType; + enum { VT_TIME_MAJOR = 4, VT_FUSED_ACTIVATION_FUNCTION = 6 }; + bool time_major() const { return GetField(VT_TIME_MAJOR, 0) != 0; } + ActivationFunctionType fused_activation_function() const { + return static_cast( + GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TIME_MAJOR) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION) && + verifier.EndTable(); + } + BidirectionalSequenceRNNOptionsT *UnPack( + const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo( + BidirectionalSequenceRNNOptionsT *_o, + const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack( + flatbuffers::FlatBufferBuilder &_fbb, + const BidirectionalSequenceRNNOptionsT *_o, + const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BidirectionalSequenceRNNOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_time_major(bool time_major) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, + static_cast(time_major), 0); + } + void add_fused_activation_function( + ActivationFunctionType fused_activation_function) { + fbb_.AddElement( + BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, + static_cast(fused_activation_function), 0); + } + explicit BidirectionalSequenceRNNOptionsBuilder( + flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + BidirectionalSequenceRNNOptionsBuilder &operator=( + const BidirectionalSequenceRNNOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset +CreateBidirectionalSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false, + ActivationFunctionType fused_activation_function = + ActivationFunctionType_NONE) { + BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_time_major(time_major); + return builder_.Finish(); +} + +flatbuffers::Offset +CreateBidirectionalSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + const BidirectionalSequenceRNNOptionsT *_o, + const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct FullyConnectedOptionsT : public flatbuffers::NativeTable { typedef FullyConnectedOptions TableType; ActivationFunctionType fused_activation_function; @@ -2541,21 +2626,14 @@ flatbuffers::Offset CreateLSTMOptions( struct ResizeBilinearOptionsT : public flatbuffers::NativeTable { typedef ResizeBilinearOptions TableType; - int32_t new_height; - int32_t new_width; - ResizeBilinearOptionsT() : new_height(0), new_width(0) {} + ResizeBilinearOptionsT() {} }; struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef ResizeBilinearOptionsT NativeTableType; - enum { VT_NEW_HEIGHT = 4, VT_NEW_WIDTH = 6 }; - int32_t new_height() const { return GetField(VT_NEW_HEIGHT, 0); } - int32_t new_width() const { return GetField(VT_NEW_WIDTH, 0); } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NEW_HEIGHT) && - VerifyField(verifier, VT_NEW_WIDTH) && verifier.EndTable(); + return VerifyTableStart(verifier) && verifier.EndTable(); } ResizeBilinearOptionsT *UnPack( const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -2570,13 +2648,6 @@ struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS struct ResizeBilinearOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_new_height(int32_t new_height) { - fbb_.AddElement(ResizeBilinearOptions::VT_NEW_HEIGHT, new_height, - 0); - } - void add_new_width(int32_t new_width) { - fbb_.AddElement(ResizeBilinearOptions::VT_NEW_WIDTH, new_width, 0); - } explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -2590,11 +2661,8 @@ struct ResizeBilinearOptionsBuilder { }; inline flatbuffers::Offset CreateResizeBilinearOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t new_height = 0, - int32_t new_width = 0) { + flatbuffers::FlatBufferBuilder &_fbb) { ResizeBilinearOptionsBuilder builder_(_fbb); - builder_.add_new_width(new_width); - builder_.add_new_height(new_height); return builder_.Finish(); } @@ -5098,6 +5166,56 @@ inline flatbuffers::Offset CreateSequenceRNNOptions( _fused_activation_function); } +inline BidirectionalSequenceRNNOptionsT * +BidirectionalSequenceRNNOptions::UnPack( + const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new BidirectionalSequenceRNNOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void BidirectionalSequenceRNNOptions::UnPackTo( + BidirectionalSequenceRNNOptionsT *_o, + const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { + auto _e = time_major(); + _o->time_major = _e; + }; + { + auto _e = fused_activation_function(); + _o->fused_activation_function = _e; + }; +} + +inline flatbuffers::Offset +BidirectionalSequenceRNNOptions::Pack( + flatbuffers::FlatBufferBuilder &_fbb, + const BidirectionalSequenceRNNOptionsT *_o, + const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset +CreateBidirectionalSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + const BidirectionalSequenceRNNOptionsT *_o, + const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { + flatbuffers::FlatBufferBuilder *__fbb; + const BidirectionalSequenceRNNOptionsT *__o; + const flatbuffers::rehasher_function_t *__rehasher; + } _va = {&_fbb, _o, _rehasher}; + (void)_va; + auto _time_major = _o->time_major; + auto _fused_activation_function = _o->fused_activation_function; + return tflite::CreateBidirectionalSequenceRNNOptions( + _fbb, _time_major, _fused_activation_function); +} + inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack( const flatbuffers::resolver_function_t *_resolver) const { auto _o = new FullyConnectedOptionsT(); @@ -5457,14 +5575,6 @@ inline void ResizeBilinearOptions::UnPackTo( const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = new_height(); - _o->new_height = _e; - }; - { - auto _e = new_width(); - _o->new_width = _e; - }; } inline flatbuffers::Offset ResizeBilinearOptions::Pack( @@ -5484,9 +5594,7 @@ inline flatbuffers::Offset CreateResizeBilinearOptions( const flatbuffers::rehasher_function_t *__rehasher; } _va = {&_fbb, _o, _rehasher}; (void)_va; - auto _new_height = _o->new_height; - auto _new_width = _o->new_width; - return tflite::CreateResizeBilinearOptions(_fbb, _new_height, _new_width); + return tflite::CreateResizeBilinearOptions(_fbb); } inline CallOptionsT *CallOptions::UnPack( -- GitLab From dfe703b3de18d512a08ef44cebc9bdc0c597866b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 20:51:06 -0800 Subject: [PATCH 177/423] Fix build: add std:: to max() in tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc. PiperOrigin-RevId: 183486778 --- tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc index cca0a37a89..6a05a2abf6 100644 --- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc @@ -107,8 +107,8 @@ int main(int argc, char** argv) { tensorflow::port::InitMain(argv[0], &argc, &argv); // Sets the minimum duration_ms and tracing attempts to one. - int duration_ms = max(FLAGS_duration_ms, 1); - int remaining_attempts = max(FLAGS_num_tracing_attempts, 1); + int duration_ms = std::max(FLAGS_duration_ms, 1); + int remaining_attempts = std::max(FLAGS_num_tracing_attempts, 1); tensorflow::ProfileOptions opts; opts.set_include_dataset_ops(FLAGS_include_dataset_ops); tensorflow::ProfileResponse response; -- GitLab From 0aed95f11379322b193945fa1e0832ee726c5278 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 26 Jan 2018 22:19:43 -0800 Subject: [PATCH 178/423] [TF:XLA] Update stale comments to match function names. PiperOrigin-RevId: 183491729 --- tensorflow/compiler/xla/util.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index 4bc2d632cd..2da9bb21b7 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -342,7 +342,7 @@ T CeilOfRatio(T dividend, T divisor) { } // Rounds the value up to a multiple of the divisor by first calling CeilOfRatio -// then multiplying by the divisor. For example: RoundUpToMultiple(13, 8) => 16 +// then multiplying by the divisor. For example: RoundUpToNearest(13, 8) => 16 template T RoundUpToNearest(T value, T divisor) { return CeilOfRatio(value, divisor) * divisor; @@ -350,7 +350,7 @@ T RoundUpToNearest(T value, T divisor) { // Rounds the value down to a multiple of the divisor by first calling // FloorOfRatio then multiplying by the divisor. For example: -// RoundUpToMultiple(13, 8) => 8 +// RoundDownToNearest(13, 8) => 8 template T RoundDownToNearest(T value, T divisor) { return FloorOfRatio(value, divisor) * divisor; -- GitLab From 5b028b2f5867ba230963a92bbc334fe538b30fdc Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 26 Jan 2018 22:57:08 -0800 Subject: [PATCH 179/423] [XLA] Make DeviceMemoryAllocator::platform() a const pointer. PiperOrigin-RevId: 183493603 --- tensorflow/compiler/jit/kernels/xla_launch_op.cc | 5 +++-- .../compiler/xla/service/device_memory_allocator.cc | 2 +- tensorflow/compiler/xla/service/device_memory_allocator.h | 8 ++++---- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc index 1d7bd22e60..17ae2bb25c 100644 --- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc +++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc @@ -45,7 +45,7 @@ namespace tensorflow { // see comment on `AllowsAsynchronousDeallocation()`. class XlaAllocator : public xla::DeviceMemoryAllocator { public: - XlaAllocator(gpu::Platform* platform, OpKernelContext* op_context); + XlaAllocator(const gpu::Platform* platform, OpKernelContext* op_context); ~XlaAllocator() override; xla::StatusOr Allocate(int device_ordinal, uint64 size, bool retry_on_failure) override; @@ -79,7 +79,8 @@ class XlaAllocator : public xla::DeviceMemoryAllocator { std::unordered_map tensors_; }; -XlaAllocator::XlaAllocator(gpu::Platform* platform, OpKernelContext* op_context) +XlaAllocator::XlaAllocator(const gpu::Platform* platform, + OpKernelContext* op_context) : xla::DeviceMemoryAllocator(platform), op_context_(op_context) {} XlaAllocator::~XlaAllocator() = default; diff --git a/tensorflow/compiler/xla/service/device_memory_allocator.cc b/tensorflow/compiler/xla/service/device_memory_allocator.cc index 2e4b0a5230..78e7aa48ac 100644 --- a/tensorflow/compiler/xla/service/device_memory_allocator.cc +++ b/tensorflow/compiler/xla/service/device_memory_allocator.cc @@ -24,7 +24,7 @@ limitations under the License. namespace xla { StreamExecutorMemoryAllocator::StreamExecutorMemoryAllocator( - perftools::gputools::Platform* platform, + const perftools::gputools::Platform* platform, tensorflow::gtl::ArraySlice stream_executors) : DeviceMemoryAllocator(platform), diff --git a/tensorflow/compiler/xla/service/device_memory_allocator.h b/tensorflow/compiler/xla/service/device_memory_allocator.h index 00caefab66..39dfad84c1 100644 --- a/tensorflow/compiler/xla/service/device_memory_allocator.h +++ b/tensorflow/compiler/xla/service/device_memory_allocator.h @@ -33,7 +33,7 @@ class DeviceMemoryAllocator { public: // Parameter platform indicates which platform the allocator allocates memory // on. Must be non-null. - explicit DeviceMemoryAllocator(perftools::gputools::Platform* platform) + explicit DeviceMemoryAllocator(const perftools::gputools::Platform* platform) : platform_(platform) {} virtual ~DeviceMemoryAllocator() {} @@ -49,14 +49,14 @@ class DeviceMemoryAllocator { int device_ordinal, perftools::gputools::DeviceMemoryBase* mem) = 0; // Return the platform that the allocator allocates memory on. - perftools::gputools::Platform* platform() const { return platform_; } + const perftools::gputools::Platform* platform() const { return platform_; } // Can we call Deallocate() as soon as a computation has been scheduled on // a stream, or do we have to wait for the computation to complete first? virtual bool AllowsAsynchronousDeallocation() const = 0; protected: - perftools::gputools::Platform* platform_; + const perftools::gputools::Platform* platform_; }; // Default memory allocator for a platform which uses @@ -64,7 +64,7 @@ class DeviceMemoryAllocator { class StreamExecutorMemoryAllocator : public DeviceMemoryAllocator { public: StreamExecutorMemoryAllocator( - perftools::gputools::Platform* platform, + const perftools::gputools::Platform* platform, tensorflow::gtl::ArraySlice stream_executors); -- GitLab From ec9592211fcd0940336c1f31dcac405709b6a247 Mon Sep 17 00:00:00 2001 From: Martin Wicke Date: Fri, 26 Jan 2018 23:35:51 -0800 Subject: [PATCH 180/423] Adds a deprecated_alias utility function with which to deprecate unmodified aliases. PiperOrigin-RevId: 183495796 --- tensorflow/python/util/deprecation.py | 131 +++++++++++++++++++-- tensorflow/python/util/deprecation_test.py | 50 ++++++++ 2 files changed, 172 insertions(+), 9 deletions(-) diff --git a/tensorflow/python/util/deprecation.py b/tensorflow/python/util/deprecation.py index 2110fc64cf..fbec8fd2d8 100644 --- a/tensorflow/python/util/deprecation.py +++ b/tensorflow/python/util/deprecation.py @@ -39,13 +39,14 @@ _PRINTED_WARNING = {} def _add_deprecated_function_notice_to_docstring(doc, date, instructions): """Adds a deprecation notice to a docstring for deprecated functions.""" + main_text = ['THIS FUNCTION IS DEPRECATED. It will be removed %s.' % + ('in a future version' if date is None else ('after %s' % date))] + if instructions: + main_text.append('Instructions for updating:') return decorator_utils.add_notice_to_docstring( doc, instructions, 'DEPRECATED FUNCTION', - '(deprecated)', [ - 'THIS FUNCTION IS DEPRECATED. It will be removed %s.' % ( - 'in a future version' if date is None else ('after %s' % date)), - 'Instructions for updating:']) + '(deprecated)', main_text) def _add_deprecated_arg_notice_to_docstring(doc, date, instructions): @@ -67,23 +68,135 @@ def _validate_deprecation_args(date, instructions): raise ValueError('Don\'t deprecate things without conversion instructions!') -def _call_location(): +def _call_location(outer=False): """Returns call location given level up from current call.""" frame = tf_inspect.currentframe() if frame: # CPython internals are available, use them for performance. # walk back two frames to get to deprecated function caller. - first_frame = frame.f_back - second_frame = first_frame.f_back - frame = second_frame if second_frame else first_frame + frame = frame.f_back + if frame.f_back: + frame = frame.f_back + if outer and frame.f_back: + frame = frame.f_back return '%s:%d' % (frame.f_code.co_filename, frame.f_lineno) else: # Slow fallback path stack = tf_inspect.stack(0) # 0 avoids generating unused context - entry = stack[2] + entry = stack[3 if outer else 2] return '%s:%d' % (entry[1], entry[2]) +def deprecated_alias(deprecated_name, name, func_or_class, warn_once=True): + """Deprecate a symbol in favor of a new name with identical semantics. + + This function is meant to be used when defining a backwards-compatibility + alias for a symbol which has been moved. For example: + + module1.py: + ```python + class NewNameForClass: pass + ``` + + module2.py: + ```python + import module1 + + DeprecatedNameForClass = deprecated_alias( + deprecated_name='module2.DeprecatedNameForClass', + name='module1.NewNameForClass', + module1.NewNameForClass) + ``` + + This function works for classes and functions. + + For classes, it creates a new class which is functionally identical (it + inherits from the original, and overrides its constructor), but which prints + a deprecation warning when an instance is created. It also adds a deprecation + notice to the class' docstring. + + For functions, it returns a function wrapped by `tf_decorator.make_decorator`. + That function prints a warning when used, and has a deprecation notice in its + docstring. This is more or less equivalent (the deprecation warning has + slightly different text) to writing: + + ```python + @deprecated + def deprecated_alias(original_args): + real_function(original_args) + ``` + + Args: + deprecated_name: The name of the symbol that is being deprecated, to be used + in the warning message. This should be its fully qualified name to avoid + confusion. + name: The name of the symbol that is to be used instead of the deprecated + name. This should be a fully qualified name to avoid confusion. + func_or_class: The (non-deprecated) class or function for which a deprecated + alias should be created. + warn_once: If True (the default), only print a deprecation warning the first + time this function is used, or the class is instantiated. + + Returns: + A wrapped version of `func_or_class` which prints a deprecation warning on + use and has a modified docstring. + """ + if tf_inspect.isclass(func_or_class): + + # Make a new class with __init__ wrapped in a warning. + class NewClass(func_or_class): # pylint: disable=missing-docstring + __doc__ = decorator_utils.add_notice_to_docstring( + func_or_class.__doc__, 'Please use %s instead.' % name, + 'DEPRECATED CLASS', + '(deprecated)', ['THIS CLASS IS DEPRECATED. ' + 'It will be removed in a future version. ']) + __name__ = func_or_class.__name__ + __module__ = _call_location(outer=True) + + def __init__(self, *args, **kwargs): + if hasattr(NewClass.__init__, '__func__'): + # Python 2 + NewClass.__init__.__func__.__doc__ = func_or_class.__init__.__doc__ + else: + # Python 3 + NewClass.__init__.__doc__ = func_or_class.__init__.__doc__ + + if _PRINT_DEPRECATION_WARNINGS: + # We're making the alias as we speak. The original may have other + # aliases, so we cannot use it to check for whether it's already been + # warned about. + if NewClass.__init__ not in _PRINTED_WARNING: + if warn_once: + _PRINTED_WARNING[NewClass.__init__] = True + logging.warning( + 'From %s: The name %s is deprecated. Please use %s instead.\n', + _call_location(), deprecated_name, name) + super(NewClass, self).__init__(*args, **kwargs) + + return NewClass + else: + decorator_utils.validate_callable(func_or_class, 'deprecated') + + # Make a wrapper for the original + @functools.wraps(func_or_class) + def new_func(*args, **kwargs): # pylint: disable=missing-docstring + if _PRINT_DEPRECATION_WARNINGS: + # We're making the alias as we speak. The original may have other + # aliases, so we cannot use it to check for whether it's already been + # warned about. + if new_func not in _PRINTED_WARNING: + if warn_once: + _PRINTED_WARNING[new_func] = True + logging.warning( + 'From %s: The name %s is deprecated. Please use %s instead.\n', + _call_location(), deprecated_name, name) + return func_or_class(*args, **kwargs) + return tf_decorator.make_decorator( + func_or_class, new_func, 'deprecated', + _add_deprecated_function_notice_to_docstring( + func_or_class.__doc__, None, 'Please use %s instead.' % name)) + + def deprecated(date, instructions, warn_once=True): """Decorator for marking functions or methods deprecated. diff --git a/tensorflow/python/util/deprecation_test.py b/tensorflow/python/util/deprecation_test.py index e61edb5cfa..bdd0bc48d2 100644 --- a/tensorflow/python/util/deprecation_test.py +++ b/tensorflow/python/util/deprecation_test.py @@ -24,6 +24,56 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import deprecation +class DeprecatedAliasTest(test.TestCase): + + @test.mock.patch.object(logging, "warning", autospec=True) + def test_function_alias(self, mock_warning): + deprecated_func = deprecation.deprecated_alias("deprecated.func", + "real.func", + logging.error) + + logging.error("fake error logged") + self.assertEqual(0, mock_warning.call_count) + deprecated_func("FAKE ERROR!") + self.assertEqual(1, mock_warning.call_count) + # Make sure the error points to the right file. + self.assertRegexpMatches(mock_warning.call_args[0][1], + r"deprecation_test\.py:") + deprecated_func("ANOTHER FAKE ERROR!") + self.assertEqual(1, mock_warning.call_count) + + @test.mock.patch.object(logging, "warning", autospec=True) + def test_class_alias(self, mock_warning): + class MyClass(object): + """My docstring.""" + + init_args = [] + + def __init__(self, arg): + MyClass.init_args.append(arg) + + deprecated_cls = deprecation.deprecated_alias("deprecated.cls", + "real.cls", + MyClass) + + print(deprecated_cls.__name__) + print(deprecated_cls.__module__) + print(deprecated_cls.__doc__) + + MyClass("test") + self.assertEqual(0, mock_warning.call_count) + deprecated_cls("deprecated") + self.assertEqual(1, mock_warning.call_count) + # Make sure the error points to the right file. + self.assertRegexpMatches(mock_warning.call_args[0][1], + r"deprecation_test\.py:") + deprecated_cls("deprecated again") + self.assertEqual(1, mock_warning.call_count) + + self.assertEqual(["test", "deprecated", "deprecated again"], + MyClass.init_args) + + class DeprecationTest(test.TestCase): @test.mock.patch.object(logging, "warning", autospec=True) -- GitLab From 88cdf2c2bf336e9d7c418d824097aa918ef0274a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 27 Jan 2018 05:12:06 -0800 Subject: [PATCH 181/423] Remove unused BUILD dependencies PiperOrigin-RevId: 183514731 --- tensorflow/cc/tools/BUILD | 1 - tensorflow/compiler/xla/service/BUILD | 2 -- tensorflow/compiler/xla/service/gpu/BUILD | 1 - tensorflow/contrib/tensorboard/db/BUILD | 1 - tensorflow/core/BUILD | 2 -- tensorflow/core/kernels/data/BUILD | 2 -- 6 files changed, 9 deletions(-) diff --git a/tensorflow/cc/tools/BUILD b/tensorflow/cc/tools/BUILD index 0a7c37383f..97f66e79b8 100644 --- a/tensorflow/cc/tools/BUILD +++ b/tensorflow/cc/tools/BUILD @@ -23,7 +23,6 @@ cc_library( "//tensorflow/core:core_cpu", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", - "//tensorflow/core:tensorflow", ], ) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 987367fc68..4b5590f5c4 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -1112,8 +1112,6 @@ cc_library( ":hlo", ":hlo_evaluator", ":hlo_pass", - ":tuple_util", - ":while_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/core:lib", ], diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 3c3328b9cd..80c2eed109 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -514,7 +514,6 @@ cc_library( "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_pass", - "@llvm//:core", ], ) diff --git a/tensorflow/contrib/tensorboard/db/BUILD b/tensorflow/contrib/tensorboard/db/BUILD index 6ff5a9e2b1..4175d8adb5 100644 --- a/tensorflow/contrib/tensorboard/db/BUILD +++ b/tensorflow/contrib/tensorboard/db/BUILD @@ -40,7 +40,6 @@ cc_library( hdrs = ["summary_db_writer.h"], copts = tf_copts(), deps = [ - ":schema", ":summary_converter", "//tensorflow/core:framework", "//tensorflow/core:lib", diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 29c515121e..455da05738 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -381,7 +381,6 @@ cc_library( srcs = ["platform/stacktrace_handler.cc"], hdrs = ["platform/stacktrace_handler.h"], deps = [ - ":abi", ":lib", ":lib_platform", ], @@ -2413,7 +2412,6 @@ cc_library( deps = [ ":lib", ":lib_internal", - ":stacktrace_handler", ":test", # buildcleaner: keep "//tensorflow/core/platform/default/build_config:test_main", ], diff --git a/tensorflow/core/kernels/data/BUILD b/tensorflow/core/kernels/data/BUILD index 500ee7b43f..45505ef716 100644 --- a/tensorflow/core/kernels/data/BUILD +++ b/tensorflow/core/kernels/data/BUILD @@ -81,9 +81,7 @@ cc_library( "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", - "//tensorflow/core:proto_text", "//tensorflow/core:protos_all_cc", - "//tensorflow/core:session_options", "//tensorflow/core/kernels:variable_ops", ], ) -- GitLab From f68404bdd10a4b6ef2a50439efac4614de024636 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 27 Jan 2018 15:01:31 -0800 Subject: [PATCH 182/423] Update docs for installing CUDA/CUDNN (#16495) * Update docs for installing CUDA/CUDNN This fix addresses the issue raised in 16479 where CUDA/CUDNN versions from the docs do not match TensorFlow v1.5.0. From the Dockerfile and docker images ENV, the version of CUDA/CUDNN for TensorFlow v1.5.0: ``` CUDA_VERSION 9.0.176 CUDNN_VERSION 7.0.5.15 ``` This fix updates the doc so that CUDA version is changed from `8.0` -> `9.0`, CUDNN version is changed from `6.0` -> `7.0`. This fix fixes 16479. Signed-off-by: Yong Tang --- tensorflow/docs_src/install/install_linux.md | 6 +++--- tensorflow/docs_src/install/install_sources.md | 10 +++++----- tensorflow/docs_src/install/install_windows.md | 6 +++--- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 03f12dff08..7289224572 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -31,13 +31,13 @@ If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system: - * CUDA® Toolkit 8.0. For details, see + * CUDA® Toolkit 9.0. For details, see [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A). Ensure that you append the relevant Cuda pathnames to the `LD_LIBRARY_PATH` environment variable as described in the NVIDIA documentation. - * The NVIDIA drivers associated with CUDA Toolkit 8.0. - * cuDNN v6.0. For details, see + * The NVIDIA drivers associated with CUDA Toolkit 9.0. + * cuDNN v7.0. For details, see [NVIDIA's documentation](https://developer.nvidia.com/cudnn). Ensure that you create the `CUDA_HOME` environment variable as described in the NVIDIA documentation. diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index f494cc7a7c..0d99f9a47d 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -133,7 +133,7 @@ The following NVIDIA hardware must be installed on your system: The following NVIDIA software must be installed on your system: - * NVIDIA's Cuda Toolkit (>= 7.0). We recommend version 8.0. + * NVIDIA's Cuda Toolkit (>= 7.0). We recommend version 9.0. For details, see [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A). Ensure that you append the relevant Cuda pathnames to the @@ -291,11 +291,11 @@ 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 -Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: 8.0 -Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: +Please specify the Cuda SDK version you want to use, e.g. 7.0. [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 6.0]: 6 -Please specify the location where cuDNN 6 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: +Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7 +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. 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. diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 8d0eb7966f..86a111c2ec 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -30,13 +30,13 @@ If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system: - * CUDA® Toolkit 8.0. For details, see + * CUDA® Toolkit 9.0. For details, see [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/) Ensure that you append the relevant Cuda pathnames to the `%PATH%` environment variable as described in the NVIDIA documentation. - * The NVIDIA drivers associated with CUDA Toolkit 8.0. - * cuDNN v6.0. For details, see + * The NVIDIA drivers associated with CUDA Toolkit 9.0. + * cuDNN v7.0. For details, see [NVIDIA's documentation](https://developer.nvidia.com/cudnn). Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed -- GitLab From f1f4169e42320419b0a5df39c454ca39a57f5a42 Mon Sep 17 00:00:00 2001 From: Zhixian Yan Date: Sat, 27 Jan 2018 17:38:59 -0800 Subject: [PATCH 183/423] Internal Change PiperOrigin-RevId: 183551521 --- .../graph_transformations/identify_lstm.cc | 18 --------------- tensorflow/contrib/lite/toco/tflite/BUILD | 1 + tensorflow/contrib/lite/toco/tflite/import.cc | 13 +++++++++-- tensorflow/contrib/lite/toco/tooling_util.cc | 23 +++++++++++++++++-- tensorflow/contrib/lite/toco/tooling_util.h | 3 +++ 5 files changed, 36 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc index 082820fddc..c363b93394 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_lstm.cc @@ -16,7 +16,6 @@ limitations under the License. #include #include -#include "absl/strings/string_view.h" #include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" #include "tensorflow/contrib/lite/toco/model.h" #include "tensorflow/contrib/lite/toco/tooling_util.h" @@ -202,23 +201,6 @@ bool MatchOperatorInputs(const Operator& op, const Model& model, return true; } -absl::string_view FindLongestCommonPrefix(absl::string_view a, - absl::string_view b) { - if (a.empty() || b.empty()) return absl::string_view(); - - const char* pa = a.data(); - const char* pb = b.data(); - size_t count = 0; - const ssize_t limit = std::min(a.size(), b.size()); - while (count < limit && *pa == *pb) { - ++pa; - ++pb; - ++count; - } - - return absl::string_view(a.data(), count); -} - } // namespace bool IdentifyLstmCell::Run(Model* model, std::size_t op_index) { diff --git a/tensorflow/contrib/lite/toco/tflite/BUILD b/tensorflow/contrib/lite/toco/tflite/BUILD index 72c9266564..a2b8145a67 100644 --- a/tensorflow/contrib/lite/toco/tflite/BUILD +++ b/tensorflow/contrib/lite/toco/tflite/BUILD @@ -117,6 +117,7 @@ cc_library( ":types", "//tensorflow/contrib/lite/schema:schema_fbs", "//tensorflow/contrib/lite/toco:model", + "//tensorflow/contrib/lite/toco:tooling_util", "@flatbuffers", ], ) diff --git a/tensorflow/contrib/lite/toco/tflite/import.cc b/tensorflow/contrib/lite/toco/tflite/import.cc index bbf201fd28..5b1ab514b2 100644 --- a/tensorflow/contrib/lite/toco/tflite/import.cc +++ b/tensorflow/contrib/lite/toco/tflite/import.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/contrib/lite/schema/schema_generated.h" #include "tensorflow/contrib/lite/toco/tflite/operator.h" #include "tensorflow/contrib/lite/toco/tflite/types.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" namespace toco { @@ -119,8 +120,16 @@ void ImportOperators( auto inputs = input_op->inputs(); for (int i = 0; i < inputs->Length(); i++) { auto input_index = inputs->Get(i); - const string& input_name = tensors_table.at(input_index); - op->inputs.push_back(input_name); + // input_index == -1 indicates optional tensor. + if (input_index != -1) { + const string& input_name = tensors_table.at(input_index); + op->inputs.push_back(input_name); + } else { + const string& tensor_name = + toco::AvailableArrayName(*model, "OptionalTensor"); + model->CreateOptionalArray(tensor_name); + op->inputs.push_back(tensor_name); + } } auto outputs = input_op->outputs(); for (int i = 0; i < outputs->Length(); i++) { diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 3728d48659..08d9ac3aff 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -33,6 +33,24 @@ limitations under the License. namespace toco { +// Find the longest common prefix of two strings. +absl::string_view FindLongestCommonPrefix(absl::string_view a, + absl::string_view b) { + if (a.empty() || b.empty()) return absl::string_view(); + + const char* pa = a.data(); + const char* pb = b.data(); + size_t count = 0; + const size_t limit = std::min(a.size(), b.size()); + while (count < limit && *pa == *pb) { + ++pa; + ++pb; + ++count; + } + + return absl::string_view(a.data(), count); +} + string LogName(const Operator& op) { const string& opname = HelpfulOperatorTypeName(op); if (op.outputs.empty()) { @@ -1314,13 +1332,14 @@ bool IsAllocatableTransientArray(const Model& model, const string& array_name) { } string AvailableArrayName(const Model& model, const string& name) { - if (!model.HasArray(name) && !model.optional_arrays.count(name)) { + if (!model.HasArray(name) && !model.IsOptionalArray(name)) { return name; } const int kNumSuffixesToTry = 1000; for (int i = 0; i < kNumSuffixesToTry; i++) { const string& name_with_suffix = toco::port::StringF("%s_%d", name, i); - if (!model.HasArray(name_with_suffix)) { + if (!model.HasArray(name_with_suffix) && + !model.IsOptionalArray(name_with_suffix)) { return name_with_suffix; } } diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index 2ac51c7e5b..4051ba3576 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -23,6 +23,7 @@ limitations under the License. #include #include +#include "absl/strings/string_view.h" #include "tensorflow/core/platform/logging.h" #if TOCO_SUPPORT_PORTABLE_PROTOS #include "third_party/protobuf/src/google/protobuf/text_format.h" @@ -49,6 +50,8 @@ namespace toco { constexpr int kLogLevelModelChanged = 1; constexpr int kLogLevelModelUnchanged = 2; +absl::string_view FindLongestCommonPrefix(absl::string_view a, + absl::string_view b); string LogName(const Operator& op); bool IsInputArray(const Model& model, const string& name); -- GitLab From 77e2f31b40228f11353654a5fde4d93f7614a11d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 27 Jan 2018 19:59:13 -0800 Subject: [PATCH 184/423] Fix use of uninitialied value. PiperOrigin-RevId: 183558128 --- tensorflow/contrib/lite/kernels/kernel_util_test.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/lite/kernels/kernel_util_test.cc b/tensorflow/contrib/lite/kernels/kernel_util_test.cc index 63a317f338..c65b68970f 100644 --- a/tensorflow/contrib/lite/kernels/kernel_util_test.cc +++ b/tensorflow/contrib/lite/kernels/kernel_util_test.cc @@ -30,6 +30,8 @@ class KernelUtilTest : public ::testing::Test { tensor1_.dims = nullptr; tensor2_.dims = nullptr; + tensor1_.allocation_type = kTfLiteMmapRo; + tensor2_.allocation_type = kTfLiteMmapRo; } ~KernelUtilTest() { TfLiteTensorFree(&tensor1_); -- GitLab From 81acc2e6c56af450cd3cd6a5eb602f862e8338bb Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Sun, 28 Jan 2018 10:12:55 -0800 Subject: [PATCH 185/423] [XLA] Reset ShapeVisitor's state between runs of the verifier. We create the ShapeVisitor once per pass pipeline. Without this change, after our ShapeVisitor has checked an instruction, it will never again check that instruction *or any of its transitive inputs*. Yikes. PiperOrigin-RevId: 183593437 --- .../compiler/xla/service/hlo_verifier.cc | 3 ++- .../compiler/xla/service/hlo_verifier.h | 17 ++++++++---- .../compiler/xla/service/hlo_verifier_test.cc | 26 +++++++++++++++++++ 3 files changed, 40 insertions(+), 6 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index 6e46f945e0..04d4656546 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -687,7 +687,8 @@ StatusOr HloVerifier::Run(HloModule* module) { instructions[instruction->name()] = instruction; } - TF_RETURN_IF_ERROR(computation->Accept(shape_verifier_.get())); + std::unique_ptr shape_verifier = shape_verifier_factory_(); + TF_RETURN_IF_ERROR(computation->Accept(shape_verifier.get())); } return false; diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h index 5a1d864e03..26d53dec1e 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.h +++ b/tensorflow/compiler/xla/service/hlo_verifier.h @@ -106,10 +106,14 @@ class ShapeVerifier : public DfsHloVisitor { class HloVerifier : public HloPassInterface { public: // Uses standard shape inference. - explicit HloVerifier() : shape_verifier_(MakeUnique()) {} + explicit HloVerifier() + : shape_verifier_factory_([] { return MakeUnique(); }) {} + // Uses custom shape verification. - explicit HloVerifier(std::unique_ptr shape_verifier) - : shape_verifier_(std::move(shape_verifier)) {} + explicit HloVerifier( + std::function()> shape_verifier_factory) + : shape_verifier_factory_(std::move(shape_verifier_factory)) {} + ~HloVerifier() override = default; tensorflow::StringPiece name() const override { return "verifier"; } @@ -121,8 +125,11 @@ class HloVerifier : public HloPassInterface { // CHECKs various invariants of a fusion instruction. Status CheckFusionInstruction(HloInstruction* fusion) const; - // Verifies shapes match inferred expectations. - std::unique_ptr shape_verifier_; + // Creates a ShapeVerifier that checks that shapes match inferred + // expectations. This is a factory function because ShapeVerifier, Note that + // ShapeVerifier, being a DfsHloVisitor, is stateful. We want a clean object + // for each run of the verifier. + std::function()> shape_verifier_factory_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_verifier_test.cc b/tensorflow/compiler/xla/service/hlo_verifier_test.cc index 2a3b55decc..c92db0be14 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier_test.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier_test.cc @@ -97,5 +97,31 @@ TEST_F(HloVerifierTest, DifferentOperandParents) { HasSubstr("is in a different computation")); } +TEST_F(HloVerifierTest, ResetsShapeVerifierState) { + HloComputation::Builder builder(TestName()); + Shape s1 = ShapeUtil::MakeShape(F32, {1}); + Shape s2 = ShapeUtil::MakeShape(F32, {2}); + + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter(0, s1, "param")); + + // Create an add instruction with the incorrect shape. + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(s2, HloOpcode::kAdd, param, param)); + + // In order to trigger the bug we're checking for, the instruction with the + // bad shape can't be the root of the computation. + builder.AddInstruction( + HloInstruction::CreateBinary(s2, HloOpcode::kMultiply, add, add)); + + auto module = CreateNewModule(); + module->AddEntryComputation(builder.Build()); + + // Run the verifier twice. It should fail both times, because it shouldn't + // carry state in its DFS visitor between runs. + EXPECT_FALSE(verifier().Run(module.get()).status().ok()); + EXPECT_FALSE(verifier().Run(module.get()).status().ok()); +} + } // namespace } // namespace xla -- GitLab From 74b5a4cf30fc1f0fa24a41d212f4aa03dcefa990 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Sun, 28 Jan 2018 10:49:33 -0800 Subject: [PATCH 186/423] [XLA] Show layouts of tuple-shaped instructions (other than kTuple) in graphs. For example the batch-norm ops return a tuple, and those values' layouts are significant. We still hide the layout on tuples, since this can be noisy. PiperOrigin-RevId: 183594622 --- .../compiler/xla/service/hlo_graph_dumper.cc | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index f7c6435002..c744c8ed81 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -1063,14 +1063,19 @@ string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) { // node -- there the shape and layout is present in the output node. if (instr->opcode() != HloOpcode::kFusion || !ShouldShowFusionSubcomputation(instr)) { - string instr_shape = ShapeUtil::HumanString(instr->shape()); - - // Show layout of non-tuple shapes with more than one dimension. - if (LayoutUtil::HasLayout(instr->shape()) && - instr->shape().dimensions_size() > 1 && - !ShapeUtil::IsTuple(instr->shape())) { - StrAppend(&instr_shape, "{", - Join(LayoutUtil::MinorToMajor(instr->shape()), ","), "}"); + // Show layout of instructions with more than one dimension. Don't show + // layout on tuples or tensors with just one dimension (which only have one + // possible layout) to avoid visual noise. + bool shape_is_multidim = false; + ShapeUtil::ForEachSubshape(instr->shape(), + [&](const Shape& s, const ShapeIndex&) { + shape_is_multidim |= s.dimensions_size() > 1; + }); + string instr_shape; + if (instr->opcode() != HloOpcode::kTuple && shape_is_multidim) { + instr_shape = ShapeUtil::HumanStringWithLayout(instr->shape()); + } else { + instr_shape = ShapeUtil::HumanString(instr->shape()); } // Some instructions have giant tuples as their shapes, so truncate the -- GitLab From a6ed38feb42021c7fdf4a76587c1bbf75f3248d1 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Sun, 28 Jan 2018 11:21:58 -0800 Subject: [PATCH 187/423] [XLA] Set layout of GTE instructions inside fusion nodes. Other than the root and parameters of a fusion computation, most other instructions in a fusion computation don't have a layout. GTEs are an exception; they should inherit their layout from their operand, which must be another GTE or a parameter. Previously LayoutAssignment left GTEs alone, assuming they came in with the right layout. But this isn't correct, and in fact LayoutAssignment cleared the layouts of every non-fused instruction before assigning them for exactly this reason. If we'd done the same to fused instructions, it would have caught this bug, so we make that change here as well. (We simplify this loop by removing the check for kOutfeed -- outfeeds do not produce a result, so there's no shape to keep.) PiperOrigin-RevId: 183595627 --- tensorflow/compiler/xla/service/BUILD | 2 + .../compiler/xla/service/layout_assignment.cc | 50 +++++++------ .../xla/service/layout_assignment_test.cc | 71 +++++++++++++++++++ 3 files changed, 103 insertions(+), 20 deletions(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 4b5590f5c4..bdccfad0d0 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -1853,7 +1853,9 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:test_utils", + "//tensorflow/compiler/xla/tools/parser:hlo_parser", "//tensorflow/core:lib", + "//tensorflow/core:test", ], ) diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index bbea6bee56..5413b95cfb 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -1236,7 +1236,8 @@ Status CopyOperandIfLayoutsDiffer(const ShapeLayout& operand_layout, // instruction itself. Status SetFusionLayouts(HloInstruction* fusion) { TF_RET_CHECK(fusion->opcode() == HloOpcode::kFusion); - for (auto* fused_instruction : fusion->fused_instructions()) { + for (auto* fused_instruction : + fusion->fused_instructions_computation()->MakeInstructionPostOrder()) { if (fused_instruction->opcode() == HloOpcode::kParameter) { const HloInstruction* fusion_operand = fusion->operand(fused_instruction->parameter_number()); @@ -1251,11 +1252,22 @@ Status SetFusionLayouts(HloInstruction* fusion) { ShapeUtil::Compatible(fusion->shape(), fused_instruction->shape())); TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( fusion->shape(), fused_instruction->mutable_shape())); - } else if (fused_instruction->opcode() != HloOpcode::kConstant && - fused_instruction->opcode() != HloOpcode::kGetTupleElement && - fused_instruction->opcode() != HloOpcode::kInfeed) { - // Internal fused instructions with the exception of constants - // and infeed need no layout. + } else if (fused_instruction->opcode() == HloOpcode::kGetTupleElement) { + // A GTE inherits its layout from its operand (which should ultimately be + // a parameter). + TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( + fused_instruction->operand(0)->shape().tuple_shapes( + fused_instruction->tuple_index()), + fused_instruction->mutable_shape())); + } else if (fused_instruction->opcode() == HloOpcode::kConstant) { + // Give constants the layout of their literal. + TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes( + fused_instruction->literal().shape(), + fused_instruction->mutable_shape())); + } else if (fused_instruction->opcode() == HloOpcode::kInfeed) { + // Nop; leave the infeed layout alone. + } else { + // Other instructions don't have layouts inside of fusion nodes. LayoutUtil::ClearLayout(fused_instruction->mutable_shape()); } } @@ -1367,20 +1379,6 @@ Status LayoutAssignment::RunOnComputation( << ")"; VLOG(2) << " ComputationLayout = " << computation_layout.ToString(); - // Clear existing layouts of the instructions. All layouts must be assigned by - // the LayoutAssignment pass, except for Infeed, Outfeed, Parameters and the - // computation result. The latter two are specified in computation_layout, so - // we only need to keep the existing layouts for Infeed and Outfeed. Clearing - // the layouts here avoids hiding potential bugs in the layout assignment pass - // that may accidently use the existing layout. - for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() == HloOpcode::kInfeed || - instruction->opcode() == HloOpcode::kOutfeed) { - continue; - } - LayoutUtil::ClearLayout(instruction->mutable_shape()); - } - // Construct LayoutConstraints with all layout constraints of the computation. LayoutConstraints constraints(points_to_analysis, computation); @@ -1458,6 +1456,18 @@ StatusOr LayoutAssignment::Run(HloModule* module) { // is handled before its caller computation. This ensures that the layout of // all callers of a computation will agree. for (auto* computation : module->MakeComputationPostOrder()) { + // Clear existing layouts of the instructions. All layouts must be assigned + // by the LayoutAssignment pass, except for those on infeeds, parameters, + // and the computation result. The latter two are specified in + // computation_layout, so we only need to keep the existing layouts for + // infeeds. Clearing the layouts here avoids hiding potential bugs in the + // layout assignment pass that may accidently use the existing layout. + for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() != HloOpcode::kInfeed) { + LayoutUtil::ClearLayout(instruction->mutable_shape()); + } + } + if (computation == module->entry_computation()) { TF_RETURN_IF_ERROR(RunOnComputation( *entry_computation_layout_, *points_to_analysis, diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc index d51c0d1dfb..e269a13459 100644 --- a/tensorflow/compiler/xla/service/layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc @@ -35,9 +35,11 @@ limitations under the License. #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/tests/test_utils.h" +#include "tensorflow/compiler/xla/tools/parser/hlo_parser.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/lib/gtl/array_slice.h" namespace op = xla::testing::opcode_matchers; @@ -587,5 +589,74 @@ TEST_F(LayoutAssignmentTest, TransposeToBitcastToUser) { EXPECT_TRUE(ShapeUtil::TransposeIsBitcast(transpose->operand(0)->shape(), transpose->shape(), {2, 3, 0, 1})); } + +// A GTE inside of a fusion node inherits the layout of its operand (which +// should, if we keep following operands, eventually be a parameter). +TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) { + const char* module_str = R"( + HloModule test_module + + fused_computation { + fparam = (f32[2,2,2], (f32[2,2,2], f32[2,2,2])) parameter(0) + gte0 = f32[2,2,2] get-tuple-element(fparam), index=0 + gte1 = (f32[2,2,2], f32[2,2,2]) get-tuple-element(fparam), index=1 + gte1a = f32[2,2,2] get-tuple-element(gte1), index=0 + gte1b = f32[2,2,2] get-tuple-element(gte1), index=1 + add = f32[2,2,2] add(gte1a, gte1b) + ROOT fresult = f32[2,2,2] add(gte0, add) + } + + ENTRY entry_computation { + param = (f32[2,2,2], (f32[2,2,2], f32[2,2,2])) parameter(0) + ROOT fusion = + f32[2,2,2] fusion(param), kind=kLoop, calls=fused_computation + } + )"; + + auto module = tools::Parse(module_str).ValueOrDie(); + ComputationLayout computation_layout( + module->entry_computation()->ComputeProgramShape()); + Shape param_shape = ShapeUtil::MakeTupleShape( + {ShapeUtil::MakeShapeWithLayout(F32, {2, 2, 2}, {0, 1, 2}), + ShapeUtil::MakeTupleShape({ + ShapeUtil::MakeShapeWithLayout(F32, {2, 2, 2}, {1, 2, 0}), + ShapeUtil::MakeShapeWithLayout(F32, {2, 2, 2}, {2, 0, 1}), + })}); + TF_ASSERT_OK( + computation_layout.mutable_parameter_layout(0)->CopyLayoutFromShape( + param_shape)); + computation_layout.mutable_result_layout()->ResetLayout( + LayoutUtil::MakeLayout({2, 1, 0})); + AssignLayouts(module.get(), &computation_layout); + + HloComputation* fused_computation = *std::find_if( + module->computations().begin(), module->computations().end(), + [](const HloComputation* c) { return c->name() == "fused_computation"; }); + + auto fused_instr = [&](const string& name) { + auto it = std::find_if( + fused_computation->instructions().begin(), + fused_computation->instructions().end(), + [&](const HloInstruction* i) { return i->name() == name; }); + CHECK(it != fused_computation->instructions().end()); + return *it; + }; + + EXPECT_THAT(fused_instr("gte0")->shape().layout().minor_to_major(), + ElementsAre(0, 1, 2)); + EXPECT_THAT( + fused_instr("gte1")->shape().tuple_shapes(0).layout().minor_to_major(), + ElementsAre(1, 2, 0)); + EXPECT_THAT( + fused_instr("gte1")->shape().tuple_shapes(1).layout().minor_to_major(), + ElementsAre(2, 0, 1)); + EXPECT_THAT(fused_instr("gte1a")->shape().layout().minor_to_major(), + ElementsAre(1, 2, 0)); + EXPECT_THAT(fused_instr("gte1b")->shape().layout().minor_to_major(), + ElementsAre(2, 0, 1)); + EXPECT_THAT(fused_instr("fresult")->shape().layout().minor_to_major(), + ElementsAre(2, 1, 0)); +} + } // namespace } // namespace xla -- GitLab From 7d7dce16b8e7aef53467d8eb08d4249ef6cd71fb Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Mon, 29 Jan 2018 07:50:48 +0900 Subject: [PATCH 188/423] Fix typo (#16509) * fix typos --- tensorflow/compiler/xla/tools/parser/hlo_parser.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc index 42e7f91f26..d9c4d094b8 100644 --- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc +++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc @@ -2173,7 +2173,7 @@ bool HloParser::ParseConvolutionDimensionNumbers( // // {[2:3:4], [5:6:7], [8:9]} // -// The the parsed result will be: +// The parsed result will be: // // {/*starts=*/{2, 5, 8}, /*limits=*/{3, 6, 9}, /*strides=*/{4, 7, 1}} // -- GitLab From ad37b47be884b1b897cc4505b856c0f9c51591a1 Mon Sep 17 00:00:00 2001 From: Shanqing Cai Date: Mon, 29 Jan 2018 07:11:29 -0800 Subject: [PATCH 189/423] tfdbg: add tensorboard debugger plugin option to three existing examples PiperOrigin-RevId: 183661140 --- .../python/debug/examples/debug_fibonacci.py | 17 ++++++++++++++++- .../python/debug/examples/debug_mnist.py | 17 ++++++++++++++++- .../debug/examples/debug_tflearn_iris.py | 18 ++++++++++++++++-- 3 files changed, 48 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/debug/examples/debug_fibonacci.py b/tensorflow/python/debug/examples/debug_fibonacci.py index 704dbda357..3821b393ec 100644 --- a/tensorflow/python/debug/examples/debug_fibonacci.py +++ b/tensorflow/python/debug/examples/debug_fibonacci.py @@ -44,6 +44,10 @@ def main(_): sess.run(tf.global_variables_initializer()) # Wrap the TensorFlow Session object for debugging. + if FLAGS.debug and FLAGS.tensorboard_debug_address: + raise ValueError( + "The --debug and --tensorboard_debug_address flags are mutually " + "exclusive.") if FLAGS.debug: sess = tf_debug.LocalCLIDebugWrapperSession(sess) @@ -52,6 +56,9 @@ def main(_): sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan) sess.add_tensor_filter("has_negative", has_negative) + elif FLAGS.tensorboard_debug_address: + sess = tf_debug.TensorBoardDebugWrapperSession( + sess, FLAGS.tensorboard_debug_address) print("Fibonacci number at position %d:\n%s" % (FLAGS.length, sess.run(n1))) @@ -82,7 +89,15 @@ if __name__ == "__main__": "--debug", dest="debug", action="store_true", - help="Use TensorFlow Debugger (tfdbg).") + help="Use TensorFlow Debugger (tfdbg). Mutually exclusive with the " + "--tensorboard_debug_address flag.") + parser.add_argument( + "--tensorboard_debug_address", + type=str, + default=None, + help="Connect to the TensorBoard Debugger Plugin backend specified by " + "the gRPC address (e.g., localhost:1234). Mutually exclusive with the " + "--debug flag.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/debug/examples/debug_mnist.py b/tensorflow/python/debug/examples/debug_mnist.py index 0a6dbf311d..ab1c90371c 100644 --- a/tensorflow/python/debug/examples/debug_mnist.py +++ b/tensorflow/python/debug/examples/debug_mnist.py @@ -120,8 +120,15 @@ def main(_): sess.run(tf.global_variables_initializer()) + if FLAGS.debug and FLAGS.tensorboard_debug_address: + raise ValueError( + "The --debug and --tensorboard_debug_address flags are mutually " + "exclusive.") if FLAGS.debug: sess = tf_debug.LocalCLIDebugWrapperSession(sess, ui_type=FLAGS.ui_type) + elif FLAGS.tensorboard_debug_address: + sess = tf_debug.TensorBoardDebugWrapperSession( + sess, FLAGS.tensorboard_debug_address) # Add this point, sess is a debug wrapper around the actual Session if # FLAGS.debug is true. In that case, calling run() will launch the CLI. @@ -173,6 +180,14 @@ if __name__ == "__main__": nargs="?", const=True, default=False, - help="Use debugger to track down bad values during training") + help="Use debugger to track down bad values during training. " + "Mutually exclusive with the --tensorboard_debug_address flag.") + parser.add_argument( + "--tensorboard_debug_address", + type=str, + default=None, + help="Connect to the TensorBoard Debugger Plugin backend specified by " + "the gRPC address (e.g., localhost:1234). Mutually exclusive with the " + "--debug flag.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/debug/examples/debug_tflearn_iris.py b/tensorflow/python/debug/examples/debug_tflearn_iris.py index 92314d8dd9..4f4666ee4f 100644 --- a/tensorflow/python/debug/examples/debug_tflearn_iris.py +++ b/tensorflow/python/debug/examples/debug_tflearn_iris.py @@ -110,10 +110,16 @@ def main(_): model_dir=model_dir) hooks = None + if FLAGS.debug and FLAGS.tensorboard_debug_address: + raise ValueError( + "The --debug and --tensorboard_debug_address flags are mutually " + "exclusive.") if FLAGS.debug: debug_hook = tf_debug.LocalCLIDebugHook(ui_type=FLAGS.ui_type, dump_root=FLAGS.dump_root) - hooks = [debug_hook] + elif FLAGS.tensorboard_debug_address: + debug_hook = tf_debug.TensorBoardDebugHook(FLAGS.tensorboard_debug_address) + hooks = [debug_hook] if not FLAGS.use_experiment: # Fit model. @@ -185,11 +191,19 @@ if __name__ == "__main__": nargs="?", const=True, default=False, - help="Use debugger to track down bad values during training") + help="Use debugger to track down bad values during training. " + "Mutually exclusive with the --tensorboard_debug_address flag.") parser.add_argument( "--dump_root", type=str, default="", help="Optional custom root directory for temporary debug dump data") + parser.add_argument( + "--tensorboard_debug_address", + type=str, + default=None, + help="Connect to the TensorBoard Debugger Plugin backend specified by " + "the gRPC address (e.g., localhost:1234). Mutually exclusive with the " + "--debug flag.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) -- GitLab From 0a53ad466e2fe001b80b8addde9a3465f7d5357f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 07:27:48 -0800 Subject: [PATCH 190/423] Remove unused class members PiperOrigin-RevId: 183662473 --- tensorflow/core/grappler/clusters/single_machine.cc | 5 +---- tensorflow/core/grappler/clusters/single_machine.h | 1 - tensorflow/core/grappler/optimizers/dependency_optimizer.h | 6 ++---- tensorflow/core/kernels/data/iterator_ops.cc | 6 ++---- tensorflow/core/kernels/lmdb_reader_op.cc | 4 +--- 5 files changed, 6 insertions(+), 16 deletions(-) diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc index 2712c5b679..862ce4ae88 100644 --- a/tensorflow/core/grappler/clusters/single_machine.cc +++ b/tensorflow/core/grappler/clusters/single_machine.cc @@ -36,10 +36,7 @@ namespace grappler { static std::atomic already_provisioned(false); SingleMachine::SingleMachine(int timeout_s, int num_cpu_cores, int num_gpus) - : Cluster(timeout_s), - num_gpus_(num_gpus), - expected_init_time_s_(0), - closing_(false) { + : Cluster(timeout_s), expected_init_time_s_(0), closing_(false) { VLOG(1) << "Number of CPU cores: " << num_cpu_cores << " Number of GPUs: " << num_gpus; thread_pool_.reset(new thread::ThreadPool( diff --git a/tensorflow/core/grappler/clusters/single_machine.h b/tensorflow/core/grappler/clusters/single_machine.h index a254f72f0c..90d6a04cab 100644 --- a/tensorflow/core/grappler/clusters/single_machine.h +++ b/tensorflow/core/grappler/clusters/single_machine.h @@ -64,7 +64,6 @@ class SingleMachine : public Cluster { Status ClearAllocatorStats() const; - const int num_gpus_; std::unique_ptr session_; std::vector queue_runner_defs_; string last_graph_id_; diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.h b/tensorflow/core/grappler/optimizers/dependency_optimizer.h index 02d8a0f32a..cfc5324439 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.h +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.h @@ -29,9 +29,8 @@ namespace grappler { // optimizations, such as removing nodes that are effectively noops. class DependencyOptimizer : public GraphOptimizer { public: - DependencyOptimizer() : opt_level_(RewriterConfig::ON) {} - explicit DependencyOptimizer(RewriterConfig::Toggle opt_level) - : opt_level_(opt_level) {} + DependencyOptimizer() {} + explicit DependencyOptimizer(RewriterConfig::Toggle /*unused*/) {} ~DependencyOptimizer() override {} string name() const override { return "dependency_optimizer"; }; @@ -62,7 +61,6 @@ class DependencyOptimizer : public GraphOptimizer { // Main driver of dependency optimizations. Status OptimizeDependencies(); - RewriterConfig::Toggle opt_level_; bool fetch_nodes_known_; std::unordered_set nodes_to_preserve_; std::unique_ptr node_map_; diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index ca22f10a85..b37bd672ad 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -82,7 +82,7 @@ class IteratorResource : public ResourceBase { public: IteratorResource(const DataTypeVector& output_dtypes, const std::vector& output_shapes, - const int graph_def_version, + const int /*unused: graph_def_version*/, std::unique_ptr device_mgr, std::unique_ptr flib_def, std::unique_ptr pflr, @@ -93,8 +93,7 @@ class IteratorResource : public ResourceBase { lib_(lib), iterator_(nullptr), output_dtypes_(output_dtypes), - output_shapes_(output_shapes), - graph_def_version_(graph_def_version) {} + output_shapes_(output_shapes) {} Status GetNext(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) { @@ -223,7 +222,6 @@ class IteratorResource : public ResourceBase { std::shared_ptr lib_def_ GUARDED_BY(mu_); const DataTypeVector output_dtypes_; const std::vector output_shapes_; - const int graph_def_version_; }; // Helper class for reading data from a VariantTensorData object. diff --git a/tensorflow/core/kernels/lmdb_reader_op.cc b/tensorflow/core/kernels/lmdb_reader_op.cc index 1335a95dce..2474fe4d56 100755 --- a/tensorflow/core/kernels/lmdb_reader_op.cc +++ b/tensorflow/core/kernels/lmdb_reader_op.cc @@ -26,9 +26,8 @@ namespace tensorflow { class LMDBReader : public ReaderBase { public: - LMDBReader(const string& node_name, Env* env) + LMDBReader(const string& node_name, Env* /*unused*/) : ReaderBase(strings::StrCat("LMDBReader '", node_name, "'")), - env_(env), mdb_env_(nullptr), mdb_dbi_(0), mdb_txn_(nullptr), @@ -107,7 +106,6 @@ class LMDBReader : public ReaderBase { } } - Env* const env_; MDB_env* mdb_env_; MDB_dbi mdb_dbi_; -- GitLab From 8fb12848d3a81a010714a4612ffd735106ea83d8 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 29 Jan 2018 08:24:13 -0800 Subject: [PATCH 191/423] Disable AWS S3 virtual addressing (#16443) * Disable AWS S3 virtual addressing This fix is related to 16397 and 15159. The fix disables the virtual addressing of AWS S3, as was suggested in the comment. Signed-off-by: Yong Tang * Fix format issue. Signed-off-by: Yong Tang * Add comment for the passed parameter of virutal addressing. Signed-off-by: Yong Tang --- tensorflow/core/platform/s3/s3_file_system.cc | 11 +++++++++-- tensorflow/core/platform/s3/s3_file_system.h | 12 ++++++++++++ 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/platform/s3/s3_file_system.cc b/tensorflow/core/platform/s3/s3_file_system.cc index 1e89fa77c1..4862fd85be 100644 --- a/tensorflow/core/platform/s3/s3_file_system.cc +++ b/tensorflow/core/platform/s3/s3_file_system.cc @@ -306,8 +306,15 @@ std::shared_ptr S3FileSystem::GetS3Client() { }; Aws::InitAPI(options); - this->s3_client_ = std::shared_ptr( - new Aws::S3::S3Client(GetDefaultClientConfig())); + // The creation of S3Client disables virtual addressing: + // S3Client(clientConfiguration, signPayloads, useVirtualAdressing = true) + // The purpose is to address the issue encountered when there is an `.` + // in the bucket name. Due to TLS hostname validation or DNS rules, + // the bucket may not be resolved. Disabling of virtual addressing + // should address the issue. See GitHub issue 16397 for details. + this->s3_client_ = std::shared_ptr(new Aws::S3::S3Client( + GetDefaultClientConfig(), + Aws::Client::AWSAuthV4Signer::PayloadSigningPolicy::Never, false)); } return this->s3_client_; diff --git a/tensorflow/core/platform/s3/s3_file_system.h b/tensorflow/core/platform/s3/s3_file_system.h index 168b8007f3..d0d6bb5949 100644 --- a/tensorflow/core/platform/s3/s3_file_system.h +++ b/tensorflow/core/platform/s3/s3_file_system.h @@ -57,6 +57,18 @@ class S3FileSystem : public FileSystem { Status RenameFile(const string& src, const string& target) override; private: // Returns the member S3 client, initializing as-needed. + // When the client tries to access the object in S3, e.g., + // s3://bucket-name/path/to/object + // the behavior could be controlled by various environmental + // variables. + // By default S3 access regional endpoint, with region + // controlled by `AWS_REGION`. The endpoint could be overridden + // with explicity `S3_ENDPOINT`. S3 use HTTPS by default. + // If S3_USE_HTTPS=0 is specified, HTTP is used. Also, + // S3_VERIFY_SSL=0 could disable SSL verification in case + // HTTPS is used. + // This S3 Client does not support Virtual Hosted–Style Method + // for a bucket. std::shared_ptr GetS3Client(); std::shared_ptr s3_client_; -- GitLab From 914b9919f98b6486127c75110034e814d53efcb2 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 19 Nov 2017 13:26:39 -0800 Subject: [PATCH 192/423] Add `tf.unravel_index` as an equivalent of `np.unravel_index` This fix tries to address the issue raised in 2075 where there was no implementation of `tf.unravel_index`. The `tf.unravel_index` could be quite useful in many places. This fix adds the `tf.unravel_index` in CPU kernel. Note `order` in `np.unravel_index` has not been added yet. Signed-off-by: Yong Tang --- tensorflow/core/kernels/BUILD | 7 ++ tensorflow/core/kernels/unravel_index_op.cc | 90 +++++++++++++++++++++ tensorflow/core/ops/array_ops.cc | 21 +++++ 3 files changed, 118 insertions(+) create mode 100644 tensorflow/core/kernels/unravel_index_op.cc diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index fd99409c9b..db309fc9da 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -629,6 +629,7 @@ cc_library( ":transpose_op", ":unique_op", ":unpack_op", + ":unravel_index_op", ":where_op", ], ) @@ -883,6 +884,12 @@ tf_kernel_library( deps = ARRAY_DEPS + [":split_lib"], ) +tf_kernel_library( + name = "unravel_index_op", + prefix = "unravel_index_op", + deps = ARRAY_DEPS, +) + tf_kernel_library( name = "where_op", srcs = ["where_op.cc"], diff --git a/tensorflow/core/kernels/unravel_index_op.cc b/tensorflow/core/kernels/unravel_index_op.cc new file mode 100644 index 0000000000..9b9f0c6f88 --- /dev/null +++ b/tensorflow/core/kernels/unravel_index_op.cc @@ -0,0 +1,90 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#define EIGEN_USE_THREADS + +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" + +namespace tensorflow { + +namespace { +template +struct mod_op { + const T operator()(const T& a, const T& b) const { + return a % b; + } +}; +} + +typedef Eigen::ThreadPoolDevice CPUDevice; + +class UnravelIndexOp : public OpKernel { + public: + explicit UnravelIndexOp(OpKernelConstruction* ctx) : OpKernel(ctx) { } + + void Compute(OpKernelContext* ctx) override { + const Tensor& indices_tensor = ctx->input(0); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(indices_tensor.shape()) || TensorShapeUtils::IsScalar(indices_tensor.shape()), errors::InvalidArgument("The indices can only be scalar or vector, got \"", indices_tensor.shape().DebugString(), "\"")); + + const Tensor& dims_tensor = ctx->input(1); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(dims_tensor.shape()), errors::InvalidArgument("The indices can only be 1-D, got \"", dims_tensor.shape().DebugString(), "\"")); + + auto dims = dims_tensor.vec(); + + Eigen::array reverse({true}); + + Tensor strides_tensor; + OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT32, TensorShape({dims_tensor.NumElements()}), &strides_tensor)); + + auto strides = strides_tensor.vec(); + strides = dims.reverse(reverse).scan(0, Eigen::internal::ProdReducer(), false).reverse(reverse); + + Tensor strides_shifted_tensor; + OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT32, TensorShape({dims_tensor.NumElements()}), &strides_shifted_tensor)); + + auto strides_shifted = strides_shifted_tensor.vec(); + strides_shifted = dims.reverse(reverse).scan(0, Eigen::internal::ProdReducer(), true).reverse(reverse); + + Tensor* output_tensor = nullptr; + if (TensorShapeUtils::IsScalar(indices_tensor.shape())) { + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}), &output_tensor)); + + auto output = output_tensor->vec(); + + output = output.constant(indices_tensor.scalar()()); + output = output.binaryExpr(strides, mod_op()) / strides_shifted; + } else { + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements(), indices_tensor.NumElements()}), &output_tensor)); + + auto output = output_tensor->matrix(); + + Eigen::array reshape{{dims_tensor.NumElements(), 1}}; + Eigen::array bcast({1, indices_tensor.NumElements()}); + Eigen::array indices_reshape{{1, indices_tensor.NumElements()}}; + Eigen::array indices_bcast({dims_tensor.NumElements(), 1}); + + output = indices_tensor.vec().reshape(indices_reshape).broadcast(indices_bcast); + output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast), mod_op()) / strides_shifted.reshape(reshape).broadcast(bcast); + } + } +}; + +REGISTER_KERNEL_BUILDER(Name("UnravelIndex").Device(DEVICE_CPU), UnravelIndexOp); + +} // namespace tensorflow diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index fb9e8ad50c..526b0caef3 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -335,6 +335,27 @@ REGISTER_OP("Unpack") return Status::OK(); }); +REGISTER_OP("UnravelIndex") + .Input("indices: int32") + .Input("dims: int32") + .Output("output: int32") + .SetShapeFn([](InferenceContext* c) { + return Status::OK(); + }) + .Doc(R"doc( +Converts a flat index or array of flat indices into a tuple of coordinate +arrays. + +This is equivalent to numpy.unravel_index + +indices: An 0-D or 1-D `int` Tensor whose elements are indices into the + flattened version of an array of dimensions dims. +dims: An 1-D `int` Tensor. The shape of the array to use for unraveling + indices. +output: An 2-D (or 1-D if indices is 0-D) tensor where each row has the + same shape as the indices array. +)doc"); + // -------------------------------------------------------------------------- // TODO(josh11b): Remove the >= 2 constraint, once we can rewrite the graph // in the N == 1 case to remove the node. -- GitLab From 88b8c4bca1bb6bdcb5d68e61f6fc32489d9d8918 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 19 Nov 2017 13:31:50 -0800 Subject: [PATCH 193/423] Sanitize unravel_index_op.cc with clang-format Signed-off-by: Yong Tang --- tensorflow/core/kernels/unravel_index_op.cc | 57 +++++++++++++++------ 1 file changed, 41 insertions(+), 16 deletions(-) diff --git a/tensorflow/core/kernels/unravel_index_op.cc b/tensorflow/core/kernels/unravel_index_op.cc index 9b9f0c6f88..e4ce333eeb 100644 --- a/tensorflow/core/kernels/unravel_index_op.cc +++ b/tensorflow/core/kernels/unravel_index_op.cc @@ -26,51 +26,71 @@ namespace tensorflow { namespace { template struct mod_op { - const T operator()(const T& a, const T& b) const { - return a % b; - } + const T operator()(const T& a, const T& b) const { return a % b; } }; -} +} // namespace typedef Eigen::ThreadPoolDevice CPUDevice; class UnravelIndexOp : public OpKernel { public: - explicit UnravelIndexOp(OpKernelConstruction* ctx) : OpKernel(ctx) { } + explicit UnravelIndexOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} void Compute(OpKernelContext* ctx) override { const Tensor& indices_tensor = ctx->input(0); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(indices_tensor.shape()) || TensorShapeUtils::IsScalar(indices_tensor.shape()), errors::InvalidArgument("The indices can only be scalar or vector, got \"", indices_tensor.shape().DebugString(), "\"")); + OP_REQUIRES(ctx, + TensorShapeUtils::IsVector(indices_tensor.shape()) || + TensorShapeUtils::IsScalar(indices_tensor.shape()), + errors::InvalidArgument( + "The indices can only be scalar or vector, got \"", + indices_tensor.shape().DebugString(), "\"")); const Tensor& dims_tensor = ctx->input(1); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(dims_tensor.shape()), errors::InvalidArgument("The indices can only be 1-D, got \"", dims_tensor.shape().DebugString(), "\"")); + OP_REQUIRES( + ctx, TensorShapeUtils::IsVector(dims_tensor.shape()), + errors::InvalidArgument("The indices can only be 1-D, got \"", + dims_tensor.shape().DebugString(), "\"")); auto dims = dims_tensor.vec(); Eigen::array reverse({true}); Tensor strides_tensor; - OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT32, TensorShape({dims_tensor.NumElements()}), &strides_tensor)); + OP_REQUIRES_OK(ctx, ctx->allocate_temp( + DT_INT32, TensorShape({dims_tensor.NumElements()}), + &strides_tensor)); auto strides = strides_tensor.vec(); - strides = dims.reverse(reverse).scan(0, Eigen::internal::ProdReducer(), false).reverse(reverse); + strides = dims.reverse(reverse) + .scan(0, Eigen::internal::ProdReducer(), false) + .reverse(reverse); Tensor strides_shifted_tensor; - OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT32, TensorShape({dims_tensor.NumElements()}), &strides_shifted_tensor)); + OP_REQUIRES_OK(ctx, ctx->allocate_temp( + DT_INT32, TensorShape({dims_tensor.NumElements()}), + &strides_shifted_tensor)); auto strides_shifted = strides_shifted_tensor.vec(); - strides_shifted = dims.reverse(reverse).scan(0, Eigen::internal::ProdReducer(), true).reverse(reverse); + strides_shifted = dims.reverse(reverse) + .scan(0, Eigen::internal::ProdReducer(), true) + .reverse(reverse); Tensor* output_tensor = nullptr; if (TensorShapeUtils::IsScalar(indices_tensor.shape())) { - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}), &output_tensor)); + OP_REQUIRES_OK( + ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}), + &output_tensor)); auto output = output_tensor->vec(); output = output.constant(indices_tensor.scalar()()); output = output.binaryExpr(strides, mod_op()) / strides_shifted; } else { - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements(), indices_tensor.NumElements()}), &output_tensor)); + OP_REQUIRES_OK( + ctx, ctx->allocate_output(0, + TensorShape({dims_tensor.NumElements(), + indices_tensor.NumElements()}), + &output_tensor)); auto output = output_tensor->matrix(); @@ -79,12 +99,17 @@ class UnravelIndexOp : public OpKernel { Eigen::array indices_reshape{{1, indices_tensor.NumElements()}}; Eigen::array indices_bcast({dims_tensor.NumElements(), 1}); - output = indices_tensor.vec().reshape(indices_reshape).broadcast(indices_bcast); - output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast), mod_op()) / strides_shifted.reshape(reshape).broadcast(bcast); + output = indices_tensor.vec() + .reshape(indices_reshape) + .broadcast(indices_bcast); + output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast), + mod_op()) / + strides_shifted.reshape(reshape).broadcast(bcast); } } }; -REGISTER_KERNEL_BUILDER(Name("UnravelIndex").Device(DEVICE_CPU), UnravelIndexOp); +REGISTER_KERNEL_BUILDER(Name("UnravelIndex").Device(DEVICE_CPU), + UnravelIndexOp); } // namespace tensorflow -- GitLab From f7443e866e994128baaed4019cb3ddbd0c0212ca Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 19 Nov 2017 13:32:14 -0800 Subject: [PATCH 194/423] Expose tf.unravel_index in python Signed-off-by: Yong Tang --- tensorflow/python/ops/array_ops.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 24a0c18619..9541b097a9 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -34,6 +34,7 @@ See the @{$python/array_ops} guide. @@reshape @@squeeze @@expand_dims +@@unravel_index @@meshgrid @@slice @@strided_slice -- GitLab From 5b6d2b0652d5b4da03c0e52e648f4b396cbb762f Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 19 Nov 2017 13:32:40 -0800 Subject: [PATCH 195/423] Add test cases for tf.unravel_index. Signed-off-by: Yong Tang --- tensorflow/python/kernel_tests/array_ops_test.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index a96b88d96f..2716c4a51f 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -1114,6 +1114,21 @@ class InvertPermutationTest(test_util.TensorFlowTestCase): self.assertAllEqual(y.get_shape(), [5]) self.assertAllEqual(y.eval(), [2, 4, 3, 0, 1]) +class UnravelIndexTest(test_util.TensorFlowTestCase): + + def testUnravelIndex(self): + with self.test_session(): + out_1 = array_ops.unravel_index(1621, [6, 7, 8, 9]) + self.assertAllEqual(out_1.eval(), [3, 1, 4, 1]) + out_2 = array_ops.unravel_index([1621], [6, 7, 8, 9]) + self.assertAllEqual(out_2.eval(), [[3], + [1], + [4], + [1]]) + out_3 = array_ops.unravel_index([22, 41, 37], [7, 6]) + self.assertAllEqual(out_3.eval(), [[3, 6, 6], + [4, 5, 1]]) + class GuaranteeConstOpTest(test_util.TensorFlowTestCase): -- GitLab From babc9bae71d31e35b8d66a715fcd527ee1ee645a Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 1 Dec 2017 11:09:11 +0000 Subject: [PATCH 196/423] Add int64 support for unravel_index Signed-off-by: Yong Tang --- tensorflow/core/kernels/unravel_index_op.cc | 59 +++++++++++---------- tensorflow/core/ops/array_ops.cc | 17 +++--- 2 files changed, 41 insertions(+), 35 deletions(-) diff --git a/tensorflow/core/kernels/unravel_index_op.cc b/tensorflow/core/kernels/unravel_index_op.cc index e4ce333eeb..da9ab01e8d 100644 --- a/tensorflow/core/kernels/unravel_index_op.cc +++ b/tensorflow/core/kernels/unravel_index_op.cc @@ -32,15 +32,15 @@ struct mod_op { typedef Eigen::ThreadPoolDevice CPUDevice; +template class UnravelIndexOp : public OpKernel { public: explicit UnravelIndexOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} void Compute(OpKernelContext* ctx) override { const Tensor& indices_tensor = ctx->input(0); - OP_REQUIRES(ctx, - TensorShapeUtils::IsVector(indices_tensor.shape()) || - TensorShapeUtils::IsScalar(indices_tensor.shape()), + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(indices_tensor.shape()) || + TensorShapeUtils::IsScalar(indices_tensor.shape()), errors::InvalidArgument( "The indices can only be scalar or vector, got \"", indices_tensor.shape().DebugString(), "\"")); @@ -51,28 +51,30 @@ class UnravelIndexOp : public OpKernel { errors::InvalidArgument("The indices can only be 1-D, got \"", dims_tensor.shape().DebugString(), "\"")); - auto dims = dims_tensor.vec(); + auto dims = dims_tensor.vec(); Eigen::array reverse({true}); Tensor strides_tensor; - OP_REQUIRES_OK(ctx, ctx->allocate_temp( - DT_INT32, TensorShape({dims_tensor.NumElements()}), - &strides_tensor)); + OP_REQUIRES_OK(ctx, + ctx->allocate_temp(DataTypeToEnum::value, + TensorShape({dims_tensor.NumElements()}), + &strides_tensor)); - auto strides = strides_tensor.vec(); + auto strides = strides_tensor.vec(); strides = dims.reverse(reverse) - .scan(0, Eigen::internal::ProdReducer(), false) + .scan(0, Eigen::internal::ProdReducer(), false) .reverse(reverse); Tensor strides_shifted_tensor; - OP_REQUIRES_OK(ctx, ctx->allocate_temp( - DT_INT32, TensorShape({dims_tensor.NumElements()}), - &strides_shifted_tensor)); + OP_REQUIRES_OK(ctx, + ctx->allocate_temp(DataTypeToEnum::value, + TensorShape({dims_tensor.NumElements()}), + &strides_shifted_tensor)); - auto strides_shifted = strides_shifted_tensor.vec(); + auto strides_shifted = strides_shifted_tensor.vec(); strides_shifted = dims.reverse(reverse) - .scan(0, Eigen::internal::ProdReducer(), true) + .scan(0, Eigen::internal::ProdReducer(), true) .reverse(reverse); Tensor* output_tensor = nullptr; @@ -81,35 +83,38 @@ class UnravelIndexOp : public OpKernel { ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}), &output_tensor)); - auto output = output_tensor->vec(); + auto output = output_tensor->vec(); - output = output.constant(indices_tensor.scalar()()); - output = output.binaryExpr(strides, mod_op()) / strides_shifted; + output = output.constant(indices_tensor.scalar()()); + output = output.binaryExpr(strides, mod_op()) / strides_shifted; } else { - OP_REQUIRES_OK( - ctx, ctx->allocate_output(0, - TensorShape({dims_tensor.NumElements(), - indices_tensor.NumElements()}), - &output_tensor)); + OP_REQUIRES_OK(ctx, ctx->allocate_output( + 0, TensorShape({dims_tensor.NumElements(), + indices_tensor.NumElements()}), + &output_tensor)); - auto output = output_tensor->matrix(); + auto output = output_tensor->matrix(); Eigen::array reshape{{dims_tensor.NumElements(), 1}}; Eigen::array bcast({1, indices_tensor.NumElements()}); Eigen::array indices_reshape{{1, indices_tensor.NumElements()}}; Eigen::array indices_bcast({dims_tensor.NumElements(), 1}); - output = indices_tensor.vec() + output = indices_tensor.vec() .reshape(indices_reshape) .broadcast(indices_bcast); output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast), - mod_op()) / + mod_op()) / strides_shifted.reshape(reshape).broadcast(bcast); } } }; -REGISTER_KERNEL_BUILDER(Name("UnravelIndex").Device(DEVICE_CPU), - UnravelIndexOp); +#define REGISTER_KERNEL(type) \ + REGISTER_KERNEL_BUILDER( \ + Name("UnravelIndex").Device(DEVICE_CPU).TypeConstraint("Tidx"), \ + UnravelIndexOp); +TF_CALL_int32(REGISTER_KERNEL) TF_CALL_int64(REGISTER_KERNEL) +#undef REGISTER_KERNEL } // namespace tensorflow diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index 526b0caef3..f9fdd50e6b 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -336,24 +336,25 @@ REGISTER_OP("Unpack") }); REGISTER_OP("UnravelIndex") - .Input("indices: int32") - .Input("dims: int32") - .Output("output: int32") - .SetShapeFn([](InferenceContext* c) { - return Status::OK(); - }) + .Input("indices: Tidx") + .Input("dims: Tidx") + .Output("output: Tidx") + .Attr("Tidx: {int32, int64} = DT_INT32") + .SetShapeFn([](InferenceContext* c) { return Status::OK(); }) .Doc(R"doc( Converts a flat index or array of flat indices into a tuple of coordinate arrays. -This is equivalent to numpy.unravel_index - indices: An 0-D or 1-D `int` Tensor whose elements are indices into the flattened version of an array of dimensions dims. dims: An 1-D `int` Tensor. The shape of the array to use for unraveling indices. output: An 2-D (or 1-D if indices is 0-D) tensor where each row has the same shape as the indices array. + +@compatibility(numpy) +Equivalent to np.unravel_index +@end_compatibility )doc"); // -------------------------------------------------------------------------- -- GitLab From b1b5c2e74d9a3e54d2e84279db94027060e20609 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Fri, 1 Dec 2017 11:11:11 +0000 Subject: [PATCH 197/423] Add test cases for int64 support of unravel_index Signed-off-by: Yong Tang --- .../python/kernel_tests/array_ops_test.py | 25 +++++++++++-------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index 2716c4a51f..68b7c3a98a 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -1118,16 +1118,21 @@ class UnravelIndexTest(test_util.TensorFlowTestCase): def testUnravelIndex(self): with self.test_session(): - out_1 = array_ops.unravel_index(1621, [6, 7, 8, 9]) - self.assertAllEqual(out_1.eval(), [3, 1, 4, 1]) - out_2 = array_ops.unravel_index([1621], [6, 7, 8, 9]) - self.assertAllEqual(out_2.eval(), [[3], - [1], - [4], - [1]]) - out_3 = array_ops.unravel_index([22, 41, 37], [7, 6]) - self.assertAllEqual(out_3.eval(), [[3, 6, 6], - [4, 5, 1]]) + for dtype in [dtypes.int32, dtypes.int64]: + indices_1 = constant_op.constant(1621, dtype=dtype) + dims_1 = constant_op.constant([6, 7, 8, 9], dtype=dtype) + out_1 = array_ops.unravel_index(indices_1, dims_1) + self.assertAllEqual(out_1.eval(), [3, 1, 4, 1]) + + indices_2 = constant_op.constant([1621], dtype=dtype) + dims_2 = constant_op.constant([6, 7, 8, 9], dtype=dtype) + out_2 = array_ops.unravel_index(indices_2, dims_2) + self.assertAllEqual(out_2.eval(), [[3], [1], [4], [1]]) + + indices_3 = constant_op.constant([22, 41, 37], dtype=dtype) + dims_3 = constant_op.constant([7, 6], dtype=dtype) + out_3 = array_ops.unravel_index(indices_3, dims_3) + self.assertAllEqual(out_3.eval(), [[3, 6, 6], [4, 5, 1]]) class GuaranteeConstOpTest(test_util.TensorFlowTestCase): -- GitLab From 25f8e54ea5021c04c11184fe134fc31b0e7ef88d Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Tue, 5 Dec 2017 22:07:01 +0000 Subject: [PATCH 198/423] Address review feedback Signed-off-by: Yong Tang --- tensorflow/core/ops/array_ops.cc | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index f9fdd50e6b..45f880265b 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -342,8 +342,12 @@ REGISTER_OP("UnravelIndex") .Attr("Tidx: {int32, int64} = DT_INT32") .SetShapeFn([](InferenceContext* c) { return Status::OK(); }) .Doc(R"doc( -Converts a flat index or array of flat indices into a tuple of coordinate -arrays. +Converts a flat index or array of flat indices into a tuple of +coordinate arrays. + +@compatibility(numpy) +Equivalent to np.unravel_index +@end_compatibility indices: An 0-D or 1-D `int` Tensor whose elements are indices into the flattened version of an array of dimensions dims. @@ -351,10 +355,6 @@ dims: An 1-D `int` Tensor. The shape of the array to use for unraveling indices. output: An 2-D (or 1-D if indices is 0-D) tensor where each row has the same shape as the indices array. - -@compatibility(numpy) -Equivalent to np.unravel_index -@end_compatibility )doc"); // -------------------------------------------------------------------------- -- GitLab From af499b49e84991152d24ee97c4ef893d6986a081 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 29 Jan 2018 16:57:37 +0000 Subject: [PATCH 199/423] Update API defs and golden Signed-off-by: Yong Tang --- .../base_api/api_def_UnravelIndex.pbtxt | 32 +++++++++++++++++++ tensorflow/core/ops/array_ops.cc | 17 +--------- tensorflow/tools/api/golden/tensorflow.pbtxt | 4 +++ 3 files changed, 37 insertions(+), 16 deletions(-) create mode 100644 tensorflow/core/api_def/base_api/api_def_UnravelIndex.pbtxt diff --git a/tensorflow/core/api_def/base_api/api_def_UnravelIndex.pbtxt b/tensorflow/core/api_def/base_api/api_def_UnravelIndex.pbtxt new file mode 100644 index 0000000000..97c380700a --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_UnravelIndex.pbtxt @@ -0,0 +1,32 @@ +op { + graph_op_name: "UnravelIndex" + in_arg { + name: "indices" + description: <= 2 constraint, once we can rewrite the graph diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index db1ed42185..dc7c3a2f45 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -2044,6 +2044,10 @@ tf_module { name: "unique_with_counts" argspec: "args=[\'x\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " } + member_method { + name: "unravel_index" + argspec: "args=[\'indices\', \'dims\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } member_method { name: "unsorted_segment_max" argspec: "args=[\'data\', \'segment_ids\', \'num_segments\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " -- GitLab From b9dae47061d4d4c9b8f8a79e73519525413ab84c Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Mon, 29 Jan 2018 09:25:42 -0800 Subject: [PATCH 200/423] Make TFLite Transpose op have parity with TF Transpose op. PiperOrigin-RevId: 183676663 --- tensorflow/contrib/lite/builtin_op_data.h | 4 - tensorflow/contrib/lite/kernels/transpose.cc | 66 +++++--- .../contrib/lite/kernels/transpose_test.cc | 158 ++++++++++++++---- tensorflow/contrib/lite/model.cc | 8 - tensorflow/contrib/lite/schema/schema.fbs | 1 - .../contrib/lite/schema/schema_generated.h | 34 +--- .../contrib/lite/testing/generate_examples.py | 27 ++- .../testing/generated_examples_zip_test.cc | 2 +- .../contrib/lite/toco/tflite/operator.cc | 5 +- .../contrib/lite/toco/tflite/operator_test.cc | 9 - 10 files changed, 197 insertions(+), 117 deletions(-) diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index 8338fde8ac..8966e5c2b6 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -204,10 +204,6 @@ typedef struct { } TfLiteGatherParams; typedef struct { - // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. - // For now we will fix the maximum possible number of dimensions. - int perm[8]; - int num_dimensions; } TfLiteTransposeParams; typedef struct { diff --git a/tensorflow/contrib/lite/kernels/transpose.cc b/tensorflow/contrib/lite/kernels/transpose.cc index 75d8136b6a..093814bc44 100644 --- a/tensorflow/contrib/lite/kernels/transpose.cc +++ b/tensorflow/contrib/lite/kernels/transpose.cc @@ -31,60 +31,78 @@ enum KernelType { kReference, }; -// TODO(nupurgarg): Permutation arrays represented as a tensor are ignored. Only -// use the `perm` specified in `params`. struct TransposeContext { TransposeContext(TfLiteContext* context, TfLiteNode* node) { - params = reinterpret_cast(node->builtin_data); input = GetInput(context, node, 0); + perm = GetInput(context, node, 1); output = GetOutput(context, node, 0); } - TfLiteTransposeParams* params; TfLiteTensor* input; + TfLiteTensor* perm; TfLiteTensor* output; }; -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + TransposeContext* op_context) { + int dims = NumDimensions(op_context->input); + const int* perm_data = GetTensorData(op_context->perm); - TransposeContext op_context(context, node); - int dims = NumDimensions(op_context.input); - - // Ensure validity of input tensor and permutation array. - TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); - TF_LITE_ENSURE_EQ(context, dims, op_context.params->num_dimensions); - TF_LITE_ENSURE_MSG(context, dims <= 4, - "Transpose op only supports 1D-4D input arrays."); + // Ensure validity of the permutations tensor as a 1D tensor. + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->perm), 1); + TF_LITE_ENSURE_EQ(context, op_context->perm->dims->data[0], dims); for (int idx = 0; idx < dims; ++idx) { - TF_LITE_ENSURE_MSG(context, - op_context.params->perm[idx] >= 0 && - op_context.params->perm[idx] < dims, + TF_LITE_ENSURE_MSG(context, (perm_data[idx] >= 0 && perm_data[idx] < dims), "Transpose op permutations array is out of bounds."); } // Determine size of output tensor. - const TfLiteIntArray* input_size = op_context.input->dims; - TfLiteIntArray* output_size = TfLiteIntArrayCreate(dims); + TfLiteIntArray* input_size = op_context->input->dims; + TfLiteIntArray* output_size = TfLiteIntArrayCopy(input_size); for (int idx = 0; idx < dims; ++idx) { - output_size->data[idx] = input_size->data[op_context.params->perm[idx]]; + output_size->data[idx] = input_size->data[perm_data[idx]]; } - return context->ResizeTensor(context, op_context.output, output_size); + return context->ResizeTensor(context, op_context->output, output_size); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + TransposeContext op_context(context, node); + + // Ensure validity of input tensor. + TF_LITE_ENSURE_MSG(context, NumDimensions(op_context.input) <= 4, + "Transpose op only supports 1D-4D input arrays."); + TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); + + if (!IsConstantTensor(op_context.perm)) { + SetTensorToDynamic(op_context.output); + return kTfLiteOk; + } + return ResizeOutputTensor(context, &op_context); } template TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TransposeContext op_context(context, node); + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + TfLiteTensorRealloc(op_context.output->bytes, op_context.output); + } + // Reverse the permuted axes and convert to 4D due to the way Dims are // constructed in GetTensorDims. + const int* perm_data = GetTensorData(op_context.perm); + const int size = op_context.perm->dims->data[0]; const int kOutputDimensionNum = 4; int reversed_perm[kOutputDimensionNum]; - int size = op_context.params->num_dimensions; + for (int output_k = 0, input_k = size - 1; output_k < size; ++output_k, --input_k) { - reversed_perm[output_k] = size - op_context.params->perm[input_k] - 1; + reversed_perm[output_k] = size - perm_data[input_k] - 1; } for (int k = size; k < kOutputDimensionNum; ++k) { reversed_perm[k] = k; diff --git a/tensorflow/contrib/lite/kernels/transpose_test.cc b/tensorflow/contrib/lite/kernels/transpose_test.cc index 7f5832cd5f..337bc144b9 100644 --- a/tensorflow/contrib/lite/kernels/transpose_test.cc +++ b/tensorflow/contrib/lite/kernels/transpose_test.cc @@ -127,61 +127,124 @@ TEST(TransposeTest, TestRefOps4D) { class TransposeOpModel : public SingleOpModel { public: - TransposeOpModel(std::initializer_list input_shape, - std::initializer_list perm) { - input_ = AddInput(TensorType_FLOAT32); - output_ = AddOutput(TensorType_FLOAT32); - SetBuiltinOp( - BuiltinOperator_TRANSPOSE, BuiltinOptions_TransposeOptions, - CreateTransposeOptions(builder_, builder_.CreateVector(perm)) - .Union()); - BuildInterpreter({input_shape}); - } - void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } + void SetPerm(std::initializer_list data) { + PopulateTensor(perm_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } std::vector GetOutputShape() { return GetTensorShape(output_); } - private: + protected: int input_; + int perm_; int output_; }; +// Tests case where perm is a const tensor. +// +// Example usage is as follows: +// SpaceToBatchNDOpConstModel m(input_shape, perm_shape, perm_data); +// m.SetInput(input_data); +// m.Invoke(); +class TransposeOpConstModel : public TransposeOpModel { + public: + TransposeOpConstModel(std::initializer_list input_shape, + std::initializer_list perm_shape, + std::initializer_list perm) { + input_ = AddInput(TensorType_FLOAT32); + perm_ = AddConstInput(TensorType_INT32, perm, perm_shape); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_TRANSPOSE, BuiltinOptions_TransposeOptions, + CreateTransposeOptions(builder_).Union()); + BuildInterpreter({input_shape}); + } +}; + +// Tests case where perm is a non-const tensor. +// +// Example usage is as follows: +// TransposeOpDynamicModel m(input_shape, perm_shape); +// m.SetInput(input_data); +// m.SetPerm(perm_data); +// m.Invoke(); +class TransposeOpDynamicModel : public TransposeOpModel { + public: + TransposeOpDynamicModel(std::initializer_list input_shape, + std::initializer_list perm_shape) { + input_ = AddInput(TensorType_FLOAT32); + perm_ = AddInput(TensorType_INT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_TRANSPOSE, BuiltinOptions_TransposeOptions, + CreateTransposeOptions(builder_).Union()); + BuildInterpreter({input_shape, perm_shape}); + } +}; + TEST(TransposeTest, TestUnequalPermSize) { - EXPECT_DEATH(TransposeOpModel({1, 3, 3, 1}, {2, 2}), - "dims != op_context.params->num_dimensions"); + EXPECT_DEATH(TransposeOpConstModel({1, 3, 3, 1}, {2}, {2, 2}), "2 != 4"); } TEST(TransposeTest, TestPermOutOfBounds) { - EXPECT_DEATH(TransposeOpModel({1, 3, 3, 1}, {0, -1, -2, -3}), + EXPECT_DEATH(TransposeOpConstModel({1, 3, 3, 1}, {4}, {0, -1, -2, -3}), "Transpose op permutations array is out of bounds."); - EXPECT_DEATH(TransposeOpModel({1, 3, 3, 1}, {0, 1, 2, 4}), + EXPECT_DEATH(TransposeOpConstModel({1, 3, 3, 1}, {4}, {0, 1, 2, 4}), "Transpose op permutations array is out of bounds."); } -TEST(TransposeTest, Test1DInputTensor) { - TransposeOpModel m({3}, {0}); +TEST(TransposeTest, Test1DInputConstTensor) { + TransposeOpConstModel m({3}, {1}, {0}); m.SetInput({1, 2, 3}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3})); } -TEST(TransposeTest, Test2DInputTensor) { - TransposeOpModel m({3, 2}, {1, 0}); +TEST(TransposeTest, Test1DInputDynamicTensor) { + TransposeOpDynamicModel m({3}, {1}); + m.SetInput({1, 2, 3}); + m.SetPerm({0}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3})); +} + +TEST(TransposeTest, Test2DInputConstTensor) { + TransposeOpConstModel m({3, 2}, {2}, {1, 0}); + m.SetInput({0, 1, 2, 3, 4, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 3})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, 2, 4, 1, 3, 5})); +} + +TEST(TransposeTest, Test2DInputDynamicTensor) { + TransposeOpDynamicModel m({3, 2}, {2}); m.SetInput({0, 1, 2, 3, 4, 5}); + m.SetPerm({1, 0}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 3})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, 2, 4, 1, 3, 5})); } -TEST(TransposeTest, Test3DInputTensor) { - TransposeOpModel m({2, 3, 4}, {2, 0, 1}); +TEST(TransposeTest, Test3DInputConstTensor) { + TransposeOpConstModel m({2, 3, 4}, {3}, {2, 0, 1}); + m.SetInput({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, + 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 3})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({0, 4, 8, 12, 16, 20, 1, 5, 9, 13, 17, 21, + 2, 6, 10, 14, 18, 22, 3, 7, 11, 15, 19, 23})); +} + +TEST(TransposeTest, Test3DInputDynamicTensor) { + TransposeOpDynamicModel m({2, 3, 4}, {3}); m.SetInput({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}); + m.SetPerm({2, 0, 1}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 3})); EXPECT_THAT(m.GetOutput(), @@ -190,28 +253,64 @@ TEST(TransposeTest, Test3DInputTensor) { } TEST(TransposeTest, Test5DInputTensor) { - EXPECT_DEATH(TransposeOpModel({1, 2, 3, 4, 5}, {0, 1, 2, 3, 4}), + EXPECT_DEATH(TransposeOpConstModel({1, 2, 3, 4, 5}, {5}, {0, 1, 2, 3, 4}), "Transpose op only supports 1D-4D input arrays."); } -TEST(TransposeTest, SimpleTestNoReorder) { - TransposeOpModel m({1, 2, 3, 1}, {0, 1, 2, 3}); +TEST(TransposeTest, SimpleTestNoReorderConstTensor) { + TransposeOpConstModel m({1, 2, 3, 1}, {4}, {0, 1, 2, 3}); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2, 3, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); +} + +TEST(TransposeTest, SimpleTestNoReorderDynamicTensor) { + TransposeOpDynamicModel m({1, 2, 3, 1}, {4}); m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetPerm({0, 1, 2, 3}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2, 3, 1})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); } -TEST(TransposeTest, SimpleTestWithReorder) { - TransposeOpModel m({1, 2, 3, 1}, {2, 1, 3, 0}); +TEST(TransposeTest, SimpleTestWithReorderConstTensor) { + TransposeOpConstModel m({1, 2, 3, 1}, {4}, {2, 1, 3, 0}); m.SetInput({1, 2, 3, 4, 5, 6}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 2, 1, 1})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6})); } -TEST(TransposeTest, ComplexTestWithReorder) { - TransposeOpModel m({2, 3, 4, 5}, {2, 0, 1, 3}); +TEST(TransposeTest, ComplexTestWithReorderConstTensor) { + TransposeOpConstModel m({2, 3, 4, 5}, {4}, {2, 0, 1, 3}); + m.SetInput({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, + 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, + 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, + 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, + 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, + 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, + 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, + 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, + 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119}); + m.Invoke(); + + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 3, 5})); + auto result = ElementsAreArray( + {0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, + 60, 61, 62, 63, 64, 80, 81, 82, 83, 84, 100, 101, 102, 103, 104, + 5, 6, 7, 8, 9, 25, 26, 27, 28, 29, 45, 46, 47, 48, 49, + 65, 66, 67, 68, 69, 85, 86, 87, 88, 89, 105, 106, 107, 108, 109, + 10, 11, 12, 13, 14, 30, 31, 32, 33, 34, 50, 51, 52, 53, 54, + 70, 71, 72, 73, 74, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, + 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 55, 56, 57, 58, 59, + 75, 76, 77, 78, 79, 95, 96, 97, 98, 99, 115, 116, 117, 118, 119}); + EXPECT_THAT(m.GetOutput(), result); +} + +TEST(TransposeTest, ComplexTestWithReorderDynamicTensor) { + TransposeOpDynamicModel m({2, 3, 4, 5}, {4}); m.SetInput({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, @@ -222,6 +321,7 @@ TEST(TransposeTest, ComplexTestWithReorder) { 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119}); + m.SetPerm({2, 0, 1, 3}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 3, 5})); diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 3f53a1abe7..ec4d6e3487 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -554,14 +554,6 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, break; } case BuiltinOperator_TRANSPOSE: { - auto* params = MallocPOD(); - if (auto* schema_params = op->builtin_options_as_TransposeOptions()) { - const auto& perm = schema_params->perm(); - FlatBufferIntVectorToArray(sizeof(params->perm), perm, params->perm, - error_reporter); - params->num_dimensions = perm->Length(); - } - builtin_data = reinterpret_cast(params); break; } case BuiltinOperator_MEAN: { diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 4c82cb9549..50709344ea 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -333,7 +333,6 @@ table GatherOptions { } table TransposeOptions { - perm:[int]; } table MeanOptions { diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index bafd28b626..f1ee925df2 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -3392,19 +3392,13 @@ flatbuffers::Offset CreateGatherOptions( struct TransposeOptionsT : public flatbuffers::NativeTable { typedef TransposeOptions TableType; - std::vector perm; TransposeOptionsT() {} }; struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef TransposeOptionsT NativeTableType; - enum { VT_PERM = 4 }; - const flatbuffers::Vector *perm() const { - return GetPointer *>(VT_PERM); - } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_PERM) && - verifier.Verify(perm()) && verifier.EndTable(); + return VerifyTableStart(verifier) && verifier.EndTable(); } TransposeOptionsT *UnPack( const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -3419,9 +3413,6 @@ struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { struct TransposeOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_perm(flatbuffers::Offset> perm) { - fbb_.AddOffset(TransposeOptions::VT_PERM, perm); - } explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -3435,20 +3426,11 @@ struct TransposeOptionsBuilder { }; inline flatbuffers::Offset CreateTransposeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> perm = 0) { + flatbuffers::FlatBufferBuilder &_fbb) { TransposeOptionsBuilder builder_(_fbb); - builder_.add_perm(perm); return builder_.Finish(); } -inline flatbuffers::Offset CreateTransposeOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *perm = nullptr) { - return tflite::CreateTransposeOptions( - _fbb, perm ? _fbb.CreateVector(*perm) : 0); -} - flatbuffers::Offset CreateTransposeOptions( flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -6107,15 +6089,6 @@ inline void TransposeOptions::UnPackTo( const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = perm(); - if (_e) { - _o->perm.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->perm[_i] = _e->Get(_i); - } - } - }; } inline flatbuffers::Offset TransposeOptions::Pack( @@ -6135,8 +6108,7 @@ inline flatbuffers::Offset CreateTransposeOptions( const flatbuffers::rehasher_function_t *__rehasher; } _va = {&_fbb, _o, _rehasher}; (void)_va; - auto _perm = _o->perm.size() ? _fbb.CreateVector(_o->perm) : 0; - return tflite::CreateTransposeOptions(_fbb, _perm); + return tflite::CreateTransposeOptions(_fbb); } inline MeanOptionsT *MeanOptions::UnPack( diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index a639351657..bc9b23aeb4 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -1397,29 +1397,44 @@ def make_transpose_tests(zip_path): "dtype": [tf.int32, tf.int64, tf.float32], "input_shape": [[2, 2, 3]], "perm": [[0, 1, 2], [0, 2, 1]], + "constant_perm": [True, False], }, { "dtype": [tf.float32], "input_shape": [[1, 2, 3, 4]], "perm": [[0, 1, 2, 3], [3, 0, 1, 2]], + "constant_perm": [True, False], }, { "dtype": [tf.float32], "input_shape": [[1, 2, 3, 4, 5]], "perm": [[0, 1, 2, 3, 4]], + "constant_perm": [True, False], }] def build_graph(parameters): + """Build a transpose graph given `parameters`.""" input_tensor = tf.placeholder( dtype=parameters["dtype"], name="input", shape=parameters["input_shape"]) - out = tf.transpose(input_tensor, perm=parameters["perm"]) - return [input_tensor], [out] + + if parameters["constant_perm"]: + perm = parameters["perm"] + input_tensors = [input_tensor] + else: + shape = [len(parameters["perm"]), 2] + perm = tf.placeholder(dtype=tf.int32, name="perm", shape=shape) + input_tensors = [input_tensor, perm] + + out = tf.transpose(input_tensor, perm=perm) + 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_perm"]: + values.append(np.array(parameters["perm"])) + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index 41652a07d2..2bbfe77a12 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -92,7 +92,7 @@ std::map kBrokenTests = { {R"(^\/resize_bilinearalign_corners=True,.*,size=\[5,6\])", "72401483"}, // Transpose only supports 1D-4D input tensors. - {R"(^\/transposedtype=.*,input_shape=\[.,.,.,.,.\],perm=.*)", "71545879"}, + {R"(^\/transpose.*input_shape=\[.,.,.,.,.\])", "71545879"}, }; // Allows test data to be unzipped into a temporary directory and makes diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 298f49025f..2d6bccce2b 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -546,14 +546,11 @@ class Transpose flatbuffers::Offset WriteOptions( const TocoOperator& op, flatbuffers::FlatBufferBuilder* builder) const override { - return ::tflite::CreateTransposeOptions(*builder, - builder->CreateVector(op.perm)); + return ::tflite::CreateTransposeOptions(*builder); } void ReadOptions(const TfLiteOptions& options, TocoOperator* op) const override { - op->perm.insert(op->perm.end(), options.perm()->begin(), - options.perm()->end()); } }; diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 9036a16d1c..78af3a767d 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -370,15 +370,6 @@ TEST_F(OperatorTest, Svdf) { EXPECT_EQ(op.rank, output_toco_op->rank); } -TEST_F(OperatorTest, Transpose) { - TransposeOperator op; - op.perm = {0, 1, 2, 3}; - - auto output_toco_op = SerializeAndDeserialize( - GetOperator("TRANSPOSE", OperatorType::kTranspose), op); - EXPECT_EQ(op.perm, output_toco_op->perm); -} - TEST_F(OperatorTest, Squeeze) { SqueezeOperator op; op.squeeze_dims = {-2, -3, 4, 1, 4}; -- GitLab From 9de5db8676a037a42da1e99de77abfcf75d10809 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Mon, 29 Jan 2018 10:46:05 -0700 Subject: [PATCH 201/423] Reverting the switch to max_pool_v2 in python (#16524) --- tensorflow/contrib/specs/python/specs_test.py | 14 +++++++------- tensorflow/python/ops/nn_ops.py | 2 +- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/specs/python/specs_test.py b/tensorflow/contrib/specs/python/specs_test.py index d5f61d1b69..41782a9fc9 100644 --- a/tensorflow/contrib/specs/python/specs_test.py +++ b/tensorflow/contrib/specs/python/specs_test.py @@ -87,7 +87,7 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (1, 8, 8, 5)) self.assertEqual( summaries.tf_spec_structure(spec, inputs), - "_ _ _ maxpoolv2 _ _ maxpoolv2 _ _ maxpoolv2") + "_ maxpool maxpool maxpool") def testAbbrevPower(self): with self.test_session(): @@ -100,10 +100,10 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (1, 8, 8, 5)) self.assertEqual( summaries.tf_spec_structure(spec, inputs), - "_ variablev2 conv variablev2 biasadd relu _ _ maxpoolv2" + "_ variablev2 conv variablev2 biasadd relu maxpool" " variablev2 conv variablev2" - " biasadd relu _ _ maxpoolv2 variablev2 conv variablev2" - " biasadd relu _ _ maxpoolv2") + " biasadd relu maxpool variablev2 conv variablev2" + " biasadd relu maxpool") def testAbbrevPower2(self): with self.test_session(): @@ -117,10 +117,10 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (1, 8, 8, 5)) self.assertEqual( summaries.tf_spec_structure(spec, inputs), - "_ variablev2 conv variablev2 biasadd relu _ _ maxpoolv2" + "_ variablev2 conv variablev2 biasadd relu maxpool" " variablev2 conv variablev2 biasadd relu" - " _ _ maxpoolv2 variablev2 conv variablev2 biasadd relu" - " _ _ maxpoolv2") + " maxpool variablev2 conv variablev2 biasadd relu" + " maxpool") def testConc(self): with self.test_session(): diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 644bb3af8a..9f0cc4a029 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -2070,7 +2070,7 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._max_pool_v2(value, + return gen_nn_ops._max_pool(value, ksize=ksize, strides=strides, padding=padding, -- GitLab From 219e22879ba981aa33fbe8f54a550cce56bc5d90 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Mon, 29 Jan 2018 09:44:35 -0800 Subject: [PATCH 202/423] TFTS: Remove a race condition in lstm_test (switch to resource variables) PiperOrigin-RevId: 183679060 --- .../contrib/timeseries/examples/lstm.py | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/timeseries/examples/lstm.py b/tensorflow/contrib/timeseries/examples/lstm.py index c7193cef69..c834430b95 100644 --- a/tensorflow/contrib/timeseries/examples/lstm.py +++ b/tensorflow/contrib/timeseries/examples/lstm.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools from os import path import numpy @@ -80,18 +81,19 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel): input_statistics: A math_utils.InputStatistics object. """ super(_LSTMModel, self).initialize_graph(input_statistics=input_statistics) - self._lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=self._num_units) - # Create templates so we don't have to worry about variable reuse. - self._lstm_cell_run = tf.make_template( - name_="lstm_cell", - func_=self._lstm_cell, - create_scope_now_=True) - # Transforms LSTM output into mean predictions. - self._predict_from_lstm_output = tf.make_template( - name_="predict_from_lstm_output", - func_= - lambda inputs: tf.layers.dense(inputs=inputs, units=self.num_features), - create_scope_now_=True) + with tf.variable_scope("", use_resource=True): + # Use ResourceVariables to avoid race conditions. + self._lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=self._num_units) + # Create templates so we don't have to worry about variable reuse. + self._lstm_cell_run = tf.make_template( + name_="lstm_cell", + func_=self._lstm_cell, + create_scope_now_=True) + # Transforms LSTM output into mean predictions. + self._predict_from_lstm_output = tf.make_template( + name_="predict_from_lstm_output", + func_=functools.partial(tf.layers.dense, units=self.num_features), + create_scope_now_=True) def get_start_state(self): """Return initial state for the time series model.""" -- GitLab From 3c7a7313738a1b5a1efa51bab79e377349bb8d7b Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Mon, 29 Jan 2018 09:48:47 -0800 Subject: [PATCH 203/423] [tf.data] Robust `output_types` and `output_shapes` if `output_classes` contains SparseTensor. PiperOrigin-RevId: 183679584 --- tensorflow/python/data/ops/iterator_ops.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index 0cbdb3ab19..53a3244ce1 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -165,8 +165,10 @@ class Iterator(object): iterator_resource = gen_dataset_ops.iterator( container="", shared_name=shared_name, - output_types=nest.flatten(output_types), - output_shapes=nest.flatten(output_shapes)) + output_types=nest.flatten( + sparse.as_dense_types(output_types, output_classes)), + output_shapes=nest.flatten( + sparse.as_dense_shapes(output_shapes, output_classes))) return Iterator(iterator_resource, None, output_types, output_shapes, output_classes) @@ -232,8 +234,10 @@ class Iterator(object): string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string) iterator_resource = gen_dataset_ops.iterator_from_string_handle( string_handle, - output_types=nest.flatten(output_types), - output_shapes=nest.flatten(output_shapes)) + output_types=nest.flatten( + sparse.as_dense_types(output_types, output_classes)), + output_shapes=nest.flatten( + sparse.as_dense_shapes(output_shapes, output_classes))) return Iterator(iterator_resource, None, output_types, output_shapes, output_classes) -- GitLab From 3dd09f3707f5c0a73127165461b430c8729d65e0 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Mon, 29 Jan 2018 09:57:20 -0800 Subject: [PATCH 204/423] Make `init_scope` preserve the active name scope. This ensures that operations nested under an `init_scope` do not ignore name scopes that were opened in a function-building graph. In light of cl/183320576, which causes `tfe.defun` to automatically hoist variables out of its function-building graph, this change also eliminates logic in make_template that special-cased the first call when `create_graph_function` was True. PiperOrigin-RevId: 183680794 --- tensorflow/python/eager/function_test.py | 18 +++++ tensorflow/python/framework/ops.py | 20 +++++- tensorflow/python/framework/ops_test.py | 26 +++++++- tensorflow/python/kernel_tests/BUILD | 1 + .../python/kernel_tests/variables_test.py | 10 +++ tensorflow/python/ops/template.py | 66 +++++++------------ 6 files changed, 96 insertions(+), 45 deletions(-) diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 0babc29f17..2cb2cfb76c 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -504,6 +504,24 @@ class FunctionTest(test.TestCase): self.assertAllEqual(ret[0][2], 10) self.assertAllEqual(ret[1], 15) + def testVariableNamesRespectNameScopesWithDefun(self): + @function.defun + def create_variable(): + with ops.name_scope('foo'): + v = resource_variable_ops.ResourceVariable(0.0, name='bar') + self.assertEqual(v.name, 'foo/bar:0') + create_variable() + + def testVariableNamesRespectNameScopesWithDefunInGraph(self): + with context.graph_mode(): + @function.defun + def create_variable(): + with ops.name_scope('foo'): + v = resource_variable_ops.ResourceVariable([1.0, 2.0], name='bar') + self.assertEqual(v.name, 'foo/bar:0') + with ops.get_default_graph().as_default(): + create_variable() + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index e3a52141a0..d5786cac68 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -5008,9 +5008,22 @@ def init_scope(): """ # pylint: enable=g-doc-return-or-yield,line-too-long + in_graph_mode = context.in_graph_mode() + # Retrieve the active name scope: entering an `init_scope` preserves + # the name scope of the current context. + if in_graph_mode: + default_graph = get_default_graph() + scope = default_graph.get_name_scope() + else: + scope = context.context().scope_name + if scope and scope[-1] != '/': + # Names that end with trailing slashes are treated by `name_scope` as + # absolute. + scope = scope + '/' + outer_context = None - if context.in_graph_mode() and not _default_graph_stack.stack: - outer_context = get_default_graph().as_default + if in_graph_mode and not _default_graph_stack.stack: + outer_context = default_graph.as_default else: for stack_entry in reversed(context.context_stack.stack): if not stack_entry.is_building_function: @@ -5022,7 +5035,8 @@ def init_scope(): "eager context was previously active.") try: - with outer_context(), control_dependencies(None), tape.stop_recording(): + with outer_context(), name_scope(scope), control_dependencies( + None), tape.stop_recording(): yield finally: pass diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index 78519f108b..c5e177d521 100644 --- a/tensorflow/python/framework/ops_test.py +++ b/tensorflow/python/framework/ops_test.py @@ -2072,10 +2072,34 @@ class InitScopeTest(test_util.TensorFlowTestCase): # pylint: disable=protected-access self.assertEqual(len(ops._default_graph_stack.stack), 0) with ops.init_scope(): - self.assertEqual(len(ops._default_graph_stack.stack), 1) + self.assertGreater(len(ops._default_graph_stack.stack), 0) self.assertEqual(len(ops._default_graph_stack.stack), 0) # pylint: enable=protected-access + def testPreservesNameScopeInGraphConstruction(self): + with ops.Graph().as_default(): + function_graph = ops.Graph() + with function_graph.as_default(): + with ops.name_scope("inner"), ops.init_scope(): + self.assertEqual(ops.get_name_scope(), "inner") + self.assertEqual(ops.get_name_scope(), "") + + def testPreservesNameScopeInEagerExecution(self): + with context.eager_mode(): + def foo(): + with ops.name_scope("inner"), ops.init_scope(): + if context.in_graph_mode(): + self.assertEqual(ops.get_name_scope(), "inner") + else: + # A trailing slash is always appended when eager execution is + # enabled. + self.assertEqual(context.context().scope_name, "inner/") + foo() + self.assertEqual(ops.get_name_scope(), "") + foo_compiled = eager_function.defun(foo) + foo_compiled() + self.assertEqual(ops.get_name_scope(), "") + @test_util.with_c_api class GraphTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index a49d6fb44a..c87b7652ad 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -1043,6 +1043,7 @@ tf_py_test( "//tensorflow/python:training", "//tensorflow/python:util", "//tensorflow/python:variables", + "//tensorflow/python/eager:function", ], ) diff --git a/tensorflow/python/kernel_tests/variables_test.py b/tensorflow/python/kernel_tests/variables_test.py index f60ebf58f6..b16c8c002c 100644 --- a/tensorflow/python/kernel_tests/variables_test.py +++ b/tensorflow/python/kernel_tests/variables_test.py @@ -22,6 +22,7 @@ import operator import numpy as np +from tensorflow.python.eager import function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl @@ -509,6 +510,15 @@ class VariablesTestCase(test.TestCase): "", repr(var)) + def testVariableNamesPreserveNameScopesWithDefun(self): + @function.defun + def create_variable(): + with ops.name_scope("foo"): + v = variables.Variable(0.0, name="bar") + self.assertEqual(v.name, "foo/bar:0") + with ops.get_default_graph().as_default(): + create_variable() + class IsInitializedTest(test.TestCase): diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py index 84449e00be..806fdd3da7 100644 --- a/tensorflow/python/ops/template.py +++ b/tensorflow/python/ops/template.py @@ -140,7 +140,7 @@ def make_template(name_, func_, create_scope_now_=False, unique_name_=None, re-enter the scope and reuse those variables. Raises: - ValueError: if the name is None. + ValueError: if `name_` is None. """ return make_template_internal( name_, @@ -176,16 +176,14 @@ def make_template_internal(name_, custom_getter_: Optional custom getter for variables used in `func_`. See the @{tf.get_variable} `custom_getter` documentation for more information. - create_graph_function_: When True, the first invocation of the template will - execute `func_` as is, to allow for variable creation; however, the second - invocation and every invocation thereafter will execute func as a graph - function. In particular, this implies that `func_` must satisfy the - properties that `function.defun` requires of functions: See the - documentation of `function.defun` for details. When executing eagerly, - setting this flag to True can improve performance. Regardless of whether - eager execution is enabled, enabling this flag gives the caller access to - graph-function semantics, i.e., accesses to variables are totally ordered - and side-effecting ops are not pruned. + create_graph_function_: When True, `func_` will be executed as a graph + function. This implies that `func_` must satisfy the properties that + `function.defun` requires of functions: See the documentation of + `function.defun` for details. When executing eagerly, setting this flag to + True can improve performance. Regardless of whether eager execution is + enabled, enabling this flag gives the caller access to graph-function + semantics, i.e., accesses to variables are totally ordered and + side-effecting ops are not pruned. **kwargs: Keyword arguments to apply to `func_`. Returns: @@ -198,8 +196,8 @@ def make_template_internal(name_, re-enter the scope and reuse those variables. Raises: - ValueError: if the name is None. - ValueError: if unique_name_ is not None and eager execution is enabled. + ValueError: if `name_` is None. + ValueError: if `unique_name_` is not None and eager execution is enabled. """ if kwargs: @@ -266,18 +264,18 @@ class Template(object): template of the same scope/unique_name already exists and reuse is false, an error is raised. Defaults to None. custom_getter: optional custom getter to pass to `variable_scope()` - create_graph_function: When True, the first invocation of the template - will execute `func` as is, to allow for variable creation; however, the - second invocation and every invocation thereafter will execute `func` as - a graph function. Enabling this flag gives the caller access to - graph-function semantics, i.e., accesses to variables are totally - ordered and side-effecting ops are not pruned. - + create_graph_function: When True, `func` will be executed as a graph + function. Enabling this flag gives the caller access to graph-function + semantics, i.e., accesses to variables are totally ordered and + side-effecting ops are not pruned. Raises: - ValueError: if the name is None. + ValueError: if `name` is None. """ - self._func = func + if create_graph_function: + self._func = function.defun(func) + else: + self._func = func self._stacktrace = traceback.format_stack()[:-2] self._name = name self._unique_name = unique_name @@ -295,19 +293,13 @@ class Template(object): # This variable keeps track of whether the template has been called yet, # which is not the same as whether the scope has been created. self._variables_created = False - self._create_graph_function = create_graph_function def _call_func(self, args, kwargs): try: vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) trainable_at_start = len( ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) - result = self._func(*args, **kwargs) - if self._create_graph_function and not self._variables_created: - # Only execute self._func as a graph function once variables are - # created. - self._func = function.defun(self._func) if self._variables_created: # Variables were previously created, implying this is not the first @@ -542,14 +534,11 @@ class EagerTemplate(Template): names of all created Tensors. If set to False, the scope will be created at the first call location. custom_getter: optional custom getter to pass to `variable_scope()` - create_graph_function: When True, the first invocation of the template - will execute `func` as is, to allow for variable creation; however, the - second invocation and every invocation thereafter will execute `func` as - a graph function. Enabling this flag allows the caller to reap the - performance benefits associated with executing graphs, at the cost of - sacrificing debuggability; however, not all functions can be compiled - into graph functions. See the documentation for `function.defun` for - details. + create_graph_function: When True, `func` will be executed as a graph + function. Enabling this flag allows the caller to reap the performance + benefits associated with executing graphs, at the cost of sacrificing + debuggability; however, not all Python functions can be compiled into + graph functions. See the documentation for `function.defun` for details. Raises: RuntimeError: if eager execution is not enabled. @@ -573,12 +562,7 @@ class EagerTemplate(Template): try: vars_at_start = self._template_store.variables() trainable_at_start = self._template_store.trainable_variables() - result = self._func(*args, **kwargs) - if self._create_graph_function and not self._variables_created: - # Only execute self._func as a graph function once variables are - # created. - self._func = function.defun(self._func) if self._variables_created: # Variables were previously created, implying this is not the first -- GitLab From c1e58361a090678534830f2cbfd868aa63fc2c98 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Mon, 29 Jan 2018 10:01:50 -0800 Subject: [PATCH 205/423] Internal change PiperOrigin-RevId: 183681594 --- tensorflow/core/grappler/inputs/file_input_yielder.h | 6 +++--- tensorflow/core/kernels/fuzzing/fuzz_session.h | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/core/grappler/inputs/file_input_yielder.h b/tensorflow/core/grappler/inputs/file_input_yielder.h index a17e1c9ff2..b597319261 100644 --- a/tensorflow/core/grappler/inputs/file_input_yielder.h +++ b/tensorflow/core/grappler/inputs/file_input_yielder.h @@ -18,8 +18,8 @@ limitations under the License. // that may be stored in the checkpoint are not restored in order to speedup the // initialization. -#ifndef LEARNING_BRAIN_EXPERIMENTAL_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ -#define LEARNING_BRAIN_EXPERIMENTAL_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ +#ifndef TENSORFLOW_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ +#define TENSORFLOW_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ #include #include @@ -53,4 +53,4 @@ class FileInputYielder : public InputYielder { } // end namespace grappler } // end namespace tensorflow -#endif // LEARNING_BRAIN_EXPERIMENTAL_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ +#endif // TENSORFLOW_GRAPPLER_INPUTS_FILE_INPUT_YIELDER_H_ diff --git a/tensorflow/core/kernels/fuzzing/fuzz_session.h b/tensorflow/core/kernels/fuzzing/fuzz_session.h index 0c0e548a90..f1f3f199df 100644 --- a/tensorflow/core/kernels/fuzzing/fuzz_session.h +++ b/tensorflow/core/kernels/fuzzing/fuzz_session.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef LEARNING_BRAIN_KERNELS_FUZZING_FUZZ_SESSION_H_ -#define LEARNING_BRAIN_KERNELS_FUZZING_FUZZ_SESSION_H_ +#ifndef TENSORFLOW_CORE_KERNELS_FUZZING_FUZZ_SESSION_H_ +#define TENSORFLOW_CORE_KERNELS_FUZZING_FUZZ_SESSION_H_ #include "tensorflow/cc/framework/scope.h" #include "tensorflow/core/graph/graph.h" @@ -153,4 +153,4 @@ class FuzzStringInputOp : public FuzzSession { } // end namespace fuzzing } // end namespace tensorflow -#endif // LEARNING_BRAIN_KERNELS_FUZZING_FUZZ_SESSION_H_ +#endif // TENSORFLOW_CORE_KERNELS_FUZZING_FUZZ_SESSION_H_ -- GitLab From 197f573f3bd830bbc4dc277e7c8b8cfaca65e14a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 10:12:42 -0800 Subject: [PATCH 206/423] Reduce memory wasted by GCS cache by shrinking buffer capacity, after a cache fill completes. PiperOrigin-RevId: 183683856 --- tensorflow/core/platform/cloud/file_block_cache.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/platform/cloud/file_block_cache.cc b/tensorflow/core/platform/cloud/file_block_cache.cc index 0375af516b..6add1142a1 100644 --- a/tensorflow/core/platform/cloud/file_block_cache.cc +++ b/tensorflow/core/platform/cloud/file_block_cache.cc @@ -131,6 +131,7 @@ Status FileBlockCache::MaybeFetch(const Key& key, block->mu.lock(); // Reacquire the lock immediately afterwards if (status.ok()) { block->data.resize(bytes_transferred, 0); + block->data.shrink_to_fit(); downloaded_block = true; block->state = FetchState::FINISHED; } else { -- GitLab From 0905a7ed035e66f3abdb123ab53cb0c640e60f0b Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Mon, 29 Jan 2018 10:19:58 -0800 Subject: [PATCH 207/423] Fix a comment PiperOrigin-RevId: 183685209 --- tensorflow/contrib/lite/interpreter.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 4f732769f9..9dc864ead8 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -108,7 +108,7 @@ class Interpreter { // Adds a node with the given parameters and returns the index of the new // node in `node_index` (optionally). Interpreter will take ownership of - // `builtin_data` and destroy it with `delete`. Ownership of 'init_data' + // `builtin_data` and destroy it with `free`. Ownership of 'init_data' // remains with the caller. TfLiteStatus AddNodeWithParameters(const std::vector& inputs, const std::vector& outputs, -- GitLab From 1f26c65254268730b7409f517d1ed1b554d01e50 Mon Sep 17 00:00:00 2001 From: Shanqing Cai Date: Mon, 29 Jan 2018 10:22:10 -0800 Subject: [PATCH 208/423] tfdbg: let session wrappers handle empty fetches correctly Fixes: #15882 PiperOrigin-RevId: 183685645 --- .../python/debug/wrappers/dumping_wrapper_test.py | 5 +++++ tensorflow/python/debug/wrappers/framework.py | 9 ++++++++- .../debug/wrappers/local_cli_wrapper_test.py | 14 ++++++++++++++ 3 files changed, 27 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py index acea9433e2..254201c393 100644 --- a/tensorflow/python/debug/wrappers/dumping_wrapper_test.py +++ b/tensorflow/python/debug/wrappers/dumping_wrapper_test.py @@ -389,6 +389,11 @@ class DumpingDebugWrapperSessionTest(test_util.TensorFlowTestCase): r"mode\."): sess.invoke_node_stepper(node_stepper) + def testDumpingWrapperWithEmptyFetchWorks(self): + sess = dumping_wrapper.DumpingDebugWrapperSession( + self.sess, session_root=self.session_root, log_usage=False) + sess.run([]) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/debug/wrappers/framework.py b/tensorflow/python/debug/wrappers/framework.py index 909150eb6a..c530204bbf 100644 --- a/tensorflow/python/debug/wrappers/framework.py +++ b/tensorflow/python/debug/wrappers/framework.py @@ -121,7 +121,9 @@ from tensorflow.python.debug.lib import debug_utils from tensorflow.python.debug.lib import stepper from tensorflow.python.framework import errors from tensorflow.python.framework import ops +from tensorflow.python.platform import tf_logging from tensorflow.python.training import monitored_session +from tensorflow.python.util import nest # Helper function. @@ -439,7 +441,12 @@ class BaseDebugWrapperSession(session.SessionInterface): "callable_runner and fetches/feed_dict are mutually exclusive, but " "are used simultaneously.") - if self._is_disabled_thread(): + empty_fetches = not nest.flatten(fetches) + if empty_fetches: + tf_logging.info( + "Due to empty fetches, tfdbg Session wrapper is letting a " + "Session.run pass through without any debugging actions.") + if self._is_disabled_thread() or empty_fetches: if callable_runner: return callable_runner(*callable_runner_args) else: diff --git a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py index 770a496aa9..490812c96d 100644 --- a/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py +++ b/tensorflow/python/debug/wrappers/local_cli_wrapper_test.py @@ -664,6 +664,20 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase): [["run"], ["run"]], monitored_sess) self.assertFalse(wrapped_monitored_sess.should_stop()) + def testRunsWithEmptyFetchWorks(self): + wrapped_sess = LocalCLIDebuggerWrapperSessionForTest( + [["run"]], self.sess, dump_root="") + + run_output = wrapped_sess.run([]) + self.assertEqual([], run_output) + + def testRunsWithEmptyNestedFetchWorks(self): + wrapped_sess = LocalCLIDebuggerWrapperSessionForTest( + [["run"]], self.sess, dump_root="") + + run_output = wrapped_sess.run({"foo": {"baz": []}, "bar": ()}) + self.assertEqual({"foo": {"baz": []}, "bar": ()}, run_output) + if __name__ == "__main__": googletest.main() -- GitLab From 730071d0dca35a9e08f3bdc49661ae34d109da74 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Mon, 29 Jan 2018 10:31:23 -0800 Subject: [PATCH 209/423] Make TFLite BatchToSpaceND op have parity with TF BatchToSpaceND op. PiperOrigin-RevId: 183687487 --- tensorflow/contrib/lite/builtin_op_data.h | 8 -- .../contrib/lite/kernels/batch_to_space_nd.cc | 69 +++++++++----- .../lite/kernels/batch_to_space_nd_test.cc | 91 +++++++++++++++---- tensorflow/contrib/lite/model.cc | 15 --- tensorflow/contrib/lite/schema/schema.fbs | 3 - .../contrib/lite/schema/schema_generated.h | 89 +----------------- .../contrib/lite/testing/generate_examples.py | 43 +++++++-- .../testing/generated_examples_zip_test.cc | 6 +- .../contrib/lite/toco/tflite/operator.cc | 15 +-- .../contrib/lite/toco/tflite/operator_test.cc | 13 --- 10 files changed, 160 insertions(+), 192 deletions(-) diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index 8966e5c2b6..6dd9cb39d2 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -127,14 +127,6 @@ typedef struct { } TfLiteSpaceToBatchNDParams; typedef struct { - // Number of spatial dimensions. - // For now only NHWC is supported, and the value should always be 2. - int num_spatial_dimensions; - // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. - // For now we will fix the maximum possible number of dimensions. - int block_shape[2]; - int before_crops[2]; - int after_crops[2]; } TfLiteBatchToSpaceNDParams; typedef struct { diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc index 0eed680fdc..d84a77039b 100644 --- a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc +++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc @@ -35,12 +35,14 @@ enum KernelType { struct BatchToSpaceNDContext { BatchToSpaceNDContext(TfLiteContext* context, TfLiteNode* node) { - params = reinterpret_cast(node->builtin_data); input = GetInput(context, node, 0); + block_shape = GetInput(context, node, 1); + crops = GetInput(context, node, 2); output = GetOutput(context, node, 0); } - TfLiteBatchToSpaceNDParams* params; TfLiteTensor* input; + TfLiteTensor* block_shape; + TfLiteTensor* crops; TfLiteTensor* output; }; @@ -48,24 +50,22 @@ struct BatchToSpaceNDContext { // The 4D array need to have exactly 2 spatial dimensions. // TODO(ycling): Support arbitrary dimension in BatchToSpaceND. const int kInputDimensionNum = 4; -const int kOutputDimensionNum = 4; +const int kBlockSizeDimensionNum = 1; const int kSpatialDimensionNum = 2; -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - // The 2nd tensor (block_shape) and the 3rd tensor (crops) are ignored now. - TF_LITE_ENSURE(context, NumInputs(node) >= 1 && NumInputs(node) <= 3); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + BatchToSpaceNDContext* op_context) { + TfLiteIntArray* input_size = op_context->input->dims; + const int* block_shape = GetTensorData(op_context->block_shape); - BatchToSpaceNDContext op_context(context, node); - TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.input), - kInputDimensionNum); - TF_LITE_ENSURE_EQ(context, op_context.params->num_spatial_dimensions, + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->block_shape), + kBlockSizeDimensionNum); + TF_LITE_ENSURE_EQ(context, op_context->block_shape->dims->data[0], + kSpatialDimensionNum); + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->crops), kSpatialDimensionNum); - TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); - - const TfLiteIntArray* input_size = op_context.input->dims; - const int* block_shape = op_context.params->block_shape; + // TODO(ycling): Add crops as part of calculation. // Number of batch must be multiple of (block_shape[0] * block_shape[1]). TF_LITE_ENSURE_EQ(context, input_size->data[0] % (block_shape[0] * block_shape[1]), 0); @@ -76,27 +76,48 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const int output_width = input_size->data[2] * block_shape[1]; const int output_channel_size = input_size->data[3]; - TfLiteIntArray* output_size = TfLiteIntArrayCreate(kOutputDimensionNum); + TfLiteIntArray* output_size = TfLiteIntArrayCopy(input_size); output_size->data[0] = output_batch_size; output_size->data[1] = output_height; output_size->data[2] = output_width; output_size->data[3] = output_channel_size; - return context->ResizeTensor(context, op_context.output, output_size); + return context->ResizeTensor(context, op_context->output, output_size); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 3); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + BatchToSpaceNDContext op_context(context, node); + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.input), + kInputDimensionNum); + TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); + + if (!IsConstantTensor(op_context.block_shape) || + !IsConstantTensor(op_context.crops)) { + SetTensorToDynamic(op_context.output); + return kTfLiteOk; + } + return ResizeOutputTensor(context, &op_context); } template TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { BatchToSpaceNDContext op_context(context, node); - int block_shape_dims_array[1] = {kSpatialDimensionNum}; - Dims<4> block_shape_dims = GetTensorDims(block_shape_dims_array, 1); + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + TfLiteTensorRealloc(op_context.output->bytes, op_context.output); + } -#define TF_LITE_BATCH_TO_SPACE_ND(type, scalar) \ - type::BatchToSpaceND(GetTensorData(op_context.input), \ - GetTensorDims(op_context.input), \ - op_context.params->block_shape, block_shape_dims, \ - GetTensorData(op_context.output), \ +#define TF_LITE_BATCH_TO_SPACE_ND(type, scalar) \ + type::BatchToSpaceND(GetTensorData(op_context.input), \ + GetTensorDims(op_context.input), \ + GetTensorData(op_context.block_shape), \ + GetTensorDims(op_context.block_shape), \ + GetTensorData(op_context.output), \ GetTensorDims(op_context.output)) switch (op_context.input->type) { // Already know in/out types are same. case kTfLiteFloat32: diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc index 3ec4efbebc..c9152bf967 100644 --- a/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc +++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc @@ -26,37 +26,88 @@ using ::testing::ElementsAreArray; class BatchToSpaceNDOpModel : public SingleOpModel { public: - BatchToSpaceNDOpModel(std::initializer_list input_shape, - std::initializer_list block_shape, - std::initializer_list before_crops, - std::initializer_list after_crops) { - input_ = AddInput(TensorType_FLOAT32); - output_ = AddOutput(TensorType_FLOAT32); - SetBuiltinOp(BuiltinOperator_BATCH_TO_SPACE_ND, - BuiltinOptions_BatchToSpaceNDOptions, - CreateBatchToSpaceNDOptions( - builder_, builder_.CreateVector(block_shape), - builder_.CreateVector(before_crops), - builder_.CreateVector(after_crops)) - .Union()); - BuildInterpreter({input_shape}); - } - void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } + void SetBlockShape(std::initializer_list data) { + PopulateTensor(block_shape_, data); + } + + void SetCrops(std::initializer_list data) { + PopulateTensor(crops_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } std::vector GetOutputShape() { return GetTensorShape(output_); } - private: + protected: int input_; + int block_shape_; + int crops_; int output_; }; -TEST(BatchToSpaceNDOpTest, SimpleTest) { - BatchToSpaceNDOpModel m({4, 2, 2, 1}, {2, 2}, {0, 0}, {0, 0}); +// Tests case where block_shape and crops are const tensors. +// +// Example usage is as follows: +// BatchToSpaceNDOpConstModel m(input_shape, block_shape, crops); +// m.SetInput(input_data); +// m.Invoke(); +class BatchToSpaceNDOpConstModel : public BatchToSpaceNDOpModel { + public: + BatchToSpaceNDOpConstModel(std::initializer_list input_shape, + std::initializer_list block_shape, + std::initializer_list crops) { + input_ = AddInput(TensorType_FLOAT32); + block_shape_ = AddConstInput(TensorType_INT32, block_shape, {2}); + crops_ = AddConstInput(TensorType_INT32, crops, {2, 2}); + output_ = AddOutput(TensorType_FLOAT32); + + SetBuiltinOp(BuiltinOperator_BATCH_TO_SPACE_ND, + BuiltinOptions_BatchToSpaceNDOptions, + CreateBatchToSpaceNDOptions(builder_).Union()); + BuildInterpreter({input_shape}); + } +}; + +// Tests case where block_shape and crops are non-const tensors. +// +// Example usage is as follows: +// BatchToSpaceNDOpDynamicModel m(input_shape); +// m.SetInput(input_data); +// m.SetBlockShape(block_shape); +// m.SetPaddings(crops); +// m.Invoke(); +class BatchToSpaceNDOpDynamicModel : public BatchToSpaceNDOpModel { + public: + BatchToSpaceNDOpDynamicModel(std::initializer_list input_shape) { + input_ = AddInput(TensorType_FLOAT32); + block_shape_ = AddInput(TensorType_INT32); + crops_ = AddInput(TensorType_INT32); + output_ = AddOutput(TensorType_FLOAT32); + + SetBuiltinOp(BuiltinOperator_BATCH_TO_SPACE_ND, + BuiltinOptions_BatchToSpaceNDOptions, + CreateBatchToSpaceNDOptions(builder_).Union()); + BuildInterpreter({input_shape, {2}, {2, 2}}); + } +}; + +TEST(BatchToSpaceNDOpTest, SimpleConstTest) { + BatchToSpaceNDOpConstModel m({4, 2, 2, 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({1, 4, 4, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 5, 2, 6, 9, 13, 10, 14, 3, 7, + 4, 8, 11, 15, 12, 16})); +} + +TEST(BatchToSpaceNDOpTest, SimpleDynamicTest) { + BatchToSpaceNDOpDynamicModel m({4, 2, 2, 1}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + m.SetBlockShape({2, 2}); + m.SetCrops({0, 0, 0, 0}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 5, 2, 6, 9, 13, 10, 14, 3, 7, @@ -64,7 +115,7 @@ TEST(BatchToSpaceNDOpTest, SimpleTest) { } TEST(BatchToSpaceNDOpTest, InvalidShapeTest) { - EXPECT_DEATH(BatchToSpaceNDOpModel({3, 2, 2, 1}, {2, 2}, {0, 0}, {0, 0}), + EXPECT_DEATH(BatchToSpaceNDOpConstModel({3, 2, 2, 1}, {2, 2}, {0, 0, 0, 0}), "Cannot allocate tensors"); } diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index ec4d6e3487..64b8f55097 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -536,21 +536,6 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, break; } case BuiltinOperator_BATCH_TO_SPACE_ND: { - auto* params = MallocPOD(); - if (auto* schema_params = - op->builtin_options_as_BatchToSpaceNDOptions()) { - const auto& block_shape = schema_params->block_shape(); - FlatBufferIntVectorToArray(sizeof(params->block_shape), block_shape, - params->block_shape, error_reporter); - const auto& before_crops = schema_params->before_crops(); - FlatBufferIntVectorToArray(sizeof(params->before_crops), before_crops, - params->before_crops, error_reporter); - const auto& after_crops = schema_params->after_crops(); - FlatBufferIntVectorToArray(sizeof(params->after_crops), after_crops, - params->after_crops, error_reporter); - params->num_spatial_dimensions = block_shape->Length(); - } - builtin_data = reinterpret_cast(params); break; } case BuiltinOperator_TRANSPOSE: { diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 50709344ea..f6217567f8 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -295,9 +295,6 @@ table SpaceToBatchNDOptions { } table BatchToSpaceNDOptions { - block_shape:[int]; - before_crops:[int]; - after_crops:[int]; } table SkipGramOptions { diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index f1ee925df2..ad758529a3 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -2929,33 +2929,14 @@ flatbuffers::Offset CreateSpaceToBatchNDOptions( struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable { typedef BatchToSpaceNDOptions TableType; - std::vector block_shape; - std::vector before_crops; - std::vector after_crops; BatchToSpaceNDOptionsT() {} }; struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef BatchToSpaceNDOptionsT NativeTableType; - enum { VT_BLOCK_SHAPE = 4, VT_BEFORE_CROPS = 6, VT_AFTER_CROPS = 8 }; - const flatbuffers::Vector *block_shape() const { - return GetPointer *>(VT_BLOCK_SHAPE); - } - const flatbuffers::Vector *before_crops() const { - return GetPointer *>(VT_BEFORE_CROPS); - } - const flatbuffers::Vector *after_crops() const { - return GetPointer *>(VT_AFTER_CROPS); - } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_BLOCK_SHAPE) && - verifier.Verify(block_shape()) && - VerifyOffset(verifier, VT_BEFORE_CROPS) && - verifier.Verify(before_crops()) && - VerifyOffset(verifier, VT_AFTER_CROPS) && - verifier.Verify(after_crops()) && verifier.EndTable(); + return VerifyTableStart(verifier) && verifier.EndTable(); } BatchToSpaceNDOptionsT *UnPack( const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -2970,18 +2951,6 @@ struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS struct BatchToSpaceNDOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_block_shape( - flatbuffers::Offset> block_shape) { - fbb_.AddOffset(BatchToSpaceNDOptions::VT_BLOCK_SHAPE, block_shape); - } - void add_before_crops( - flatbuffers::Offset> before_crops) { - fbb_.AddOffset(BatchToSpaceNDOptions::VT_BEFORE_CROPS, before_crops); - } - void add_after_crops( - flatbuffers::Offset> after_crops) { - fbb_.AddOffset(BatchToSpaceNDOptions::VT_AFTER_CROPS, after_crops); - } explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -2995,29 +2964,11 @@ struct BatchToSpaceNDOptionsBuilder { }; inline flatbuffers::Offset CreateBatchToSpaceNDOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> block_shape = 0, - flatbuffers::Offset> before_crops = 0, - flatbuffers::Offset> after_crops = 0) { + flatbuffers::FlatBufferBuilder &_fbb) { BatchToSpaceNDOptionsBuilder builder_(_fbb); - builder_.add_after_crops(after_crops); - builder_.add_before_crops(before_crops); - builder_.add_block_shape(block_shape); return builder_.Finish(); } -inline flatbuffers::Offset -CreateBatchToSpaceNDOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *block_shape = nullptr, - const std::vector *before_crops = nullptr, - const std::vector *after_crops = nullptr) { - return tflite::CreateBatchToSpaceNDOptions( - _fbb, block_shape ? _fbb.CreateVector(*block_shape) : 0, - before_crops ? _fbb.CreateVector(*before_crops) : 0, - after_crops ? _fbb.CreateVector(*after_crops) : 0); -} - flatbuffers::Offset CreateBatchToSpaceNDOptions( flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -5774,33 +5725,6 @@ inline void BatchToSpaceNDOptions::UnPackTo( const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = block_shape(); - if (_e) { - _o->block_shape.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->block_shape[_i] = _e->Get(_i); - } - } - }; - { - auto _e = before_crops(); - if (_e) { - _o->before_crops.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->before_crops[_i] = _e->Get(_i); - } - } - }; - { - auto _e = after_crops(); - if (_e) { - _o->after_crops.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->after_crops[_i] = _e->Get(_i); - } - } - }; } inline flatbuffers::Offset BatchToSpaceNDOptions::Pack( @@ -5820,14 +5744,7 @@ inline flatbuffers::Offset CreateBatchToSpaceNDOptions( const flatbuffers::rehasher_function_t *__rehasher; } _va = {&_fbb, _o, _rehasher}; (void)_va; - auto _block_shape = - _o->block_shape.size() ? _fbb.CreateVector(_o->block_shape) : 0; - auto _before_crops = - _o->before_crops.size() ? _fbb.CreateVector(_o->before_crops) : 0; - auto _after_crops = - _o->after_crops.size() ? _fbb.CreateVector(_o->after_crops) : 0; - return tflite::CreateBatchToSpaceNDOptions(_fbb, _block_shape, _before_crops, - _after_crops); + return tflite::CreateBatchToSpaceNDOptions(_fbb); } inline SkipGramOptionsT *SkipGramOptions::UnPack( diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index bc9b23aeb4..4ae6ccb765 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -94,7 +94,8 @@ KNOWN_BUGS = { r"softmax.*input_shape=\[1,3,4,3\]": "67749831", # SpaceToDepth only supports float32. r"space_to_depth.*(float16|int32|uint8|int64)": "68018134", - # BatchToSpaceND doesn't support cropping. + # BatchToSpaceND doesn't support cropping. This catches test cases with + # const tensors as crops. r"batch_to_space_nd.*crops=\[\[1,1\],\[1,1\]\]": "70594634", # BatchToSpaceND only supports 4D tensors. r"batch_to_space_nd.*input_shape=\[8,2,2,2,1,1\]": "70594733", @@ -1361,6 +1362,8 @@ def make_batch_to_space_nd_tests(zip_path): "input_shape": [[12, 2, 2, 1]], "block_shape": [[1, 4], [2, 2], [3, 4]], "crops": [[[0, 0], [0, 0]], [[1, 1], [1, 1]]], + "constant_block_shape": [True, False], + "constant_crops": [True, False], }, # Non-4D use case: 1 bath dimension, 3 spatial dimensions, 2 others. { @@ -1368,23 +1371,47 @@ def make_batch_to_space_nd_tests(zip_path): "input_shape": [[8, 2, 2, 2, 1, 1]], "block_shape": [[2, 2, 2]], "crops": [[[0, 0], [0, 0], [0, 0]]], + "constant_block_shape": [True, False], + "constant_crops": [True, False], }, ] def build_graph(parameters): + """Build a batch_to_space graph given `parameters`.""" input_tensor = tf.placeholder( dtype=parameters["dtype"], name="input", shape=parameters["input_shape"]) - out = tf.batch_to_space_nd(input_tensor, parameters["block_shape"], - parameters["crops"]) - return [input_tensor], [out] + input_tensors = [input_tensor] + + # Get block_shape either as a const or as a placeholder (tensor). + if parameters["constant_block_shape"]: + block_shape = parameters["block_shape"] + else: + shape = [len(parameters["block_shape"])] + block_shape = tf.placeholder(dtype=tf.int32, name="shape", shape=shape) + input_tensors.append(block_shape) + + # Get crops either as a const or as a placeholder (tensor). + if parameters["constant_crops"]: + crops = parameters["crops"] + else: + shape = [len(parameters["crops"]), 2] + crops = tf.placeholder(dtype=tf.int32, name="crops", shape=shape) + input_tensors.append(crops) + + out = tf.batch_to_space_nd(input_tensor, block_shape, crops) + 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_block_shape"]: + values.append(np.array(parameters["block_shape"])) + if not parameters["constant_crops"]: + values.append(np.array(parameters["crops"])) + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index 2bbfe77a12..e6b782472a 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -67,7 +67,11 @@ std::map kBrokenTests = { // L2Norm only supports tensors with 4D or fewer. {R"(^\/l2normdim=.*,epsilon=.*,input_shape=\[.,.,.,.,.*\])", "67963684"}, - // SpaceToBatch only supports 4D tensors. + // BatchToSpaceND doesn't support cropping. This catches test cases with + // non-const tensors as crops. + {R"(^\/batch_to_space_nd.*crops=\[\[1,1\],\[1,1\]\])", "70594634"}, + + // SpaceToBatchND only supports 4D tensors. {R"(^\/space_to_batch_nd.*input_shape=\[1,4,4,4,1,1\])", "70848787"}, // L2Norm only works for dim=-1. diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 2d6bccce2b..853a9f46c2 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -211,24 +211,11 @@ class BatchToSpaceND flatbuffers::Offset WriteOptions( const TocoOperator& op, flatbuffers::FlatBufferBuilder* builder) const override { - auto block_shape = builder->CreateVector(op.block_shape); - auto before_crops = builder->CreateVector(op.before_crops); - auto after_crops = builder->CreateVector(op.after_crops); - return ::tflite::CreateBatchToSpaceNDOptions(*builder, block_shape, - before_crops, after_crops); + return ::tflite::CreateBatchToSpaceNDOptions(*builder); } void ReadOptions(const TfLiteOptions& options, TocoOperator* op) const override { - op->block_shape.insert(op->block_shape.end(), - options.block_shape()->begin(), - options.block_shape()->end()); - op->before_crops.insert(op->before_crops.end(), - options.before_crops()->begin(), - options.before_crops()->end()); - op->after_crops.insert(op->after_crops.end(), - options.after_crops()->begin(), - options.after_crops()->end()); } }; diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 78af3a767d..4df9071095 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -132,19 +132,6 @@ TEST_F(OperatorTest, BuiltinSpaceToBatchND) { EXPECT_EQ(op.after_paddings, output_toco_op->after_paddings); } -TEST_F(OperatorTest, BuiltinBatchToSpaceND) { - BatchToSpaceNDOperator op; - op.block_shape = {2, 2}; - op.before_crops = {1, 2}; - op.after_crops = {3, 4}; - - auto output_toco_op = SerializeAndDeserialize( - GetOperator("BATCH_TO_SPACE_ND", OperatorType::kBatchToSpaceND), op); - EXPECT_EQ(op.block_shape, output_toco_op->block_shape); - EXPECT_EQ(op.before_crops, output_toco_op->before_crops); - EXPECT_EQ(op.after_crops, output_toco_op->after_crops); -} - TEST_F(OperatorTest, BuiltinMean) { MeanOperator op; op.axis = {1, 2}; -- GitLab From fd63d4e30a01cf860baf60b990b223cd54bc895c Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Mon, 29 Jan 2018 10:42:32 -0800 Subject: [PATCH 210/423] Add C0326 bad-whitespace error to pylint sanity check. PiperOrigin-RevId: 183689499 --- .../python/ops/accumulate_n_v2_test.py | 20 +- .../learn/python/learn/datasets/base.py | 19 +- tensorflow/contrib/lite/tools/visualize.py | 104 +++---- .../examples/cifar10/cifar10_input.py | 52 ++-- .../python/kernel_tests/core_rnn_cell_test.py | 258 ++++++++++-------- .../examples/learn/text_classification.py | 19 +- tensorflow/python/client/notebook.py | 11 +- tensorflow/python/estimator/training.py | 56 ++-- .../python/kernel_tests/tensordot_op_test.py | 29 +- tensorflow/tools/ci_build/ci_sanity.sh | 3 +- tensorflow/tools/compatibility/tf_upgrade.py | 93 ++++--- 11 files changed, 354 insertions(+), 310 deletions(-) diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py b/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py index b5e9f8df79..6f65fe771e 100644 --- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py +++ b/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py @@ -31,7 +31,6 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import googletest - class AccumulateNV2Test(test_util.TensorFlowTestCase): """Tests of the new, differentiable version of accumulate_n""" @@ -62,8 +61,9 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): accum_n = av2.accumulate_n_v2(input_vars) sess.run(variables.global_variables_initializer()) accum_n_grad = gradients.gradients(accum_n, input_vars) - self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 - [g.eval() for g in accum_n_grad]) + self.assertAllEqual( + np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 + [g.eval() for g in accum_n_grad]) # The tests below used to be in a separate class under cwise_ops_test.py, # which did not run in the default test target. @@ -75,8 +75,8 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): np.random.rand(16, 16, 16, 16).astype(np.float32) for _ in range(20) ] random_tensors = [ - ops.convert_to_tensor( - x, dtype=dtypes_lib.float32) for x in random_arrays + ops.convert_to_tensor(x, dtype=dtypes_lib.float32) + for x in random_arrays ] tf_val = av2.accumulate_n_v2(random_tensors) np_val = random_arrays[0] @@ -95,21 +95,21 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): a = variables.Variable(0.2) b = variables.Variable(0.1) - tf_val = av2.accumulate_n_v2([a,b], shape=[2,2]) # Should be shape=[] + tf_val = av2.accumulate_n_v2([a, b], shape=[2, 2]) # Should be shape=[] def testIncompatibleShapes(self): with self.test_session(): with self.assertRaises(ValueError): - a = variables.Variable(np.array([0.1,0.2])) - b = variables.Variable(np.array([[0.3],[0.4]])) - tf_val = av2.accumulate_n_v2([a,b]) + a = variables.Variable(np.array([0.1, 0.2])) + b = variables.Variable(np.array([[0.3], [0.4]])) + tf_val = av2.accumulate_n_v2([a, b]) def testWrongType(self): with self.test_session(): with self.assertRaises(TypeError): a = variables.Variable(0.2, dtype=np.float32) b = variables.Variable(0.1, dtype=np.float32) - tf_val = av2.accumulate_n_v2([a,b], tensor_dtype=np.int32) + tf_val = av2.accumulate_n_v2([a, b], tensor_dtype=np.int32) def testWrongTypeOneInput(self): # Scenario that used to trigger a bug, even when testWrongType() worked diff --git a/tensorflow/contrib/learn/python/learn/datasets/base.py b/tensorflow/contrib/learn/python/learn/datasets/base.py index 71978d4394..18bf16e246 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/base.py +++ b/tensorflow/contrib/learn/python/learn/datasets/base.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Base utilities for loading datasets.""" from __future__ import absolute_import @@ -100,9 +99,7 @@ def load_iris(data_path=None): module_path = path.dirname(__file__) data_path = path.join(module_path, 'data', 'iris.csv') return load_csv_with_header( - data_path, - target_dtype=np.int, - features_dtype=np.float) + data_path, target_dtype=np.int, features_dtype=np.float) def load_boston(data_path=None): @@ -118,16 +115,10 @@ def load_boston(data_path=None): module_path = path.dirname(__file__) data_path = path.join(module_path, 'data', 'boston_house_prices.csv') return load_csv_with_header( - data_path, - target_dtype=np.float, - features_dtype=np.float) + data_path, target_dtype=np.float, features_dtype=np.float) -def retry(initial_delay, - max_delay, - factor=2.0, - jitter=0.25, - is_retriable=None): +def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None): """Simple decorator for wrapping retriable functions. Args: @@ -152,7 +143,7 @@ def retry(initial_delay, def delays(): delay = initial_delay while delay <= max_delay: - yield delay * random.uniform(1 - jitter, 1 + jitter) + yield delay * random.uniform(1 - jitter, 1 + jitter) delay *= factor def wrap(fn): @@ -172,7 +163,9 @@ def retry(initial_delay, else: raise return fn(*args, **kwargs) + return wrapped_fn + return wrap diff --git a/tensorflow/contrib/lite/tools/visualize.py b/tensorflow/contrib/lite/tools/visualize.py index d0d78e3afa..f571dd59da 100644 --- a/tensorflow/contrib/lite/tools/visualize.py +++ b/tensorflow/contrib/lite/tools/visualize.py @@ -198,10 +198,13 @@ class TensorMapper(object): def GenerateGraph(subgraph_idx, g, opcode_mapper): """Produces the HTML required to have a d3 visualization of the dag.""" + def TensorName(idx): - return "t%d"%idx + return "t%d" % idx + def OpName(idx): - return "o%d"%idx + return "o%d" % idx + edges = [] nodes = [] first = {} @@ -210,27 +213,35 @@ def GenerateGraph(subgraph_idx, g, opcode_mapper): for tensor_input_position, tensor_index in enumerate(op["inputs"]): if tensor_index not in first: first[tensor_index] = ( - op_index*pixel_mult, - tensor_input_position*pixel_mult - pixel_mult/2) - edges.append( - {"source": TensorName(tensor_index), "target": OpName(op_index)}) + op_index * pixel_mult, + tensor_input_position * pixel_mult - pixel_mult / 2) + edges.append({ + "source": TensorName(tensor_index), + "target": OpName(op_index) + }) for tensor_index in op["outputs"]: - edges.append( - {"target": TensorName(tensor_index), "source": OpName(op_index)}) - nodes.append({"id": OpName(op_index), - "name": opcode_mapper(op["opcode_index"]), - "group": 2, - "x": pixel_mult, - "y": op_index * pixel_mult}) + edges.append({ + "target": TensorName(tensor_index), + "source": OpName(op_index) + }) + nodes.append({ + "id": OpName(op_index), + "name": opcode_mapper(op["opcode_index"]), + "group": 2, + "x": pixel_mult, + "y": op_index * pixel_mult + }) for tensor_index, tensor in enumerate(g["tensors"]): - initial_y = (first[tensor_index] if tensor_index in first - else len(g["operators"])) - - nodes.append({"id": TensorName(tensor_index), - "name": "%s (%d)" % (tensor["name"], tensor_index), - "group": 1, - "x": 2, - "y": initial_y}) + initial_y = ( + first[tensor_index] if tensor_index in first else len(g["operators"])) + + nodes.append({ + "id": TensorName(tensor_index), + "name": "%s (%d)" % (tensor["name"], tensor_index), + "group": 1, + "x": 2, + "y": initial_y + }) graph_str = json.dumps({"nodes": nodes, "edges": edges}) html = _D3_HTML_TEMPLATE % (graph_str, subgraph_idx) @@ -267,7 +278,7 @@ def GenerateTableHtml(items, keys_to_print, display_index=True): for h, mapper in keys_to_print: val = tensor[h] if h in tensor else None val = val if mapper is None else mapper(val) - html += "\n"%val + html += "\n" % val html += "\n" html += "
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.5.0-rc1CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.5.0-rc1GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.5.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.5.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.4.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.4.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.368
tensorflow-1.3.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
%s%s
\n" @@ -279,18 +290,19 @@ def CreateHtmlFile(tflite_input, html_output): # Convert the model into a JSON flatbuffer using flatc (build if doesn't # exist. - if not os.path.exists(tflite_input): + if not os.path.exists(tflite_input): raise RuntimeError("Invalid filename %r" % tflite_input) if tflite_input.endswith(".tflite") or tflite_input.endswith(".bin"): # Run convert - cmd = (_BINARY + " -t " - "--strict-json --defaults-json -o /tmp {schema} -- {input}".format( - input=tflite_input, schema=_SCHEMA)) + cmd = ( + _BINARY + " -t " + "--strict-json --defaults-json -o /tmp {schema} -- {input}".format( + input=tflite_input, schema=_SCHEMA)) print(cmd) os.system(cmd) - real_output = ("/tmp/"+ os.path.splitext(os.path.split(tflite_input)[-1])[0] - + ".json") + real_output = ("/tmp/" + os.path.splitext( + os.path.split(tflite_input)[-1])[0] + ".json") data = json.load(open(real_output)) elif tflite_input.endswith(".json"): @@ -302,12 +314,13 @@ def CreateHtmlFile(tflite_input, html_output): html += "

TensorFlow Lite Model

" data["filename"] = tflite_input # Avoid special case - toplevel_stuff = [("filename", None), ("version", None), - ("description", None)] + toplevel_stuff = [("filename", None), ("version", None), ("description", + None)] html += "\n" for key, mapping in toplevel_stuff: - if not mapping: mapping = lambda x: x + if not mapping: + mapping = lambda x: x html += "\n" % (key, mapping(data[key])) html += "
%s%s
\n" @@ -320,22 +333,22 @@ def CreateHtmlFile(tflite_input, html_output): html += "
" tensor_mapper = TensorMapper(g) opcode_mapper = OpCodeMapper(data) - op_keys_to_display = [ - ("inputs", tensor_mapper), ("outputs", tensor_mapper), - ("builtin_options", None), ("opcode_index", opcode_mapper)] - tensor_keys_to_display = [ - ("name", None), ("type", None), ("shape", None), ("buffer", None), - ("quantization", None)] + op_keys_to_display = [("inputs", tensor_mapper), ("outputs", tensor_mapper), + ("builtin_options", None), ("opcode_index", + opcode_mapper)] + tensor_keys_to_display = [("name", None), ("type", None), ("shape", None), + ("buffer", None), ("quantization", None)] html += "

Subgraph %d

\n" % subgraph_idx # Inputs and outputs. html += "

Inputs/Outputs

\n" - html += GenerateTableHtml([{"inputs": g["inputs"], - "outputs": g["outputs"]}], - [("inputs", tensor_mapper), - ("outputs", tensor_mapper)], - display_index=False) + html += GenerateTableHtml( + [{ + "inputs": g["inputs"], + "outputs": g["outputs"] + }], [("inputs", tensor_mapper), ("outputs", tensor_mapper)], + display_index=False) # Print the tensors. html += "

Tensors

\n" @@ -357,8 +370,7 @@ def CreateHtmlFile(tflite_input, html_output): # Operator codes html += "

Operator Codes

\n" - html += GenerateTableHtml(data["operator_codes"], - operator_keys_to_display) + html += GenerateTableHtml(data["operator_codes"], operator_keys_to_display) html += "\n" @@ -370,10 +382,10 @@ def main(argv): tflite_input = argv[1] html_output = argv[2] except IndexError: - print ("Usage: %s " % (argv[0])) + print("Usage: %s " % (argv[0])) else: CreateHtmlFile(tflite_input, html_output) + if __name__ == "__main__": main(sys.argv) - diff --git a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py index d07fece4bc..6a3b535eb4 100644 --- a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py +++ b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py @@ -58,6 +58,7 @@ def read_cifar10(filename_queue): class CIFAR10Record(object): pass + result = CIFAR10Record() # Dimensions of the images in the CIFAR-10 dataset. @@ -147,8 +148,9 @@ def distorted_inputs(data_dir, batch_size): images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. labels: Labels. 1D tensor of [batch_size] size. """ - filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) - for i in xrange(1, 6)] + filenames = [ + os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6) + ] for f in filenames: if not tf.gfile.Exists(f): raise ValueError('Failed to find file: ' + f) @@ -174,10 +176,9 @@ def distorted_inputs(data_dir, batch_size): # Because these operations are not commutative, consider randomizing # the order their operation. - distorted_image = tf.image.random_brightness(distorted_image, - max_delta=63) - distorted_image = tf.image.random_contrast(distorted_image, - lower=0.2, upper=1.8) + distorted_image = tf.image.random_brightness(distorted_image, max_delta=63) + distorted_image = tf.image.random_contrast( + distorted_image, lower=0.2, upper=1.8) # Subtract off the mean and divide by the variance of the pixels. float_image = tf.image.per_image_standardization(distorted_image) @@ -188,15 +189,18 @@ def distorted_inputs(data_dir, batch_size): # Ensure that the random shuffling has good mixing properties. min_fraction_of_examples_in_queue = 0.4 - min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * - min_fraction_of_examples_in_queue) - print ('Filling queue with %d CIFAR images before starting to train. ' - 'This will take a few minutes.' % min_queue_examples) + min_queue_examples = int( + NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue) + print('Filling queue with %d CIFAR images before starting to train. ' + 'This will take a few minutes.' % min_queue_examples) # Generate a batch of images and labels by building up a queue of examples. - return _generate_image_and_label_batch(float_image, read_input.label, - min_queue_examples, batch_size, - shuffle=True) + return _generate_image_and_label_batch( + float_image, + read_input.label, + min_queue_examples, + batch_size, + shuffle=True) def inputs(eval_data, data_dir, batch_size): @@ -212,8 +216,9 @@ def inputs(eval_data, data_dir, batch_size): labels: Labels. 1D tensor of [batch_size] size. """ if not eval_data: - filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) - for i in xrange(1, 6)] + filenames = [ + os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6) + ] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN else: filenames = [os.path.join(data_dir, 'test_batch.bin')] @@ -235,8 +240,8 @@ def inputs(eval_data, data_dir, batch_size): # Image processing for evaluation. # Crop the central [height, width] of the image. - resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, - width, height) + resized_image = tf.image.resize_image_with_crop_or_pad( + reshaped_image, width, height) # Subtract off the mean and divide by the variance of the pixels. float_image = tf.image.per_image_standardization(resized_image) @@ -247,10 +252,13 @@ def inputs(eval_data, data_dir, batch_size): # Ensure that the random shuffling has good mixing properties. min_fraction_of_examples_in_queue = 0.4 - min_queue_examples = int(num_examples_per_epoch * - min_fraction_of_examples_in_queue) + min_queue_examples = int( + num_examples_per_epoch * min_fraction_of_examples_in_queue) # Generate a batch of images and labels by building up a queue of examples. - return _generate_image_and_label_batch(float_image, read_input.label, - min_queue_examples, batch_size, - shuffle=False) + return _generate_image_and_label_batch( + float_image, + read_input.label, + min_queue_examples, + batch_size, + shuffle=False) diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index cafeb56ad8..e1838c1739 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -42,7 +42,6 @@ from tensorflow.python.platform import test from tensorflow.python.framework import test_util from tensorflow.contrib.rnn.python.ops import rnn_cell as contrib_rnn_cell - # pylint: enable=protected-access Linear = core_rnn_cell._Linear # pylint: disable=invalid-name @@ -84,19 +83,22 @@ class RNNCellTest(test.TestCase): ], [v.name for v in cell.trainable_variables]) self.assertFalse(cell.non_trainable_variables) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g], {x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) self.assertEqual(res[0].shape, (1, 2)) def testBasicRNNCellNotTrainable(self): with self.test_session() as sess: + def not_trainable_getter(getter, *args, **kwargs): kwargs["trainable"] = False return getter(*args, **kwargs) with variable_scope.variable_scope( - "root", initializer=init_ops.constant_initializer(0.5), + "root", + initializer=init_ops.constant_initializer(0.5), custom_getter=not_trainable_getter): x = array_ops.zeros([1, 2]) m = array_ops.zeros([1, 2]) @@ -108,9 +110,10 @@ class RNNCellTest(test.TestCase): "root/basic_rnn_cell/%s:0" % rnn_cell_impl._BIAS_VARIABLE_NAME ], [v.name for v in cell.non_trainable_variables]) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g], {x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) self.assertEqual(res[0].shape, (1, 2)) def testGRUCell(self): @@ -121,9 +124,10 @@ class RNNCellTest(test.TestCase): m = array_ops.zeros([1, 2]) g, _ = rnn_cell_impl.GRUCell(2)(x, m) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g], {x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) # Smoke test self.assertAllClose(res[0], [[0.175991, 0.175991]]) with variable_scope.variable_scope( @@ -133,10 +137,10 @@ class RNNCellTest(test.TestCase): m = array_ops.zeros([1, 2]) g, _ = rnn_cell_impl.GRUCell(2)(x, m) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g], - {x.name: np.array([[1., 1., 1.]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g], { + x.name: np.array([[1., 1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) # Smoke test self.assertAllClose(res[0], [[0.156736, 0.156736]]) @@ -148,11 +152,12 @@ class RNNCellTest(test.TestCase): m = array_ops.zeros([1, 2]) g, _ = contrib_rnn_cell.SRUCell(2)(x, m) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g], {x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) # Smoke test - self.assertAllClose(res[0], [[0.509682, 0.509682]]) + self.assertAllClose(res[0], [[0.509682, 0.509682]]) def testBasicLSTMCell(self): for dtype in [dtypes.float16, dtypes.float32]: @@ -164,8 +169,7 @@ class RNNCellTest(test.TestCase): m = array_ops.zeros([1, 8], dtype=dtype) cell = rnn_cell_impl.MultiRNNCell( [ - rnn_cell_impl.BasicLSTMCell( - 2, state_is_tuple=False) + rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False) for _ in range(2) ], state_is_tuple=False) @@ -183,22 +187,21 @@ class RNNCellTest(test.TestCase): "root/multi_rnn_cell/cell_1/basic_lstm_cell/%s:0" % rnn_cell_impl._BIAS_VARIABLE_NAME ] - self.assertEqual( - expected_variable_names, - [v.name for v in cell.trainable_variables]) + self.assertEqual(expected_variable_names, + [v.name for v in cell.trainable_variables]) self.assertFalse(cell.non_trainable_variables) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g, out_m], - {x.name: np.array([[1., 1.]]), - m.name: 0.1 * np.ones([1, 8])}) + res = sess.run([g, out_m], { + x.name: np.array([[1., 1.]]), + m.name: 0.1 * np.ones([1, 8]) + }) self.assertEqual(len(res), 2) variables = variables_lib.global_variables() self.assertEqual(expected_variable_names, [v.name for v in variables]) # The numbers in results were not calculated, this is just a # smoke test. - self.assertAllClose( - res[0], np.array([[0.240, 0.240]], dtype=np_dtype), 1e-2) + self.assertAllClose(res[0], np.array( + [[0.240, 0.240]], dtype=np_dtype), 1e-2) expected_mem = np.array( [[0.689, 0.689, 0.448, 0.448, 0.398, 0.398, 0.240, 0.240]], dtype=np_dtype) @@ -208,13 +211,13 @@ class RNNCellTest(test.TestCase): # Test BasicLSTMCell with input_size != num_units. x = array_ops.zeros([1, 3], dtype=dtype) m = array_ops.zeros([1, 4], dtype=dtype) - g, out_m = rnn_cell_impl.BasicLSTMCell( - 2, state_is_tuple=False)(x, m) + g, out_m = rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)(x, m) sess.run([variables_lib.global_variables_initializer()]) res = sess.run( - [g, out_m], - {x.name: np.array([[1., 1., 1.]], dtype=np_dtype), - m.name: 0.1 * np.ones([1, 4], dtype=np_dtype)}) + [g, out_m], { + x.name: np.array([[1., 1., 1.]], dtype=np_dtype), + m.name: 0.1 * np.ones([1, 4], dtype=np_dtype) + }) self.assertEqual(len(res), 2) def testBasicLSTMCellDimension0Error(self): @@ -232,9 +235,11 @@ class RNNCellTest(test.TestCase): g, out_m = rnn_cell_impl.BasicLSTMCell( num_units, state_is_tuple=False)(x, m) sess.run([variables_lib.global_variables_initializer()]) - sess.run([g, out_m], - {x.name: 1 * np.ones([batch_size, input_size]), - m.name: 0.1 * np.ones([batch_size - 1, state_size])}) + sess.run( + [g, out_m], { + x.name: 1 * np.ones([batch_size, input_size]), + m.name: 0.1 * np.ones([batch_size - 1, state_size]) + }) def testBasicLSTMCellStateSizeError(self): """Tests that state_size must be num_units * 2.""" @@ -251,9 +256,11 @@ class RNNCellTest(test.TestCase): g, out_m = rnn_cell_impl.BasicLSTMCell( num_units, state_is_tuple=False)(x, m) sess.run([variables_lib.global_variables_initializer()]) - sess.run([g, out_m], - {x.name: 1 * np.ones([batch_size, input_size]), - m.name: 0.1 * np.ones([batch_size, state_size])}) + sess.run( + [g, out_m], { + x.name: 1 * np.ones([batch_size, input_size]), + m.name: 0.1 * np.ones([batch_size, state_size]) + }) def testBasicLSTMCellStateTupleType(self): with self.test_session(): @@ -301,11 +308,12 @@ class RNNCellTest(test.TestCase): state_is_tuple=True) g, (out_m0, out_m1) = cell(x, (m0, m1)) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run([g, out_m0, out_m1], { - x.name: np.array([[1., 1.]]), - m0.name: 0.1 * np.ones([1, 4]), - m1.name: 0.1 * np.ones([1, 4]) - }) + res = sess.run( + [g, out_m0, out_m1], { + x.name: np.array([[1., 1.]]), + m0.name: 0.1 * np.ones([1, 4]), + m1.name: 0.1 * np.ones([1, 4]) + }) self.assertEqual(len(res), 3) # The numbers in results were not calculated, this is just a smoke test. # Note, however, these values should match the original @@ -336,10 +344,11 @@ class RNNCellTest(test.TestCase): state_is_tuple=False) output, state = cell(x, m) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run([output, state], { - x.name: np.array([[1., 1.], [2., 2.], [3., 3.]]), - m.name: 0.1 * np.ones((batch_size, state_size)) - }) + res = sess.run( + [output, state], { + x.name: np.array([[1., 1.], [2., 2.], [3., 3.]]), + m.name: 0.1 * np.ones((batch_size, state_size)) + }) self.assertEqual(len(res), 2) # The numbers in results were not calculated, this is mostly just a # smoke test. @@ -442,10 +451,10 @@ class RNNCellTest(test.TestCase): rnn_cell_impl.GRUCell(3), num_proj=3) g, new_m = cell(x, m) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g, new_m], - {x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1, 0.1]])}) + res = sess.run([g, new_m], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1, 0.1]]) + }) self.assertEqual(res[1].shape, (1, 3)) # The numbers in results were not calculated, this is just a smoke test. self.assertAllClose(res[0], [[0.154605, 0.154605, 0.154605]]) @@ -479,9 +488,11 @@ class RNNCellTest(test.TestCase): base_cell = rnn_cell_impl.GRUCell(3) g, m_new = base_cell(x, m) variable_scope.get_variable_scope().reuse_variables() + def residual_with_slice_fn(inp, out): inp_sliced = array_ops.slice(inp, [0, 0], [-1, 3]) return inp_sliced + out + g_res, m_new_res = rnn_cell_impl.ResidualWrapper( base_cell, residual_with_slice_fn)(x, m) sess.run([variables_lib.global_variables_initializer()]) @@ -551,10 +562,10 @@ class RNNCellTest(test.TestCase): self.assertEqual(embedding_cell.output_size, 2) g, new_m = embedding_cell(x, m) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g, new_m], - {x.name: np.array([[1]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g, new_m], { + x.name: np.array([[1]]), + m.name: np.array([[0.1, 0.1]]) + }) self.assertEqual(res[1].shape, (1, 2)) # The numbers in results were not calculated, this is just a smoke test. self.assertAllClose(res[0], [[0.17139, 0.17139]]) @@ -584,8 +595,8 @@ class RNNCellTest(test.TestCase): x = array_ops.zeros([1, 2]) m = array_ops.zeros([1, 4]) _, ml = rnn_cell_impl.MultiRNNCell( - [rnn_cell_impl.GRUCell(2) - for _ in range(2)], state_is_tuple=False)(x, m) + [rnn_cell_impl.GRUCell(2) for _ in range(2)], + state_is_tuple=False)(x, m) sess.run([variables_lib.global_variables_initializer()]) res = sess.run(ml, { x.name: np.array([[1., 1.]]), @@ -605,19 +616,20 @@ class RNNCellTest(test.TestCase): # Test incorrectness of state with self.assertRaisesRegexp(ValueError, "Expected state .* a tuple"): rnn_cell_impl.MultiRNNCell( - [rnn_cell_impl.GRUCell(2) - for _ in range(2)], state_is_tuple=True)(x, m_bad) + [rnn_cell_impl.GRUCell(2) for _ in range(2)], + state_is_tuple=True)(x, m_bad) _, ml = rnn_cell_impl.MultiRNNCell( - [rnn_cell_impl.GRUCell(2) - for _ in range(2)], state_is_tuple=True)(x, m_good) + [rnn_cell_impl.GRUCell(2) for _ in range(2)], + state_is_tuple=True)(x, m_good) sess.run([variables_lib.global_variables_initializer()]) - res = sess.run(ml, { - x.name: np.array([[1., 1.]]), - m_good[0].name: np.array([[0.1, 0.1]]), - m_good[1].name: np.array([[0.1, 0.1]]) - }) + res = sess.run( + ml, { + x.name: np.array([[1., 1.]]), + m_good[0].name: np.array([[0.1, 0.1]]), + m_good[1].name: np.array([[0.1, 0.1]]) + }) # The numbers in results were not calculated, this is just a # smoke test. However, these numbers should match those of @@ -628,8 +640,11 @@ class RNNCellTest(test.TestCase): class DropoutWrapperTest(test.TestCase): - def _testDropoutWrapper(self, batch_size=None, time_steps=None, - parallel_iterations=None, **kwargs): + def _testDropoutWrapper(self, + batch_size=None, + time_steps=None, + parallel_iterations=None, + **kwargs): with self.test_session() as sess: with variable_scope.variable_scope( "root", initializer=init_ops.constant_initializer(0.5)): @@ -640,14 +655,14 @@ class DropoutWrapperTest(test.TestCase): x = constant_op.constant( [[[2., 2., 2.]], [[1., 1., 1.]]], dtype=dtypes.float32) m = rnn_cell_impl.LSTMStateTuple( - *[constant_op.constant([[0.1, 0.1, 0.1]], dtype=dtypes.float32) - ] * 2) + *[constant_op.constant([[0.1, 0.1, 0.1]], dtype=dtypes.float32 + )] * 2) else: x = constant_op.constant( np.random.randn(time_steps, batch_size, 3).astype(np.float32)) m = rnn_cell_impl.LSTMStateTuple(*[ - constant_op.constant( - [[0.1, 0.1, 0.1]] * batch_size, dtype=dtypes.float32) + constant_op. + constant([[0.1, 0.1, 0.1]] * batch_size, dtype=dtypes.float32) ] * 2) outputs, final_state = rnn.dynamic_rnn( cell=rnn_cell_impl.DropoutWrapper( @@ -674,8 +689,8 @@ class DropoutWrapperTest(test.TestCase): res = self._testDropoutWrapper( input_keep_prob=keep, output_keep_prob=keep, state_keep_prob=keep) true_full_output = np.array( - [[[0.751109, 0.751109, 0.751109]], - [[0.895509, 0.895509, 0.895509]]], dtype=np.float32) + [[[0.751109, 0.751109, 0.751109]], [[0.895509, 0.895509, 0.895509]]], + dtype=np.float32) true_full_final_c = np.array( [[1.949385, 1.949385, 1.949385]], dtype=np.float32) self.assertAllClose(true_full_output, res[0]) @@ -687,8 +702,8 @@ class DropoutWrapperTest(test.TestCase): res = self._testDropoutWrapper( input_keep_prob=keep, output_keep_prob=keep, state_keep_prob=keep) true_full_output = np.array( - [[[0.751109, 0.751109, 0.751109]], - [[0.895509, 0.895509, 0.895509]]], dtype=np.float32) + [[[0.751109, 0.751109, 0.751109]], [[0.895509, 0.895509, 0.895509]]], + dtype=np.float32) true_full_final_c = np.array( [[1.949385, 1.949385, 1.949385]], dtype=np.float32) self.assertAllClose(true_full_output, res[0]) @@ -703,16 +718,20 @@ class DropoutWrapperTest(test.TestCase): ## consistent across both calls. Otherwise the seed may not end ## up being munged consistently across both graphs. res_standard_1 = self._testDropoutWrapper( - input_keep_prob=keep_some, output_keep_prob=keep_some, - state_keep_prob=keep_some, seed=10, + input_keep_prob=keep_some, + output_keep_prob=keep_some, + state_keep_prob=keep_some, + seed=10, parallel_iterations=1) # Clear away the graph and the test session (which keeps variables around) ops.reset_default_graph() self._ClearCachedSession() random_seed.set_random_seed(2) res_standard_2 = self._testDropoutWrapper( - input_keep_prob=keep_some, output_keep_prob=keep_some, - state_keep_prob=keep_some, seed=10, + input_keep_prob=keep_some, + output_keep_prob=keep_some, + state_keep_prob=keep_some, + seed=10, parallel_iterations=1) self.assertAllClose(res_standard_1[0], res_standard_2[0]) self.assertAllClose(res_standard_1[1].c, res_standard_2[1].c) @@ -722,11 +741,12 @@ class DropoutWrapperTest(test.TestCase): keep_all = variable_scope.get_variable("all", initializer=1.0) keep_none = variable_scope.get_variable("none", initializer=1e-10) res = self._testDropoutWrapper( - input_keep_prob=keep_all, output_keep_prob=keep_none, + input_keep_prob=keep_all, + output_keep_prob=keep_none, state_keep_prob=keep_all) true_full_output = np.array( - [[[0.751109, 0.751109, 0.751109]], - [[0.895509, 0.895509, 0.895509]]], dtype=np.float32) + [[[0.751109, 0.751109, 0.751109]], [[0.895509, 0.895509, 0.895509]]], + dtype=np.float32) true_full_final_c = np.array( [[1.949385, 1.949385, 1.949385]], dtype=np.float32) self.assertAllClose(np.zeros(res[0].shape), res[0]) @@ -739,13 +759,13 @@ class DropoutWrapperTest(test.TestCase): # Even though we dropout state, by default DropoutWrapper never # drops out the memory ("c") term of an LSTMStateTuple. res = self._testDropoutWrapper( - input_keep_prob=keep_all, output_keep_prob=keep_all, + input_keep_prob=keep_all, + output_keep_prob=keep_all, state_keep_prob=keep_none) - true_c_state = np.array( - [[1.713925, 1.713925, 1.713925]], dtype=np.float32) + true_c_state = np.array([[1.713925, 1.713925, 1.713925]], dtype=np.float32) true_full_output = np.array( - [[[0.751109, 0.751109, 0.751109]], - [[0.895509, 0.895509, 0.895509]]], dtype=np.float32) + [[[0.751109, 0.751109, 0.751109]], [[0.895509, 0.895509, 0.895509]]], + dtype=np.float32) self.assertAllClose(true_full_output[0], res[0][0]) # Second output is modified by zero input state self.assertGreater(np.linalg.norm(true_full_output[1] - res[0][1]), 1e-4) @@ -758,13 +778,14 @@ class DropoutWrapperTest(test.TestCase): keep_all = variable_scope.get_variable("all", initializer=1.0) keep_none = variable_scope.get_variable("none", initializer=1e-10) true_full_output = np.array( - [[[0.751109, 0.751109, 0.751109]], - [[0.895509, 0.895509, 0.895509]]], dtype=np.float32) + [[[0.751109, 0.751109, 0.751109]], [[0.895509, 0.895509, 0.895509]]], + dtype=np.float32) true_full_final_c = np.array( [[1.949385, 1.949385, 1.949385]], dtype=np.float32) # All outputs are different because inputs are zeroed out res = self._testDropoutWrapper( - input_keep_prob=keep_none, output_keep_prob=keep_all, + input_keep_prob=keep_none, + output_keep_prob=keep_all, state_keep_prob=keep_all) self.assertGreater(np.linalg.norm(res[0] - true_full_output), 1e-4) self.assertGreater(np.linalg.norm(res[1].h - true_full_output[1]), 1e-4) @@ -774,9 +795,13 @@ class DropoutWrapperTest(test.TestCase): keep_some = 0.8 keep_all = variable_scope.get_variable("all", initializer=1.0) res = self._testDropoutWrapper( - input_keep_prob=keep_all, output_keep_prob=keep_some, - state_keep_prob=keep_all, variational_recurrent=True, - input_size=3, batch_size=5, time_steps=7) + input_keep_prob=keep_all, + output_keep_prob=keep_some, + state_keep_prob=keep_all, + variational_recurrent=True, + input_size=3, + batch_size=5, + time_steps=7) # Ensure the same dropout pattern for all time steps output_mask = np.abs(res[0]) > 1e-6 for m in output_mask[1:]: @@ -785,9 +810,13 @@ class DropoutWrapperTest(test.TestCase): def testDropoutWrapperRecurrentStateInputAndOutput(self): keep_some = 0.9 res = self._testDropoutWrapper( - input_keep_prob=keep_some, output_keep_prob=keep_some, - state_keep_prob=keep_some, variational_recurrent=True, - input_size=3, batch_size=5, time_steps=7) + input_keep_prob=keep_some, + output_keep_prob=keep_some, + state_keep_prob=keep_some, + variational_recurrent=True, + input_size=3, + batch_size=5, + time_steps=7) # Smoke test for the state/input masks. output_mask = np.abs(res[0]) > 1e-6 @@ -811,17 +840,27 @@ class DropoutWrapperTest(test.TestCase): random_seed.set_random_seed(2347) np.random.seed(23487) res0 = self._testDropoutWrapper( - input_keep_prob=keep_some, output_keep_prob=keep_some, - state_keep_prob=keep_some, variational_recurrent=True, - input_size=3, batch_size=5, time_steps=7, seed=-234987) + input_keep_prob=keep_some, + output_keep_prob=keep_some, + state_keep_prob=keep_some, + variational_recurrent=True, + input_size=3, + batch_size=5, + time_steps=7, + seed=-234987) ops.reset_default_graph() self._ClearCachedSession() random_seed.set_random_seed(2347) np.random.seed(23487) res1 = self._testDropoutWrapper( - input_keep_prob=keep_some, output_keep_prob=keep_some, - state_keep_prob=keep_some, variational_recurrent=True, - input_size=3, batch_size=5, time_steps=7, seed=-234987) + input_keep_prob=keep_some, + output_keep_prob=keep_some, + state_keep_prob=keep_some, + variational_recurrent=True, + input_size=3, + batch_size=5, + time_steps=7, + seed=-234987) output_mask = np.abs(res0[0]) > 1e-6 for time_step in output_mask: @@ -858,9 +897,10 @@ class SlimRNNCellTest(test.TestCase): g, _ = rnn_cell_impl._SlimRNNCell(my_cell)(x, m) # pylint: enable=protected-access sess.run([variables_lib.global_variables_initializer()]) - res = sess.run( - [g], {x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1]])}) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) self.assertEqual(res[0].shape, (1, 2)) def testBasicRNNCellMatch(self): diff --git a/tensorflow/examples/learn/text_classification.py b/tensorflow/examples/learn/text_classification.py index eb117c39a1..e4e61862b0 100644 --- a/tensorflow/examples/learn/text_classification.py +++ b/tensorflow/examples/learn/text_classification.py @@ -34,8 +34,7 @@ MAX_LABEL = 15 WORDS_FEATURE = 'words' # Name of the input words feature. -def estimator_spec_for_softmax_classification( - logits, labels, mode): +def estimator_spec_for_softmax_classification(logits, labels, mode): """Returns EstimatorSpec instance for softmax classification.""" predicted_classes = tf.argmax(logits, 1) if mode == tf.estimator.ModeKeys.PREDICT: @@ -53,8 +52,8 @@ def estimator_spec_for_softmax_classification( return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op) eval_metric_ops = { - 'accuracy': tf.metrics.accuracy( - labels=labels, predictions=predicted_classes) + 'accuracy': + tf.metrics.accuracy(labels=labels, predictions=predicted_classes) } return tf.estimator.EstimatorSpec( mode=mode, loss=loss, eval_metric_ops=eval_metric_ops) @@ -67,8 +66,7 @@ def bag_of_words_model(features, labels, mode): bow_embedding_column = tf.feature_column.embedding_column( bow_column, dimension=EMBEDDING_SIZE) bow = tf.feature_column.input_layer( - features, - feature_columns=[bow_embedding_column]) + features, feature_columns=[bow_embedding_column]) logits = tf.layers.dense(bow, MAX_LABEL, activation=None) return estimator_spec_for_softmax_classification( @@ -110,9 +108,9 @@ def main(unused_argv): # Prepare training and testing data dbpedia = tf.contrib.learn.datasets.load_dataset( 'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data) - x_train = pandas.Series(dbpedia.train.data[:,1]) + x_train = pandas.Series(dbpedia.train.data[:, 1]) y_train = pandas.Series(dbpedia.train.target) - x_test = pandas.Series(dbpedia.test.data[:,1]) + x_test = pandas.Series(dbpedia.test.data[:, 1]) y_test = pandas.Series(dbpedia.test.target) # Process vocabulary @@ -152,10 +150,7 @@ def main(unused_argv): # Predict. test_input_fn = tf.estimator.inputs.numpy_input_fn( - x={WORDS_FEATURE: x_test}, - y=y_test, - num_epochs=1, - shuffle=False) + x={WORDS_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False) predictions = classifier.predict(input_fn=test_input_fn) y_predicted = np.array(list(p['class'] for p in predictions)) y_predicted = y_predicted.reshape(np.array(y_test).shape) diff --git a/tensorflow/python/client/notebook.py b/tensorflow/python/client/notebook.py index 8babe35b32..4b6a0f71ae 100644 --- a/tensorflow/python/client/notebook.py +++ b/tensorflow/python/client/notebook.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Notebook front-end to TensorFlow. When you run this binary, you'll see something like below, which indicates @@ -43,10 +42,8 @@ from tensorflow.python.platform import app os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "cpp" os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION"] = "2" - FLAGS = None - ORIG_ARGV = sys.argv # Main notebook process calls itself with argv[1]="kernel" to start kernel # subprocesses. @@ -73,8 +70,8 @@ def main(unused_argv): notebookapp.ip = "0.0.0.0" notebookapp.password = passwd(FLAGS.password) else: - print ("\nNo password specified; Notebook server will only be available" - " on the local machine.\n") + print("\nNo password specified; Notebook server will only be available" + " on the local machine.\n") notebookapp.initialize(argv=["--notebook-dir", FLAGS.notebook_dir]) if notebookapp.ip == "0.0.0.0": @@ -125,8 +122,8 @@ if __name__ == "__main__": # kernel app. if IS_KERNEL: # Drop everything except --flagfile. - sys.argv = ([sys.argv[0]] + - [x for x in sys.argv[1:] if x.startswith("--flagfile")]) + sys.argv = ( + [sys.argv[0]] + [x for x in sys.argv[1:] if x.startswith("--flagfile")]) FLAGS, unparsed = parser.parse_known_args() app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index 52fb1d39ae..2e84c5014f 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== - """Classes and functions related to train_and_evaluate.""" from __future__ import absolute_import @@ -37,7 +36,6 @@ from tensorflow.python.training import server_lib from tensorflow.python.training import session_run_hook from tensorflow.python.util import compat - _MAX_DELAY_SECS = 60 _DELAY_SECS_PER_WORKER = 5 _TF_CONFIG_ENV = 'TF_CONFIG' @@ -50,8 +48,7 @@ _TRAINER_JOBS = (run_config_lib.TaskType.CHIEF, run_config_lib.TaskType.MASTER, def _validate_input_fn(input_fn): """Validates the `input_fn`.""" if not callable(input_fn): - raise TypeError( - '`input_fn` must be callable, given: {}'.format(input_fn)) + raise TypeError('`input_fn` must be callable, given: {}'.format(input_fn)) def _validate_hooks(hooks): @@ -125,10 +122,7 @@ class TrainSpec( duration. Optional hooks run at various stages of training. """ - def __new__(cls, - input_fn, - max_steps=None, - hooks=None): + def __new__(cls, input_fn, max_steps=None, hooks=None): """Creates a validated `TrainSpec` instance. Args: @@ -161,16 +155,13 @@ class TrainSpec( hooks = _validate_hooks(hooks) return super(TrainSpec, cls).__new__( - cls, - input_fn=input_fn, - max_steps=max_steps, - hooks=hooks) + cls, input_fn=input_fn, max_steps=max_steps, hooks=hooks) class EvalSpec( collections.namedtuple('EvalSpec', [ - 'input_fn', 'steps', 'name', 'hooks', 'exporters', - 'start_delay_secs', 'throttle_secs' + 'input_fn', 'steps', 'name', 'hooks', 'exporters', 'start_delay_secs', + 'throttle_secs' ])): """Configuration for the "eval" part for the `train_and_evaluate` call. @@ -417,8 +408,8 @@ def train_and_evaluate(estimator, train_spec, eval_spec): Raises: ValueError: if environment variable `TF_CONFIG` is incorrectly set. """ - executor = _TrainingExecutor(estimator=estimator, train_spec=train_spec, - eval_spec=eval_spec) + executor = _TrainingExecutor( + estimator=estimator, train_spec=train_spec, eval_spec=eval_spec) config = estimator.config if (config.task_type == run_config_lib.TaskType.EVALUATOR and @@ -561,9 +552,8 @@ class _TrainingExecutor(object): self._timer.update_last_triggered_step(global_step_value) self._evaluator.evaluate_and_export() else: - logging.info( - 'Skip the current checkpoint eval due to throttle secs ' - '({} secs).'.format(self._eval_throttle_secs)) + logging.info('Skip the current checkpoint eval due to throttle secs ' + '({} secs).'.format(self._eval_throttle_secs)) # Final export signal: For any eval result with global_step >= train # max_steps, the evaluator will send the final export signal. There is a @@ -576,8 +566,8 @@ class _TrainingExecutor(object): # # But here, throttle_secs will skip the next intermediate checkpoint and, # so, the double final export chance is very small. - evaluator = _TrainingExecutor._Evaluator( - self._estimator, self._eval_spec, self._train_spec.max_steps) + evaluator = _TrainingExecutor._Evaluator(self._estimator, self._eval_spec, + self._train_spec.max_steps) # When the underlying `Estimator` object saves a new checkpoint, we would # like this callback to be called so that evaluation and export can trigger. @@ -617,8 +607,7 @@ class _TrainingExecutor(object): raise ValueError('eval_spec.throttle_secs should be positive, given: {}.' 'It is used do determine how long each training ' 'iteration should go when train and evaluate ' - 'locally.'.format( - self._eval_spec.throttle_secs)) + 'locally.'.format(self._eval_spec.throttle_secs)) stop_hook = _StopAtSecsHook(self._eval_spec.throttle_secs) train_hooks = ( @@ -663,8 +652,9 @@ class _TrainingExecutor(object): if not config.master: jobs = config.cluster_spec.jobs - if (len(jobs) == 1 and len(config.cluster_spec.job_tasks(jobs[0])) == 1 - and config.task_type in _TRAINER_JOBS): + if (len(jobs) == 1 and + len(config.cluster_spec.job_tasks(jobs[0])) == 1 and + config.task_type in _TRAINER_JOBS): # For distributed training, config.master is empty if and only if it has # a single node in the cluster spec. In this case, we should not start # the server. @@ -679,9 +669,9 @@ class _TrainingExecutor(object): logging.info('Start Tensorflow server.') if config.session_config is None: - session_config=config_pb2.ConfigProto(log_device_placement=False) + session_config = config_pb2.ConfigProto(log_device_placement=False) else: - session_config=config_pb2.ConfigProto( + session_config = config_pb2.ConfigProto( log_device_placement=False, gpu_options=config.session_config.gpu_options) @@ -744,8 +734,7 @@ class _TrainingExecutor(object): global_step >= self._train_spec.max_steps): logging.info( 'Exiting evaluation, global_step=%s >= train max_steps=%s', - global_step, - self._train_spec.max_steps) + global_step, self._train_spec.max_steps) return latest_eval_result, should_early_stop = self._execute_evaluator_once( @@ -781,10 +770,9 @@ class _TrainingExecutor(object): # Throttle if necessary. elapsed_time = time.time() - start - difference = throttle_secs - elapsed_time + difference = throttle_secs - elapsed_time if difference > 0: - logging.info('Waiting %f secs before starting next eval run.', - difference) + logging.info('Waiting %f secs before starting next eval run.', difference) time.sleep(difference) return (eval_result, should_early_stop) @@ -929,8 +917,8 @@ class _EvalResult( if checkpoint_path: raise ValueError( 'checkpoint must be `None` if status is not {}; got status {}, ' - 'checkpoint_path {}'.format( - _EvalStatus.EVALUATED, status, checkpoint_path)) + 'checkpoint_path {}'.format(_EvalStatus.EVALUATED, status, + checkpoint_path)) return super(_EvalResult, cls).__new__(cls, status, metrics, checkpoint_path) diff --git a/tensorflow/python/kernel_tests/tensordot_op_test.py b/tensorflow/python/kernel_tests/tensordot_op_test.py index f375157287..f1670a47f5 100644 --- a/tensorflow/python/kernel_tests/tensordot_op_test.py +++ b/tensorflow/python/kernel_tests/tensordot_op_test.py @@ -56,9 +56,11 @@ class TensordotTest(test_lib.TestCase): axes_ph = array_ops.placeholder(dtypes.int32) output = math_ops.tensordot(a_ph, b_ph, axes_ph) _ = sess.run( - [output], feed_dict={a_ph: a, - b_ph: b, - axes_ph: (a_axes, b_axes)}) + [output], feed_dict={ + a_ph: a, + b_ph: b, + axes_ph: (a_axes, b_axes) + }) def test_invalid_axes(self): a = [[1, 2], [3, 4]] @@ -81,26 +83,27 @@ class TensordotTest(test_lib.TestCase): with self.test_session() as sess: with self.assertRaises(errors_impl.InvalidArgumentError): _ = sess.run( - [output], feed_dict={a_ph: a, - b_ph: b, - axes_ph: axes_value}) + [output], feed_dict={ + a_ph: a, + b_ph: b, + axes_ph: axes_value + }) # Test case for 11950 def test_valid_axis(self): for axes_value in [1, 2], [[1], [2]]: with self.test_session() as sess: - np_a = np.ones((3,3)) + np_a = np.ones((3, 3)) np_b = np.array([2, 3, 1])[None, None] np_ans = np.tensordot(np_a, np_b, axes_value) - tf_a = array_ops.ones((3,3), dtype=dtypes.float32) + tf_a = array_ops.ones((3, 3), dtype=dtypes.float32) tf_b = constant_op.constant([2, 3, 1], dtype=dtypes.float32)[None, None] tf_ans = math_ops.tensordot(tf_a, tf_b, axes_value).eval() self.assertAllEqual(tf_ans.shape, np_ans.shape) self.assertAllEqual(tf_ans, np_ans) - def test_partial_shape_inference(self): a = array_ops.placeholder(dtypes.float32) b = array_ops.placeholder(dtypes.float32) @@ -169,9 +172,11 @@ def _get_tensordot_tests(dtype_, rank_a_, rank_b_, num_dims_, dynamic_shape_): axes = array_ops.placeholder(dtypes.int32) c = math_ops.tensordot(a, b, axes) tf_ans = sess.run( - c, feed_dict={a: a_np, - b: b_np, - axes: (a_dims_np, b_dims_np)}) + c, feed_dict={ + a: a_np, + b: b_np, + axes: (a_dims_np, b_dims_np) + }) else: tf_ans = math_ops.tensordot(a_np, b_np, (a_dims_np, b_dims_np)).eval() self.assertAllClose(tf_ans, np_ans, rtol=tol, atol=tol) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 6f4f0f9859..106ea19d46 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -184,7 +184,8 @@ do_pylint() { # W0312 mixed-indentation # C0330 bad-continuation # C0301 line-too-long - grep -E '(\[E|\[W0311|\[W0312|\[C0330|\[C0301)' ${OUTPUT_FILE} > ${ERRORS_FILE} + # C0326 bad-whitespace + grep -E '(\[E|\[W0311|\[W0312|\[C0330|\[C0301|\[C0326)' ${OUTPUT_FILE} > ${ERRORS_FILE} N_ERRORS=0 while read -r LINE; do diff --git a/tensorflow/tools/compatibility/tf_upgrade.py b/tensorflow/tools/compatibility/tf_upgrade.py index f678681dac..6e90b286c9 100644 --- a/tensorflow/tools/compatibility/tf_upgrade.py +++ b/tensorflow/tools/compatibility/tf_upgrade.py @@ -46,8 +46,9 @@ class APIChangeSpec(object): """ -class _FileEditTuple(collections.namedtuple( - "_FileEditTuple", ["comment", "line", "start", "old", "new"])): +class _FileEditTuple( + collections.namedtuple("_FileEditTuple", + ["comment", "line", "start", "old", "new"])): """Each edit that is recorded by a _FileEditRecorder. Fields: @@ -179,8 +180,7 @@ class _ASTCallVisitor(ast.NodeVisitor): 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), + 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 @@ -227,7 +227,7 @@ class _ASTCallVisitor(ast.NodeVisitor): # loop over lines while 1: # Reverse the text to and regular expression search for whitespace - text = self._lines[line-1] + 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. @@ -248,8 +248,8 @@ class _ASTCallVisitor(ast.NodeVisitor): # 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 + if comment_start == -1: + col = len(prev_line) - 1 elif find_string_chars.search(prev_line[comment_start:]) is None: col = comment_start else: @@ -260,7 +260,6 @@ class _ASTCallVisitor(ast.NodeVisitor): # 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. @@ -268,7 +267,6 @@ class _ASTCallVisitor(ast.NodeVisitor): node: Current Node """ - # Find a simple attribute name path e.g. "tf.foo.bar" full_name = self._get_attribute_full_path(node.func) @@ -293,18 +291,21 @@ class _ASTCallVisitor(ast.NodeVisitor): 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, - "", "", + "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 + "=") + 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 @@ -322,11 +323,11 @@ class _ASTCallVisitor(ast.NodeVisitor): # 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 + "="): + 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, + (argkey, + renamed_keywords[argkey]), argval_lineno, argval_col_offset - len(argkey) - 1, argkey + "=", renamed_keywords[argkey] + "=") continue @@ -335,7 +336,8 @@ class _ASTCallVisitor(ast.NodeVisitor): (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) @@ -352,7 +354,7 @@ class _ASTCallVisitor(ast.NodeVisitor): 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), + 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) @@ -380,8 +382,8 @@ class ASTCodeUpgrader(object): # 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) + ret = self.process_opened_file(in_filename, in_file, out_filename, + temp_file) shutil.move(temp_file.name, out_filename) return ret @@ -424,6 +426,7 @@ class ASTCodeUpgrader(object): 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, @@ -444,16 +447,16 @@ class ASTCodeUpgrader(object): # 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)) + 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)) + 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 @@ -465,14 +468,16 @@ class ASTCodeUpgrader(object): 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)) + 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)) + fullpath_output = os.path.join(output_root_directory, + os.path.relpath( + fullpath, root_directory)) files_to_copy.append((fullpath, fullpath_output)) file_count = 0 @@ -641,18 +646,17 @@ class TFAPIChangeSpec(APIChangeSpec): "tf.concat": ["concat_dim", "values", "name"], "tf.svd": ["tensor", "compute_uv", "full_matrices", "name"], "tf.nn.softmax_cross_entropy_with_logits": [ - "logits", "labels", "dim", "name"], + "logits", "labels", "dim", "name" + ], "tf.nn.sparse_softmax_cross_entropy_with_logits": [ - "logits", "labels", "name"], - "tf.nn.sigmoid_cross_entropy_with_logits": [ - "logits", "labels", "name"], + "logits", "labels", "name" + ], + "tf.nn.sigmoid_cross_entropy_with_logits": ["logits", "labels", "name"], "tf.op_scope": ["values", "name", "default_name"], } # Specially handled functions. - self.function_handle = { - "tf.reverse": self._reverse_handler - } + self.function_handle = {"tf.reverse": self._reverse_handler} @staticmethod def _reverse_handler(file_edit_recorder, node): @@ -661,12 +665,13 @@ class TFAPIChangeSpec(APIChangeSpec): comment = ("ERROR: tf.reverse has had its argument semantics changed\n" "significantly the converter cannot detect this reliably, so you" "need to inspect this usage manually.\n") - file_edit_recorder.add(comment, - node.lineno, - node.col_offset, - "tf.reverse", - "tf.reverse", - error="tf.reverse requires manual check.") + file_edit_recorder.add( + comment, + node.lineno, + node.col_offset, + "tf.reverse", + "tf.reverse", + error="tf.reverse requires manual check.") if __name__ == "__main__": -- GitLab From 19c579d97f689b6fc0581f4a1973c3305b5693c5 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Mon, 29 Jan 2018 11:20:39 -0800 Subject: [PATCH 211/423] Make optimize_for_inference_test.py work the C API enabled. PiperOrigin-RevId: 183696425 --- tensorflow/python/tools/optimize_for_inference_test.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/tools/optimize_for_inference_test.py b/tensorflow/python/tools/optimize_for_inference_test.py index 6dd24c0dca..7686bb0f14 100644 --- a/tensorflow/python/tools/optimize_for_inference_test.py +++ b/tensorflow/python/tools/optimize_for_inference_test.py @@ -29,6 +29,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import importer 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 gen_nn_ops from tensorflow.python.ops import image_ops @@ -38,6 +39,7 @@ from tensorflow.python.platform import test from tensorflow.python.tools import optimize_for_inference_lib +@test_util.with_c_api class OptimizeForInferenceTest(test.TestCase): def create_node_def(self, op, name, inputs): @@ -145,7 +147,7 @@ class OptimizeForInferenceTest(test.TestCase): np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32) gamma_op = constant_op.constant( np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32) - ops.get_default_graph().graph_def_versions.producer = 8 + test_util.set_producer_version(ops.get_default_graph(), 8) gen_nn_ops._batch_norm_with_global_normalization( conv_op, mean_op, -- GitLab From b981f2e93c7edb7f07b82677e5ac85d966dd0ab5 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Mon, 29 Jan 2018 11:31:07 -0800 Subject: [PATCH 212/423] [TF:XLA] Implement ReverseSequence operator. PiperOrigin-RevId: 183698184 --- tensorflow/compiler/tests/BUILD | 12 ++ .../tests/reverse_sequence_op_test.py | 93 +++++++++ tensorflow/compiler/tf2xla/kernels/BUILD | 1 + .../tf2xla/kernels/reverse_sequence_op.cc | 182 ++++++++++++++++++ 4 files changed, 288 insertions(+) create mode 100644 tensorflow/compiler/tests/reverse_sequence_op_test.py create mode 100644 tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 314f5506b1..a3a82df9ad 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -437,6 +437,18 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "reverse_sequence_op_test", + size = "small", + srcs = ["reverse_sequence_op_test.py"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform_test", + ], +) + tf_xla_py_test( name = "rmsprop_test", size = "small", diff --git a/tensorflow/compiler/tests/reverse_sequence_op_test.py b/tensorflow/compiler/tests/reverse_sequence_op_test.py new file mode 100644 index 0000000000..1a5d05094e --- /dev/null +++ b/tensorflow/compiler/tests/reverse_sequence_op_test.py @@ -0,0 +1,93 @@ +# 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 tensorflow.ops.reverse_sequence_op.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class ReverseSequenceTest(XLATestCase): + + def _testReverseSequence(self, + x, + batch_axis, + seq_axis, + seq_lengths, + truth, + expected_err_re=None): + with self.test_session(): + p = array_ops.placeholder(dtypes.as_dtype(x.dtype)) + lengths = array_ops.placeholder(dtypes.as_dtype(seq_lengths.dtype)) + with self.test_scope(): + ans = array_ops.reverse_sequence( + p, batch_axis=batch_axis, seq_axis=seq_axis, seq_lengths=lengths) + if expected_err_re is None: + tf_ans = ans.eval(feed_dict={p: x, lengths: seq_lengths}) + self.assertAllClose(tf_ans, truth, atol=1e-10) + else: + with self.assertRaisesOpError(expected_err_re): + ans.eval(feed_dict={p: x, lengths: seq_lengths}) + + def testSimple(self): + x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) + expected = np.array([[1, 2, 3], [6, 5, 4], [8, 7, 9]], dtype=np.int32) + self._testReverseSequence( + x, + batch_axis=0, + seq_axis=1, + seq_lengths=np.array([1, 3, 2], np.int32), + truth=expected) + + def _testBasic(self, dtype, len_dtype): + x = np.asarray( + [[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]], + [[17, 18, 19, 20], [21, 22, 23, 24]]], + dtype=dtype) + x = x.reshape(3, 2, 4, 1, 1) + x = x.transpose([2, 1, 0, 3, 4]) # permute axes 0 <=> 2 + + # reverse dim 2 up to (0:3, none, 0:4) along dim=0 + seq_lengths = np.asarray([3, 0, 4], dtype=len_dtype) + + truth_orig = np.asarray( + [ + [[3, 2, 1, 4], [7, 6, 5, 8]], # reverse 0:3 + [[9, 10, 11, 12], [13, 14, 15, 16]], # reverse none + [[20, 19, 18, 17], [24, 23, 22, 21]] + ], # reverse 0:4 (all) + dtype=dtype) + truth_orig = truth_orig.reshape(3, 2, 4, 1, 1) + truth = truth_orig.transpose([2, 1, 0, 3, 4]) # permute axes 0 <=> 2 + + seq_axis = 0 # permute seq_axis and batch_axis (originally 2 and 0, resp.) + batch_axis = 2 + self._testReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth) + + def testSeqLength(self): + for dtype in self.all_types: + for seq_dtype in self.int_types: + self._testBasic(dtype, seq_dtype) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 5e1b01878b..a7e00cb12f 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -58,6 +58,7 @@ tf_kernel_library( "reshape_op.cc", "retval_op.cc", "reverse_op.cc", + "reverse_sequence_op.cc", "scan_ops.cc", "segment_reduction_ops.cc", "select_op.cc", diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc new file mode 100644 index 0000000000..6bc5d3adb0 --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.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 "tensorflow/compiler/tf2xla/shape_util.h" +#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/core/framework/tensor_shape.h" + +namespace tensorflow { +namespace { + +class ReverseSequenceOp : public XlaOpKernel { + public: + explicit ReverseSequenceOp(OpKernelConstruction* context) + : XlaOpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("batch_dim", &batch_dim_)); + OP_REQUIRES_OK(context, context->GetAttr("seq_dim", &seq_dim_)); + } + + void Compile(XlaOpKernelContext* context) override { + const TensorShape input_shape = context->InputShape(0); + const TensorShape seq_lens_shape = context->InputShape(1); + + OP_REQUIRES(context, TensorShapeUtils::IsVector(seq_lens_shape), + errors::InvalidArgument("seq_lens input must be 1-dim, not ", + seq_lens_shape.dims())); + OP_REQUIRES(context, batch_dim_ != seq_dim_, + errors::InvalidArgument("batch_dim == seq_dim == ", seq_dim_)); + OP_REQUIRES( + context, seq_dim_ < input_shape.dims(), + errors::InvalidArgument("seq_dim must be < input.dims()", "( ", + seq_dim_, " vs. ", input_shape.dims(), ")")); + OP_REQUIRES( + context, batch_dim_ < input_shape.dims(), + errors::InvalidArgument("batch_dim must be < input.dims()", "( ", + batch_dim_, " vs. ", input_shape.dims(), ")")); + OP_REQUIRES( + context, + seq_lens_shape.num_elements() == input_shape.dim_size(batch_dim_), + errors::InvalidArgument("len(seq_lens) != input.dims(", batch_dim_, + "), ", "(", seq_lens_shape.num_elements(), + " vs. ", input_shape.dim_size(batch_dim_))); + + xla::ComputationBuilder* builder = context->builder(); + const auto input = context->Input(0); + const auto seq_lens = context->Input(1); + + const int64 batch_size = input_shape.dim_size(batch_dim_); + + const DataType input_type = context->input_type(0); + const DataType seq_lens_type = context->input_type(1); + const int64 max_seq_len = input_shape.dim_size(seq_dim_); + + xla::Shape input_xla_shape; + OP_REQUIRES_OK(context, TensorShapeToXLAShape(input_type, input_shape, + &input_xla_shape)); + xla::Shape seq_lens_xla_shape; + OP_REQUIRES_OK(context, TensorShapeToXLAShape(seq_lens_type, seq_lens_shape, + &seq_lens_xla_shape)); + + const auto tuple_shape = xla::ShapeUtil::MakeTupleShape({ + xla::ShapeUtil::MakeShape(seq_lens_xla_shape.element_type(), {}), + seq_lens_xla_shape, + input_xla_shape, + }); + + // For each entry in the batch, reverse the sequence. + // TODO(b/65689298): generalize the Map() operator to non-scalar cases and + // use it here, instead of a While loop. + + // Condition: lambda (i, _, _): i < batch_size + auto condition_builder = + builder->CreateSubBuilder("reverse_sequence_condition"); + { + auto param = condition_builder->Parameter(0, tuple_shape, "param"); + auto i = condition_builder->GetTupleElement(param, 0); + condition_builder->Lt( + i, XlaHelpers::IntegerLiteral(condition_builder.get(), seq_lens_type, + batch_size)); + } + auto condition = condition_builder->Build(); + OP_REQUIRES_OK(context, condition.status()); + + auto body_builder = builder->CreateSubBuilder("reverse_sequence_body"); + { + auto param = body_builder->Parameter(0, tuple_shape, "param"); + auto i = body_builder->GetTupleElement(param, 0); + auto seq_lens = body_builder->GetTupleElement(param, 1); + auto output = body_builder->GetTupleElement(param, 2); + + // seq_len is the sequence length of the current batch element (rank 1) + auto seq_len = body_builder->DynamicSlice( + seq_lens, body_builder->Reshape(i, {1}), {1}); + + // Indices is the offset of the batch element in the input. + auto indices = body_builder->Broadcast( + XlaHelpers::Zero(body_builder.get(), seq_lens_type), + {input_shape.dims()}); + indices = body_builder->DynamicUpdateSlice( + indices, body_builder->Reshape(i, {1}), + body_builder->Reshape( + XlaHelpers::IntegerLiteral(body_builder.get(), seq_lens_type, + batch_dim_), + {1})); + + // slice_indices is the offset of the start of the reversed sequence in + // the input. + auto slice_indices = body_builder->DynamicUpdateSlice( + indices, + body_builder->Sub(XlaHelpers::IntegerLiteral( + body_builder.get(), seq_lens_type, max_seq_len), + seq_len), + body_builder->Reshape( + XlaHelpers::IntegerLiteral(body_builder.get(), seq_lens_type, + seq_dim_), + {1})); + + // Slice out the reversed sequence. The slice will overflow the end of the + // sequence, and the contents of the overflow are implementation-defined. + // However, we will mask off these elements and replace them with elements + // from the original input so their values do not matter. + TensorShape slice_shape = input_shape; + slice_shape.set_dim(batch_dim_, 1); + auto slice = body_builder->DynamicSlice(output, slice_indices, + slice_shape.dim_sizes()); + + // Shift the reversed sequence to the left. + output = body_builder->DynamicUpdateSlice(output, slice, indices); + + body_builder->Tuple( + {body_builder->Add( + i, XlaHelpers::One(body_builder.get(), seq_lens_type)), + seq_lens, output}); + } + auto body = body_builder->Build(); + OP_REQUIRES_OK(context, body.status()); + + auto loop_output = builder->While( + condition.ValueOrDie(), body.ValueOrDie(), + builder->Tuple({XlaHelpers::Zero(builder, seq_lens_type), seq_lens, + builder->Rev(input, {seq_dim_})})); + auto output = builder->GetTupleElement(loop_output, 2); + + // Mask out elements after the sequence length. + xla::ComputationDataHandle iota; + OP_REQUIRES_OK( + context, XlaHelpers::Iota(builder, seq_lens_type, max_seq_len, &iota)); + std::vector dims(input_shape.dims(), 1); + dims[batch_dim_] = batch_size; + auto mask = builder->Lt(iota, builder->Reshape(seq_lens, dims), {seq_dim_}); + + // Broadcast the mask up to the input shape. + mask = + builder->Or(mask, builder->Broadcast(builder->ConstantR0(false), + input_shape.dim_sizes())); + + output = builder->Select(mask, output, input); + context->SetOutput(0, output); + } + + private: + int32 batch_dim_; + int32 seq_dim_; +}; + +REGISTER_XLA_OP(Name("ReverseSequence"), ReverseSequenceOp); + +} // namespace +} // namespace tensorflow -- GitLab From dd808e926da2ccfb1dbe0086bcef5dee55a454a9 Mon Sep 17 00:00:00 2001 From: Nick Desaulniers Date: Mon, 29 Jan 2018 11:41:31 -0800 Subject: [PATCH 213/423] [TF:XLA] Add xla_dump_per_pass_hlo_proto_to flag and rename xla_dump_hlo_proto_to and xla_dump_prepass_hlo_proto_to. Renamed: xla_dump_hlo_proto_to -> xla_dump_optimized_hlo_proto_to xla_dump_prepass_hlo_proto_to -> xla_dump_unoptimized_hlo_proto_to xla_dump_per_pass_hlo_proto_to takes a directory from which to dump the serialized HLO module protos after each HLO pass. This will help us compare HLO modules as they change during HLO passes, such as what they evaluate to, or how fast they perform. The directory passed will contain multiple protos with the filename format: module_...after_.pb example: module_0000.0009.simplification.after_dce.pb such that pass number 0000_* is the first pass run, followed immediately by pass number 0001_*. PiperOrigin-RevId: 183700119 --- .../xla/legacy_flags/debug_options_flags.cc | 18 ++++++---- tensorflow/compiler/xla/service/BUILD | 1 + .../compiler/xla/service/cpu/cpu_compiler.cc | 20 +++++------ .../compiler/xla/service/gpu/gpu_compiler.cc | 8 ++--- tensorflow/compiler/xla/service/hlo_module.cc | 12 +++++-- tensorflow/compiler/xla/service/hlo_module.h | 10 ++++++ .../compiler/xla/service/hlo_module_test.cc | 6 ++++ .../compiler/xla/service/hlo_pass_pipeline.cc | 36 ++++++++++++++++--- tensorflow/compiler/xla/service/service.cc | 8 ++--- .../compiler/xla/tools/hlo_proto_to_json.cc | 2 +- tensorflow/compiler/xla/util.cc | 2 +- tensorflow/compiler/xla/xla.proto | 15 +++++--- 12 files changed, 100 insertions(+), 38 deletions(-) diff --git a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc index fe3a4d2f6d..c8ed3e3a2b 100644 --- a/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc +++ b/tensorflow/compiler/xla/legacy_flags/debug_options_flags.cc @@ -221,13 +221,19 @@ void AllocateFlags() { flag_values->xla_gpu_disable_multi_streaming(), "If true, multi-streaming in the GPU backend is disabled."), tensorflow::Flag( - "xla_dump_hlo_proto_to", flag_values->mutable_xla_dump_hlo_proto_to(), - "Dump compilation artifacts as proto binary into this directory."), + "xla_dump_optimized_hlo_proto_to", + flag_values->mutable_xla_dump_optimized_hlo_proto_to(), + "Dump Hlo after all hlo passes are executed as proto binary into " + "this directory."), tensorflow::Flag( - "xla_dump_prepass_hlo_proto_to", - flag_values->mutable_xla_dump_prepass_hlo_proto_to(), - "Dump compilation artifacts, before hlo passes are executed, as " - "proto binary into this directory."), + "xla_dump_unoptimized_hlo_proto_to", + flag_values->mutable_xla_dump_unoptimized_hlo_proto_to(), + "Dump HLO before any hlo passes are executed as proto binary into " + "this directory."), + tensorflow::Flag("xla_dump_per_pass_hlo_proto_to", + flag_values->mutable_xla_dump_per_pass_hlo_proto_to(), + "Dump HLO after each pass as an HloProto in binary file " + "format into this directory."), tensorflow::Flag( "xla_test_all_output_layouts", bool_setter_for(&DebugOptions::set_xla_test_all_output_layouts), diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index bdccfad0d0..a04ba7ae3e 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -1886,6 +1886,7 @@ cc_library( ":hlo", ":hlo_graph_dumper", ":hlo_pass", + ":hlo_proto_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 3fdb3d5ca6..d13a97bcc9 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -519,8 +519,8 @@ StatusOr> CpuCompiler::RunBackend( // ownership is std::moved. const bool embed_ir_in_executable = module->config().debug_options().xla_embed_ir_in_executable(); - const string xla_dump_hlo_proto_to = - module->config().debug_options().xla_dump_hlo_proto_to(); + const string xla_dump_optimized_hlo_proto_to = + module->config().debug_options().xla_dump_optimized_hlo_proto_to(); if (options::CpuParallelBackendRequested(module->config())) { VLOG(1) << "Using parallel cpu backend"; @@ -540,10 +540,10 @@ StatusOr> CpuCompiler::RunBackend( // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); - if (!xla_dump_hlo_proto_to.empty()) { + if (!xla_dump_optimized_hlo_proto_to.empty()) { HloProto proto = MakeHloProto(*module, *assignment); TF_RETURN_IF_ERROR(protobuf_util::DumpProtoToDirectory( - proto, xla_dump_hlo_proto_to, module->name())); + proto, xla_dump_optimized_hlo_proto_to, module->name())); } // If we are using the parallel CPU backend, we need to create map from @@ -649,10 +649,10 @@ StatusOr> CpuCompiler::RunBackend( // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); - if (!xla_dump_hlo_proto_to.empty()) { + if (!xla_dump_optimized_hlo_proto_to.empty()) { HloProto proto = MakeHloProto(*module, *assignment); TF_RETURN_IF_ERROR(protobuf_util::DumpProtoToDirectory( - proto, xla_dump_hlo_proto_to, module->name())); + proto, xla_dump_optimized_hlo_proto_to, module->name())); } // Each computation is a single function. Emit all embedded computations @@ -828,12 +828,12 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); - const string xla_dump_hlo_proto_to = - module->config().debug_options().xla_dump_hlo_proto_to(); - if (!xla_dump_hlo_proto_to.empty()) { + const string xla_dump_optimized_hlo_proto_to = + module->config().debug_options().xla_dump_optimized_hlo_proto_to(); + if (!xla_dump_optimized_hlo_proto_to.empty()) { HloProto proto = MakeHloProto(*module, *assignment); TF_RETURN_IF_ERROR(protobuf_util::DumpProtoToDirectory( - proto, xla_dump_hlo_proto_to, module->name())); + proto, xla_dump_optimized_hlo_proto_to, module->name())); } IrEmitter ir_emitter(*module, *assignment, &llvm_module, diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc index 495ae1710f..07543d42e3 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc @@ -468,12 +468,12 @@ StatusOr> GpuCompiler::RunBackend( // print one ourselves. XLA_VLOG_LINES(2, buffer_assignment->ToString()); XLA_VLOG_LINES(2, module->ToString()); - const string xla_dump_hlo_proto_to = - module->config().debug_options().xla_dump_hlo_proto_to(); - if (!xla_dump_hlo_proto_to.empty()) { + const string xla_dump_optimized_hlo_proto_to = + module->config().debug_options().xla_dump_optimized_hlo_proto_to(); + if (!xla_dump_optimized_hlo_proto_to.empty()) { HloProto proto = MakeHloProto(*module, *buffer_assignment); TF_RETURN_IF_ERROR(protobuf_util::DumpProtoToDirectory( - proto, xla_dump_hlo_proto_to, module->name())); + proto, xla_dump_optimized_hlo_proto_to, module->name())); } IrEmitterContext ir_emitter_context(module.get(), buffer_assignment.get(), diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 99d8dd04e5..60270b0595 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -38,12 +38,16 @@ HloModule::HloModule(const string& name, : name_(NameUniquer::GetSanitizedName(name)), config_(config), has_entry_computation_handle_(true), - entry_computation_handle_(entry_computation_handle) {} + entry_computation_handle_(entry_computation_handle), + unique_id_(next_unique_module_id_++) {} HloModule::HloModule(const string& name) - : name_(NameUniquer::GetSanitizedName(name)) {} + : name_(NameUniquer::GetSanitizedName(name)), + unique_id_(next_unique_module_id_++) {} HloModule::HloModule(const string& name, const HloModuleConfig& config) - : name_(NameUniquer::GetSanitizedName(name)), config_(config) {} + : name_(NameUniquer::GetSanitizedName(name)), + config_(config), + unique_id_(next_unique_module_id_++) {} HloComputation* HloModule::AddComputationInternal( std::unique_ptr computation, bool is_entry, @@ -564,4 +568,6 @@ uint64 HloModule::RandomNew64() const { return rng_(); } +/* static */ std::atomic HloModule::next_unique_module_id_(0); + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h index e377654d02..4bfe8d89ce 100644 --- a/tensorflow/compiler/xla/service/hlo_module.h +++ b/tensorflow/compiler/xla/service/hlo_module.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_MODULE_H_ +#include #include #include #include @@ -201,6 +202,10 @@ class HloModule { // this point are guaranteed to be in the range [0..NumUniqueInstructionIds()) int NumUniqueInstructionIds() const { return next_unique_id_; } + // Returns an id that is unique to this module across all modules created over + // the lifetime of this process. + int unique_id() const { return unique_id_; } + private: HloComputation* AddComputationInternal( std::unique_ptr computation, bool is_entry, @@ -227,6 +232,11 @@ class HloModule { NameUniquer computation_name_uniquer_{/*separator=*/"."}; NameUniquer instruction_name_uniquer_{/*separator=*/"."}; int next_unique_id_ = 0; + + // Used to keep track of the next unique module id that should be assigned. + static std::atomic next_unique_module_id_; + // A unique id to label modules with. + int unique_id_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_module_test.cc b/tensorflow/compiler/xla/service/hlo_module_test.cc index cd51fa4e85..7f28a804bf 100644 --- a/tensorflow/compiler/xla/service/hlo_module_test.cc +++ b/tensorflow/compiler/xla/service/hlo_module_test.cc @@ -188,6 +188,12 @@ TEST_F(HloModuleTest, LargeConstantToString) { module->ToString(HloPrintOptions().set_print_large_constants(true))); } +TEST_F(HloModuleTest, UniqueModuleId) { + auto module_a = CreateNewModule(); + auto module_b = CreateNewModule(); + EXPECT_NE(module_a->unique_id(), module_b->unique_id()); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc index 53bd46a641..5120775737 100644 --- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc +++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" +#include "tensorflow/compiler/xla/service/hlo_proto_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" @@ -32,12 +33,28 @@ using ::tensorflow::strings::StrCat; namespace xla { namespace { -void DumpModule(const HloModule& module, - const string& message) { +void DumpModuleGraph(const HloModule& module, const string& message) { hlo_graph_dumper::MaybeDumpHloModule(module, message); VLOG(3) << "HLO " << message << ":"; XLA_VLOG_LINES(3, module.ToString()); } + +void DumpModuleProto(const HloModule& module, const string& dump_to, + const string& pipeline_name, const string& pass_name) { + static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED); + static auto* const module_id_to_pass_number = + new tensorflow::gtl::FlatMap(); + + tensorflow::mutex_lock lock(mu); + const int64 pass_number = (*module_id_to_pass_number)[module.unique_id()]++; + + const string mod_name = SanitizeFileName(tensorflow::strings::Printf( + "module_%04d.%04lld.%s.after_%s", module.unique_id(), pass_number, + pipeline_name.c_str(), pass_name.c_str())); + + TF_QCHECK_OK(protobuf_util::DumpProtoToDirectory(MakeHloProto(module), + dump_to, mod_name)); +} } // namespace StatusOr HloPassPipeline::Run(HloModule* module) { @@ -78,6 +95,13 @@ StatusOr HloPassPipeline::Run(HloModule* module) { string message; TF_RETURN_IF_ERROR( run_invariant_checkers(StrCat("before running pipeline: ", name()))); + const string xla_dump_per_pass_hlo_proto_to = + module->config().debug_options().xla_dump_per_pass_hlo_proto_to(); + if (!xla_dump_per_pass_hlo_proto_to.empty()) { + DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to, name().ToString(), + "pipeline_start"); + } + for (auto& pass : passes_) { if (disabled_passes.count(pass->name().ToString()) > 0) { VLOG(1) << " Skipping HLO pass " << pass->name() @@ -90,17 +114,21 @@ StatusOr HloPassPipeline::Run(HloModule* module) { // Emit label containing: "after foo-pass, before bar-pass". message.clear(); StrAppend(&message, prefix, ", before ", pass->name()); - DumpModule(*module, message); + DumpModuleGraph(*module, message); TF_ASSIGN_OR_RETURN(bool changed_this_pass, pass->Run(module)); TF_RETURN_IF_ERROR( run_invariant_checkers(StrCat("after running pass: ", pass->name()))); + if (!xla_dump_per_pass_hlo_proto_to.empty()) { + DumpModuleProto(*module, xla_dump_per_pass_hlo_proto_to, + name().ToString(), pass->name().ToString()); + } changed |= changed_this_pass; prefix.clear(); StrAppend(&prefix, name(), ": after ", pass->name()); } - DumpModule(*module, prefix + ", pipeline end"); + DumpModuleGraph(*module, prefix + ", pipeline end"); return changed; } diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index fea6956345..a57b7e5717 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -1619,14 +1619,14 @@ StatusOr> Service::Replicas( } Status Service::MaybeDumpHloModule(const HloModule& module) const { - const string xla_dump_prepass_hlo_proto_to = - module.config().debug_options().xla_dump_prepass_hlo_proto_to(); - if (xla_dump_prepass_hlo_proto_to.empty()) { + const string xla_dump_unoptimized_hlo_proto_to = + module.config().debug_options().xla_dump_unoptimized_hlo_proto_to(); + if (xla_dump_unoptimized_hlo_proto_to.empty()) { return Status::OK(); } HloProto proto = MakeHloProto(module); return protobuf_util::DumpProtoToDirectory( - proto, xla_dump_prepass_hlo_proto_to, module.name()); + proto, xla_dump_unoptimized_hlo_proto_to, module.name()); } } // namespace xla diff --git a/tensorflow/compiler/xla/tools/hlo_proto_to_json.cc b/tensorflow/compiler/xla/tools/hlo_proto_to_json.cc index 4e02e17db6..8460ae3e49 100644 --- a/tensorflow/compiler/xla/tools/hlo_proto_to_json.cc +++ b/tensorflow/compiler/xla/tools/hlo_proto_to_json.cc @@ -19,7 +19,7 @@ limitations under the License. // // Reads one serilized Hlo module, convert it into JSON format and dump into // some output directory. some_binaray_proto is obtained by serializing Hlo -// module to disk using --xla_dump_hlo_proto_to debug optoin. +// module to disk using --xla_dump_optimized_hlo_proto_to debug option. #include #include diff --git a/tensorflow/compiler/xla/util.cc b/tensorflow/compiler/xla/util.cc index b020905035..1f0c626bbb 100644 --- a/tensorflow/compiler/xla/util.cc +++ b/tensorflow/compiler/xla/util.cc @@ -339,7 +339,7 @@ std::vector> CommonFactors( string SanitizeFileName(string file_name) { for (char& c : file_name) { - if (c == '/' || c == '\\' || c == '[' || c == ']') { + if (c == '/' || c == '\\' || c == '[' || c == ']' || c == ' ') { c = '_'; } } diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index e1ed08c848..56162ab44e 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -82,8 +82,9 @@ message DebugOptions { // Dump all HLO modules as text into the provided directory path. string xla_generate_hlo_text_to = 7; - // Dump compilation artifacts in binary proto into this directory. - string xla_dump_hlo_proto_to = 8; + // Dump Hlo after all hlo passes are executed as proto binary into this + // directory. + string xla_dump_optimized_hlo_proto_to = 8; // Instrument the computation to collect per-HLO cycle counts. bool xla_hlo_profile = 9; @@ -179,9 +180,13 @@ message DebugOptions { // ops. bool xla_gpu_use_cudnn_batchnorm = 94; - // Dump compilation artifacts, before hlo passes are executed, in binary proto - // into this directory. - string xla_dump_prepass_hlo_proto_to = 95; + // Dump HLO before any hlo passes are executed as proto binary into this + // directory. + string xla_dump_unoptimized_hlo_proto_to = 95; + + // Dump HLO after each pass as an HloProto in binary file format into this + // directory. + string xla_dump_per_pass_hlo_proto_to = 96; // Extra options to pass to the compilation backend; specific interpretation // of these values is left to the backend. -- GitLab From 0050ce16d425b3367010d58a5a3dca30aab894a4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 11:47:02 -0800 Subject: [PATCH 214/423] Make TFLite Mean op have parity with TF Reduce Mean op by changing the representation of axis from an attribute to a tensor. PiperOrigin-RevId: 183701017 --- tensorflow/contrib/lite/builtin_op_data.h | 4 - tensorflow/contrib/lite/kernels/mean.cc | 129 +++++++++++------- tensorflow/contrib/lite/kernels/mean_test.cc | 100 ++++++++++---- tensorflow/contrib/lite/model.cc | 4 - tensorflow/contrib/lite/schema/schema.fbs | 1 - .../contrib/lite/schema/schema_generated.h | 36 +---- .../contrib/lite/testing/generate_examples.py | 32 +++-- .../contrib/lite/toco/tflite/operator.cc | 5 +- .../contrib/lite/toco/tflite/operator_test.cc | 2 - 9 files changed, 186 insertions(+), 127 deletions(-) diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index 6dd9cb39d2..7a7e20a41e 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -199,10 +199,6 @@ typedef struct { } TfLiteTransposeParams; typedef struct { - // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. - // For now we will fix the maximum possible number of dimensions. - int axis[8]; - int num_axis_dimensions; bool keep_dims; } TfLiteMeanParams; diff --git a/tensorflow/contrib/lite/kernels/mean.cc b/tensorflow/contrib/lite/kernels/mean.cc index 540e5a364d..ec1c402027 100644 --- a/tensorflow/contrib/lite/kernels/mean.cc +++ b/tensorflow/contrib/lite/kernels/mean.cc @@ -35,10 +35,12 @@ struct MeanContext { MeanContext(TfLiteContext* context, TfLiteNode* node) { params = reinterpret_cast(node->builtin_data); input = GetInput(context, node, 0); + axis = GetInput(context, node, 1); output = GetOutput(context, node, 0); } TfLiteMeanParams* params; TfLiteTensor* input; + TfLiteTensor* axis; TfLiteTensor* output; }; @@ -54,45 +56,26 @@ void Free(TfLiteContext* context, void* buffer) { delete reinterpret_cast(buffer); } -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - MeanContext op_context(context, node); - int input_num_dims = NumDimensions(op_context.input); - int axis_num_dims = op_context.params->num_axis_dimensions; - - // Creates a temp index to iterate through input data. - int* scratch_tensor_index = reinterpret_cast(node->user_data); - TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(2); - node->temporaries->data[0] = *scratch_tensor_index; - TfLiteTensor* scratch_tensor = &context->tensors[node->temporaries->data[0]]; - scratch_tensor->type = kTfLiteInt32; - scratch_tensor->allocation_type = kTfLiteArenaRw; - TfLiteIntArray* index_size = TfLiteIntArrayCreate(1); - index_size->data[0] = input_num_dims; - TF_LITE_ENSURE_OK(context, - context->ResizeTensor(context, scratch_tensor, index_size)); - - // Creates a temp tensor to store resolved axis given input data. - node->temporaries->data[1] = *scratch_tensor_index + 1; - TfLiteTensor* axis_tensor = &context->tensors[node->temporaries->data[1]]; - axis_tensor->type = kTfLiteInt32; - axis_tensor->allocation_type = kTfLiteArenaRw; +// Resizes the temp tensor that stores resolved axis. +TfLiteStatus ResizeTempAxis(TfLiteContext* context, MeanContext* op_context, + TfLiteTensor* resolved_axis) { TfLiteIntArray* axis_size = TfLiteIntArrayCreate(1); - axis_size->data[0] = op_context.params->num_axis_dimensions; - TF_LITE_ENSURE_OK(context, - context->ResizeTensor(context, axis_tensor, axis_size)); + axis_size->data[0] = static_cast(NumElements(op_context->axis)); + return context->ResizeTensor(context, resolved_axis, axis_size); +} - // Determines size of output tensor. - const TfLiteIntArray* input_dims = op_context.input->dims; - const int* axis = op_context.params->axis; - if (op_context.params->keep_dims) { +// Resizes output array based on the input size and resolved axis. +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + MeanContext* 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); + const int* axis = GetTensorData(op_context->axis); + if (op_context->params->keep_dims) { TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_num_dims); for (int idx = 0; idx < input_num_dims; ++idx) { bool is_axis = false; - for (int axis_idx = 0; axis_idx < axis_num_dims; ++axis_idx) { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) { is_axis = true; break; @@ -104,11 +87,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_dims->data[idx] = input_dims->data[idx]; } } - return context->ResizeTensor(context, op_context.output, output_dims); + return context->ResizeTensor(context, op_context->output, output_dims); } else { // Calculates size of reducing axis. - int num_reduce_axis = axis_num_dims; - for (int i = 0; i < axis_num_dims; ++i) { + int num_reduce_axis = num_axis; + for (int i = 0; i < num_axis; ++i) { int current = axis[i]; if (current < 0) { current += input_num_dims; @@ -131,7 +114,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { int num_skip_axis = 0; for (int idx = 0; idx < input_num_dims; ++idx) { bool is_axis = false; - for (int axis_idx = 0; axis_idx < axis_num_dims; ++axis_idx) { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) { ++num_skip_axis; is_axis = true; @@ -142,24 +125,76 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_dims->data[idx - num_skip_axis] = input_dims->data[idx]; } } - return context->ResizeTensor(context, op_context.output, output_dims); + return context->ResizeTensor(context, op_context->output, output_dims); + } +} + +// Initializes temp tensors to store index and resolved axis. +TfLiteStatus InitializeTemporaries(TfLiteContext* context, TfLiteNode* node, + MeanContext* op_context) { + // Creates a temp index to iterate through input data. + int* scratch_tensor_index = reinterpret_cast(node->user_data); + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(2); + node->temporaries->data[0] = *scratch_tensor_index; + TfLiteTensor* scratch_tensor = &context->tensors[node->temporaries->data[0]]; + scratch_tensor->type = kTfLiteInt32; + scratch_tensor->allocation_type = kTfLiteArenaRw; + TfLiteIntArray* index_size = TfLiteIntArrayCreate(1); + index_size->data[0] = NumDimensions(op_context->input); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, scratch_tensor, index_size)); + + // Creates a temp tensor to store resolved axis given input data. + node->temporaries->data[1] = *scratch_tensor_index + 1; + TfLiteTensor* resolved_axis = &context->tensors[node->temporaries->data[1]]; + resolved_axis->type = kTfLiteInt32; + return kTfLiteOk; +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + MeanContext op_context(context, node); + TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); + + TfLiteTensor* resolved_axis = &context->tensors[node->temporaries->data[1]]; + // Leaves work to Eval if axis is not constant; else resizes output. + if (!IsConstantTensor(op_context.axis)) { + SetTensorToDynamic(op_context.output); + SetTensorToDynamic(resolved_axis); + return kTfLiteOk; } + resolved_axis->allocation_type = kTfLiteArenaRw; + TF_LITE_ENSURE_OK(context, + ResizeTempAxis(context, &op_context, resolved_axis)); + return ResizeOutputTensor(context, &op_context); } template TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { MeanContext op_context(context, node); + int num_axis = static_cast(NumElements(op_context.axis)); TfLiteTensor* temp_index = &context->tensors[node->temporaries->data[0]]; TfLiteTensor* resolved_axis = &context->tensors[node->temporaries->data[1]]; + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, + ResizeTempAxis(context, &op_context, resolved_axis)); + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + TfLiteTensorRealloc(resolved_axis->bytes, resolved_axis); + TfLiteTensorRealloc(op_context.output->bytes, op_context.output); + } -#define TF_LITE_MEAN(kernel_type, data_type) \ - kernel_type::Mean<>( \ - GetTensorData(op_context.input), \ - op_context.input->dims->data, op_context.input->dims->size, \ - GetTensorData(op_context.output), \ - op_context.output->dims->data, op_context.output->dims->size, \ - op_context.params->axis, op_context.params->num_axis_dimensions, \ - op_context.params->keep_dims, GetTensorData(temp_index), \ +#define TF_LITE_MEAN(kernel_type, data_type) \ + kernel_type::Mean<>( \ + GetTensorData(op_context.input), \ + op_context.input->dims->data, op_context.input->dims->size, \ + GetTensorData(op_context.output), \ + op_context.output->dims->data, op_context.output->dims->size, \ + GetTensorData(op_context.axis), num_axis, \ + op_context.params->keep_dims, GetTensorData(temp_index), \ GetTensorData(resolved_axis)) if (kernel_type == kReference) { diff --git a/tensorflow/contrib/lite/kernels/mean_test.cc b/tensorflow/contrib/lite/kernels/mean_test.cc index 4305c0632f..c4c53c2ded 100644 --- a/tensorflow/contrib/lite/kernels/mean_test.cc +++ b/tensorflow/contrib/lite/kernels/mean_test.cc @@ -25,58 +25,108 @@ using ::testing::ElementsAreArray; class BaseMeanOpModel : public SingleOpModel { public: - BaseMeanOpModel(const TensorData& input, const TensorData& output, - std::initializer_list axis, bool keep_dims) { - input_ = AddInput(input); - output_ = AddOutput(output); - SetBuiltinOp( - BuiltinOperator_MEAN, BuiltinOptions_MeanOptions, - CreateMeanOptions(builder_, builder_.CreateVector(axis), keep_dims) - .Union()); - BuildInterpreter({GetShape(input_)}); + void SetAxis(std::initializer_list data) { PopulateTensor(axis_, data); } + + template + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); } - int input() { return input_; } + template + std::vector GetOutput() { + return ExtractVector(output_); + } + + std::vector GetOutputShape() { return GetTensorShape(output_); } protected: int input_; + int axis_; int output_; }; -class FloatMeanOpModel : public BaseMeanOpModel { +// Model for the tests case where axis is a const tensor. +class MeanOpConstModel : public BaseMeanOpModel { public: - using BaseMeanOpModel::BaseMeanOpModel; - - void SetInput(std::initializer_list data) { - PopulateTensor(input_, data); + MeanOpConstModel(const TensorData& input, const TensorData& output, + std::initializer_list axis_shape, + std::initializer_list axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_MeanOptions, + CreateMeanOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); } +}; - std::vector GetOutput() { return ExtractVector(output_); } - std::vector GetOutputShape() { return GetTensorShape(output_); } +// Model for the tests case where axis is a dynamic tensor. +class MeanOpDynamicModel : public BaseMeanOpModel { + public: + MeanOpDynamicModel(const TensorData& input, const TensorData& output, + const TensorData& axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddInput(axis); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_MeanOptions, + CreateMeanOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } }; -TEST(FloatMeanOpTest, NotKeepDims) { +TEST(ConstMeanOpTest, NotKeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); +} + +TEST(ConstMeanOpTest, KeepDims) { + std::initializer_list data = { + 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, + 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); +} + +TEST(DynamicMeanOpTest, NotKeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; - FloatMeanOpModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, - {1, 0, -3, -3}, false); + MeanOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}}, + false); + std::initializer_list axis = {1, 0, -3, -3}; + m.SetAxis(axis); m.SetInput(data); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); - EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); } -TEST(FloatMeanOpTest, KeepDims) { +TEST(DynamicMeanOpTest, KeepDims) { std::initializer_list data = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; - FloatMeanOpModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, - {0, 2}, true); + MeanOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, + true); + std::initializer_list axis = {0, 2}; + m.SetAxis(axis); m.SetInput(data); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); - EXPECT_THAT(m.GetOutput(), + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); } diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 64b8f55097..c82ae27953 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -544,11 +544,7 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_MEAN: { auto* params = MallocPOD(); if (auto* schema_params = op->builtin_options_as_MeanOptions()) { - const auto& axis = schema_params->axis(); - FlatBufferIntVectorToArray(sizeof(params->axis), axis, params->axis, - error_reporter); params->keep_dims = schema_params->keep_dims(); - params->num_axis_dimensions = axis->Length(); } builtin_data = reinterpret_cast(params); break; diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index f6217567f8..91eac2ab48 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -333,7 +333,6 @@ table TransposeOptions { } table MeanOptions { - axis:[int]; keep_dims: bool; } diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index ad758529a3..a8370b34c6 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -3388,21 +3388,16 @@ flatbuffers::Offset CreateTransposeOptions( struct MeanOptionsT : public flatbuffers::NativeTable { typedef MeanOptions TableType; - std::vector axis; bool keep_dims; MeanOptionsT() : keep_dims(false) {} }; struct MeanOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef MeanOptionsT NativeTableType; - enum { VT_AXIS = 4, VT_KEEP_DIMS = 6 }; - const flatbuffers::Vector *axis() const { - return GetPointer *>(VT_AXIS); - } + enum { VT_KEEP_DIMS = 4 }; bool keep_dims() const { return GetField(VT_KEEP_DIMS, 0) != 0; } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_AXIS) && - verifier.Verify(axis()) && + return VerifyTableStart(verifier) && VerifyField(verifier, VT_KEEP_DIMS) && verifier.EndTable(); } MeanOptionsT *UnPack( @@ -3418,9 +3413,6 @@ struct MeanOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { struct MeanOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_axis(flatbuffers::Offset> axis) { - fbb_.AddOffset(MeanOptions::VT_AXIS, axis); - } void add_keep_dims(bool keep_dims) { fbb_.AddElement(MeanOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); @@ -3438,22 +3430,12 @@ struct MeanOptionsBuilder { }; inline flatbuffers::Offset CreateMeanOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> axis = 0, - bool keep_dims = false) { + flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false) { MeanOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); builder_.add_keep_dims(keep_dims); return builder_.Finish(); } -inline flatbuffers::Offset CreateMeanOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *axis = nullptr, bool keep_dims = false) { - return tflite::CreateMeanOptions( - _fbb, axis ? _fbb.CreateVector(*axis) : 0, keep_dims); -} - flatbuffers::Offset CreateMeanOptions( flatbuffers::FlatBufferBuilder &_fbb, const MeanOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -6039,15 +6021,6 @@ inline void MeanOptions::UnPackTo( MeanOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = axis(); - if (_e) { - _o->axis.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->axis[_i] = _e->Get(_i); - } - } - }; { auto _e = keep_dims(); _o->keep_dims = _e; @@ -6071,9 +6044,8 @@ inline flatbuffers::Offset CreateMeanOptions( const flatbuffers::rehasher_function_t *__rehasher; } _va = {&_fbb, _o, _rehasher}; (void)_va; - auto _axis = _o->axis.size() ? _fbb.CreateVector(_o->axis) : 0; auto _keep_dims = _o->keep_dims; - return tflite::CreateMeanOptions(_fbb, _axis, _keep_dims); + return tflite::CreateMeanOptions(_fbb, _keep_dims); } inline SqueezeOptionsT *SqueezeOptions::UnPack( diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 4ae6ccb765..fc8149bef9 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -695,6 +695,7 @@ def make_mean_tests(zip_path): [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] ], + "const_axis": [True, False], "keep_dims": [True, False], }, { "input_dtype": [tf.float32, tf.int32, tf.int64], @@ -705,6 +706,7 @@ def make_mean_tests(zip_path): -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], "keep_dims": [True, False], }] @@ -714,17 +716,31 @@ def make_mean_tests(zip_path): dtype=parameters["input_dtype"], name="input", shape=parameters["input_shape"]) + + # Get axis as either a placeholder or constants. + if parameters["const_axis"]: + axis = parameters["axis"] + input_tensors = [input_tensor] + else: + if isinstance(parameters["axis"], list): + shape = [len(parameters["axis"])] + else: + shape = [0] # shape for None or integers. + axis = tf.placeholder(dtype=tf.int32, name="axis", shape=shape) + input_tensors = [input_tensor, axis] + out = tf.reduce_mean( - input_tensor, - axis=parameters["axis"], - keep_dims=parameters["keep_dims"]) - return [input_tensor], [out] + input_tensor, axis=axis, keep_dims=parameters["keep_dims"]) + return input_tensors, [out] def build_inputs(parameters, sess, inputs, outputs): - input_values = create_tensor_data(parameters["input_dtype"], - parameters["input_shape"]) - return [input_values], sess.run( - outputs, feed_dict=dict(zip(inputs, [input_values]))) + values = [ + create_tensor_data(parameters["input_dtype"], parameters["input_shape"]) + ] + if not parameters["const_axis"]: + if 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) diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 853a9f46c2..e33a5788d8 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -548,14 +548,11 @@ class Mean : public BuiltinOperator WriteOptions( const TocoOperator& op, flatbuffers::FlatBufferBuilder* builder) const override { - auto axis = builder->CreateVector(op.axis); - return ::tflite::CreateMeanOptions(*builder, axis, op.keep_dims); + return ::tflite::CreateMeanOptions(*builder, op.keep_dims); } void ReadOptions(const TfLiteOptions& options, TocoOperator* op) const override { - op->axis.insert(op->axis.end(), options.axis()->begin(), - options.axis()->end()); op->keep_dims = options.keep_dims(); } }; diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 4df9071095..b4ec7bbd50 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -134,12 +134,10 @@ TEST_F(OperatorTest, BuiltinSpaceToBatchND) { TEST_F(OperatorTest, BuiltinMean) { MeanOperator op; - op.axis = {1, 2}; op.keep_dims = false; auto output_toco_op = SerializeAndDeserialize(GetOperator("MEAN", OperatorType::kMean), op); - EXPECT_EQ(op.axis, output_toco_op->axis); EXPECT_EQ(op.keep_dims, output_toco_op->keep_dims); } -- GitLab From 5fb13ffc145af5a9c707a1388c38dd45f793b0a0 Mon Sep 17 00:00:00 2001 From: Adam Roberts Date: Mon, 29 Jan 2018 11:51:08 -0800 Subject: [PATCH 215/423] Add input and sequence_length accessor to TrainingHelper. PiperOrigin-RevId: 183701716 --- tensorflow/contrib/seq2seq/python/ops/helper.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/tensorflow/contrib/seq2seq/python/ops/helper.py b/tensorflow/contrib/seq2seq/python/ops/helper.py index ef3722ee41..3245cc5e72 100644 --- a/tensorflow/contrib/seq2seq/python/ops/helper.py +++ b/tensorflow/contrib/seq2seq/python/ops/helper.py @@ -184,6 +184,7 @@ class TrainingHelper(Helper): """ with ops.name_scope(name, "TrainingHelper", [inputs, sequence_length]): inputs = ops.convert_to_tensor(inputs, name="inputs") + self._inputs = inputs if not time_major: inputs = nest.map_structure(_transpose_batch_time, inputs) @@ -200,6 +201,14 @@ class TrainingHelper(Helper): self._batch_size = array_ops.size(sequence_length) + @property + def inputs(self): + return self._inputs + + @property + def sequence_length(self): + return self._sequence_length + @property def batch_size(self): return self._batch_size -- GitLab From 8bfaa9213b640201b6886f3f245a1ad1a7461030 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Mon, 29 Jan 2018 12:09:05 -0800 Subject: [PATCH 216/423] [tf.data] Handle `tf.SparseTensor` elements in the stats ops. PiperOrigin-RevId: 183705008 --- tensorflow/contrib/data/python/ops/stats_ops.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index 1dd0729513..9cd1701c39 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -20,6 +20,7 @@ from __future__ import print_function from tensorflow.python.data.ops import dataset_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.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops @@ -161,8 +162,10 @@ class _StatsDataset(dataset_ops.Dataset): return self._op_function( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access self._tag, - output_shapes=nest.flatten(self.output_shapes), - output_types=nest.flatten(self.output_types)) + output_types=nest.flatten( + sparse.as_dense_types(self.output_types, self.output_classes)), + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) @property def output_shapes(self): -- GitLab From 95a8af24058c168ce8a5327451e1cfcbc56461eb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 12:12:41 -0800 Subject: [PATCH 217/423] Ensure that non-recursive conversion is identity transformation wrt all types of function calls by only failing on unresolved symbols if they're needed. Simplify code structure all around. Remove the awkward activity analysis that deemed a function parameter as "modified". Consolidate activity analysis by tracking function parameters and returned symbols separately. Strengthen the type inference a little by using more interpret-like constructs. PiperOrigin-RevId: 183705547 --- tensorflow/contrib/py2tf/conversion.py | 33 ++-- .../contrib/py2tf/converters/call_trees.py | 159 ++++++++++-------- .../py2tf/converters/call_trees_test.py | 46 ++++- .../py2tf/converters/converter_test_base.py | 19 ++- .../py2tf/converters/side_effect_guards.py | 5 +- tensorflow/contrib/py2tf/naming.py | 68 ++++---- tensorflow/contrib/py2tf/naming_test.py | 14 +- tensorflow/contrib/py2tf/pyct/context.py | 3 +- tensorflow/contrib/py2tf/pyct/parser.py | 10 +- tensorflow/contrib/py2tf/pyct/parser_test.py | 11 +- .../py2tf/pyct/static_analysis/access.py | 67 ++++++-- .../py2tf/pyct/static_analysis/access_test.py | 42 ++--- .../py2tf/pyct/static_analysis/live_values.py | 52 +++--- .../pyct/static_analysis/live_values_test.py | 56 ++++-- .../py2tf/pyct/static_analysis/type_info.py | 66 +++----- .../pyct/static_analysis/type_info_test.py | 60 ++++--- tensorflow/contrib/py2tf/pyct/templates.py | 3 +- tensorflow/contrib/py2tf/pyct/transformer.py | 17 +- 18 files changed, 442 insertions(+), 289 deletions(-) diff --git a/tensorflow/contrib/py2tf/conversion.py b/tensorflow/contrib/py2tf/conversion.py index b484eebbd5..e277eadec4 100644 --- a/tensorflow/contrib/py2tf/conversion.py +++ b/tensorflow/contrib/py2tf/conversion.py @@ -171,7 +171,8 @@ def class_to_graph(c, conversion_map): def function_to_graph(f, conversion_map, arg_values, arg_types, owner_type=None): """Specialization of `entity_to_graph` for callable functions.""" - node = parser.parse_object(f).body[0] + node, source = parser.parse_entity(f) + node = node.body[0] namespace = six.get_function_globals(f) # This is needed for non-global functions. @@ -185,28 +186,29 @@ def function_to_graph(f, conversion_map, arg_values, arg_types, namer = conversion_map.new_namer(namespace) ctx = context.EntityContext( namer=namer, - source_code=tf_inspect.getsource(f), - source_file=tf_inspect.getfile(f), + source_code=source, + source_file='', namespace=namespace, arg_values=arg_values, - arg_types=arg_types) + arg_types=arg_types, + recursive=conversion_map.recursive) node = node_to_graph(node, ctx, conversion_map.nocompile_decorators) - # Simulate a rename to ensure the top level is in the name map. This is needed - # for top level functions, and it also helps the consistency verification made - # by update_name_map. - if owner_type is not None: - new_name = namer.compiled_function_name(f.__name__, f, owner_type) - else: - new_name = namer.compiled_function_name(f.__name__, f) + # TODO(mdan): This somewhat duplicates the call rename logic in call_treest.py + new_name, did_rename = namer.compiled_function_name(f.__name__, f, owner_type) + if not did_rename: + new_name = f.__name__ + if node.name != f.__name__: + raise NotImplementedError('Strange corner case. Send us offending code!') + node.name = new_name conversion_map.update_name_map(namer) - return node, conversion_map.name_map[f] + return node, new_name def _static_analysis_pass(node, ctx): - node = access.resolve(node) - node = live_values.resolve(node, ctx.namespace, config.PYTHON_LITERALS) + node = access.resolve(node, ctx) + node = live_values.resolve(node, ctx, config.PYTHON_LITERALS) node = type_info.resolve(node, ctx) return node @@ -259,8 +261,7 @@ def node_to_graph(node, ctx, nocompile_decorators): node = _static_analysis_pass(node, ctx) node = print_functions.transform(node) - node = call_trees.transform(node, ctx.namer, ctx.namespace, - config.DEFAULT_UNCOMPILED_MODULES, + node = call_trees.transform(node, ctx, config.DEFAULT_UNCOMPILED_MODULES, nocompile_decorators) node = control_flow.transform(node, ctx.namer) node = logical_expressions.transform(node) diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py index 0aae030450..4c238b7fb9 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees.py +++ b/tensorflow/contrib/py2tf/converters/call_trees.py @@ -29,46 +29,46 @@ import gast from tensorflow.contrib.py2tf.pyct import anno from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer +from tensorflow.python.util import tf_inspect class FunctionNamer(object): """Describes the interface for CallTreeTransformer's namer.""" def compiled_function_name(self, - original_name, - live_object=None, + original_fqn, + live_entity=None, owner_type=None): """Generate the name corresponding to the compiled version of a function. Args: - original_name: String - live_object: Callable, the actual target function, if known. + original_fqn: string or tuple(string) + live_entity: Callable, the actual target function, if known. owner_type: Optional object. If present, it indicates that the function is a member of the given type. Returns: - String. + string, bool """ raise NotImplementedError() - def compiled_class_name(self, original_name, live_object=None): + def compiled_class_name(self, original_fqn, live_entity=None): """Generate the name corresponding to the compiled version of a class. Args: - original_name: String - live_object: The actual target class, if known. + original_fqn: string or tuple(string) + live_entity: The actual target class, if known. Returns: - String. + string """ raise NotImplementedError() -class CallTreeTransformer(gast.NodeTransformer): +class CallTreeTransformer(transformer.Base): """Transforms the call tree by renaming transformed symbols.""" - def __init__(self, namer, namespace, uncompiled_modules, - nocompile_decorators): - self.namer = namer - self.namespace = namespace + def __init__(self, context, uncompiled_modules, nocompile_decorators): + super(CallTreeTransformer, self).__init__(context) self.uncompiled_modules = uncompiled_modules self.nocompile_decorators = nocompile_decorators @@ -78,7 +78,7 @@ class CallTreeTransformer(gast.NodeTransformer): if isinstance(node, gast.Call): return self._resolve_name(node.func) if isinstance(node, gast.Name): - return self.namespace.get(node.id) + return self.context.namespace.get(node.id) if isinstance(node, gast.Attribute): parent = self._resolve_name(node.value) if parent is not None: @@ -91,8 +91,12 @@ class CallTreeTransformer(gast.NodeTransformer): if anno.hasanno(node, 'live_val'): return anno.getanno(node, 'live_val') if isinstance(node, gast.Attribute) and anno.hasanno(node, 'type'): - member = getattr(anno.getanno(node, 'type'), node.attr) - return member + owner_type = anno.getanno(node, 'type') + if hasattr(owner_type, node.attr): + return getattr(owner_type, node.attr) + else: + raise ValueError('Type "%s" has not attribute "%s". Is it dynamic?' % + (owner_type, node.attr)) return None def _should_compile(self, node, fqn): @@ -106,14 +110,14 @@ class CallTreeTransformer(gast.NodeTransformer): # The decorators themselves are not to be converted. # If present, the decorators should appear as static functions. - target_obj = self._try_resolve_target(node.func) - if target_obj is not None: + target_entity = self._try_resolve_target(node.func) + if target_entity is not None: # This attribute is set by the decorator itself. # TODO(mdan): This may not play nicely with other wrapping decorators. - if hasattr(target_obj, '__pyct_is_compile_decorator'): + if hasattr(target_entity, '__pyct_is_compile_decorator'): return False - if target_obj in self.nocompile_decorators: + if target_entity in self.nocompile_decorators: return False # Inspect the target function decorators. If any include a @convert @@ -122,7 +126,8 @@ class CallTreeTransformer(gast.NodeTransformer): # To parse and re-analize each function for every call site could be quite # wasteful. Maybe we could cache the parsed AST? try: - target_node = parser.parse_object(target_obj).body[0] + target_node, _ = parser.parse_entity(target_entity) + target_node = target_node.body[0] except TypeError: # Functions whose source we cannot access are compilable (e.g. wrapped # to py_func). @@ -136,48 +141,57 @@ class CallTreeTransformer(gast.NodeTransformer): return True + def _determine_function_owner(self, m): + # TODO(mdan): The parent type should be known at analysis. Use that instead. + if hasattr(m, 'im_class'): # Python 2 + return m.im_class + if hasattr(m, '__qualname__'): # Python 3 + # Object attributes: should be bound to "self". + if hasattr(m, '__self__'): + return type(m.__self__) + + # Class attributes: should have the owner name in their namespace. + qn = m.__qualname__.split('.') + if len(qn) < 2: + return None + owner_name, func_name = qn[-2:] + if func_name != m.__name__: + raise ValueError('Inconsistent names detected ' + '(__qualname__[1] = "%s", __name__ = "%s") for %s.' % + (func_name, m.__name__, m)) + if owner_name == '': + return None + if owner_name not in self.context.namespace: + raise ValueError( + 'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' % + (owner_name, m, self.context.namespace)) + return self.context.namespace[owner_name] + return None + def _rename_compilable_function(self, node): assert anno.hasanno(node.func, 'live_val') assert anno.hasanno(node.func, 'fqn') - target_obj = anno.getanno(node.func, 'live_val') + target_entity = anno.getanno(node.func, 'live_val') target_fqn = anno.getanno(node.func, 'fqn') if not self._should_compile(node, target_fqn): return node if anno.hasanno(node, 'is_constructor'): - new_name = self.namer.compiled_class_name( - '__'.join(target_fqn), live_object=target_obj) + new_name = self.context.namer.compiled_class_name( + target_fqn, live_entity=target_entity) + do_rename = True else: - new_name = self.namer.compiled_function_name( - '__'.join(target_fqn), live_object=target_obj) - node.func = gast.Name(new_name, gast.Load(), None) - return node - - def _rename_member_function_of_known_type(self, node): - assert isinstance(node.func, gast.Attribute) - - type_fqn = anno.getanno(node.func, 'type_fqn') - assert anno.hasanno(node.func, 'type') - target_type = anno.getanno(node.func, 'type') - - if not self._should_compile(node, type_fqn): - return node - - # TODO(mdan): We should not assume that the namer only needs the - # member function name. - method_name = node.func.attr - method_object = getattr(target_type, method_name) - new_name = self.namer.compiled_function_name( - method_name, live_object=method_object, owner_type=target_type) - if new_name != node.func.attr: - # If a member function call is renamed, then the new function is no - # longer bound to the target object. We then refactor the call from: - # foo.bar(...) - # to: - # renamed_foo(bar, ...) - # TODO(mdan): This risks causing duplication, if target_type is renamed. - node.args = [node.func.value] + node.args + owner_type = self._determine_function_owner(target_entity) + new_name, do_rename = self.context.namer.compiled_function_name( + target_fqn, live_entity=target_entity, owner_type=owner_type) + + if do_rename: + if target_entity is not None: + if tf_inspect.ismethod(target_entity): + # The renaming process will transform it into a regular function. + # TODO(mdan): Is this complete? How does it work with nested members? + node.args = [node.func.value] + node.args node.func = gast.Name(new_name, gast.Load(), None) return node @@ -193,7 +207,7 @@ class CallTreeTransformer(gast.NodeTransformer): wrapper_def, call_expr = templates.replace( template, call=node.func, - wrapper=self.namer.compiled_function_name(node.func.id), + wrapper=self.context.namer.compiled_function_name(node.func.id)[0], args=tuple(gast.Name(n, gast.Load(), None) for n in args_scope.used)) anno.setanno(call_expr.value, 'args_scope', args_scope) # TODO(mdan): Rename this annotation to 'graph_ready' @@ -201,15 +215,15 @@ class CallTreeTransformer(gast.NodeTransformer): return (wrapper_def, call_expr) - def _function_is_compilable(self, target_obj): + def _function_is_compilable(self, target_entity): # TODO(mdan): This is just a placeholder. Implement. - return not isinstance(target_obj, types.BuiltinFunctionType) + return not isinstance(target_entity, types.BuiltinFunctionType) def visit_Expr(self, node): if isinstance(node.value, gast.Call): if anno.hasanno(node.value.func, 'live_val'): - target_obj = anno.getanno(node.value.func, 'live_val') - if not self._function_is_compilable(target_obj): + target_entity = anno.getanno(node.value.func, 'live_val') + if not self._function_is_compilable(target_entity): if anno.hasanno(node.value.func, 'fqn'): target_fqn = anno.getanno(node.value.func, 'fqn') if not self._should_compile(node.value, target_fqn): @@ -227,8 +241,8 @@ class CallTreeTransformer(gast.NodeTransformer): # If the function is wrapped by one of the marker decorators, # consider it graph ready. if anno.hasanno(node.func, 'live_val'): - target_obj = anno.getanno(node.func, 'live_val') - if target_obj in self.nocompile_decorators: + target_entity = anno.getanno(node.func, 'live_val') + if target_entity in self.nocompile_decorators: if len(node.args) < 1: raise ValueError( 'Found call to decorator function "%s", but it had no arguments. ' @@ -237,28 +251,28 @@ class CallTreeTransformer(gast.NodeTransformer): self.generic_visit(node) if anno.hasanno(node.func, 'live_val'): - target_obj = anno.getanno(node.func, 'live_val') - if self._function_is_compilable(target_obj): + target_entity = anno.getanno(node.func, 'live_val') + if self._function_is_compilable(target_entity): node = self._rename_compilable_function(node) else: raise NotImplementedError('py_func with return values') - elif anno.hasanno(node.func, 'type_fqn'): - node = self._rename_member_function_of_known_type(node) else: - raise NotImplementedError( - 'Member function call (of unknown type): %s.' % node.func.id) + if self.context.recursive: + raise NotImplementedError('Could not resolve target function.') + else: + # TODO(mdan): Double check. Is this reachable code? + pass return node # pylint:enable=invalid-name -def transform(node, namer, namespace, uncompiled_modules, nocompile_decorators): +def transform(node, context, uncompiled_modules, nocompile_decorators): """Transform function call to the compiled counterparts. Args: node: AST to transform. - namer: FunctionNamer-like. - namespace: Dict mapping symbol names to their corresponding live objects. + context: An EntityContext object. uncompiled_modules: set of string tuples, each tuple represents the fully qualified name of a package containing functions that will not be compiled. @@ -269,7 +283,6 @@ def transform(node, namer, namespace, uncompiled_modules, nocompile_decorators): node: The transformed AST new_names: set(string), containing any newly-generated names """ - transformer = CallTreeTransformer(namer, namespace, uncompiled_modules, - nocompile_decorators) - node = transformer.visit(node) + t = CallTreeTransformer(context, uncompiled_modules, nocompile_decorators) + node = t.visit(node) return node diff --git a/tensorflow/contrib/py2tf/converters/call_trees_test.py b/tensorflow/contrib/py2tf/converters/call_trees_test.py index 8cb8d7be0f..e63c10de0f 100644 --- a/tensorflow/contrib/py2tf/converters/call_trees_test.py +++ b/tensorflow/contrib/py2tf/converters/call_trees_test.py @@ -28,8 +28,13 @@ from tensorflow.python.platform import test class TestNamer(call_trees.FunctionNamer): - def compiled_function_name(self, original_name, live_object=None): - return 'renamed_%s' % original_name + def compiled_function_name(self, + original_fqn, + live_entity=None, + owner_type=None): + if owner_type is not None: + return None, False + return ('renamed_%s' % '_'.join(original_fqn)), True class CallTreesTest(converter_test_base.TestCase): @@ -45,14 +50,35 @@ class CallTreesTest(converter_test_base.TestCase): def test_fn_2(a): return test_fn_1(a) + 1 - node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1}) - node = call_trees.transform(node, TestNamer(), {}, (), ()) + node = self.parse_and_analyze( + test_fn_2, {'test_fn_1': test_fn_1}, namer=TestNamer()) + node = call_trees.transform(node, self.ctx, (), ()) result = compiler.ast_to_object(node) # Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 manually. setattr(result, 'renamed_test_fn_1', renamed_test_fn_1) self.assertEquals(3, result.test_fn_2(1)) + def test_simple_methods(self): + + class TestClass(object): + + def test_fn_1(self, a): + return a + 1 + + def test_fn_2(self, a): + return self.test_fn_1(a) + 1 + + node = self.parse_and_analyze( + TestClass.test_fn_2, {'TestClass': TestClass}, + namer=TestNamer(), + arg_types={'self': (TestClass.__name__, TestClass)}) + node = call_trees.transform(node, self.ctx, (), ()) + result = compiler.ast_to_object(node) + + tc = TestClass() + self.assertEquals(3, result.test_fn_2(tc, 1)) + def test_uncompiled_modules(self): def test_fn(a): @@ -60,11 +86,13 @@ class CallTreesTest(converter_test_base.TestCase): a = math_ops.add(a, constant_op.constant(1)) return a - node = self.parse_and_analyze(test_fn, { - 'math_ops': math_ops, - 'constant_op': constant_op - }) - node = call_trees.transform(node, TestNamer(), {}, + node = self.parse_and_analyze( + test_fn, { + 'math_ops': math_ops, + 'constant_op': constant_op + }, + namer=TestNamer()) + node = call_trees.transform(node, self.ctx, set(((math_ops.__name__,), (constant_op.__name__,))), ()) result = compiler.ast_to_object(node) diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py index ed006bad6d..6bfa55443c 100644 --- a/tensorflow/contrib/py2tf/converters/converter_test_base.py +++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py @@ -31,18 +31,23 @@ class TestCase(test.TestCase): def parse_and_analyze(self, test_fn, namespace, + namer=None, arg_types=None, - include_type_analysis=True): + include_type_analysis=True, + recursive=True): + node, source = parser.parse_entity(test_fn) ctx = context.EntityContext( - namer=None, - source_code=None, + namer=namer, + source_code=source, source_file=None, namespace=namespace, arg_values=None, - arg_types=arg_types) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, namespace, {}) + arg_types=arg_types, + recursive=recursive) + node = access.resolve(node, ctx) + node = live_values.resolve(node, ctx, {}) if include_type_analysis: node = type_info.resolve(node, ctx) + node = live_values.resolve(node, ctx, {}) + self.ctx = ctx return node diff --git a/tensorflow/contrib/py2tf/converters/side_effect_guards.py b/tensorflow/contrib/py2tf/converters/side_effect_guards.py index a88828ff80..46a2269c20 100644 --- a/tensorflow/contrib/py2tf/converters/side_effect_guards.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards.py @@ -94,6 +94,7 @@ class SideEffectGuardTransformer(gast.NodeTransformer): return node def _gate_symbols(self, guard_statement, guarded_args): + # TODO(mdan): This won't work for variables. template = """ (args,) = (tf.identity(a) for a in (args,)) """ @@ -133,8 +134,8 @@ class SideEffectGuardTransformer(gast.NodeTransformer): # First, attempt to gate future evaluation of args. If that's not # possible, gate all remaining statements (and that may fail too, see # _visit_and_reindent. - guarded_args = tuple( - n for n in args_scope.used if n in args_scope.parent.modified) + guarded_args = tuple(args_scope.used & (args_scope.parent.modified + | args_scope.parent.returned)) if guarded_args: node = tuple(statements[:-1]) + ( self._gate_symbols(control_deps_guard, guarded_args),) diff --git a/tensorflow/contrib/py2tf/naming.py b/tensorflow/contrib/py2tf/naming.py index a90758962b..5c7e4c5f95 100644 --- a/tensorflow/contrib/py2tf/naming.py +++ b/tensorflow/contrib/py2tf/naming.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.util import tf_inspect - class Namer(object): """Implementation of the namer interfaces required by various converters. @@ -45,10 +43,15 @@ class Namer(object): self.generated_names = set() - def compiled_class_name(self, original_name, live_object=None): + def compiled_class_name(self, original_fqn, live_entity=None): """See call_trees.FunctionNamer.compiled_class_name.""" - if live_object is not None and live_object in self.renamed_calls: - return self.renamed_calls[live_object] + if live_entity is not None and live_entity in self.renamed_calls: + return self.renamed_calls[live_entity] + + if isinstance(original_fqn, tuple): + original_name = '__'.join(original_fqn) + else: + original_name = original_fqn new_name_root = 'Tf%s' % original_name new_name = new_name_root @@ -57,41 +60,46 @@ class Namer(object): n += 1 new_name = '%s_%d' % (new_name_root, n) - if live_object is not None: - self.renamed_calls[live_object] = new_name + if live_entity is not None: + self.renamed_calls[live_entity] = new_name self.generated_names.add(new_name) + if live_entity is not None: + self.renamed_calls[live_entity] = new_name return new_name def compiled_function_name(self, - original_name, - live_object=None, + original_fqn, + live_entity=None, owner_type=None): """See call_trees.FunctionNamer.compiled_function_name.""" - if live_object is not None and live_object in self.renamed_calls: - return self.renamed_calls[live_object] if not self.recursive: - new_name = original_name - elif owner_type is None or owner_type in self.partial_types: - # Top level functions: rename - new_name_root = 'tf__%s' % original_name - new_name = new_name_root - n = 0 - while new_name in self.global_namespace: - n += 1 - new_name = '%s_%d' % (new_name_root, n) + return None, False + + if owner_type is not None and owner_type not in self.partial_types: + # Members are not renamed when part of an entire converted class. + return None, False + + if isinstance(original_fqn, tuple): + original_name = '__'.join(original_fqn) else: - if tf_inspect.isclass(owner_type): - # Class members: do not rename (the entire class will be renamed) - new_name = original_name - else: - raise NotImplementedError('Member function "%s" of non-class type: %s' % - (original_name, owner_type)) - - if live_object is not None: - self.renamed_calls[live_object] = new_name + original_name = original_fqn + + if live_entity is not None and live_entity in self.renamed_calls: + return self.renamed_calls[live_entity], True + + new_name_root = 'tf__%s' % original_name + new_name = new_name_root + n = 0 + while new_name in self.global_namespace: + n += 1 + new_name = '%s_%d' % (new_name_root, n) + + if live_entity is not None: + self.renamed_calls[live_entity] = new_name self.generated_names.add(new_name) - return new_name + + return new_name, True def new_symbol(self, name_root, reserved_locals): """See control_flow.SymbolNamer.new_symbol.""" diff --git a/tensorflow/contrib/py2tf/naming_test.py b/tensorflow/contrib/py2tf/naming_test.py index 7bfc9b8733..5cf0a3da2c 100644 --- a/tensorflow/contrib/py2tf/naming_test.py +++ b/tensorflow/contrib/py2tf/naming_test.py @@ -29,8 +29,9 @@ class NamerTest(test.TestCase): pass namer = naming.Namer({}, True, None, ()) - self.assertEqual('tf__foo', namer.compiled_function_name('foo')) - self.assertEqual('tf__bar', namer.compiled_function_name('bar', bar)) + self.assertEqual(('tf__foo', True), namer.compiled_function_name('foo')) + self.assertEqual(('tf__bar', True), namer.compiled_function_name( + 'bar', bar)) self.assertEqual({bar: 'tf__bar'}, namer.renamed_calls) self.assertItemsEqual(('tf__bar', 'tf__foo'), namer.generated_names) @@ -39,15 +40,18 @@ class NamerTest(test.TestCase): pass namer = naming.Namer({}, True, None, ()) - self.assertEqual('tf__foo', namer.compiled_function_name('foo', foo)) - self.assertEqual('tf__foo', namer.compiled_function_name('foo', foo)) + self.assertEqual(('tf__foo', True), namer.compiled_function_name( + 'foo', foo)) + self.assertEqual(('tf__foo', True), namer.compiled_function_name( + 'foo', foo)) def test_compiled_function_name_avoids_global_conflicts(self): def foo(): pass namer = naming.Namer({'tf__foo': 1}, True, None, ()) - self.assertEqual('tf__foo_1', namer.compiled_function_name('foo', foo)) + self.assertEqual(('tf__foo_1', True), + namer.compiled_function_name('foo', foo)) def test_new_symbol_tracks_names(self): namer = naming.Namer({}, True, None, ()) diff --git a/tensorflow/contrib/py2tf/pyct/context.py b/tensorflow/contrib/py2tf/pyct/context.py index 73f3613d09..fef74ebefa 100644 --- a/tensorflow/contrib/py2tf/pyct/context.py +++ b/tensorflow/contrib/py2tf/pyct/context.py @@ -33,10 +33,11 @@ class EntityContext(object): """ def __init__(self, namer, source_code, source_file, namespace, arg_values, - arg_types): + arg_types, recursive): self.namer = namer self.source_code = source_code self.source_file = source_file self.namespace = namespace self.arg_values = {} if arg_values is None else arg_values self.arg_types = {} if arg_types is None else arg_types + self.recursive = recursive diff --git a/tensorflow/contrib/py2tf/pyct/parser.py b/tensorflow/contrib/py2tf/pyct/parser.py index 3daa69b9ce..dc7df883b3 100644 --- a/tensorflow/contrib/py2tf/pyct/parser.py +++ b/tensorflow/contrib/py2tf/pyct/parser.py @@ -28,11 +28,13 @@ import gast from tensorflow.python.util import tf_inspect -def parse_object(obj): - """Return the AST of given object.""" - return parse_str(tf_inspect.getsource(obj)) +def parse_entity(entity): + """Return the AST of given entity.""" + source = tf_inspect.getsource(entity) + source = textwrap.dedent(source) + return parse_str(source), source def parse_str(src): """Return the AST of given piece of code.""" - return gast.parse(textwrap.dedent(src)) + return gast.parse(src) diff --git a/tensorflow/contrib/py2tf/pyct/parser_test.py b/tensorflow/contrib/py2tf/pyct/parser_test.py index 46f9aa8207..f35dfa04c7 100644 --- a/tensorflow/contrib/py2tf/pyct/parser_test.py +++ b/tensorflow/contrib/py2tf/pyct/parser_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import textwrap + from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.platform import test @@ -28,15 +30,16 @@ def f(x): class ParserTest(test.TestCase): - def test_parse_object(self): - mod = parser.parse_object(f) + def test_parse_entity(self): + mod, _ = parser.parse_entity(f) self.assertEqual('f', mod.body[0].name) def test_parse_str(self): - mod = parser.parse_str(""" + mod = parser.parse_str( + textwrap.dedent(""" def f(x): return x + 1 - """) + """)) self.assertEqual('f', mod.body[0].name) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/access.py b/tensorflow/contrib/py2tf/pyct/static_analysis/access.py index 8f3ac48b68..33629f87d1 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/access.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/access.py @@ -23,6 +23,7 @@ import copy import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import transformer # TODO(mdan): Add support for PY3 (e.g. Param vs arg). @@ -53,6 +54,8 @@ class Scope(object): self.modified = set() self.created = set() self.used = set() + self.params = set() + self.returned = set() # TODO(mdan): Rename to `locals` @property @@ -69,42 +72,73 @@ class Scope(object): self.modified = copy.copy(other.modified) self.created = copy.copy(other.created) self.used = copy.copy(other.used) + self.params = copy.copy(other.params) + self.returned = copy.copy(other.returned) def merge_from(self, other): self.modified |= other.modified self.created |= other.created self.used |= other.used + self.params |= other.params + self.returned |= other.returned def has(self, name): - if name in self.modified: + if name in self.modified or name in self.params: return True elif self.parent is not None: return self.parent.has(name) return False + def is_modified_since_entry(self, name): + if name in self.modified: + return True + elif self.parent is not None and not self.isolated: + return self.parent.is_modified_since_entry(name) + return False + + def is_param(self, name): + if name in self.params: + return True + elif self.parent is not None and not self.isolated: + return self.parent.is_param(name) + return False + def mark_read(self, name): self.used.add(name) if self.parent is not None and name not in self.created: self.parent.mark_read(name) + def mark_param(self, name): + self.params.add(name) + + def mark_creation(self, name): + self.created.add(name) + def mark_write(self, name): self.modified.add(name) if self.isolated: - self.created.add(name) + self.mark_creation(name) else: if self.parent is None: - self.created.add(name) + self.mark_creation(name) else: if not self.parent.has(name): - self.created.add(name) + self.mark_creation(name) self.parent.mark_write(name) + def mark_returned(self, name): + self.returned.add(name) + if not self.isolated and self.parent is not None: + self.parent.mark_returned(name) + -class AccessResolver(gast.NodeTransformer): +class AccessResolver(transformer.Base): """Annotates nodes with local scope information. See Scope.""" - def __init__(self): + def __init__(self, context): + super(AccessResolver, self).__init__(context) self.scope = Scope(None) + self._in_return_statement = False def visit_Name(self, node): # TODO(mdan): This is insufficient for object fields, e.g. hp.learning_rate. @@ -120,10 +154,17 @@ class AccessResolver(gast.NodeTransformer): # TODO(mdan): This bay be incorrect with nested functions. # For nested functions, we'll have to add the notion of hiding args from # the parent scope, not writing to them. - self.scope.mark_write(node.id) + self.scope.mark_creation(node.id) + self.scope.mark_param(node.id) else: raise ValueError('Unknown context %s for node %s.' % (type(node.ctx), node.id)) + anno.setanno(node, 'is_modified_since_entry', + self.scope.is_modified_since_entry(node.id)) + anno.setanno(node, 'is_param', self.scope.is_param(node.id)) + + if self._in_return_statement: + self.scope.mark_returned(node.id) return node def visit_Print(self, node): @@ -138,7 +179,7 @@ class AccessResolver(gast.NodeTransformer): def visit_Call(self, node): current_scope = self.scope - args_scope = Scope(current_scope) + args_scope = Scope(current_scope, isolated=False) self.scope = args_scope for n in node.args: self.visit(n) @@ -200,6 +241,12 @@ class AccessResolver(gast.NodeTransformer): node, ((node.body, 'body'), (node.orelse, 'orelse'))) return node + def visit_Return(self, node): + self._in_return_statement = True + node = self.generic_visit(node) + self._in_return_statement = False + return node + -def resolve(node): - return AccessResolver().visit(node) +def resolve(node, context): + return AccessResolver(context).visit(node) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/access_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/access_test.py index 0912ebb4c3..df0283b54d 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/access_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/access_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct.static_analysis import access from tensorflow.python.platform import test @@ -95,6 +96,19 @@ class ScopeTest(test.TestCase): class AccessResolverTest(test.TestCase): + def _parse_and_analyze(self, test_fn): + node, source = parser.parse_entity(test_fn) + ctx = context.EntityContext( + namer=None, + source_code=source, + source_file=None, + namespace={}, + arg_values=None, + arg_types=None, + recursive=True) + node = access.resolve(node, ctx) + return node + def test_local_markers(self): def test_fn(a): # pylint:disable=unused-argument @@ -103,9 +117,7 @@ class AccessResolverTest(test.TestCase): b -= 1 return b - node = parser.parse_object(test_fn) - node = access.resolve(node) - + node = self._parse_and_analyze(test_fn) self.assertFalse(anno.getanno(node.body[0].body[0].value, 'is_local')) # c in b = c self.assertTrue(anno.getanno(node.body[0].body[1].test.left, @@ -126,9 +138,7 @@ class AccessResolverTest(test.TestCase): print(a, b) return c - node = parser.parse_object(test_fn) - node = access.resolve(node) - + node = self._parse_and_analyze(test_fn) print_node = node.body[0].body[2] if isinstance(print_node, gast.Print): # Python 2 @@ -151,9 +161,7 @@ class AccessResolverTest(test.TestCase): foo(a, b) # pylint:disable=undefined-variable return c - node = parser.parse_object(test_fn) - node = access.resolve(node) - + node = self._parse_and_analyze(test_fn) call_node = node.body[0].body[2].value # We basically need to detect which variables are captured by the call # arguments. @@ -169,15 +177,13 @@ class AccessResolverTest(test.TestCase): b -= 1 return b, c - node = parser.parse_object(test_fn) - node = access.resolve(node) - + node = self._parse_and_analyze(test_fn) while_node = node.body[0].body[1] self.assertScopeIs( anno.getanno(while_node, 'body_scope'), ('b',), ('b', 'c'), ('c',)) self.assertScopeIs( anno.getanno(while_node, 'body_parent_scope'), ('a', 'b', 'c'), - ('a', 'b', 'c'), ('a', 'b', 'c')) + ('b', 'c'), ('a', 'b', 'c')) def test_for(self): @@ -188,15 +194,13 @@ class AccessResolverTest(test.TestCase): b -= 1 return b, c - node = parser.parse_object(test_fn) - node = access.resolve(node) - + node = self._parse_and_analyze(test_fn) for_node = node.body[0].body[1] self.assertScopeIs( anno.getanno(for_node, 'body_scope'), ('b',), ('b', 'c'), ('c',)) self.assertScopeIs( anno.getanno(for_node, 'body_parent_scope'), ('a', 'b', 'c'), - ('a', 'b', 'c', '_'), ('a', 'b', 'c', '_')) + ('b', 'c', '_'), ('a', 'b', 'c', '_')) def test_if(self): @@ -211,9 +215,7 @@ class AccessResolverTest(test.TestCase): u = -y return z, u - node = parser.parse_object(test_fn) - node = access.resolve(node) - + node = self._parse_and_analyze(test_fn) if_node = node.body[0].body[0] self.assertScopeIs( anno.getanno(if_node, 'body_scope'), ('x', 'y'), ('x', 'y', 'z'), diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py index 242e544b52..5a2903e6b5 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py @@ -26,26 +26,19 @@ from __future__ import print_function import gast from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import transformer -class LiveValueResolver(gast.NodeTransformer): +class LiveValueResolver(transformer.Base): """Annotates nodes with live values.""" - def __init__(self, namespace, literals): - """Create a new resolver. - - Args: - namespace: A dict representing the namespace visible to the AST in the - intended execution context. - literals: A dict mapping literal lymbol names to their value. An example - literal is "None". - """ - self.namespace = namespace + def __init__(self, context, literals): + super(LiveValueResolver, self).__init__(context) self.literals = literals def visit_ClassDef(self, node): self.generic_visit(node) - anno.setanno(node, 'live_val', self.namespace[node.name]) + anno.setanno(node, 'live_val', self.context.namespace[node.name]) return node def visit_Name(self, node): @@ -53,20 +46,31 @@ class LiveValueResolver(gast.NodeTransformer): if isinstance(node.ctx, gast.Load): assert anno.hasanno(node, 'is_local'), node symbol_is_local = anno.getanno(node, 'is_local') - if not symbol_is_local: + assert anno.hasanno(node, 'is_modified_since_entry'), node + symbol_is_modified = anno.getanno(node, 'is_modified_since_entry') + assert anno.hasanno(node, 'is_param'), node + symbol_is_param = anno.getanno(node, 'is_param') + + if not symbol_is_local and not symbol_is_param: if node.id in self.literals: anno.setanno(node, 'live_val', self.literals[node.id]) # TODO(mdan): Could live values have FQNs? i.e. 'a'.join() - elif node.id in self.namespace: - obj = self.namespace[node.id] + elif node.id in self.context.namespace: + obj = self.context.namespace[node.id] anno.setanno(node, 'live_val', obj) anno.setanno(node, 'fqn', (obj.__name__,)) else: - raise ValueError('Could not find global symbol %s.' % node.id) + raise ValueError('Could not resolve symbol "%s".' % node.id) else: pass # TODO(mdan): Attempt to trace its value through the local chain. # TODO(mdan): Use type annotations as fallback. + + if not symbol_is_modified: + if node.id in self.context.arg_values: + obj = self.context.arg_values[node.id] + anno.setanno(node, 'live_val', obj) + anno.setanno(node, 'fqn', (obj.__class__.__name__,)) return node def visit_Attribute(self, node): @@ -79,15 +83,25 @@ class LiveValueResolver(gast.NodeTransformer): node.attr)) anno.setanno(node, 'live_val', getattr(parent_object, node.attr)) anno.setanno(node, 'fqn', anno.getanno(node.value, 'fqn') + (node.attr,)) + # TODO(mdan): Investigate the role built-in annotations can play here. + elif anno.hasanno(node.value, 'type'): + parent_type = anno.getanno(node.value, 'type') + if hasattr(parent_type, node.attr): + # This should hold for static members like methods. + # This would not hold for dynamic members like function attributes. + # For the dynamic case, we simply leave the node without an annotation, + # and let downstream consumers figure out what to do. + anno.setanno(node, 'live_val', getattr(parent_type, node.attr)) + anno.setanno(node, 'fqn', + anno.getanno(node.value, 'type_fqn') + (node.attr,)) elif isinstance(node.value, gast.Name): stem_name = node.value # All nonlocal symbols should be fully resolved. assert anno.hasanno(stem_name, 'is_local'), stem_name - assert anno.getanno(stem_name, 'is_local'), stem_name # TODO(mdan): Figure out what to do when calling attribute on local object # Maybe just leave as-is? return node -def resolve(node, namespace, literals): - return LiveValueResolver(namespace, literals).visit(node) +def resolve(node, context, literals): + return LiveValueResolver(context, literals).visit(node) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py index e77497654a..f3057b3466 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py @@ -19,24 +19,45 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.py2tf.pyct import anno +from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct.static_analysis import access from tensorflow.contrib.py2tf.pyct.static_analysis import live_values +from tensorflow.contrib.py2tf.pyct.static_analysis import type_info from tensorflow.python.framework import constant_op from tensorflow.python.platform import test class LiveValuesResolverTest(test.TestCase): + def _parse_and_analyze(self, + test_fn, + namespace, + literals=None, + arg_types=None): + literals = literals or {} + arg_types = arg_types or {} + node, source = parser.parse_entity(test_fn) + ctx = context.EntityContext( + namer=None, + source_code=source, + source_file=None, + namespace=namespace, + arg_values=None, + arg_types=arg_types, + recursive=True) + node = access.resolve(node, ctx) + node = live_values.resolve(node, ctx, literals) + node = type_info.resolve(node, ctx) + node = live_values.resolve(node, ctx, literals) + return node + def test_literals(self): def test_fn(): return Foo # pylint: disable=undefined-variable - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {}, {'Foo': 'bar'}) - + node = self._parse_and_analyze(test_fn, {}, {'Foo': 'bar'}) retval_node = node.body[0].body[0].value self.assertEquals('bar', anno.getanno(retval_node, 'live_val')) @@ -48,10 +69,7 @@ class LiveValuesResolverTest(test.TestCase): def test_fn(): return foo() - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'foo': foo}, {}) - + node = self._parse_and_analyze(test_fn, {'foo': foo}) func_node = node.body[0].body[0].value.func self.assertEquals(foo, anno.getanno(func_node, 'live_val')) self.assertEquals(('foo',), anno.getanno(func_node, 'fqn')) @@ -61,15 +79,29 @@ class LiveValuesResolverTest(test.TestCase): def test_fn(): return constant_op.constant(0) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, {'constant_op': constant_op}, {}) - + node = self._parse_and_analyze(test_fn, {'constant_op': constant_op}) func_node = node.body[0].body[0].value.func self.assertEquals(constant_op.constant, anno.getanno(func_node, 'live_val')) self.assertEquals((constant_op.__name__, 'constant'), anno.getanno(func_node, 'fqn')) + def test_attributes_with_type_hints(self): + + class TestClass(object): + + def member(self): + pass + + def test_fn(self): + return self.member() + + node = self._parse_and_analyze( + TestClass.test_fn, {'constant_op': constant_op}, + arg_types={'self': (TestClass.__name__, TestClass)}) + func_node = node.body[0].body[0].value.func + self.assertEquals(TestClass.member, anno.getanno(func_node, 'live_val')) + self.assertEquals(('TestClass', 'member'), anno.getanno(func_node, 'fqn')) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py index 0042aa90ed..cf74142cbe 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info.py @@ -36,8 +36,6 @@ class Scope(object): most recently assigned to the symbol. """ - # TODO(mdan): Should rather use a CFG here? - def __init__(self, parent): """Create a new scope. @@ -117,18 +115,32 @@ class TypeInfoResolver(transformer.Base): node.orelse = self._visit_block(node.orelse) return node + def _process_function_arg(self, arg_name): + if self.function_level == 1 and arg_name in self.context.arg_types: + # Forge a node to hold the type information, so that method calls on + # it can resolve the type. + type_holder = gast.Name(arg_name, gast.Load(), None) + type_string, type_obj = self.context.arg_types[arg_name] + anno.setanno(type_holder, 'type', type_obj) + anno.setanno(type_holder, 'type_fqn', tuple(type_string.split('.'))) + self.scope.setval(arg_name, type_holder) + + def visit_arg(self, node): + self._process_function_arg(node.arg) + return node + def visit_Name(self, node): self.generic_visit(node) if isinstance(node.ctx, gast.Param): - self.scope.setval(node.id, gast.Name(node.id, gast.Load(), None)) - if self.function_level == 1 and node.id in self.context.arg_types: - # Forge a node to hold the type information, so that method calls on - # it can resolve the type. - type_holder = gast.Name(node.id, gast.Load(), None) - type_string, type_obj = self.context.arg_types[node.id] - anno.setanno(type_holder, 'type', type_obj) - anno.setanno(type_holder, 'type_fqn', tuple(type_string.split('.'))) - self.scope.setval(node.id, type_holder) + self._process_function_arg(node.id) + elif isinstance(node.ctx, gast.Load) and self.scope.hasval(node.id): + # E.g. if we had + # a = b + # then for future references to `a` we should have traced_source = `b` + traced_source = self.scope.getval(node.id) + if anno.hasanno(traced_source, 'type'): + anno.setanno(node, 'type', anno.getanno(traced_source, 'type')) + anno.setanno(node, 'type_fqn', anno.getanno(traced_source, 'type_fqn')) return node def _process_variable_assignment(self, source, targets): @@ -172,38 +184,6 @@ class TypeInfoResolver(transformer.Base): self._process_variable_assignment(node.value, node.targets) return node - def visit_Call(self, node): - target = node.func - if not anno.hasanno(target, 'live_val'): - if not isinstance(target, gast.Attribute): - # Suspecting this pattern would reach here: - # foo = bar - # foo() - raise ValueError('Dont know how to handle dynamic functions.') - if not isinstance(target.value, gast.Name): - # Possible example of this kind: - # foo = module.Foo() - # foo.bar.baz() - # TODO(mdan): This should be doable by using the FQN. - raise ValueError('Dont know how to handle object properties yet.') - # In the example below, object_source is 'tr.train.Optimizer()': - # opt = tf.train.Optimizer() - # opt.foo() - if self.scope.hasval(target.value.id): - object_source = self.scope.getval(target.value.id) - if not anno.hasanno(object_source, 'type'): - raise ValueError('Could not determine type of "%s". Is it dynamic?' % - (target.value.id)) - anno.setanno(target, 'type', anno.getanno(object_source, 'type')) - anno.setanno(target, 'type_fqn', anno.getanno(object_source, - 'type_fqn')) - else: - # TODO(mdan): Figure out what could the user do to get past this. - raise ValueError('No info on "%s". Is it dynamically built?' % - (target.value.id)) - self.generic_visit(node) - return node - def resolve(node, context): return TypeInfoResolver(context).visit(node) diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py index a491f49ca3..68fa1ee92a 100644 --- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py +++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py @@ -21,7 +21,6 @@ from __future__ import print_function from tensorflow.contrib.py2tf.pyct import anno from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser -from tensorflow.contrib.py2tf.pyct import transformer from tensorflow.contrib.py2tf.pyct.static_analysis import access from tensorflow.contrib.py2tf.pyct.static_analysis import live_values from tensorflow.contrib.py2tf.pyct.static_analysis import type_info @@ -57,17 +56,19 @@ class ScopeTest(test.TestCase): class TypeInfoResolverTest(test.TestCase): def _parse_and_analyze(self, test_fn, namespace, arg_types=None): + node, source = parser.parse_entity(test_fn) ctx = context.EntityContext( namer=None, - source_code=None, + source_code=source, source_file=None, namespace=namespace, arg_values=None, - arg_types=arg_types) - node = parser.parse_object(test_fn) - node = access.resolve(node) - node = live_values.resolve(node, namespace, {}) + arg_types=arg_types, + recursive=True) + node = access.resolve(node, ctx) + node = live_values.resolve(node, ctx, {}) node = type_info.resolve(node, ctx) + node = live_values.resolve(node, ctx, {}) return node def test_constructor_detection(self): @@ -83,16 +84,16 @@ class TypeInfoResolverTest(test.TestCase): self.assertEquals((training.__name__, 'GradientDescentOptimizer'), anno.getanno(call_node, 'type_fqn')) - def test_class_members(self): + def test_class_members_of_detected_constructor(self): def test_fn(): opt = training.GradientDescentOptimizer(0.1) opt.minimize(0) node = self._parse_and_analyze(test_fn, {'training': training}) - attr_call_node = node.body[0].body[1].value.func - self.assertEquals((training.__name__, 'GradientDescentOptimizer'), - anno.getanno(attr_call_node, 'type_fqn')) + method_call = node.body[0].body[1].value.func + self.assertEquals(training.GradientDescentOptimizer.minimize, + anno.getanno(method_call, 'live_val')) def test_class_members_in_with_stmt(self): @@ -106,11 +107,11 @@ class TypeInfoResolverTest(test.TestCase): self.assertEquals((session.__name__, 'Session'), anno.getanno(constructor_call, 'type_fqn')) - member_call = node.body[0].body[0].body[0].value.func - self.assertEquals((session.__name__, 'Session'), - anno.getanno(member_call, 'type_fqn')) + method_call = node.body[0].body[0].body[0].value.func + self.assertEquals(session.Session.run, anno.getanno(method_call, + 'live_val')) - def test_constructor_deta_dependent(self): + def test_constructor_data_dependent(self): def test_fn(x): if x > 0: @@ -119,16 +120,18 @@ class TypeInfoResolverTest(test.TestCase): opt = training.GradientDescentOptimizer(0.01) opt.minimize(0) - with self.assertRaises(transformer.PyFlowParseError): - self._parse_and_analyze(test_fn, {'training': training}) + node = self._parse_and_analyze(test_fn, {'training': training}) + method_call = node.body[0].body[1].value.func + self.assertFalse(anno.hasanno(method_call, 'live_val')) def test_parameter_class_members(self): def test_fn(opt): opt.minimize(0) - with self.assertRaises(transformer.PyFlowParseError): - self._parse_and_analyze(test_fn, {'training': training}) + node = self._parse_and_analyze(test_fn, {}) + method_call = node.body[0].body[0].value.func + self.assertFalse(anno.hasanno(method_call, 'live_val')) def test_parameter_class_members_with_value_hints(self): @@ -138,14 +141,13 @@ class TypeInfoResolverTest(test.TestCase): node = self._parse_and_analyze( test_fn, {'training': training}, arg_types={ - 'opt': (('%s.GradientDescentOptimizer' % training.__name__), - training.GradientDescentOptimizer(0.1)) + 'opt': (training.GradientDescentOptimizer.__name__, + training.GradientDescentOptimizer) }) - attr_call_node = node.body[0].body[0].value.func - self.assertEquals( - tuple(training.__name__.split('.')) + ('GradientDescentOptimizer',), - anno.getanno(attr_call_node, 'type_fqn')) + method_call = node.body[0].body[0].value.func + self.assertEquals(training.GradientDescentOptimizer.minimize, + anno.getanno(method_call, 'live_val')) def test_function_variables(self): @@ -156,8 +158,9 @@ class TypeInfoResolverTest(test.TestCase): foo = bar foo() - with self.assertRaises(transformer.PyFlowParseError): - self._parse_and_analyze(test_fn, {'bar': bar}) + node = self._parse_and_analyze(test_fn, {'bar': bar}) + method_call = node.body[0].body[1].value.func + self.assertFalse(anno.hasanno(method_call, 'live_val')) def test_nested_members(self): @@ -165,8 +168,9 @@ class TypeInfoResolverTest(test.TestCase): foo = training.GradientDescentOptimizer(0.1) foo.bar.baz() - with self.assertRaises(transformer.PyFlowParseError): - self._parse_and_analyze(test_fn, {'training': training}) + node = self._parse_and_analyze(test_fn, {'training': training}) + method_call = node.body[0].body[1].value.func + self.assertFalse(anno.hasanno(method_call, 'live_val')) if __name__ == '__main__': diff --git a/tensorflow/contrib/py2tf/pyct/templates.py b/tensorflow/contrib/py2tf/pyct/templates.py index 77c5fbe02a..6be526f20d 100644 --- a/tensorflow/contrib/py2tf/pyct/templates.py +++ b/tensorflow/contrib/py2tf/pyct/templates.py @@ -23,6 +23,7 @@ from __future__ import print_function import ast import copy +import textwrap import gast @@ -119,7 +120,7 @@ def replace(template, **replacements): """ if not isinstance(template, str): raise ValueError('Expected string template, got %s' % type(template)) - tree = parser.parse_str(template) + tree = parser.parse_str(textwrap.dedent(template)) for k in replacements: replacements[k] = _strings_to_names(replacements[k]) return ReplaceTransformer(replacements).visit(tree).body diff --git a/tensorflow/contrib/py2tf/pyct/transformer.py b/tensorflow/contrib/py2tf/pyct/transformer.py index d5aa23eaeb..8a836b7c1b 100644 --- a/tensorflow/contrib/py2tf/pyct/transformer.py +++ b/tensorflow/contrib/py2tf/pyct/transformer.py @@ -18,7 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import sys + import gast +import six from tensorflow.contrib.py2tf.pyct import pretty_printer @@ -48,11 +51,15 @@ class Base(gast.NodeTransformer): self._lineno = node.lineno self._col_offset = node.col_offset return super(Base, self).visit(node) - except ValueError as e: - msg = '%s\nOccurred at node:\n%s' % (str(e), pretty_printer.fmt(node)) + except (ValueError, AttributeError, NotImplementedError) as e: + msg = '%s: %s\nOccurred at node:\n%s' % (e.__class__.__name__, str(e), + pretty_printer.fmt(node)) if source_code: - line = self._source.splitlines()[self._lineno - 1] + line = source_code.splitlines()[self._lineno - 1] else: line = '' - raise PyFlowParseError( - msg, (source_file, self._lineno, self._col_offset + 1, line)) + six.reraise(PyFlowParseError, + PyFlowParseError( + msg, + (source_file, self._lineno, self._col_offset + 1, line)), + sys.exc_info()[2]) -- GitLab From 76ba86d308f1bb255fdca930deed19ae9233ee86 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 12:14:15 -0800 Subject: [PATCH 218/423] Pass maximum_iterations hint to tf.while_loop if possible. PiperOrigin-RevId: 183705773 --- .../python/ops/bijectors/masked_autoregressive.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py index dc8ae1eed1..5251dbcb57 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py @@ -237,6 +237,11 @@ class MaskedAutoregressiveFlow(bijector_lib.Bijector): return y event_size = array_ops.shape(x)[-1] + # If the event size is available at graph construction time, we can inform + # the graph compiler of the maximum number of steps. If not, + # static_event_size will be None, and the maximum_iterations argument will + # have no effect. + static_event_size = x.shape.with_rank_at_least(1)[-1].value y0 = array_ops.zeros_like(x, name="y0") # call the template once to ensure creation _ = self._shift_and_log_scale_fn(y0) @@ -258,7 +263,8 @@ class MaskedAutoregressiveFlow(bijector_lib.Bijector): _, y = control_flow_ops.while_loop( cond=lambda index, _: index < event_size, body=_loop_body, - loop_vars=[0, y0]) + loop_vars=(0, y0), + maximum_iterations=static_event_size) return y def _inverse(self, y): -- GitLab From b78134d0d5ea7f17468bea9276c35fab4a9cb388 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 12:15:12 -0800 Subject: [PATCH 219/423] A few tweaks to add const to functions and member fields. PiperOrigin-RevId: 183705898 --- tensorflow/c/eager/BUILD | 1 + tensorflow/c/eager/c_api.cc | 10 +--------- tensorflow/c/eager/c_api_internal.h | 19 +++++++++++++------ .../process_function_library_runtime.cc | 2 +- .../process_function_library_runtime.h | 2 +- 5 files changed, 17 insertions(+), 17 deletions(-) diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD index 74190cb135..e62310d811 100644 --- a/tensorflow/c/eager/BUILD +++ b/tensorflow/c/eager/BUILD @@ -46,6 +46,7 @@ tf_cuda_library( "//tensorflow/c:c_api", "//tensorflow/c:c_api_internal", "//tensorflow/core:core_cpu_lib", + "//tensorflow/core:framework", "//tensorflow/core:framework_internal", "//tensorflow/core:framework_lite", "//tensorflow/core:lib_internal", diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index a76c8f5ec0..fd6cecd77b 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -85,15 +85,7 @@ TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) { return nullptr; } - TFE_Context* ret = new TFE_Context(session); - ret->policy = opts->policy; - ret->pflr.reset(new tensorflow::ProcessFunctionLibraryRuntime( - ret->session->device_mgr, opts->session_options.options.env, - TF_GRAPH_DEF_VERSION, &ret->func_lib_def, {})); - ret->rendezvous = - new tensorflow::IntraProcessRendezvous(ret->session->device_mgr); - - return ret; + return new TFE_Context(*opts, session); } void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status) { diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h index a6f76c732f..dda68471a8 100644 --- a/tensorflow/c/eager/c_api_internal.h +++ b/tensorflow/c/eager/c_api_internal.h @@ -35,6 +35,7 @@ limitations under the License. #include "tensorflow/core/lib/gtl/stl_util.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/thread_annotations.h" +#include "tensorflow/core/public/version.h" struct TFE_ContextOptions { TF_SessionOptions session_options; @@ -43,9 +44,15 @@ struct TFE_ContextOptions { }; struct TFE_Context { - explicit TFE_Context(TF_Session* s) : session(s) {} + explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s) + : policy(opts.policy), + session(s), + rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)), + pflr(new tensorflow::ProcessFunctionLibraryRuntime( + session->device_mgr, opts.session_options.options.env, + TF_GRAPH_DEF_VERSION, &func_lib_def, {})) {} - TFE_ContextDevicePlacementPolicy policy; + const TFE_ContextDevicePlacementPolicy policy; // Note: we cannot use C++11 thread_local here as there is no concept of a // thread-local-object-local variable in C++11. @@ -54,8 +61,8 @@ struct TFE_Context { thread_local_policies GUARDED_BY(policy_map_mu); // TFE_Context is an extension of TF_Session. And TF_Session needs a TF_Graph. - TF_Session* session; - tensorflow::Rendezvous* rendezvous; + TF_Session* const session; + tensorflow::Rendezvous* const rendezvous; tensorflow::mutex functions_mu; tensorflow::FunctionLibraryDefinition func_lib_def GUARDED_BY(functions_mu){ @@ -64,14 +71,14 @@ struct TFE_Context { // One FunctionLibraryRuntime per device. // func_libs[i] is the FunctionLibraryRuntime corresponding to // session->devices[i]. - std::unique_ptr pflr; + const std::unique_ptr pflr; tensorflow::mutex cache_mu; std::unordered_map kernel_cache GUARDED_BY(cache_mu); - tensorflow::FunctionLibraryRuntime* func_lib(tensorflow::Device* d) { + tensorflow::FunctionLibraryRuntime* func_lib(tensorflow::Device* d) const { return pflr->GetFLR(d->name()); } diff --git a/tensorflow/core/common_runtime/process_function_library_runtime.cc b/tensorflow/core/common_runtime/process_function_library_runtime.cc index 12947e284a..dd4bf6a345 100644 --- a/tensorflow/core/common_runtime/process_function_library_runtime.cc +++ b/tensorflow/core/common_runtime/process_function_library_runtime.cc @@ -158,7 +158,7 @@ Status ProcessFunctionLibraryRuntime::GetDeviceContext( } FunctionLibraryRuntime* ProcessFunctionLibraryRuntime::GetFLR( - const string& device_name) { + const string& device_name) const { Device* device = nullptr; if (device_name != kDefaultFLRDevice) { if (!device_mgr_->LookupDevice(device_name, &device).ok()) { diff --git a/tensorflow/core/common_runtime/process_function_library_runtime.h b/tensorflow/core/common_runtime/process_function_library_runtime.h index a1adc4b6b3..9c9c92f1ea 100644 --- a/tensorflow/core/common_runtime/process_function_library_runtime.h +++ b/tensorflow/core/common_runtime/process_function_library_runtime.h @@ -85,7 +85,7 @@ class ProcessFunctionLibraryRuntime { static const char kDefaultFLRDevice[]; // Returns the FunctionLibraryRuntime for the corresponding device_name. - FunctionLibraryRuntime* GetFLR(const string& device_name); + FunctionLibraryRuntime* GetFLR(const string& device_name) const; // Returns the device incarnation for the given device_name. Status GetDeviceIncarnation(const string& device_name, int64* incarnation); -- GitLab From 0a34211774c8c45c8f290e6c51335b99873dcbb9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 12:42:09 -0800 Subject: [PATCH 220/423] Remove the trailing '/' in the tensor name when loading checkpoints PiperOrigin-RevId: 183709590 --- tensorflow/python/training/checkpoint_utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index b5d3e78797..63235a1454 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -242,6 +242,9 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map): full_tensor_name = full_tensor_name[1:] if tensor_name_in_ckpt != "/": full_tensor_name = tensor_name_in_ckpt + full_tensor_name + # Remove trailing '/', if any, in the full_tensor_name + if full_tensor_name.endswith("/"): + full_tensor_name = full_tensor_name[:-1] if full_tensor_name not in variable_map: raise ValueError( "Tensor %s (%s in %s) is not found in %s checkpoint" % ( -- GitLab From 1fcb66651d386e1430b3e83a3ce3d971256ffe70 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 12:43:46 -0800 Subject: [PATCH 221/423] Support shrink_axis_mask argument of StridedSlice Op for TfLite. PiperOrigin-RevId: 183709796 --- .../internal/reference/reference_ops.h | 32 +++- .../contrib/lite/kernels/strided_slice.cc | 37 ++-- .../lite/kernels/strided_slice_test.cc | 160 +++++++++++++++++- tensorflow/contrib/lite/testing/BUILD | 2 +- .../contrib/lite/testing/generate_examples.py | 15 +- 5 files changed, 216 insertions(+), 30 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 31bade26f9..4bcf4993e9 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -2370,13 +2370,15 @@ inline int StartIndex(int start, int stride, int dim, bool masked) { return masked ? (stride > 0 ? 0 : dim - 1) : start; } -inline int StopIndex(int stop, int stride, int dim, bool masked) { - return masked ? (stride > 0 ? dim : -1) : stop; +inline int StopIndex(int start, int stop, int stride, int dim, bool masked, + bool shrink_axis_masked) { + return shrink_axis_masked ? stride > 0 ? start + 1 : start - 1 + : masked ? (stride > 0 ? dim : -1) : stop; } template inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, - int begin_mask, int end_mask, + int begin_mask, int end_mask, int shrink_axis_mask, const std::vector& starts, const std::vector& stops, const std::vector& strides, T* output_data, @@ -2387,19 +2389,23 @@ inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, const int start_b = StartIndex(starts[3], strides[3], input_dims.sizes[3], begin_mask & 8); const int stop_b = - StopIndex(stops[3], strides[3], input_dims.sizes[3], end_mask & 8); + StopIndex(start_b, stops[3], strides[3], input_dims.sizes[3], + end_mask & 8, shrink_axis_mask & 8); const int start_h = StartIndex(starts[2], strides[2], input_dims.sizes[2], begin_mask & 4); const int stop_h = - StopIndex(stops[2], strides[2], input_dims.sizes[2], end_mask & 4); + StopIndex(start_h, stops[2], strides[2], input_dims.sizes[2], + end_mask & 4, shrink_axis_mask & 4); const int start_w = StartIndex(starts[1], strides[1], input_dims.sizes[1], begin_mask & 2); const int stop_w = - StopIndex(stops[1], strides[1], input_dims.sizes[1], end_mask & 2); + StopIndex(start_w, stops[1], strides[1], input_dims.sizes[1], + end_mask & 2, shrink_axis_mask & 2); const int start_d = StartIndex(starts[0], strides[0], input_dims.sizes[0], begin_mask & 1); const int stop_d = - StopIndex(stops[0], strides[0], input_dims.sizes[0], end_mask & 1); + StopIndex(start_d, stops[0], strides[0], input_dims.sizes[0], + end_mask & 1, shrink_axis_mask & 1); T* out_ptr = output_data; for (int in_b = start_b; LoopCondition(in_b, stop_b, strides[3]); @@ -2417,6 +2423,18 @@ inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, } } +template +inline void StridedSlice(const T* input_data, const Dims<4>& input_dims, + int begin_mask, int end_mask, + const std::vector& starts, + const std::vector& stops, + const std::vector& strides, T* output_data, + const Dims<4>& output_dims) { + StridedSlice(input_data, input_dims, begin_mask, end_mask, + /*shrink_axis_mask=*/0, starts, stops, strides, output_data, + output_dims); +} + template inline void Slice(const T* input_data, const Dims<4>& input_dims, const std::vector& begin, const std::vector& size, diff --git a/tensorflow/contrib/lite/kernels/strided_slice.cc b/tensorflow/contrib/lite/kernels/strided_slice.cc index 91ba4a9b78..c510ee3b9f 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice.cc @@ -81,8 +81,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { "ellipsis_mask is not implemented yet."); TF_LITE_ENSURE_MSG(context, op_context.params->new_axis_mask == 0, "new_axis_mask is not implemented yet."); - TF_LITE_ENSURE_MSG(context, op_context.params->shrink_axis_mask == 0, - "shrink_axis_mask is not implemented yet."); // TODO(soroosh): optimize for constant tensors to do allocation in Prepare op_context.output->allocation_type = kTfLiteDynamic; @@ -153,9 +151,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { std::vector starts; std::vector stops; std::vector strides; + std::vector output_shape_vector; - // Determine size of output tensor and map indices - TfLiteIntArray* output_shape = TfLiteIntArrayCreate(op_context.dims); for (int idx = op_context.dims - 1; idx >= 0; --idx) { int dim = op_context.input->dims->data[idx]; int32_t stride = GetTensorData(op_context.strides)[idx]; @@ -174,14 +171,24 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { pos_stride); // This is valid for both positive and negative strides - output_shape->data[idx] = ceil((end - begin) / static_cast(stride)); - output_shape->data[idx] = - output_shape->data[idx] < 0 ? 0 : output_shape->data[idx]; + int32_t dim_shape = ceil((end - begin) / static_cast(stride)); + dim_shape = dim_shape < 0 ? 0 : dim_shape; + + if (!(op_context.params->shrink_axis_mask & (1 << idx))) { + output_shape_vector.push_back(dim_shape); + } + starts.emplace_back(begin); stops.emplace_back(end); strides.emplace_back(stride); } + TfLiteIntArray* output_shape = + TfLiteIntArrayCreate(output_shape_vector.size()); + + std::reverse_copy(output_shape_vector.begin(), output_shape_vector.end(), + output_shape->data); + for (int i = op_context.dims; i < kMaxDim; i++) { starts.emplace_back(0); stops.emplace_back(1); @@ -202,13 +209,15 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { ReverseMaskBits(op_context.params->begin_mask, op_context.dims); op_context.params->end_mask = ReverseMaskBits(op_context.params->end_mask, op_context.dims); - -#define TF_LITE_STRIDED_SLICE(kernel_type, data_type) \ - kernel_type::StridedSlice( \ - GetTensorData(op_context.input), \ - GetTensorDims(op_context.input), op_context.params->begin_mask, \ - op_context.params->end_mask, starts, stops, strides, \ - GetTensorData(op_context.output), \ + op_context.params->shrink_axis_mask = + ReverseMaskBits(op_context.params->shrink_axis_mask, op_context.dims); + +#define TF_LITE_STRIDED_SLICE(kernel_type, data_type) \ + kernel_type::StridedSlice( \ + GetTensorData(op_context.input), \ + GetTensorDims(op_context.input), op_context.params->begin_mask, \ + op_context.params->end_mask, op_context.params->shrink_axis_mask, \ + starts, stops, strides, GetTensorData(op_context.output), \ GetTensorDims(op_context.output)) switch (op_context.input->type) { diff --git a/tensorflow/contrib/lite/kernels/strided_slice_test.cc b/tensorflow/contrib/lite/kernels/strided_slice_test.cc index cd4a364682..5bc7dc353b 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice_test.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice_test.cc @@ -79,8 +79,6 @@ TEST(StridedSliceOpTest, UnssupportedArgs) { "ellipsis_mask is not implemented yet."); EXPECT_DEATH(StridedSliceOpModel({3, 2}, {2}, {2}, {2}, 0, 0, 0, 1, 0), "new_axis_mask is not implemented yet."); - EXPECT_DEATH(StridedSliceOpModel({3, 2}, {2}, {2}, {2}, 0, 0, 0, 0, 1), - "shrink_axis_mask is not implemented yet."); } TEST(StridedSliceOpTest, In1D) { @@ -213,6 +211,7 @@ TEST(StridedSliceOpTest, In1D_EndMask) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 3, 4})); } + TEST(StridedSliceOpTest, In1D_NegStride) { StridedSliceOpModel m({3}, {1}, {1}, {1}, 0, 0, 0, 0, 0); m.SetInput({1, 2, 3}); @@ -234,6 +233,7 @@ TEST(StridedSliceOpTest, In1D_EvenLenStride2) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); } + TEST(StridedSliceOpTest, In1D_OddLenStride2) { StridedSliceOpModel m({3}, {1}, {1}, {1}, 0, 0, 0, 0, 0); m.SetInput({1, 2, 3}); @@ -255,6 +255,7 @@ TEST(StridedSliceOpTest, In2D_Identity) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 3})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); } + TEST(StridedSliceOpTest, In2D) { StridedSliceOpModel m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 0); m.SetInput({1, 2, 3, 4, 5, 6}); @@ -320,6 +321,7 @@ TEST(StridedSliceOpTest, In2D_NegStrideBeginMask) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3})); EXPECT_THAT(m.GetOutput(), ElementsAreArray({6, 5, 4})); } + TEST(StridedSliceOpTest, In2D_NegStrideEndMask) { StridedSliceOpModel m({2, 3}, {2}, {2}, {2}, 0, 2, 0, 0, 0); m.SetInput({1, 2, 3, 4, 5, 6}); @@ -354,6 +356,7 @@ TEST(StridedSliceOpTest, In3D_NegStride) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1})); } + TEST(StridedSliceOpTest, In3D_Strided2) { StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 0); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); @@ -365,6 +368,159 @@ TEST(StridedSliceOpTest, In3D_Strided2) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 5})); } +TEST(StridedSliceOpTest, In1D_ShrinkAxisMask1) { + StridedSliceOpModel m({4}, {1}, {1}, {1}, 0, 0, 0, 0, 1); + m.SetInput({1, 2, 3, 4}); + m.SetBegin({1}); + m.SetEnd({3}); + m.SetStrides({1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({2})); +} + +TEST(StridedSliceOpTest, In1D_EmptyOutputShrinkAxisMask1) { + StridedSliceOpModel m({4}, {1}, {1}, {1}, 0, 0, 0, 0, 1); + m.SetInput({1, 2, 3, 4}); + m.SetBegin({2}); + m.SetEnd({1}); + m.SetStrides({1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({3})); +} + +TEST(StridedSliceOpTest, In1D_BeginMaskShrinkAxisMask1) { + StridedSliceOpModel m({4}, {1}, {1}, {1}, 1, 0, 0, 0, 1); + m.SetInput({1, 2, 3, 4}); + m.SetBegin({1}); + m.SetEnd({3}); + m.SetStrides({1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); +} + +TEST(StridedSliceOpTest, In1D_NegativeBeginNegativeStrideShrinkAxisMask1) { + StridedSliceOpModel m({4}, {1}, {1}, {1}, 0, 0, 0, 0, 1); + m.SetInput({1, 2, 3, 4}); + m.SetBegin({-2}); + m.SetEnd({-3}); + m.SetStrides({-1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({3})); +} + +TEST(StridedSliceOpTest, In2D_ShrinkAxisMask1) { + StridedSliceOpModel m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 1); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetBegin({0, 0}); + m.SetEnd({2, 3}); + m.SetStrides({1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3})); +} + +TEST(StridedSliceOpTest, In2D_ShrinkAxisMask2) { + StridedSliceOpModel m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 2); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetBegin({0, 0}); + m.SetEnd({2, 3}); + m.SetStrides({1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 4})); +} + +TEST(StridedSliceOpTest, In2D_ShrinkAxisMask3) { + 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({2, 3}); + m.SetStrides({1, 1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); +} + +TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis1) { + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 1); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); + m.SetBegin({0, 0, 0}); + m.SetEnd({2, 3, 2}); + m.SetStrides({1, 1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); +} + +TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis2) { + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 2); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); + m.SetBegin({0, 0, 0}); + m.SetEnd({2, 3, 2}); + m.SetStrides({1, 1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 7, 8})); +} + +TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis3) { + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 3); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); + m.SetBegin({0, 0, 0}); + m.SetEnd({2, 3, 2}); + m.SetStrides({1, 1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2})); +} + +TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis4) { + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 4); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); + m.SetBegin({0, 0, 0}); + m.SetEnd({2, 3, 2}); + m.SetStrides({1, 1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 3})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 5, 7, 9, 11})); +} + +TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis5) { + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 5); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); + m.SetBegin({0, 0, 0}); + m.SetEnd({2, 3, 2}); + m.SetStrides({1, 1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 5})); +} + +TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis6) { + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 6); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); + m.SetBegin({0, 0, 0}); + m.SetEnd({2, 3, 2}); + m.SetStrides({1, 1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 7})); +} + +TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis7) { + StridedSliceOpModel m({2, 3, 2}, {3}, {3}, {3}, 0, 0, 0, 0, 7); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); + m.SetBegin({0, 0, 0}); + m.SetEnd({2, 3, 2}); + m.SetStrides({1, 1, 1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); +} } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 50e8ca75f8..7f84a0ab9b 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -197,7 +197,7 @@ cc_binary( tf_cc_test( name = "generated_examples_zip_test", - size = "medium", + size = "large", srcs = ["generated_examples_zip_test.cc"], args = [ "--zip_files_dir=tensorflow/contrib/lite/testing/optest", diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index fc8149bef9..f75d7c4bb9 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -1533,8 +1533,9 @@ def make_strided_slice_tests(zip_path): "begin": [[0, 0, 0, 0], [1, 0, 1, 0]], "end": [[8, 2, 2, 3], [12, 2, 2, 5]], "strides": [None, [1, 1, 1, 1], [2, 1, 3, 1]], - "begin_mask": [None, 0, 1, 2, 8], - "end_mask": [None, 0, 1, 2, 8], + "begin_mask": [None, 1, 2, 8], + "end_mask": [None, 1, 2, 8], + "shrink_axis_mask": [None, 1, 2, 4, 8, 11, 15, -1], }, # 2-D { @@ -1544,8 +1545,9 @@ def make_strided_slice_tests(zip_path): "begin": [[0, 0], [1, 0]], "end": [[2, 3], [2, 2]], "strides": [None, [1, 1], [2, 2]], - "begin_mask": [None, 0, 1, 2], - "end_mask": [None, 0, 1, 2], + "begin_mask": [None, 1, 2], + "end_mask": [None, 1, 2], + "shrink_axis_mask": [None, 1, 2, 3, -1], }, # Negative strides { @@ -1555,8 +1557,9 @@ def make_strided_slice_tests(zip_path): "begin": [[0, -1]], "end": [[2, -3]], "strides": [[1, -1]], - "begin_mask": [None, 0, 1, 2], - "end_mask": [None, 0, 1, 2], + "begin_mask": [None, 1, 2], + "end_mask": [None, 1, 2], + "shrink_axis_mask": [None, 1, 2, 3, -1], }, ] -- GitLab From ea8bd26b997ec75ca2b8eb48b2ffe4e3d0e7c855 Mon Sep 17 00:00:00 2001 From: Po-Hsien Chu Date: Tue, 30 Jan 2018 05:33:48 +0800 Subject: [PATCH 222/423] remove SRU num_units == x.shape[-1] restriction (#16404) --- .../python/kernel_tests/core_rnn_cell_test.py | 14 +++++++++++ tensorflow/contrib/rnn/python/ops/rnn_cell.py | 24 ++++--------------- 2 files changed, 18 insertions(+), 20 deletions(-) diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index cafeb56ad8..5711f41cc3 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -153,6 +153,20 @@ class RNNCellTest(test.TestCase): m.name: np.array([[0.1, 0.1]])}) # Smoke test self.assertAllClose(res[0], [[0.509682, 0.509682]]) + + def testSRUCellWithDiffSize(self): + with self.test_session() as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 3]) + m = array_ops.zeros([1, 2]) + g, _ = contrib_rnn_cell.SRUCell(2)(x, m) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run( + [g], {x.name: np.array([[1., 1., 1.]]), + m.name: np.array([[0.1, 0.1]])}) + # Smoke test + self.assertAllClose(res[0], [[0.55255556, 0.55255556]]) def testBasicLSTMCell(self): for dtype in [dtypes.float16, dtypes.float32]: diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py index 8adf5dce6e..5fee2e93e4 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py +++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py @@ -2729,25 +2729,9 @@ class SRUCell(rnn_cell_impl._LayerRNNCell): input_depth = inputs_shape[1].value - # Here the contributor believes that the following constraints - # are implied. The reasoning is explained here with reference to - # the paper https://arxiv.org/pdf/1709.02755.pdf upon which this - # implementation is based. - # In section 2.1 Equation 5, specifically: - # h_t = r_t \odot g(c_t) + (1 - r_t) \odot x_t - # the pointwise operation between r_t and x_t means they have - # the same shape (since we are implementing an RNN cell, braodcasting - # does not happen to input of a single timestep); by the same - # reasons, x_t has the same shape as h_t, essentially mandating that - # input_depth = unit_num. - if input_depth != self._num_units: - raise ValueError("SRU requires input_depth == num_units, got " - "input_depth = %s, num_units = %s" % (input_depth, - self._num_units)) - self._kernel = self.add_variable( rnn_cell_impl._WEIGHTS_VARIABLE_NAME, - shape=[input_depth, 3 * self._num_units]) + shape=[input_depth, 4 * self._num_units]) self._bias = self.add_variable( rnn_cell_impl._BIAS_VARIABLE_NAME, @@ -2760,8 +2744,8 @@ class SRUCell(rnn_cell_impl._LayerRNNCell): """Simple recurrent unit (SRU) with num_units cells.""" U = math_ops.matmul(inputs, self._kernel) - x_bar, f_intermediate, r_intermediate = array_ops.split( - value=U, num_or_size_splits=3, axis=1) + x_bar, f_intermediate, r_intermediate, x_tx = array_ops.split( + value=U, num_or_size_splits=4, axis=1) f_r = math_ops.sigmoid( nn_ops.bias_add( @@ -2769,7 +2753,7 @@ class SRUCell(rnn_cell_impl._LayerRNNCell): f, r = array_ops.split(value=f_r, num_or_size_splits=2, axis=1) c = f * state + (1.0 - f) * x_bar - h = r * self._activation(c) + (1.0 - r) * inputs + h = r * self._activation(c) + (1.0 - r) * x_tx return h, c -- GitLab From 5a756fe53b421bf7276551aa64eda0d4b6d50d95 Mon Sep 17 00:00:00 2001 From: Loo Rong Jie Date: Tue, 30 Jan 2018 05:35:45 +0800 Subject: [PATCH 223/423] Check more cpu features for Clang on Windows (#16508) --- tensorflow/core/platform/cpu_feature_guard.cc | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/platform/cpu_feature_guard.cc b/tensorflow/core/platform/cpu_feature_guard.cc index b0d7b3a67a..7caf9d4db6 100644 --- a/tensorflow/core/platform/cpu_feature_guard.cc +++ b/tensorflow/core/platform/cpu_feature_guard.cc @@ -97,14 +97,17 @@ std::once_flag g_cpu_feature_guard_warn_once_flag; void InfoAboutUnusedCPUFeatures() { std::call_once(g_cpu_feature_guard_warn_once_flag, [] { string missing_instructions; -#ifdef PLATFORM_WINDOWS +#if defined(_MSC_VER) && !defined(__clang__) + #ifndef __AVX__ CheckIfFeatureUnused(CPUFeature::AVX, "AVX", missing_instructions); #endif // __AVX__ #ifndef __AVX2__ CheckIfFeatureUnused(CPUFeature::AVX2, "AVX2", missing_instructions); #endif // __AVX2__ -#else // ifdef platform windows + +#else // if defined(_MSC_VER) && !defined(__clang__) + #ifndef __SSE__ CheckIfFeatureUnused(CPUFeature::SSE, "SSE", missing_instructions); #endif // __SSE__ @@ -132,7 +135,7 @@ void InfoAboutUnusedCPUFeatures() { #ifndef __FMA__ CheckIfFeatureUnused(CPUFeature::FMA, "FMA", missing_instructions); #endif // __FMA__ -#endif // else of ifdef platform windows +#endif // else of if defined(_MSC_VER) && !defined(__clang__) if (!missing_instructions.empty()) { LOG(INFO) << "Your CPU supports instructions that this TensorFlow " << "binary was not compiled to use:" << missing_instructions; -- GitLab From aeee7753013155cb0a73de24b62a03def11b3655 Mon Sep 17 00:00:00 2001 From: mjwen Date: Mon, 29 Jan 2018 15:36:17 -0600 Subject: [PATCH 224/423] Allow step callback for scipy SLSQP (#16312) * Allow step callback for scipy SLSQP * Add test_callbacks() for scipy optimizer --- .../opt/python/training/external_optimizer.py | 4 -- .../training/external_optimizer_test.py | 39 +++++++++++++++++++ 2 files changed, 39 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/opt/python/training/external_optimizer.py b/tensorflow/contrib/opt/python/training/external_optimizer.py index f243317f1d..82ebca7f20 100644 --- a/tensorflow/contrib/opt/python/training/external_optimizer.py +++ b/tensorflow/contrib/opt/python/training/external_optimizer.py @@ -397,10 +397,6 @@ class ScipyOptimizerInterface(ExternalOptimizerInterface): 'automatically and cannot be injected manually'.format(kwarg)) minimize_kwargs.update(optimizer_kwargs) - if method == 'SLSQP': - # SLSQP doesn't support step callbacks. Obviate associated warning - # message. - del minimize_kwargs['callback'] import scipy.optimize # pylint: disable=g-import-not-at-top result = scipy.optimize.minimize(*minimize_args, **minimize_kwargs) diff --git a/tensorflow/contrib/opt/python/training/external_optimizer_test.py b/tensorflow/contrib/opt/python/training/external_optimizer_test.py index 0f597d0a24..953586ee70 100644 --- a/tensorflow/contrib/opt/python/training/external_optimizer_test.py +++ b/tensorflow/contrib/opt/python/training/external_optimizer_test.py @@ -299,6 +299,45 @@ class ScipyOptimizerInterfaceTest(TestCase): method = optimizer.optimizer_kwargs.get('method') self.assertEqual('SLSQP', method) + def test_callbacks(self): + vector_val = np.array([7., -2.], dtype=np.float32) + vector = variables.Variable(vector_val, 'vector') + + minimum_location_val = np.arange(2) + minimum_location = constant_op.constant( + minimum_location_val, dtype=dtypes.float32) + + loss = math_ops.reduce_sum(math_ops.square(vector - minimum_location)) / 2. + loss_val_first = ((vector_val - minimum_location_val)**2).sum() / 2. + + optimizer = external_optimizer.ScipyOptimizerInterface(loss, method='SLSQP') + + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + + initial_vector_val = sess.run(vector) + + extra_fetches = [loss] + + step_callback = test.mock.Mock() + loss_callback = test.mock.Mock() + + optimizer.minimize( + sess, + fetches=extra_fetches, + loss_callback=loss_callback, + step_callback=step_callback) + + loss_val_last = sess.run(loss) + + call_first = test.mock.call(loss_val_first) + call_last = test.mock.call(loss_val_last) + loss_calls = [call_first, call_last] + loss_callback.assert_has_calls(loss_calls, any_order=True) + + args, _ = step_callback.call_args + self.assertAllClose(minimum_location_val, args[0]) + if __name__ == '__main__': test.main() -- GitLab From 4f15f9197c4de1a6c7357a3340c6de12a901ed3a Mon Sep 17 00:00:00 2001 From: cclauss Date: Mon, 29 Jan 2018 22:50:39 +0100 Subject: [PATCH 225/423] contrib/learn: Typo in variable name x_exrta --> x_extra (#16500) * contrib/learn: Typo in variable name x_exrta --> x_extra flake8 testing of https://github.com/tensorflow/tensorflow --- tensorflow/contrib/learn/python/learn/datasets/synthetic.py | 2 +- .../contrib/learn/python/learn/datasets/synthetic_test.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py index 649996c49c..9a843168c2 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py @@ -151,7 +151,7 @@ def spirals(n_samples=100, # Add more points if n_samples is not divisible by n_classes (unbalanced!) extras = n_samples % n_classes if extras > 0: - x_exrta, y_extra = _modes[mode](np.random.rand(extras) * 2 * np.pi, *args, + x_extra, y_extra = _modes[mode](np.random.rand(extras) * 2 * np.pi, *args, **kwargs) spir_x = np.append(spir_x, x_extra) spir_y = np.append(spir_y, y_extra) diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py index 613d8d39a3..19791d7759 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py @@ -136,6 +136,9 @@ class SyntheticTest(test.TestCase): self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data) + def test_spirals(self): + synthetic.spirals(3) + if __name__ == '__main__': test.main() -- GitLab From 32c7607ff8117f1454c62e1762cadaf577f242fd Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Mon, 29 Jan 2018 13:36:09 -0800 Subject: [PATCH 226/423] Java: Update to 1.5.0 PiperOrigin-RevId: 183718481 --- tensorflow/java/maven/libtensorflow/pom.xml | 2 +- tensorflow/java/maven/libtensorflow_jni/pom.xml | 2 +- tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml | 2 +- tensorflow/java/maven/pom.xml | 2 +- tensorflow/java/maven/proto/pom.xml | 2 +- tensorflow/java/maven/tensorflow/pom.xml | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index 6285ee0483..a9ce5372ae 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0-rc1 + 1.5.0 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index b0e5c44fec..fe34ca83ff 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0-rc1 + 1.5.0 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index 02c5dca13f..390152808e 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 - 1.5.0-rc1 + 1.5.0 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index 949597ca7f..524ec45f48 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.5.0-rc1 + 1.5.0 pom https://www.tensorflow.org diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml index 9f0ebcf84c..9cf3217f51 100644 --- a/tensorflow/java/maven/proto/pom.xml +++ b/tensorflow/java/maven/proto/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0-rc1 + 1.5.0 ../ proto diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index 88d897362a..d619f986a9 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.5.0-rc1 + 1.5.0 ../ tensorflow -- GitLab From 82336ab8a1c04961817db19fc42c84872b17d954 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 29 Jan 2018 13:59:08 -0800 Subject: [PATCH 227/423] Fix incorrect docs for DecodeVideoOp (#16525) This fix fixes incorrect docs for DecodeVideoOp Signed-off-by: Yong Tang --- tensorflow/contrib/ffmpeg/decode_video_op.cc | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/ffmpeg/decode_video_op.cc b/tensorflow/contrib/ffmpeg/decode_video_op.cc index d44032968d..6f8ad486d1 100644 --- a/tensorflow/contrib/ffmpeg/decode_video_op.cc +++ b/tensorflow/contrib/ffmpeg/decode_video_op.cc @@ -102,16 +102,12 @@ REGISTER_OP("DecodeVideo") return Status::OK(); }) .Doc(R"doc( -Processes the contents of an audio file into a tensor using FFmpeg to decode +Processes the contents of an video file into a tensor using FFmpeg to decode the file. -One row of the tensor is created for each channel in the audio file. Each -channel contains audio samples starting at the beginning of the audio and -having `1/samples_per_second` time between them. If the `channel_count` is -different from the contents of the file, channels will be merged or created. - -contents: The binary audio file contents, as a string or rank-0 string - tensor. +contents: The binary contents of the video file to decode. This is a + scalar. +output: A rank-4 `Tensor` that has `[frames, height, width, 3]` RGB as output. )doc"); } // namespace ffmpeg -- GitLab From 412a905ebc68984eee862a29ecdb2a08dae359c0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 14:04:16 -0800 Subject: [PATCH 228/423] Revise the decorator transformer and add tests that clarify when can decorator information appear in the AST. PiperOrigin-RevId: 183723446 --- .../contrib/py2tf/converters/decorators.py | 24 +++-- .../py2tf/converters/decorators_test.py | 96 +++++++++++++++++++ tensorflow/contrib/py2tf/pyct/BUILD | 1 + tensorflow/contrib/py2tf/pyct/compiler.py | 6 +- 4 files changed, 117 insertions(+), 10 deletions(-) create mode 100644 tensorflow/contrib/py2tf/converters/decorators_test.py diff --git a/tensorflow/contrib/py2tf/converters/decorators.py b/tensorflow/contrib/py2tf/converters/decorators.py index a4313bfa51..3f620c1cd2 100644 --- a/tensorflow/contrib/py2tf/converters/decorators.py +++ b/tensorflow/contrib/py2tf/converters/decorators.py @@ -12,7 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Handles decorators.""" +"""Handles decorators. + +Note: this module only deals with functions whose decorators are still recorded +in the AST. This does not always happen. See the unit test for an example. +""" from __future__ import absolute_import from __future__ import division @@ -34,17 +38,19 @@ class DecoratorsTransformer(gast.NodeTransformer): def visit_FunctionDef(self, node): self.generic_visit(node) + kept_decorators = [] for dec in node.decorator_list: if isinstance(dec, gast.Call): - dec = dec.func - if not anno.hasanno(dec, 'live_val'): + dec_func = dec.func + else: + dec_func = dec + if not anno.hasanno(dec_func, 'live_val'): raise ValueError( - 'Could not resolve decorator: %s' % pretty_printer.fmt(dec)) - dec_value = anno.getanno(dec, 'live_val') - if dec_value in self.remove_decorators: - continue - raise ValueError('Dont know how to convert decorators for now.') - node.decorator_list = [] + 'Could not resolve decorator: %s' % pretty_printer.fmt(dec_func)) + dec_value = anno.getanno(dec_func, 'live_val') + if dec_value not in self.remove_decorators: + kept_decorators.append(dec) + node.decorator_list = kept_decorators return node # pylint:enable=invalid-name diff --git a/tensorflow/contrib/py2tf/converters/decorators_test.py b/tensorflow/contrib/py2tf/converters/decorators_test.py new file mode 100644 index 0000000000..f50d593043 --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/decorators_test.py @@ -0,0 +1,96 @@ +# 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 decorators module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import textwrap + +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.contrib.py2tf.converters import decorators +from tensorflow.contrib.py2tf.pyct import compiler +from tensorflow.python.platform import test +from tensorflow.python.util import tf_inspect + + +class DecoratorsTest(converter_test_base.TestCase): + + def test_function_decorator(self): + + def function_decorator(): + + def decorator(f): + return lambda a: f(a) + 1 + + return decorator + + # The Python parser does capture decorators into the AST. + # However, the interpreter desugars them on load, and refering to the + # decorated function at runtime usually loses any trace of the decorator. + # Below is an example when that doesn't happen. + def static_wrapper(): + + @function_decorator() + def test_fn(a): # pylint:disable=unused-variable + return a + + node = self.parse_and_analyze(static_wrapper, + {'function_decorator': function_decorator}) + node = node.body[0].body[0] + + node = decorators.transform(node, remove_decorators=()) + result = compiler.ast_to_object( + node, + source_prefix=textwrap.dedent(tf_inspect.getsource(function_decorator))) + self.assertEqual(2, result.test_fn(1)) + + node = decorators.transform(node, remove_decorators=(function_decorator,)) + result = compiler.ast_to_object(node) + self.assertEqual(1, result.test_fn(1)) + + def test_simple_decorator(self): + + def simple_decorator(f): + return lambda a: f(a) + 1 + + # The Python parser does capture decorators into the AST. + # However, the interpreter desugars them upon load, and refering to the + # decorated function at runtime usually loses any trace of the decorator. + # Below is an example when that doesn't happen. + def static_wrapper(): + + @simple_decorator + def test_fn(a): # pylint:disable=unused-variable + return a + + node = self.parse_and_analyze(static_wrapper, + {'simple_decorator': simple_decorator}) + node = node.body[0].body[0] + + node = decorators.transform(node, remove_decorators=()) + result = compiler.ast_to_object( + node, + source_prefix=textwrap.dedent(tf_inspect.getsource(simple_decorator))) + self.assertEqual(2, result.test_fn(1)) + + node = decorators.transform(node, remove_decorators=(simple_decorator,)) + result = compiler.ast_to_object(node) + self.assertEqual(1, result.test_fn(1)) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/py2tf/pyct/BUILD b/tensorflow/contrib/py2tf/pyct/BUILD index 88902dea84..1b2408ba0e 100644 --- a/tensorflow/contrib/py2tf/pyct/BUILD +++ b/tensorflow/contrib/py2tf/pyct/BUILD @@ -31,6 +31,7 @@ py_library( deps = [ "@astor_archive//:astor", "@gast_archive//:gast", + "@six_archive//:six", "@termcolor_archive//:termcolor", ], ) diff --git a/tensorflow/contrib/py2tf/pyct/compiler.py b/tensorflow/contrib/py2tf/pyct/compiler.py index b09353cc72..fc71469d1e 100644 --- a/tensorflow/contrib/py2tf/pyct/compiler.py +++ b/tensorflow/contrib/py2tf/pyct/compiler.py @@ -41,7 +41,7 @@ def ast_to_source(node, indentation): return astor.source_repr.pretty_source(generator.result).lstrip() -def ast_to_object(node, indentation=' '): +def ast_to_object(node, indentation=' ', source_prefix=None): """Return the Python objects represented by given AST. Compiling the AST code this way ensures that the source code is readable by @@ -50,6 +50,7 @@ def ast_to_object(node, indentation=' '): 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. Returns: A module object containing the compiled source code. @@ -58,5 +59,8 @@ def ast_to_object(node, indentation=' '): with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f: module_name = os.path.basename(f.name[:-3]) + if source_prefix: + f.write(source_prefix) + f.write('\n') f.write(source) return imp.load_source(module_name, f.name) -- GitLab From 8fdef24247814d322991127091cc6d0c1eb60380 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 29 Jan 2018 14:26:43 -0800 Subject: [PATCH 229/423] Switch over to max_pool_v2 in Python This fix is a follow up to 11875 so that MaxPool in Python use v2 version. As 11875 has been merged some time ago, this fix conforms to the deprecation policy. This fix is realted to 11875 and 4746. Signed-off-by: Yong Tang PiperOrigin-RevId: 183727668 --- .../compiler/tf2xla/kernels/pooling_ops.cc | 84 ++++++++++++++----- .../python/util/parse_layer_parameters.py | 63 ++++++++++---- .../python/kernel_tests/pooling_ops_test.py | 21 ++--- tensorflow/python/ops/nn_ops.py | 13 ++- 4 files changed, 125 insertions(+), 56 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index 0b5a38967a..2ba572fd0e 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -37,21 +37,9 @@ class PoolingOp : public XlaOpKernel { public: PoolingOp(OpKernelConstruction* ctx, int num_spatial_dims) : XlaOpKernel(ctx), num_spatial_dims_(num_spatial_dims) { - std::vector ksize_int; - std::vector stride_int; - OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_int)); - OP_REQUIRES(ctx, ksize_int.size() == num_dims(), - errors::InvalidArgument("Sliding window ksize field must " - "specify ", - num_dims(), " dimensions")); - OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_int)); - OP_REQUIRES(ctx, stride_int.size() == num_dims(), - errors::InvalidArgument("Sliding window stride field must " - "specify ", - num_dims(), " dimensions")); - for (int i = 0; i < num_dims(); ++i) { - ksize_.push_back(ksize_int[i]); - stride_.push_back(stride_int[i]); + if (ctx->num_inputs() == 1) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_)); } Padding padding; OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding)); @@ -77,6 +65,28 @@ class PoolingOp : public XlaOpKernel { xla::ComputationDataHandle input = ctx->Input(0); const TensorShape input_shape = ctx->InputShape(0); + if (ctx->num_inputs() != 1) { + const TensorShape ksize_shape = ctx->InputShape(1); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(ksize_shape), + errors::InvalidArgument("ksize must be a vector, not shape ", + ksize_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(1, &ksize_)); + + const TensorShape stride_shape = ctx->InputShape(2); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(stride_shape), + errors::InvalidArgument("stride must be a vector, not shape ", + stride_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(2, &stride_)); + } + + OP_REQUIRES(ctx, ksize_.size() == num_dims(), + errors::InvalidArgument("Sliding window ksize field must " + "specify ", + num_dims(), " dimensions")); + OP_REQUIRES(ctx, stride_.size() == num_dims(), + errors::InvalidArgument("Sliding window stride field must " + "specify ", + num_dims(), " dimensions")); OP_REQUIRES(ctx, input_shape.dims() == num_dims(), errors::InvalidArgument("Input to ", type_string(), " operator must have ", num_dims(), @@ -130,6 +140,10 @@ class MaxPool2DOp : public MaxPoolOp { } }; REGISTER_XLA_OP(Name("MaxPool"), MaxPool2DOp); +REGISTER_XLA_OP(Name("MaxPoolV2") + .CompileTimeConstInput("ksize") + .CompileTimeConstInput("strides"), + MaxPool2DOp); class MaxPool3DOp : public MaxPoolOp { public: @@ -243,22 +257,44 @@ class MaxPoolGradOp : public XlaOpKernel { public: MaxPoolGradOp(OpKernelConstruction* ctx, int num_spatial_dims) : XlaOpKernel(ctx), num_spatial_dims_(num_spatial_dims) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_)); + if (ctx->num_inputs() == 3) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("ksize", &ksize_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_)); + } + OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding_)); + } + + int num_dims() const { return num_spatial_dims_ + 2; } + + void Compile(XlaOpKernelContext* ctx) override { + if (ctx->num_inputs() != 3) { + OP_REQUIRES( + ctx, ctx->num_inputs() == 5, + errors::InvalidArgument("Must supply ksize and stride arguments.")); + const TensorShape ksize_shape = ctx->InputShape(3); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(ksize_shape), + errors::InvalidArgument("ksize must be a vector, not shape ", + ksize_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(3, &ksize_)); + + const TensorShape stride_shape = ctx->InputShape(4); + // Validate input sizes. + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(stride_shape), + errors::InvalidArgument("stride must be a vector, not shape ", + stride_shape.DebugString())); + OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(4, &stride_)); + } + OP_REQUIRES(ctx, ksize_.size() == num_dims(), errors::InvalidArgument("Sliding window ksize field must " "specify ", num_dims(), " dimensions")); - OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &stride_)); OP_REQUIRES(ctx, stride_.size() == num_dims(), errors::InvalidArgument("Sliding window strides field must " "specify ", num_dims(), " dimensions")); - OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding_)); - } - - int num_dims() const { return num_spatial_dims_ + 2; } - void Compile(XlaOpKernelContext* ctx) override { const TensorShape tensor_in_shape = ctx->InputShape(0); const TensorShape tensor_out_shape = ctx->InputShape(1); const TensorShape out_backprop_shape = ctx->InputShape(2); @@ -315,6 +351,10 @@ class MaxPool2DGradOp : public MaxPoolGradOp { } }; REGISTER_XLA_OP(Name("MaxPoolGrad"), MaxPool2DGradOp); +REGISTER_XLA_OP(Name("MaxPoolGradV2") + .CompileTimeConstInput("ksize") + .CompileTimeConstInput("strides"), + MaxPool2DGradOp); class MaxPool3DGradOp : public MaxPoolGradOp { public: diff --git a/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py b/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py index 44998b3b65..bc383a8034 100644 --- a/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py +++ b/tensorflow/contrib/receptive_field/python/util/parse_layer_parameters.py @@ -35,20 +35,34 @@ _VALID_PADDING = ["VALID", b"VALID"] _SAME_PADDING = ["SAME", b"SAME"] -def _stride_size(node): +def _stride_size(node, name_to_node): """Computes stride size given a TF node. Args: node: Tensorflow node (NodeDef proto). + name_to_node: For MaxPoolV2, mapping from variable name Tensorflow node. Returns: stride_x: Stride size for horizontal direction (integer). stride_y: Stride size for vertical direction (integer). + + Raises: + ValueError: If stride input cannot be found in `name_to_node`. """ - strides_attr = node.attr["strides"] - logging.vlog(4, "strides_attr = %s", strides_attr) - stride_y = strides_attr.list.i[1] - stride_x = strides_attr.list.i[2] + if node.op == "MaxPoolV2": + strides_input_name = node.input[2] + if not strides_input_name.endswith("/strides"): + raise ValueError("Strides name does not end with '/strides'") + strides_node = name_to_node[strides_input_name] + value = strides_node.attr["value"] + t = make_ndarray(value.tensor) + stride_y = t[1] + stride_x = t[2] + else: + strides_attr = node.attr["strides"] + logging.vlog(4, "strides_attr = %s", strides_attr) + stride_y = strides_attr.list.i[1] + stride_x = strides_attr.list.i[2] return stride_x, stride_y @@ -144,11 +158,12 @@ def _padding_size_conv_pool(node, kernel_size, stride, input_resolution=None): return total_padding, padding -def _pool_kernel_size(node): +def _pool_kernel_size(node, name_to_node): """Computes kernel size given a TF pooling node. Args: node: Tensorflow node (NodeDef proto). + name_to_node: For MaxPoolV2, mapping from node name to NodeDef. Returns: kernel_size_x: Kernel size for horizontal direction (integer). @@ -157,13 +172,27 @@ def _pool_kernel_size(node): Raises: ValueError: If pooling is invalid. """ - ksize = node.attr["ksize"] - kernel_size_y = ksize.list.i[1] - kernel_size_x = ksize.list.i[2] - if ksize.list.i[0] != 1: - raise ValueError("pool ksize for first dim is not 1") - if ksize.list.i[3] != 1: - raise ValueError("pool ksize for last dim is not 1") + if node.op == "MaxPoolV2": + ksize_input_name = node.input[1] + if not ksize_input_name.endswith("/ksize"): + raise ValueError("Kernel size name does not end with '/ksize'") + ksize_node = name_to_node[ksize_input_name] + value = ksize_node.attr["value"] + t = make_ndarray(value.tensor) + kernel_size_y = t[1] + kernel_size_x = t[2] + if t[0] != 1: + raise ValueError("pool ksize for first dim is not 1") + if t[3] != 1: + raise ValueError("pool ksize for last dim is not 1") + else: + ksize = node.attr["ksize"] + kernel_size_y = ksize.list.i[1] + kernel_size_x = ksize.list.i[2] + if ksize.list.i[0] != 1: + raise ValueError("pool ksize for first dim is not 1") + if ksize.list.i[3] != 1: + raise ValueError("pool ksize for last dim is not 1") return kernel_size_x, kernel_size_y @@ -243,7 +272,7 @@ def get_layer_params(node, name_to_node, input_resolution=None, force=False): logging.vlog(3, "node.op = %s", node.op) logging.vlog(4, "node = %s", node) if node.op == "Conv2D" or node.op == "DepthwiseConv2dNative": - stride_x, stride_y = _stride_size(node) + stride_x, stride_y = _stride_size(node, name_to_node) kernel_size_x, kernel_size_y = _conv_kernel_size(node, name_to_node) # Compute the padding for this node separately for each direction. total_padding_x, padding_x = _padding_size_conv_pool( @@ -260,9 +289,9 @@ def get_layer_params(node, name_to_node, input_resolution=None, force=False): stride_y = 1 total_padding_x, padding_x, total_padding_y, padding_y = ( _padding_size_pad_layer(node, name_to_node)) - elif node.op == "MaxPool" or node.op == "AvgPool": - stride_x, stride_y = _stride_size(node) - kernel_size_x, kernel_size_y = _pool_kernel_size(node) + elif node.op == "MaxPool" or node.op == "MaxPoolV2" or node.op == "AvgPool": + stride_x, stride_y = _stride_size(node, name_to_node) + kernel_size_x, kernel_size_y = _pool_kernel_size(node, name_to_node) # Compute the padding for this node separately for each direction. total_padding_x, padding_x = _padding_size_conv_pool( node, kernel_size_x, stride_x, input_resolution[1] diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index 3263ed1a60..4466beeec9 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -1811,16 +1811,17 @@ class PoolingTest(test.TestCase): if test.is_gpu_available(): pool_funcs.append(nn_ops.max_pool_with_argmax) for pool_func in pool_funcs: - # Illegal strides. - with self.assertRaisesRegexp( - errors_impl.UnimplementedError, - "Pooling is not yet supported on the batch"): - sess.run( - pool_func( - array_ops.placeholder(dtypes.float32), - ksize=[1, 1, 1, 1], - strides=[2, 1, 1, 1], - padding="SAME")) + if pool_func != nn_ops.max_pool: + # Illegal strides. + with self.assertRaisesRegexp( + errors_impl.UnimplementedError, + "Pooling is not yet supported on the batch"): + sess.run( + pool_func( + array_ops.placeholder(dtypes.float32), + ksize=[1, 1, 1, 1], + strides=[2, 1, 1, 1], + padding="SAME")) # Filter too large. with self.assertRaisesRegexp(ValueError, "Negative dimension size"): diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 9c875b4bcb..a691e281ee 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -2116,13 +2116,12 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """ with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") - return gen_nn_ops._max_pool( - value, - ksize=ksize, - strides=strides, - padding=padding, - data_format=data_format, - name=name) + return gen_nn_ops._max_pool(value, + ksize=ksize, + strides=strides, + padding=padding, + data_format=data_format, + name=name) @ops.RegisterStatistics("Conv2D", "flops") -- GitLab From f751a8a016a4d486073cec0c061c877cb94ca9d4 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Mon, 29 Jan 2018 14:31:51 -0800 Subject: [PATCH 230/423] [TF:XLA] Bump open source llvm revision to r323630 PiperOrigin-RevId: 183728562 --- tensorflow/workspace.bzl | 8 ++++---- third_party/llvm/llvm.BUILD | 24 ++++++++++++++++++++++++ 2 files changed, 28 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 26abebe2de..357b04d75f 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -472,11 +472,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/11a2ca6eea8a7fe240a14c0c35fd2017341279be.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/11a2ca6eea8a7fe240a14c0c35fd2017341279be.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/674f4d74284e9c431c7b69e30f6e10fc4fcbc9ef.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/674f4d74284e9c431c7b69e30f6e10fc4fcbc9ef.tar.gz", ], - sha256 = "b5429ccf8d57273cb8489714f728c997cd720ec66fc2c0292422ab8f0e729ce0", - strip_prefix = "llvm-11a2ca6eea8a7fe240a14c0c35fd2017341279be", + sha256 = "3330c50efc9fc5d742e227dc810c2f586c7e36a60ecacd8251fafd2ea591e404", + strip_prefix = "llvm-674f4d74284e9c431c7b69e30f6e10fc4fcbc9ef", build_file = str(Label("//third_party/llvm:llvm.BUILD")), ) diff --git a/third_party/llvm/llvm.BUILD b/third_party/llvm/llvm.BUILD index 5344525ba8..a9e1341a03 100644 --- a/third_party/llvm/llvm.BUILD +++ b/third_party/llvm/llvm.BUILD @@ -670,6 +670,28 @@ cc_library( ], ) +cc_library( + name = "aggressive_inst_combine", + srcs = glob([ + "lib/Transforms/AggressiveInstCombine/*.c", + "lib/Transforms/AggressiveInstCombine/*.cpp", + "lib/Transforms/AggressiveInstCombine/*.inc", + "lib/Transforms/AggressiveInstCombine/*.h", + ]), + hdrs = glob([ + "include/llvm/Transforms/AggressiveInstCombine/*.h", + "include/llvm/Transforms/AggressiveInstCombine/*.def", + "include/llvm/Transforms/AggressiveInstCombine/*.inc", + ]), + deps = [ + ":analysis", + ":config", + ":core", + ":support", + ":transform_utils", + ], +) + cc_library( name = "analysis", srcs = glob([ @@ -1405,6 +1427,7 @@ cc_library( "include/llvm/Transforms/IPO/*.inc", ]), deps = [ + ":aggressive_inst_combine", ":analysis", ":bit_reader", ":bit_writer", @@ -1931,6 +1954,7 @@ cc_library( "include/llvm/Transforms/IPO/SCCP.h", ]), deps = [ + ":aggressive_inst_combine", ":analysis", ":config", ":core", -- GitLab From 1aa6e1a67a8c952501058c24e8af687fe9340560 Mon Sep 17 00:00:00 2001 From: resec Date: Tue, 30 Jan 2018 06:36:21 +0800 Subject: [PATCH 231/423] Add nsync lib dep. to cc_library rule android_tensorflow_lib_selective_registration (#16117) --- tensorflow/core/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 455da05738..3b4a10eedb 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1151,6 +1151,7 @@ cc_library( deps = [ ":protos_all_cc_impl", "//third_party/eigen3", + "@nsync//:nsync_cpp", "@protobuf_archive//:protobuf", ], alwayslink = 1, -- GitLab From c7351055a89b90c3114fb4c24f880a947a15352e Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 29 Jan 2018 14:37:17 -0800 Subject: [PATCH 232/423] Set default signal handler for SIGINT (keyboard interrupt). The default signal handling for Python delivers the signal to the thread for processing; if broad exceptions are being caught, this can often result in the interrupt being swallowed. PiperOrigin-RevId: 183729637 --- .../contrib/tpu/python/tpu/tpu_estimator.py | 29 +++++++++++++++---- 1 file changed, 24 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 2ae3a26a85..7907d0baa5 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -21,6 +21,7 @@ from __future__ import print_function import collections from contextlib import contextmanager import copy +import signal import threading import time import traceback @@ -467,12 +468,12 @@ class _OpQueueContext(object): def read_iteration_counts(self): while True: - signal = self._queue.get(block=True) - logging.debug('%s read signal %s', self._name, signal) - if signal == _SIGNAL.STOP: - logging.info('%s received signal, stopping.', self._name) + iterations = self._queue.get(block=True) + logging.debug('%s read iterations %s', self._name, iterations) + if iterations == _SIGNAL.STOP: + logging.info('%s received shutdown signal, stopping.', self._name) return - yield signal + yield iterations def join(self): logging.info('Shutting down %s thread.' % self._name) @@ -1387,6 +1388,23 @@ class ExamplesPerSecondHook(basic_session_run_hooks.StepCounterHook): logging.info('examples/sec: %g', examples_per_sec) +class InstallSignalHandlerHook(session_run_hook.SessionRunHook): + """Change SIGINT (CTRL^C) handler to force quit the process. + + The default behavior often results in hanging processes. + The original handler is restored after training/evaluation. + """ + + def __init__(self): + self._signal_fn = signal.getsignal(signal.SIGINT) + + def before_run(self, run_context): + signal.signal(signal.SIGINT, signal.SIG_DFL) + + def end(self, session): + signal.signal(signal.SIGINT, self._signal_fn) + + class TPUEstimator(estimator_lib.Estimator): """Estimator with TPU support. @@ -1704,6 +1722,7 @@ class TPUEstimator(estimator_lib.Estimator): hooks = [ TPUInfeedOutfeedSessionHook(ctx, enqueue_ops), ExamplesPerSecondHook(ctx.global_batch_size), + InstallSignalHandlerHook(), training.LoggingTensorHook( { 'loss': array_ops.identity(loss), -- GitLab From a807755db184c2d2c3b9bcca65457e9915508650 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Mon, 29 Jan 2018 14:40:07 -0800 Subject: [PATCH 233/423] Use Popen.communicate() instead of read() in stacktrace_handler_test.py. This avoids potential deadlock, see the warnings in https://docs.python.org/2/library/subprocess.html#popen-objects. I found that enabling the C API caused us to deadlock without this change. PiperOrigin-RevId: 183730170 --- tensorflow/python/platform/stacktrace_handler_test.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/platform/stacktrace_handler_test.py b/tensorflow/python/platform/stacktrace_handler_test.py index 3f0e534f4c..f2071f9d54 100644 --- a/tensorflow/python/platform/stacktrace_handler_test.py +++ b/tensorflow/python/platform/stacktrace_handler_test.py @@ -57,7 +57,8 @@ class StacktraceHandlerTest(test.TestCase): # Capture its output. capture both stdout and stderr and append them. # We are not worried about timing or order of messages in this test. - child_output = child_process.stdout.read() + child_process.stderr.read() + child_stdout, child_stderr = child_process.communicate() + child_output = child_stdout + child_stderr # Make sure the child process is dead before we proceed. child_process.wait() -- GitLab From 70d608a5bd58f8da2dfb8b76a8ce94ac8d03aa4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Carl=20Thom=C3=A9?= Date: Tue, 30 Jan 2018 00:06:28 +0100 Subject: [PATCH 234/423] Spelling (#16547) --- tensorflow/python/training/basic_session_run_hooks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index 752d585cd1..864c2e4406 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -331,7 +331,7 @@ class CheckpointSaverListener(object): `CheckpointSaverHook`, as in this example: ```python - class ExampleCheckpointSaverListerner(CheckpointSaverListener): + class ExampleCheckpointSaverListener(CheckpointSaverListener): def begin(self): # You can add ops to the graph here. print('Starting the session.') @@ -347,7 +347,7 @@ class CheckpointSaverListener(object): print('Done with the session.') ... - listener = ExampleCheckpointSaverListerner() + listener = ExampleCheckpointSaverListener() saver_hook = tf.train.CheckpointSaverHook( checkpoint_dir, listeners=[listener]) with tf.train.MonitoredTrainingSession(chief_only_hooks=[saver_hook]): -- GitLab From aaaeef5bbc4698ea48c2476ad5c84a94712c8d2f Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Mon, 29 Jan 2018 15:06:30 -0800 Subject: [PATCH 235/423] Make TFLite SpaceToBatchND op have parity with TF SpaceToBatchND op. PiperOrigin-RevId: 183734695 --- tensorflow/contrib/lite/builtin_op_data.h | 8 - .../contrib/lite/kernels/space_to_batch_nd.cc | 95 ++++++------ .../lite/kernels/space_to_batch_nd_test.cc | 141 ++++++++++++++---- tensorflow/contrib/lite/model.cc | 17 --- tensorflow/contrib/lite/schema/schema.fbs | 3 - .../contrib/lite/schema/schema_generated.h | 89 +---------- .../contrib/lite/testing/generate_examples.py | 42 +++++- .../contrib/lite/toco/tflite/operator.cc | 15 +- .../contrib/lite/toco/tflite/operator_test.cc | 13 -- 9 files changed, 204 insertions(+), 219 deletions(-) diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index 7a7e20a41e..a1037a525c 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -116,14 +116,6 @@ typedef struct { } TfLiteAddParams; typedef struct { - // Number of spatial dimensions. - // For now only NHWC is supported, and the value should always be 2. - int num_spatial_dimensions; - // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. - // For now we will fix the maximum possible number of dimensions. - int block_shape[2]; - int before_paddings[2]; - int after_paddings[2]; } TfLiteSpaceToBatchNDParams; typedef struct { diff --git a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc index 2e22d0db56..e2e1873f77 100644 --- a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc +++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc @@ -33,17 +33,16 @@ enum KernelType { kGenericOptimized, }; -// Inputs specified in the 2nd tensor (block_shape) and 3rd tensor (paddings) -// are ignored. Only use the `block_shape` and `paddings` specified in params. -// TODO(nupurgarg): Support inputs as tensors in SpaceToBatchND. struct SpaceToBatchNDContext { SpaceToBatchNDContext(TfLiteContext* context, TfLiteNode* node) { - params = reinterpret_cast(node->builtin_data); input = GetInput(context, node, 0); + block_shape = GetInput(context, node, 1); + paddings = GetInput(context, node, 2); output = GetOutput(context, node, 0); } - TfLiteSpaceToBatchNDParams* params; TfLiteTensor* input; + TfLiteTensor* block_shape; + TfLiteTensor* paddings; TfLiteTensor* output; }; @@ -51,32 +50,29 @@ struct SpaceToBatchNDContext { // The 4D array need to have exactly 2 spatial dimensions. // TODO(nupurgarg): Support arbitrary dimension in SpaceToBatchND. const int kInputDimensionNum = 4; -const int kOutputDimensionNum = 4; +const int kBlockSizeDimensionNum = 1; const int kSpatialDimensionNum = 2; -const int kPaddingDimensionNum = 4; -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TF_LITE_ENSURE(context, NumInputs(node) >= 1 && NumInputs(node) <= 3); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + SpaceToBatchNDContext* op_context) { + TfLiteIntArray* input_size = op_context->input->dims; + const int32* block_shape = GetTensorData(op_context->block_shape); + const int32* paddings_data = GetTensorData(op_context->paddings); - SpaceToBatchNDContext op_context(context, node); - TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.input), - kInputDimensionNum); - TF_LITE_ENSURE_EQ(context, op_context.params->num_spatial_dimensions, + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->block_shape), + kBlockSizeDimensionNum); + TF_LITE_ENSURE_EQ(context, op_context->block_shape->dims->data[0], + kSpatialDimensionNum); + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->paddings), kSpatialDimensionNum); - TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); - - const TfLiteIntArray* input_size = op_context.input->dims; - const int* block_shape = op_context.params->block_shape; - TfLiteIntArray* output_size = TfLiteIntArrayCreate(kOutputDimensionNum); + TfLiteIntArray* output_size = TfLiteIntArrayCopy(input_size); // Ensures the input height and width (with padding) is a multiple of block // shape height and width. for (int dim = 0; dim < kSpatialDimensionNum; ++dim) { - int final_dim_size = - (input_size->data[dim + 1] + op_context.params->before_paddings[dim] + - op_context.params->after_paddings[dim]); + int final_dim_size = (input_size->data[dim + 1] + paddings_data[dim * 2] + + paddings_data[dim * 2 + 1]); TF_LITE_ENSURE_EQ(context, final_dim_size % block_shape[dim], 0); output_size->data[dim + 1] = final_dim_size / block_shape[dim]; } @@ -88,33 +84,44 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_size->data[0] = output_batch_size; output_size->data[3] = output_channel_size; - return context->ResizeTensor(context, op_context.output, output_size); + return context->ResizeTensor(context, op_context->output, output_size); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 3); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + SpaceToBatchNDContext op_context(context, node); + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.input), + kInputDimensionNum); + TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); + + if (!IsConstantTensor(op_context.block_shape) || + !IsConstantTensor(op_context.paddings)) { + SetTensorToDynamic(op_context.output); + return kTfLiteOk; + } + return ResizeOutputTensor(context, &op_context); } template TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { SpaceToBatchNDContext op_context(context, node); - int block_shape_dims_array[1] = {kSpatialDimensionNum}; - Dims<4> block_shape_dims = GetTensorDims(block_shape_dims_array, 1); - - // Initialize padding array in the format accepted by the kernel code. - // TODO(nupurgarg): Make kernel code accept padding array format that is - // consistent with Pad operation (i.e. before_paddings and after_paddings). - TfLiteIntArray* padding_data = TfLiteIntArrayCreate(kPaddingDimensionNum); - padding_data->data[0] = op_context.params->before_paddings[0]; - padding_data->data[1] = op_context.params->after_paddings[0]; - padding_data->data[2] = op_context.params->before_paddings[1]; - padding_data->data[3] = op_context.params->after_paddings[1]; - int padding_dims_array[1] = {kPaddingDimensionNum}; - Dims<4> padding_dims = GetTensorDims(padding_dims_array, 1); - -#define TF_LITE_SPACE_TO_BATCH_ND(type, scalar) \ - type::SpaceToBatchND(GetTensorData(op_context.input), \ - GetTensorDims(op_context.input), \ - op_context.params->block_shape, block_shape_dims, \ - padding_data->data, padding_dims, \ - GetTensorData(op_context.output), \ + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + TfLiteTensorRealloc(op_context.output->bytes, op_context.output); + } + +#define TF_LITE_SPACE_TO_BATCH_ND(type, scalar) \ + type::SpaceToBatchND(GetTensorData(op_context.input), \ + GetTensorDims(op_context.input), \ + GetTensorData(op_context.block_shape), \ + GetTensorDims(op_context.block_shape), \ + GetTensorData(op_context.paddings), \ + GetTensorDims(op_context.paddings), \ + GetTensorData(op_context.output), \ GetTensorDims(op_context.output)) switch (op_context.input->type) { // Already know in/out types are same. case kTfLiteFloat32: @@ -151,8 +158,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteError; } #undef TF_LITE_SPACE_TO_BATCH_ND - - TfLiteIntArrayFree(padding_data); return kTfLiteOk; } 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 45a6aef73d..92a4a037d5 100644 --- a/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc +++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc @@ -26,41 +26,81 @@ using ::testing::ElementsAreArray; class SpaceToBatchNDOpModel : public SingleOpModel { public: - SpaceToBatchNDOpModel(std::initializer_list input_shape, - std::initializer_list block_shape, - std::initializer_list before_paddings, - std::initializer_list after_paddings) { - input_ = AddInput(TensorType_FLOAT32); - output_ = AddOutput(TensorType_FLOAT32); - SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, - BuiltinOptions_SpaceToBatchNDOptions, - CreateSpaceToBatchNDOptions( - builder_, builder_.CreateVector(block_shape), - builder_.CreateVector(before_paddings), - builder_.CreateVector(after_paddings)) - .Union()); - BuildInterpreter({input_shape}); - } - 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_); } - private: + protected: int input_; + int block_shape_; + int paddings_; int output_; }; +// Tests case where block_shape and paddings are const tensors. +// +// Example usage is as follows: +// SpaceToBatchNDOpConstModel m(input_shape, block_shape, paddings); +// m.SetInput(input_data); +// m.Invoke(); +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}); + } +}; + +// Tests case where block_shape and paddings are non-const tensors. +// +// Example usage is as follows: +// SpaceToBatchNDOpDynamicModel m(input_shape); +// m.SetInput(input_data); +// m.SetBlockShape(block_shape); +// m.SetPaddings(paddings); +// m.Invoke(); +class SpaceToBatchNDOpDynamicModel : public SpaceToBatchNDOpModel { + public: + SpaceToBatchNDOpDynamicModel(std::initializer_list input_shape) { + input_ = AddInput(TensorType_FLOAT32); + block_shape_ = AddInput(TensorType_INT32); + paddings_ = AddInput(TensorType_INT32); + output_ = AddOutput(TensorType_FLOAT32); + + SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, + BuiltinOptions_SpaceToBatchNDOptions, + CreateSpaceToBatchNDOptions(builder_).Union()); + BuildInterpreter({input_shape, {2}, {2, 2}}); + } +}; + TEST(SpaceToBatchNDOpTest, InvalidShapeTest) { - EXPECT_DEATH(SpaceToBatchNDOpModel({1, 3, 3, 1}, {2, 2}, {0, 0}, {0, 0}), + EXPECT_DEATH(SpaceToBatchNDOpConstModel({1, 3, 3, 1}, {2, 2}, {0, 0, 0, 0}), "Cannot allocate tensors"); } -TEST(SpaceToBatchNDOpTest, SimpleTest) { - SpaceToBatchNDOpModel m({1, 4, 4, 1}, {2, 2}, {0, 0}, {0, 0}); +TEST(SpaceToBatchNDOpTest, SimpleConstTest) { + 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})); @@ -68,17 +108,39 @@ TEST(SpaceToBatchNDOpTest, SimpleTest) { 13, 15, 6, 8, 14, 16})); } -TEST(SpaceToBatchNDOpTest, MultipleInputBatches) { - SpaceToBatchNDOpModel m({2, 2, 4, 1}, {2, 2}, {0, 0}, {0, 0}); +TEST(SpaceToBatchNDOpTest, SimpleDynamicTest) { + SpaceToBatchNDOpDynamicModel m({1, 4, 4, 1}); 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}); + 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(SpaceToBatchNDOpTest, MultipleInputBatchesConstTest) { + 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(SpaceToBatchNDOpTest, MultipleInputBatchesDynamicTest) { + SpaceToBatchNDOpDynamicModel m({2, 2, 4, 1}); + 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}); 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(SpaceToBatchNDOpTest, SimplePadding) { - SpaceToBatchNDOpModel m({1, 5, 2, 1}, {3, 2}, {1, 2}, {0, 0}); +TEST(SpaceToBatchNDOpTest, SimplePaddingConstTest) { + 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})); @@ -88,9 +150,36 @@ TEST(SpaceToBatchNDOpTest, SimplePadding) { })); } -TEST(SpaceToBatchNDOpTest, ComplexPadding) { - SpaceToBatchNDOpModel m({1, 4, 2, 1}, {3, 2}, {1, 2}, {1, 4}); +TEST(SpaceToBatchNDOpTest, SimplePaddingDynamicTest) { + SpaceToBatchNDOpDynamicModel m({1, 5, 2, 1}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); + m.SetBlockShape({3, 2}); + m.SetPaddings({1, 0, 2, 0}); + 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(SpaceToBatchNDOpTest, ComplexPaddingConstTest) { + 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, + })); +} + +TEST(SpaceToBatchNDOpTest, ComplexPaddingDynamicTest) { + SpaceToBatchNDOpDynamicModel m({1, 4, 2, 1}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 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.GetOutput(), ElementsAreArray({ diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index c82ae27953..b36bfcef84 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -516,23 +516,6 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type, break; } case BuiltinOperator_SPACE_TO_BATCH_ND: { - auto* params = MallocPOD(); - if (auto* schema_params = - op->builtin_options_as_SpaceToBatchNDOptions()) { - const auto& block_shape = schema_params->block_shape(); - FlatBufferIntVectorToArray(sizeof(params->block_shape), block_shape, - params->block_shape, error_reporter); - const auto& before_paddings = schema_params->before_paddings(); - FlatBufferIntVectorToArray(sizeof(params->before_paddings), - before_paddings, params->before_paddings, - error_reporter); - const auto& after_paddings = schema_params->after_paddings(); - FlatBufferIntVectorToArray(sizeof(params->after_paddings), - after_paddings, params->after_paddings, - error_reporter); - params->num_spatial_dimensions = block_shape->Length(); - } - builtin_data = reinterpret_cast(params); break; } case BuiltinOperator_BATCH_TO_SPACE_ND: { diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 91eac2ab48..c0b220e872 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -289,9 +289,6 @@ table ReshapeOptions { } table SpaceToBatchNDOptions { - block_shape:[int]; - before_paddings:[int]; - after_paddings:[int]; } table BatchToSpaceNDOptions { diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index a8370b34c6..29f3a17be7 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -2834,33 +2834,14 @@ flatbuffers::Offset CreateReshapeOptions( struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable { typedef SpaceToBatchNDOptions TableType; - std::vector block_shape; - std::vector before_paddings; - std::vector after_paddings; SpaceToBatchNDOptionsT() {} }; struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { typedef SpaceToBatchNDOptionsT NativeTableType; - enum { VT_BLOCK_SHAPE = 4, VT_BEFORE_PADDINGS = 6, VT_AFTER_PADDINGS = 8 }; - const flatbuffers::Vector *block_shape() const { - return GetPointer *>(VT_BLOCK_SHAPE); - } - const flatbuffers::Vector *before_paddings() const { - return GetPointer *>(VT_BEFORE_PADDINGS); - } - const flatbuffers::Vector *after_paddings() const { - return GetPointer *>(VT_AFTER_PADDINGS); - } bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_BLOCK_SHAPE) && - verifier.Verify(block_shape()) && - VerifyOffset(verifier, VT_BEFORE_PADDINGS) && - verifier.Verify(before_paddings()) && - VerifyOffset(verifier, VT_AFTER_PADDINGS) && - verifier.Verify(after_paddings()) && verifier.EndTable(); + return VerifyTableStart(verifier) && verifier.EndTable(); } SpaceToBatchNDOptionsT *UnPack( const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -2875,18 +2856,6 @@ struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS struct SpaceToBatchNDOptionsBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; - void add_block_shape( - flatbuffers::Offset> block_shape) { - fbb_.AddOffset(SpaceToBatchNDOptions::VT_BLOCK_SHAPE, block_shape); - } - void add_before_paddings( - flatbuffers::Offset> before_paddings) { - fbb_.AddOffset(SpaceToBatchNDOptions::VT_BEFORE_PADDINGS, before_paddings); - } - void add_after_paddings( - flatbuffers::Offset> after_paddings) { - fbb_.AddOffset(SpaceToBatchNDOptions::VT_AFTER_PADDINGS, after_paddings); - } explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -2900,29 +2869,11 @@ struct SpaceToBatchNDOptionsBuilder { }; inline flatbuffers::Offset CreateSpaceToBatchNDOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> block_shape = 0, - flatbuffers::Offset> before_paddings = 0, - flatbuffers::Offset> after_paddings = 0) { + flatbuffers::FlatBufferBuilder &_fbb) { SpaceToBatchNDOptionsBuilder builder_(_fbb); - builder_.add_after_paddings(after_paddings); - builder_.add_before_paddings(before_paddings); - builder_.add_block_shape(block_shape); return builder_.Finish(); } -inline flatbuffers::Offset -CreateSpaceToBatchNDOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *block_shape = nullptr, - const std::vector *before_paddings = nullptr, - const std::vector *after_paddings = nullptr) { - return tflite::CreateSpaceToBatchNDOptions( - _fbb, block_shape ? _fbb.CreateVector(*block_shape) : 0, - before_paddings ? _fbb.CreateVector(*before_paddings) : 0, - after_paddings ? _fbb.CreateVector(*after_paddings) : 0); -} - flatbuffers::Offset CreateSpaceToBatchNDOptions( flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); @@ -5639,33 +5590,6 @@ inline void SpaceToBatchNDOptions::UnPackTo( const flatbuffers::resolver_function_t *_resolver) const { (void)_o; (void)_resolver; - { - auto _e = block_shape(); - if (_e) { - _o->block_shape.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->block_shape[_i] = _e->Get(_i); - } - } - }; - { - auto _e = before_paddings(); - if (_e) { - _o->before_paddings.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->before_paddings[_i] = _e->Get(_i); - } - } - }; - { - auto _e = after_paddings(); - if (_e) { - _o->after_paddings.resize(_e->size()); - for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { - _o->after_paddings[_i] = _e->Get(_i); - } - } - }; } inline flatbuffers::Offset SpaceToBatchNDOptions::Pack( @@ -5685,14 +5609,7 @@ inline flatbuffers::Offset CreateSpaceToBatchNDOptions( const flatbuffers::rehasher_function_t *__rehasher; } _va = {&_fbb, _o, _rehasher}; (void)_va; - auto _block_shape = - _o->block_shape.size() ? _fbb.CreateVector(_o->block_shape) : 0; - auto _before_paddings = - _o->before_paddings.size() ? _fbb.CreateVector(_o->before_paddings) : 0; - auto _after_paddings = - _o->after_paddings.size() ? _fbb.CreateVector(_o->after_paddings) : 0; - return tflite::CreateSpaceToBatchNDOptions(_fbb, _block_shape, - _before_paddings, _after_paddings); + return tflite::CreateSpaceToBatchNDOptions(_fbb); } inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack( diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index f75d7c4bb9..e7606eecc4 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -1335,12 +1335,16 @@ def make_space_to_batch_nd_tests(zip_path): "input_shape": [[1, 2, 2, 3], [2, 2, 4, 1]], "block_shape": [[1, 3], [2, 2]], "paddings": [[[0, 0], [0, 0]], [[0, 0], [2, 0]], [[1, 1], [1, 1]]], + "constant_block_shape": [True, False], + "constant_paddings": [True, False], }, { "dtype": [tf.float32], "input_shape": [[2, 3, 7, 3]], "block_shape": [[1, 3], [2, 2]], "paddings": [[[0, 0], [2, 0]], [[1, 0], [1, 0]]], + "constant_block_shape": [True, False], + "constant_paddings": [True, False], }, # Non-4D use case: 1 bath dimension, 3 spatial dimensions, 2 others. { @@ -1348,23 +1352,47 @@ def make_space_to_batch_nd_tests(zip_path): "input_shape": [[1, 4, 4, 4, 1, 1]], "block_shape": [[2, 2, 2]], "paddings": [[[0, 0], [0, 0], [0, 0]]], + "constant_block_shape": [True, False], + "constant_paddings": [True, False], }, ] def build_graph(parameters): + """Build a space_to_batch graph given `parameters`.""" input_tensor = tf.placeholder( dtype=parameters["dtype"], name="input", shape=parameters["input_shape"]) - out = tf.space_to_batch_nd(input_tensor, parameters["block_shape"], - parameters["paddings"]) - return [input_tensor], [out] + input_tensors = [input_tensor] + + # Get block_shape either as a const or as a placeholder (tensor). + if parameters["constant_block_shape"]: + block_shape = parameters["block_shape"] + else: + shape = [len(parameters["block_shape"])] + block_shape = tf.placeholder(dtype=tf.int32, name="shape", shape=shape) + input_tensors.append(block_shape) + + # Get paddings either as a const or as a placeholder (tensor). + if parameters["constant_paddings"]: + paddings = parameters["paddings"] + else: + shape = [len(parameters["paddings"]), 2] + paddings = tf.placeholder(dtype=tf.int32, name="paddings", shape=shape) + input_tensors.append(paddings) + + out = tf.space_to_batch_nd(input_tensor, block_shape, paddings) + 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_block_shape"]: + values.append(np.array(parameters["block_shape"])) + if not parameters["constant_paddings"]: + values.append(np.array(parameters["paddings"])) + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index e33a5788d8..e2162e1493 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -140,24 +140,11 @@ class SpaceToBatchND flatbuffers::Offset WriteOptions( const TocoOperator& op, flatbuffers::FlatBufferBuilder* builder) const override { - auto block_shape = builder->CreateVector(op.block_shape); - auto before_paddings = builder->CreateVector(op.before_paddings); - auto after_paddings = builder->CreateVector(op.after_paddings); - return ::tflite::CreateSpaceToBatchNDOptions( - *builder, block_shape, before_paddings, after_paddings); + return ::tflite::CreateSpaceToBatchNDOptions(*builder); } void ReadOptions(const TfLiteOptions& options, TocoOperator* op) const override { - op->block_shape.insert(op->block_shape.end(), - options.block_shape()->begin(), - options.block_shape()->end()); - op->before_paddings.insert(op->before_paddings.end(), - options.before_paddings()->begin(), - options.before_paddings()->end()); - op->after_paddings.insert(op->after_paddings.end(), - options.after_paddings()->begin(), - options.after_paddings()->end()); } }; diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index b4ec7bbd50..6daa296282 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -119,19 +119,6 @@ TEST_F(OperatorTest, BuiltinAdd) { output_toco_op->fused_activation_function); } -TEST_F(OperatorTest, BuiltinSpaceToBatchND) { - SpaceToBatchNDOperator op; - op.block_shape = {2, 2}; - op.before_paddings = {1, 2}; - op.after_paddings = {3, 4}; - - auto output_toco_op = SerializeAndDeserialize( - GetOperator("SPACE_TO_BATCH_ND", OperatorType::kSpaceToBatchND), op); - EXPECT_EQ(op.block_shape, output_toco_op->block_shape); - EXPECT_EQ(op.before_paddings, output_toco_op->before_paddings); - EXPECT_EQ(op.after_paddings, output_toco_op->after_paddings); -} - TEST_F(OperatorTest, BuiltinMean) { MeanOperator op; op.keep_dims = false; -- GitLab From f6c1dd3264d518d74928676d49171af77a823692 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 15:11:32 -0800 Subject: [PATCH 236/423] Add transformation that exchanges a Reshape followed by an activation function. PiperOrigin-RevId: 183735457 --- tensorflow/contrib/lite/toco/BUILD | 1 + .../fuse_activation_functions.cc | 7 +- .../graph_transformations.h | 1 + .../reorder_activation_functions.cc | 85 +++++++++++++++++++ tensorflow/contrib/lite/toco/toco_tooling.cc | 1 + tensorflow/contrib/lite/toco/tooling_util.cc | 13 +++ tensorflow/contrib/lite/toco/tooling_util.h | 2 + 7 files changed, 104 insertions(+), 6 deletions(-) create mode 100644 tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 6fc7e5e3fd..20c156a932 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -205,6 +205,7 @@ cc_library( "graph_transformations/remove_trivial_quantized_activation_func.cc", "graph_transformations/remove_trivial_reshape.cc", "graph_transformations/remove_unused_op.cc", + "graph_transformations/reorder_activation_functions.cc", "graph_transformations/resolve_batch_normalization.cc", "graph_transformations/resolve_batch_to_space_nd_attributes.cc", "graph_transformations/resolve_constant_binary.cc", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/fuse_activation_functions.cc b/tensorflow/contrib/lite/toco/graph_transformations/fuse_activation_functions.cc index 88e59664ec..ab943f72d1 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/fuse_activation_functions.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/fuse_activation_functions.cc @@ -68,12 +68,7 @@ bool FuseActivationFunctions::Run(Model* model, std::size_t op_index) { return false; } - // TODO(b/72172404): Great many ops don't support activation function - // fusing. Switch to a categorizing function instead. - if (op->type == OperatorType::kConcatenation || - op->type == OperatorType::kSlice || - op->type == OperatorType::kTensorFlowReshape || - op->type == OperatorType::kTensorFlowSplit) { + if (!OperatorSupportsFusedActivation(op->type)) { AddMessageF( "Not fusing activation function because the %s op doesn't support it", 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 e11bebcd4e..cf90ebe996 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -144,6 +144,7 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveConstantUnaryOperator) DECLARE_GRAPH_TRANSFORMATION(CreateIm2colArrays) DECLARE_GRAPH_TRANSFORMATION(DropIm2colArrays) DECLARE_GRAPH_TRANSFORMATION(ReadFakeQuantMinMax) +DECLARE_GRAPH_TRANSFORMATION(ReorderActivationFunctions) DECLARE_GRAPH_TRANSFORMATION(ResolveReorderAxes) DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowConcat) DECLARE_GRAPH_TRANSFORMATION(ResolveTensorFlowMatMul) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc b/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc new file mode 100644 index 0000000000..cabbc4d313 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/reorder_activation_functions.cc @@ -0,0 +1,85 @@ +/* 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 +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/runtime/types.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +bool ReorderActivationFunctions::Run(Model* model, std::size_t op_index) { + const auto ac_it = model->operators.begin() + op_index; + std::unique_ptr& ac_op = *ac_it; + DCHECK(ac_op); + + if (ac_op->type != OperatorType::kRelu6 && + ac_op->type != OperatorType::kRelu1 && + ac_op->type != OperatorType::kRelu) { + return false; + } + + auto exchange_it = FindOpWithOutput(*model, ac_op->inputs[0]); + if (exchange_it == model->operators.end()) return false; + // Find the op producing the array passed to this activation function + std::unique_ptr& exchange_op = *exchange_it; + DCHECK(exchange_op); + + if (exchange_op->type != OperatorType::kTensorFlowReshape) { + return false; + } + + DCHECK_EQ(exchange_op->outputs[0], ac_op->inputs[0]); + const auto& exchange_op_input = exchange_op->inputs[0]; + const auto& intermediate_array = exchange_op->outputs[0]; + const auto& ac_op_output = ac_op->outputs[0]; + + int count_ops_consuming_output = + CountOpsWithInput(*model, intermediate_array); + DCHECK_GE(count_ops_consuming_output, 1); + if (count_ops_consuming_output > 1) { + AddMessageF( + "Not exchanging activation function with %s because it is consumed by " + "more than 1 other operator", + LogName(*exchange_op)); + return false; + } + + // Rewire by changing inputs, including all consumers. + Operator* consumer = GetFirstOpWithInput(*model, ac_op_output); + while (consumer) { + for (int i = 0; i < consumer->inputs.size(); ++i) { + if (consumer->inputs[i] == ac_op_output) { + consumer->inputs[i] = intermediate_array; + } + } + consumer = GetFirstOpWithInput(*model, ac_op_output); + } + ac_op->inputs[0] = exchange_op_input; + exchange_op->inputs[0] = ac_op_output; + + // Finally, reorder operators. Note that this only works when there are no + // other direct descendents of the exchange_op. + ac_op.swap(exchange_op); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index 727df1cc76..b715881774 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -68,6 +68,7 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveTensorFlowMatMul); transformations->Add(new FuseBinaryIntoPrecedingAffine); transformations->Add(new FuseBinaryIntoFollowingAffine); + transformations->Add(new ReorderActivationFunctions); transformations->Add(new ResolveBatchNormalization); transformations->Add(new ResolveConstantBinaryOperator); transformations->Add(new ResolveConstantFill); diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 08d9ac3aff..d2741a5e9b 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -304,6 +304,19 @@ string HelpfulOperatorTypeName(const Operator& op) { return OperatorTypeName(op.type); } +bool OperatorSupportsFusedActivation(OperatorType type) { + switch (type) { + case OperatorType::kConcatenation: + case OperatorType::kSlice: + case OperatorType::kSqueeze: + case OperatorType::kTensorFlowReshape: + case OperatorType::kTensorFlowSplit: + return false; + default: + return true; + } +} + void LogSummary(int log_level, const Model& model) { VLOG(log_level) << "Operators summary (" << model.operators.size() << " operators):"; diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index 4051ba3576..a7e77a02eb 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -82,6 +82,8 @@ std::vector>::iterator FindOp(Model& model, const char* OperatorTypeName(OperatorType type); string HelpfulOperatorTypeName(const Operator& op); +bool OperatorSupportsFusedActivation(OperatorType type); + void DumpGraphvizVideoFrame(const Model& model); void LogDump(int log_level, const string& message, const Model& model); void LogSummary(int log_level, const string& message, const Model& model); -- GitLab From e4021e7060166ead2fc14a94c048b5fc5336e495 Mon Sep 17 00:00:00 2001 From: Bjarke Hammersholt Roune Date: Mon, 29 Jan 2018 15:21:16 -0800 Subject: [PATCH 237/423] Add new StrCat utility functions for creating failed Status'es. PiperOrigin-RevId: 183737063 --- tensorflow/compiler/xla/util.h | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index 2da9bb21b7..08df5b12b3 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -217,6 +217,24 @@ Status Unavailable(const char* format, ...) TF_PRINTF_ATTRIBUTE(1, 2); // Passed-varargs variant of the InvalidArgument factory above. Status InvalidArgumentV(const char* format, va_list args); +template +Status UnimplementedStrCat(Args&&... concat) { + return Unimplemented( + "%s", tensorflow::strings::StrCat(std::forward(concat)...).c_str()); +} + +template +Status InternalErrorStrCat(Args&&... concat) { + return InternalError( + "%s", tensorflow::strings::StrCat(std::forward(concat)...).c_str()); +} + +template +Status ResourceExhaustedStrCat(Args&&... concat) { + return ResourceExhausted( + "%s", tensorflow::strings::StrCat(std::forward(concat)...).c_str()); +} + // Splits the lines of the original, replaces leading whitespace with the prefix // given by "indentation", and returns the string joined by newlines again. As a // side effect, any additional trailing whitespace is removed. -- GitLab From 6b23b788980787684fd9fcc716660d06c5e02ad6 Mon Sep 17 00:00:00 2001 From: elilienstein Date: Mon, 29 Jan 2018 15:27:57 -0800 Subject: [PATCH 238/423] Fix typos 'followings' 'optionanl' (#16549) --- configure.py | 4 ++-- tensorflow/contrib/coder/README.md | 2 +- tensorflow/core/kernels/fractional_pool_common.h | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/configure.py b/configure.py index 16763b8c0d..27519b4aba 100644 --- a/configure.py +++ b/configure.py @@ -298,7 +298,7 @@ def get_var(environ_cp, System". enabled_by_default: boolean for default behavior. question: optional string for how to ask for user input. - yes_reply: optionanl string for reply when feature is enabled. + yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. Returns: @@ -411,7 +411,7 @@ def set_action_env_var(environ_cp, System". enabled_by_default: boolean for default behavior. question: optional string for how to ask for user input. - yes_reply: optionanl string for reply when feature is enabled. + yes_reply: optional string for reply when feature is enabled. no_reply: optional string for reply when feature is disabled. """ var = int( diff --git a/tensorflow/contrib/coder/README.md b/tensorflow/contrib/coder/README.md index e1e867db5a..c6c379c458 100644 --- a/tensorflow/contrib/coder/README.md +++ b/tensorflow/contrib/coder/README.md @@ -30,7 +30,7 @@ following sense: around, - The number of CDF axes does not extend, i.e., `CDF.ndim == data.ndim + 1`. -In the previous example where data has shape (10, 10), the followings are +In the previous example where data has shape (10, 10), the following are acceptable CDF shapes: - (10, 10, 65) diff --git a/tensorflow/core/kernels/fractional_pool_common.h b/tensorflow/core/kernels/fractional_pool_common.h index df0bbbfa06..2d7a230fc0 100644 --- a/tensorflow/core/kernels/fractional_pool_common.h +++ b/tensorflow/core/kernels/fractional_pool_common.h @@ -57,7 +57,7 @@ static inline void RandomShuffle(Iter first, Iter last, const Random& uniform) { // * sum(generated_diff_pooling_sequence) = input_length // * Let's define floor(input_length / output_length) = K, then // K <= generated_diff_pooling_sequence[i] <= K+1 -// For example, when input_length = 10, output_length = 6, the followings are +// For example, when input_length = 10, output_length = 6, the following are // valid pooling sequence: // * [1, 2, 2, 1, 2, 2] // * [1, 1, 2, 2, 2, 2] -- GitLab From 8c1f3b3fcdc5f897dbce3b7315c1b7000069f9c4 Mon Sep 17 00:00:00 2001 From: ImSheridan Date: Tue, 30 Jan 2018 07:28:27 +0800 Subject: [PATCH 239/423] Fix some typos in the documation (tensorflow/docs_src) (#16519) * Fix some typos in the documation (tensorflow/docs_src) * update another minor typo fix * revert typo correction It seems "infed" was being used as a past-tense verb here and so was correct. --- tensorflow/docs_src/api_guides/python/regression_examples.md | 2 +- tensorflow/docs_src/get_started/custom_estimators.md | 2 +- tensorflow/docs_src/get_started/datasets_quickstart.md | 4 ++-- tensorflow/docs_src/get_started/feature_columns.md | 4 ++-- tensorflow/docs_src/get_started/premade_estimators.md | 2 +- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tensorflow/docs_src/api_guides/python/regression_examples.md b/tensorflow/docs_src/api_guides/python/regression_examples.md index 45cb9d829c..dae50a8f03 100644 --- a/tensorflow/docs_src/api_guides/python/regression_examples.md +++ b/tensorflow/docs_src/api_guides/python/regression_examples.md @@ -229,4 +229,4 @@ passed through to the `model_fn` when the `model_fn` is called. The `model_fn` returns an @{tf.estimator.EstimatorSpec$`EstimatorSpec`} which is a simple structure indicating to the `Estimator` which operations should be run to accomplish -varions tasks. +various tasks. diff --git a/tensorflow/docs_src/get_started/custom_estimators.md b/tensorflow/docs_src/get_started/custom_estimators.md index 6343cc4ee4..79c4ee75d0 100644 --- a/tensorflow/docs_src/get_started/custom_estimators.md +++ b/tensorflow/docs_src/get_started/custom_estimators.md @@ -15,7 +15,7 @@ git clone https://github.com/tensorflow/models/ cd models/samples/core/get_started ``` -In this document we wil be looking at +In this document we will be looking at [`custom_estimator.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/custom_estimator.py). You can run it with the following command: diff --git a/tensorflow/docs_src/get_started/datasets_quickstart.md b/tensorflow/docs_src/get_started/datasets_quickstart.md index ecfbf160f0..a8a2ab6e56 100644 --- a/tensorflow/docs_src/get_started/datasets_quickstart.md +++ b/tensorflow/docs_src/get_started/datasets_quickstart.md @@ -169,7 +169,7 @@ the number of examples in the `Dataset` ensures that the data is completely shuffled. The Iris data set only contains 150 examples. The @{tf.data.Dataset.repeat$`repeat`} method has the `Dataset` restart when -it reaches the end. To limit the number of epochss, set the `count` argument. +it reaches the end. To limit the number of epochs, set the `count` argument. The @{tf.data.Dataset.repeat$`batch`} method collects a number of examples and stacks them, to create batches. This adds a dimension to their shape. The new @@ -282,7 +282,7 @@ produce the necessary `(features, label)` pairs. We will start by building a function to parse a single line. -The following `iris_data.parse_line` function acomplishes this taks using the +The following `iris_data.parse_line` function accomplishes this task using the @{tf.decode_csv} function, and some simple python code: We must parse each of the lines in the dataset in order to generate the diff --git a/tensorflow/docs_src/get_started/feature_columns.md b/tensorflow/docs_src/get_started/feature_columns.md index e3308ed716..ad3e1fe3e3 100644 --- a/tensorflow/docs_src/get_started/feature_columns.md +++ b/tensorflow/docs_src/get_started/feature_columns.md @@ -461,8 +461,8 @@ permitting a richer palette of numbers for every cell, an embedding column contains far fewer cells than an indicator column. Let's look at an example comparing indicator and embedding columns. Suppose our -input examples consists of different words from a limited palette of only 81 -words. Further suppose that the data set provides provides the following input +input examples consist of different words from a limited palette of only 81 +words. Further suppose that the data set provides the following input words in 4 separate examples: * `"dog"` diff --git a/tensorflow/docs_src/get_started/premade_estimators.md b/tensorflow/docs_src/get_started/premade_estimators.md index dbc35065ab..4ef212a5b5 100644 --- a/tensorflow/docs_src/get_started/premade_estimators.md +++ b/tensorflow/docs_src/get_started/premade_estimators.md @@ -363,7 +363,7 @@ Test set accuracy: 0.967 We now have a trained model that produces good evaluation results. We can now use the trained model to predict the species of an Iris flower -based on some unlabeled measurments. As with training and evaluation, we make +based on some unlabeled measurements. As with training and evaluation, we make predictions using a single function call: ```python -- GitLab From e08f0080f822543d0a306075878c2e35dabf8cc0 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Mon, 29 Jan 2018 15:34:09 -0800 Subject: [PATCH 240/423] Make with_c_api more robust and enable C API in most of saved_model_test.py. This change makes the test_util.with_c_api decorator call reset_default_graph() after enabling or disabling the C API instead of creating a new Graph. This makes it more robust to tests that call reset_default_graph(), which requires that the current default graph isn't nested (which the C API-enabled Graph previously was). In addition, enables the C API with saved_model_test.py (which required the above change). A few tests still need further changes, which I'll post in subsequent patches. PiperOrigin-RevId: 183739148 --- tensorflow/python/framework/test_util.py | 13 ++-- .../python/saved_model/saved_model_test.py | 61 +++++++++++-------- 2 files changed, 43 insertions(+), 31 deletions(-) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 4a8aa2e258..70f6a2acba 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -320,12 +320,17 @@ def _use_c_api_wrapper(fn, use_c_api, *args, **kwargs): prev_value = ops._USE_C_API ops._USE_C_API = use_c_api try: - with ops.Graph().as_default(): - fn(*args, **kwargs) + # Reset the default graph so it has the C API enabled. We call + # reset_default_graph() instead of creating a new default Graph context to + # make this robust to tests that call reset_default_graph(), which requires + # that the current default graph isn't nested. + ops.reset_default_graph() + fn(*args, **kwargs) finally: ops._USE_C_API = prev_value - - + # Make sure default graph reflects prev_value in case next test doesn't call + # reset_default_graph(). + ops.reset_default_graph() # pylint: disable=protected-access diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index 1ea619ff55..f92247d52e 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -54,8 +54,14 @@ def tearDownModule(): file_io.delete_recursively(test.get_temp_dir()) +@test_util.with_c_api class SavedModelTest(test.TestCase): + def _get_export_dir(self, label): + if ops._USE_C_API: + label += "_c_api" + return os.path.join(test.get_temp_dir(), label) + def _init_and_validate_variable(self, sess, variable_name, variable_value): v = variables.Variable(variable_value, name=variable_name) sess.run(variables.global_variables_initializer()) @@ -123,8 +129,7 @@ class SavedModelTest(test.TestCase): self.assertFalse(loader.maybe_saved_model_directory(base_path)) def testBadSavedModelFileFormat(self): - export_dir = os.path.join(test.get_temp_dir(), - "test_bad_saved_model_file_format") + export_dir = self._get_export_dir("test_bad_saved_model_file_format") # Attempt to load a SavedModel from an export directory that does not exist. with self.test_session(graph=ops.Graph()) as sess: with self.assertRaisesRegexp(IOError, @@ -157,8 +162,7 @@ class SavedModelTest(test.TestCase): loader.load(sess, ["foo"], export_dir) def testVerifySessionGraphUsage(self): - export_dir = os.path.join(test.get_temp_dir(), - "test_verify_session_graph_usage") + export_dir = self._get_export_dir("test_verify_session_graph_usage") builder = saved_model_builder.SavedModelBuilder(export_dir) with self.test_session(graph=ops.Graph()) as sess: @@ -178,7 +182,7 @@ class SavedModelTest(test.TestCase): 42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) def testSequence(self): - export_dir = os.path.join(test.get_temp_dir(), "test_sequence") + export_dir = self._get_export_dir("test_sequence") builder = saved_model_builder.SavedModelBuilder(export_dir) # Expect an assertion error since add_meta_graph_and_variables() should be @@ -195,7 +199,7 @@ class SavedModelTest(test.TestCase): sess, ["baz"]) def testTags(self): - export_dir = os.path.join(test.get_temp_dir(), "test_tags") + export_dir = self._get_export_dir("test_tags") builder = saved_model_builder.SavedModelBuilder(export_dir) # Graph with a single variable. SavedModel invoked to: @@ -284,7 +288,7 @@ class SavedModelTest(test.TestCase): export_dir) def testVariables(self): - export_dir = os.path.join(test.get_temp_dir(), "test_variables") + export_dir = self._get_export_dir("test_variables") builder = saved_model_builder.SavedModelBuilder(export_dir) # Graph with two variables. SavedModel invoked to: @@ -336,7 +340,7 @@ class SavedModelTest(test.TestCase): export_dir) def testGraphWithoutVariables(self): - export_dir = os.path.join(test.get_temp_dir(), "test_graph_has_variables") + export_dir = self._get_export_dir("test_graph_has_variables") builder = saved_model_builder.SavedModelBuilder(export_dir) # Graph with no variables. @@ -371,7 +375,7 @@ class SavedModelTest(test.TestCase): self.assertEqual(30.0, sess.run(c)) def testNoOverwrite(self): - export_dir = os.path.join(test.get_temp_dir(), "test_no_overwrite") + export_dir = self._get_export_dir("test_no_overwrite") builder = saved_model_builder.SavedModelBuilder(export_dir) # Graph with a single variable. SavedModel invoked to: @@ -395,7 +399,7 @@ class SavedModelTest(test.TestCase): export_dir) def testSaveAsText(self): - export_dir = os.path.join(test.get_temp_dir(), "test_astext") + export_dir = self._get_export_dir("test_astext") builder = saved_model_builder.SavedModelBuilder(export_dir) # Graph with a single variable. SavedModel invoked to: @@ -426,7 +430,7 @@ class SavedModelTest(test.TestCase): 42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) def testCollections(self): - export_dir = os.path.join(test.get_temp_dir(), "test_collections") + export_dir = self._get_export_dir("test_collections") builder = saved_model_builder.SavedModelBuilder(export_dir) # Graph with a single variable added to a collection. SavedModel invoked to: @@ -476,7 +480,7 @@ class SavedModelTest(test.TestCase): self.assertEqual(len(ops.get_collection("foo_vars")), 0) def testSignatureDefs(self): - export_dir = os.path.join(test.get_temp_dir(), "test_signature_defs") + export_dir = self._get_export_dir("test_signature_defs") builder = saved_model_builder.SavedModelBuilder(export_dir) # Graph with a single variable and a single entry in the signature def map. @@ -536,8 +540,7 @@ class SavedModelTest(test.TestCase): self.assertEqual("foo_new", bar_signature["foo_key"].method_name) def testSignatureDefValidation(self): - export_dir = os.path.join(test.get_temp_dir(), - "test_signature_def_validation") + export_dir = self._get_export_dir("test_signature_def_validation") builder = saved_model_builder.SavedModelBuilder(export_dir) tensor_without_name = meta_graph_pb2.TensorInfo() @@ -555,7 +558,7 @@ class SavedModelTest(test.TestCase): self._validate_outputs_tensor_info(builder, tensor_empty) def testAssets(self): - export_dir = os.path.join(test.get_temp_dir(), "test_assets") + export_dir = self._get_export_dir("test_assets") builder = saved_model_builder.SavedModelBuilder(export_dir) with self.test_session(graph=ops.Graph()) as sess: @@ -588,7 +591,7 @@ class SavedModelTest(test.TestCase): self.assertFalse(file_io.file_exists(ignored_asset_path)) def testCustomMainOp(self): - export_dir = os.path.join(test.get_temp_dir(), "test_main_op") + export_dir = self._get_export_dir("test_main_op") builder = saved_model_builder.SavedModelBuilder(export_dir) with self.test_session(graph=ops.Graph()) as sess: @@ -623,7 +626,7 @@ class SavedModelTest(test.TestCase): self.assertEqual(3, ops.get_collection("v")[2].eval()) def testLegacyInitOp(self): - export_dir = os.path.join(test.get_temp_dir(), "test_legacy_init_op") + export_dir = self._get_export_dir("test_legacy_init_op") builder = saved_model_builder.SavedModelBuilder(export_dir) with self.test_session(graph=ops.Graph()) as sess: @@ -657,8 +660,8 @@ class SavedModelTest(test.TestCase): self.assertEqual(3, ops.get_collection("v")[2].eval()) def testLegacyInitOpWithNonEmptyCollection(self): - export_dir = os.path.join(test.get_temp_dir(), - "test_legacy_init_op_with_non_empty_collection") + export_dir = self._get_export_dir( + "test_legacy_init_op_with_non_empty_collection") builder = saved_model_builder.SavedModelBuilder(export_dir) with self.test_session(graph=ops.Graph()) as sess: @@ -685,7 +688,7 @@ class SavedModelTest(test.TestCase): sess, ["foo"], legacy_init_op=legacy_init_op) def testMultipleAssets(self): - export_dir = os.path.join(test.get_temp_dir(), "test_multiple_assets") + export_dir = self._get_export_dir("test_multiple_assets") builder = saved_model_builder.SavedModelBuilder(export_dir) with self.test_session(graph=ops.Graph()) as sess: @@ -727,7 +730,7 @@ class SavedModelTest(test.TestCase): "asset_file_tensor:0") def testDuplicateAssets(self): - export_dir = os.path.join(test.get_temp_dir(), "test_duplicate_assets") + export_dir = self._get_export_dir("test_duplicate_assets") builder = saved_model_builder.SavedModelBuilder(export_dir) with self.test_session(graph=ops.Graph()) as sess: @@ -775,7 +778,7 @@ class SavedModelTest(test.TestCase): "asset_file_tensor:0") def testOp(self): - export_dir = os.path.join(test.get_temp_dir(), "test_op") + export_dir = self._get_export_dir("test_op") builder = saved_model_builder.SavedModelBuilder(export_dir) with session.Session( @@ -818,7 +821,7 @@ class SavedModelTest(test.TestCase): self.assertEqual(3, ops.get_collection("v")[2].eval()) def testCustomSaveable(self): - export_dir = os.path.join(test.get_temp_dir(), "custom_saveable") + export_dir = self._get_export_dir("custom_saveable") builder = saved_model_builder.SavedModelBuilder(export_dir) with session.Session( @@ -847,7 +850,7 @@ class SavedModelTest(test.TestCase): self.assertEqual(3.0, v1.values().eval()) def testClearDevices(self): - export_dir = os.path.join(test.get_temp_dir(), "test_clear_devices") + export_dir = self._get_export_dir("test_clear_devices") builder = saved_model_builder.SavedModelBuilder(export_dir) # Specify a device and save a variable. @@ -871,7 +874,9 @@ class SavedModelTest(test.TestCase): 42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) def testStripDefaultAttrs(self): - export_dir = os.path.join(test.get_temp_dir(), "test_strip_default_attrs") + if ops._USE_C_API: return # TODO(skyewm): get this working + + export_dir = self._get_export_dir("test_strip_default_attrs") builder = saved_model_builder.SavedModelBuilder(export_dir) # Add a graph with two float32 variables and a Complex Op composing them @@ -941,8 +946,10 @@ class SavedModelTest(test.TestCase): self.assertIn("Tout", node_def.attr) def testStripDefaultAttrsInconsistentConsumerDefaults(self): - export_dir = os.path.join(test.get_temp_dir(), - "test_strip_default_attrs_no_consumer_defaults") + if ops._USE_C_API: return # TODO(skyewm): get this working + + export_dir = self._get_export_dir( + "test_strip_default_attrs_no_consumer_defaults") builder = saved_model_builder.SavedModelBuilder(export_dir) # Add a graph with two float32 variables and a Complex Op composing them -- GitLab From fd48cf4fed0be0abdb77b56a377836b1be0f7257 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Mon, 29 Jan 2018 15:41:11 -0800 Subject: [PATCH 241/423] GCS: Add client-side throttle. This throttle is loosely based on a leaky bucket configuration that captures both the cost of the requests as well as the bandwidth consumed into a single token value. PiperOrigin-RevId: 183740223 --- tensorflow/core/platform/cloud/BUILD | 25 +++ .../core/platform/cloud/gcs_file_system.cc | 99 +++++++---- .../core/platform/cloud/gcs_file_system.h | 2 + .../core/platform/cloud/gcs_throttle.cc | 62 +++++++ tensorflow/core/platform/cloud/gcs_throttle.h | 156 ++++++++++++++++++ .../core/platform/cloud/gcs_throttle_test.cc | 101 ++++++++++++ 6 files changed, 415 insertions(+), 30 deletions(-) create mode 100644 tensorflow/core/platform/cloud/gcs_throttle.cc create mode 100644 tensorflow/core/platform/cloud/gcs_throttle.h create mode 100644 tensorflow/core/platform/cloud/gcs_throttle_test.cc diff --git a/tensorflow/core/platform/cloud/BUILD b/tensorflow/core/platform/cloud/BUILD index 07aecf8483..9ba25dea4f 100644 --- a/tensorflow/core/platform/cloud/BUILD +++ b/tensorflow/core/platform/cloud/BUILD @@ -57,6 +57,17 @@ cc_library( ], ) +cc_library( + name = "gcs_throttle", + srcs = ["gcs_throttle.cc"], + hdrs = ["gcs_throttle.h"], + copts = tf_copts(), + visibility = ["//tensorflow:__subpackages__"], + deps = [ + "//tensorflow/core:lib", + ], +) + cc_library( name = "gcs_file_system", srcs = ["gcs_file_system.cc"], @@ -69,6 +80,7 @@ cc_library( ":expiring_lru_cache", ":file_block_cache", ":gcs_dns_cache", + ":gcs_throttle", ":google_auth_provider", ":http_request", ":retrying_file_system", @@ -271,6 +283,19 @@ tf_cc_test( ], ) +tf_cc_test( + name = "gcs_throttle_test", + size = "small", + srcs = ["gcs_throttle_test.cc"], + linkopts = if_windows(["-DEFAULTLIB:ws2_32.lib"]), + deps = [ + ":gcs_throttle", + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + tf_cc_test( name = "curl_http_request_test", size = "small", diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc index 91d381bd6f..01ca0d76ba 100644 --- a/tensorflow/core/platform/cloud/gcs_file_system.cc +++ b/tensorflow/core/platform/cloud/gcs_file_system.cc @@ -116,6 +116,15 @@ constexpr char kWriteRequestTimeout[] = "GCS_WRITE_REQUEST_TIMEOUT_SECS"; // The environment variable to configure an additional header to send with // all requests to GCS (format HEADERNAME:HEADERCONTENT) constexpr char kAdditionalRequestHeader[] = "GCS_ADDITIONAL_REQUEST_HEADER"; +// The environment variable to configure the throttle (format: ) +constexpr char kThrottleRate[] = "GCS_THROTTLE_TOKEN_RATE"; +// The environment variable to configure the token bucket size (format: ) +constexpr char kThrottleBucket[] = "GCS_THROTTLE_BUCKET_SIZE"; +// The environment variable that controls the number of tokens per request. +// (format: ) +constexpr char kTokensPerRequest[] = "GCS_TOKENS_PER_REQUEST"; +// The environment variable to configure the initial tokens (format: ) +constexpr char kInitialTokens[] = "GCS_INITIAL_TOKENS"; // TODO: DO NOT use a hardcoded path Status GetTmpFilename(string* filename) { @@ -721,6 +730,26 @@ GcsFileSystem::GcsFileSystem() if (GetEnvVar(kWriteRequestTimeout, strings::safe_strtou32, &timeout_value)) { timeouts_.write = timeout_value; } + + int64 token_value; + if (GetEnvVar(kThrottleRate, strings::safe_strto64, &token_value)) { + GcsThrottleConfig config; + config.enabled = true; + config.token_rate = token_value; + + if (GetEnvVar(kThrottleBucket, strings::safe_strto64, &token_value)) { + config.bucket_size = token_value; + } + + if (GetEnvVar(kTokensPerRequest, strings::safe_strto64, &token_value)) { + config.tokens_per_request = token_value; + } + + if (GetEnvVar(kInitialTokens, strings::safe_strto64, &token_value)) { + config.initial_tokens = token_value; + } + throttle_.SetConfig(config); + } } GcsFileSystem::GcsFileSystem( @@ -774,7 +803,9 @@ Status GcsFileSystem::LoadBufferFromGCS(const string& filename, size_t offset, TF_RETURN_IF_ERROR(ParseGcsPath(filename, false, &bucket, &object)); std::unique_ptr request; - TF_RETURN_IF_ERROR(CreateHttpRequest(&request)); + TF_RETURN_WITH_CONTEXT_IF_ERROR(CreateHttpRequest(&request), + "when reading gs://", bucket, "/", object); + request->SetUri(strings::StrCat("https://", kStorageHost, "/", bucket, "/", request->EscapeString(object))); request->SetRange(offset, offset + n - 1); @@ -789,6 +820,8 @@ Status GcsFileSystem::LoadBufferFromGCS(const string& filename, size_t offset, VLOG(1) << "Successful read of gs://" << bucket << "/" << object << " @ " << offset << " of size: " << bytes_read; + throttle_.RecordResponse(bytes_read); + if (bytes_read < block_size()) { // Check stat cache to see if we encountered an interrupted read. FileStatistics stat; @@ -926,41 +959,43 @@ Status GcsFileSystem::StatForObject(const string& fname, const string& bucket, "'object' must be a non-empty string. (File: %s)", fname.c_str())); } - StatCache::ComputeFunc compute_func = - [this, &bucket, &object](const string& fname, FileStatistics* stat) { - std::vector output_buffer; - std::unique_ptr request; - TF_RETURN_IF_ERROR(CreateHttpRequest(&request)); - request->SetUri(strings::StrCat(kGcsUriBase, "b/", bucket, "/o/", - request->EscapeString(object), - "?fields=size%2Cupdated")); - request->SetResultBuffer(&output_buffer); - request->SetTimeouts(timeouts_.connect, timeouts_.idle, - timeouts_.metadata); + StatCache::ComputeFunc compute_func = [this, &bucket, &object]( + const string& fname, + FileStatistics* stat) { + std::vector output_buffer; + std::unique_ptr request; + TF_RETURN_WITH_CONTEXT_IF_ERROR(CreateHttpRequest(&request), + " when reading metadata of gs://", bucket, + "/", object); + + request->SetUri(strings::StrCat(kGcsUriBase, "b/", bucket, "/o/", + request->EscapeString(object), + "?fields=size%2Cupdated")); + request->SetResultBuffer(&output_buffer); + request->SetTimeouts(timeouts_.connect, timeouts_.idle, timeouts_.metadata); - TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), - " when reading metadata of gs://", - bucket, "/", object); + TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), + " when reading metadata of gs://", bucket, + "/", object); - Json::Value root; - TF_RETURN_IF_ERROR(ParseJson(output_buffer, &root)); + Json::Value root; + TF_RETURN_IF_ERROR(ParseJson(output_buffer, &root)); - // Parse file size. - TF_RETURN_IF_ERROR(GetInt64Value(root, "size", &stat->length)); + // Parse file size. + TF_RETURN_IF_ERROR(GetInt64Value(root, "size", &stat->length)); - // Parse file modification time. - string updated; - TF_RETURN_IF_ERROR(GetStringValue(root, "updated", &updated)); - TF_RETURN_IF_ERROR(ParseRfc3339Time(updated, &(stat->mtime_nsec))); + // Parse file modification time. + string updated; + TF_RETURN_IF_ERROR(GetStringValue(root, "updated", &updated)); + TF_RETURN_IF_ERROR(ParseRfc3339Time(updated, &(stat->mtime_nsec))); - VLOG(1) << "Stat of: gs://" << bucket << "/" << object << " -- " - << " length: " << stat->length - << "; mtime_nsec: " << stat->mtime_nsec - << "; updated: " << updated; + VLOG(1) << "Stat of: gs://" << bucket << "/" << object << " -- " + << " length: " << stat->length + << "; mtime_nsec: " << stat->mtime_nsec << "; updated: " << updated; - stat->is_directory = false; - return Status::OK(); - }; + stat->is_directory = false; + return Status::OK(); + }; TF_RETURN_IF_ERROR(stat_cache_->LookupOrCompute(fname, stat, compute_func)); if (stat->is_directory) { @@ -1438,6 +1473,10 @@ Status GcsFileSystem::CreateHttpRequest(std::unique_ptr* request) { additional_header_->second); } + if (!throttle_.AdmitRequest()) { + return errors::Unavailable("Request throttled"); + } + *request = std::move(new_request); return Status::OK(); } diff --git a/tensorflow/core/platform/cloud/gcs_file_system.h b/tensorflow/core/platform/cloud/gcs_file_system.h index 2eae39608e..e8edde8a44 100644 --- a/tensorflow/core/platform/cloud/gcs_file_system.h +++ b/tensorflow/core/platform/cloud/gcs_file_system.h @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/core/platform/cloud/expiring_lru_cache.h" #include "tensorflow/core/platform/cloud/file_block_cache.h" #include "tensorflow/core/platform/cloud/gcs_dns_cache.h" +#include "tensorflow/core/platform/cloud/gcs_throttle.h" #include "tensorflow/core/platform/cloud/http_request.h" #include "tensorflow/core/platform/cloud/retrying_file_system.h" #include "tensorflow/core/platform/file_system.h" @@ -194,6 +195,7 @@ class GcsFileSystem : public FileSystem { std::unique_ptr http_request_factory_; std::unique_ptr file_block_cache_; std::unique_ptr dns_cache_; + GcsThrottle throttle_; using StatCache = ExpiringLRUCache; std::unique_ptr stat_cache_; diff --git a/tensorflow/core/platform/cloud/gcs_throttle.cc b/tensorflow/core/platform/cloud/gcs_throttle.cc new file mode 100644 index 0000000000..eb5f8958a3 --- /dev/null +++ b/tensorflow/core/platform/cloud/gcs_throttle.cc @@ -0,0 +1,62 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/platform/cloud/gcs_throttle.h" + +#include + +namespace tensorflow { + +GcsThrottle::GcsThrottle(EnvTime* env_time) + : last_updated_secs_(env_time->NowSeconds()), + available_tokens_(0), + env_time_(env_time) {} + +bool GcsThrottle::AdmitRequest() { + mutex_lock l(mu_); + if (!config_.enabled) return true; + UpdateState(); + if (available_tokens_ < config_.tokens_per_request) { + return false; + } + available_tokens_ -= config_.tokens_per_request; + return true; +} + +void GcsThrottle::RecordResponse(size_t num_bytes) { + mutex_lock l(mu_); + if (!config_.enabled) return; + UpdateState(); + available_tokens_ -= request_bytes_to_tokens(num_bytes); +} + +void GcsThrottle::SetConfig(GcsThrottleConfig config) { + mutex_lock l(mu_); + config_ = config; + available_tokens_ = config.initial_tokens; + last_updated_secs_ = env_time_->NowSeconds(); +} + +void GcsThrottle::UpdateState() { + // TODO(b/72643279): Switch to a monotonic clock. + int64 now = env_time_->NowSeconds(); + uint64 delta_secs = + std::max(0LL, now - static_cast(last_updated_secs_)); + available_tokens_ += delta_secs * config_.token_rate; + available_tokens_ = std::min(available_tokens_, config_.bucket_size); + last_updated_secs_ = now; +} + +} // namespace tensorflow diff --git a/tensorflow/core/platform/cloud/gcs_throttle.h b/tensorflow/core/platform/cloud/gcs_throttle.h new file mode 100644 index 0000000000..8e46fca6ca --- /dev/null +++ b/tensorflow/core/platform/cloud/gcs_throttle.h @@ -0,0 +1,156 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CORE_PLATFORM_CLOUD_GCS_THROTTLE_H_ +#define TENSORFLOW_CORE_PLATFORM_CLOUD_GCS_THROTTLE_H_ + +#include "tensorflow/core/platform/env.h" + +namespace tensorflow { + +/** + * GcsThrottleConfig is used to configure the GcsThrottle. + */ +struct GcsThrottleConfig { + /** + * enabled is true if GcsThrottle should throttle requests, false otherwise. + */ + bool enabled = false; + + /** + * token_rate is the number of tokens accrued every second that can be used + * for making requests to the GCS service. + */ + int64 token_rate = 100000; // Approximately 800 MBits/second bandwidth-only. + + /** + * bucket_size is the maximum number of available tokens the GcsThrottle can + * accrue. + */ + int64 bucket_size = 10000000; // 10 million tokens total + + /** + * tokens_per_request determines the number of tokens consumed for every + * request. + * + * Note: tokens are also consumed in proportion to the response size. + */ + int64 tokens_per_request = 100; + + /** + * initial_tokens determines how many tokens should be available immediately + * after the GcsThrottle is constructed. + */ + int64 initial_tokens = 0; +}; + +/** + * GcsThrottle is used to ensure fair use of the available GCS capacity. + * + * GcsThrottle operates around a concept of tokens. Tokens are consumed when + * making requests to the GCS service. Tokens are consumed both based on the + * number of requests made, as well as the bandwidth consumed (response sizes). + * + * GcsThrottle is thread safe and can be used from multiple threads. + */ +class GcsThrottle { + public: + /** + * Constructs a GcsThrottle. + */ + explicit GcsThrottle(EnvTime* env_time = EnvTime::Default()); + + /** + * AdmitRequest updates the GcsThrottle to record a request will be made. + * + * AdmitRequest should be called before any request is made. AdmitRequest + * returns false if the request should be denied. If AdmitRequest + * returns false, no tokens are consumed. If true is returned, the configured + * number of tokens are consumed. + */ + bool AdmitRequest(); + + /** + * RecordResponse updates the GcsThrottle to record a request has been made. + * + * RecordResponse should be called after the response has been received. + * RecordResponse will update the internal state based on the number of bytes + * in the response. + * + * Note: we split up the request and the response in this fashion in order to + * avoid penalizing consumers who are using large readahead buffers at higher + * layers of the I/O stack. + */ + void RecordResponse(size_t num_bytes); + + /** + * SetConfig sets the configuration for GcsThrottle and re-initializes state. + * + * After calling this, the token pool will be config.initial_tokens. + */ + void SetConfig(GcsThrottleConfig config); + + /** + * available_tokens gives a snapshot of how many tokens are available. + * + * The returned value should not be used to make admission decisions. The + * purpose of this function is to make available to monitoring or other + * instrumentation the number of available tokens in the pool. + */ + inline int64 available_tokens() { + mutex_lock l(mu_); + if (!config_.enabled) return 0; + UpdateState(); + return available_tokens_; + } + + private: + /** + * UpdateState updates the available_tokens_ and last_updated_secs_ variables. + * + * UpdateState should be called in order to mark the passage of time, and + * therefore add tokens to the availble_tokens_ pool. + */ + void UpdateState() EXCLUSIVE_LOCKS_REQUIRED(mu_); + + inline uint64 request_bytes_to_tokens(size_t num_bytes) { + return num_bytes >> 8; + } + + mutex mu_; + + /** + * last_updated_secs_ records the number of seconds since the Unix epoch that + * the internal state of the GcsThrottle was updated. This is important when + * determining the number of tokens to add to the available_tokens_ pool. + */ + uint64 last_updated_secs_ GUARDED_BY(mu_) = 0; + + /** + * available_tokens_ records how many tokens are available to be consumed. + * + * Note: it is possible for available_tokens_ to become negative. If a + * response comes back that consumes more than the available tokens, the count + * will go negative, and block future requests until we have available tokens. + */ + int64 available_tokens_ GUARDED_BY(mu_) = 0; + + EnvTime* const env_time_; + GcsThrottleConfig config_ GUARDED_BY(mu_); +}; + +} // namespace tensorflow + +#endif // TENSORFLOW_CORE_PLATFORM_CLOUD_GCS_THROTTLE_H_ diff --git a/tensorflow/core/platform/cloud/gcs_throttle_test.cc b/tensorflow/core/platform/cloud/gcs_throttle_test.cc new file mode 100644 index 0000000000..a1e8167c27 --- /dev/null +++ b/tensorflow/core/platform/cloud/gcs_throttle_test.cc @@ -0,0 +1,101 @@ +/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/platform/cloud/gcs_throttle.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { + +namespace { + +class TestTime : public EnvTime { + public: + uint64 NowMicros() override { return now_; } + + void SetTime(uint64 now_micros) { now_ = now_micros; } + + void AdvanceSeconds(int64 secs) { now_ += secs * 1000000L; } + + private: + uint64 now_ = 1234567890000000ULL; +}; + +class GcsThrottleTest : public ::testing::Test { + protected: + GcsThrottleTest() : throttle_(&time_) { + config_.enabled = true; + throttle_.SetConfig(config_); + } + + GcsThrottleConfig config_; + TestTime time_; + GcsThrottle throttle_; +}; + +TEST_F(GcsThrottleTest, ReplenishTokens) { + EXPECT_EQ(0, throttle_.available_tokens()); + time_.AdvanceSeconds(1); + EXPECT_EQ(100000, throttle_.available_tokens()); + time_.AdvanceSeconds(2); + EXPECT_EQ(300000, throttle_.available_tokens()); +} + +TEST_F(GcsThrottleTest, RejectRequest) { + EXPECT_EQ(0, throttle_.available_tokens()); + time_.AdvanceSeconds(1); + EXPECT_TRUE(throttle_.AdmitRequest()); + EXPECT_EQ(99900, throttle_.available_tokens()); + for (int i = 1; i < 1000; i++) { + EXPECT_TRUE(throttle_.AdmitRequest()); + } + EXPECT_FALSE(throttle_.AdmitRequest()); +} + +TEST_F(GcsThrottleTest, MarkResponses) { + time_.AdvanceSeconds(1); + EXPECT_TRUE(throttle_.AdmitRequest()); + throttle_.RecordResponse(32000000); // 32 MB response + EXPECT_EQ(-25100, throttle_.available_tokens()); + EXPECT_FALSE(throttle_.AdmitRequest()); + time_.AdvanceSeconds(1); + EXPECT_TRUE(throttle_.AdmitRequest()) + << "Available tokens: " << throttle_.available_tokens(); +} + +TEST_F(GcsThrottleTest, Skippingtime_) { + EXPECT_EQ(0, throttle_.available_tokens()); + time_.AdvanceSeconds(90); + EXPECT_EQ(9000000, throttle_.available_tokens()); +} + +TEST_F(GcsThrottleTest, BucketLimit) { + time_.AdvanceSeconds(120); + EXPECT_EQ(10000000, throttle_.available_tokens()); +} + +TEST_F(GcsThrottleTest, ReverseTime) { + time_.AdvanceSeconds(1); + EXPECT_EQ(100000, throttle_.available_tokens()); + time_.AdvanceSeconds(-3600); + EXPECT_EQ(100000, throttle_.available_tokens()); + time_.AdvanceSeconds(1); + EXPECT_EQ(200000, throttle_.available_tokens()); +} + +} // namespace + +} // namespace tensorflow -- GitLab From 4ab2d8531c461169cd6a33bc0fef1129b419e9df Mon Sep 17 00:00:00 2001 From: Jean Flaherty Date: Mon, 29 Jan 2018 17:03:31 -0700 Subject: [PATCH 242/423] Tensor roll op implementation (#14953) * Half migrated to manip Half migrated to tf.manip * Roll op: polymorphism & GPU attempt * Roll op: Added support for gradients Added compile script * Rebase for roll op code * Roll op: Migrated to manip namespace * Roll op: Supports CPU thread pooling fix namespace error * Remove roll from user_ops * Roll op: Optimization * Roll op: Pylint fix * Roll op: Updated documentation * Roll op: Two versions for CPU thread pooling * Roll op: Huge CPU speed up Fixed thread pooling issue that was due to a bad cost_per_unit parameter Also improved readability * Roll op: Rough draft of DoRollV2 DoRollV2 copies memory in groups instead of element by element. Not thoroughly tested yet. Polished DoRollV2 algorithm * Roll op: Restrict tensor size for GPU implementation * Roll op: Fixed clang-format and missing include * Roll op: Minor change * Roll op GPU bug fix Roll op GPU bug fix GPU bug fix Roll op GPU bug fix Roll op GPU fix Roll GPU test BUILD update * Roll op: Remove GPU code Fully remove roll op GPU code Remove compile_cpu.sh * Roll op: Fixes problems with array_ops_test.py and a size 1 dimension bug * Roll op: Migrated to manip Migrated to tf.manip Roll op registered Roll op uses InlinedVector Small improvements * Roll op: Revert array op changes * Roll op: Api def fix * Roll op: review changes * Roll op: API review changes Roll op: Docstring fix * Roll op: Review changes round 1 * Roll op: resolve conflicts * Roll op: Resolve conflicts * Roll op: clang-tidy * Roll op: Review round 2 changes Roll op: fixed BUILD file Roll op: api docs update * Roll op: failure fixes 1 - updates goldens and fixes api compatibility issue - fixes python op test issue for windows - fixes makefile issues * Roll op: Windows CMake failure fix Windows CMake checks were failing because numpy was on an older version that did not support np.roll on multiple shifts that was use to check the correctness of tf.manip.roll. manip_ops_test.py now checks for numpy version 1.12.0 before testing multiple shifts, otherwise it'll just test single shift roll. * Roll op: pylint changes --- tensorflow/cc/BUILD | 1 + tensorflow/contrib/cmake/tf_core_ops.cmake | 1 + tensorflow/contrib/cmake/tf_python.cmake | 1 + tensorflow/contrib/makefile/tf_op_files.txt | 2 + tensorflow/core/BUILD | 3 + .../core/api_def/base_api/api_def_Roll.pbtxt | 52 ++ tensorflow/core/graph/testlib.cc | 12 +- tensorflow/core/graph/testlib.h | 4 + tensorflow/core/kernels/BUILD | 39 ++ tensorflow/core/kernels/roll_op.cc | 334 ++++++++++++ tensorflow/core/kernels/roll_op_test.cc | 484 ++++++++++++++++++ tensorflow/core/ops/manip_ops.cc | 33 ++ tensorflow/python/BUILD | 36 ++ tensorflow/python/__init__.py | 2 + tensorflow/python/kernel_tests/BUILD | 13 + .../python/kernel_tests/manip_ops_test.py | 137 +++++ tensorflow/python/ops/gradients_impl.py | 1 + tensorflow/python/ops/manip_grad.py | 32 ++ tensorflow/python/ops/manip_ops.py | 36 ++ tensorflow/python/ops/standard_ops.py | 4 + .../tools/api/golden/tensorflow.manip.pbtxt | 7 + tensorflow/tools/api/golden/tensorflow.pbtxt | 4 + 22 files changed, 1237 insertions(+), 1 deletion(-) create mode 100644 tensorflow/core/api_def/base_api/api_def_Roll.pbtxt create mode 100644 tensorflow/core/kernels/roll_op.cc create mode 100644 tensorflow/core/kernels/roll_op_test.cc create mode 100644 tensorflow/core/ops/manip_ops.cc create mode 100644 tensorflow/python/kernel_tests/manip_ops_test.py create mode 100644 tensorflow/python/ops/manip_grad.py create mode 100644 tensorflow/python/ops/manip_ops.py create mode 100644 tensorflow/tools/api/golden/tensorflow.manip.pbtxt diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD index c9ade5fb83..9060c19e9d 100644 --- a/tensorflow/cc/BUILD +++ b/tensorflow/cc/BUILD @@ -433,6 +433,7 @@ tf_gen_op_wrappers_cc( "linalg_ops", "logging_ops", "lookup_ops", + "manip_ops", "math_ops", "nn_ops", "no_op", diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake index 138993db35..c42bc35ce7 100644 --- a/tensorflow/contrib/cmake/tf_core_ops.cmake +++ b/tensorflow/contrib/cmake/tf_core_ops.cmake @@ -30,6 +30,7 @@ set(tf_op_lib_names "list_ops" "lookup_ops" "logging_ops" + "manip_ops" "math_ops" "nn_ops" "no_op" diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 8862390d2b..b7c816c24f 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -335,6 +335,7 @@ GENERATE_PYTHON_OP_LIB("list_ops") GENERATE_PYTHON_OP_LIB("logging_ops") GENERATE_PYTHON_OP_LIB("lookup_ops") GENERATE_PYTHON_OP_LIB("nn_ops") +GENERATE_PYTHON_OP_LIB("manip_ops") GENERATE_PYTHON_OP_LIB("parsing_ops") GENERATE_PYTHON_OP_LIB("random_ops") GENERATE_PYTHON_OP_LIB("remote_fused_graph_ops" diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt index 5f27566398..9a1ab50317 100644 --- a/tensorflow/contrib/makefile/tf_op_files.txt +++ b/tensorflow/contrib/makefile/tf_op_files.txt @@ -91,6 +91,7 @@ tensorflow/core/kernels/reduction_ops_max.cc tensorflow/core/kernels/reduction_ops_common.cc tensorflow/core/kernels/reduction_ops_any.cc tensorflow/core/kernels/reduction_ops_all.cc +tensorflow/core/kernels/roll_op.cc tensorflow/core/kernels/queue_ops.cc tensorflow/core/kernels/queue_base.cc tensorflow/core/kernels/pooling_ops_common.cc @@ -270,6 +271,7 @@ tensorflow/core/ops/parsing_ops.cc tensorflow/core/ops/no_op.cc tensorflow/core/ops/nn_ops.cc tensorflow/core/ops/nn_grad.cc +tensorflow/core/ops/manip_ops.cc tensorflow/core/ops/math_ops.cc tensorflow/core/ops/math_grad.cc tensorflow/core/ops/logging_ops.cc diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 3b4a10eedb..90c2823ea4 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -610,6 +610,7 @@ tf_gen_op_libs( "list_ops", "lookup_ops", "logging_ops", + "manip_ops", "math_ops", "nn_ops", "no_op", @@ -692,6 +693,7 @@ cc_library( ":list_ops_op_lib", ":logging_ops_op_lib", ":lookup_ops_op_lib", + ":manip_ops_op_lib", ":math_ops_op_lib", ":nn_ops_op_lib", ":no_op_op_lib", @@ -829,6 +831,7 @@ cc_library( "//tensorflow/core/kernels:list_kernels", "//tensorflow/core/kernels:lookup", "//tensorflow/core/kernels:logging", + "//tensorflow/core/kernels:manip", "//tensorflow/core/kernels:math", "//tensorflow/core/kernels:multinomial_op", "//tensorflow/core/kernels:nn", diff --git a/tensorflow/core/api_def/base_api/api_def_Roll.pbtxt b/tensorflow/core/api_def/base_api/api_def_Roll.pbtxt new file mode 100644 index 0000000000..b308ad1f9d --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_Roll.pbtxt @@ -0,0 +1,52 @@ +op { + graph_op_name: "Roll" + in_arg { + name: "shift" + description: < [3, 4, 0, 1, 2] + +# shifting along multiple dimensions +# 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]] + +# shifting along the same axis multiple times +# 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] +``` +END +} diff --git a/tensorflow/core/graph/testlib.cc b/tensorflow/core/graph/testlib.cc index 172471e34b..0d88d1ff72 100644 --- a/tensorflow/core/graph/testlib.cc +++ b/tensorflow/core/graph/testlib.cc @@ -40,7 +40,7 @@ REGISTER_KERNEL_BUILDER( #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER( Name("HostConst").Device(DEVICE_SYCL).HostMemory("output"), HostConstantOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register the HostConst Op // Returns a constant tensor on the host. Useful for writing C++ tests @@ -273,6 +273,16 @@ Node* Reverse(Graph* g, Node* tensor, Node* axis) { return Binary(g, "ReverseV2", tensor, axis); } +Node* Roll(Graph* g, Node* input, Node* shift, Node* axis) { + Node* ret; + TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Roll", g->op_registry()) + .Input(input) + .Input(shift) + .Input(axis) + .Finalize(g, &ret)); + return ret; +} + Node* Error(Graph* g, Node* input, const string& errmsg) { Node* ret; TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Error") diff --git a/tensorflow/core/graph/testlib.h b/tensorflow/core/graph/testlib.h index 06597778bb..eb9038d619 100644 --- a/tensorflow/core/graph/testlib.h +++ b/tensorflow/core/graph/testlib.h @@ -117,6 +117,10 @@ Node* RandomGamma(Graph* g, Node* shape, Node* alpha); // Output dtype determined by lam. Node* RandomPoisson(Graph* g, Node* shape, Node* lam); +// Rolls tensor by an offset of along the corresponding +// dimensions. +Node* Roll(Graph* g, Node* input, Node* shift, Node* axis); + // Generates random parameters from the truncated standard normal distribution // of the nput shape Node* TruncatedNormal(Graph* g, Node* input, DataType dtype); diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index db309fc9da..e7192ec42f 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -2589,6 +2589,45 @@ tf_cc_tests( ], ) +cc_library( + name = "manip", + deps = [ + ":roll_op", + ], +) + +MANIP_DEPS = [ + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:manip_ops_op_lib", + "//third_party/eigen3", +] + +tf_kernel_library( + name = "roll_op", + prefix = "roll_op", + deps = MANIP_DEPS, +) + +tf_cc_test( + name = "roll_op_test", + size = "small", + srcs = ["roll_op_test.cc"], + deps = [ + ":ops_testutil", + ":ops_util", + ":roll_op", + "//tensorflow/core:core_cpu", + "//tensorflow/core:core_cpu_internal", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + MATH_DEPS = [ ":bounds_check", ":fill_functor", diff --git a/tensorflow/core/kernels/roll_op.cc b/tensorflow/core/kernels/roll_op.cc new file mode 100644 index 0000000000..bcbdbee058 --- /dev/null +++ b/tensorflow/core/kernels/roll_op.cc @@ -0,0 +1,334 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/framework/register_types_traits.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/platform/types.h" +#include "tensorflow/core/util/work_sharder.h" + +namespace tensorflow { + +#define EIGEN_USE_THREADS +using CPUDevice = Eigen::ThreadPoolDevice; + +// dim_size - the size of each dimension +// dim_range - the number of indices over in the flattened tensor +// you need to skip in order to make it over from one side of a dimension +// to the other. Used to make the shifts wrap around after a threshold. +// threshold - the index for each dimension that the roll starts to wrap +// back to the front +template +void DoRoll(OpKernelContext* context, const int64 num_elements, + const int num_dims, const gtl::ArraySlice& dim_size, + const T* input, T* output, const gtl::ArraySlice& threshold, + const gtl::ArraySlice& dim_range) { + auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range]( + int64 start, int64 end) { + // array of indices for each dimension + gtl::InlinedVector indices(num_dims); + int offset = 0; // the shift along the flattened tensor for current element + // initialize indices and offset + for (int i = 0; i < num_dims; i++) { + // stride is the number of indices over in the flattened tensor + // you need to skip in order to make it over to an adjacent element + // along a dimension. dim_size[i] != 0 because we set it to max(dim, 1) + const int64 stride = dim_range[i] / dim_size[i]; + const int shift = dim_size[i] - threshold[i]; + const int indx = (start / stride) % dim_size[i]; + indices[i] = indx; + // calculate dimension index after the shift + const int shifted_indx = (indx + shift) % dim_size[i]; + offset += (shifted_indx - indx) * stride; + } + + for (int64 i = start; i < end; i++) { + output[i + offset] = input[i]; + // create next combination of indices + // while at it adjust offset if needed + for (int j = num_dims - 1; j >= 0; j--) { + const int indx = (indices[j] + 1) % dim_size[j]; + indices[j] = indx; + if (indx != 0) { + if (indx == threshold[j]) { // we've reached the threshold + // dim_range[j] = threshold[j] + shift[j] + // offset = shift[j] + ... other offsets + // offset - dim_range[j] = -threshold[j] + ... other offsets + // thus we undo our previous offset as well as add a new offset of + // -threshold[j] in one operation + offset -= dim_range[j]; // now wraps around + } + break; // indx != 0 don't need to carry + } else if (threshold[j] != 0) { // if threshold is 0 shift is 0 + offset += dim_range[j]; // indx became 0 so reverse wrap around + } + } + } + }; + // Shard + auto worker_threads = context->device()->tensorflow_cpu_worker_threads(); + // 15 - expiramentally determined with float and bool types + const int cost_per_element = 15 * sizeof(T); // rough esitmate + Shard(worker_threads->num_threads, worker_threads->workers, num_elements, + cost_per_element, std::move(work)); +} + +// dim_size - the size of each dimension +// dim_range - the number of indices over in the flattened tensor +// you need to skip in order to make it over from one side of a dimension +// to the other. Used to make the shifts wrap around after a threshold. +// threshold - the index for each dimension that the roll starts to wrap +// back to the front +// isd - inner shift dimension +template +// Use memcpy to copy memory in groups when the data type supports memcpy +void DoRollWithMemcpy(OpKernelContext* context, const int64 num_elements, + const int num_dims, const gtl::ArraySlice& dim_size, + const T* input, T* output, + const gtl::ArraySlice& threshold, + const gtl::ArraySlice& dim_range, + const int64 isd) { + auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range, isd]( + int64 start, int64 end) { + // the number of indices over in the flattened tensor you need to skip in + // order to make it over from one side of the isd to the other + const int64 isd_range = std::max(dim_range[isd], 1); + // the distance along the flattend tensor to the next element in the isd + const int64 isd_stride = isd_range / std::max(dim_size[isd], 1); + + // start and end represent the i-th group currently so we will convert + // them into numbers representing the i-th elements. + // there are 2 groups per isd one for all elements before threshold[isd] + // and another for all elements after threshold[isd]. + const int64 start_remainder = (start % 2) * threshold[isd] * isd_stride; + const int64 end_remainder = (end % 2) * threshold[isd] * isd_stride; + start = (start / 2) * isd_range + start_remainder; + end = (end / 2) * isd_range + end_remainder; + + const T* in_ptr = &input[0]; + T* out_ptr = &output[0]; + in_ptr += start; + out_ptr += start; + + // array of indices for each dimension + // indicies = [i, j, k, l, m, n] + gtl::InlinedVector indicies(num_dims); + // the offset needed to make all inner non-shifting dimensions become 0 + int64 remainder_offset = 0; + // initialize indicies + for (int i = 0; i < num_dims; i++) { + // stride is the number of indices over in the flattened tensor + // you need to skip in order to make it over to an adjacent element + // along a dimension. dim_size[i] != 0 because we set it to max(dim, 1) + const int64 stride = dim_range[i] / dim_size[i]; + const int shift = dim_size[i] - threshold[i]; + const int indx = (start / stride) % dim_size[i]; + indicies[i] = indx; + // calculate dimension index after the shift + int out_indx = (indx + shift) % dim_size[i]; + if (i > isd) { + // trailing zeroes for indices after the inner shifted dimension + out_indx = 0; + remainder_offset += (out_indx - indx) * stride; + } + out_ptr += (out_indx - indx) * stride; + } + // set trailing zeroes for indices after the inner shifted dimension + for (int i = num_dims - 1; i > isd; i--) indicies[i] = 0; + + // the number of indices in the isd dimension the next group will skip + // to make it to the next threshold or end point + int isd_indx_skip = 0; + // the size of the next group + int64 group_size = 0; + // initialize isd_indx_skip and group_size + if (indicies[isd] < threshold[isd]) { + isd_indx_skip = threshold[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride + remainder_offset; + } else { + isd_indx_skip = dim_size[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride + remainder_offset; + } + + int64 i = start; + while (i < end) { + // copy group of elements + memcpy(out_ptr, in_ptr, group_size * sizeof(T)); + + // shift i and the pointers over to the next group position + i += group_size; + out_ptr += group_size; + in_ptr += group_size; + + // produce next combination of indices and adjust the out_ptr position + // to fix the offset if necessary + // the isd (inner shift dim) should skip to next threshold or endpoint + // all dimensions to the left increment by 1 when a digit is carried + // all dimensions to the right remain set to 0 + // +1 +1 +1 +isd_indx_skip + // indicies = [i, j, k, l, 0, 0] + // ^isd + for (int j = isd; j >= 0; j--) { + int inc = 1; + if (j == isd) inc = isd_indx_skip; + const int indx = (indicies[j] + inc) % dim_size[j]; + indicies[j] = indx; + if (indx != 0) { + if (indx == threshold[j]) { + out_ptr -= dim_range[j]; // now wraps around + } + break; // indx != 0 don't need to carry + } else if (threshold[j] != 0) { // if threshold is 0 shift is 0 + out_ptr += dim_range[j]; // indx became 0 so reverse wrap around + } + } + + // set isd_indx_skip and group_size for next iteration + if (indicies[isd] < threshold[isd]) { + isd_indx_skip = threshold[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride; + } else { + isd_indx_skip = dim_size[isd] - indicies[isd]; + group_size = isd_indx_skip * isd_stride; + } + } + }; + // Shard + auto worker_threads = context->device()->tensorflow_cpu_worker_threads(); + const int64 ave_group_size = dim_range[isd] / 2; + const int total_work = 2 * num_elements / std::max(dim_range[isd], 1); + // 25000 - expiramentally determined with float and bool types + const int cost_per_group = 25000 * sizeof(T) * ave_group_size; + Shard(worker_threads->num_threads, worker_threads->workers, total_work, + cost_per_group, std::move(work)); +} + +template +class RollOp : public OpKernel { + public: + explicit RollOp(OpKernelConstruction* context) : OpKernel(context) {} + + void Compute(OpKernelContext* context) override { + // Grab the input tensor + const Tensor& input = context->input(0); + const Tensor& shift = context->input(1); + const Tensor& axis = context->input(2); + + auto shift_flat = shift.flat(); + auto axis_flat = axis.flat(); + + OP_REQUIRES(context, TensorShapeUtils::IsVectorOrHigher(input.shape()), + errors::InvalidArgument("input must be 1-D or higher")); + OP_REQUIRES(context, shift.shape().dims() <= 1, + errors::InvalidArgument( + "shift must be a scalar or a 1-D vector. Found: ", + shift.shape().DebugString())); + OP_REQUIRES(context, axis.shape().dims() <= 1, + errors::InvalidArgument( + "axis must be a scalar or a 1-D vector. Found: ", + axis.shape().DebugString())); + OP_REQUIRES( + context, shift.shape() == axis.shape(), + errors::InvalidArgument("shift and axis must have the same size")); + const int64 num_elements = input.NumElements(); + const int num_shifts = static_cast(shift_flat.size()); + const int num_dims = input.dims(); + + // if there are any duplicate axes, shift_mod_sum will have the + // total modulo sum of shifts for each dimension + gtl::InlinedVector shift_mod_sum(num_dims, 0); + for (int i = 0; i < num_shifts; i++) { + const int axis = axis_flat(i); + OP_REQUIRES(context, axis < num_dims, + errors::InvalidArgument("axis ", axis, " is out of range")); + const int ds = std::max(static_cast(input.dim_size(axis)), 1); + const int sum = shift_mod_sum[axis] + static_cast(shift_flat(i)); + // modulo that works with negatives: ((x % y) + y) % y + shift_mod_sum[axis] = (sum % ds + ds) % ds; + } + // the size of each dimension + gtl::InlinedVector dim_size(num_dims); + // threshold[i] is the index that the roll starts to wrap back to the front + gtl::InlinedVector threshold(num_dims); + // dim_range is the number of indices over in the flattened tensor + // you need to skip in order to make it over from one side of a dimension + // to the other. Used to make the shifts wrap around after a threshold. + gtl::InlinedVector dim_range(num_dims); + int64 dim_size_prod = 1; // dimension size product + // inner shift dimension (inner most shifted dimension) + int64 isd = 0; + for (int i = num_dims - 1; i >= 0; i--) { + if (isd == 0 && shift_mod_sum[i] != 0) isd = i; + const int ds = std::max(static_cast(input.dim_size(i)), 1); + dim_size[i] = ds; + threshold[i] = (ds - shift_mod_sum[i]) % ds; + dim_size_prod *= static_cast(input.dim_size(i)); + dim_range[i] = dim_size_prod; + } + + Tensor* output = NULL; + OP_REQUIRES_OK(context, + context->allocate_output(0, input.shape(), &output)); + auto input_flat = input.flat().data(); + auto output_flat = output->flat().data(); + + if (std::is_same::value) { + if (DataTypeCanUseMemcpy(DataTypeToEnum::v())) { + // V2 copies memory in groups instead of element by element + DoRollWithMemcpy(context, num_elements, num_dims, dim_size, + input_flat, output_flat, threshold, dim_range, isd); + } else { + // incase memcpy does not work for current data type + DoRoll(context, num_elements, num_dims, dim_size, input_flat, + output_flat, threshold, dim_range); + } + } + } +}; + +// Register the CPU kernels. +#define REGISTER_CPU(type) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tshift") \ + .TypeConstraint("Taxis"), \ + RollOp) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tshift") \ + .TypeConstraint("Taxis"), \ + RollOp) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tshift") \ + .TypeConstraint("Taxis"), \ + RollOp) \ + REGISTER_KERNEL_BUILDER(Name("Roll") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T") \ + .TypeConstraint("Tshift") \ + .TypeConstraint("Taxis"), \ + RollOp) + +TF_CALL_ALL_TYPES(REGISTER_CPU); +#undef REGISTER_CPU +} // namespace tensorflow diff --git a/tensorflow/core/kernels/roll_op_test.cc b/tensorflow/core/kernels/roll_op_test.cc new file mode 100644 index 0000000000..90b6f8d0f3 --- /dev/null +++ b/tensorflow/core/kernels/roll_op_test.cc @@ -0,0 +1,484 @@ +/* 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/core/common_runtime/device.h" +#include "tensorflow/core/common_runtime/device_factory.h" +#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" +#include "tensorflow/core/framework/allocator.h" +#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/framework/types.pb.h" +#include "tensorflow/core/kernels/ops_testutil.h" +#include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/test_benchmark.h" + +namespace tensorflow { +namespace { + +class RollOpTest : public OpsTestBase { + protected: + void MakeOp(DataType data_type, DataType index_type) { + TF_ASSERT_OK(NodeDefBuilder("myop", "Roll") + .Input(FakeInput(data_type)) + .Input(FakeInput(index_type)) + .Input(FakeInput(index_type)) + .Finalize(node_def())); + TF_ASSERT_OK(InitOp()); + } +}; + +TEST_F(RollOpTest, ScalarIndices) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({5}), {0, 1, 2, 3, 4}); + AddInputFromArray(TensorShape({}), {3}); + AddInputFromArray(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({5})); + test::FillValues(&expected, {2, 3, 4, 0, 1}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ScalarIndices_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({5}), {"a", "b", "c", "d", "e"}); + AddInputFromArray(TensorShape({}), {3}); + AddInputFromArray(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({5})); + test::FillValues(&expected, {"c", "d", "e", "a", "b"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ScalarIndices_Complex) { + MakeOp(DT_COMPLEX64, DT_INT32); + + // Feed and run + AddInputFromArray>( + TensorShape({5}), {std::complex(0, 10), std::complex(1, 11), + std::complex(2, 12), std::complex(3, 13), + std::complex(4, 14)}); + AddInputFromArray(TensorShape({}), {3}); + AddInputFromArray(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_COMPLEX64, TensorShape({5})); + test::FillValues>( + &expected, {std::complex(2, 12), std::complex(3, 13), + std::complex(4, 14), std::complex(0, 10), + std::complex(1, 11)}); + test::ExpectTensorEqual>(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({3, 5}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}); + AddInputFromArray(TensorShape({2}), {2, -1}); + AddInputFromArray(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({3, 5})); + test::FillValues(&expected, + {6, 7, 8, 9, 5, 11, 12, 13, 14, 10, 1, 2, 3, 4, 0}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({3, 5}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", + "k", "l", "m", "n", "o"}); + AddInputFromArray(TensorShape({2}), {2, -1}); + AddInputFromArray(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({3, 5})); + test::FillValues(&expected, {"g", "h", "i", "j", "f", "l", "m", "n", + "o", "k", "b", "c", "d", "e", "a"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({2, 2, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + AddInputFromArray(TensorShape({3}), {1, -1, -1}); + AddInputFromArray(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 3})); + test::FillValues(&expected, {10, 11, 9, 7, 8, 6, 4, 5, 3, 1, 2, 0}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray( + TensorShape({2, 2, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + AddInputFromArray(TensorShape({3}), {1, -1, -1}); + AddInputFromArray(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({2, 2, 3})); + test::FillValues( + &expected, {"k", "l", "j", "h", "i", "g", "e", "f", "d", "b", "c", "a"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD64) { + MakeOp(DT_FLOAT, DT_INT64); + + // Feed and run + AddInputFromArray(TensorShape({5, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}); + AddInputFromArray(TensorShape({2}), {-1, 4}); + AddInputFromArray(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 3})); + test::FillValues(&expected, + {5, 3, 4, 8, 6, 7, 11, 9, 10, 14, 12, 13, 2, 0, 1}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_TwoD64_NoMemcpy) { + MakeOp(DT_STRING, DT_INT64); + + // Feed and run + AddInputFromArray(TensorShape({5, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", + "k", "l", "m", "n", "o"}); + AddInputFromArray(TensorShape({2}), {-1, 4}); + AddInputFromArray(TensorShape({2}), {0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({5, 3})); + test::FillValues(&expected, {"f", "d", "e", "i", "g", "h", "l", "j", + "k", "o", "m", "n", "c", "a", "b"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD64) { + MakeOp(DT_FLOAT, DT_INT64); + + // Feed and run + AddInputFromArray(TensorShape({4, 1, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + AddInputFromArray(TensorShape({3}), {4, 3, 2}); + AddInputFromArray(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({4, 1, 3})); + test::FillValues(&expected, {1, 2, 0, 4, 5, 3, 7, 8, 6, 10, 11, 9}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Simple_ThreeD64_NoMemcpy) { + MakeOp(DT_STRING, DT_INT64); + + // Feed and run + AddInputFromArray( + TensorShape({4, 1, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + AddInputFromArray(TensorShape({3}), {4, 3, 2}); + AddInputFromArray(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({4, 1, 3})); + test::FillValues( + &expected, {"b", "c", "a", "e", "f", "d", "h", "i", "g", "k", "l", "j"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroShift_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({2, 2, 3}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + AddInputFromArray(TensorShape({3}), {0, 0, 0}); + AddInputFromArray(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 2, 3})); + test::FillValues(&expected, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroShift_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray( + TensorShape({2, 2, 3}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + AddInputFromArray(TensorShape({3}), {0, 0, 0}); + AddInputFromArray(TensorShape({3}), {0, 1, 2}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({2, 2, 3})); + test::FillValues( + &expected, {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroSize_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({5, 0, 0}), {}); + AddInputFromArray(TensorShape({}), {1}); + AddInputFromArray(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 0, 0})); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, ZeroSize_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({5, 0, 0}), {}); + AddInputFromArray(TensorShape({}), {1}); + AddInputFromArray(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({5, 0, 0})); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, OneSize_ThreeD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({1, 1, 1}), {5}); + AddInputFromArray(TensorShape({}), {1}); + AddInputFromArray(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 1, 1})); + test::FillValues(&expected, {5}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, OneSize_ThreeD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({1, 1, 1}), {"a"}); + AddInputFromArray(TensorShape({}), {1}); + AddInputFromArray(TensorShape({}), {0}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({1, 1, 1})); + test::FillValues(&expected, {"a"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, MultiShifts_TwoD32) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({3, 5}), + {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}); + AddInputFromArray(TensorShape({4}), {-2, 2, -1, 1}); + AddInputFromArray(TensorShape({4}), {1, 0, 0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_FLOAT, TensorShape({3, 5})); + test::FillValues(&expected, + {11, 12, 13, 14, 10, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, MultiShifts_TwoD32_NoMemcpy) { + MakeOp(DT_STRING, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({3, 5}), + {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", + "k", "l", "m", "n", "o"}); + AddInputFromArray(TensorShape({4}), {-2, 2, -1, 1}); + AddInputFromArray(TensorShape({4}), {1, 0, 0, 1}); + TF_ASSERT_OK(RunOpKernel()); + + // Check the output. + Tensor expected(allocator(), DT_STRING, TensorShape({3, 5})); + test::FillValues(&expected, {"l", "m", "n", "o", "k", "b", "c", "d", + "e", "a", "g", "h", "i", "j", "f"}); + test::ExpectTensorEqual(expected, *GetOutput(0)); +} + +TEST_F(RollOpTest, Error_InputMustBeVectorOrHigher) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({}), {7}); + AddInputFromArray(TensorShape({}), {1}); + AddInputFromArray(TensorShape({}), {0}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()).contains("input must be 1-D or higher")) + << s; +} + +TEST_F(RollOpTest, Error_AxisMustBeScalarOrVector) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({2, 2}), {1, 2, 3, 4}); + AddInputFromArray(TensorShape({}), {1}); + AddInputFromArray(TensorShape({1, 2}), {0, 1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()) + .contains("axis must be a scalar or a 1-D vector")) + << s; +} + +TEST_F(RollOpTest, Error_ShiftMustBeScalarOrVector) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({2, 2}), {1, 2, 3, 4}); + AddInputFromArray(TensorShape({1, 2}), {0, 1}); + AddInputFromArray(TensorShape({}), {1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()) + .contains("shift must be a scalar or a 1-D vector")) + << s; +} + +TEST_F(RollOpTest, Error_ShiftAndAxisMustBeSameSize) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({2, 2}), {1, 2, 3, 4}); + AddInputFromArray(TensorShape({1}), {1}); + AddInputFromArray(TensorShape({2}), {0, 1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()) + .contains("shift and axis must have the same size")) + << s; +} + +TEST_F(RollOpTest, Error_AxisOutOfRange) { + MakeOp(DT_FLOAT, DT_INT32); + + // Feed and run + AddInputFromArray(TensorShape({4}), {1, 2, 3, 4}); + AddInputFromArray(TensorShape({}), {1}); + AddInputFromArray(TensorShape({}), {1}); + Status s = RunOpKernel(); + EXPECT_TRUE(StringPiece(s.ToString()).contains("is out of range")) << s; +} + +// isd - (inner shift dimension) The inner most dimension to be shifted. +// All outer dimensions will also be shifted for testing. +static Graph* RollGraph(const TensorShape& shape, int isd) { + Graph* g = new Graph(OpRegistry::Global()); + Tensor input(DT_FLOAT, shape); + input.flat().setRandom(); + const int dims = static_cast(input.dims()); + Tensor shift(DT_INT32, TensorShape({dims})); + for (int i = 0; i < dims; i++) { + // shift the inner shift dimension and all outer dimensions + shift.flat()(i) = (i <= isd) ? 2 : 0; + } + Tensor axis(DT_INT32, TensorShape({dims})); + for (int i = 0; i < dims; i++) { + axis.flat()(i) = i; + } + test::graph::Roll(g, test::graph::Constant(g, input), + test::graph::Constant(g, shift), + test::graph::Constant(g, axis)); + return g; +} + +#define BM_ROLL_OUTER(DEVICE) \ + static void BM_##DEVICE##_roll_outer(int iters, int rows, int columns) { \ + TensorShape shape{rows, columns}; \ + const int64 num_items = static_cast(iters) * shape.num_elements(); \ + testing::ItemsProcessed(num_items); \ + testing::BytesProcessed(num_items * sizeof(float)); \ + testing::UseRealTime(); \ + test::Benchmark(#DEVICE, RollGraph(shape, 0)).Run(iters); \ + } \ + BENCHMARK(BM_##DEVICE##_roll_outer) \ + ->ArgPair(256, 256) \ + ->ArgPair(512, 512) \ + ->ArgPair(1024, 1024) \ + ->ArgPair(2048, 2048) + +#define BM_ROLL_ALL(DEVICE) \ + static void BM_##DEVICE##_roll_all(int iters, int rows, int columns) { \ + TensorShape shape{rows, columns}; \ + const int64 num_items = static_cast(iters) * shape.num_elements(); \ + testing::ItemsProcessed(num_items); \ + testing::BytesProcessed(num_items * sizeof(float)); \ + testing::UseRealTime(); \ + test::Benchmark(#DEVICE, RollGraph(shape, 1)).Run(iters); \ + } \ + BENCHMARK(BM_##DEVICE##_roll_all) \ + ->ArgPair(256, 256) \ + ->ArgPair(512, 512) \ + ->ArgPair(1024, 1024) \ + ->ArgPair(2048, 2048) + +BM_ROLL_OUTER(cpu); +BM_ROLL_ALL(cpu); +} // namespace +} // namespace tensorflow diff --git a/tensorflow/core/ops/manip_ops.cc b/tensorflow/core/ops/manip_ops.cc new file mode 100644 index 0000000000..95b4774fe6 --- /dev/null +++ b/tensorflow/core/ops/manip_ops.cc @@ -0,0 +1,33 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +// -------------------------------------------------------------------------- +REGISTER_OP("Roll") + .Input("input: T") + .Input("shift: Tshift") + .Input("axis: Taxis") + .Output("output: T") + .Attr("T: type") + .Attr("Tshift: {int32,int64}") + .Attr("Taxis: {int32,int64}") + .SetShapeFn(shape_inference::UnchangedShape); + +} // namespace tensorflow diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index a323d5bc39..c73d6c37ee 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -76,6 +76,7 @@ py_library( ":layers", ":lib", ":list_ops", + ":manip_ops", ":math_ops", ":metrics", ":nn", @@ -1394,6 +1395,14 @@ tf_gen_op_wrapper_private_py( ], ) +tf_gen_op_wrapper_private_py( + name = "manip_ops_gen", + visibility = [ + "//learning/brain/python/ops:__pkg__", + "//tensorflow/python/kernel_tests:__pkg__", + ], +) + tf_gen_op_wrapper_private_py( name = "math_ops_gen", visibility = [ @@ -1726,6 +1735,8 @@ py_library( ":linalg_grad", ":linalg_ops", ":logging_ops", + ":manip_grad", + ":manip_ops", ":math_grad", ":math_ops", ":platform", @@ -1848,6 +1859,29 @@ py_library( ], ) +py_library( + name = "manip_grad", + srcs = ["ops/manip_grad.py"], + srcs_version = "PY2AND3", + deps = [ + ":control_flow_ops", + ":framework_for_generated_wrappers", + ":manip_ops", + ], +) + +py_library( + name = "manip_ops", + srcs = ["ops/manip_ops.py"], + srcs_version = "PY2AND3", + deps = [ + ":dtypes", + ":framework_ops", + ":manip_ops_gen", + "//third_party/py/numpy", + ], +) + py_library( name = "logging_ops", srcs = ["ops/logging_ops.py"], @@ -2310,6 +2344,8 @@ py_library( ":linalg_ops", ":logging_ops", ":lookup_ops", + ":manip_grad", + ":manip_ops", ":math_grad", ":math_ops", ":numerics", diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py index bc9ddec2a5..ea7604d30f 100644 --- a/tensorflow/python/__init__.py +++ b/tensorflow/python/__init__.py @@ -84,6 +84,7 @@ from tensorflow.python.feature_column import feature_column_lib as feature_colum from tensorflow.python.layers import layers from tensorflow.python.ops import bitwise_ops as bitwise from tensorflow.python.ops import image_ops as image +from tensorflow.python.ops import manip_ops as manip from tensorflow.python.ops import metrics from tensorflow.python.ops import nn from tensorflow.python.ops import sets @@ -241,6 +242,7 @@ _allowed_symbols.extend([ 'linalg', 'logging', 'losses', + 'manip', 'metrics', 'newaxis', 'nn', diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index c87b7652ad..3a6058054b 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -1601,6 +1601,19 @@ cuda_py_test( ], ) +cuda_py_test( + name = "manip_ops_test", + size = "small", + srcs = ["manip_ops_test.py"], + additional_deps = [ + "//third_party/py/numpy", + "//tensorflow/python:manip_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + ], + tags = ["no_windows_gpu"], +) + cuda_py_test( name = "matmul_op_test", size = "small", diff --git a/tensorflow/python/kernel_tests/manip_ops_test.py b/tensorflow/python/kernel_tests/manip_ops_test.py new file mode 100644 index 0000000000..3044b21aa4 --- /dev/null +++ b/tensorflow/python/kernel_tests/manip_ops_test.py @@ -0,0 +1,137 @@ +# 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 manip_ops.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import errors_impl +from tensorflow.python.framework import test_util +from tensorflow.python.ops import manip_ops +from tensorflow.python.ops import gradient_checker +from tensorflow.python.platform import test as test_lib + +import numpy as np + +# pylint: disable=g-import-not-at-top +try: + from distutils.version import StrictVersion as Version + # numpy.roll for multiple shifts was introduced in numpy version 1.12.0 + NP_ROLL_CAN_MULTISHIFT = Version(np.version.version) >= Version('1.12.0') +except ImportError: + NP_ROLL_CAN_MULTISHIFT = False +# pylint: enable=g-import-not-at-top + +class RollTest(test_util.TensorFlowTestCase): + def _testRoll(self, np_input, shift, axis): + expected_roll = np.roll(np_input, shift, axis) + with self.test_session(): + roll = manip_ops.roll(np_input, shift, axis) + self.assertAllEqual(roll.eval(), expected_roll) + + def _testGradient(self, np_input, shift, axis): + with self.test_session(): + inx = constant_op.constant(np_input.tolist()) + xs = list(np_input.shape) + y = manip_ops.roll(inx, shift, axis) + # Expected y's shape to be the same + ys = xs + jacob_t, jacob_n = gradient_checker.compute_gradient( + inx, xs, y, ys, x_init_value=np_input) + self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5) + + def _testAll(self, np_input, shift, axis): + self._testRoll(np_input, shift, axis) + if np_input.dtype == np.float32: + self._testGradient(np_input, shift, axis) + + def testIntTypes(self): + for t in [np.int32, np.int64]: + self._testAll(np.random.randint(-100, 100, (5)).astype(t), 3, 0) + if NP_ROLL_CAN_MULTISHIFT: + self._testAll(np.random.randint(-100, 100, (4, 4, 3)).astype(t), + [1, -2, 3], [0, 1, 2]) + self._testAll(np.random.randint(-100, 100, (4, 2, 1, 3)).astype(t), + [0, 1, -2], [1, 2, 3]) + + def testFloatTypes(self): + for t in [np.float32, np.float64]: + self._testAll(np.random.rand(5).astype(t), 2, 0) + if NP_ROLL_CAN_MULTISHIFT: + self._testAll(np.random.rand(3, 4).astype(t), [1, 2], [1, 0]) + self._testAll(np.random.rand(1, 3, 4).astype(t), [1, 0, -3], [0, 1, 2]) + + def testComplexTypes(self): + for t in [np.complex64, np.complex128]: + x = np.random.rand(4, 4).astype(t) + self._testAll(x + 1j * x, 2, 0) + if NP_ROLL_CAN_MULTISHIFT: + x = np.random.rand(2, 5).astype(t) + self._testAll(x + 1j * x, [1, 2], [1, 0]) + x = np.random.rand(3, 2, 1, 1).astype(t) + self._testAll(x + 1j * x, [2, 1, 1, 0], [0, 3, 1, 2]) + + + def testRollInputMustVectorHigherRaises(self): + tensor = 7 + shift = 1 + axis = 0 + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "input must be 1-D or higher"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollAxisMustBeScalarOrVectorRaises(self): + tensor = [[1, 2], + [3, 4]] + shift = 1 + axis = [[0, 1]] + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "axis must be a scalar or a 1-D vector"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollShiftMustBeScalarOrVectorRaises(self): + tensor = [[1, 2], + [3, 4]] + shift = [[0, 1]] + axis = 1 + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "shift must be a scalar or a 1-D vector"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollShiftAndAxisMustBeSameSizeRaises(self): + tensor = [[1, 2], + [3, 4]] + shift = [1] + axis = [0, 1] + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "shift and axis must have the same size"): + manip_ops.roll(tensor, shift, axis).eval() + + def testRollAxisOutOfRangeRaises(self): + tensor = [1, 2] + shift = 1 + axis = 1 + with self.test_session(): + with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, + "is out of range"): + manip_ops.roll(tensor, shift, axis).eval() + +if __name__ == "__main__": + test_lib.main() diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index 314726ede6..230b6c5946 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -44,6 +44,7 @@ from tensorflow.python.ops import image_grad # pylint: disable=unused-import from tensorflow.python.ops import linalg_grad # pylint: disable=unused-import from tensorflow.python.ops import linalg_ops # pylint: disable=unused-import from tensorflow.python.ops import logging_ops # pylint: disable=unused-import +from tensorflow.python.ops import manip_grad # pylint: disable=unused-import from tensorflow.python.ops import math_grad # pylint: disable=unused-import from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops diff --git a/tensorflow/python/ops/manip_grad.py b/tensorflow/python/ops/manip_grad.py new file mode 100644 index 0000000000..573e8c0a0d --- /dev/null +++ b/tensorflow/python/ops/manip_grad.py @@ -0,0 +1,32 @@ +# 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. +# ============================================================================== + +"""Gradients for operators defined in manip_ops.py.""" + +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 manip_ops + + +@ops.RegisterGradient("Roll") +def _RollGrad(op, grad): + # The gradient is just the roll reversed + shift = op.inputs[1] + axis = op.inputs[2] + roll_grad = manip_ops.roll(grad, -shift, axis) + return roll_grad, None, None diff --git a/tensorflow/python/ops/manip_ops.py b/tensorflow/python/ops/manip_ops.py new file mode 100644 index 0000000000..c5f39784f4 --- /dev/null +++ b/tensorflow/python/ops/manip_ops.py @@ -0,0 +1,36 @@ +# 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. +# ============================================================================== +"""Operators for manipulating tensors. + +@@roll +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.ops import gen_manip_ops as _gen_manip_ops +from tensorflow.python.util.all_util import remove_undocumented + +# pylint: disable=protected-access +def roll(input, shift, axis): + return _gen_manip_ops.roll(input, shift, axis) + +roll.__doc__ = _gen_manip_ops.roll.__doc__ +# pylint: enable=protected-access + +_allowed_symbols = ['roll'] + +remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py index 30bf4e4ef1..737b923415 100644 --- a/tensorflow/python/ops/standard_ops.py +++ b/tensorflow/python/ops/standard_ops.py @@ -26,6 +26,7 @@ import sys as _sys from tensorflow.python.ops import array_grad from tensorflow.python.ops import data_flow_grad from tensorflow.python.ops import math_grad +from tensorflow.python.ops import manip_grad from tensorflow.python.ops import sparse_grad from tensorflow.python.ops import spectral_grad from tensorflow.python.ops import state_grad @@ -59,6 +60,7 @@ from tensorflow.python.ops.logging_ops import Print from tensorflow.python.ops.logging_ops import get_summary_op from tensorflow.python.ops.lookup_ops import initialize_all_tables from tensorflow.python.ops.lookup_ops import tables_initializer +from tensorflow.python.ops.manip_ops import * from tensorflow.python.ops.math_ops import * from tensorflow.python.ops.numerics import * from tensorflow.python.ops.parsing_ops import * @@ -105,6 +107,7 @@ from tensorflow.python.ops import init_ops as _init_ops from tensorflow.python.ops import io_ops as _io_ops from tensorflow.python.ops import linalg_ops as _linalg_ops from tensorflow.python.ops import logging_ops as _logging_ops +from tensorflow.python.ops import manip_ops as _manip_ops from tensorflow.python.ops import math_ops as _math_ops from tensorflow.python.ops import numerics as _numerics from tensorflow.python.ops import parsing_ops as _parsing_ops @@ -280,6 +283,7 @@ remove_undocumented(__name__, _allowed_symbols, _io_ops, _linalg_ops, _logging_ops, + _manip_ops, _math_ops, _numerics, _parsing_ops, diff --git a/tensorflow/tools/api/golden/tensorflow.manip.pbtxt b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt new file mode 100644 index 0000000000..0b84165285 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.manip.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.manip" +tf_module { + member_method { + name: "roll" + argspec: "args=[\'input\', \'shift\', \'axis\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index dc7c3a2f45..e8890e9cc0 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -396,6 +396,10 @@ tf_module { name: "losses" mtype: "" } + member { + name: "manip" + mtype: "" + } member { name: "metrics" mtype: "" -- GitLab From ee69436c2dd034ade90e5e278ef233917ad8afcc Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Mon, 29 Jan 2018 16:22:43 -0800 Subject: [PATCH 243/423] Adds prediction_hooks into EstimatorSpec. PiperOrigin-RevId: 183747105 --- tensorflow/python/estimator/estimator.py | 5 ++++- tensorflow/python/estimator/estimator_test.py | 19 +++++++++++++++++++ tensorflow/python/estimator/model_fn.py | 15 +++++++++++---- tensorflow/python/estimator/model_fn_test.py | 15 +++++++++++++-- ...tensorflow.estimator.-estimator-spec.pbtxt | 4 ++++ 5 files changed, 51 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index face20d530..8d1f1afcff 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -477,13 +477,16 @@ class Estimator(object): estimator_spec = self._call_model_fn( features, None, model_fn_lib.ModeKeys.PREDICT, self.config) predictions = self._extract_keys(estimator_spec.predictions, predict_keys) + all_hooks = list(input_hooks) + all_hooks.extend(hooks) + all_hooks.extend(list(estimator_spec.prediction_hooks or [])) with training.MonitoredSession( session_creator=training.ChiefSessionCreator( checkpoint_filename_with_path=checkpoint_path, master=self._config.master, scaffold=estimator_spec.scaffold, config=self._session_config), - hooks=input_hooks + hooks) as mon_sess: + hooks=all_hooks) as mon_sess: while not mon_sess.should_stop(): preds_evaluated = mon_sess.run(predictions) if not isinstance(predictions, dict): diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index 833f3dcac3..39a5b998eb 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -1355,6 +1355,25 @@ class EstimatorPredictTest(test.TestCase): est.train(dummy_input_fn, steps=1) self.assertEqual(10., next(est.predict(dummy_input_fn))) + def test_predictionhooks_are_used(self): + hook = test.mock.MagicMock( + wraps=training.SessionRunHook(), spec=training.SessionRunHook) + + def _model_fn_hooks(features, labels, mode): + _, _ = features, labels + return model_fn_lib.EstimatorSpec( + mode=mode, + loss=constant_op.constant(0.), + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions=constant_op.constant([[10.]]), + prediction_hooks=[hook]) + + est = estimator.Estimator(model_fn=_model_fn_hooks) + est.train(dummy_input_fn, steps=1) + self.assertFalse(hook.begin.called) + next(est.predict(dummy_input_fn)) + self.assertTrue(hook.begin.called) + def test_warn_if_no_queue_runner(self): def _model_fn(features, labels, mode): diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index da202408c3..b08f83fc56 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -56,7 +56,7 @@ class EstimatorSpec( collections.namedtuple('EstimatorSpec', [ 'mode', 'predictions', 'loss', 'train_op', 'eval_metric_ops', 'export_outputs', 'training_chief_hooks', 'training_hooks', 'scaffold', - 'evaluation_hooks' + 'evaluation_hooks', 'prediction_hooks' ])): """Ops and objects returned from a `model_fn` and passed to an `Estimator`. @@ -73,7 +73,8 @@ class EstimatorSpec( training_chief_hooks=None, training_hooks=None, scaffold=None, - evaluation_hooks=None): + evaluation_hooks=None, + prediction_hooks=None): """Creates a validated `EstimatorSpec` instance. Depending on the value of `mode`, different arguments are required. Namely @@ -154,6 +155,8 @@ class EstimatorSpec( initialization, saver, and more to be used in training. evaluation_hooks: Iterable of `tf.train.SessionRunHook` objects to run during evaluation. + prediction_hooks: Iterable of `tf.train.SessionRunHook` objects to + run during predictions. Returns: A validated `EstimatorSpec` object. @@ -282,7 +285,10 @@ class EstimatorSpec( training_chief_hooks = tuple(training_chief_hooks or []) training_hooks = tuple(training_hooks or []) evaluation_hooks = tuple(evaluation_hooks or []) - for hook in training_hooks + training_chief_hooks + evaluation_hooks: + prediction_hooks = tuple(prediction_hooks or []) + + for hook in (training_hooks + training_chief_hooks + evaluation_hooks + + prediction_hooks): if not isinstance(hook, session_run_hook.SessionRunHook): raise TypeError( 'All hooks must be SessionRunHook instances, given: {}'.format( @@ -305,7 +311,8 @@ class EstimatorSpec( training_chief_hooks=training_chief_hooks, training_hooks=training_hooks, scaffold=scaffold, - evaluation_hooks=evaluation_hooks) + evaluation_hooks=evaluation_hooks, + prediction_hooks=prediction_hooks) def _replace(self, **kwds): """Return a new EstimatorSpec replacing specified fields with new values.""" diff --git a/tensorflow/python/estimator/model_fn_test.py b/tensorflow/python/estimator/model_fn_test.py index d67c4b7161..b7eeeb437c 100644 --- a/tensorflow/python/estimator/model_fn_test.py +++ b/tensorflow/python/estimator/model_fn_test.py @@ -72,7 +72,8 @@ class EstimatorSpecTrainTest(test.TestCase): training_chief_hooks=[_FakeHook()], training_hooks=[_FakeHook()], scaffold=monitored_session.Scaffold(), - evaluation_hooks=[_FakeHook()]) + evaluation_hooks=[_FakeHook()], + prediction_hooks=[_FakeHook()]) def testLossNumber(self): """Tests that error is raised when loss is a number (not Tensor).""" @@ -465,7 +466,17 @@ class EstimatorSpecInferTest(test.TestCase): training_chief_hooks=[_FakeHook()], training_hooks=[_FakeHook()], scaffold=monitored_session.Scaffold(), - evaluation_hooks=[_FakeHook()]) + evaluation_hooks=[_FakeHook()], + prediction_hooks=[_FakeHook()]) + + def testPredictionHookInvalid(self): + with ops.Graph().as_default(), self.test_session(): + with self.assertRaisesRegexp( + TypeError, 'All hooks must be SessionRunHook instances'): + model_fn.EstimatorSpec( + mode=model_fn.ModeKeys.PREDICT, + predictions=constant_op.constant(1.), + prediction_hooks=[_InvalidHook()]) def testPredictionsMissing(self): with ops.Graph().as_default(), self.test_session(): diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt index dbcc187f94..aa6ac46613 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt @@ -23,6 +23,10 @@ tf_class { name: "mode" mtype: "" } + member { + name: "prediction_hooks" + mtype: "" + } member { name: "predictions" mtype: "" -- GitLab From a9db14e45d799d62914b5cde31d4d85f007b85eb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 16:50:27 -0800 Subject: [PATCH 244/423] Making a bunch of edits to the Getting Started with Premade Estimators doc for consistency and correctness. PiperOrigin-RevId: 183751330 --- .../get_started/premade_estimators.md | 115 ++++++++++-------- 1 file changed, 62 insertions(+), 53 deletions(-) diff --git a/tensorflow/docs_src/get_started/premade_estimators.md b/tensorflow/docs_src/get_started/premade_estimators.md index dbc35065ab..45850a8996 100644 --- a/tensorflow/docs_src/get_started/premade_estimators.md +++ b/tensorflow/docs_src/get_started/premade_estimators.md @@ -2,37 +2,39 @@ # Getting Started with TensorFlow This document introduces the TensorFlow programming environment and shows you -how to write the Iris classification problem in TensorFlow. +how to solve the Iris classification problem in TensorFlow. -Prior to reading this document, do the following: +## Prerequisites + +Prior to using the sample code in this document, you'll need to do the +following: * @{$install$Install TensorFlow}. * If you installed TensorFlow with virtualenv or Anaconda, activate your TensorFlow environment. -* To keep the data import simple, our Iris example uses Pandas. You can - install Pandas with: +* Install or upgrade pandas by issuing the following command: - `pip install pandas` + pip install pandas ## Getting the sample code -Take the following steps to get the sample code for this program: +Take the following steps to get the sample code we'll be going through: -1. Clone the TensorFlow Models repository from github by entering the following +1. Clone the TensorFlow Models repository from GitHub by entering the following command: - `git clone https://github.com/tensorflow/models` + git clone https://github.com/tensorflow/models 1. Change directory within that branch to the location containing the examples used in this document: - `cd models/samples/core/get_started/` + cd models/samples/core/get_started/ The program described in this document is [`premade_estimator.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/premade_estimator.py). This program uses [`iris_data.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/iris_data.py) -To fetch its training data. +to fetch its training data. ### Running the program @@ -45,7 +47,7 @@ python premade_estimator.py The program should output training logs followed by some predictions against the test set. For example, the first line in the following output shows that the model thinks there is a 99.6% chance that the first example in the test -set is a Setosa. Since the test set `expected "Setosa"`, this appears to be +set is a Setosa. Since the test set expected Setosa, this appears to be a good prediction. ``` None @@ -61,9 +63,9 @@ If the program generates errors instead of answers, ask yourself the following questions: * Did you install TensorFlow properly? -* Are you using the correct version of tensorflow? +* Are you using the correct version of TensorFlow? * Did you activate the environment you installed TensorFlow in? (This is - only relevant in certain installation environments.) + only relevant in certain installation mechanisms.) ## The programming stack @@ -74,18 +76,15 @@ provides a programming stack consisting of multiple API layers:
-
-The TensorFlow Programming Environment -
We strongly recommend writing TensorFlow programs with the following APIs: -* @{tf.estimator$Estimators}, which represent a complete model. +* @{$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$Datasets}, which build a data input pipeline. The Dataset API has methods to load and manipulate data, and feed - it into your model. The Datasets API meshes well with the Estimators API. + it into your model. The Dataset API meshes well with the Estimators API. ## Classifying irises: an overview @@ -120,7 +119,7 @@ individual Iris flowers: * petal length * petal width -Our model will represent these features as float32 numerical data. +Our model will represent these features as `float32` numerical data. The label identifies the Iris species, which must be one of the following: @@ -154,9 +153,6 @@ The following figure illustrates the features, hidden layers, and predictions alt="A diagram of the network architecture: Inputs, 2 hidden layers, and outputs" src="../images/custom_estimators/full_network.png">
-
-The Model. -
### Inference @@ -174,12 +170,12 @@ example is an Iris Versicolor. ## Overview of programming with Estimators -An Estimator is TensorFlow's high level representation of a complete model. It +An Estimator is TensorFlow's high-level representation of a complete model. It handles the details of initialization, logging, saving and restoring, and many other features so you can concentrate on your model. For more details see @{$programmers_guide/estimators}. -An "Estimator" is any class derived from @{tf.estimator.Estimator}. TensorFlow +An Estimator is any class derived from @{tf.estimator.Estimator}. TensorFlow provides a collection of [pre-made Estimators](https://developers.google.com/machine-learning/glossary/#pre-made_Estimator) (for example, `LinearRegressor`) to implement common ML algorithms. Beyond @@ -199,7 +195,7 @@ following tasks: * Call one or more methods on the Estimator object, passing the appropriate input function as the source of the data. -Let's see how those tasks are implemented in Iris. +Let's see how those tasks are implemented for Iris classification. ## Create input functions @@ -209,17 +205,30 @@ evaluating, and prediction. An **input function** is a function that returns a @{tf.data.Dataset} object which outputs the following two-element tuple: -* "features" - A Python dictionary in which: +* [`features`](https://developers.google.com/machine-learning/glossary/#feature) - A Python dictionary in which: * Each key is the name of a feature. * Each value is an array containing all of that feature's values. -* "label" - An array containing the values of the +* `label` - An array containing the values of the [label](https://developers.google.com/machine-learning/glossary/#label) for every example. -Your input function may generate the "features" dictionary and "label" list any -way you like. However, we recommend using TensorFlow's @{tf.data.Dataset} API, -which can deftly parse all sorts of data. At a high-level, -the @{tf.data.Dataset} API consists of the following classes: +Just to demonstrate the format of the input function, here's a simple +implementation: + +```python +def input_evaluation_set(): + features = {'SepalLength': np.array([6.4, 5.0]), + 'SepalWidth': np.array([2.8, 2.3]), + 'PetalLength': np.array([5.6, 3.3]), + 'PetalWidth': np.array([2.2, 1.0])} + labels = np.array([2, 1]) + return features, labels +``` + +Your input function may generate the `features` dictionary and `label` list any +way you like. However, we recommend using TensorFlow's Dataset API, which can +parse all sorts of data. At a high level, the Dataset API consists of the +following classes:
+Where the individual members are: -Where: - -* Dataset: Base class containing methods to create and transform datasets. Also - allows you to initialize a dataset from data in memory, or from a Python - generator. -* TextLineDataset: Reads lines from text files. -* TFRecordDataset: Reads records from TFRecord files. -* FixedLengthRecordDataset: Reads fixed size records from binary files. -* Iterator: Provides a way to access one data set element at a time. +* `Dataset` - Base class containing methods to create and transform + datasets. Also allows you to initialize a dataset from data in memory, or from + a Python generator. +* `TextLineDataset` - Reads lines from text files. +* `TFRecordDataset` - Reads records from TFRecord files. +* `FixedLengthRecordDataset` - Reads fixed size records from binary files. +* `Iterator` - Provides a way to access one data set element at a time. The Dataset API can handle a lot of common cases for you. For example, using the Dataset API, you can easily read in records from a large collection of files in parallel and join them into a single stream. -To keep things simple in this example we are going to load the data with pandas, -and build our input pipeline from this in-memory data. +To keep things simple in this example we are going to load the data with +[pandas](https://pandas.pydata.org/), and build our input pipeline from this +in-memory data. Here is the input function used for training in this program, which is available in [`iris_data.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/iris_data.py): @@ -258,9 +267,9 @@ def train_input_fn(features, labels, batch_size): return dataset.shuffle(1000).repeat().batch(batch_size) ``` -## Define the Feature Columns +## Define the feature columns -A [**Feature Column**](https://developers.google.com/machine-learning/glossary/#feature_columns) +A [**feature column**](https://developers.google.com/machine-learning/glossary/#feature_columns) is an object describing how the model should use raw input data from the features dictionary. When you build an Estimator model, you pass it a list of feature columns that describes each of the features you want the model to use. @@ -270,7 +279,7 @@ to the model. For Iris, the 4 raw features are numeric values, so we'll build a list of feature columns to tell the Estimator model to represent each of the four features as 32-bit floating-point values. Therefore, the code to create the -Feature Column is simply: +feature column is: ```python # Feature columns describe how to use the input. @@ -279,29 +288,29 @@ for key in train_x.keys(): my_feature_columns.append(tf.feature_column.numeric_column(key=key)) ``` -Feature Columns can be far more sophisticated than those we're showing here. -We detail feature columns @{$get_started/feature_columns$later on} in -getting started. +Feature columns can be far more sophisticated than those we're showing here. We +detail feature columns @{$get_started/feature_columns$later on} in our Getting +Started guide. Now that we have the description of how we want the model to represent the raw features, we can build the estimator. -## Instantiate an Estimator +## Instantiate an estimator The Iris problem is a classic classification problem. Fortunately, TensorFlow provides several pre-made classifier Estimators, including: -* @{tf.estimator.DNNClassifier}—for deep models that perform multi-class +* @{tf.estimator.DNNClassifier} for deep models that perform multi-class classification. -* @{tf.estimator.DNNLinearCombinedClassifier}—for wide-n-deep models. -* @{tf.estimator.LinearClassifier}— for classifiers based on linear models. +* @{tf.estimator.DNNLinearCombinedClassifier} for wide & deep models. +* @{tf.estimator.LinearClassifier} for classifiers based on linear models. For the Iris problem, `tf.estimator.DNNClassifier` seems like the best choice. Here's how we instantiated this Estimator: ```python -# Build 2 hidden layer DNN with 10, 10 units respectively. +# Build a DNN with 2 hidden layers and 10 nodes in each hidden layer. classifier = tf.estimator.DNNClassifier( feature_columns=my_feature_columns, # Two hidden layers of 10 nodes each. -- GitLab From 89a6ff9bff0fac47788f8cfe6693a72316e0eb5d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 17:05:21 -0800 Subject: [PATCH 245/423] Add support for the assert statement. PiperOrigin-RevId: 183753774 --- tensorflow/contrib/py2tf/conversion.py | 2 + tensorflow/contrib/py2tf/converters/BUILD | 23 ++++++++ .../contrib/py2tf/converters/asserts.py | 53 +++++++++++++++++++ .../contrib/py2tf/converters/asserts_test.py | 42 +++++++++++++++ 4 files changed, 120 insertions(+) create mode 100644 tensorflow/contrib/py2tf/converters/asserts.py create mode 100644 tensorflow/contrib/py2tf/converters/asserts_test.py diff --git a/tensorflow/contrib/py2tf/conversion.py b/tensorflow/contrib/py2tf/conversion.py index e277eadec4..67ca52d194 100644 --- a/tensorflow/contrib/py2tf/conversion.py +++ b/tensorflow/contrib/py2tf/conversion.py @@ -23,6 +23,7 @@ import six from tensorflow.contrib.py2tf import config from tensorflow.contrib.py2tf import naming +from tensorflow.contrib.py2tf.converters import asserts from tensorflow.contrib.py2tf.converters import break_canonicalization from tensorflow.contrib.py2tf.converters import builtin_functions from tensorflow.contrib.py2tf.converters import call_trees @@ -245,6 +246,7 @@ def node_to_graph(node, ctx, nocompile_decorators): node = _static_analysis_pass(node, ctx) node = decorators.transform(node, nocompile_decorators) node = break_canonicalization.transform(node, ctx.namer) + node = asserts.transform(node, ctx) # Note: sequencing continue canonicalization before for loop one avoids # dealing with the extra loop increment operation that the for diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD index 4f90f94e09..b61fda3e91 100644 --- a/tensorflow/contrib/py2tf/converters/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -17,6 +17,7 @@ filegroup( py_library( name = "converters", srcs = [ + "asserts.py", "break_canonicalization.py", "builtin_functions.py", "call_trees.py", @@ -49,6 +50,17 @@ py_library( ], ) +py_test( + name = "asserts_test", + srcs = ["asserts_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":test_lib", + "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "break_canonicalization_test", srcs = ["break_canonicalization_test.py"], @@ -71,6 +83,17 @@ py_test( ], ) +py_test( + name = "decorators_test", + srcs = ["decorators_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":test_lib", + "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "continue_canonicalization_test", srcs = ["continue_canonicalization_test.py"], diff --git a/tensorflow/contrib/py2tf/converters/asserts.py b/tensorflow/contrib/py2tf/converters/asserts.py new file mode 100644 index 0000000000..2d6ee1d098 --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/asserts.py @@ -0,0 +1,53 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Converts Assert statements to their corresponding TF calls.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.py2tf.pyct import templates +from tensorflow.contrib.py2tf.pyct import transformer + + +class AssertsTransformer(transformer.Base): + """Transforms Print nodes to Call so they can be handled as functions.""" + + # pylint:disable=invalid-name + + def visit_Assert(self, node): + self.generic_visit(node) + + # Note: The lone tf.Assert call will be wrapped with control_dependencies + # by side_effect_guards. + template = """ + tf.Assert(test, [tf.constant(msg)]) + """ + + if node.msg is None: + return templates.replace( + template, test=node.test, msg=gast.Str('Assertion error')) + elif isinstance(node.msg, gast.Str): + return templates.replace(template, test=node.test, msg=node.msg) + else: + raise NotImplementedError('Can only convert string messages for now.') + + # pylint:enable=invalid-name + + +def transform(node, context): + return AssertsTransformer(context).visit(node) diff --git a/tensorflow/contrib/py2tf/converters/asserts_test.py b/tensorflow/contrib/py2tf/converters/asserts_test.py new file mode 100644 index 0000000000..6611f2777a --- /dev/null +++ b/tensorflow/contrib/py2tf/converters/asserts_test.py @@ -0,0 +1,42 @@ +# 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 asserts module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.py2tf.converters import asserts +from tensorflow.contrib.py2tf.converters import converter_test_base +from tensorflow.python.platform import test + + +class AssertsTest(converter_test_base.TestCase): + + def test_transform(self): + + def test_fn(a): + assert a > 0 + + node = self.parse_and_analyze(test_fn, {}) + node = asserts.transform(node, self.ctx) + + self.assertTrue(isinstance(node.body[0].body[0].value, gast.Call)) + + +if __name__ == '__main__': + test.main() -- GitLab From 18447358543598abc3296f5df9e7774528dc53f0 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Mon, 29 Jan 2018 17:09:21 -0800 Subject: [PATCH 246/423] Remove HloRunner::ReadModule. Replace with methods which explicitly specify the HLO file format. ReadModule would automatically determine the format of the file (HLO text, text proto, or binary proto). However, automatic determination did not work well because the underlying TF code which read protos from files unconditionally emitted error messages to stderr in case of parsing error resulting in confusing and irrelevant error messages to the user. PiperOrigin-RevId: 183754369 --- tensorflow/compiler/xla/service/hlo_runner.cc | 54 +++++++++---------- tensorflow/compiler/xla/service/hlo_runner.h | 16 ++---- .../compiler/xla/tests/hlo_test_base.cc | 4 +- 3 files changed, 32 insertions(+), 42 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_runner.cc b/tensorflow/compiler/xla/service/hlo_runner.cc index e281538848..41b079eb79 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.cc +++ b/tensorflow/compiler/xla/service/hlo_runner.cc @@ -47,22 +47,11 @@ HloRunner::CreateModuleFromString(const tensorflow::StringPiece hlo_string, return tools::Parse(hlo_string, config); } -/*static*/ StatusOr> -HloRunner::ReadModuleFromHloProtoFile(const std::string& filename, - const DebugOptions& debug_options) { - HloProto proto; - - const Status s = - tensorflow::ReadBinaryProto(tensorflow::Env::Default(), filename, &proto); - - if (!s.ok()) { - const Status s2 = - tensorflow::ReadTextProto(tensorflow::Env::Default(), filename, &proto); - if (!s2.ok()) { - return Status(s2.code(), s.error_message() + "\n" + s2.error_message()); - } - } +namespace { +// Creates an HloModule from the given proto. +StatusOr> HloProtoToModule( + const HloProto& proto, const DebugOptions& debug_options) { TF_ASSIGN_OR_RETURN( HloModuleConfig config, HloModule::CreateModuleConfigFromProto(proto.hlo_module())); @@ -72,9 +61,29 @@ HloRunner::ReadModuleFromHloProtoFile(const std::string& filename, return std::move(module); } +} // namespace + /*static*/ StatusOr> -HloRunner::ReadModuleFromHloTextDumpFile(const std::string& filename, +HloRunner::ReadModuleFromBinaryProtoFile(const std::string& filename, const DebugOptions& debug_options) { + HloProto proto; + TF_RETURN_IF_ERROR(tensorflow::ReadBinaryProto(tensorflow::Env::Default(), + filename, &proto)); + return HloProtoToModule(proto, debug_options); +} + +/*static*/ StatusOr> +HloRunner::ReadModuleFromTextProtoFile(const std::string& filename, + const DebugOptions& debug_options) { + HloProto proto; + TF_RETURN_IF_ERROR( + tensorflow::ReadTextProto(tensorflow::Env::Default(), filename, &proto)); + return HloProtoToModule(proto, debug_options); +} + +/*static*/ StatusOr> +HloRunner::ReadModuleFromHloTextFile(const std::string& filename, + const DebugOptions& debug_options) { string hlo_string; TF_RETURN_IF_ERROR(tensorflow::ReadFileToString(tensorflow::Env::Default(), filename, &hlo_string)); @@ -83,19 +92,6 @@ HloRunner::ReadModuleFromHloTextDumpFile(const std::string& filename, return tools::Parse(hlo_string, config); } -/*static*/ StatusOr> HloRunner::ReadModule( - const std::string& filename, const DebugOptions& debug_options) { - auto module = HloRunner::ReadModuleFromHloProtoFile(filename, debug_options); - if (module.ok()) { - return module; - } - const std::string e = module.status().error_message(); - module = HloRunner::ReadModuleFromHloTextDumpFile(filename, debug_options); - return module.ok() ? std::move(module) - : Status(module.status().code(), - e + "\n" + module.status().error_message()); -} - // Define this in .cc file to avoid having to include eigen or forward declare // these types in the header. struct HloRunner::EigenThreadPoolWrapper { diff --git a/tensorflow/compiler/xla/service/hlo_runner.h b/tensorflow/compiler/xla/service/hlo_runner.h index d4b221fb52..cbaebc68be 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.h +++ b/tensorflow/compiler/xla/service/hlo_runner.h @@ -52,21 +52,15 @@ class HloRunner { const DebugOptions& debug_options); // Reads the proto file in xla.HloProto format, creates and returns the - // HloModule. Will try to parse the filename as binary proto, then try as - // text proto if that fails. - static StatusOr> ReadModuleFromHloProtoFile( + // HloModule. + static StatusOr> ReadModuleFromBinaryProtoFile( + const std::string& filename, const DebugOptions& debug_options); + static StatusOr> ReadModuleFromTextProtoFile( const std::string& filename, const DebugOptions& debug_options); // Reads the hlo text dump file in HloModule::ToString format, creates and // returns the HloModule. - static StatusOr> ReadModuleFromHloTextDumpFile( - const std::string& filename, const DebugOptions& debug_options); - - // Tries to parse the filename specified first as binary proto format, then - // as a textual proto format, then textual IR, then gives up if both fail. - // ReadModuleFromHloProtoFile or ReadModuleFromHloTextDumpFile should be used - // explicitly when you know the format, this if you don't. - static StatusOr> ReadModule( + static StatusOr> ReadModuleFromHloTextFile( const std::string& filename, const DebugOptions& debug_options); // Executes the given module with given literals as input and returns the diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index 7c1a993b47..9f5806c5e1 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -230,7 +230,7 @@ template const string& filename, const tensorflow::gtl::optional& error, const std::function& reference_preprocessor) { auto module_or_status = - HloRunner::ReadModule(filename, GetDebugOptionsForTest()); + HloRunner::ReadModuleFromHloTextFile(filename, GetDebugOptionsForTest()); if (!module_or_status.ok()) { return ::testing::AssertionFailure() << "failed reading hlo module from file"; @@ -258,7 +258,7 @@ template const string& filename, const tensorflow::gtl::optional& error, const std::function& reference_preprocessor) { auto module_or_status = - HloRunner::ReadModule(filename, GetDebugOptionsForTest()); + HloRunner::ReadModuleFromHloTextFile(filename, GetDebugOptionsForTest()); if (!module_or_status.ok()) { return ::testing::AssertionFailure() << "failed reading hlo module from file"; -- GitLab From aca9f4257f064d381be171a7ff2ee114002a8fab Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 17:25:45 -0800 Subject: [PATCH 247/423] [TF:XLA] Complete the TriangularSolve implementation. The previous version only handled the case of left_side=false, lower=true, transpose_a=true, conjugate_a=false. This updated implementation handles all 16 combinations of those boolean options, and also instantiates the corresponding MatrixTriangularSolve TF op. To improve compile times and potentially FLOP performance, when lower=true the within-block subroutine used on the diagonal blocks is now a left-looking variant implemented with an XLA HLO While loop. This update also slightly generalizes BatchDot in tf2xla/lib to accept separate conjugation arguments (in addition to transpose arguments). PiperOrigin-RevId: 183756639 --- tensorflow/compiler/tests/BUILD | 15 + .../tests/matrix_triangular_solve_op_test.py | 130 +++++ .../tf2xla/g3doc/cpu_supported_ops.md | 24 +- .../tf2xla/g3doc/gpu_supported_ops.md | 24 +- tensorflow/compiler/tf2xla/kernels/BUILD | 2 + .../tf2xla/kernels/batch_matmul_op.cc | 5 +- .../compiler/tf2xla/kernels/cholesky_op.cc | 2 +- .../kernels/matrix_triangular_solve_op.cc | 50 ++ tensorflow/compiler/tf2xla/lib/BUILD | 2 + tensorflow/compiler/tf2xla/lib/batch_dot.cc | 9 +- tensorflow/compiler/tf2xla/lib/batch_dot.h | 10 +- tensorflow/compiler/tf2xla/lib/cholesky.cc | 14 +- tensorflow/compiler/tf2xla/lib/cholesky.h | 1 + .../compiler/tf2xla/lib/triangular_solve.cc | 469 ++++++++++++++++-- .../compiler/tf2xla/lib/triangular_solve.h | 51 +- .../tf2xla/lib/triangular_solve_test.cc | 324 +++++++++++- tensorflow/compiler/tf2xla/lib/util.cc | 11 + tensorflow/compiler/tf2xla/lib/util.h | 4 + 18 files changed, 1055 insertions(+), 92 deletions(-) create mode 100644 tensorflow/compiler/tests/matrix_triangular_solve_op_test.py create mode 100644 tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.cc diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index a3a82df9ad..9e64f3e9a3 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -144,6 +144,21 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "matrix_triangular_solve_op_test", + size = "small", + srcs = ["matrix_triangular_solve_op_test.py"], + tags = ["optonly"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + "//tensorflow/python:training", + ], +) + tf_xla_py_test( name = "clustering_test", size = "small", diff --git a/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py b/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py new file mode 100644 index 0000000000..cccb7f5789 --- /dev/null +++ b/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py @@ -0,0 +1,130 @@ +# 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.ops.tf.MatrixTriangularSolve.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import itertools + +import numpy as np + +from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import linalg_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +def MakePlaceholder(x): + return array_ops.placeholder(dtypes.as_dtype(x.dtype), shape=x.shape) + + +class MatrixTriangularSolveOpTest(XLATestCase): + + def _VerifyTriangularSolveBase(self, sess, placeholder_a, placeholder_ca, + placeholder_b, a, clean_a, b, verification, + atol): + feed_dict = {placeholder_a: a, placeholder_ca: clean_a, placeholder_b: b} + verification_np = sess.run(verification, feed_dict) + self.assertAllClose(b, verification_np, atol=atol) + + def _VerifyTriangularSolve(self, a, b, lower, adjoint, atol): + clean_a = np.tril(a) if lower else np.triu(a) + with self.test_session() as sess: + placeholder_a = MakePlaceholder(a) + placeholder_ca = MakePlaceholder(clean_a) + placeholder_b = MakePlaceholder(b) + with self.test_scope(): + x = linalg_ops.matrix_triangular_solve( + placeholder_a, placeholder_b, lower=lower, adjoint=adjoint) + verification = math_ops.matmul(placeholder_ca, x, adjoint_a=adjoint) + self._VerifyTriangularSolveBase(sess, placeholder_a, placeholder_ca, + placeholder_b, a, clean_a, b, + verification, atol) + + def _VerifyTriangularSolveCombo(self, a, b, atol=1e-4): + transp = lambda x: np.swapaxes(x, -1, -2) + for lower, adjoint in itertools.product([True, False], repeat=2): + self._VerifyTriangularSolve( + a if lower else transp(a), b, lower, adjoint, atol) + + def testBasic(self): + rng = np.random.RandomState(0) + a = np.tril(rng.randn(5, 5)) + b = rng.randn(5, 7) + for dtype in self.float_types: + self._VerifyTriangularSolveCombo(a.astype(dtype), b.astype(dtype)) + + def testBasicNotActuallyTriangular(self): + rng = np.random.RandomState(0) + a = rng.randn(5, 5) # the `a` matrix is not lower-triangular + b = rng.randn(5, 7) + for dtype in self.float_types: + self._VerifyTriangularSolveCombo(a.astype(dtype), b.astype(dtype)) + + def testBasicComplexDtypes(self): + rng = np.random.RandomState(0) + a = np.tril(rng.randn(5, 5) + rng.randn(5, 5) * 1j) + b = rng.randn(5, 7) + rng.randn(5, 7) * 1j + for dtype in self.complex_types: + self._VerifyTriangularSolveCombo(a.astype(dtype), b.astype(dtype)) + + def testBatch(self): + rng = np.random.RandomState(0) + shapes = [((4, 3, 3), (4, 3, 5)), ((1, 2, 2), (1, 2, 1)), + ((1, 1, 1), (1, 1, 2)), ((2, 3, 4, 4), (2, 3, 4, 1))] + tuples = itertools.product(self.float_types, shapes) + for dtype, (a_shape, b_shape) in tuples: + n = a_shape[-1] + a = np.tril(rng.rand(*a_shape) - 0.5) / (2.0 * n) + np.eye(n) + b = rng.randn(*b_shape) + self._VerifyTriangularSolveCombo( + a.astype(dtype), b.astype(dtype), atol=1e-3) + + def testLarge(self): + n = 1024 + rng = np.random.RandomState(0) + a = np.tril(rng.rand(n, n) - 0.5) / (2.0 * n) + np.eye(n) + b = rng.randn(n, n) + self._VerifyTriangularSolve( + a.astype(np.float32), b.astype(np.float32), True, False, 1e-4) + + def testNonSquareCoefficientMatrix(self): + rng = np.random.RandomState(0) + for dtype in self.float_types: + a = rng.randn(3, 4).astype(dtype) + b = rng.randn(4, 4).astype(dtype) + with self.assertRaises(ValueError): + linalg_ops.matrix_triangular_solve(a, b) + with self.assertRaises(ValueError): + linalg_ops.matrix_triangular_solve(a, b) + + def testWrongDimensions(self): + randn = np.random.RandomState(0).randn + for dtype in self.float_types: + lhs = constant_op.constant(randn(3, 3), dtype=dtype) + rhs = constant_op.constant(randn(4, 3), dtype=dtype) + with self.assertRaises(ValueError): + linalg_ops.matrix_triangular_solve(lhs, rhs) + with self.assertRaises(ValueError): + linalg_ops.matrix_triangular_solve(lhs, rhs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md index 82b3b46a2f..44f7db5ffd 100644 --- a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md @@ -6,6 +6,9 @@ Operator | Type Constraint `Acosh` | `T={complex64,double,float}` `Add` | `T={complex64,double,float,int32,int64}` `AddN` | `T={complex64,double,float,int32,int64,uint32,uint64}` +`AdjustContrastv2` | +`AdjustHue` | +`AdjustSaturation` | `All` | `Tidx={int32,int64}` `Angle` | `Tout={double,float}`
`T={complex64}` `Any` | `Tidx={int32,int64}` @@ -34,7 +37,7 @@ Operator | Type Constraint `BroadcastGradientArgs` | `T={int32,int64}` `Cast` | `DstT={bool,complex64,double,float,int32,int64,uint32,uint64}`
`SrcT={bool,complex64,double,float,int32,int64,uint32,uint64}` `Ceil` | `T={double,float}` -`Cholesky` | `T={complex64,double,float}` +`Cholesky` | `T={double,float}` `Complex` | `Tout={complex64}`
`T={double,float}` `ComplexAbs` | `Tout={double,float}`
`T={complex64}` `Concat` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` @@ -68,7 +71,10 @@ Operator | Type Constraint `Exp` | `T={complex64,double,float}` `ExpandDims` | `Tdim={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Expm1` | `T={complex64,double,float}` -`Fill` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`FFT` | +`FFT2D` | +`FFT3D` | +`Fill` | `index_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Floor` | `T={double,float}` `FloorDiv` | `T={complex64,double,float,int32,int64}` `FloorMod` | `T={double,float,int32,int64}` @@ -80,6 +86,13 @@ Operator | Type Constraint `GatherV2` | `Taxis={int32,int64}`
`Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `Greater` | `T={double,float,int32,int64,uint32,uint64}` `GreaterEqual` | `T={double,float,int32,int64,uint32,uint64}` +`HSVToRGB` | `T={double,float}` +`IFFT` | +`IFFT2D` | +`IFFT3D` | +`IRFFT` | +`IRFFT2D` | +`IRFFT3D` | `Identity` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `IdentityN` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Imag` | `Tout={double,float}`
`T={complex64}` @@ -105,6 +118,7 @@ Operator | Type Constraint `MatMul` | `T={complex64,double,float}` `MatrixDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiagPart` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`MatrixTriangularSolve` | `T={complex64,double,float}` `Max` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `MaxPool` | `T={double,float,int32,int64}` `MaxPool3D` | `T={float}` @@ -131,6 +145,10 @@ Operator | Type Constraint `PreventGradient` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Prod` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `QuantizeAndDequantizeV2` | `T={double,float}` +`RFFT` | +`RFFT2D` | +`RFFT3D` | +`RGBToHSV` | `T={double,float}` `RandomStandardNormal` | `dtype={float}` `RandomUniform` | `T={int32,int64}`
`dtype={double,float}` `RandomUniformInt` | `T={int32,int64}`
`Tout={int32,int64}` @@ -146,6 +164,8 @@ Operator | Type Constraint `Relu6Grad` | `T={double,float,int32,int64,uint32,uint64}` `ReluGrad` | `T={double,float,int32,int64,uint32,uint64}` `Reshape` | `Tshape={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`ResizeBilinear` | `T={double,float,int32,int64}` +`ResizeBilinearGrad` | `T={double,float}` `ResourceApplyAdagrad` | `T={double,float}` `ResourceApplyAdam` | `T={double,float}` `ResourceApplyFtrl` | `T={double,float}` diff --git a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md index d4b7621ad2..eb1f891125 100644 --- a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md @@ -6,6 +6,9 @@ Operator | Type Constraint `Acosh` | `T={complex64,double,float}` `Add` | `T={complex64,double,float,int32,int64}` `AddN` | `T={complex64,double,float,int32,int64,uint32,uint64}` +`AdjustContrastv2` | +`AdjustHue` | +`AdjustSaturation` | `All` | `Tidx={int32,int64}` `Angle` | `Tout={double,float}`
`T={complex64}` `Any` | `Tidx={int32,int64}` @@ -34,7 +37,7 @@ Operator | Type Constraint `BroadcastGradientArgs` | `T={int32,int64}` `Cast` | `DstT={bool,complex64,double,float,int32,int64,uint32,uint64}`
`SrcT={bool,complex64,double,float,int32,int64,uint32,uint64}` `Ceil` | `T={double,float}` -`Cholesky` | `T={complex64,double,float}` +`Cholesky` | `T={double,float}` `Complex` | `Tout={complex64}`
`T={double,float}` `ComplexAbs` | `Tout={double,float}`
`T={complex64}` `Concat` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` @@ -68,7 +71,10 @@ Operator | Type Constraint `Exp` | `T={complex64,double,float}` `ExpandDims` | `Tdim={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Expm1` | `T={complex64,double,float}` -`Fill` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`FFT` | +`FFT2D` | +`FFT3D` | +`Fill` | `index_type={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Floor` | `T={double,float}` `FloorDiv` | `T={complex64,double,float,int32,int64}` `FloorMod` | `T={double,float,int32,int64}` @@ -80,6 +86,13 @@ Operator | Type Constraint `GatherV2` | `Taxis={int32,int64}`
`Tindices={int32,int64}`
`Tparams={bool,complex64,double,float,int32,int64,uint32,uint64}` `Greater` | `T={double,float,int32,int64,uint32,uint64}` `GreaterEqual` | `T={double,float,int32,int64,uint32,uint64}` +`HSVToRGB` | `T={double,float}` +`IFFT` | +`IFFT2D` | +`IFFT3D` | +`IRFFT` | +`IRFFT2D` | +`IRFFT3D` | `Identity` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `IdentityN` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Imag` | `Tout={double,float}`
`T={complex64}` @@ -105,6 +118,7 @@ Operator | Type Constraint `MatMul` | `T={complex64,double,float}` `MatrixDiag` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `MatrixDiagPart` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`MatrixTriangularSolve` | `T={complex64,double,float}` `Max` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `MaxPool` | `T={double,float,int32,int64}` `MaxPool3D` | `T={float}` @@ -131,6 +145,10 @@ Operator | Type Constraint `PreventGradient` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Prod` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `QuantizeAndDequantizeV2` | `T={double,float}` +`RFFT` | +`RFFT2D` | +`RFFT3D` | +`RGBToHSV` | `T={double,float}` `Range` | `Tidx={double,float,int32,int64}` `Rank` | `T={bool,complex64,double,float,int32,int64,uint32,uint64}` `ReadVariableOp` | `dtype={bool,complex64,double,float,int32,int64,uint32,uint64}` @@ -143,6 +161,8 @@ Operator | Type Constraint `Relu6Grad` | `T={double,float,int32,int64,uint32,uint64}` `ReluGrad` | `T={double,float,int32,int64,uint32,uint64}` `Reshape` | `Tshape={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` +`ResizeBilinear` | `T={double,float,int32,int64}` +`ResizeBilinearGrad` | `T={double,float}` `ResourceApplyAdagrad` | `T={double,float}` `ResourceApplyAdam` | `T={double,float}` `ResourceApplyFtrl` | `T={double,float}` diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index a7e00cb12f..84fa43f4fb 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -43,6 +43,7 @@ tf_kernel_library( "l2loss_op.cc", "lrn_ops.cc", "matmul_op.cc", + "matrix_triangular_solve_op.cc", "mirror_pad_op.cc", "no_op.cc", "one_hot_op.cc", @@ -93,6 +94,7 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/lib:batch_dot", "//tensorflow/compiler/tf2xla/lib:cholesky", + "//tensorflow/compiler/tf2xla/lib:triangular_solve", "//tensorflow/compiler/tf2xla/lib:util", "//tensorflow/compiler/tf2xla/ops:sendrecv_ops", "//tensorflow/compiler/xla:array4d", diff --git a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc index a015b8e0e8..b0ba25b998 100644 --- a/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batch_matmul_op.cc @@ -28,8 +28,9 @@ class BatchMatMulOp : public XlaOpKernel { } void Compile(XlaOpKernelContext* ctx) override { - auto result = - BatchDot(ctx->builder(), ctx->Input(0), ctx->Input(1), adj_x_, adj_y_); + auto result = BatchDot(ctx->builder(), ctx->Input(0), ctx->Input(1), + /*transpose_x=*/adj_x_, /*transpose_y=*/adj_y_, + /*conjugate_x=*/adj_x_, /*conjugate_y=*/adj_y_); OP_REQUIRES_OK(ctx, result.status()); ctx->SetOutput(0, result.ValueOrDie()); } diff --git a/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc b/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc index 87d858f763..fe6651793d 100644 --- a/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cholesky_op.cc @@ -33,7 +33,7 @@ class CholeskyOp : public XlaOpKernel { } }; -REGISTER_XLA_OP(Name("Cholesky"), CholeskyOp); +REGISTER_XLA_OP(Name("Cholesky").TypeConstraint("T", kFloatTypes), CholeskyOp); } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.cc new file mode 100644 index 0000000000..eaed931464 --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/matrix_triangular_solve_op.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/tf2xla/lib/triangular_solve.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" + +namespace tensorflow { +namespace { + +class MatrixTriangularSolveOp : public XlaOpKernel { + public: + explicit MatrixTriangularSolveOp(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("lower", &lower_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("adjoint", &adjoint_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + auto result = TriangularSolve( + ctx->builder(), ctx->Input(0), ctx->Input(1), /*left_side=*/true, + /*lower=*/lower_, /*transpose_a=*/adjoint_, /*conjugate_a=*/adjoint_); + if (!result.ok()) { + ctx->SetStatus(result.status()); + return; + } + ctx->SetOutput(0, result.ValueOrDie()); + } + + private: + bool lower_; + bool adjoint_; +}; + +REGISTER_XLA_OP(Name("MatrixTriangularSolve"), MatrixTriangularSolveOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index 21ad21f737..d184f59e01 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -60,6 +60,8 @@ cc_library( "//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/client:computation", "//tensorflow/compiler/xla/client:computation_builder", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.cc b/tensorflow/compiler/tf2xla/lib/batch_dot.cc index 9b0e617447..798f0fa780 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.cc +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.cc @@ -25,11 +25,10 @@ limitations under the License. namespace tensorflow { -// The current implementation simply unrolls the computation along the batch -// dimension. xla::StatusOr BatchDot( xla::ComputationBuilder* builder, xla::ComputationDataHandle x, - xla::ComputationDataHandle y, bool transpose_x, bool transpose_y) { + xla::ComputationDataHandle y, bool transpose_x, bool transpose_y, + bool conjugate_x, bool conjugate_y) { TF_ASSIGN_OR_RETURN(std::unique_ptr x_shape, builder->GetShape(x)); TF_ASSIGN_OR_RETURN(std::unique_ptr y_shape, @@ -89,10 +88,10 @@ xla::StatusOr BatchDot( dimensions); } - if (x_shape->element_type() == xla::C64 && transpose_x) { + if (x_shape->element_type() == xla::C64 && conjugate_x) { x = builder->Conj(x); } - if (y_shape->element_type() == xla::C64 && transpose_y) { + if (y_shape->element_type() == xla::C64 && conjugate_y) { y = builder->Conj(y); } diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.h b/tensorflow/compiler/tf2xla/lib/batch_dot.h index b46bc7417d..b230e885f1 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.h +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.h @@ -27,7 +27,10 @@ namespace tensorflow { // viewed as an element of a batch), and arranges the individual results // in a single output tensor of the same batch size. Each of the // individual slices can optionally be transposed before multiplication by -// setting the `transpose_x` or `transpose_y` flag to `true`. +// setting the `transpose_x` or `transpose_y` flag to `true`. Similarly, each +// can be elementwise-complex-conjugated by setting the `conjugate_x` or +// `conjugate_y` flag to `true`. To apply a Hermitian adjoint to `x`, set both +// `transpose_x` and `conjugate_x` to `true`, and analogously for `y`. // // The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` // and `[..., r_y, c_y]`. @@ -40,11 +43,10 @@ namespace tensorflow { // It is computed as: // // output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) -// TODO(phawkins): add an option to take the complex conjugate of the LHS or -// RHS. xla::StatusOr BatchDot( xla::ComputationBuilder* builder, xla::ComputationDataHandle x, - xla::ComputationDataHandle y, bool transpose_x, bool transpose_y); + xla::ComputationDataHandle y, bool transpose_x, bool transpose_y, + bool conjugate_x = false, bool conjugate_y = false); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.cc b/tensorflow/compiler/tf2xla/lib/cholesky.cc index b3cc489adf..e795701181 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.cc +++ b/tensorflow/compiler/tf2xla/lib/cholesky.cc @@ -71,11 +71,14 @@ xla::StatusOr CholeskyUnblocked( SliceInMinorDims(builder, l, {j + 1, 0}, {n, j})); TF_ASSIGN_OR_RETURN(auto r_squared, BatchDot(builder, r, r, /*transpose_x=*/false, - /*transpose_y=*/true)); + /*transpose_y=*/true, /*conjugate_x=*/false, + /*conjugate_y=*/false)); new_d_squared = builder->Sub(new_d_squared, r_squared); TF_ASSIGN_OR_RETURN(br, BatchDot(builder, b, r, /*transpose_x=*/false, - /*transpose_y=*/true)); + /*transpose_y=*/true, + /*conjugate_x=*/false, + /*conjugate_y=*/false)); } auto new_d_inv = builder->Pow( new_d_squared, FloatLiteral(builder, shape->element_type(), -0.5)); @@ -134,7 +137,8 @@ xla::StatusOr Cholesky( SliceInMinorDims(builder, l, {i, 0}, {i + k, i})); TF_ASSIGN_OR_RETURN(auto delta, BatchDot(builder, lhs, rhs, /*transpose_x=*/false, - /*transpose_y=*/true)); + /*transpose_y=*/true, /*conjugate_x=*/false, + /*conjugate_y=*/false)); TF_ASSIGN_OR_RETURN(auto before, SliceInMinorDims(builder, a, {i, i}, {n, i + k})); TF_ASSIGN_OR_RETURN( @@ -155,6 +159,10 @@ xla::StatusOr Cholesky( SliceInMinorDims(builder, a, {i + k, i}, {n, i + k})); TF_ASSIGN_OR_RETURN(auto update, TriangularSolve(builder, factorized, panel, + /*left_side=*/false, + /*lower=*/true, + /*transpose_a=*/true, + /*conjugate_a=*/false, /*block_size=*/8)); TF_ASSIGN_OR_RETURN( l, UpdateSliceInMinorDims(builder, l, update, {i + k, i})); diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/tf2xla/lib/cholesky.h index 2bead7359b..e083a383be 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/tf2xla/lib/cholesky.h @@ -29,6 +29,7 @@ namespace tensorflow { // the block size to use. // TODO(phawkins): check for negative values on the diagonal and return an // error, instead of silently yielding NaNs. +// TODO(mattjj): handle the complex Hermitian case xla::StatusOr Cholesky( xla::ComputationBuilder* builder, xla::ComputationDataHandle a, int64 block_size = 256); diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index 579944c3a3..5f0445dd44 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -24,13 +24,15 @@ limitations under the License. #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" namespace tensorflow { xla::StatusOr TriangularSolve( xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, - xla::ComputationDataHandle b, int64 block_size) { + xla::ComputationDataHandle b, bool left_side, bool lower, bool transpose_a, + bool conjugate_a, int64 block_size) { TF_ASSIGN_OR_RETURN(std::unique_ptr a_shape, builder->GetShape(a)); TF_ASSIGN_OR_RETURN(std::unique_ptr b_shape, @@ -60,14 +62,15 @@ xla::StatusOr TriangularSolve( batch_dimensions.push_back(a_size); } - const int64 n = xla::ShapeUtil::GetDimension(*a_shape, -1); - const int64 m = xla::ShapeUtil::GetDimension(*b_shape, -2); - if (n != xla::ShapeUtil::GetDimension(*a_shape, -2)) { + if (xla::ShapeUtil::GetDimension(*a_shape, -1) != + xla::ShapeUtil::GetDimension(*a_shape, -2)) { return errors::InvalidArgument( "The 'a' arguments to TriangularSolve must be square matrices: ", xla::ShapeUtil::HumanString(*a_shape)); } - if (n != xla::ShapeUtil::GetDimension(*b_shape, -1)) { + const int64 m = xla::ShapeUtil::GetDimension(*b_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(*b_shape, -1); + if ((left_side ? m : n) != xla::ShapeUtil::GetDimension(*a_shape, -1)) { return errors::InvalidArgument( "Arguments to TriangularSolve have incompatible matrix shapes: ", xla::ShapeUtil::HumanString(*a_shape), " vs ", @@ -89,6 +92,14 @@ xla::StatusOr TriangularSolve( return output; }; + // Applies a complex conjugation operation if `a` is complex and `conjugate_a` + // is true, otherwise returns its argument. + auto maybe_conj = [&](xla::ComputationBuilder* builder, + xla::ComputationDataHandle x) { + auto perform_conj = a_shape->element_type() == xla::C64 && conjugate_a; + return perform_conj ? builder->Conj(x) : x; + }; + std::map base_computations; auto get_base_triangular_solve = [&](int k) -> xla::StatusOr { @@ -103,19 +114,35 @@ xla::StatusOr TriangularSolve( prepend_batch_dims({k, k})), "a"); + std::array b_lastd; + if (left_side) { + b_lastd = {k, n}; + } else { + b_lastd = {m, k}; + } auto b_param = sub->Parameter(1, xla::ShapeUtil::MakeShape(b_shape->element_type(), - prepend_batch_dims({m, k})), + prepend_batch_dims(b_lastd)), "b"); - // TODO(phawkins): it might make sense to use a while loop here, rather - // than unrolling. - // TODO(phawkins): the left-looking variant of the algorithm might be more - // efficient at block size 1. - TF_RETURN_IF_ERROR(TriangularSolve(sub.get(), a_param, b_param, - /*block_size=*/1) - .status()); + // We use a left-looking subroutine on the block diagonal in some common + // cases, while falling back to a recursive call in unsupported cases. The + // left-looking subroutine is written with a While loop and so yields much + // faster compile times. Moreover, the left-looking variant can give + // higher performance on smaller (sub)problems. + if (left_side && lower) { + TF_RETURN_IF_ERROR(TriangularSolveLeftLooking(sub.get(), a_param, + b_param, transpose_a, + conjugate_a) + .status()); + } else { + TF_RETURN_IF_ERROR(TriangularSolve(sub.get(), a_param, b_param, + left_side, lower, transpose_a, + conjugate_a, + /*block_size=*/1) + .status()); + } TF_ASSIGN_OR_RETURN(computation, sub->Build()); } @@ -129,47 +156,397 @@ xla::StatusOr TriangularSolve( // 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. - for (int64 i = 0; i < n; i += block_size) { - int64 k = std::min(block_size, n - i); - // if k > 1: - // output[..., :, i:i+k] = triangular_solve( - // a[..., i:i+k, ..., i:i+k], b[..., :, i:i+k], side='Right', - // kind='Lower', transpose=True, block_size=1) - // else: - // output[..., :, i] = b[..., :, i] / a[..., i, i] + // 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], ..., block_size=1) + TF_ASSIGN_OR_RETURN(auto a_slice, + SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); + TF_ASSIGN_OR_RETURN(auto b_slice, + SliceInMinorDims(builder, b, {0, i}, {m, i + k})); + xla::ComputationDataHandle update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::Computation * solve, + get_base_triangular_solve(k)); + update = builder->Call(*solve, {a_slice, b_slice}); + } else { + update = builder->Div(b_slice, maybe_conj(builder, a_slice)); + } + TF_ASSIGN_OR_RETURN( + output, UpdateSliceInMinorDims(builder, output, update, {0, i})); + + // if i + k < a.shape[-1]: + // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., :, i+k:] -= np.matmul(output[..., :, i:i+k], a_slice_2) + if (i + k < n) { + xla::ComputationDataHandle a_slice_2; + if (lower) { + TF_ASSIGN_OR_RETURN( + a_slice_2, SliceInMinorDims(builder, a, {i + k, i}, {n, i + k})); + } else { + TF_ASSIGN_OR_RETURN( + a_slice_2, SliceInMinorDims(builder, a, {i, i + k}, {i + k, n})); + } + + TF_ASSIGN_OR_RETURN(auto b_update, + BatchDot(builder, update, a_slice_2, + /*transpose_x=*/false, + /*transpose_y=*/transpose_a, + /*conjugate_x=*/false, + /*conjugate_y=*/conjugate_a)); + TF_ASSIGN_OR_RETURN(auto b_slice_2, + SliceInMinorDims(builder, b, {0, i + k}, {m, n})); + b_update = builder->Sub(b_slice_2, b_update); + TF_ASSIGN_OR_RETURN( + b, UpdateSliceInMinorDims(builder, b, b_update, {0, i + k})); + } + } + + } 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, :], ..., block_size=1) + TF_ASSIGN_OR_RETURN(auto a_slice, + SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); + TF_ASSIGN_OR_RETURN(auto b_slice, + SliceInMinorDims(builder, b, {i, 0}, {i + k, n})); + xla::ComputationDataHandle update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::Computation * solve, + get_base_triangular_solve(k)); + update = builder->Call(*solve, {a_slice, b_slice}); + } else { + update = builder->Div(b_slice, maybe_conj(builder, a_slice)); + } + TF_ASSIGN_OR_RETURN( + output, UpdateSliceInMinorDims(builder, output, update, {i, 0})); + + // if i + k < a.shape[-1]: + // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., i+k:, :] -= np.matmul(a_slice_2, output[..., i:i+k, :]) + if (i + k < m) { + xla::ComputationDataHandle a_slice_2; + if (lower) { + TF_ASSIGN_OR_RETURN( + a_slice_2, SliceInMinorDims(builder, a, {i + k, i}, {m, i + k})); + } else { + TF_ASSIGN_OR_RETURN( + a_slice_2, SliceInMinorDims(builder, a, {i, i + k}, {i + k, m})); + } + + TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(builder, a_slice_2, update, + /*transpose_x=*/transpose_a, + /*transpose_y=*/false, + /*conjugate_x=*/conjugate_a, + /*conjugate_y=*/false)); + TF_ASSIGN_OR_RETURN(auto b_slice_2, + SliceInMinorDims(builder, b, {i + k, 0}, {m, n})); + b_update = builder->Sub(b_slice_2, b_update); + TF_ASSIGN_OR_RETURN( + b, UpdateSliceInMinorDims(builder, b, b_update, {i + k, 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], ..., block_size=1) + TF_ASSIGN_OR_RETURN(auto a_slice, + SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); + TF_ASSIGN_OR_RETURN(auto b_slice, + SliceInMinorDims(builder, b, {0, i}, {m, i + k})); + xla::ComputationDataHandle update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::Computation * solve, + get_base_triangular_solve(k)); + update = builder->Call(*solve, {a_slice, b_slice}); + } else { + update = builder->Div(b_slice, maybe_conj(builder, a_slice)); + } + TF_ASSIGN_OR_RETURN( + output, UpdateSliceInMinorDims(builder, output, update, {0, i})); + + // if i - k >= 0: + // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., :, :i] -= np.matmul(out[..., :, i:i+k], a_slice_2) + if (i - k >= 0) { + xla::ComputationDataHandle a_slice_2; + if (lower) { + TF_ASSIGN_OR_RETURN(a_slice_2, + SliceInMinorDims(builder, a, {i, 0}, {i + k, i})); + } else { + TF_ASSIGN_OR_RETURN(a_slice_2, + SliceInMinorDims(builder, a, {0, i}, {i, i + k})); + } + + TF_ASSIGN_OR_RETURN(auto b_update, + BatchDot(builder, update, a_slice_2, + /*transpose_x=*/false, + /*transpose_y=*/transpose_a, + /*conjugate_x=*/false, + /*conjugate_y=*/conjugate_a)); + TF_ASSIGN_OR_RETURN(auto b_slice_2, + SliceInMinorDims(builder, b, {0, 0}, {m, i})); + b_update = builder->Sub(b_slice_2, b_update); + TF_ASSIGN_OR_RETURN( + b, UpdateSliceInMinorDims(builder, b, b_update, {0, 0})); + } + } + } 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, :], ..., block_size=1) + TF_ASSIGN_OR_RETURN(auto a_slice, + SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); + TF_ASSIGN_OR_RETURN(auto b_slice, + SliceInMinorDims(builder, b, {i, 0}, {i + k, n})); + xla::ComputationDataHandle update; + if (k > 1) { + TF_ASSIGN_OR_RETURN(xla::Computation * solve, + get_base_triangular_solve(k)); + update = builder->Call(*solve, {a_slice, b_slice}); + } else { + update = builder->Div(b_slice, maybe_conj(builder, a_slice)); + } + TF_ASSIGN_OR_RETURN( + output, UpdateSliceInMinorDims(builder, output, update, {i, 0})); + + // if i - k >= 0: + // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] + // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 + // b[..., :i, :] -= np.matmul(a_slice_2, out[..., i:i+k, :]) + if (i - k >= 0) { + xla::ComputationDataHandle a_slice_2; + if (lower) { + TF_ASSIGN_OR_RETURN(a_slice_2, + SliceInMinorDims(builder, a, {i, 0}, {i + k, i})); + } else { + TF_ASSIGN_OR_RETURN(a_slice_2, + SliceInMinorDims(builder, a, {0, i}, {i, i + k})); + } + + TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(builder, a_slice_2, update, + /*transpose_x=*/transpose_a, + /*transpose_y=*/false, + /*conjugate_x=*/conjugate_a, + /*conjugate_y=*/false)); + TF_ASSIGN_OR_RETURN(auto b_slice_2, + SliceInMinorDims(builder, b, {0, 0}, {i, n})); + b_update = builder->Sub(b_slice_2, b_update); + TF_ASSIGN_OR_RETURN( + b, UpdateSliceInMinorDims(builder, b, b_update, {0, 0})); + } + } + } + + return output; +} + +xla::StatusOr TriangularSolveLeftLooking( + xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, + const xla::ComputationDataHandle& b, bool transpose_a, bool conjugate_a) { + TF_ASSIGN_OR_RETURN(std::unique_ptr a_shape, + builder->GetShape(a)); + TF_ASSIGN_OR_RETURN(std::unique_ptr 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; + for (int i = 0; i < ndims - 2; ++i) { + int64 a_size = a_shape->dimensions(i); + batch_dimensions.push_back(a_size); + } + + auto prepend_batch_dims = [&](std::array indices) { + std::vector output(ndims); + std::copy(batch_dimensions.begin(), batch_dimensions.end(), output.begin()); + std::copy(indices.begin(), indices.end(), + output.begin() + batch_dimensions.size()); + return output; + }; + + auto maybe_conj = [&](xla::ComputationBuilder* builder, + xla::ComputationDataHandle x) { + auto perform_conj = a_shape->element_type() == xla::C64 && conjugate_a; + return perform_conj ? builder->Conj(x) : x; + }; + + // 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::ComputationDataHandle output = Zeros(builder, *b_shape); + { + auto i = transpose_a ? m - 1 : 0; TF_ASSIGN_OR_RETURN(auto a_slice, - SliceInMinorDims(builder, a, {i, i}, {i + k, i + k})); + SliceInMinorDims(builder, a, {i, i}, {i + 1, i + 1})); TF_ASSIGN_OR_RETURN(auto b_slice, - SliceInMinorDims(builder, b, {0, i}, {m, i + k})); - xla::ComputationDataHandle update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::Computation * solve, - get_base_triangular_solve(k)); - update = builder->Call(*solve, {a_slice, b_slice}); + SliceInMinorDims(builder, b, {i, 0}, {i + 1, n})); + auto update = builder->Div(b_slice, maybe_conj(builder, a_slice)); + TF_ASSIGN_OR_RETURN( + output, UpdateSliceInMinorDims(builder, 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. + xla::ShapeUtil::MakeShape(b_shape->element_type(), b_shape->dimensions()), + // The coefficient matrix a is a loop invariant. + xla::ShapeUtil::MakeShape(a_shape->element_type(), a_shape->dimensions()), + // The right-hand-side matrix b is a loop invariant. + xla::ShapeUtil::MakeShape(b_shape->element_type(), b_shape->dimensions()), + }); + xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); + auto init_i = builder->ConstantR0(transpose_a ? m - 2 : 1); + auto init = builder->Tuple({init_i, output, 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 = condb->GetTupleElement( + condb->Parameter(0, tuple_shape, + "TriangularSolveLeftLookingWhileTuple"), + 0); + if (transpose_a) { + condb->Ge(i, condb->ConstantR0(0)); } else { - update = builder->Div(b_slice, a_slice); + condb->Lt(i, condb->ConstantR0(m)); } + } + TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); - TF_ASSIGN_OR_RETURN( - output, UpdateSliceInMinorDims(builder, output, update, {0, i})); - // b[..., :, i+k:] -= np.dot(output[..., :, i:i+k], - // np.transpose(..., a[i+k:, i:i+k])) - if (i + k < n) { - TF_ASSIGN_OR_RETURN(auto a_slice_2, - SliceInMinorDims(builder, a, {i + k, i}, {n, i + k})); - TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(builder, update, a_slice_2, - /*transpose_x=*/false, - /*transpose_y=*/true)); - - TF_ASSIGN_OR_RETURN(auto b_slice_2, - SliceInMinorDims(builder, b, {0, i + k}, {m, n})); - b_update = builder->Sub(b_slice_2, b_update); - TF_ASSIGN_OR_RETURN( - b, UpdateSliceInMinorDims(builder, b, b_update, {0, i + k})); + // 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 = bodyb->Parameter(0, tuple_shape, + "TriangularSolveLeftLookingWhileTuple"); + + // i, output, a, b = loop_carry + auto i = bodyb->GetTupleElement(input_tuple, 0); + auto body_out = bodyb->GetTupleElement(input_tuple, 1); + auto body_a = bodyb->GetTupleElement(input_tuple, 2); + auto body_b = bodyb->GetTupleElement(input_tuple, 3); + auto zero = bodyb->ConstantR0(0); + + // Set up some helper functions. + auto prepend_zeros = [&](std::array starts) { + auto zero = bodyb->Reshape(bodyb->ConstantR0(0), {1}); + std::vector padded_starts(ndims, zero); + padded_starts[ndims - 2] = bodyb->Reshape(starts[0], {1}); + padded_starts[ndims - 1] = bodyb->Reshape(starts[1], {1}); + return bodyb->ConcatInDim(padded_starts, 0); + }; + + auto dynamic_slice = [&](xla::ComputationDataHandle x, + std::array starts, + std::array sizes) { + auto padded_starts = prepend_zeros(starts); + auto padded_sizes = prepend_batch_dims(sizes); + return bodyb->DynamicSlice(x, padded_starts, padded_sizes); + }; + + auto update = [&](xla::ComputationDataHandle x, + xla::ComputationDataHandle update, + std::array starts) { + auto padded_starts = prepend_zeros(starts); + return bodyb->DynamicUpdateSlice(x, update, padded_starts); + }; + + // 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::ComputationDataHandle a_row; + if (transpose_a) { + a_row = dynamic_slice(body_a, {zero, i}, {m, 1}); + } else { + a_row = dynamic_slice(body_a, {i, zero}, {1, m}); } + TF_ASSIGN_OR_RETURN(auto b_update, BatchDot(bodyb.get(), a_row, body_out, + /*transpose_x=*/transpose_a, + /*transpose_y=*/false, + /*conjugate_x=*/conjugate_a, + /*conjugate_y=*/false)); + auto result_row = + bodyb->Sub(dynamic_slice(body_b, {i, zero}, {1, n}), b_update); + + // body_out[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] + auto a_elt = dynamic_slice(body_a, {i, i}, {1, 1}); + auto div_result = bodyb->Div(result_row, maybe_conj(bodyb.get(), a_elt)); + body_out = update(body_out, div_result, {i, zero}); + + // if transpose_a: + // return (i - 1, body_out, a, b) + // else: + // return (i + 1, body_out, a, b) + auto next_i = bodyb->Add(i, bodyb->ConstantR0(transpose_a ? -1 : 1)); + bodyb->Tuple({next_i, body_out, body_a, body_b}); } - return output; + 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 = builder->While(cond, body, init); + return builder->GetTupleElement(triangular_solve_left_looking_while, 1); } } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index 501d026411..e32223bfdd 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -21,25 +21,50 @@ limitations under the License. namespace tensorflow { -// Solves systems of linear equations with upper or lower triangular matrices by -// backsubstitution. +// Solves systems of linear equations with lower or upper triangular coefficient +// matrices by forward- or back-substitution. Broadcasting along leading +// dimensions, this routine solves one of the matrix systems +// `op(a) * x = b`, or `x * op(a) = b`, +// for the variable `x` given `a` and `b`, where `op(a)` is either +// `op(a) = a`, or `op(a) = transpose(a)`, or `op(a) = conj(transpose(a))`. +// That is, the innermost matrices in the output satisfy a scalar system +// depending on the value of the value of (left_side, transpose_a, conjugate_a) +// according to: +// (F, F, F) => `output[..., i, k] a[..., k, j] = b[..., i, j]`, +// (F, F, T) => `output[..., i, k] a*[..., k, j] = b[..., i, j]`, +// (F, T, F) => `output[..., i, k] a[..., j, k] = b[..., i, j]`, +// (F, T, T) => `output[..., i, k] a*[..., j, k] = b[..., i, j]`, +// (T, F, F) => ` a[..., i, k] output[..., k, j] = b[..., i, j]`, +// (T, F, T) => `a*[..., i, k] output[..., k, j] = b[..., i, j]`, +// (T, T, F) => ` a[..., i, k] output[..., j, k] = b[..., i, j]`, +// (T, T, T) => `a*[..., i, k] output[..., j, k] = b[..., i, j]`, +// where * denotes complex conjugation and where the index `k` is summed over. // -// `a` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form -// square matrices. The strictly upper triangular part of each inner-most matrix -// is assumed to be zero and not accessed. -// `b` is a tensor of shape `[..., M, K]`. -// -// The innermost matrices in the output satisfy matrix equations -// `output[..., i, j] * adjoint(a[..., k, j]) = b[..., i, k]`. +// `a` is a tensor of shape `[..., M, M]` whose innermost 2 dimensions form +// square matrices. If lower is true (false), then the strictly upper (lower) +// triangular part of each innermost matrix in `a` is assumed to be zero and is +// not accessed. +// `b` is a tensor of shape `[..., M, K]` if left_side is true, otherwise a +// tensor of shape `[..., K, M]`. +// `left_side` is a boolean, indicating whether to solve a system of the form +// op(a) * x = b (true) or x * op(a) = b (false). +// `lower` is a boolean, indicating whether the argument `a` is lower-triangular +// (true) or upper-triangular (false). +// `transpose_a` is a boolean indicating whether the matrix `a` is transposed. +// `conjugate_a` is a boolean indicating whether the entries of `a` are complex +// conjugated (independently of whether they are transposed), so that when both +// transpose_a and conjugate_a are true the effect is a Hermitian adjoint. // // Uses a blocked algorithm if `block_size` is > 1; if block_size == 1 then no // blocking is used. -// TODO(phawkins): equivalent to the BLAS TRSM routine with side=right, -// kind=lower, and transposed_a=true. Implement the other possible combinations -// of side, kind and transposed_a. xla::StatusOr TriangularSolve( xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, - xla::ComputationDataHandle b, int64 block_size = 256); + xla::ComputationDataHandle b, bool left_side, bool lower, bool transpose_a, + bool conjugate_a, int64 block_size = 256); + +xla::StatusOr TriangularSolveLeftLooking( + xla::ComputationBuilder* builder, const xla::ComputationDataHandle& a, + const xla::ComputationDataHandle& b, bool transpose_a, bool conjugate_a); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc index 671d9aa4fe..6617070629 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc @@ -27,32 +27,134 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/core/status_test_util.h" namespace tensorflow { namespace { using TriangularSolveTest = xla::ClientLibraryTestBase; +using TriangularSolveLeftLookingTest = xla::ClientLibraryTestBase; +using complex64 = xla::complex64; -XLA_TEST_F(TriangularSolveTest, Simple) { +xla::Array2D AValsLower() { + return {{2, 0, 0, 0}, {3, 6, 0, 0}, {4, 7, 9, 0}, {5, 8, 10, 11}}; +} + +xla::Array2D AValsUpper() { + return {{2, 3, 4, 5}, {0, 6, 7, 8}, {0, 0, 9, 10}, {0, 0, 0, 11}}; +} + +xla::Array2D BValsRight() { + return {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}; +} + +xla::Array2D BValsLeft() { + return {{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {10, 11, 12}}; +} + +xla::Array2D AValsLowerComplex() { + return {{2, 0, 0, 0}, + {complex64(3, 1), 6, 0, 0}, + {4, complex64(7, 2), 9, 0}, + {5, 8, complex64(10, 3), 11}}; +} + +xla::Array2D AValsUpperComplex() { + return {{2, 3, complex64(4, 3), 5}, + {0, 6, complex64(7, 2), 8}, + {0, 0, complex64(9, 1), 10}, + {0, 0, 0, 11}}; +} + +xla::Array2D BValsRightComplex() { + return {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}; +} + +xla::Array2D BValsLeftComplex() { + return {{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {10, 11, 12}}; +} + +xla::Array2D AValsFull() { + return {{2, 0, 1, 2}, {3, 6, 0, 1}, {4, 7, 9, 0}, {5, 8, 10, 11}}; +} + +XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTranspose) { xla::ComputationBuilder builder(client_, TestName()); - xla::Array2D a_vals({ - {2, 0, 0, 0}, - {3, 6, 0, 0}, - {4, 7, 9, 0}, - {5, 8, 10, 11}, + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/false, /*lower=*/true, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + xla::Array2D expected({ + {0.5, 0.08333334, 0.04629629, 0.03367003}, + {2.5, -0.25, -0.1388889, -0.1010101}, + {4.5, -0.58333331, -0.32407406, -0.23569024}, + }); + + ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveTest, SimpleRightLowerNotranspose) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/false, /*lower=*/true, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + xla::Array2D expected({ + {-0.16414141, -0.06902357, -0.07070707, 0.36363636}, + {0.64393939, 0.06565657, -0.03030303, 0.72727273}, + {1.4520202, 0.2003367, 0.01010101, 1.09090909}, }); - xla::Array2D b_vals({ - {1, 2, 3, 4}, - {5, 6, 7, 8}, - {9, 10, 11, 12}, + + ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveTest, SimpleRightUpperTranspose) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/false, /*lower=*/false, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + xla::Array2D expected({ + {-0.16414141, -0.06902357, -0.07070707, 0.36363636}, + {0.64393939, 0.06565657, -0.03030303, 0.72727273}, + {1.4520202, 0.2003367, 0.01010101, 1.09090909}, }); + ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveTest, SimpleRightUpperNotranspose) { + xla::ComputationBuilder builder(client_, TestName()); + xla::ComputationDataHandle a, b; - auto a_data = CreateR2Parameter(a_vals, 0, "a", &builder, &a); - auto b_data = CreateR2Parameter(b_vals, 1, "b", &builder, &b); - auto result = TriangularSolve(&builder, a, b, /*block_size=*/2); + auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsRight(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/false, /*lower=*/false, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); TF_ASSERT_OK(result.status()); xla::Array2D expected({ @@ -62,7 +164,201 @@ XLA_TEST_F(TriangularSolveTest, Simple) { }); ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, - xla::ErrorSpec(2e-3, 2e-3)); + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerTranspose) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/true, /*lower=*/true, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + xla::Array2D expected({ + {-0.89646465, -0.69444444, -0.49242424}, + {-0.27441077, -0.24074074, -0.20707071}, + {-0.23232323, -0.22222222, -0.21212121}, + {0.90909091, 1., 1.09090909}, + }); + + ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotranspose) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/true, /*lower=*/true, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + 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::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/true, /*lower=*/false, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + 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, SimpleLeftUpperNotranspose) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsUpper(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/true, /*lower=*/false, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + xla::Array2D expected({ + {-0.89646465, -0.69444444, -0.49242424}, + {-0.27441077, -0.24074074, -0.20707071}, + {-0.23232323, -0.22222222, -0.21212121}, + {0.90909091, 1., 1.09090909}, + }); + + ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveTest, SimpleRightLowerTransposeConjugate) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = + CreateR2Parameter(AValsLowerComplex(), 0, "a", &builder, &a); + auto b_data = + CreateR2Parameter(BValsRightComplex(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/false, /*lower=*/true, + /*transpose_a=*/true, /*conjugate_a=*/true, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + xla::Array2D expected({ + {0.5, complex64(0.08333333, 0.08333333), + complex64(0.02777778, -0.0462963), complex64(0.06313131, -0.01094276)}, + {2.5, complex64(-0.25, 0.41666667), complex64(-0.23148148, -0.37962963), + complex64(0.08670034, -0.02104377)}, + {4.5, complex64(-0.58333333, 0.75), complex64(-0.49074074, -0.71296296), + complex64(0.11026936, -0.03114478)}, + }); + + ComputeAndCompareR2(&builder, expected, + {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTransposeNoconjugate) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = + CreateR2Parameter(AValsUpperComplex(), 0, "a", &builder, &a); + auto b_data = + CreateR2Parameter(BValsLeftComplex(), 1, "b", &builder, &b); + auto result = TriangularSolve(&builder, a, b, + /*left_side=*/true, /*lower=*/false, + /*transpose_a=*/true, /*conjugate_a=*/false, + /*block_size=*/2); + TF_ASSERT_OK(result.status()); + + xla::Array2D expected({ + {0.5, 1., 1.5}, + {0.41666667, 0.33333333, 0.25}, + {complex64(0.20020325, -2.81504065e-01), + complex64(0.13821138, -4.22764228e-01), + complex64(0.07621951, -5.64024390e-01)}, + {complex64(0.19678492, 2.55912786e-01), + complex64(0.17738359, 3.84331116e-01), + complex64(0.15798226, 5.12749446e-01)}, + }); + + ComputeAndCompareR2(&builder, expected, + {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + +XLA_TEST_F(TriangularSolveLeftLookingTest, Simple) { + xla::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + auto result = TriangularSolveLeftLooking(&builder, a, b, + /*transpose_a=*/false, + /*conjugate_a=*/false); + TF_ASSERT_OK(result.status()); + + 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::ComputationBuilder builder(client_, TestName()); + + xla::ComputationDataHandle a, b; + auto a_data = CreateR2Parameter(AValsFull(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + auto result = TriangularSolveLeftLooking(&builder, a, b, + /*transpose_a=*/false, + /*conjugate_a=*/false); + TF_ASSERT_OK(result.status()); + + 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 diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc index ce24b61b5d..9b7492f8cf 100644 --- a/tensorflow/compiler/tf2xla/lib/util.cc +++ b/tensorflow/compiler/tf2xla/lib/util.cc @@ -107,4 +107,15 @@ xla::StatusOr UpdateSliceInMinorDims( return UpdateSlice(builder, x, update, padded_start); } +xla::StatusOr TransposeInMinorDims( + xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x) { + TF_ASSIGN_OR_RETURN(std::unique_ptr shape, builder->GetShape(x)); + const int64 n_dims = xla::ShapeUtil::Rank(*shape); + TF_RET_CHECK(n_dims >= 2); + std::vector permutation(n_dims); + std::iota(permutation.begin(), permutation.end(), 0); + std::swap(permutation[n_dims - 1], permutation[n_dims - 2]); + return builder->Transpose(x, permutation); +} + } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index fb138b4f73..7f93102ee7 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -49,6 +49,10 @@ xla::StatusOr UpdateSliceInMinorDims( xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x, const xla::ComputationDataHandle& update, gtl::ArraySlice start); +// Transposes a stack of matrices `x` by swapping the last two dimensions. +xla::StatusOr TransposeInMinorDims( + xla::ComputationBuilder* builder, const xla::ComputationDataHandle& x); + } // namespace tensorflow #endif // TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ -- GitLab From a4b88a5b795d5496bffe4ff80875a5bf0954a4d6 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Mon, 29 Jan 2018 17:48:17 -0800 Subject: [PATCH 248/423] Use new Operation._set_attr method instead of modifying node_def directly. Once calling the C API from TF's Python code is enabled, the NodeDef returned by Operation.node_def will no longer be the NodeDef sent to TF's runtime, meaning any changes to it will have no effect. Use _set_attr instead, which works with and without the C API enabled. PiperOrigin-RevId: 183759464 --- tensorflow/contrib/tpu/python/tpu/tpu.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index 8fec379aad..d5f54ff4fd 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -153,10 +153,11 @@ class TPUReplicateContext(control_flow_ops.XLAControlFlowContext): raise NotImplementedError( "Non-resource Variables are not supported inside TPU computations " "(operator name: %s)" % op.name) - # pylint: enable=protected-access if _TPU_REPLICATE_ATTR in op.node_def.attr: raise ValueError("TPU computations cannot be nested") - op.node_def.attr[_TPU_REPLICATE_ATTR].s = compat.as_bytes(self._name) + op._set_attr(_TPU_REPLICATE_ATTR, + attr_value_pb2.AttrValue(s=compat.as_bytes(self._name))) + # pylint: enable=protected-access op.graph.prevent_feeding(op) op.graph.prevent_fetching(op) -- GitLab From e3d99c92975efc2010d0e1e2dd4c3eb787a8d67c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 29 Jan 2018 17:50:56 -0800 Subject: [PATCH 249/423] Remove Identity nodes if num_inputs * num_outputs <= num_inputs + num_outputs. Exceptions are Identity nodes after Variable nodes, and Identity nodes after Switch nodes when removing the node would require anchoring a control dependency on the Switch. Another exception is Identity nodes where inputs or outputs cross a device boundary, since we are not allowed to remove Identity nodes after _Recv that might be inserted in the graph later. PiperOrigin-RevId: 183759826 --- .../optimizers/dependency_optimizer.cc | 209 +++++++++++++----- .../optimizers/dependency_optimizer.h | 9 +- .../optimizers/dependency_optimizer_test.cc | 150 ++++++++++++- .../python/debug/lib/debug_gradients_test.py | 7 +- .../debug/lib/session_debug_grpc_test.py | 3 +- 5 files changed, 305 insertions(+), 73 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc index d2da125236..0842fc92a8 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc @@ -36,20 +36,20 @@ namespace grappler { namespace { -int RemoveInput(NodeDef* node, const string& input, NodeMap* node_map) { - int num_removed = 0; +bool RemoveInput(NodeDef* node, const string& input, NodeMap* node_map) { + bool removed_input = false; int pos = 0; while (pos < node->input_size()) { if (node->input(pos) == input) { node->mutable_input()->SwapElements(pos, node->input_size() - 1); node->mutable_input()->RemoveLast(); node_map->RemoveOutput(NodeName(input), node->name()); + removed_input = true; } else { ++pos; } - ++num_removed; } - return num_removed; + return removed_input; } // Remove duplicate control inputs. @@ -71,6 +71,43 @@ void PruneControlInputs(NodeDef* node) { } // namespace +bool DependencyOptimizer::SafeToRemoveIdentity(const NodeDef& node) { + if (!IsIdentity(node)) { + return true; + } + if (nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) { + return false; + } + if (!fetch_nodes_known_) { + // The output values of this node may be needed. + return false; + } + const NodeDef* input = node_map_->GetNode(NodeName(node.input(0))); + CHECK(input != nullptr) << "node = " << node.name() + << " input = " << node.input(0); + // Don't remove Identity nodes corresponding to Variable reads or following + // Recv. + if (IsVariable(*input) || IsRecv(*input)) { + return false; + } else if (IsSwitch(*input)) { + // Don't turn Identity nodes following Switch into NoOp or remove them + // if it requires anchoring a control dependencies the Switch node, which + // is not valid. + if (StringPiece(node.name()).starts_with(kConstantFoldingCtrl)) { + // TODO(rmlarsen): Try to remove this artificial contraint. + return false; + } + for (auto consumer : node_map_->GetOutputs(node.name())) { + for (const string& consumer_input : consumer->input()) { + if (consumer_input == AsControlDependency(node.name())) { + return false; + } + } + } + } + return true; +} + bool DependencyOptimizer::SafeToConvertToNoOp(const NodeDef& node) { if (nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) { return false; @@ -100,18 +137,8 @@ bool DependencyOptimizer::SafeToConvertToNoOp(const NodeDef& node) { return false; } - // Don't turn Identity nodes inserted by Grappler after Switch into NoOp, - // since we cannot anchor control dependencies on Switch nodes. - // Don't remove Identity nodes corresponding to Variable reads. - if (IsIdentity(node)) { - const NodeDef* input = node_map_->GetNode(NodeName(node.input(0))); - if (input != nullptr) { - if (IsVariable(*input) || - (StringPiece(node.name()).starts_with(kConstantFoldingCtrl) && - IsSwitch(*input))) { - return false; - } - } + if (!SafeToRemoveIdentity(node)) { + return false; } const std::unordered_set do_not_rewrite_ops{ @@ -124,19 +151,22 @@ bool DependencyOptimizer::SafeToConvertToNoOp(const NodeDef& node) { void DependencyOptimizer::OptimizeNode(int node_idx, SetVector* nodes_to_simplify, std::set* nodes_to_delete) { + const bool is_aggressive = opt_level_ == RewriterConfig::AGGRESSIVE; NodeDef* node = optimized_graph_->mutable_node(node_idx); - + const bool is_noop = IsNoOp(*node); + const bool is_identity = IsIdentity(*node); + const string node_name = node->name(); // Constant nodes with no input control dependency are always executed early, // so we can prune all their output control dependencies. if (IsConstant(*node) && node->input_size() == 0) { - const std::set output_nodes = node_map_->GetOutputs(node->name()); + const std::set output_nodes = node_map_->GetOutputs(node_name); for (NodeDef* fanout : output_nodes) { bool optimize_fanout = false; bool data_connection = false; for (int i = fanout->input_size() - 1; i >= 0; --i) { int pos; string input_name = ParseNodeName(fanout->input(i), &pos); - if (input_name == node->name()) { + if (input_name == node_name) { if (pos < 0) { fanout->mutable_input()->SwapElements(i, fanout->input_size() - 1); fanout->mutable_input()->RemoveLast(); @@ -149,22 +179,21 @@ void DependencyOptimizer::OptimizeNode(int node_idx, if (optimize_fanout) { nodes_to_simplify->PushBack(node_to_idx_[fanout]); if (!data_connection) { - node_map_->RemoveOutput(node->name(), fanout->name()); + node_map_->RemoveOutput(node_name, fanout->name()); } } } - if (node_map_->GetOutputs(node->name()).empty() && fetch_nodes_known_ && - nodes_to_preserve_.find(node->name()) == nodes_to_preserve_.end()) { + if (node_map_->GetOutputs(node_name).empty() && fetch_nodes_known_ && + nodes_to_preserve_.find(node_name) == nodes_to_preserve_.end()) { // Mark the node for deletion. nodes_to_delete->insert(node_to_idx_[node]); } - return; } // Change ops that only have control dependencies as outputs to NoOps. - if (node->op() != "NoOp" && SafeToConvertToNoOp(*node)) { - VLOG(1) << "***** Replacing " << node->name() << " (" << node->op() + if (!is_noop && SafeToConvertToNoOp(*node)) { + VLOG(1) << "***** Replacing " << node_name << " (" << node->op() << ") with NoOp."; // The outputs of this node are not consumed. Replace its inputs with // control dependencies and replace the op itself with the NoOp op. @@ -186,7 +215,7 @@ void DependencyOptimizer::OptimizeNode(int node_idx, old_input, optimized_graph_, node_map_.get()); if (ctrl_inputs.insert(ctrl_input).second) { node->set_input(pos, ctrl_input); - node_map_->UpdateInput(node->name(), old_input, ctrl_input); + node_map_->UpdateInput(node_name, old_input, ctrl_input); const NodeDef* old_input_node = node_map_->GetNode(old_input); nodes_to_simplify->PushBack(node_to_idx_[old_input_node]); } @@ -194,6 +223,8 @@ void DependencyOptimizer::OptimizeNode(int node_idx, } node->set_op("NoOp"); node->clear_attr(); + nodes_to_simplify->PushBack(node_to_idx_[node]); + return; } // Remove NoOp nodes if the product of their fan-in and fan-out is less than @@ -222,9 +253,30 @@ void DependencyOptimizer::OptimizeNode(int node_idx, // a and x, respectively, are on the same device. Control edges across device // boundaries require inter-device communication (Send/Recv pairs to be // inserted in the graph), which is very costly. + // + // We also remove identity nodes, subject to the same constraints on number of + // resulting control edges and device boundary crossings: + // + // Case a) + // +----------+ ---> a +---+ ---> a + // x --> | Identity | --^> b ==> | x | --^> b + // | | ... | | ... + // +----------+ --^> c +---+ --^> c + // + // Case b) + // x ---> +----------+ ---> a x ---> +---+ + // y --^> | Identity | ==> y --^> | a | + // ... | | ... | | + // z --^> +----------+ z --^> +---+ + // + // Case c) + // +----------+ x ---> +---+ + // x ---> | Identity | ---> a ==> \--^> | a | + // y --^> | | --^> b /\ +---+ + // +----------+ y --^> b - if (node->op() == "NoOp") { - const auto& output_node_set = node_map_->GetOutputs(node->name()); + if (is_noop || (is_identity && is_aggressive)) { + const auto& output_node_set = node_map_->GetOutputs(node_name); const std::vector output_nodes(output_node_set.begin(), output_node_set.end()); const int num_outputs = output_nodes.size(); @@ -233,15 +285,14 @@ void DependencyOptimizer::OptimizeNode(int node_idx, if (num_inputs * num_outputs > num_inputs + num_outputs) { return; } - VLOG(1) << "***** Rerouting input around " << node->name(); std::vector input_nodes; for (int i = 0; i < num_inputs; ++i) { - NodeDef* tmp = node_map_->GetNode(node->input(i)); - CHECK_NE(tmp, nullptr); - input_nodes.push_back(tmp); + NodeDef* input_node = node_map_->GetNode(node->input(i)); + CHECK_NE(input_node, nullptr); + input_nodes.push_back(input_node); } - // Make sure that we don't increase the number of control edges that cross + // Make sure that we don't increase the number of edges that cross // device boundaries. if ((num_inputs == 1 && num_outputs > 1 && input_nodes[0]->device() != node->device()) || @@ -266,40 +317,75 @@ void DependencyOptimizer::OptimizeNode(int node_idx, if (num_cross_after > num_cross_before) { return; } + // To avoid potentially removing Identity nodes following _Recv nodes, + // we require that no device crossings occur in that case. + // TODO(rmlarsen): See if we can relax this condition. + if (is_identity && (num_cross_after > 0 || num_cross_before > 0)) { + return; + } + } + if (is_identity && !SafeToRemoveIdentity(*node)) { + return; } + + VLOG(1) << "***** Rerouting input around\n" << node->DebugString(); + // Now remove the node and re-wire its inputs to its outputs. for (auto consumer : output_nodes) { bool updated_consumer = false; - VLOG(1) << "***** Considering consumer " << consumer->name() << "\n" - << consumer->DebugString(); + VLOG(1) << "consumer before:\n" << consumer->DebugString(); for (int i = 0; i < num_inputs; ++i) { const NodeDef* input = input_nodes[i]; // Forward dependency from input to consumer if it doesn't already // depend on it. - if (node_map_->GetOutputs(input->name()).count(consumer) == 0) { - consumer->add_input(AsControlDependency(input->name())); + if (is_identity && i == 0) { + // Replace regular input from Identity node. + bool found_input = false; + string new_input; + const string& input_to_forward = node->input(0); + CHECK(!IsControlInput(input_to_forward)); + for (int j = 0; j < consumer->input_size(); ++j) { + const string& old_input = consumer->input(j); + if (old_input == node_name) { + new_input = input_to_forward; + node_map_->UpdateInput(consumer->name(), old_input, new_input); + consumer->set_input(j, new_input); + found_input = true; + } else if (old_input == AsControlDependency(NodeName(node_name))) { + new_input = AsControlDependency(NodeName(input_to_forward)); + node_map_->UpdateInput(consumer->name(), old_input, new_input); + consumer->set_input(j, new_input); + found_input = true; + } + } + CHECK(found_input); updated_consumer = true; - node_map_->AddOutput(input->name(), consumer->name()); - nodes_to_simplify->PushBack(node_to_idx_[input]); + } else { + // Forward dependency from input to consumer if it doesn't already + // depend on it. + if (node_map_->GetOutputs(input->name()).count(consumer) == 0) { + consumer->add_input(AsControlDependency(input->name())); + node_map_->AddOutput(input->name(), consumer->name()); + nodes_to_simplify->PushBack(node_to_idx_[input]); + updated_consumer = true; + } } } // Remove dependency on node from consumer. - updated_consumer |= RemoveInput( - consumer, AsControlDependency(node->name()), node_map_.get()); + updated_consumer |= RemoveInput(consumer, AsControlDependency(node_name), + node_map_.get()); if (updated_consumer) { - VLOG(1) << "***** Updated consumer " << consumer->name() << " (" - << consumer->op() << ")"; nodes_to_simplify->PushBack(node_to_idx_[consumer]); } + VLOG(1) << "consumer after:\n" << consumer->DebugString(); } - - node_map_->RemoveOutputs(node->name()); + node_map_->RemoveOutputs(node_name); if (fetch_nodes_known_ && - nodes_to_preserve_.find(node->name()) == nodes_to_preserve_.end()) { + nodes_to_preserve_.find(node_name) == nodes_to_preserve_.end()) { // Mark the node for deletion. nodes_to_delete->insert(node_idx); - // Unconnect the node from its inputs to enable further optimizations. - node_map_->RemoveInputs(node->name()); + // Disconnect the node from its inputs to enable further optimizations. + node_map_->RemoveInputs(node_name); node->clear_input(); } } @@ -330,13 +416,18 @@ Status DependencyOptimizer::OptimizeDependencies() { std::set nodes_to_delete; for (int i = 0; i < optimized_graph_->node_size(); ++i) { const NodeDef& node = optimized_graph_->node(i); - if (node.op() == "NoOp" || IsConstant(node) || SafeToConvertToNoOp(node)) { + if (IsNoOp(node) || IsIdentity(node) || IsConstant(node) || + SafeToConvertToNoOp(node)) { nodes_to_simplify.PushBack(i); } } while (!nodes_to_simplify.Empty()) { - OptimizeNode(nodes_to_simplify.PopBack(), &nodes_to_simplify, - &nodes_to_delete); + int node_to_simplify = nodes_to_simplify.PopBack(); + // Discard nodes that were marked for deletion already. + while (nodes_to_delete.find(node_to_simplify) != nodes_to_delete.end()) { + node_to_simplify = nodes_to_simplify.PopBack(); + } + OptimizeNode(node_to_simplify, &nodes_to_simplify, &nodes_to_delete); } if (fetch_nodes_known_) { @@ -431,9 +522,10 @@ Status DependencyOptimizer::TransitiveReduction() { if (longest_distance[target] > 1) { const int input_slot = control_output.second; control_edges_to_remove[target].emplace(input_slot, source); - VLOG(1) << "Removing edge from:\n" - << optimized_graph_->node(source).DebugString() << "\n\nto:\n\n" - << optimized_graph_->node(target).DebugString(); + // VLOG(1) << "Removing edge from:\n" + // << optimized_graph_->node(source).DebugString() << + // "\n\nto:\n\n" + // << optimized_graph_->node(target).DebugString(); } } } @@ -473,8 +565,8 @@ Status DependencyOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, *optimized_graph_ = item.graph; nodes_to_preserve_ = item.NodesToPreserve(); fetch_nodes_known_ = !item.fetch.empty(); - CleanControlInputs(); + const int num_iterations = 2; for (int iteration = 0; iteration < num_iterations; ++iteration) { Status topo_sort_status; @@ -491,9 +583,12 @@ Status DependencyOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item, } else { LOG(ERROR) << topo_sort_status.error_message(); } - - // Turn nodes with only control outputs into NoOps, prune NoOps. + // Turn nodes with only control outputs into NoOps, prune NoOp and Identity + // nodes. TF_RETURN_IF_ERROR(OptimizeDependencies()); + + // Dedup control inputs. + CleanControlInputs(); } return Status::OK(); diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.h b/tensorflow/core/grappler/optimizers/dependency_optimizer.h index cfc5324439..0f47528a04 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.h +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.h @@ -29,8 +29,9 @@ namespace grappler { // optimizations, such as removing nodes that are effectively noops. class DependencyOptimizer : public GraphOptimizer { public: - DependencyOptimizer() {} - explicit DependencyOptimizer(RewriterConfig::Toggle /*unused*/) {} + DependencyOptimizer() : opt_level_(RewriterConfig::ON) {} + explicit DependencyOptimizer(RewriterConfig::Toggle opt_level) + : opt_level_(opt_level) {} ~DependencyOptimizer() override {} string name() const override { return "dependency_optimizer"; }; @@ -42,6 +43,9 @@ class DependencyOptimizer : public GraphOptimizer { const GraphDef& optimized_graph, double result) override; private: + // Returns true if node is not an Identity node or if it is an Identity + // that is safe to remove. + bool SafeToRemoveIdentity(const NodeDef& node); // Returns true if it is safe to convert node to NoOp. bool SafeToConvertToNoOp(const NodeDef& node); // Removes all duplicate control dependencies. @@ -61,6 +65,7 @@ class DependencyOptimizer : public GraphOptimizer { // Main driver of dependency optimizations. Status OptimizeDependencies(); + RewriterConfig::Toggle opt_level_; bool fetch_nodes_known_; std::unordered_set nodes_to_preserve_; std::unique_ptr node_map_; diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc index f5027a4a99..b8facb9dea 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer_test.cc @@ -167,14 +167,16 @@ TEST_F(DependencyOptimizerTest, ChangeToNoop_SwitchIdentity) { ops::Const(scope.WithOpName("c2").WithControlDependencies(ctrl_dep_id), {1.0f, 2.0f}, {1, 2}); Output neg1 = ops::Neg(scope.WithOpName("neg1"), s.output_false); + Output neg2 = ops::Neg(scope.WithOpName("neg2"), ctrl_dep_id); GrapplerItem item; TF_CHECK_OK(scope.ToGraphDef(&item.graph)); item.fetch.push_back("c1"); item.fetch.push_back("c2"); item.fetch.push_back("neg1"); + item.fetch.push_back("neg2"); - DependencyOptimizer optimizer; + DependencyOptimizer optimizer(RewriterConfig::AGGRESSIVE); GraphDef output; Status status = optimizer.Optimize(nullptr, item, &output); TF_EXPECT_OK(status); @@ -323,25 +325,148 @@ TEST_F(DependencyOptimizerTest, RemoveNoOps_SingleInputOrOutput) { } } +TEST_F(DependencyOptimizerTest, RemoveIdentity) { + tensorflow::Scope s = tensorflow::Scope::NewRootScope(); + Output x = ops::RandomUniform(s.WithOpName("x"), {1, 2}, DT_FLOAT); + Output y = ops::RandomUniform(s.WithOpName("y"), {1, 2}, DT_FLOAT); + Output z = ops::RandomUniform(s.WithOpName("z"), {1, 2}, DT_FLOAT); + + // Identity nodes to be removed. + // Case a) with a single input- and multiple outputs. + auto id_a = ops::Identity(s.WithOpName("id_a"), x); + // Case b) with multiple inputs and a single output. + auto id_b = ops::Identity( + s.WithOpName("id_b").WithControlDependencies(y).WithControlDependencies( + z), + x); + // Case c) with two inputs and two outputs. + auto id_c = ops::Identity(s.WithOpName("id_c").WithControlDependencies(y), x); + + // Output for Case a. + Output a_a = ops::Identity(s.WithOpName("a_a"), id_a); + Output a_b = ops::Identity(s.WithOpName("a_b"), id_a); + Output a_c = + ops::Identity(s.WithOpName("a_c").WithControlDependencies(id_a), z); + Output a_d = + ops::Identity(s.WithOpName("a_d").WithControlDependencies(id_a), z); + // Output for Case b. + Output b_a = ops::Identity(s.WithOpName("b_a"), id_b); + // Output for Case c. + Output c_a = ops::Identity(s.WithOpName("c_a"), id_c); + Output c_b = + ops::Identity(s.WithOpName("c_b").WithControlDependencies(id_c), z); + + GrapplerItem item; + TF_CHECK_OK(s.ToGraphDef(&item.graph)); + item.fetch = {"a_a", "a_b", "a_c", "a_d", "b_a", "c_a", "c_b"}; + + DependencyOptimizer optimizer(RewriterConfig::AGGRESSIVE); + GraphDef output; + Status status = optimizer.Optimize(nullptr, item, &output); + TF_EXPECT_OK(status); + + EXPECT_EQ(item.graph.node_size() - 3, output.node_size()); + for (const NodeDef& node : output.node()) { + EXPECT_NE("id_a", node.name()); + EXPECT_NE("id_b", node.name()); + EXPECT_NE("id_c", node.name()); + if (node.name() == "a_a" || node.name() == "a_b") { + EXPECT_EQ(1, node.input_size()); + EXPECT_EQ("x", node.input(0)); + } + if (node.name() == "a_c" || node.name() == "a_d") { + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("z", node.input(0)); + EXPECT_EQ("^x", node.input(1)); + } + if (node.name() == "b_a") { + EXPECT_EQ(3, node.input_size()); + EXPECT_EQ("x", node.input(0)); + EXPECT_EQ("^y", node.input(1)); + EXPECT_EQ("^z", node.input(2)); + } + if (node.name() == "c_a") { + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("x", node.input(0)); + EXPECT_EQ("^y", node.input(1)); + } + if (node.name() == "c_b") { + EXPECT_EQ(3, node.input_size()); + EXPECT_EQ("z", node.input(0)); + EXPECT_EQ("^x", node.input(1)); + EXPECT_EQ("^y", node.input(2)); + } + } +} + +TEST_F(DependencyOptimizerTest, RemoveIdentity_RepeatedInputs) { + // Corner cases with repeated inputs. + tensorflow::Scope scope = tensorflow::Scope::NewRootScope(); + ops::Variable x(scope.WithOpName("x"), {}, DT_BOOL); + ops::Variable y(scope.WithOpName("y"), {}, DT_BOOL); + ops::Switch sw(scope.WithOpName("switch"), x, x); + // id0 should be removed. + Output id0 = ops::Identity(scope.WithOpName("id0"), sw.output_true); + // id1 should not be removed, since it would anchor a control dependency + // on the switch. + Output id1 = ops::Identity(scope.WithOpName("id1"), sw.output_false); + Output or0 = ops::LogicalOr(scope.WithOpName("or0"), id0, id0); + Output or1 = ops::LogicalOr(scope.WithOpName("or1"), id0, y); + Output or2 = ops::LogicalOr( + scope.WithOpName("or2").WithControlDependencies(id1), y, y); + + GrapplerItem item; + TF_CHECK_OK(scope.ToGraphDef(&item.graph)); + item.fetch.push_back("or0"); + item.fetch.push_back("or1"); + item.fetch.push_back("or2"); + DependencyOptimizer optimizer(RewriterConfig::AGGRESSIVE); + GraphDef output; + Status status = optimizer.Optimize(nullptr, item, &output); + TF_EXPECT_OK(status); + + EXPECT_EQ(item.graph.node_size() - 1, output.node_size()); + for (const NodeDef& node : output.node()) { + EXPECT_NE("id0", node.name()); + if (node.name() == "or0") { + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("switch:1", node.input(0)); + EXPECT_EQ("switch:1", node.input(1)); + } + if (node.name() == "or1") { + EXPECT_EQ(2, node.input_size()); + EXPECT_EQ("switch:1", node.input(0)); + EXPECT_EQ("y", node.input(1)); + } + if (node.name() == "or2") { + // or1 should be unchanged. + EXPECT_EQ(3, node.input_size()); + EXPECT_EQ("y", node.input(0)); + EXPECT_EQ("y", node.input(1)); + EXPECT_EQ("^id1", node.input(2)); + } + } +} + TEST_F(DependencyOptimizerTest, Transitive_Reduction_Simple) { tensorflow::Scope s = tensorflow::Scope::NewRootScope(); Output c = ops::Const(s.WithOpName("c"), {1.0f, 2.0f}, {1, 2}); Output x = ops::Square(s.WithOpName("x"), c); - Output id1 = ops::Identity(s.WithOpName("id1"), x); - Output id2 = - ops::Identity(s.WithOpName("id2").WithControlDependencies({x}), id1); + Output neg1 = ops::Neg(s.WithOpName("neg1"), x); + Output neg2 = + ops::Neg(s.WithOpName("neg2").WithControlDependencies({x}), neg1); GrapplerItem item; TF_CHECK_OK(s.ToGraphDef(&item.graph)); - item.fetch.push_back("id2"); - DependencyOptimizer optimizer; + item.fetch.push_back("neg2"); + DependencyOptimizer optimizer(RewriterConfig::AGGRESSIVE); GraphDef output; Status status = optimizer.Optimize(nullptr, item, &output); TF_EXPECT_OK(status); EXPECT_EQ(4, output.node_size()); - EXPECT_EQ("id2", output.node(3).name()); + EXPECT_EQ("neg2", output.node(3).name()); EXPECT_EQ(1, output.node(3).input_size()); - EXPECT_EQ("id1", output.node(3).input(0)); + EXPECT_EQ("neg1", output.node(3).input(0)); } TEST_F(DependencyOptimizerTest, ChangeToNoop_Identity) { @@ -356,20 +481,21 @@ TEST_F(DependencyOptimizerTest, ChangeToNoop_Identity) { Output grappler_added_id = ops::Identity( scope.WithOpName("ConstantFoldingCtrl/switch_1"), s.output_true); Output c1 = ops::Const(scope.WithOpName("c1") - .WithControlDependencies(id0) .WithControlDependencies(id_after_var) .WithControlDependencies(grappler_added_id), {1.0f, 2.0f}, {1, 2}); Output id1 = ops::Identity(scope.WithOpName("id1"), c1); + Output id2 = ops::Identity(scope.WithOpName("id2"), id0); Output fetch = ops::Identity(scope.WithOpName("fetch").WithControlDependencies(id1), c1); GrapplerItem item; TF_CHECK_OK(scope.ToGraphDef(&item.graph)); item.fetch.push_back("c1"); + item.fetch.push_back("id2"); item.fetch.push_back("fetch"); - DependencyOptimizer optimizer; + DependencyOptimizer optimizer(RewriterConfig::AGGRESSIVE); GraphDef output; Status status = optimizer.Optimize(nullptr, item, &output); TF_EXPECT_OK(status); @@ -377,8 +503,8 @@ TEST_F(DependencyOptimizerTest, ChangeToNoop_Identity) { EXPECT_EQ(item.graph.node_size() - 2, output.node_size()); for (int i = 0; i < output.node_size(); ++i) { const NodeDef& node = output.node(i); - // "id0" and "id1" but neither "ConstantFoldingCtrl/switch_1" nor - // "id_after_var" should be eliminated. + // "id0" and "id1" but neither "ConstantFoldingCtrl/switch_1", + // "id_after_var, nor "id2"" should be eliminated. EXPECT_NE("id0", node.name()); EXPECT_NE("id1", node.name()); if (node.name() == "c1") { diff --git a/tensorflow/python/debug/lib/debug_gradients_test.py b/tensorflow/python/debug/lib/debug_gradients_test.py index b6c7280a41..c1e9869d97 100644 --- a/tensorflow/python/debug/lib/debug_gradients_test.py +++ b/tensorflow/python/debug/lib/debug_gradients_test.py @@ -22,6 +22,7 @@ import shutil import tempfile from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.debug.lib import debug_data from tensorflow.python.debug.lib import debug_gradients @@ -38,7 +39,11 @@ from tensorflow.python.training import gradient_descent class IdentifyGradientTest(test_util.TensorFlowTestCase): def setUp(self): - self.sess = session.Session() + rewriter_config = rewriter_config_pb2.RewriterConfig( + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) + graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) + config = config_pb2.ConfigProto(graph_options=graph_options) + self.sess = session.Session(config=config) with self.sess.as_default(): self.u = variables.Variable(2.0, name="u") self.v = variables.Variable(3.0, name="v") diff --git a/tensorflow/python/debug/lib/session_debug_grpc_test.py b/tensorflow/python/debug/lib/session_debug_grpc_test.py index 367b353545..b623ee31c5 100644 --- a/tensorflow/python/debug/lib/session_debug_grpc_test.py +++ b/tensorflow/python/debug/lib/session_debug_grpc_test.py @@ -54,7 +54,8 @@ from tensorflow.python.training import monitored_session def no_rewrite_session_config(): rewriter_config = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, - arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF) + arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, + dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF) graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config) return config_pb2.ConfigProto(graph_options=graph_options) -- GitLab From 87a3c967973641d3b0d2a16d17add184ed967392 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Mon, 29 Jan 2018 18:30:59 -0800 Subject: [PATCH 250/423] TFE: Register a GPU kernel for tfe.py_func. PiperOrigin-RevId: 183765122 --- tensorflow/python/BUILD | 1 + .../python/kernel_tests/py_func_test.py | 86 +++++++++---------- tensorflow/python/lib/core/py_func.cc | 72 +++++++++++----- tensorflow/python/ops/script_ops.py | 38 +++++--- 4 files changed, 120 insertions(+), 77 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index a323d5bc39..363ff6fae9 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -298,6 +298,7 @@ cc_library( ":safe_ptr", "//tensorflow/c:tf_status_helper", "//tensorflow/c/eager:c_api", + "//tensorflow/c/eager:c_api_internal", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 92fb68820e..c7181497d8 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -396,66 +396,66 @@ class PyFuncTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testEagerSingleOutputFloat32(self): - a = array_ops.ones((3, 3), dtype=dtypes.float32) - x = array_ops.ones((3, 1), dtype=dtypes.float32) - output = script_ops.eager_py_func(matmul, inp=[a, x], Tout=dtypes.float32) - with self.test_session(): + with test_util.device(use_gpu=True): + a = array_ops.ones((3, 3), dtype=dtypes.float32) + x = array_ops.ones((3, 1), dtype=dtypes.float32) + output = script_ops.eager_py_func(matmul, inp=[a, x], Tout=dtypes.float32) ret = self.evaluate(output) self.assertAllClose(ret, [[3.0], [3.0], [3.0]]) @test_util.run_in_graph_and_eager_modes() def testEagerArrayOutput(self): - a = array_ops.ones((3, 3), dtype=dtypes.int32) - x = array_ops.ones((3, 1), dtype=dtypes.int32) - output = script_ops.eager_py_func( - lambda a, x: [matmul(a, x)], inp=[a, x], Tout=[dtypes.int32]) - - with self.test_session(): + with test_util.device(use_gpu=True): + a = array_ops.ones((3, 3), dtype=dtypes.float32) + x = array_ops.ones((3, 1), dtype=dtypes.float32) + output = script_ops.eager_py_func( + lambda a, x: [matmul(a, x)], inp=[a, x], Tout=[dtypes.float32]) ret = self.evaluate(output) - self.assertAllEqual(ret, [[[3], [3], [3]]]) + self.assertAllEqual(ret, [[[3.0], [3.0], [3.0]]]) @test_util.run_in_graph_and_eager_modes() def testEagerReturnNone(self): + with test_util.device(use_gpu=True): + def no_return_value(): + return - def no_return_value(): - return - - output = script_ops.eager_py_func(no_return_value, inp=[], Tout=[]) - ret = self.evaluate(output) - if context.in_eager_mode(): - self.assertEquals(len(ret), 0) - else: - self.assertIsNone(ret) + output = script_ops.eager_py_func(no_return_value, inp=[], Tout=[]) + ret = self.evaluate(output) + if context.in_eager_mode(): + self.assertEquals(len(ret), 0) + else: + self.assertIsNone(ret) @test_util.run_in_graph_and_eager_modes() def testEagerPyFuncInDefun(self): + with test_util.device(use_gpu=True): + def wrapper(): + a = array_ops.ones((3, 3), dtype=dtypes.float32) + x = array_ops.ones((3, 1), dtype=dtypes.float32) + return script_ops.eager_py_func(matmul, inp=[a, x], Tout=dtypes.float32) - def wrapper(): - a = array_ops.ones((3, 3), dtype=dtypes.int32) - x = array_ops.ones((3, 1), dtype=dtypes.int32) - return script_ops.eager_py_func(matmul, inp=[a, x], Tout=dtypes.int32) - - wrapped = function.defun(wrapper) - ret = self.evaluate(wrapped()) - self.assertAllEqual(ret, [[3], [3], [3]]) + wrapped = function.defun(wrapper) + ret = self.evaluate(wrapped()) + self.assertAllEqual(ret, [[3.0], [3.0], [3.0]]) @test_util.run_in_graph_and_eager_modes() def testEagerExceptionHandling(self): - self._testExceptionHandling( - ValueError, errors.InvalidArgumentError, eager=True) - self._testExceptionHandling( - TypeError, errors.InvalidArgumentError, eager=True) - self._testExceptionHandling( - StopIteration, errors.OutOfRangeError, eager=True) - self._testExceptionHandling( - MemoryError, errors.ResourceExhaustedError, eager=True) - self._testExceptionHandling( - NotImplementedError, errors.UnimplementedError, eager=True) - - class WeirdError(Exception): - pass - - self._testExceptionHandling(WeirdError, errors.UnknownError, eager=True) + with test_util.device(use_gpu=True): + self._testExceptionHandling( + ValueError, errors.InvalidArgumentError, eager=True) + self._testExceptionHandling( + TypeError, errors.InvalidArgumentError, eager=True) + self._testExceptionHandling( + StopIteration, errors.OutOfRangeError, eager=True) + self._testExceptionHandling( + MemoryError, errors.ResourceExhaustedError, eager=True) + self._testExceptionHandling( + NotImplementedError, errors.UnimplementedError, eager=True) + + class WeirdError(Exception): + pass + + self._testExceptionHandling(WeirdError, errors.UnknownError, eager=True) if __name__ == "__main__": diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc index d3bfa0ee33..e0422ef80a 100644 --- a/tensorflow/python/lib/core/py_func.cc +++ b/tensorflow/python/lib/core/py_func.cc @@ -19,6 +19,7 @@ limitations under the License. #include "numpy/arrayobject.h" #include "tensorflow/c/eager/c_api.h" +#include "tensorflow/c/eager/c_api_internal.h" #include "tensorflow/c/tf_status_helper.h" #include "tensorflow/core/framework/allocation_description.pb.h" #include "tensorflow/core/framework/op_kernel.h" @@ -53,6 +54,12 @@ struct PyCall { // with this "token". string token; + // The device on which Tensors are stored; only used for EagerPyFunc. + Device* device; + + // True if and only if the op has been placed on a GPU. + bool gpu; + // True if the call is associated with an EagerPyFunc. bool eager; @@ -71,7 +78,12 @@ Status MakeArgTuple(const PyCall* call, PyObject** tuple) { PyObject* arg = nullptr; const Tensor& t = call->ins[i]; if (call->eager) { - arg = EagerTensorFromHandle(TFE_NewTensorHandle(t)); + if (call->gpu) { + arg = EagerTensorFromHandle(new TFE_TensorHandle(t, call->device)); + } else { + // TFE_TensorHandle assumes that CPU is identified by `nullptr`. + arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr)); + } if (arg == nullptr) { return errors::Internal("Unable to procure EagerTensor from Tensor."); } @@ -84,7 +96,8 @@ Status MakeArgTuple(const PyCall* call, PyObject** tuple) { } PyList_SetItem(lst, i, arg); } - *tuple = Py_BuildValue("(sN)", call->token.c_str(), lst); + *tuple = Py_BuildValue("(sON)", call->token.c_str(), + call->gpu ? Py_True : Py_False, lst); CHECK(*tuple); return Status::OK(); } @@ -150,15 +163,9 @@ bool IsSingleNone(PyObject* obj) { } // Retrieves a Tensor from `eager_tensor` and stores it in `output_tensor`. -Status ExtractTensorFromEagerTensor(const PyObject* eager_tensor, - Tensor* output_tensor, - TF_Status* tf_status) { - // TODO(akshayka): Lift the restriction requiring output tensors to - // lie in host memory; EagerPyFunc should be able to dispatch ops on GPU - // tensors, so we should eventually implement a GPU kernel for EagerPyFunc. - *output_tensor = *TFE_TensorHandleUnderlyingTensorInHostMemory( - EagerTensor_Handle(eager_tensor), tf_status); - return StatusFromTF_Status(tf_status); +void ExtractTensorFromEagerTensor(const PyObject* eager_tensor, + Tensor* output_tensor) { + *output_tensor = EagerTensor_Handle(eager_tensor)->t; } // Calls the registered py function through the trampoline. @@ -201,15 +208,23 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { } // Process the return values and convert them to TF Tensors. - Status s; + Status s = Status::OK(); if (PyList_Check(result)) { + // `result` is a Python list; if this operation is an `EagerPyFunc`, then + // every item in the list must be an `EagerTensor`; otherwise, every element + // must be a NumPy array. call->out.clear(); for (int i = 0; i < PyList_Size(result); ++i) { Tensor t; if (call->eager) { - auto tf_status = tensorflow::make_safe(TF_NewStatus()); - s = ExtractTensorFromEagerTensor(PyList_GetItem(result, i), &t, - tf_status.get()); + const PyObject* item = PyList_GetItem(result, i); + if (EagerTensor_CheckExact(item)) { + ExtractTensorFromEagerTensor(item, &t); + } else { + s = errors::FailedPrecondition( + "Expected EagerTensor, found PyObject of type: ", + Py_TYPE(item)->tp_name); + } } else { s = ConvertNdarrayToTensor(PyList_GetItem(result, i), &t); } @@ -220,16 +235,15 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { call->out.push_back(t); } } else if (EagerTensor_CheckExact(result) || result == Py_None) { + // result is an `EagerTensor` or `None`. DCHECK(call->eager); Tensor t; if (result != Py_None) { - auto tf_status = tensorflow::make_safe(TF_NewStatus()); - s = ExtractTensorFromEagerTensor(result, &t, tf_status.get()); - if (s.ok()) { - call->out.push_back(t); - } + ExtractTensorFromEagerTensor(result, &t); + call->out.push_back(t); } } else if (PyArray_Check(result)) { + // `result` is a NumPy array. DCHECK(!call->eager); if (!IsSingleNone(result)) { Tensor t; @@ -239,7 +253,7 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) { } } } else { - s = errors::Internal("Unexpected pyobject is returned: ", + s = errors::Internal("Unexpected PyObject was returned: ", Py_TYPE(result)->tp_name); } Py_DECREF(result); @@ -429,12 +443,24 @@ class PyFuncOp : public OpKernel { explicit PyFuncOp(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("token", &token_)); eager_ = type_string() == "EagerPyFunc"; + gpu_ = ctx->device_type().type_string() == DEVICE_GPU; } void Compute(OpKernelContext* ctx) override { PyCall call; call.token = token_; + call.gpu = gpu_; call.eager = eager_; + if (call.eager) { + // Eager's C API uses `Device`, whereas `OpKernelContext` stores a + // `DeviceBase`; attempt to downcast. + call.device = dynamic_cast(ctx->device()); + if (call.device == nullptr) { + ctx->CtxFailureWithWarning( + errors::Internal("Unrecognized device class")); + } + } + for (int i = 0; i < ctx->num_inputs(); ++i) { call.ins.push_back(ctx->input(i)); } @@ -476,6 +502,9 @@ class PyFuncOp : public OpKernel { private: string token_; + // True if and only if this op has been placed on a GPU. + bool gpu_; + // True if and only if this op should execute the python function eagerly, // i.e., if and only if the eager attribute is set. bool eager_; @@ -486,5 +515,6 @@ class PyFuncOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("PyFunc").Device(DEVICE_CPU), PyFuncOp); REGISTER_KERNEL_BUILDER(Name("PyFuncStateless").Device(DEVICE_CPU), PyFuncOp); REGISTER_KERNEL_BUILDER(Name("EagerPyFunc").Device(DEVICE_CPU), PyFuncOp); +REGISTER_KERNEL_BUILDER(Name("EagerPyFunc").Device(DEVICE_GPU), PyFuncOp); } // end namespace tensorflow diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 4b5072fd67..1b9071ee93 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -50,19 +50,21 @@ class EagerFunc(object): self._func = func self._out_dtypes = Tout - def __call__(self, *args, **kwargs): - """Passes args, kwargs to `self._func`, which is executed eagerly.""" + def __call__(self, on_gpu, args): + """Passes `args` to `self._func`, which is executed eagerly.""" with context.eager_mode(): - ret = self._func(*args, **kwargs) + ret = self._func(*args) + maybe_copy_to_gpu = lambda x: x if not on_gpu else x.gpu() if isinstance(ret, (tuple, list)): return [ - ops.convert_to_tensor(x, dtype=dtype) + maybe_copy_to_gpu(ops.convert_to_tensor(x, dtype=dtype)) for (x, dtype) in zip(ret, self._out_dtypes) ] elif ret is None: return ret else: - return ops.convert_to_tensor(ret, dtype=self._out_dtypes[0]) + return maybe_copy_to_gpu( + ops.convert_to_tensor(ret, dtype=self._out_dtypes[0])) class FuncRegistry(object): @@ -116,16 +118,29 @@ class FuncRegistry(object): else: return result - def __call__(self, token, args): - """Calls the registered function for `token` with args.""" + def __call__(self, token, on_gpu, args): + """Calls the registered function for `token` with args. + + Args: + token: A key into this `FuncRegistry` identifying which function to call. + on_gpu: A boolean indicating whether or not `token`'s corresponding + operation was placed on GPU; only used if the function registered for + `token` is an `EagerPyFunc`. + args: The arguments to pass to the function registered for `token`. + + Returns: + The output of the function registered for `token`. + + Raises: + ValueError: if no function is registered for `token`. + """ func = self._funcs[token] if func is None: raise ValueError("callback %s is not found" % token) - ret = func(*args) - if isinstance(func, EagerFunc): - return ret + return func(on_gpu, args) else: + ret = func(*args) # Strings seem to lead to a memory leak here if they're not wrapped in a # list. if isinstance(ret, six.binary_type): @@ -302,8 +317,5 @@ def py_func(func, inp, Tout, stateful=True, name=None): func=func, inp=inp, Tout=Tout, stateful=stateful, eager=False, name=name) -# TODO(akshayka): PyFuncs where the 'eager' attribute is set to True should be -# differentiable, i.e., the gradient of PyFunc should propagate Nones if the -# eager attribute is not set, and otherwise, it should return the gradient. ops.NotDifferentiable("PyFunc") ops.NotDifferentiable("PyFuncStateless") -- GitLab From 5ded6fc13bb451d41a0d3a8d874f1c23b2e7c424 Mon Sep 17 00:00:00 2001 From: Makoto Uchida Date: Mon, 29 Jan 2018 22:27:11 -0800 Subject: [PATCH 251/423] Adds loss_reduction argument to baseline estimators. PiperOrigin-RevId: 183783628 --- .../python/estimator/canned/baseline.py | 20 ++++++++++++++----- ...rflow.estimator.-baseline-classifier.pbtxt | 2 +- ...orflow.estimator.-baseline-regressor.pbtxt | 2 +- 3 files changed, 17 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/estimator/canned/baseline.py b/tensorflow/python/estimator/canned/baseline.py index 96e4ecd29f..138152ac1c 100644 --- a/tensorflow/python/estimator/canned/baseline.py +++ b/tensorflow/python/estimator/canned/baseline.py @@ -57,6 +57,7 @@ from tensorflow.python.ops import check_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.ops.losses import losses from tensorflow.python.training import training_util # The default learning rate of 0.3 is a historical artifact of the initial @@ -220,7 +221,8 @@ class BaselineClassifier(estimator.Estimator): weight_column=None, label_vocabulary=None, optimizer='Ftrl', - config=None): + config=None, + loss_reduction=losses.Reduction.SUM): """Initializes a BaselineClassifier instance. Args: @@ -240,6 +242,8 @@ class BaselineClassifier(estimator.Estimator): optimizer to use for training. If not specified, will use `FtrlOptimizer` with a default learning rate of 0.3. config: `RunConfig` object to configure the runtime settings. + loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how + to reduce training loss over batch. Defaults to `SUM`. Returns: A `BaselineClassifier` estimator. @@ -249,11 +253,13 @@ class BaselineClassifier(estimator.Estimator): if n_classes == 2: head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( # pylint: disable=protected-access weight_column=weight_column, - label_vocabulary=label_vocabulary) + label_vocabulary=label_vocabulary, + loss_reduction=loss_reduction) else: head = head_lib._multi_class_head_with_softmax_cross_entropy_loss( # pylint: disable=protected-access n_classes, weight_column=weight_column, - label_vocabulary=label_vocabulary) + label_vocabulary=label_vocabulary, + loss_reduction=loss_reduction) def _model_fn(features, labels, mode, config): return _baseline_model_fn( features=features, @@ -311,7 +317,8 @@ class BaselineRegressor(estimator.Estimator): label_dimension=1, weight_column=None, optimizer='Ftrl', - config=None): + config=None, + loss_reduction=losses.Reduction.SUM): """Initializes a BaselineRegressor instance. Args: @@ -328,13 +335,16 @@ class BaselineRegressor(estimator.Estimator): optimizer to use for training. If not specified, will use `FtrlOptimizer` with a default learning rate of 0.3. config: `RunConfig` object to configure the runtime settings. + loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how + to reduce training loss over batch. Defaults to `SUM`. Returns: A `BaselineRegressor` estimator. """ head = head_lib._regression_head_with_mean_squared_error_loss( # pylint: disable=protected-access label_dimension=label_dimension, - weight_column=weight_column) + weight_column=weight_column, + loss_reduction=loss_reduction) def _model_fn(features, labels, mode, config): return _baseline_model_fn( features=features, diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt index ab697b1b95..874a73f661 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\'], " + argspec: "args=[\'self\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\', \'weighted_sum\'], " } member_method { name: "evaluate" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt index b73f6433e2..8da2a2b686 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\'], " + argspec: "args=[\'self\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\', \'weighted_sum\'], " } member_method { name: "evaluate" -- GitLab From 7052c6a77985dccd8754f552f051be6b802e90fe Mon Sep 17 00:00:00 2001 From: Qiumin Xu Date: Mon, 29 Jan 2018 22:58:19 -0800 Subject: [PATCH 252/423] Add options to enable new features for cloud-tpu-profiler. (#16550) --- .../pip_package/cloud_tpu_profiler/main.py | 21 +++++++++++++------ .../contrib/tpu/profiler/pip_package/setup.py | 2 +- 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py index 846db13329..885466e5d1 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl import flags import os import subprocess @@ -24,13 +25,19 @@ import sys import tensorflow as tf -tf.flags.DEFINE_string('service_addr', '', +flags.DEFINE_string('service_addr', None, 'Address of TPU profiler service e.g. localhost:8466') -tf.flags.DEFINE_string('logdir', '', - 'Path of TensorBoard log directory e.g. /tmp/tb_log') -tf.flags.DEFINE_integer('duration_ms', 2000, 'Duration of tracing in ms.') - -FLAGS = tf.flags.FLAGS +flags.DEFINE_string('logdir', None, + "Path of TensorBoard log directory e.g. /tmp/tb_log, " + "gs://tb_bucket") +flags.DEFINE_integer('duration_ms', 2000, 'Duration of tracing in ms.') +flags.DEFINE_integer('num_tracing_attempts', 3, + "Automatically retry N times when no trace event is " + "collected.") +flags.DEFINE_boolean('include_dataset_ops', True, + "Set to false to profile longer TPU device traces.") + +FLAGS = flags.FLAGS EXECUTABLE = 'data/capture_tpu_profile' @@ -47,6 +54,8 @@ def main(unused_argv=None): cmd.append('--logdir='+logdir) cmd.append('--service_addr='+FLAGS.service_addr) cmd.append('--duration_ms='+str(FLAGS.duration_ms)) + cmd.append('--num_tracing_attempts='+str(FLAGS.num_tracing_attempts)) + cmd.append('--include_dataset_ops='+str(FLAGS.include_dataset_ops).lower()) subprocess.call(cmd) diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py index 9219663831..3dffebe668 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py @@ -20,7 +20,7 @@ from __future__ import print_function from setuptools import setup -_VERSION = '1.4.3-a2' +_VERSION = '1.5.0-rc1' CONSOLE_SCRIPTS = [ 'capture_tpu_profile=cloud_tpu_profiler.main:run_main', -- GitLab From ba64f5334d4bba31d22c30e09a96f806ea0e2f7e Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Tue, 30 Jan 2018 15:59:05 +0900 Subject: [PATCH 253/423] Fix typo (#16562) * fix typos * fix typos * fix typo * fix typo --- .../contrib/lite/nnapi/NeuralNetworksShim.h | 2 +- .../reduce_slice_ops/ops/reduce_slice_ops.cc | 18 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h index 7019c29959..76032771af 100644 --- a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h +++ b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h @@ -1571,7 +1571,7 @@ inline int ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, } /** - * Specfifies which operands will be the model's inputs and outputs. + * Specifies which operands will be the model's inputs and outputs. * * An operand cannot be used for both input and output. Doing so will * return an error. diff --git a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc index b8b56c0e22..31e565027f 100644 --- a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc +++ b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc @@ -87,9 +87,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 2, 3] - [ 0, 0, 0] - [41,52,63]]. +the output will be [[ 1, 2, 3] + [ 0, 0, 0] + [41,52,63]]. ``` The data must be at least rank 1. The indices must be of shape (?,2) where the @@ -132,9 +132,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 2, 3] - [ 1, 1, 1] - [40,100,180]]. +the output will be [[ 1, 2, 3] + [ 1, 1, 1] + [40,100,180]]. ``` The data must be at least rank 1. The indices can be of shape (?,2) where the @@ -189,9 +189,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 20, 3] - [ -BIG_VALUE, -BIG_VALUE, -BIG_VALUE] - [ 400, 20, 60]]. +the output will be [[ 1, 20, 3] + [ -BIG_VALUE, -BIG_VALUE, -BIG_VALUE] + [ 400, 20, 60]]. ``` The data must be at least rank 1. The indices can be of shape (?,2) where the -- GitLab From 9560054b18ad3fc9faa296d344e062876858817a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 08:08:27 -0800 Subject: [PATCH 254/423] Support `mode` option to discriminator function in GANEstimator. This supports operations like batch normalization, which have different train and eval behavior. PiperOrigin-RevId: 183833519 --- .../gan/python/estimator/python/gan_estimator_impl.py | 10 ++++++++-- .../gan/python/estimator/python/gan_estimator_test.py | 4 ++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py index 0d51c282a8..082c42eba1 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py @@ -59,7 +59,11 @@ _summary_type_map = { class GANEstimator(estimator.Estimator): """An estimator for Generative Adversarial Networks (GANs). - This Estimator is backed by TFGAN. + This Estimator is backed by TFGAN. The network functions follow the TFGAN API + except for one exception: if either `generator_fn` or `discriminator_fn` have + an argument called `mode`, then the tf.Estimator mode is passed in for that + argument. This helps with operations like batch normalization, which have + different train and evaluation behavior. Example: @@ -233,9 +237,11 @@ def _gan_model_fn( def _make_gan_model(generator_fn, discriminator_fn, real_data, generator_inputs, generator_scope, add_summaries, mode): """Make a `GANModel`, and optionally pass in `mode`.""" - # If `generator_fn` has an argument `mode`, pass mode to it. + # If network functions have an argument `mode`, pass mode to it. if 'mode' in inspect.getargspec(generator_fn).args: generator_fn = functools.partial(generator_fn, mode=mode) + if 'mode' in inspect.getargspec(discriminator_fn).args: + discriminator_fn = functools.partial(discriminator_fn, mode=mode) gan_model = tfgan_train.gan_model( generator_fn, discriminator_fn, diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py index e752f0bccc..387a62bd74 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py @@ -54,7 +54,8 @@ def generator_fn(noise_dict, mode): return layers.fully_connected(noise, noise.shape[1].value) -def discriminator_fn(data, _): +def discriminator_fn(data, unused_conditioning, mode): + del unused_conditioning, mode return layers.fully_connected(data, 1) @@ -99,7 +100,6 @@ def mock_head(testcase, expected_generator_inputs, expected_real_data, else: testcase.assertEqual(discriminator_scope_name, gan_model.discriminator_scope.name) - testcase.assertEqual(_or_none(discriminator_fn), gan_model.discriminator_fn) with ops.control_dependencies(assertions): if mode == model_fn_lib.ModeKeys.TRAIN: -- GitLab From 4d3c6ca4fc99aeefa1370b51c9a3e1bcd26dd04a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 08:35:46 -0800 Subject: [PATCH 255/423] Changed the heap simulator to allow it to be configured about whether to issue Alloc/Free for constants, and enable buffer sharing. PiperOrigin-RevId: 183836922 --- .../compiler/xla/service/buffer_assignment.cc | 10 ++-- .../compiler/xla/service/heap_simulator.cc | 53 ++++++++++--------- .../compiler/xla/service/heap_simulator.h | 30 +++++++---- 3 files changed, 54 insertions(+), 39 deletions(-) diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index d5594dc07c..774b11478c 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -997,14 +997,15 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( auto color = single_colored_set.first; VLOG(2) << "Simulating heap for color " << color; int64 alignment = assignment->color_alignment_(color); + HeapSimulator::Options options; + options.buffers_to_assign = &single_colored_set.second; TF_ASSIGN_OR_RETURN( const HeapSimulator::Result result, HeapSimulator::Run(MakeUnique( MakeUnique(alignment)), assignment->module(), module_sequence, assignment->points_to_analysis(), - assignment->buffer_size_, - &single_colored_set.second)); + assignment->buffer_size_, options)); AssignBuffersFromHeapSimulator(result, assignment, single_colored_set.first); } @@ -1024,14 +1025,15 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( auto color = single_colored_set.first; VLOG(2) << "Simulating heap for color " << color; int64 alignment = assignment->color_alignment_(color); + HeapSimulator::Options options; + options.buffers_to_assign = &single_colored_set.second; TF_ASSIGN_OR_RETURN( const HeapSimulator::Result result, HeapSimulator::Run(MakeUnique( MakeUnique(alignment)), *computation, *instruction_sequence, assignment->points_to_analysis(), - assignment->buffer_size_, - &single_colored_set.second)); + assignment->buffer_size_, options)); AssignBuffersFromHeapSimulator(result, assignment, single_colored_set.first); } diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc index 34e2f7ee20..cde5877e29 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.cc +++ b/tensorflow/compiler/xla/service/heap_simulator.cc @@ -64,10 +64,8 @@ StatusOr HeapSimulator::Run( std::unique_ptr algorithm, const HloModule& module, const SequentialHloOrdering::HloModuleSequence& module_sequence, const TuplePointsToAnalysis& points_to_analysis, - const LogicalBuffer::SizeFunction& size_fn, - const FlatSet* buffers_to_assign) { - HeapSimulator heap(std::move(algorithm), size_fn, buffers_to_assign, - &module_sequence); + const LogicalBuffer::SizeFunction& size_fn, const Options& options) { + HeapSimulator heap(std::move(algorithm), size_fn, options, &module_sequence); const HloComputation* entry_computation = module.entry_computation(); const std::vector& instruction_sequence = FindOrDie(module_sequence, entry_computation); @@ -81,9 +79,8 @@ StatusOr HeapSimulator::Run( std::unique_ptr algorithm, const HloComputation& computation, const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, - const LogicalBuffer::SizeFunction& size_fn, - const FlatSet* buffers_to_assign) { - HeapSimulator heap(std::move(algorithm), size_fn, buffers_to_assign, + const LogicalBuffer::SizeFunction& size_fn, const Options& options) { + HeapSimulator heap(std::move(algorithm), size_fn, options, /*module_sequence=*/nullptr); TF_RETURN_IF_ERROR(heap.RunComputation(computation, instruction_sequence, points_to_analysis)); @@ -199,15 +196,17 @@ Status HeapSimulator::RunComputation( // We can only share with the operand buffer if it is about to be freed; // we must be the last user of the buffer. bool shared = false; - for (const LogicalBuffer* operand_buffer : operand_buffers_to_free) { - if (buffer->instruction()->IsUserOf(operand_buffer->instruction()) && - buffer->instruction()->opcode() != HloOpcode::kCopy && - CanShareOperandBufferWithUser( - operand_buffer->instruction(), operand_buffer->index(), - buffer->instruction(), buffer->index(), points_to_analysis)) { - ShareBuffer(buffer, operand_buffer, instruction); - shared = true; - break; + if (options_.may_reuse_operand_buffers) { + for (const LogicalBuffer* operand_buffer : operand_buffers_to_free) { + if (buffer->instruction()->IsUserOf(operand_buffer->instruction()) && + buffer->instruction()->opcode() != HloOpcode::kCopy && + CanShareOperandBufferWithUser( + operand_buffer->instruction(), operand_buffer->index(), + buffer->instruction(), buffer->index(), points_to_analysis)) { + ShareBuffer(buffer, operand_buffer, instruction); + shared = true; + break; + } } } @@ -266,13 +265,12 @@ Status HeapSimulator::RunComputation( HeapSimulator::HeapSimulator( std::unique_ptr algorithm, - const LogicalBuffer::SizeFunction& size_fn, - const FlatSet* buffers_to_assign, + const LogicalBuffer::SizeFunction& size_fn, const Options& options, const SequentialHloOrdering::HloModuleSequence* module_sequence) : no_fragmentation_stats_(MakeUnique()), algorithm_(std::move(algorithm)), size_fn_(size_fn), - buffers_to_assign_(buffers_to_assign), + options_(options), module_sequence_(module_sequence) { debug_trace_.set_whole_module_simulation(module_sequence_ != nullptr); } @@ -280,13 +278,16 @@ HeapSimulator::HeapSimulator( HeapSimulator::~HeapSimulator() {} bool HeapSimulator::IgnoreBuffer(const LogicalBuffer* buffer) const { - // Buffers for constants are ignored, as with BufferAssigner. Also ignore - // buffers that we're not meant to assign. + // Buffers for constants are ignored unless the alloc_constants option is + // set. Also ignore buffers that we're not meant to assign. // // TODO(b/32248867): For consistency, constants should get allocations. - return buffer->instruction()->opcode() == HloOpcode::kConstant || - (buffers_to_assign_ != nullptr && - buffers_to_assign_->count(buffer) == 0); + if (!options_.alloc_constants && + buffer->instruction()->opcode() == HloOpcode::kConstant) { + return true; + } + return options_.buffers_to_assign != nullptr && + options_.buffers_to_assign->count(buffer) == 0; } // Alloc always calls the underlying heap algorithm. @@ -400,8 +401,8 @@ HeapSimulator::Result HeapSimulator::Finish() { } // If we were told to assign specific buffers, make sure we've assigned // exactly that many buffers. - if (buffers_to_assign_ != nullptr) { - CHECK_EQ(buffers_to_assign_->size(), result.chunk_map.size()); + if (options_.buffers_to_assign != nullptr) { + CHECK_EQ(options_.buffers_to_assign->size(), result.chunk_map.size()); } } diff --git a/tensorflow/compiler/xla/service/heap_simulator.h b/tensorflow/compiler/xla/service/heap_simulator.h index 88a8698d16..636f19dd39 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.h +++ b/tensorflow/compiler/xla/service/heap_simulator.h @@ -67,6 +67,23 @@ class HeapSimulator { HeapSimulatorTrace debug_trace; }; + // The different options to be passed to the Run() APIs. + struct Options { + Options() + : may_reuse_operand_buffers(true), + alloc_constants(false), + buffers_to_assign(nullptr) {} + + // Whether a buffer about to be Free()-ed, can be recycled for a new born + // one, hence collapsing Free()+Alloc() calls (default true). + bool may_reuse_operand_buffers; + // Whether to issue Alloc() and Free() calls for constants (default false). + bool alloc_constants; + // If 'buffers_to_assign' is provided, only those buffers are assigned + // offsets, otherwise all buffers defined by the instructions are assigned. + const tensorflow::gtl::FlatSet* buffers_to_assign; + }; + // Run the heap simulation with the given algorithm, assuming the given // module_sequence, which must contain a topologically-consistent total // ordering of all instructions within each computation. The result is invalid @@ -76,15 +93,12 @@ class HeapSimulator { // to running on a per-computation basis, since we can re-use buffer space for // called sub-computations. // - // If 'buffers_to_assign' is provided, only those buffers are assigned - // offsets, otherwise all buffers defined by the instructions are assigned. static StatusOr Run( std::unique_ptr algorithm, const HloModule& module, const SequentialHloOrdering::HloModuleSequence& module_sequence, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_fn, - const tensorflow::gtl::FlatSet* buffers_to_assign = - nullptr); + const Options& options = Options()); // Same as above, but runs on a single computation. The 'instruction_sequence' // must contain a topologically-consistent total ordering of all instructions @@ -96,8 +110,7 @@ class HeapSimulator { const std::vector& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_fn, - const tensorflow::gtl::FlatSet* buffers_to_assign = - nullptr); + const Options& options = Options()); private: // If 'module_sequence' is non-null, it is used to find kCall and kWhile @@ -105,8 +118,7 @@ class HeapSimulator { // be run recursively. I.e. the simulation is run over the whole module. HeapSimulator( std::unique_ptr algorithm, - const LogicalBuffer::SizeFunction& size_fn, - const tensorflow::gtl::FlatSet* buffers_to_assign, + const LogicalBuffer::SizeFunction& size_fn, const Options& options, const SequentialHloOrdering::HloModuleSequence* module_sequence); ~HeapSimulator(); @@ -130,7 +142,7 @@ class HeapSimulator { const std::unique_ptr no_fragmentation_stats_; const std::unique_ptr algorithm_; const LogicalBuffer::SizeFunction size_fn_; - const tensorflow::gtl::FlatSet* buffers_to_assign_; + const Options options_; const SequentialHloOrdering::HloModuleSequence* module_sequence_; // In addition to Alloc and Free, the heap simulator exposes a concept of -- GitLab From c8b884c683e260b42c15883f1c14caac4ea8d000 Mon Sep 17 00:00:00 2001 From: Chris Leary Date: Tue, 30 Jan 2018 08:43:37 -0800 Subject: [PATCH 256/423] [XLA] Plumb build options via local API. * Break build options into their own translation unit for use from local client and to mirror ExecutableRunOptions. * Add some ToString()s to aid debugging. * Add HLO graph generation regex to build options. * Add SWIG type map for ExecutableBuildOptions. Also fix a build issue occurring on some platforms with triangular_solve. PiperOrigin-RevId: 183837856 --- .../compiler/tf2xla/lib/triangular_solve.cc | 9 +- tensorflow/compiler/xla/BUILD | 4 + tensorflow/compiler/xla/client/BUILD | 13 +++ .../xla/client/executable_build_options.cc | 79 +++++++++++++ .../xla/client/executable_build_options.h | 74 ++++++++++++ .../compiler/xla/client/local_client.cc | 107 +++++++----------- tensorflow/compiler/xla/client/local_client.h | 58 ++-------- .../compiler/xla/executable_run_options.cc | 17 +++ .../compiler/xla/executable_run_options.h | 7 ++ tensorflow/compiler/xla/python/BUILD | 1 + .../xla/python/local_computation_builder.cc | 6 +- .../xla/python/local_computation_builder.h | 4 +- .../xla/python/local_computation_builder.i | 34 ++++++ tensorflow/compiler/xla/python/xla_client.py | 21 +++- tensorflow/compiler/xla/service/BUILD | 1 + .../compiler/xla/service/hlo_graph_dumper.cc | 6 +- .../compiler/xla/service/local_service.cc | 27 +++-- .../compiler/xla/service/local_service.h | 4 +- 18 files changed, 332 insertions(+), 140 deletions(-) create mode 100644 tensorflow/compiler/xla/client/executable_build_options.cc create mode 100644 tensorflow/compiler/xla/client/executable_build_options.h diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index 5f0445dd44..7f72a6073d 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -419,16 +419,15 @@ xla::StatusOr TriangularSolveLeftLooking( // init = (m-2, output, a, b) // else: // init = (1, output, a, b) - std::vector tuple_shapes({ + 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. - xla::ShapeUtil::MakeShape(b_shape->element_type(), b_shape->dimensions()), + *b_shape, // The coefficient matrix a is a loop invariant. - xla::ShapeUtil::MakeShape(a_shape->element_type(), a_shape->dimensions()), + *a_shape, // The right-hand-side matrix b is a loop invariant. - xla::ShapeUtil::MakeShape(b_shape->element_type(), b_shape->dimensions()), - }); + *b_shape}; xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); auto init_i = builder->ConstantR0(transpose_a ? m - 2 : 1); auto init = builder->Tuple({init_i, output, a, b}); diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index c22fd37129..38e39afdc0 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -444,6 +444,10 @@ cc_library( srcs = ["executable_run_options.cc"], hdrs = ["executable_run_options.h"], visibility = ["//visibility:public"], + deps = [ + ":types", + "//tensorflow/core:lib", + ], ) cc_library( diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index 952109dde2..02356699a2 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -80,6 +80,18 @@ cc_library( ], ) +cc_library( + name = "executable_build_options", + srcs = ["executable_build_options.cc"], + hdrs = ["executable_build_options.h"], + deps = [ + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:device_memory_allocator", + "//tensorflow/core:lib", + ], +) + cc_library( name = "local_client", srcs = ["local_client.cc"], @@ -87,6 +99,7 @@ cc_library( deps = [ ":client", ":computation", + ":executable_build_options", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", diff --git a/tensorflow/compiler/xla/client/executable_build_options.cc b/tensorflow/compiler/xla/client/executable_build_options.cc new file mode 100644 index 0000000000..804e34f5e7 --- /dev/null +++ b/tensorflow/compiler/xla/client/executable_build_options.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/compiler/xla/client/executable_build_options.h" + +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/lib/strings/stringprintf.h" + +namespace xla { + +ExecutableBuildOptions& ExecutableBuildOptions::set_device_allocator( + DeviceMemoryAllocator* allocator) { + device_allocator_ = allocator; + return *this; +} + +DeviceMemoryAllocator* ExecutableBuildOptions::device_allocator() const { + return device_allocator_; +} + +ExecutableBuildOptions& ExecutableBuildOptions::set_device_ordinal( + int device_ordinal) { + CHECK_GE(device_ordinal, 0); + device_ordinal_ = device_ordinal; + return *this; +} + +int ExecutableBuildOptions::device_ordinal() const { return device_ordinal_; } + +ExecutableBuildOptions& ExecutableBuildOptions::set_result_layout( + const Shape& shape_with_layout) { + result_layout_set_ = true; + result_layout_ = shape_with_layout; + return *this; +} + +const Shape* ExecutableBuildOptions::result_layout() const { + return result_layout_set_ ? &result_layout_ : nullptr; +} + +string ExecutableBuildOptions::ToString() const { + string result_layout = "nullopt"; + if (result_layout_set_) { + result_layout = ShapeUtil::HumanStringWithLayout(result_layout_); + } + string generate_hlo_graph = "nullopt"; + if (generate_hlo_graph_.has_value()) { + generate_hlo_graph = generate_hlo_graph_.value(); + } + return tensorflow::strings::Printf( + "ExecutableBuildOptions{device_ordinal=%d, result_layout=%s, " + "generate_hlo_graph=%s}", + device_ordinal_, result_layout.c_str(), generate_hlo_graph.c_str()); +} + +ExecutableBuildOptions& ExecutableBuildOptions::set_generate_hlo_graph( + string regex) { + generate_hlo_graph_ = std::move(regex); + return *this; +} + +const tensorflow::gtl::optional& +ExecutableBuildOptions::generate_hlo_graph() const { + return generate_hlo_graph_; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/client/executable_build_options.h b/tensorflow/compiler/xla/client/executable_build_options.h new file mode 100644 index 0000000000..3a52dbac9a --- /dev/null +++ b/tensorflow/compiler/xla/client/executable_build_options.h @@ -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. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_EXECUTABLE_BUILD_OPTIONS_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_EXECUTABLE_BUILD_OPTIONS_H_ + +#include "tensorflow/compiler/xla/service/device_memory_allocator.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/gtl/optional.h" + +namespace xla { + +// Class containing options for building an LocalExecutable with +// LocalClient::Compile. +class ExecutableBuildOptions { + public: + // If set, this is the device to build the computation for. Valid + // device_ordinal values are: 0 to # of devices - 1. These values are + // identical to the device ordinal values used by StreamExecutor. The built + // executable will be executable on any device equivalent to the specified + // device as determined by Backend::devices_equivalent(). A value of -1 + // indicates this option has not been set. + ExecutableBuildOptions& set_device_ordinal(int device_ordinal); + int device_ordinal() const; + + // If set, this specifies the layout of the result of the computation. If not + // set, the service will chose the layout of the result. A Shape is used to + // store the layout to accommodate tuple result shapes. A value of nullptr + // indicates the option has not been set. + ExecutableBuildOptions& set_result_layout(const Shape& shape_with_layout); + const Shape* result_layout() const; + + // If set, this specifies an allocator that can be used to allocate temporary + // space on the device during compilation. For example, the compiler might + // want to run various algorithms on the device and pick the fastest one -- it + // might allocate buffers for use by these algorithms using this allocator. + // + // This does not need to be the same as the DeviceMemoryAllocator passed when + // running the executable. + ExecutableBuildOptions& set_device_allocator( + DeviceMemoryAllocator* allocator); + DeviceMemoryAllocator* device_allocator() const; + + // If set, specifies a regexp of HLO graphs to dump (as in DebugOptions). + ExecutableBuildOptions& set_generate_hlo_graph(string regex); + const tensorflow::gtl::optional& generate_hlo_graph() const; + + // Returns a string representation of the build options, suitable for + // debugging. + string ToString() const; + + private: + int device_ordinal_ = -1; + Shape result_layout_; + bool result_layout_set_ = false; + tensorflow::gtl::optional generate_hlo_graph_; + DeviceMemoryAllocator* device_allocator_ = nullptr; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_EXECUTABLE_BUILD_OPTIONS_H_ diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index e45787fca6..ef98dbb640 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -30,35 +30,6 @@ using xla::source_map_util::InvalidParameterArgument; namespace xla { -ExecutableBuildOptions& ExecutableBuildOptions::set_device_ordinal( - int device_ordinal) { - device_ordinal_ = device_ordinal; - return *this; -} - -int ExecutableBuildOptions::device_ordinal() const { return device_ordinal_; } - -ExecutableBuildOptions& ExecutableBuildOptions::set_result_layout( - const Shape& shape_with_layout) { - result_layout_set_ = true; - result_layout_ = shape_with_layout; - return *this; -} - -const Shape* ExecutableBuildOptions::result_layout() const { - return result_layout_set_ ? &result_layout_ : nullptr; -} - -ExecutableBuildOptions& ExecutableBuildOptions::set_device_allocator( - DeviceMemoryAllocator* allocator) { - device_allocator_ = allocator; - return *this; -} - -DeviceMemoryAllocator* ExecutableBuildOptions::device_allocator() const { - return device_allocator_; -} - namespace { StatusOr BorrowStreamForDevice(int device_ordinal, Backend* backend) { @@ -70,16 +41,18 @@ StatusOr BorrowStreamForDevice(int device_ordinal, } // namespace LocalExecutable::LocalExecutable(std::unique_ptr executable, - Backend* backend, int device_ordinal, - const ExecutableBuildOptions& build_options) + Backend* backend, + ExecutableBuildOptions build_options) : executable_(std::move(executable)), backend_(backend), - build_device_ordinal_(device_ordinal), - build_options_(build_options) {} + build_options_(std::move(build_options)) { + CHECK_GE(build_options_.device_ordinal(), 0) + << "Must have a valid device ordinal that the executable was built for."; +} tensorflow::Status LocalExecutable::ValidateExecutionOptions( const tensorflow::gtl::ArraySlice arguments, - const ExecutableRunOptions& options, const Backend& backend) { + const ExecutableRunOptions& run_options, const Backend& backend) { const ComputationLayout& computation_layout = executable_->module_config().entry_computation_layout(); @@ -103,14 +76,14 @@ tensorflow::Status LocalExecutable::ValidateExecutionOptions( } } - if (options.stream() != nullptr) { - if (!options.stream()->ok()) { + if (run_options.stream() != nullptr) { + if (!run_options.stream()->ok()) { return InvalidArgument("stream is uninitialized or in an error state"); } // Check stream matches service platform. const se::Platform* stream_platform = - options.stream()->parent()->platform(); + run_options.stream()->parent()->platform(); if (stream_platform != backend_->platform()) { return InvalidArgument( "stream is for platform %s, but service targets platform %s", @@ -120,7 +93,7 @@ tensorflow::Status LocalExecutable::ValidateExecutionOptions( // Cannot specify device_ordinal with a stream. The stream determines these // values. - if (options.device_ordinal() != -1) { + if (run_options.device_ordinal() != -1) { return InvalidArgument( "cannot set both device ordinal and stream options in " "ExecutableRunOptions; the stream determines the device ordinal"); @@ -129,34 +102,34 @@ tensorflow::Status LocalExecutable::ValidateExecutionOptions( // Verify that the device the executable was built for is equivalent to the // device it will run on. - int run_device_ordinal = options.device_ordinal() == -1 + int run_device_ordinal = run_options.device_ordinal() == -1 ? backend_->default_device_ordinal() - : options.device_ordinal(); - TF_ASSIGN_OR_RETURN( - bool devices_equivalent, - backend_->devices_equivalent(run_device_ordinal, build_device_ordinal_)); + : run_options.device_ordinal(); + TF_ASSIGN_OR_RETURN(bool devices_equivalent, + backend_->devices_equivalent( + run_device_ordinal, build_options_.device_ordinal())); if (!devices_equivalent) { TF_ASSIGN_OR_RETURN(se::StreamExecutor * run_executor, backend_->stream_executor(run_device_ordinal)); TF_ASSIGN_OR_RETURN(se::StreamExecutor * build_executor, - backend_->stream_executor(build_device_ordinal_)); + backend_->stream_executor(build_device_ordinal())); return InvalidArgument( "executable is built for device %s of type \"%s\"; cannot run it on " "device %s of type \"%s\"", - backend_->device_name(build_device_ordinal_).c_str(), + backend_->device_name(build_device_ordinal()).c_str(), build_executor->GetDeviceDescription().name().c_str(), backend_->device_name(run_device_ordinal).c_str(), run_executor->GetDeviceDescription().name().c_str()); } - if (!options.allocator()) { + if (!run_options.allocator()) { return InvalidArgument("an allocator must be provided to ExecuteLocally"); } - if (options.allocator()->platform() != backend.platform()) { + if (run_options.allocator()->platform() != backend.platform()) { return InvalidArgument( "allocator platform (%s) does not match service platform (%s)", - options.allocator()->platform()->Name().c_str(), + run_options.allocator()->platform()->Name().c_str(), backend.platform()->Name().c_str()); } @@ -165,23 +138,22 @@ tensorflow::Status LocalExecutable::ValidateExecutionOptions( StatusOr> LocalExecutable::Run( const tensorflow::gtl::ArraySlice arguments, - const ExecutableRunOptions& options) { - TF_RETURN_IF_ERROR(ValidateExecutionOptions(arguments, options, *backend_)); - - ExecutableRunOptions actual_options = options; + ExecutableRunOptions run_options) { + TF_RETURN_IF_ERROR( + ValidateExecutionOptions(arguments, run_options, *backend_)); Backend::StreamPtr stream; - if (options.stream() == nullptr) { + 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 // ExecuteOnStreamWrapper), which is why it isn't declared in the inner "if" // scope. TF_ASSIGN_OR_RETURN( - stream, BorrowStreamForDevice(options.device_ordinal(), backend_)); - actual_options.set_stream(stream.get()); + stream, BorrowStreamForDevice(run_options.device_ordinal(), backend_)); + run_options.set_stream(stream.get()); } - if (options.allocator() == nullptr) { - actual_options.set_allocator(backend_->memory_allocator()); + if (run_options.allocator() == nullptr) { + run_options.set_allocator(backend_->memory_allocator()); } // For local client execution on CPU backends: @@ -190,7 +162,7 @@ StatusOr> LocalExecutable::Run( // *) The thread pool used for XLA CPU ops is from // backend_->eigen_intra_op_thread_pool(). ServiceExecutableRunOptions service_options( - actual_options, backend_->StreamBorrower(), + run_options, backend_->StreamBorrower(), backend_->eigen_intra_op_thread_pool()); if (executable_->dumping()) { @@ -199,9 +171,8 @@ StatusOr> LocalExecutable::Run( TF_ASSIGN_OR_RETURN( std::unique_ptr result, executable_->ExecuteOnStreamWrapper( - &service_options, options.execution_profile(), arguments)); - return ScopedShapedBuffer::MakeScoped(result.get(), - actual_options.allocator()); + &service_options, run_options.execution_profile(), arguments)); + return ScopedShapedBuffer::MakeScoped(result.get(), run_options.allocator()); } StatusOr> LocalExecutable::ExecuteAndDump( @@ -277,17 +248,19 @@ StatusOr> LocalClient::Compile( const Computation& computation, const tensorflow::gtl::ArraySlice argument_layouts, const ExecutableBuildOptions& options) { - int device_ordinal = options.device_ordinal() == -1 - ? default_device_ordinal() - : options.device_ordinal(); + ExecutableBuildOptions updated_options = options; + if (options.device_ordinal() == -1) { + updated_options.set_device_ordinal(default_device_ordinal()); + VLOG(3) << "Set device ordinal to default value of: " + << updated_options.device_ordinal(); + } TF_ASSIGN_OR_RETURN( std::unique_ptr executable, local_service_->CompileExecutable(computation.handle(), argument_layouts, - options.result_layout(), device_ordinal, - options.device_allocator())); + updated_options)); return WrapUnique(new LocalExecutable(std::move(executable), local_service_->mutable_backend(), - device_ordinal, options)); + updated_options)); } StatusOr> diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h index 843ad7aa85..b52a30f5a0 100644 --- a/tensorflow/compiler/xla/client/local_client.h +++ b/tensorflow/compiler/xla/client/local_client.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/computation.h" +#include "tensorflow/compiler/xla/client/executable_build_options.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" @@ -33,51 +34,13 @@ limitations under the License. namespace xla { -// Class containing options for building an LocalExecutable with -// LocalClient::Compile. -class ExecutableBuildOptions { - public: - // If set, this is the device to build the computation for. Valid - // device_ordinal values are: 0 to # of devices - 1. These values are - // identical to the device ordinal values used by StreamExecutor. The built - // executable will be executable on any device equivalent to the specified - // device as determined by Backend::devices_equivalent(). A value of -1 - // indicates this option has not been set. - ExecutableBuildOptions& set_device_ordinal(int device_ordinal); - int device_ordinal() const; - - // If set, this specifies the layout of the result of the computation. If not - // set, the service will chose the layout of the result. A Shape is used to - // store the layout to accommodate tuple result shapes. A value of nullptr - // indicates the option has not been set. - ExecutableBuildOptions& set_result_layout(const Shape& shape_with_layout); - const Shape* result_layout() const; - - // If set, this specifies an allocator that can be used to allocate temporary - // space on the device during compilation. For example, the compiler might - // want to run various algorithms on the device and pick the fastest one -- it - // might allocate buffers for use by these algorithms using this allocator. - // - // This does not need to be the same as the DeviceMemoryAllocator passed when - // running the executable. - ExecutableBuildOptions& set_device_allocator( - DeviceMemoryAllocator* allocator); - DeviceMemoryAllocator* device_allocator() const; - - private: - int device_ordinal_ = -1; - Shape result_layout_; - bool result_layout_set_ = false; - DeviceMemoryAllocator* device_allocator_ = nullptr; -}; - class LocalExecutable { public: // Run the compiled computation with the given arguments and options and // return the result. StatusOr> Run( const tensorflow::gtl::ArraySlice arguments, - const ExecutableRunOptions& options); + ExecutableRunOptions run_options); // Return the layout (contained in a shape) of the result produced by the // computation. @@ -100,8 +63,7 @@ class LocalExecutable { // Constructor invoked by LocalClient. LocalExecutable(std::unique_ptr executable, Backend* backend, - int device_ordinal, - const ExecutableBuildOptions& build_options); + ExecutableBuildOptions build_options); // Validates that the given arguments and options satisfy various constraints // of the computation. @@ -129,19 +91,19 @@ class LocalExecutable { StatusOr> LiteralFromShapedBuffer( const ShapedBuffer& shaped_buffer); + // The ordinal of the device which this executable was compiled for. The + // executable can run on all equivalent devices (as determined by + // Backend::devices_equivalent). + int build_device_ordinal() const { return build_options_.device_ordinal(); } + // Compiled computation. std::unique_ptr executable_; // Execution backend. - Backend* backend_; - - // The ordinal of the device which this executable was compiled for. The - // executable can run on all equivalent devices (as determined by - // Backend::devices_equivalent). - int build_device_ordinal_; + Backend* backend_ = nullptr; // Options used to build the executable. - const ExecutableBuildOptions& build_options_; + const ExecutableBuildOptions build_options_; }; // An XLA Client specialization for use when the client and service run in diff --git a/tensorflow/compiler/xla/executable_run_options.cc b/tensorflow/compiler/xla/executable_run_options.cc index 392ad9010a..f8bb8e52c7 100644 --- a/tensorflow/compiler/xla/executable_run_options.cc +++ b/tensorflow/compiler/xla/executable_run_options.cc @@ -15,6 +15,8 @@ limitations under the License. #include "tensorflow/compiler/xla/executable_run_options.h" +#include "tensorflow/core/lib/strings/stringprintf.h" + namespace xla { ExecutableRunOptions& ExecutableRunOptions::set_device_ordinal( @@ -87,4 +89,19 @@ const DeviceAssignment* ExecutableRunOptions::device_assignment() const { return device_assignment_; } +string ExecutableRunOptions::ToString() const { + return tensorflow::strings::Printf( + "ExecutableRunOptions{allocator=%p, device_ordinal=%d, " + "device_assignment=%p, stream=%p, inter_op_thread_pool=%p, " + "intra_op_thread_pool=%p, execution_profile=%p}", + allocator_, device_ordinal_, device_assignment_, stream_, + inter_op_thread_pool_, intra_op_thread_pool_, execution_profile_); +} + +std::ostream& operator<<(std::ostream& out, + const ExecutableRunOptions& options) { + out << options.ToString(); + return out; +} + } // namespace xla diff --git a/tensorflow/compiler/xla/executable_run_options.h b/tensorflow/compiler/xla/executable_run_options.h index d4fcbf0493..c7a20bb33c 100644 --- a/tensorflow/compiler/xla/executable_run_options.h +++ b/tensorflow/compiler/xla/executable_run_options.h @@ -16,6 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ #define TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ +#include "tensorflow/compiler/xla/types.h" + // Intentionally forward declared so that ExecutableRunOptions can be linked // into an XLA-compiled binary without having to link all of the pointed-to // objects (e.g., for an ahead-of-time compiled CPU binary, the gpu tools don't @@ -84,6 +86,8 @@ class ExecutableRunOptions { DeviceAssignment* device_assignment); const DeviceAssignment* device_assignment() const; + string ToString() const; + private: DeviceMemoryAllocator* allocator_ = nullptr; int device_ordinal_ = -1; @@ -94,6 +98,9 @@ class ExecutableRunOptions { ExecutionProfile* execution_profile_ = nullptr; }; +std::ostream& operator<<(std::ostream& out, + const ExecutableRunOptions& options); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ diff --git a/tensorflow/compiler/xla/python/BUILD b/tensorflow/compiler/xla/python/BUILD index a8ca0e3ea0..e2972f0601 100644 --- a/tensorflow/compiler/xla/python/BUILD +++ b/tensorflow/compiler/xla/python/BUILD @@ -49,6 +49,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:computation_builder", + "//tensorflow/compiler/xla/client:executable_build_options", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/core:framework_lite", diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 37f1eada2b..5772532b84 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -233,7 +233,8 @@ LocalComputation::LocalComputation(Computation computation) : computation_(std::move(computation)) {} StatusOr LocalComputation::Compile( - const std::vector& argument_shapes) { + const std::vector& argument_shapes, + const ExecutableBuildOptions* build_options) { std::vector argument_shape_pointers; argument_shape_pointers.reserve(argument_shapes.size()); for (auto& argument_shape : argument_shapes) { @@ -242,6 +243,9 @@ StatusOr LocalComputation::Compile( LocalClient* client = GetOrCreateLocalClient(); ExecutableBuildOptions options; + if (build_options != nullptr) { + options = *build_options; + } TF_ASSIGN_OR_RETURN( auto local_executable, client->Compile(computation_, argument_shape_pointers, options)); diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index e5503cd52f..6851c2644d 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/computation_builder.h" +#include "tensorflow/compiler/xla/client/executable_build_options.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -93,7 +94,8 @@ class LocalComputation { public: LocalComputation(Computation computation); StatusOr Compile( - const std::vector& argument_shapes); + const std::vector& argument_shapes, + const ExecutableBuildOptions* build_options); const Computation& computation() const; private: diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index 3178925960..6a52a088dd 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -176,6 +176,16 @@ tensorflow::ImportNumpy(); } } +%typemap(out) StatusOr< std::unique_ptr > { + if ($1.ok()) { + std::unique_ptr value = $1.ConsumeValueOrDie(); + $result = numpy::PyObjectFromXlaLiteral(*value); + } else { + PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str()); + return NULL; + } +} + %typemap(out) StatusOr { if ($1.ok()) { auto* value = $1.ValueOrDie(); @@ -623,6 +633,30 @@ tensorflow::ImportNumpy(); $1 = &dimension_numbers; } +// ExecutableBuildOptions + +%typemap(in) const ExecutableBuildOptions* + (ExecutableBuildOptions build_options) { + if ($input == Py_None) { + $1 = NULL; + } else { + PyObject* o = PyObject_GetAttrString($input, "generate_hlo_graph"); + if (!o) { + return NULL; + } + if (o != Py_None) { + if (!PyString_Check(o)) { + PyErr_SetString(PyExc_TypeError, "ExecutableBuildOptions.generate_hlo_graph must be a string or None."); + return NULL; + } + build_options.set_generate_hlo_graph(PyString_AsString(o)); + } + Py_DECREF(o); + + $1 = &build_options; + } +} + %ignoreall %unignore xla; %unignore xla::swig; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index 66ace613a0..a89e2643c8 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -260,6 +260,17 @@ def require_numpy_array_layout(value): return np.require(value, requirements=['C', 'A']) +class CompileOptions(object): + """Python object for XLA compile options. + + These options can be passed to the 'compile' step when using a local XLA + client. + """ + + def __init__(self): + self.generate_hlo_graph = None + + def transfer_to_infeed(value, replica_number=None): """Transfers the given value into the XLA infeed queue. @@ -313,16 +324,18 @@ class LocalComputation(object): else: self._delete = c_api.DeleteLocalComputation - def Compile(self, argument_shapes=()): + def Compile(self, argument_shapes=(), compile_options=None): if self.is_compiled: raise ValueError('Attempt to compile a compiled local XLA computation.') return LocalComputation( - self.c_local_computation.Compile(_unwrap_shapes(argument_shapes)), + self.c_local_computation.Compile( + _unwrap_shapes(argument_shapes), compile_options), is_compiled=True) - def CompileWithExampleArguments(self, arguments=()): + def CompileWithExampleArguments(self, arguments=(), compile_options=None): return self.Compile( - argument_shapes=[Shape.from_numpy(arg) for arg in arguments]) + argument_shapes=[Shape.from_numpy(arg) for arg in arguments], + compile_options=compile_options) def Execute(self, arguments=()): if not self.is_compiled: diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index a04ba7ae3e..93cc5ab1a9 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -511,6 +511,7 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:executable_build_options", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", ], diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index c744c8ed81..44fcd36370 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -1426,9 +1426,11 @@ void DumpText(const HloModule& module, const string& label, string MaybeDumpHloModule(const HloModule& module, const string& label, const HloExecutionProfile* profile) { - VLOG(2) << "MaybeDumpHloModule called on module " << module.name(); - string graph_url; const DebugOptions& debug_options = module.config().debug_options(); + VLOG(2) << "MaybeDumpHloModule called on module " << module.name() + << " with generate_hlo_graph regex \"" + << debug_options.xla_generate_hlo_graph() << "\""; + string graph_url; if (!debug_options.xla_generate_hlo_graph().empty() && RE2::PartialMatch(module.name(), debug_options.xla_generate_hlo_graph())) { diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index bb9fd447d9..07f989d4fa 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -19,6 +19,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/client/executable_build_options.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/backend.h" @@ -71,8 +72,7 @@ LocalService::LocalService(const ServiceOptions& options, StatusOr> LocalService::CompileExecutable( const ComputationHandle& computation, const tensorflow::gtl::ArraySlice argument_layouts, - const Shape* result_layout, int device_ordinal, - DeviceMemoryAllocator* device_allocator) { + const ExecutableBuildOptions& build_options) { TF_ASSIGN_OR_RETURN(UserComputation * user_computation, computation_tracker_.Resolve(computation)); VersionedComputationHandle versioned_handle = @@ -113,14 +113,19 @@ StatusOr> LocalService::CompileExecutable( ShapeUtil::HumanString(argument_shape).c_str()); } } - if (result_layout != nullptr) { - TF_RETURN_IF_ERROR( - ValidateResultShapeWithLayout(*result_layout, program_shape->result())); + if (build_options.result_layout() != nullptr) { + TF_RETURN_IF_ERROR(ValidateResultShapeWithLayout( + *build_options.result_layout(), program_shape->result())); } ExecutionOptions execution_options = CreateDefaultExecutionOptions(); - if (result_layout != nullptr) { - *execution_options.mutable_shape_with_output_layout() = *result_layout; + if (build_options.generate_hlo_graph().has_value()) { + execution_options.mutable_debug_options()->set_xla_generate_hlo_graph( + build_options.generate_hlo_graph().value()); + } + if (build_options.result_layout() != nullptr) { + *execution_options.mutable_shape_with_output_layout() = + *build_options.result_layout(); } else { *execution_options.mutable_shape_with_output_layout() = program_shape->result(); @@ -132,11 +137,13 @@ StatusOr> LocalService::CompileExecutable( CreateModuleConfig(*program_shape, argument_layouts, &execution_options, *user_computation)); - TF_ASSIGN_OR_RETURN(se::StreamExecutor * executor, - execute_backend_->stream_executor(device_ordinal)); + TF_ASSIGN_OR_RETURN( + se::StreamExecutor * executor, + execute_backend_->stream_executor(build_options.device_ordinal())); return BuildExecutable(versioned_handle, std::move(module_config), - execute_backend_.get(), executor, device_allocator); + execute_backend_.get(), executor, + build_options.device_allocator()); } StatusOr LocalService::ReplicaNumberToDeviceOrdinal(int replica_number) { diff --git a/tensorflow/compiler/xla/service/local_service.h b/tensorflow/compiler/xla/service/local_service.h index 16c71b25c4..15e120685e 100644 --- a/tensorflow/compiler/xla/service/local_service.h +++ b/tensorflow/compiler/xla/service/local_service.h @@ -18,6 +18,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/client/executable_build_options.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" @@ -47,8 +48,7 @@ class LocalService : public Service { StatusOr> CompileExecutable( const ComputationHandle& computation, const tensorflow::gtl::ArraySlice argument_layouts, - const Shape* result_layout, int device_ordinal, - DeviceMemoryAllocator* device_allocator); + const ExecutableBuildOptions& options); // Returns the device ordinal that corresponds to the given replica number. // -- GitLab From b5ee0be2df79efd6df150d1dcc8fadeaa99cb558 Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Tue, 30 Jan 2018 09:21:57 -0800 Subject: [PATCH 257/423] Fixes broken link in documentation. PiperOrigin-RevId: 183842485 --- tensorflow/docs_src/get_started/checkpoints.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/get_started/checkpoints.md b/tensorflow/docs_src/get_started/checkpoints.md index 680e1c0d3f..dfa2110e69 100644 --- a/tensorflow/docs_src/get_started/checkpoints.md +++ b/tensorflow/docs_src/get_started/checkpoints.md @@ -16,7 +16,7 @@ This document focuses on checkpoints. For details on SavedModel, see the ## Sample code This document relies on the same -[https://github.com/tensorflow/models/blob/master/samples/core/get_started/premade_estimator.py](Iris classification example) detailed in @{$premade_estimators$Getting Started with TensorFlow}. +[Iris classification example](https://github.com/tensorflow/models/blob/master/samples/core/get_started/premade_estimator.py) detailed in @{$premade_estimators$Getting Started with TensorFlow}. To download and access the example, invoke the following two commands: ```shell -- GitLab From ca20a8382b9484a3ad406ae9a78ab6a164ec72f9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 09:42:48 -0800 Subject: [PATCH 258/423] Reenable 'constant' test. PiperOrigin-RevId: 183845007 --- tensorflow/contrib/lite/testing/generated_examples_zip_test.cc | 3 +-- 1 file changed, 1 insertion(+), 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 e6b782472a..d73c9937ce 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -243,8 +243,7 @@ INSTANTIATE_TESTS(avg_pool) INSTANTIATE_TESTS(space_to_batch_nd) INSTANTIATE_TESTS(batch_to_space_nd) INSTANTIATE_TESTS(concat) -// TODO(b/71642435) re-enable this test -// INSTANTIATE_TESTS(constant) +INSTANTIATE_TESTS(constant) INSTANTIATE_TESTS(control_dep) INSTANTIATE_TESTS(conv) INSTANTIATE_TESTS(depthwiseconv) -- GitLab From 5ab07fcfc51fd524622e2c583f81f0cd8eca97d5 Mon Sep 17 00:00:00 2001 From: Yuanzhong Xu Date: Tue, 30 Jan 2018 09:54:46 -0800 Subject: [PATCH 259/423] Add BF16 test cases for pad. PiperOrigin-RevId: 183846616 --- tensorflow/compiler/xla/tests/pad_test.cc | 193 +++++++++++----------- 1 file changed, 100 insertions(+), 93 deletions(-) diff --git a/tensorflow/compiler/xla/tests/pad_test.cc b/tensorflow/compiler/xla/tests/pad_test.cc index 3fd83a4c3b..8cef8dd34d 100644 --- a/tensorflow/compiler/xla/tests/pad_test.cc +++ b/tensorflow/compiler/xla/tests/pad_test.cc @@ -33,6 +33,14 @@ limitations under the License. namespace xla { namespace { +#ifdef XLA_BACKEND_SUPPORTS_BFLOAT16 +// Tests both F32 and BF16. +static std::array use_bfloat16_params{false, true}; +#else +// Only tests F32. +static std::array use_bfloat16_params{false}; +#endif + class PadTest : public ClientLibraryTestBase { protected: PadTest() { @@ -61,8 +69,22 @@ class PadTest : public ClientLibraryTestBase { PaddingConfig r4_padding_on_dim0_dim1_; }; +class PadTestFloat : public PadTest, + public ::testing::WithParamInterface { + protected: + PadTestFloat() { set_use_bfloat16(GetParam()); } + + ErrorSpec DefaultErrorSpec() const { + if (use_bfloat16()) { + return ErrorSpec(1e-3, 1e-3); + } else { + return ErrorSpec(1e-5, 1e-5); + } + } +}; + // Tests a Pad() with a zero-element input and output. -XLA_TEST_F(PadTest, Pad1DS0ToS0Array) { +XLA_TEST_P(PadTestFloat, Pad1DS0ToS0Array) { ComputationBuilder b(client_, TestName()); // Set up the padding configuration {low: 0, high: 0, interior: 0}. PaddingConfig padding_config; @@ -71,12 +93,13 @@ XLA_TEST_F(PadTest, Pad1DS0ToS0Array) { dimension->set_edge_padding_high(0); dimension->set_interior_padding(0); - b.Pad(b.ConstantR1({}), b.ConstantR0(0.1), padding_config); - ComputeAndCompareR1(&b, {}, {}, ErrorSpec(0.0001)); + b.Pad(AddParam(*Literal::CreateR1({}), &b), + AddParam(*Literal::CreateR0(0.1), &b), padding_config); + ComputeAndCompareR1(&b, {}, {}, DefaultErrorSpec()); } // Tests a Pad() with a zero-element input but a non-zero-element output. -XLA_TEST_F(PadTest, Pad1DS0ToS5Array) { +XLA_TEST_P(PadTestFloat, Pad1DS0ToS5Array) { ComputationBuilder b(client_, TestName()); // Set up the padding configuration {low: 3, high: 0, interior: 1}. PaddingConfig padding_config; @@ -85,12 +108,13 @@ XLA_TEST_F(PadTest, Pad1DS0ToS5Array) { dimension->set_edge_padding_high(4); dimension->set_interior_padding(7); - b.Pad(b.ConstantR1({}), b.ConstantR0(0.1), padding_config); + b.Pad(AddParam(*Literal::CreateR1({}), &b), + AddParam(*Literal::CreateR0(0.1), &b), padding_config); ComputeAndCompareR1(&b, std::vector(5, 0.1), {}, - ErrorSpec(0.0001)); + DefaultErrorSpec()); } -XLA_TEST_F(PadTest, Pad1DS3Array) { +XLA_TEST_P(PadTestFloat, Pad1DS3Array) { ComputationBuilder b(client_, TestName()); // Set up the padding configuration {low: 3, high: 0, interior: 1}. PaddingConfig padding_config; @@ -99,21 +123,21 @@ XLA_TEST_F(PadTest, Pad1DS3Array) { dimension->set_edge_padding_high(0); dimension->set_interior_padding(1); - b.Pad(b.ConstantR1({1, 2, 3}), b.ConstantR0(0.1), - padding_config); + b.Pad(AddParam(*Literal::CreateR1({1, 2, 3}), &b), + AddParam(*Literal::CreateR0(0.1), &b), padding_config); std::vector expected({0.1, 0.1, 0.1, 1, 0.1, 2, 0.1, 3}); - ComputeAndCompareR1(&b, expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR1(&b, expected, {}, DefaultErrorSpec()); } -XLA_TEST_F(PadTest, Pad4D_2x0x3x2_FloatArray) { +XLA_TEST_P(PadTestFloat, Pad4D_2x0x3x2_FloatArray) { ComputationBuilder b(client_, TestName()); - b.Pad(b.ConstantR4FromArray4D(Array4D(2, 0, 3, 2)), - b.ConstantR0(1.5), r4_padding_on_dim0_dim1_); + b.Pad(AddParam(Array4D(2, 0, 3, 2), &b), + AddParam(*Literal::CreateR0(1.5), &b), r4_padding_on_dim0_dim1_); ComputeAndCompareR4(&b, Array4D(5, 2, 3, 2, 1.5f), {}, - ErrorSpec(0.0001)); + DefaultErrorSpec()); } -TEST_F(PadTest, Pad4DFloat_1x1x3x2_Array) { +TEST_P(PadTestFloat, Pad4DFloat_1x1x3x2_Array) { ComputationBuilder b(client_, TestName()); auto input = MakeUnique>(1, 1, 3, 2); Array2D input_xy({ @@ -123,7 +147,7 @@ TEST_F(PadTest, Pad4DFloat_1x1x3x2_Array) { }); input->FillWithYX(input_xy); - b.Pad(b.ConstantR4FromArray4D(*input), b.ConstantR0(1.5), + b.Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(1.5), &b), r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(2, 3, 3, 2); @@ -134,15 +158,15 @@ TEST_F(PadTest, Pad4DFloat_1x1x3x2_Array) { (*expected)(1, 0, 1, 1) = 4.0f; (*expected)(1, 0, 2, 0) = 5.0f; (*expected)(1, 0, 2, 1) = 6.0f; - ComputeAndCompareR4(&b, *expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR4(&b, *expected, {}, DefaultErrorSpec()); } -TEST_F(PadTest, Pad4DFloatArrayWithInteriorPadding) { +TEST_P(PadTestFloat, Pad4DFloatArrayWithInteriorPadding) { ComputationBuilder b(client_, TestName()); const float pad_value = 1.5f; Array4D input(3, 2, 1, 1, {1, 2, 3, 4, 5, 6}); - b.Pad(b.ConstantR4FromArray4D(input), b.ConstantR0(pad_value), + b.Pad(AddParam(input, &b), AddParam(*Literal::CreateR0(pad_value), &b), r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(8, 5, 1, 1); @@ -156,7 +180,7 @@ TEST_F(PadTest, Pad4DFloatArrayWithInteriorPadding) { ComputeAndCompareR4(&b, *expected, {}, ErrorSpec(0.0001)); } -TEST_F(PadTest, Pad4DFloatArrayMinorFirstSmall) { +TEST_P(PadTestFloat, Pad4DFloatArrayMinorFirstSmall) { ComputationBuilder b(client_, TestName()); PaddingConfig padding_config; @@ -184,7 +208,8 @@ TEST_F(PadTest, Pad4DFloatArrayMinorFirstSmall) { auto input = Literal::CreateR4FromArray4D(input_array); input = input->Relayout(layout); - b.Pad(b.ConstantLiteral(*input), b.ConstantR0(pad_value), padding_config); + b.Pad(AddParam(*input, &b), + AddParam(*Literal::CreateR0(pad_value), &b), padding_config); Array4D expected_array(1, 1, 5, 8); expected_array.Fill(pad_value); @@ -197,7 +222,7 @@ TEST_F(PadTest, Pad4DFloatArrayMinorFirstSmall) { ComputeAndCompareR4(&b, expected_array, {}, ErrorSpec(0.0001)); } -XLA_TEST_F(PadTest, Pad4DFloatArrayMinorFirstNonTrivialMinorDimensions) { +XLA_TEST_P(PadTestFloat, Pad4DFloatArrayMinorFirstNonTrivialMinorDimensions) { ComputationBuilder b(client_, TestName()); PaddingConfig padding_config; @@ -229,7 +254,8 @@ XLA_TEST_F(PadTest, Pad4DFloatArrayMinorFirstNonTrivialMinorDimensions) { auto input = Literal::CreateR4FromArray4D(input_array); input = input->Relayout(layout); - b.Pad(b.ConstantLiteral(*input), b.ConstantR0(pad_value), padding_config); + b.Pad(AddParam(*input, &b), + AddParam(*Literal::CreateR0(pad_value), &b), padding_config); Array4D expected_array(1, 25, 17, 11); expected_array.Fill(pad_value); @@ -249,7 +275,7 @@ XLA_TEST_F(PadTest, Pad4DU8Array) { }); input->FillWithYX(input_xy); - b.Pad(b.ConstantR4FromArray4D(*input), b.ConstantR0(35), + b.Pad(AddParam(*input, &b), b.ConstantR0(35), r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(2, 3, 3, 2); @@ -277,8 +303,7 @@ XLA_TEST_F(PadTest, Pad4DPredArray) { auto ones = MakeUnique>(2, 3, 3, 2); zeros->Fill(0); ones->Fill(1); - b.Select(padded, b.ConstantR4FromArray4D(*ones), - b.ConstantR4FromArray4D(*zeros)); + b.Select(padded, AddParam(*ones, &b), AddParam(*zeros, &b)); auto expected = MakeUnique>(2, 3, 3, 2); expected->Fill(0); @@ -291,10 +316,12 @@ XLA_TEST_F(PadTest, Pad4DPredArray) { ComputeAndCompareR4(&b, *expected, {}); } -XLA_TEST_F(PadTest, Large2DPad) { +XLA_TEST_P(PadTestFloat, Large2DPad) { ComputationBuilder b(client_, TestName()); - auto input = b.Parameter(0, ShapeUtil::MakeShape(F32, {4, 4}), "input"); + auto ones = MakeUnique>(4, 4); + ones->Fill(1.0f); + auto input = AddParam(*ones, &b); PaddingConfig padding_config = MakeNoPaddingConfig(2); for (int dim : {0, 1}) { padding_config.mutable_dimensions(dim)->set_edge_padding_low( @@ -302,25 +329,22 @@ XLA_TEST_F(PadTest, Large2DPad) { padding_config.mutable_dimensions(dim)->set_edge_padding_high(58 + 100 * dim); } - auto padded = b.Pad(input, b.ConstantR0(0.0f), padding_config); - - auto ones = MakeUnique>(4, 4); - ones->Fill(1.0f); - auto input_literal = Literal::CreateR2FromArray2D(*ones); - std::unique_ptr input_data = - client_->TransferToServer(*input_literal).ConsumeValueOrDie(); + auto padded = b.Pad(input, AddParam(*Literal::CreateR0(0.0f), &b), + padding_config); auto expected = ReferenceUtil::PadArray2D(*ones, padding_config, 0.0f); - ComputeAndCompareR2(&b, *expected, {input_data.get()}); + ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); } -XLA_TEST_F(PadTest, AllTypes2DPad) { +XLA_TEST_P(PadTestFloat, AllTypes2DPad) { ComputationBuilder b(client_, TestName()); constexpr int64 in_rows = 35; constexpr int64 in_cols = 35; - auto input = - b.Parameter(0, ShapeUtil::MakeShape(F32, {in_rows, in_cols}), "input"); + auto operand = MakeUnique>(in_rows, in_cols); + operand->FillUnique(0.0f); + auto input = AddParam(*operand, &b); + PaddingConfig padding_config = MakeNoPaddingConfig(2); padding_config.mutable_dimensions(0)->set_edge_padding_low(7); padding_config.mutable_dimensions(0)->set_edge_padding_high(5); @@ -328,20 +352,14 @@ XLA_TEST_F(PadTest, AllTypes2DPad) { padding_config.mutable_dimensions(1)->set_edge_padding_low(6); padding_config.mutable_dimensions(1)->set_edge_padding_high(4); padding_config.mutable_dimensions(1)->set_interior_padding(2); - auto padded = b.Pad(input, b.ConstantR0(3.14f), padding_config); - - auto operand = MakeUnique>(in_rows, in_cols); - operand->FillUnique(0.0f); - auto input_literal = Literal::CreateR2FromArray2D(*operand); - std::unique_ptr input_data = - client_->TransferToServer(*input_literal).ConsumeValueOrDie(); + auto padded = b.Pad(input, AddParam(*Literal::CreateR0(3.14f), &b), + padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 3.14f); - ComputeAndCompareR2(&b, *expected, {input_data.get()}, - ErrorSpec{0.0001}); + ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); } -XLA_TEST_F(PadTest, High2DPad) { +XLA_TEST_P(PadTestFloat, High2DPad) { ComputationBuilder b(client_, TestName()); constexpr int64 in_rows = 129; @@ -349,8 +367,9 @@ XLA_TEST_F(PadTest, High2DPad) { constexpr int64 low_padding = 0; int64 high_padding[2] = {5, 7}; constexpr int64 interior_padding = 0; - auto input = - b.Parameter(0, ShapeUtil::MakeShape(F32, {in_rows, in_cols}), "input"); + auto operand = MakeUnique>(in_rows, in_cols); + operand->FillUnique(1.0f); + auto input = AddParam(*operand, &b); PaddingConfig padding_config = MakeNoPaddingConfig(2); for (int dim : {0, 1}) { padding_config.mutable_dimensions(dim)->set_edge_padding_low(low_padding); @@ -359,20 +378,15 @@ XLA_TEST_F(PadTest, High2DPad) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding); } - auto padded = b.Pad(input, b.ConstantR0(2.718f), padding_config); + auto padded = b.Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), + padding_config); - auto operand = MakeUnique>(in_rows, in_cols); - operand->FillUnique(1.0f); - auto input_literal = Literal::CreateR2FromArray2D(*operand); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); - std::unique_ptr input_data = - client_->TransferToServer(*input_literal).ConsumeValueOrDie(); - ComputeAndCompareR2(&b, *expected, {input_data.get()}, - ErrorSpec(0.0001)); + ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); } -XLA_TEST_F(PadTest, NegativePadding2D) { +XLA_TEST_P(PadTestFloat, NegativePadding2D) { ComputationBuilder b(client_, TestName()); constexpr int64 in_rows = 129; @@ -380,8 +394,9 @@ XLA_TEST_F(PadTest, NegativePadding2D) { int64 low_padding[2] = {-1, -2}; int64 high_padding[2] = {-3, 4}; constexpr int64 interior_padding = 0; - auto input = - b.Parameter(0, ShapeUtil::MakeShape(F32, {in_rows, in_cols}), "input"); + auto operand = MakeUnique>(in_rows, in_cols); + operand->FillUnique(1.0f); + auto input = AddParam(*operand, &b); PaddingConfig padding_config = MakeNoPaddingConfig(2); for (int dim : {0, 1}) { padding_config.mutable_dimensions(dim)->set_edge_padding_low( @@ -391,20 +406,15 @@ XLA_TEST_F(PadTest, NegativePadding2D) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding); } - auto padded = b.Pad(input, b.ConstantR0(2.718f), padding_config); + auto padded = b.Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), + padding_config); - auto operand = MakeUnique>(in_rows, in_cols); - operand->FillUnique(1.0f); - auto input_literal = Literal::CreateR2FromArray2D(*operand); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); - std::unique_ptr input_data = - client_->TransferToServer(*input_literal).ConsumeValueOrDie(); - ComputeAndCompareR2(&b, *expected, {input_data.get()}, - ErrorSpec(0.0001)); + ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); } -XLA_TEST_F(PadTest, NegativeAndInteriorPadding2D) { +XLA_TEST_P(PadTestFloat, NegativeAndInteriorPadding2D) { ComputationBuilder b(client_, TestName()); constexpr int64 in_rows = 8; @@ -412,8 +422,9 @@ XLA_TEST_F(PadTest, NegativeAndInteriorPadding2D) { int64 low_padding[2] = {4, -1}; int64 high_padding[2] = {-2, -4}; int64 interior_padding[2] = {1, 2}; - auto input = - b.Parameter(0, ShapeUtil::MakeShape(F32, {in_rows, in_cols}), "input"); + auto operand = MakeUnique>(in_rows, in_cols); + operand->FillUnique(1.0f); + auto input = AddParam(*operand, &b); PaddingConfig padding_config = MakeNoPaddingConfig(2); for (int dim : {0, 1}) { padding_config.mutable_dimensions(dim)->set_edge_padding_low( @@ -423,44 +434,40 @@ XLA_TEST_F(PadTest, NegativeAndInteriorPadding2D) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding[dim]); } - auto padded = b.Pad(input, b.ConstantR0(2.718f), padding_config); + auto padded = b.Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), + padding_config); - auto operand = MakeUnique>(in_rows, in_cols); - operand->FillUnique(1.0f); - auto input_literal = Literal::CreateR2FromArray2D(*operand); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); - std::unique_ptr input_data = - client_->TransferToServer(*input_literal).ConsumeValueOrDie(); - ComputeAndCompareR2(&b, *expected, {input_data.get()}, - ErrorSpec(0.0001)); + ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); } // Regression test for b/31827337. -XLA_TEST_F(PadTest, ReducePad) { +XLA_TEST_P(PadTestFloat, ReducePad) { ComputationBuilder b(client_, TestName()); - auto input = b.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2, 2, 2}), "input"); + auto ones = MakeUnique>(2, 2, 2, 2); + ones->Fill(1.0); + auto input = AddParam(*ones, &b); - Computation add_f32 = CreateScalarAddComputation(F32, &b); - auto reduce = b.Reduce(input, b.ConstantR0(0.0), add_f32, {0}); + Computation add = CreateScalarAddComputation(FloatType(), &b); + auto reduce = + b.Reduce(input, AddParam(*Literal::CreateR0(0.0), &b), add, {0}); PaddingConfig padding_config = MakeNoPaddingConfig(3); padding_config.mutable_dimensions(0)->set_edge_padding_low(1); padding_config.mutable_dimensions(0)->set_edge_padding_high(1); - auto pad = b.Pad(reduce, b.ConstantR0(0.0), padding_config); - - auto ones = MakeUnique>(2, 2, 2, 2); - ones->Fill(1.0); - auto input_literal = Literal::CreateR4FromArray4D(*ones); - std::unique_ptr input_data = - client_->TransferToServer(*input_literal).ConsumeValueOrDie(); + auto padded = b.Pad(reduce, AddParam(*Literal::CreateR0(0.0f), &b), + padding_config); Array3D expected({{{0.0, 0.0}, {0.0, 0.0}}, {{2.0, 2.0}, {2.0, 2.0}}, {{2.0, 2.0}, {2.0, 2.0}}, {{0.0, 0.0}, {0.0, 0.0}}}); - ComputeAndCompareR3(&b, expected, {input_data.get()}); + ComputeAndCompareR3(&b, expected, {}, DefaultErrorSpec()); } +INSTANTIATE_TEST_CASE_P(PadTestFloatInstantiation, PadTestFloat, + ::testing::ValuesIn(use_bfloat16_params)); + } // namespace } // namespace xla -- GitLab From 39bc42ebcf0df005b378fa88a4650a5bebb1eb0c Mon Sep 17 00:00:00 2001 From: Andrew Selle Date: Tue, 30 Jan 2018 09:55:38 -0800 Subject: [PATCH 260/423] Create an interface to create hints for future toco conversions. Specifically, tf.contrib.lite.OpHint can create "breadcrumb" hints that describe encapsulation of multiple TensorFlow ops that make up a TensorFlow lite builtin or custom op. These can later be replaced with stub versions in a GraphDef or SavedModel. PiperOrigin-RevId: 183846742 --- tensorflow/contrib/framework/__init__.py | 1 + tensorflow/contrib/lite/python/BUILD | 13 + tensorflow/contrib/lite/python/lite.py | 7 +- tensorflow/contrib/lite/python/lite_test.py | 118 +++++++- tensorflow/contrib/lite/python/op_hint.py | 291 ++++++++++++++++++++ 5 files changed, 428 insertions(+), 2 deletions(-) create mode 100644 tensorflow/contrib/lite/python/op_hint.py diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index 673c517842..503b868aaa 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -53,6 +53,7 @@ See the @{$python/contrib.framework} guide. @@assign_from_values_fn @@create_global_step @@filter_variables +@@fuse_op @@get_global_step @@get_or_create_global_step @@get_local_variables diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD index 3d6a3ec0fd..2d8c49b7d7 100644 --- a/tensorflow/contrib/lite/python/BUILD +++ b/tensorflow/contrib/lite/python/BUILD @@ -13,6 +13,7 @@ py_library( srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ + ":op_hint", "//tensorflow/contrib/lite/toco:model_flags_proto_py", "//tensorflow/contrib/lite/toco:toco_flags_proto_py", "//tensorflow/contrib/lite/toco/python:tensorflow_wrap_toco", @@ -20,6 +21,17 @@ py_library( ], ) +py_library( + name = "op_hint", + srcs = ["op_hint.py"], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/contrib/framework:framework_py", + "//tensorflow/python:platform", + ], +) + py_test( name = "lite_test", srcs = ["lite_test.py"], @@ -27,6 +39,7 @@ py_test( tags = ["no_oss"], deps = [ ":lite", + ":op_hint", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:dtypes", diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 3c369774be..5d2f216537 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -18,16 +18,21 @@ EXPERIMENTAL: APIs here are unstable and likely to change without notice. @@toco_convert @@toco_convert_protos +@@OpHint +@@convert_op_hints_to_stubs """ from __future__ import absolute_import from __future__ import division from __future__ import print_function - import os import subprocess import tempfile +# pylint: disable=unused-import +from tensorflow.contrib.lite.python.op_hint import convert_op_hints_to_stubs +from tensorflow.contrib.lite.python.op_hint import OpHint +# pylint: enable=unused-import from tensorflow.contrib.lite.toco import model_flags_pb2 as _model_flags_pb2 from tensorflow.contrib.lite.toco import toco_flags_pb2 as _toco_flags_pb2 from tensorflow.contrib.lite.toco import types_pb2 as _types_pb2 diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py index 7d55f3fe6f..b8b4510188 100644 --- a/tensorflow/contrib/lite/python/lite_test.py +++ b/tensorflow/contrib/lite/python/lite_test.py @@ -18,10 +18,14 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.lite.python import lite +from tensorflow.contrib.lite.python.op_hint import _tensor_name_base as _tensor_name_base from tensorflow.python.client import session from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util +from tensorflow.python.framework.graph_util_impl import _bfs_for_reachable_nodes +from tensorflow.python.framework.graph_util_impl import _extract_graph_summary from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops from tensorflow.python.platform import test @@ -35,7 +39,8 @@ class LiteTest(test_util.TensorFlowTestCase): # Try running on valid graph result = lite.toco_convert(sess.graph_def, [in_tensor], [out_tensor]) self.assertTrue(result) - # TODO(aselle): remove tests that fail. + # TODO(aselle): remove tests that fail (we must get TOCO to not fatal + # all the time). # Try running on identity graph (known fail) # with self.assertRaisesRegexp(RuntimeError, "!model->operators.empty()"): # result = lite.toco_convert(sess.graph_def, [in_tensor], [in_tensor]) @@ -51,5 +56,116 @@ class LiteTest(test_util.TensorFlowTestCase): quantized_input_stats=[(0., 1.)]) self.assertTrue(result) + +class LiteTestOpHint(test_util.TensorFlowTestCase): + """Test the hint to stub functionality.""" + + def _getGraphOpTypes(self, graphdef, output_nodes): + """Returns used op types in `graphdef` reachable from `output_nodes`. + + This is used to check that after the stub transformation the expected + nodes are there. Typically use this with self.assertCountEqual(...). + + NOTE: this is not a exact test that the graph is the correct output, but + it balances compact expressibility of test with sanity checking. + + Args: + graphdef: TensorFlow proto graphdef. + output_nodes: A list of output node names that we need to reach. + + Returns: + A set of node types reachable from `output_nodes`. + """ + name_to_input_name, name_to_node, _ = ( + _extract_graph_summary(graphdef)) + # Find all nodes that are needed by the outputs + used_node_names = _bfs_for_reachable_nodes(output_nodes, name_to_input_name) + return set([name_to_node[node_name].op for node_name in used_node_names]) + + def _countIdentities(self, nodes): + """Count the number of "Identity" op types in the list of proto nodes. + + Args: + nodes: NodeDefs of the graph. + + Returns: + The number of nodes with op type "Identity" found. + """ + return len([x for x in nodes if x.op == "Identity"]) + + def testSwishLiteHint(self): + """Makes a custom op swish and makes sure it gets converted as a unit.""" + image = array_ops.constant([1., 2., 3., 4.]) + swish_scale = array_ops.constant(1.0) + + def _swish(input_tensor, scale): + custom = lite.OpHint("cool_activation") + input_tensor, scale = custom.add_inputs(input_tensor, scale) + output = math_ops.sigmoid(input_tensor) * input_tensor * scale + output, = custom.add_outputs(output) + return output + output = array_ops.identity(_swish(image, swish_scale), name="ModelOutput") + + with self.test_session() as sess: + # check if identities have been put into the graph (2 input, 1 output, + # and 1 final output). + self.assertEqual(self._countIdentities(sess.graph_def.node), 4) + + stubbed_graphdef = lite.convert_op_hints_to_stubs(sess) + + self.assertCountEqual( + self._getGraphOpTypes( + stubbed_graphdef, output_nodes=[_tensor_name_base(output)]), + ["cool_activation", "Const", "Identity"]) + + def testScaleAndBiasAndIdentity(self): + """This tests a scaled add which has 3 inputs and 2 outputs.""" + a = array_ops.constant(1.) + x = array_ops.constant([2., 3.]) + b = array_ops.constant([4., 5.]) + + def _scaled_and_bias_and_identity(a, x, b): + custom = lite.OpHint("scale_and_bias_and_identity") + a, x, b = custom.add_inputs(a, x, b) + return custom.add_outputs(a * x + b, x) + output = array_ops.identity(_scaled_and_bias_and_identity(a, x, b), + name="ModelOutput") + + with self.test_session() as sess: + # make sure one identity for each input (3) and output (2) => 3 + 2 = 5 + # +1 for the final output + self.assertEqual(self._countIdentities(sess.graph_def.node), 6) + + stubbed_graphdef = lite.convert_op_hints_to_stubs(sess) + + self.assertCountEqual( + self._getGraphOpTypes( + stubbed_graphdef, output_nodes=[_tensor_name_base(output)]), + ["scale_and_bias_and_identity", "Const", "Identity", "Pack"]) + + def testTwoFunctions(self): + """Tests if two functions are converted correctly.""" + a = array_ops.constant([1.]) + b = array_ops.constant([1.]) + def _double_values(x): + custom = lite.OpHint("add_test") + x = custom.add_inputs(x) + output = math_ops.multiply(x, x) + output, = custom.add_outputs(output) + return output + output = array_ops.identity( + math_ops.add(_double_values(a), _double_values(b)), name="ModelOutput") + + with self.test_session() as sess: + # make sure one identity for each input (2) and output (2) => 2 + 2 + # +1 for the final output + self.assertEqual(self._countIdentities(sess.graph_def.node), 5) + stubbed_graphdef = lite.convert_op_hints_to_stubs(sess) + self.assertCountEqual( + self._getGraphOpTypes( + stubbed_graphdef, output_nodes=[_tensor_name_base(output)]), + ["add_test", "Const", "Identity", "Add"]) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/lite/python/op_hint.py b/tensorflow/contrib/lite/python/op_hint.py new file mode 100644 index 0000000000..7c587e38b1 --- /dev/null +++ b/tensorflow/contrib/lite/python/op_hint.py @@ -0,0 +1,291 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Define tflite op hints (intrinsic operations). + +This essentially allows defining a TensorFlow API for tflite operations in +Python with hints on how they are represented in TensorFlow Lite. This basically +is a form of tflite intrinsic. It wraps a subpart of a TensorFlow execution +graph and is useful for LSTMs and other complicated TensorFlow constructions +that are difficult to pattern match in TOCO, but are represented by a single +accelerated tflite op. + +Example: + def tflite_cool_activation(input): + # A cool activation function. + custom = tf.contrib.lite.OpHint("cool_activation") + input = custom.add_inputs(input) + output = tf.sigmoid(input) * input + custom.add_outputs(output) + return output + + image = tf.placeholder(tf.float32, (1, 16, 16, 1)) + output = tf.identity(tflite_cool_activation(image)) + + session = tf.Session() + + graphdef_to_convert = tf.contrib.lite.convert_op_hints_to_stubs(session) + tflite_graph = tf.contrib.lite.toco_convert(graphdef_to_convert, + [image], [output]) + [image], [output]) + with open("/tmp/graph.fb", "wb") as fp: + fp.write(tflite_graph) + +How does it work?: + +OpHint is a helper that you use when defining a vanilla python function. +It allows you to wrap arguments with tf.identities with some custom attributes. +These attributes allow you to find the original block of ops that was created. +For example, if you use cool_activation above you essentially get: + +a_input = tf.identity() +result = tf.multiply(tf.sigmoid(a_input), a_input) +output = tf.identity() + +a_input, output are identities that have parameters representing +what argument they are, what the name of the function they should turn into +in tf lite as well as a guid that uniquely identifies a particular invocation. + +Once you have built your whole tensorflow graph, you can run it and train it +as usual, but after you have done that, you need to convert the graph into +a form that replaces these subgraphs wrapped in identities to stub ops. These +ops don't actually exist in the normal TensorFlow runtime, but will be +understood by toco later. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections as _collections +import itertools as _itertools +import uuid as _uuid + +from tensorflow.contrib import framework as _framework +from tensorflow.python.framework import ops as _ops +from tensorflow.python.ops import array_ops as _array_ops +from tensorflow.python.util.all_util import remove_undocumented + + +class OpHint(object): + """A class that helps build tflite function invocations. + + It allows you to take a bunch of TensorFlow ops and annotate the construction + such that toco knows how to convert it to tflite. This embeds a pseudo + function in a TensorFlow graph. This allows embedding high-level API usage + information in a lower level TensorFlow implementation so that an alternative + implementation can be substituted later. + + Essentially, any "input" into this pseudo op is fed into an identity, and + attributes are added to that input before being used by the constituent ops + that make up the pseudo op. A similar process is done to any output that + is to be exported from the current op. + + TODO(aselle): When TensorFlow functions functionality works for arbitrary + constructs, this mechanism can be retired and changed to use python defun's. + """ + + # Attr constants that are used for representation in the GraphDef + FUNCTION_NAME_ATTR = "_tflite_function_name" + FUNCTION_UUID_ATTR = "_tflite_function_uuid" + FUNCTION_INPUT_INDEX_ATTR = "_tflite_function_input_index" + FUNCTION_OUTPUT_INDEX_ATTR = "_tflite_function_output_index" + + def __init__(self, function_name, **kwargs): + """Create a OpHint. + + Args: + function_name: Name of the function (the custom op name in tflite) + **kwargs: Keyword arguments of any constant attributes for the function. + """ + self._function_name = function_name + self._unique_function_id = _uuid.uuid1().hex # TODO(aselle): Unique enough? + self._curr_input_index = 0 + self._curr_output_index = 0 + self._attrs_to_store_later = kwargs + self._stored_attrs = False + + def _setattr(self, dest_op, name, value): + tensor_value = _ops.convert_to_tensor(value) + dest_op.op.node_def.attr[name].tensor.CopyFrom( + tensor_value.op.node_def.attr["value"].tensor) + + def add_inputs(self, *args): + """Add a sequence of inputs to the function invocation. + + Args: + *args: List of inputs to be converted (should be Tf.Tensor). + Returns: + Wrapped inputs (identity standins that have additional metadata). These + are also are also tf.Tensor's. + """ + + def augmented_identity(arg): + identity_op = _array_ops.identity(arg) + attr = identity_op.op.node_def.attr + attr[OpHint.FUNCTION_NAME_ATTR].s = self._function_name + attr[OpHint.FUNCTION_UUID_ATTR].s = self._unique_function_id + attr[OpHint.FUNCTION_INPUT_INDEX_ATTR].i = self._curr_input_index + self._curr_input_index += 1 + return identity_op + + return [augmented_identity(arg) for arg in args] + + def add_outputs(self, *args): + """Add a sequence of outputs to the function invocation. + + Args: + *args: List of outputs to be converted (should be tf.Tensor). + Returns: + Wrapped outputs (identity standins that have additional metadata). These + are also tf.Tensor's. + """ + + def augmented_identity(arg): + identity_op = _array_ops.identity(arg) + attr = identity_op.op.node_def.attr + attr[OpHint.FUNCTION_NAME_ATTR].s = self._function_name + attr[OpHint.FUNCTION_UUID_ATTR].s = self._unique_function_id + attr[OpHint.FUNCTION_OUTPUT_INDEX_ATTR].i = self._curr_output_index + self._curr_output_index += 1 + return identity_op + + wrapped_outputs = [augmented_identity(arg) for arg in args] + + if not self._stored_attrs: + for key, value in self._attrs_to_store_later.iteritems(): + self._setattr(wrapped_outputs[0], "_tflite_attr_" + key, value) + self._stored_attrs = True + + return wrapped_outputs + + +class _LiteFuncCall(object): + """Represent a TensorFlow Lite custom function. + + This is uses to accumulate found hints in the graphdef into a single + conceptual unit. + + Properties: + self.inputs: inputs to the op (hash from index # to argument) + self.outputs: outputs to the op (hash from index # to argument) + self.function_name: the tflite custom op name to use + self.uuid: a unique call id for this particular call (i.e. + multiple function calls would have the same function_name but different + uuids. + self.params: A param name to key value for op constant data. I.e. for + axis on a reduction, strides on a convolution, etc. + """ + + def __init__(self): + self.inputs = {} + self.outputs = {} + self.function_name = None + self.uuid = None + self.params = {} + + def __str__(self): + return "tflite function %s call %s\n\tinputs: %r\n\toutputs: %r" % ( + self.function_name, self.uuid, self.inputs, self.outputs) + + +def _find_all_hints_in_graph_def(session): + """Look at the current default graph and return a list of LiteFuncCall objs. + + Args: + session: A TensorFlow session that contains the graph to convert. + Returns: + a list of `LifeFuncCall` objects in the form + + """ + func_calls = _collections.defaultdict(_LiteFuncCall) + seen_ops = set() + + for op in session.graph.get_operations(): + for operand in _itertools.chain(op.inputs, op.outputs): + if operand in seen_ops: + continue + seen_ops.add(operand) + attr = operand.op.node_def.attr + uuid = attr[OpHint.FUNCTION_UUID_ATTR].s + if OpHint.FUNCTION_UUID_ATTR not in attr: + continue + call_def = func_calls[uuid] + call_def.uuid = uuid + if OpHint.FUNCTION_UUID_ATTR in attr: + call_def.function_name = attr[OpHint.FUNCTION_NAME_ATTR].s + if OpHint.FUNCTION_INPUT_INDEX_ATTR in attr: + call_def.inputs[attr[OpHint.FUNCTION_INPUT_INDEX_ATTR].i] = operand + if OpHint.FUNCTION_OUTPUT_INDEX_ATTR in attr: + call_def.outputs[attr[OpHint.FUNCTION_OUTPUT_INDEX_ATTR].i] = operand + + for a in attr: + if a.startswith("_tflite_attr_"): + # TODO(aselle): Remember the attribute tensors so we can put them + # in collapse. + call_def.params[a.replace("_tflite_attr_,", "")] = attr[a].tensor + + return func_calls + + +def _tensor_name_base(full_tensor_name): + """Removes the device assignment code from a tensor. + + e.g. _tensor_name_base("foo:3") => "foo" + + Args: + full_tensor_name: A tensor name that is annotated with a device placement + (this is what tensor flow introspection gives). + Returns: + A name without any device assignment. + """ + return full_tensor_name.name.split(":")[0] + + +def convert_op_hints_to_stubs(session): + """Converts a graphdef with LiteOp hints into stub operations. + + This is used to prepare for toco conversion of complex intrinsic usages. + + Args: + session: A TensorFlow session that contains the graph to convert. + Returns: + A new graphdef with all ops contained in OpHints being replaced by + a single op call with the right parameters. + """ + hints = _find_all_hints_in_graph_def(session) + current_graph_def = session.graph_def + for call in hints.values(): + input_names = [None] * len(call.inputs) + output_names = [None] * len(call.outputs) + output_dtypes = [None] * len(call.outputs) + output_quantized = False + for input_index, tensor in call.inputs.items(): + input_names[input_index] = _tensor_name_base(tensor) + for output_index, tensor in call.outputs.items(): + output_names[output_index] = _tensor_name_base(tensor) + output_dtypes[output_index] = tensor.dtype.as_datatype_enum + # TODO(aselle): Support quantized flag properly + current_graph_def = _framework.fuse_op( + current_graph_def, input_names, output_names, output_dtypes, + output_quantized, call.uuid, call.function_name) + for node in current_graph_def.node: + if node.name == call.uuid: + for param, tensor in call.params.items(): + node.attr[param].tensor.CopyFrom(tensor) + return current_graph_def + + +_allowed_symbols = ["OpHint", "convert_op_hints_to_stubs"] +remove_undocumented(__name__, _allowed_symbols) -- GitLab From 88eb6c61ef7659c2b5bb1ec6586c7d3cca5e4e9c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 09:57:16 -0800 Subject: [PATCH 261/423] TensorFlow SavedModel loader: avoid segmentation fault when NewSession returns null PiperOrigin-RevId: 183846994 --- tensorflow/cc/saved_model/loader.cc | 4 +++- tensorflow/cc/saved_model/loader_test.cc | 18 ++++++++++++++++++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index acef098c7d..faa1e378d0 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -96,7 +96,9 @@ Status FindMetaGraphDefToLoad(const SavedModel& saved_model_proto, Status LoadMetaGraphIntoSession(const MetaGraphDef& meta_graph_def, const SessionOptions& session_options, std::unique_ptr* session) { - session->reset(NewSession(session_options)); + Session* session_p = nullptr; + TF_RETURN_IF_ERROR(NewSession(session_options, &session_p)); + session->reset(session_p); return (*session)->Create(meta_graph_def.graph_def()); } diff --git a/tensorflow/cc/saved_model/loader_test.cc b/tensorflow/cc/saved_model/loader_test.cc index 0ad6b33bba..4c64d2cfe3 100644 --- a/tensorflow/cc/saved_model/loader_test.cc +++ b/tensorflow/cc/saved_model/loader_test.cc @@ -155,6 +155,24 @@ TEST_F(LoaderTest, NoTagMatchMultiple) { << st.error_message(); } +TEST_F(LoaderTest, SessionCreationFailure) { + SavedModelBundle bundle; + // Use invalid SessionOptions to cause session creation to fail. Default + // options work, so provide an invalid value for the target field. + SessionOptions session_options; + constexpr char kInvalidTarget[] = "invalid target"; + session_options.target = kInvalidTarget; + RunOptions run_options; + + const string export_dir = + io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataSharded); + Status st = LoadSavedModel(session_options, run_options, export_dir, + {kSavedModelTagServe}, &bundle); + EXPECT_FALSE(st.ok()); + EXPECT_TRUE(StringPiece(st.error_message()).contains(kInvalidTarget)) + << st.error_message(); +} + TEST_F(LoaderTest, PbtxtFormat) { SavedModelBundle bundle; SessionOptions session_options; -- GitLab From c7b3d4be6113b9207ca25a3687e918cde61e210b Mon Sep 17 00:00:00 2001 From: Omar Aflak Date: Tue, 30 Jan 2018 11:10:56 -0800 Subject: [PATCH 262/423] Update README.md (#16558) added a friendly badge to get the latest android tensorflow version --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0c93813e58..c754c3f0db 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ | **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** | |-----------------|---------------------|------------------|-------------------|---------------| -| [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-cpu)](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-linux-gpu)](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-mac)](https://ci.tensorflow.org/job/tensorflow-master-mac) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) | +| [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-cpu)](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-linux-gpu)](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-mac)](https://ci.tensorflow.org/job/tensorflow-master-mac) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) [ ![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg) ](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) | **TensorFlow** is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while -- GitLab From a58f26d7265428ef026877cb24cc6bbd7693687b Mon Sep 17 00:00:00 2001 From: ManHyuk Date: Wed, 31 Jan 2018 05:20:47 +0900 Subject: [PATCH 263/423] Fix typo (#16583) * fix typos * fix typos * fix typo * fix typo * fix typo * fix typo * fix typo * tweak grammar --- tensorflow/contrib/coder/kernels/range_coder.cc | 2 +- tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc | 6 +++--- tensorflow/core/platform/s3/s3_file_system.h | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/coder/kernels/range_coder.cc b/tensorflow/contrib/coder/kernels/range_coder.cc index f4f076b6c4..21b35155ff 100644 --- a/tensorflow/contrib/coder/kernels/range_coder.cc +++ b/tensorflow/contrib/coder/kernels/range_coder.cc @@ -276,7 +276,7 @@ void RangeEncoder::Finalize(string* sink) { } } else if (base_ != 0) { // If base == 0, then pick 0 from [base, base + size) and no zeros are - // explcitly written. + // explicitly written. // // Otherwise, pick (base + (2^16 - base[16:0])), i.e., round up base to the // next multiple of 2^16. As 2^16 < size, this value should be in the diff --git a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc index 31e565027f..92879ab535 100644 --- a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc +++ b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc @@ -246,9 +246,9 @@ and 'indices' is [[0,1] [1,1] [0,2]], -the the output will be [[ 1, 20, 3] - [ +BIG_VALUE, +BIG_VALUE, +BIG_VALUE] - [ 1, 5, 3]]. +the output will be [[ 1, 20, 3] + [ +BIG_VALUE, +BIG_VALUE, +BIG_VALUE] + [ 1, 5, 3]]. ``` The data must be at least rank 1. The indices can be of shape (?,2) where the diff --git a/tensorflow/core/platform/s3/s3_file_system.h b/tensorflow/core/platform/s3/s3_file_system.h index d0d6bb5949..8177e48dba 100644 --- a/tensorflow/core/platform/s3/s3_file_system.h +++ b/tensorflow/core/platform/s3/s3_file_system.h @@ -63,7 +63,7 @@ class S3FileSystem : public FileSystem { // variables. // By default S3 access regional endpoint, with region // controlled by `AWS_REGION`. The endpoint could be overridden - // with explicity `S3_ENDPOINT`. S3 use HTTPS by default. + // explicitly with `S3_ENDPOINT`. S3 uses HTTPS by default. // If S3_USE_HTTPS=0 is specified, HTTP is used. Also, // S3_VERIFY_SSL=0 could disable SSL verification in case // HTTPS is used. -- GitLab From 7149a2e2e2f549035f23e21224ee41afe8df3876 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 10:05:04 -0800 Subject: [PATCH 264/423] Cleanup: Ran clang-format on files in tensorflow/core/.../*.{cc,h}. PiperOrigin-RevId: 183848459 --- .../common_runtime/accumulate_n_optimizer.cc | 3 - .../core/common_runtime/bfc_allocator.h | 8 +- .../core/common_runtime/device_set_test.cc | 4 +- .../core/common_runtime/direct_session.cc | 7 +- .../common_runtime/direct_session_test.cc | 68 ++-- ...direct_session_with_tracking_alloc_test.cc | 8 +- tensorflow/core/common_runtime/executor.cc | 2 +- tensorflow/core/common_runtime/function.cc | 2 +- .../gpu/gpu_bfc_allocator_test.cc | 5 +- .../core/common_runtime/gpu/gpu_device.cc | 5 +- .../gpu/gpu_stream_util_test.cc | 3 +- .../core/common_runtime/gpu/process_state.cc | 4 +- .../common_runtime/graph_execution_state.h | 4 +- .../core/common_runtime/memory_types.cc | 2 +- .../core/common_runtime/memory_types_test.cc | 6 +- tensorflow/core/common_runtime/placer.h | 6 +- tensorflow/core/common_runtime/placer_test.cc | 6 +- .../core/common_runtime/session_factory.cc | 4 +- .../common_runtime/step_stats_collector.cc | 19 +- .../common_runtime/sycl/sycl_allocator.cc | 2 +- .../core/common_runtime/sycl/sycl_allocator.h | 5 +- .../core/common_runtime/sycl/sycl_device.h | 6 +- .../sycl/sycl_device_factory.cc | 15 +- .../core/common_runtime/sycl/sycl_util.h | 2 +- tensorflow/core/debug/debug_gateway.cc | 53 +-- tensorflow/core/debug/debug_gateway_test.cc | 32 +- tensorflow/core/debug/debug_grpc_testlib.cc | 2 +- tensorflow/core/debug/debug_io_utils_test.cc | 3 +- .../core/distributed_runtime/graph_mgr.h | 2 +- tensorflow/core/distributed_runtime/master.cc | 4 +- .../core/distributed_runtime/master_test.cc | 4 +- .../rpc/grpc_master_service.cc | 33 +- .../rpc/grpc_master_service_impl.h | 6 +- .../rpc/grpc_remote_master.cc | 8 +- .../rpc/grpc_testlib_ops.cc | 14 +- .../core/distributed_runtime/rpcbench_test.cc | 4 +- .../core/distributed_runtime/scheduler.cc | 2 +- .../core/distributed_runtime/scheduler.h | 4 +- .../worker_cache_logger.cc | 5 +- tensorflow/core/graph/costmodel.cc | 4 +- tensorflow/core/graph/costmodel.h | 4 +- tensorflow/core/graph/graph.h | 4 +- .../core/graph/graph_def_builder_test.cc | 1 - tensorflow/core/graph/mkl_graph_util.h | 171 +++++---- tensorflow/core/graph/mkl_layout_pass.cc | 348 +++++++++--------- tensorflow/core/graph/mkl_layout_pass_test.cc | 107 ++++-- .../core/graph/mkl_tfconversion_pass.cc | 22 +- tensorflow/core/graph/testlib.cc | 2 +- .../kernels/batching_util/periodic_function.h | 2 +- .../shared_batch_scheduler_test.cc | 25 +- .../core/kernels/data/batch_dataset_op.cc | 1 - .../core/kernels/data/captured_function.cc | 4 +- .../core/kernels/data/skip_dataset_op.cc | 11 +- .../kernels/fuzzing/decode_base64_fuzz.cc | 2 +- .../core/kernels/fuzzing/decode_jpeg_fuzz.cc | 2 +- .../fuzzing/decode_json_example_fuzz.cc | 2 +- .../core/kernels/fuzzing/decode_png_fuzz.cc | 2 +- .../kernels/fuzzing/encode_base64_fuzz.cc | 2 +- .../core/kernels/fuzzing/encode_jpeg_fuzz.cc | 2 +- .../example_proto_fast_parsing_fuzz.cc | 2 +- .../core/kernels/fuzzing/identity_fuzz.cc | 2 +- .../kernels/fuzzing/parse_tensor_op_fuzz.cc | 2 +- .../core/kernels/fuzzing/string_split_fuzz.cc | 2 +- .../kernels/fuzzing/string_to_number_fuzz.cc | 2 +- .../kernels/hexagon/graph_transfer_utils.cc | 2 +- .../core/kernels/hexagon/graph_transferer.h | 4 +- .../kernels/hexagon/graph_transferer_test.cc | 5 +- .../hexagon/hexagon_graph_execution_test.cc | 8 +- .../kernels/neon/neon_depthwise_conv_op.cc | 8 +- tensorflow/core/lib/core/status.h | 2 +- tensorflow/core/lib/core/threadpool.cc | 4 +- tensorflow/core/lib/core/threadpool_test.cc | 4 +- tensorflow/core/lib/db/sqlite.h | 23 +- tensorflow/core/lib/db/sqlite_test.cc | 8 +- tensorflow/core/lib/gtl/cleanup.h | 13 +- tensorflow/core/lib/gtl/cleanup_test.cc | 14 +- tensorflow/core/lib/gtl/inlined_vector.h | 4 +- tensorflow/core/lib/gtl/int_type.h | 34 +- tensorflow/core/lib/gtl/int_type_test.cc | 6 +- tensorflow/core/lib/gtl/optional.h | 14 +- tensorflow/core/lib/gtl/optional_test.cc | 26 +- tensorflow/core/lib/gtl/top_n_test.cc | 2 +- tensorflow/core/lib/io/compression.cc | 6 +- tensorflow/core/lib/io/compression.h | 6 +- tensorflow/core/lib/io/record_reader.cc | 2 +- tensorflow/core/lib/io/recordio_test.cc | 4 +- tensorflow/core/lib/png/png_io.cc | 4 +- .../lib/random/philox_random_test_utils.h | 4 +- .../core/lib/random/random_distributions.h | 3 +- .../lib/random/random_distributions_test.cc | 4 +- tensorflow/core/lib/strings/ordered_code.cc | 3 +- tensorflow/core/lib/strings/strcat.h | 2 +- .../compat/backwards_compatibility_test.cc | 5 +- .../core/platform/cloud/gcs_dns_cache.cc | 2 +- .../core/platform/cloud/http_request_fake.h | 3 +- .../core/platform/cloud/oauth_client_test.cc | 12 +- .../platform/cloud/retrying_file_system.cc | 1 - .../core/platform/cuda_libdevice_path_test.cc | 3 +- .../core/platform/default/device_tracer.cc | 6 +- tensorflow/core/platform/default/logging.cc | 5 +- tensorflow/core/platform/default/logging.h | 26 +- tensorflow/core/platform/denormal.cc | 4 +- .../core/platform/device_tracer_test.cc | 3 +- tensorflow/core/platform/env.h | 3 +- tensorflow/core/platform/file_system.cc | 25 +- tensorflow/core/platform/gif.h | 3 +- .../platform/hadoop/hadoop_file_system.cc | 5 +- tensorflow/core/platform/jpeg.h | 3 +- tensorflow/core/platform/png.h | 3 +- tensorflow/core/platform/posix/error.cc | 32 +- .../android_armv7a_cpu_utils_helper.h | 4 +- .../core/platform/profile_utils/cpu_utils.cc | 16 +- .../core/platform/profile_utils/cpu_utils.h | 18 +- .../platform/profile_utils/cpu_utils_test.cc | 20 +- .../profile_utils/i_cpu_utils_helper.h | 4 +- tensorflow/core/platform/protobuf_internal.h | 4 +- tensorflow/core/platform/s3/aws_logging.cc | 2 +- tensorflow/core/platform/setround.cc | 1 - tensorflow/core/platform/test_benchmark.h | 2 +- tensorflow/core/platform/windows/env.cc | 26 +- tensorflow/core/platform/windows/error.cc | 2 +- tensorflow/core/platform/windows/error.h | 4 +- .../core/platform/windows/integral_types.h | 30 +- tensorflow/core/platform/windows/net.cc | 18 +- tensorflow/core/platform/windows/subprocess.h | 3 +- tensorflow/core/platform/windows/test.cc | 2 +- .../platform/windows/windows_file_system.cc | 96 ++--- .../platform/windows/windows_file_system.h | 40 +- .../internal/advisor/tfprof_advisor_test.cc | 9 +- .../core/profiler/internal/tfprof_op.cc | 16 +- tensorflow/core/profiler/internal/tfprof_op.h | 5 +- .../core/profiler/internal/tfprof_show.h | 71 ++-- .../profiler/internal/tfprof_show_multi.h | 2 +- .../core/profiler/internal/tfprof_timeline.h | 2 +- tensorflow/core/util/bcast.cc | 6 +- .../core/util/ctc/ctc_loss_calculator.h | 21 +- .../core/util/cuda_kernel_helper_test.cu.cc | 112 +++--- tensorflow/core/util/example_proto_helper.cc | 10 +- .../core/util/memmapped_file_system_test.cc | 4 +- tensorflow/core/util/mkl_util.h | 104 +++--- tensorflow/core/util/presized_cuckoo_map.h | 2 +- tensorflow/core/util/reporter_test.cc | 4 +- tensorflow/core/util/sparse/sparse_tensor.h | 6 +- .../core/util/sparse/sparse_tensor_test.cc | 2 +- tensorflow/core/util/stream_executor_util.h | 7 +- .../core/util/tensor_bundle/tensor_bundle.cc | 2 +- .../core/util/tensor_slice_reader_cache.cc | 2 +- tensorflow/core/util/tensor_slice_set.cc | 6 +- tensorflow/core/util/tensor_slice_util.h | 6 +- tensorflow/core/util/tensor_slice_writer.h | 10 +- 150 files changed, 1101 insertions(+), 1063 deletions(-) diff --git a/tensorflow/core/common_runtime/accumulate_n_optimizer.cc b/tensorflow/core/common_runtime/accumulate_n_optimizer.cc index a1e3b21e4f..832a55f255 100644 --- a/tensorflow/core/common_runtime/accumulate_n_optimizer.cc +++ b/tensorflow/core/common_runtime/accumulate_n_optimizer.cc @@ -13,11 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ - #include "tensorflow/core/common_runtime/optimization_registry.h" #include "tensorflow/core/graph/node_builder.h" - namespace tensorflow { namespace { @@ -44,7 +42,6 @@ Tensor make_zeros(const DataType& dtype, const TensorShapeProto& shape) { // third-party libraries aren't currently supported. class AccumulateNV2RemovePass : public GraphOptimizationPass { public: - Status Run(const GraphOptimizationPassOptions& options) override { // TODO(freiss.oss@gmail.com): Substantial shared code with // ParallelConcatRemovePass::Run(). Consider refactoring if someone makes diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h index 3dd011a58e..9353997753 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.h +++ b/tensorflow/core/common_runtime/bfc_allocator.h @@ -127,10 +127,10 @@ class BFCAllocator : public VisitableAllocator { string DebugString(BFCAllocator* a, bool recurse) NO_THREAD_SAFETY_ANALYSIS { string dbg; - strings::StrAppend(&dbg, " Size: ", strings::HumanReadableNumBytes(size), - " | Requested Size: ", - strings::HumanReadableNumBytes(requested_size), - " | in_use: ", in_use()); + strings::StrAppend( + &dbg, " Size: ", strings::HumanReadableNumBytes(size), + " | Requested Size: ", strings::HumanReadableNumBytes(requested_size), + " | in_use: ", in_use()); if (recurse && prev != BFCAllocator::kInvalidChunkHandle) { Chunk* p = a->ChunkFromHandle(prev); strings::StrAppend(&dbg, ", prev: ", p->DebugString(a, false)); diff --git a/tensorflow/core/common_runtime/device_set_test.cc b/tensorflow/core/common_runtime/device_set_test.cc index 0507076c8c..fd9c4222a7 100644 --- a/tensorflow/core/common_runtime/device_set_test.cc +++ b/tensorflow/core/common_runtime/device_set_test.cc @@ -88,7 +88,9 @@ TEST_F(DeviceSetTest, PrioritizedDeviceTypeList) { // D3 is prioritized below D1. AddDevice("d3", "/job:a/replica:0/task:0/device:d3:0"); EXPECT_EQ((std::vector{ - DeviceType("d2"), DeviceType("d1"), DeviceType("d3"), + DeviceType("d2"), + DeviceType("d1"), + DeviceType("d3"), }), types()); } diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc index 20c59ad42b..df6f4b8877 100644 --- a/tensorflow/core/common_runtime/direct_session.cc +++ b/tensorflow/core/common_runtime/direct_session.cc @@ -61,7 +61,6 @@ limitations under the License. #include "tensorflow/core/util/device_name_utils.h" #include "tensorflow/core/util/env_var.h" - namespace tensorflow { namespace { @@ -472,9 +471,9 @@ Status DirectSession::Run(const RunOptions& run_options, Executor::Args args; args.step_id = step_id_counter_.fetch_add(1); - TF_RETURN_IF_ERROR( - GetOrCreateExecutors(input_tensor_names, output_names, target_nodes, - &executors_and_keys, &run_state_args)); + TF_RETURN_IF_ERROR(GetOrCreateExecutors(input_tensor_names, output_names, + target_nodes, &executors_and_keys, + &run_state_args)); const int64 executor_step_count = executors_and_keys->step_count.fetch_add(1); std::unique_ptr debugger_state; diff --git a/tensorflow/core/common_runtime/direct_session_test.cc b/tensorflow/core/common_runtime/direct_session_test.cc index 99b33e2ef0..b75a4f76d9 100644 --- a/tensorflow/core/common_runtime/direct_session_test.cc +++ b/tensorflow/core/common_runtime/direct_session_test.cc @@ -436,10 +436,7 @@ TEST(DirectSessionTest, FetchMultipleTimes) { } } -REGISTER_OP("Darth") - .Input("x: float") - .Output("y: float") - .Doc(R"doc( +REGISTER_OP("Darth").Input("x: float").Output("y: float").Doc(R"doc( Darth promises one return value. x: float @@ -972,39 +969,38 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib, std::atomic num_done(0); // Runs session to compute :0 using inter_op thread pool . - auto add_session_run_call = [use_global_pools, &def, &options, &sessions, - &sessions_mu, - &num_done](thread::ThreadPool* tp, Node* node, - int inter_op_pool) { - auto fn = [use_global_pools, &def, &options, &sessions, &sessions_mu, - inter_op_pool, node, &num_done]() { - RunOptions run_options; - run_options.set_inter_op_thread_pool(inter_op_pool); - std::vector outputs; - - Session* session; - if (use_global_pools) { - std::unique_ptr s(NewSession(options)); - TF_ASSERT_OK(s->Create(def)); - session = s.get(); - - mutex_lock l(sessions_mu); - sessions.emplace_back(std::move(s)); - } else { - session = sessions[0].get(); - } + auto add_session_run_call = + [use_global_pools, &def, &options, &sessions, &sessions_mu, &num_done]( + thread::ThreadPool* tp, Node* node, int inter_op_pool) { + auto fn = [use_global_pools, &def, &options, &sessions, &sessions_mu, + inter_op_pool, node, &num_done]() { + RunOptions run_options; + run_options.set_inter_op_thread_pool(inter_op_pool); + std::vector outputs; + + Session* session; + if (use_global_pools) { + std::unique_ptr s(NewSession(options)); + TF_ASSERT_OK(s->Create(def)); + session = s.get(); + + mutex_lock l(sessions_mu); + sessions.emplace_back(std::move(s)); + } else { + session = sessions[0].get(); + } - Status s = session->Run(run_options, {} /* inputs */, - {node->name() + ":0"} /* output_names */, {}, - &outputs, nullptr /* run_metadata */); - TF_CHECK_OK(s); - ASSERT_EQ(1, outputs.size()); - auto flat = outputs[0].flat(); - EXPECT_FLOAT_EQ(1.2, flat(0)); - num_done.fetch_add(1); - }; - tp->Schedule(fn); - }; + Status s = session->Run(run_options, {} /* inputs */, + {node->name() + ":0"} /* output_names */, {}, + &outputs, nullptr /* run_metadata */); + TF_CHECK_OK(s); + ASSERT_EQ(1, outputs.size()); + auto flat = outputs[0].flat(); + EXPECT_FLOAT_EQ(1.2, flat(0)); + num_done.fetch_add(1); + }; + tp->Schedule(fn); + }; // For blocking states: // - Starts at 0, BlockingOp::Compute will move to 1. diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index df9cf0c91f..31fb128f93 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -161,14 +161,14 @@ static void TestHWAccelerator(bool enableHWTrace) { x->set_assigned_device_name("/job:localhost/replica:0/task:0/device:GPU:0"); #ifdef TENSORFLOW_USE_SYCL x->set_assigned_device_name("/job:localhost/replica:0/task:0/device:SYCL:0"); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // y = A * x Node* y = test::graph::Matmul(&graph, a, x, false, false); y->set_assigned_device_name("/job:localhost/replica:0/task:0/device:GPU:0"); #ifdef TENSORFLOW_USE_SYCL -y->set_assigned_device_name("/job:localhost/replica:0/task:0/device:SYCL:0"); -#endif // TENSORFLOW_USE_SYCL + y->set_assigned_device_name("/job:localhost/replica:0/task:0/device:SYCL:0"); +#endif // TENSORFLOW_USE_SYCL Node* y_neg = test::graph::Unary(&graph, "Neg", y); y_neg->set_assigned_device_name("/job:localhost/replica:0/task:0/cpu:0"); @@ -181,7 +181,7 @@ y->set_assigned_device_name("/job:localhost/replica:0/task:0/device:SYCL:0"); (*options.config.mutable_device_count())["GPU"] = 1; #ifdef TENSORFLOW_USE_SYCL (*options.config.mutable_device_count())["SYCL"] = 1; -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL options.config.set_allow_soft_placement(true); options.config.mutable_graph_options()->set_build_cost_model(1); std::unique_ptr session(NewSession(options)); diff --git a/tensorflow/core/common_runtime/executor.cc b/tensorflow/core/common_runtime/executor.cc index 9d03caff1e..f515590b28 100644 --- a/tensorflow/core/common_runtime/executor.cc +++ b/tensorflow/core/common_runtime/executor.cc @@ -1609,7 +1609,7 @@ void ExecutorState::Process(TaggedNode tagged_node, int64 scheduled_usec) { auto done = [this, state]() { Device* device = impl_->params_.device; NodeExecStatsWrapper* stats = state->stats; // Shorthand - Entry* first_input = state->first_input; // Shorthand + Entry* first_input = state->first_input; // Shorthand nodestats::SetOpEnd(stats); EntryVector outputs; diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc index e9c4328f29..150fb85c70 100644 --- a/tensorflow/core/common_runtime/function.cc +++ b/tensorflow/core/common_runtime/function.cc @@ -205,7 +205,7 @@ class FunctionLibraryRuntimeImpl : public FunctionLibraryRuntime { // The instantiated and transformed function is encoded as a Graph // object, and an executor is created for the graph. struct Item : public core::RefCounted { - const Graph* graph = nullptr; // Owned by exec. + const Graph* graph = nullptr; // Owned by exec. const FunctionLibraryDefinition* overlay_lib = nullptr; // Not owned. FunctionBody* func_graph = nullptr; Executor* exec = nullptr; diff --git a/tensorflow/core/common_runtime/gpu/gpu_bfc_allocator_test.cc b/tensorflow/core/common_runtime/gpu/gpu_bfc_allocator_test.cc index 9e4b617d2b..67caeb3495 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_bfc_allocator_test.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_bfc_allocator_test.cc @@ -154,8 +154,9 @@ TEST(GPUBFCAllocatorTest, ExerciseCoalescing) { a.DeallocateRaw(t3); a.DeallocateRaw(t4); } - CheckStats(&a, 4097, 0, 1024 * sizeof(float) + 1048576 * sizeof(int64) + - 2048 * sizeof(double) + 10485760 * sizeof(float), + CheckStats(&a, 4097, 0, + 1024 * sizeof(float) + 1048576 * sizeof(int64) + + 2048 * sizeof(double) + 10485760 * sizeof(float), 10485760 * sizeof(float)); // At the end, we should have coalesced all memory into one region diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc index 0e5b6b7ef8..e26cb0fc3e 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc @@ -763,8 +763,9 @@ int64 MinSystemMemory(int64 available_memory) { min_system_memory *= 2; #endif #if defined(NVIDIA_TEGRA) - // 1GB system mem for NVIDIA Tegra devices since they use the same mem for RAM and Video RAM - min_system_memory = 1<<30; + // 1GB system mem for NVIDIA Tegra devices since they use the same mem for RAM + // and Video RAM + min_system_memory = 1 << 30; #endif return min_system_memory; } diff --git a/tensorflow/core/common_runtime/gpu/gpu_stream_util_test.cc b/tensorflow/core/common_runtime/gpu/gpu_stream_util_test.cc index 7763a4f2e6..2500425359 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_stream_util_test.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_stream_util_test.cc @@ -108,7 +108,8 @@ TEST_F(GpuStreamUtilTest, StreamOverrides) { ops::_Recv(root.WithOpName("input"), DT_FLOAT, "input", "/cpu:0", 0, "/device:GPU:0"); Output n = ops::MatMul(root, {}, {}); - ops::_Send(root.WithOpName("output"), n, "output", "/device:GPU:0", 0, "/cpu:0"); + ops::_Send(root.WithOpName("output"), n, "output", "/device:GPU:0", 0, + "/cpu:0"); Graph g(OpRegistry::Global()); TF_ASSERT_OK(root.ToGraph(&g)); diff --git a/tensorflow/core/common_runtime/gpu/process_state.cc b/tensorflow/core/common_runtime/gpu/process_state.cc index 2f13cf8bd7..b195de7cba 100644 --- a/tensorflow/core/common_runtime/gpu/process_state.cc +++ b/tensorflow/core/common_runtime/gpu/process_state.cc @@ -88,8 +88,8 @@ ProcessState::~ProcessState() { } string ProcessState::MemDesc::DebugString() { - return strings::StrCat((loc == CPU ? "CPU " : "GPU "), dev_index, ", dma: ", - gpu_registered, ", nic: ", nic_registered); + return strings::StrCat((loc == CPU ? "CPU " : "GPU "), dev_index, + ", dma: ", gpu_registered, ", nic: ", nic_registered); } ProcessState::MemDesc ProcessState::PtrType(const void* ptr) { diff --git a/tensorflow/core/common_runtime/graph_execution_state.h b/tensorflow/core/common_runtime/graph_execution_state.h index db2686ce2c..2312e1a89f 100644 --- a/tensorflow/core/common_runtime/graph_execution_state.h +++ b/tensorflow/core/common_runtime/graph_execution_state.h @@ -139,9 +139,7 @@ class GraphExecutionState { // The graph returned by BuildGraph may contain only the pruned // graph, whereas some clients may want access to the full graph. - const Graph* full_graph() { - return graph_; - } + const Graph* full_graph() { return graph_; } // Returns the node with the given name, or null if it does not exist. const Node* get_node_by_name(const string& name) const { diff --git a/tensorflow/core/common_runtime/memory_types.cc b/tensorflow/core/common_runtime/memory_types.cc index 76b926ba40..090a16ebeb 100644 --- a/tensorflow/core/common_runtime/memory_types.cc +++ b/tensorflow/core/common_runtime/memory_types.cc @@ -47,7 +47,7 @@ struct EndpointEq { static Status ProcessMemoryTypes( const DeviceType& device_type, const Graph* g, const std::function& fn) { - if (device_type != DEVICE_GPU && device_type != DEVICE_SYCL ) { + if (device_type != DEVICE_GPU && device_type != DEVICE_SYCL) { // On non-GPU and non-SYCL devices, HOST_MEMORY and DEVICE_MEMORY are always // compatible. return Status::OK(); diff --git a/tensorflow/core/common_runtime/memory_types_test.cc b/tensorflow/core/common_runtime/memory_types_test.cc index 2a834ddca4..a093585571 100644 --- a/tensorflow/core/common_runtime/memory_types_test.cc +++ b/tensorflow/core/common_runtime/memory_types_test.cc @@ -36,7 +36,7 @@ TEST(MemoryTypeChecker, Int32OK) { #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL TF_EXPECT_OK(ValidateMemoryTypes(DEVICE_SYCL, g)); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL delete g; } @@ -64,7 +64,7 @@ TEST(MemoryTypeChecker, Int32NotOk) { // But we can insert _HostSend/_HostRecv to ensure the invariant. TF_EXPECT_OK(EnsureMemoryTypes(DEVICE_SYCL, "/device:SYCL:0", g)); TF_EXPECT_OK(ValidateMemoryTypes(DEVICE_SYCL, g)); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL delete g; } @@ -91,7 +91,7 @@ TEST(MemoryTypeChecker, MemoryTypeForOutput) { TF_EXPECT_OK(MemoryTypeForOutput(DEVICE_SYCL, g, si, 0, &memory_type)); // int Switch's output on GPU has HOST_MEMORY constraint. EXPECT_EQ(memory_type, HOST_MEMORY); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL delete g; } diff --git a/tensorflow/core/common_runtime/placer.h b/tensorflow/core/common_runtime/placer.h index c5b76592e1..75dce7c7fe 100644 --- a/tensorflow/core/common_runtime/placer.h +++ b/tensorflow/core/common_runtime/placer.h @@ -88,9 +88,9 @@ class Placer { void AssignAndLog(int assigned_device, Node* node) const; void LogDeviceAssignment(const Node* node) const; - Graph* const graph_; // Not owned. - const DeviceSet* const devices_; // Not owned. - const SessionOptions* options_; // Not owned. + Graph* const graph_; // Not owned. + const DeviceSet* const devices_; // Not owned. + const SessionOptions* options_; // Not owned. const bool log_device_placement_; TF_DISALLOW_COPY_AND_ASSIGN(Placer); diff --git a/tensorflow/core/common_runtime/placer_test.cc b/tensorflow/core/common_runtime/placer_test.cc index 5d87b1e279..02c9cd5313 100644 --- a/tensorflow/core/common_runtime/placer_test.cc +++ b/tensorflow/core/common_runtime/placer_test.cc @@ -619,9 +619,9 @@ TEST_F(PlacerTest, TestReferenceConnectionIgnoreInfeasible) { Node* input = ops::SourceOp( "TestDevice", b.opts().WithName("in").WithDevice("/job:a/task:0/device:fakegpu:0")); - Node* var = ops::SourceOp("TestVariable", - b.opts().WithName("var_0").WithDevice( - "/job:a/task:0/device:fakegpu:0")); + Node* var = + ops::SourceOp("TestVariable", b.opts().WithName("var_0").WithDevice( + "/job:a/task:0/device:fakegpu:0")); // This op is specified on CPU, but in practice will be ignored, // because the reference edges forces it on GPU. diff --git a/tensorflow/core/common_runtime/session_factory.cc b/tensorflow/core/common_runtime/session_factory.cc index 0234d4c372..4dbe113e44 100644 --- a/tensorflow/core/common_runtime/session_factory.cc +++ b/tensorflow/core/common_runtime/session_factory.cc @@ -60,8 +60,8 @@ const string RegisteredFactoriesErrorMessageLocked() { str_util::Join(factory_types, ", "), "}."); } string SessionOptionsToString(const SessionOptions& options) { - return strings::StrCat("target: \"", options.target, "\" config: ", - ProtoShortDebugString(options.config)); + return strings::StrCat("target: \"", options.target, + "\" config: ", ProtoShortDebugString(options.config)); } } // namespace diff --git a/tensorflow/core/common_runtime/step_stats_collector.cc b/tensorflow/core/common_runtime/step_stats_collector.cc index d7e01144c9..cb900db10a 100644 --- a/tensorflow/core/common_runtime/step_stats_collector.cc +++ b/tensorflow/core/common_runtime/step_stats_collector.cc @@ -226,22 +226,23 @@ void StepStatsCollector::BuildCostModel( if (node) { for (int i = 0; i < stats.output_size(); ++i) { const auto& output = stats.output(i); - cm->RecordMaxMemorySize(node, i, Bytes(output.tensor_description() - .allocation_description() - .allocated_bytes()), + cm->RecordMaxMemorySize(node, i, + Bytes(output.tensor_description() + .allocation_description() + .allocated_bytes()), stats.output(i).tensor_description().shape(), node->output_types()[i]); - cm->RecordAllocationId(node, i, output.tensor_description() - .allocation_description() - .allocation_id()); + cm->RecordAllocationId(node, i, + output.tensor_description() + .allocation_description() + .allocation_id()); } cm->RecordMemoryStats(node, stats.memory_stats()); // Use hardware stats to record the execution time if they're available, // otherwise use the regular (less accurate) stats string node_name = dev_stats.regular_stats->node_stats(i).node_name(); - if (dev_stats.hardware_stats && - name_to_hw_node_stats.find(node_name) != - name_to_hw_node_stats.end()) { + if (dev_stats.hardware_stats && name_to_hw_node_stats.find(node_name) != + name_to_hw_node_stats.end()) { const NodeExecStats& hw_stats = name_to_hw_node_stats[node_name]; cm->RecordMaxExecutionTime( node, Microseconds(hw_stats.op_end_rel_micros())); diff --git a/tensorflow/core/common_runtime/sycl/sycl_allocator.cc b/tensorflow/core/common_runtime/sycl/sycl_allocator.cc index 9094824ee7..02bd8b8f3b 100644 --- a/tensorflow/core/common_runtime/sycl/sycl_allocator.cc +++ b/tensorflow/core/common_runtime/sycl/sycl_allocator.cc @@ -80,7 +80,7 @@ void SYCLAllocator::ClearStats() override { size_t SYCLAllocator::RequestedSize(void* ptr) { mutex_lock lock(mu_); - if(!sycl_device_) { + if (!sycl_device_) { return 0; } const auto& buffer = sycl_device_->get_sycl_buffer(ptr); diff --git a/tensorflow/core/common_runtime/sycl/sycl_allocator.h b/tensorflow/core/common_runtime/sycl/sycl_allocator.h index cca9f92c62..550f193332 100644 --- a/tensorflow/core/common_runtime/sycl/sycl_allocator.h +++ b/tensorflow/core/common_runtime/sycl/sycl_allocator.h @@ -20,10 +20,10 @@ limitations under the License. #ifndef TENSORFLOW_COMMON_RUNTIME_SYCL_SYCL_ALLOCATOR_H_ #define TENSORFLOW_COMMON_RUNTIME_SYCL_SYCL_ALLOCATOR_H_ +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/allocator.h" #include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/types.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { @@ -56,14 +56,13 @@ class SYCLAllocator : public Allocator { // Clear the SYCL device used by the Allocator void ClearSYCLDevice() { mutex_lock lock(mu_); - if(sycl_device_) { + if (sycl_device_) { delete sycl_device_; sycl_device_ = nullptr; } } private: - mutable mutex mu_; Eigen::SyclDevice* sycl_device_ GUARDED_BY(mu_); // owned AllocatorStats stats_ GUARDED_BY(mu_); diff --git a/tensorflow/core/common_runtime/sycl/sycl_device.h b/tensorflow/core/common_runtime/sycl/sycl_device.h index cc272d156e..7c09e0b8f1 100644 --- a/tensorflow/core/common_runtime/sycl/sycl_device.h +++ b/tensorflow/core/common_runtime/sycl/sycl_device.h @@ -187,9 +187,9 @@ class GSYCLInterface { type = "Unknown"; } - return strings::StrCat("id: ", device_id, ", type: ", type, ", name: ", - name.c_str(), ", vendor: ", vendor.c_str(), - ", profile: ", profile.c_str()); + return strings::StrCat( + "id: ", device_id, ", type: ", type, ", name: ", name.c_str(), + ", vendor: ", vendor.c_str(), ", profile: ", profile.c_str()); } }; diff --git a/tensorflow/core/common_runtime/sycl/sycl_device_factory.cc b/tensorflow/core/common_runtime/sycl/sycl_device_factory.cc index 19c14770dc..14f7727659 100644 --- a/tensorflow/core/common_runtime/sycl/sycl_device_factory.cc +++ b/tensorflow/core/common_runtime/sycl/sycl_device_factory.cc @@ -26,7 +26,6 @@ class SYCLDeviceFactory : public DeviceFactory { public: Status CreateDevices(const SessionOptions &options, const string &name_prefix, std::vector *devices) override { - auto syclInterface = GSYCLInterface::instance(); size_t n = 1; @@ -37,13 +36,11 @@ class SYCLDeviceFactory : public DeviceFactory { for (int i = 0; i < n; i++) { string name = strings::StrCat(name_prefix, "/device:SYCL:", i); - devices->push_back( - new SYCLDevice(options, name, Bytes(256 << 20), DeviceLocality() - , syclInterface->GetShortDeviceDescription(i) - , syclInterface->GetSYCLAllocator(i) - , syclInterface->GetCPUAllocator(i) - , syclInterface->GetSYCLContext(i)) - ); + devices->push_back(new SYCLDevice( + options, name, Bytes(256 << 20), DeviceLocality(), + syclInterface->GetShortDeviceDescription(i), + syclInterface->GetSYCLAllocator(i), syclInterface->GetCPUAllocator(i), + syclInterface->GetSYCLContext(i))); } return Status::OK(); @@ -51,6 +48,6 @@ class SYCLDeviceFactory : public DeviceFactory { }; REGISTER_LOCAL_DEVICE_FACTORY("SYCL", SYCLDeviceFactory, 200); -} +} // namespace tensorflow #endif // TENSORFLOW_USE_SYCL diff --git a/tensorflow/core/common_runtime/sycl/sycl_util.h b/tensorflow/core/common_runtime/sycl/sycl_util.h index 83016b706a..3124ed23c9 100644 --- a/tensorflow/core/common_runtime/sycl/sycl_util.h +++ b/tensorflow/core/common_runtime/sycl/sycl_util.h @@ -20,8 +20,8 @@ limitations under the License. #ifndef TENSORFLOW_CORE_COMMON_RUNTIME_SYCL_SYCL_UTIL_H_ #define TENSORFLOW_CORE_COMMON_RUNTIME_SYCL_SYCL_UTIL_H_ -#include "tensorflow/core/common_runtime/device.h" #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/common_runtime/device.h" // For DMA helper #include "tensorflow/core/common_runtime/dma_helper.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/core/debug/debug_gateway.cc b/tensorflow/core/debug/debug_gateway.cc index 616ced3d0f..2e1aabd1cc 100644 --- a/tensorflow/core/debug/debug_gateway.cc +++ b/tensorflow/core/debug/debug_gateway.cc @@ -24,31 +24,31 @@ limitations under the License. namespace tensorflow { DebugGateway::DebugGateway(DirectSession* session) : session_(session) { - session_->node_outputs_callback_ = [this]( - const string& node_name, const int output_slot, const Tensor* tensor, - const bool is_ref, OpKernelContext* ctx) { - if (comp_cb_ != nullptr && output_slot <= 0) { - // The node completion callback is invoked once for a node regardless - // of whether the node has zero, one or more outputs. - // The output_slot can be negative (-1, or kControlSlot) if - // node_outputs_callback_ is invoked for a node with no output. If that - // is the case, notify the callback that the node in question has no - // output. - comp_cb_(node_name, output_slot == 0); - } - - // Copy tensor values (e.g., from GPU to host) only if the - // value callback is not nullptr. - if (val_cb_ != nullptr && output_slot >= 0) { - CopyTensor( - node_name, output_slot, tensor, ctx, - [this, node_name, output_slot, is_ref](const Tensor* copied_tensor) { - val_cb_(node_name, output_slot, *copied_tensor, is_ref); - }); - } - - return Status::OK(); - }; + session_->node_outputs_callback_ = + [this](const string& node_name, const int output_slot, + const Tensor* tensor, const bool is_ref, OpKernelContext* ctx) { + if (comp_cb_ != nullptr && output_slot <= 0) { + // The node completion callback is invoked once for a node regardless + // of whether the node has zero, one or more outputs. + // The output_slot can be negative (-1, or kControlSlot) if + // node_outputs_callback_ is invoked for a node with no output. If + // that is the case, notify the callback that the node in question has + // no output. + comp_cb_(node_name, output_slot == 0); + } + + // Copy tensor values (e.g., from GPU to host) only if the + // value callback is not nullptr. + if (val_cb_ != nullptr && output_slot >= 0) { + CopyTensor(node_name, output_slot, tensor, ctx, + [this, node_name, output_slot, + is_ref](const Tensor* copied_tensor) { + val_cb_(node_name, output_slot, *copied_tensor, is_ref); + }); + } + + return Status::OK(); + }; } DebugGateway::~DebugGateway() { @@ -86,7 +86,8 @@ void DebugGateway::CopyTensor(const string& node_name, const int output_slot, // Determine if the tensor is on device (GPU) or host (CPU). // The second part of the check is necessary because even an OpKernel on // may have output tensors allocated on CPU. - if ((device->name().find("GPU:") != string::npos || device->name().find("SYCL:") != string::npos) && + if ((device->name().find("GPU:") != string::npos || + device->name().find("SYCL:") != string::npos) && !ctx->output_alloc_attr(output_slot).on_host()) { // GPU tensors: Copy it to host (CPU). DeviceContext* device_ctxt = ctx->op_device_context(); diff --git a/tensorflow/core/debug/debug_gateway_test.cc b/tensorflow/core/debug/debug_gateway_test.cc index 5758334906..b1bbd3f698 100644 --- a/tensorflow/core/debug/debug_gateway_test.cc +++ b/tensorflow/core/debug/debug_gateway_test.cc @@ -390,9 +390,9 @@ TEST_F(SessionDebugMinusAXTest, debug_gateway.SetNodeValueCallback( [this, &mu, &val_callback_count, &a_debug_identity_node_name, &x_debug_identity_node_name, &y_debug_identity_node_name, - &debug_identity_tensor_vals, &callbacks_done, &kConcurrentRuns]( - const string& node_name, const int output_slot, - const Tensor& tensor_value, const bool is_ref) { + &debug_identity_tensor_vals, &callbacks_done, + &kConcurrentRuns](const string& node_name, const int output_slot, + const Tensor& tensor_value, const bool is_ref) { mutex_lock l(mu); if (node_name == a_debug_identity_node_name && output_slot == 0) { @@ -560,21 +560,21 @@ TEST_F(SessionDebugOutputSlotWithoutOutgoingEdgeTest, Notification callbacks_done; std::vector debug_identity_tensor_vals; - debug_gateway.SetNodeValueCallback([this, &mu, &callbacks_done, - &debug_identity_node_name, - &debug_identity_tensor_vals]( - const string& node_name, const int output_slot, - const Tensor& tensor_value, const bool is_ref) { - mutex_lock l(mu); + debug_gateway.SetNodeValueCallback( + [this, &mu, &callbacks_done, &debug_identity_node_name, + &debug_identity_tensor_vals]( + const string& node_name, const int output_slot, + const Tensor& tensor_value, const bool is_ref) { + mutex_lock l(mu); - if (node_name == debug_identity_node_name && output_slot == 0) { - debug_identity_tensor_vals.push_back(tensor_value); + if (node_name == debug_identity_node_name && output_slot == 0) { + debug_identity_tensor_vals.push_back(tensor_value); - if (!callbacks_done.HasBeenNotified()) { - callbacks_done.Notify(); - } - } - }); + if (!callbacks_done.HasBeenNotified()) { + callbacks_done.Notify(); + } + } + }); // Add DebugIdentity watch on c:0, which does not have an outgoing edge. RunOptions run_opts; diff --git a/tensorflow/core/debug/debug_grpc_testlib.cc b/tensorflow/core/debug/debug_grpc_testlib.cc index a312f789d8..f70931e926 100644 --- a/tensorflow/core/debug/debug_grpc_testlib.cc +++ b/tensorflow/core/debug/debug_grpc_testlib.cc @@ -30,7 +30,7 @@ namespace test { ::grpc::Status TestEventListenerImpl::SendEvents( ::grpc::ServerContext* context, - ::grpc::ServerReaderWriter< ::tensorflow::EventReply, ::tensorflow::Event>* + ::grpc::ServerReaderWriter<::tensorflow::EventReply, ::tensorflow::Event>* stream) { Event event; diff --git a/tensorflow/core/debug/debug_io_utils_test.cc b/tensorflow/core/debug/debug_io_utils_test.cc index 2f83c2415b..0807a85b8b 100644 --- a/tensorflow/core/debug/debug_io_utils_test.cc +++ b/tensorflow/core/debug/debug_io_utils_test.cc @@ -57,7 +57,8 @@ class DebugIOUtilsTest : public ::testing::Test { TEST_F(DebugIOUtilsTest, ConstructDebugNodeKey) { DebugNodeKey debug_node_key("/job:worker/replica:1/task:0/device:GPU:2", "hidden_1/MatMul", 0, "DebugIdentity"); - EXPECT_EQ("/job:worker/replica:1/task:0/device:GPU:2", debug_node_key.device_name); + EXPECT_EQ("/job:worker/replica:1/task:0/device:GPU:2", + debug_node_key.device_name); EXPECT_EQ("hidden_1/MatMul", debug_node_key.node_name); EXPECT_EQ(0, debug_node_key.output_slot); EXPECT_EQ("DebugIdentity", debug_node_key.debug_op); diff --git a/tensorflow/core/distributed_runtime/graph_mgr.h b/tensorflow/core/distributed_runtime/graph_mgr.h index d0ca2a6257..cc35264b8f 100644 --- a/tensorflow/core/distributed_runtime/graph_mgr.h +++ b/tensorflow/core/distributed_runtime/graph_mgr.h @@ -140,7 +140,7 @@ class GraphMgr { GraphMgr* graph_mgr; }; - const WorkerEnv* worker_env_; // Not owned. + const WorkerEnv* worker_env_; // Not owned. DeviceMgr* device_mgr_; CostModelManager cost_model_manager_; diff --git a/tensorflow/core/distributed_runtime/master.cc b/tensorflow/core/distributed_runtime/master.cc index d1dc622ce7..1a488303ac 100644 --- a/tensorflow/core/distributed_runtime/master.cc +++ b/tensorflow/core/distributed_runtime/master.cc @@ -528,8 +528,8 @@ void Master::ListDevices(const ListDevicesRequest* req, auto session = FindMasterSession(req->session_handle()); if (session == nullptr) { done(errors::InvalidArgument( - "Session ", req->session_handle(), - " is not found. Possibly, this master has restarted.")); + "Session ", req->session_handle(), + " is not found. Possibly, this master has restarted.")); return; } core::ScopedUnref ref(session); diff --git a/tensorflow/core/distributed_runtime/master_test.cc b/tensorflow/core/distributed_runtime/master_test.cc index 121c58762f..f2c1f3489c 100644 --- a/tensorflow/core/distributed_runtime/master_test.cc +++ b/tensorflow/core/distributed_runtime/master_test.cc @@ -61,7 +61,7 @@ class MasterTest : public ::testing::Test { // rpc calls. Status CreateSession(const GraphDef& def, string* handle, - int64* initial_version) { + int64* initial_version) { ::grpc::ClientContext ctx; CreateSessionRequest req; *(req.mutable_graph_def()) = def; @@ -77,7 +77,7 @@ class MasterTest : public ::testing::Test { } Status ExtendSession(const string& handle, const GraphDef& def, - int64 current_version, int64* new_version) { + int64 current_version, int64* new_version) { ::grpc::ClientContext ctx; ExtendSessionRequest req; req.set_session_handle(handle); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc index ac27993773..b4d18d8607 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc @@ -185,23 +185,22 @@ class GrpcMasterService : public AsyncServiceInterface { MutableRunStepResponseWrapper* wrapped_response = new NonOwnedProtoRunStepResponse(&call->response); call->SetCancelCallback([call_opts]() { call_opts->StartCancel(); }); - master_impl_->RunStep(call_opts, wrapped_request, wrapped_response, - [call, call_opts, wrapped_request, wrapped_response, - trace](const Status& status) { - call->ClearCancelCallback(); - delete call_opts; - delete wrapped_request; - delete trace; - if (call->request.store_errors_in_response_body() && - !status.ok()) { - call->response.set_status_code(status.code()); - call->response.set_status_error_message( - status.error_message()); - call->SendResponse(ToGrpcStatus(Status::OK())); - } else { - call->SendResponse(ToGrpcStatus(status)); - } - }); + master_impl_->RunStep( + call_opts, wrapped_request, wrapped_response, + [call, call_opts, wrapped_request, wrapped_response, + trace](const Status& status) { + call->ClearCancelCallback(); + delete call_opts; + delete wrapped_request; + delete trace; + if (call->request.store_errors_in_response_body() && !status.ok()) { + call->response.set_status_code(status.code()); + call->response.set_status_error_message(status.error_message()); + call->SendResponse(ToGrpcStatus(Status::OK())); + } else { + call->SendResponse(ToGrpcStatus(status)); + } + }); ENQUEUE_REQUEST(RunStep, true); } diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h index 4e203e260a..6ae94b7441 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h +++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.h @@ -89,9 +89,9 @@ class MasterService final { ::grpc::Status ExtendSession(::grpc::ClientContext* context, const ExtendSessionRequest& request, ExtendSessionResponse* response) override; - ::grpc::Status PartialRunSetup( - ::grpc::ClientContext* context, const PartialRunSetupRequest& request, - PartialRunSetupResponse* response) override; + ::grpc::Status PartialRunSetup(::grpc::ClientContext* context, + const PartialRunSetupRequest& request, + PartialRunSetupResponse* response) override; ::grpc::Status RunStep(::grpc::ClientContext* context, const RunStepRequest& request, RunStepResponse* response) override; diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc index 70418f6368..1088e9be66 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc @@ -69,8 +69,7 @@ class GrpcRemoteMaster : public MasterInterface { ::grpc::ClientContext ctx; auto trace = TraceRpc("RunStep/Client", &ctx); return Call(&ctx, call_options, &request->ToProto(), - get_proto_from_wrapper(response), - &MasterServiceStub::RunStep); + get_proto_from_wrapper(response), &MasterServiceStub::RunStep); } Status CloseSession(CallOptions* call_options, @@ -114,8 +113,9 @@ class GrpcRemoteMaster : public MasterInterface { template Status Call(::grpc::ClientContext* ctx, CallOptions* call_options, const Request* request, Response* response, - ::grpc::Status (MasterServiceStub::*pfunc)( - ::grpc::ClientContext*, const Request&, Response*)) { + ::grpc::Status (MasterServiceStub::*pfunc)(::grpc::ClientContext*, + const Request&, + Response*)) { ctx->set_fail_fast(false); SetDeadline(ctx, call_options->GetTimeout()); return FromGrpcStatus((stub_.get()->*pfunc)(ctx, *request, response)); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_testlib_ops.cc b/tensorflow/core/distributed_runtime/rpc/grpc_testlib_ops.cc index 373eecffca..5597ee7a76 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_testlib_ops.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_testlib_ops.cc @@ -21,11 +21,8 @@ namespace tensorflow { namespace test { // ErrorOp::Compute returns an error. -REGISTER_OP("Error") - .Input("in: T") - .Output("out: T") - .Attr("T: type") - .Attr("message: string"); +REGISTER_OP("Error").Input("in: T").Output("out: T").Attr("T: type").Attr( + "message: string"); class ErrorOp : public OpKernel { public: explicit ErrorOp(OpKernelConstruction* ctx) : OpKernel(ctx) { @@ -66,11 +63,8 @@ REGISTER_KERNEL_BUILDER(Name("InvalidRefType").Device(DEVICE_CPU), // DelayOp::AsyncCompute sleeps for "micros"-econd and then returns // its input. -REGISTER_OP("Delay") - .Input("in: T") - .Output("out: T") - .Attr("T: type") - .Attr("micros: int"); +REGISTER_OP("Delay").Input("in: T").Output("out: T").Attr("T: type").Attr( + "micros: int"); class DelayOp : public AsyncOpKernel { public: explicit DelayOp(OpKernelConstruction* ctx) : AsyncOpKernel(ctx) { diff --git a/tensorflow/core/distributed_runtime/rpcbench_test.cc b/tensorflow/core/distributed_runtime/rpcbench_test.cc index b2668fae25..d3af7417e6 100644 --- a/tensorflow/core/distributed_runtime/rpcbench_test.cc +++ b/tensorflow/core/distributed_runtime/rpcbench_test.cc @@ -184,8 +184,8 @@ static void BM_Helper(int iters, int width, int num_stages, int tensor_size, testing::SetLabel( strings::StrCat(def.node_size(), " nodes; ", - use_multiple_devices ? "Multi device" : "Single device", - "; tensor bytes/send: ", tensor_size * sizeof(float))); + use_multiple_devices ? "Multi device" : "Single device", + "; tensor bytes/send: ", tensor_size * sizeof(float))); std::vector outputs; diff --git a/tensorflow/core/distributed_runtime/scheduler.cc b/tensorflow/core/distributed_runtime/scheduler.cc index 4766f4c33b..9dae5b3b92 100644 --- a/tensorflow/core/distributed_runtime/scheduler.cc +++ b/tensorflow/core/distributed_runtime/scheduler.cc @@ -17,9 +17,9 @@ limitations under the License. #include -#include "tensorflow/core/graph/graph.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/device_set.h" +#include "tensorflow/core/graph/graph.h" #include "tensorflow/core/util/util.h" namespace tensorflow { diff --git a/tensorflow/core/distributed_runtime/scheduler.h b/tensorflow/core/distributed_runtime/scheduler.h index eabcaccdd1..ef87b9834d 100644 --- a/tensorflow/core/distributed_runtime/scheduler.h +++ b/tensorflow/core/distributed_runtime/scheduler.h @@ -16,15 +16,15 @@ limitations under the License. #ifndef TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_SCHEDULER_H_ #define TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_SCHEDULER_H_ -#include #include +#include #include #include #include -#include "tensorflow/core/graph/costmodel.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/device_set.h" +#include "tensorflow/core/graph/costmodel.h" namespace tensorflow { diff --git a/tensorflow/core/distributed_runtime/worker_cache_logger.cc b/tensorflow/core/distributed_runtime/worker_cache_logger.cc index 702af78c88..95ca3c3b4d 100644 --- a/tensorflow/core/distributed_runtime/worker_cache_logger.cc +++ b/tensorflow/core/distributed_runtime/worker_cache_logger.cc @@ -97,9 +97,8 @@ void WorkerCacheLogger::RecordDataTransfer(int64 step_id, int64 start_usecs, const string& tensor_name, const string& src_device, const string& dst_device, - int64 bytes, - const string& details, - const string& transfer_method_name){ + int64 bytes, const string& details, + const string& transfer_method_name) { NodeExecStats* ns = new NodeExecStats; ns->set_node_name(transfer_method_name); if (details.empty()) { diff --git a/tensorflow/core/graph/costmodel.cc b/tensorflow/core/graph/costmodel.cc index 4118f14f8b..f47c983086 100644 --- a/tensorflow/core/graph/costmodel.cc +++ b/tensorflow/core/graph/costmodel.cc @@ -158,8 +158,8 @@ void CostModel::SetNumOutputs(const Node* node, int num_outputs) { Ensure(id, 0); auto perslot = &slot_bytes_[id]; if (!perslot->empty()) { - CHECK_EQ(num_outputs, perslot->size()) << "Cannot resize slot_bytes, node=" - << node->name(); + CHECK_EQ(num_outputs, perslot->size()) + << "Cannot resize slot_bytes, node=" << node->name(); } Ensure(id, num_outputs); } diff --git a/tensorflow/core/graph/costmodel.h b/tensorflow/core/graph/costmodel.h index c60a946c2c..9b703e4693 100644 --- a/tensorflow/core/graph/costmodel.h +++ b/tensorflow/core/graph/costmodel.h @@ -198,7 +198,7 @@ class CostModel { // Cumulative execution time. std::vector time_; // Cumulative Bytes output on each channel. - std::vector > slot_bytes_; + std::vector> slot_bytes_; // Maximum execution time std::vector max_exec_time_; @@ -217,7 +217,7 @@ class CostModel { }; std::vector max_mem_usage_; - std::vector > output_port_alloc_ids_; + std::vector> output_port_alloc_ids_; std::set persistent_alloc_ids_; std::map> persistent_alloc_ids_by_devices_; diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h index b620127d90..93d8dd6f11 100644 --- a/tensorflow/core/graph/graph.h +++ b/tensorflow/core/graph/graph.h @@ -62,8 +62,8 @@ class Node; class VersionDef; class WhileContext; -class NeighborIter; // Declared below -class NodeIter; // Declared below +class NeighborIter; // Declared below +class NodeIter; // Declared below class NodeProperties; // Defined in .cc class Node { diff --git a/tensorflow/core/graph/graph_def_builder_test.cc b/tensorflow/core/graph/graph_def_builder_test.cc index e85de71ef7..e928c81b45 100644 --- a/tensorflow/core/graph/graph_def_builder_test.cc +++ b/tensorflow/core/graph/graph_def_builder_test.cc @@ -26,7 +26,6 @@ namespace tensorflow { namespace { TEST(GraphDefBuilderTest, Version) { - // Verify that our assertions will be nontrivial ASSERT_LT(0, TF_GRAPH_DEF_VERSION); diff --git a/tensorflow/core/graph/mkl_graph_util.h b/tensorflow/core/graph/mkl_graph_util.h index 3df981437a..1b99d54e8e 100644 --- a/tensorflow/core/graph/mkl_graph_util.h +++ b/tensorflow/core/graph/mkl_graph_util.h @@ -21,102 +21,101 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { - // Since our ops are going to produce and also consume N addition tensors - // (Mkl) for N Tensorflow tensors, we can have following different - // orderings among these 2N tensors. - // - // E.g., for Tensorflow tensors A, B, and C, our ops will produce and - // consume A_m, B_m, and C_m additionally. - // - // INTERLEAVED: in this case 2N tensors are interleaved. So for above - // example, the ordering looks like: A, A_m, B, B_m, C, C_m. - // - // CONTIGUOUS: in thi case N Tensorflow tensors are contiguous followed - // by N Mkl tensors. So for above example, the ordering looks - // like: A, B, C, A_m, B_m, C_m - // - // Following APIs map index of original Tensorflow tensors to their - // appropriate position based on selected ordering. For contiguous ordering, - // we need to know the total number of tensors (parameter total). - // - typedef enum { TENSORS_INTERLEAVED, TENSORS_CONTIGUOUS } MklTfTensorOrdering; - // NOTE: Currently, we use contiguous ordering. If you change this, then you - // would need to change Mkl op definitions in nn_ops.cc. - static MklTfTensorOrdering kTensorOrdering = TENSORS_CONTIGUOUS; +// Since our ops are going to produce and also consume N addition tensors +// (Mkl) for N Tensorflow tensors, we can have following different +// orderings among these 2N tensors. +// +// E.g., for Tensorflow tensors A, B, and C, our ops will produce and +// consume A_m, B_m, and C_m additionally. +// +// INTERLEAVED: in this case 2N tensors are interleaved. So for above +// example, the ordering looks like: A, A_m, B, B_m, C, C_m. +// +// CONTIGUOUS: in thi case N Tensorflow tensors are contiguous followed +// by N Mkl tensors. So for above example, the ordering looks +// like: A, B, C, A_m, B_m, C_m +// +// Following APIs map index of original Tensorflow tensors to their +// appropriate position based on selected ordering. For contiguous ordering, +// we need to know the total number of tensors (parameter total). +// +typedef enum { TENSORS_INTERLEAVED, TENSORS_CONTIGUOUS } MklTfTensorOrdering; +// NOTE: Currently, we use contiguous ordering. If you change this, then you +// would need to change Mkl op definitions in nn_ops.cc. +static MklTfTensorOrdering kTensorOrdering = TENSORS_CONTIGUOUS; - // Get index of MetaData tensor from index 'n' of Data tensor. - inline int DataIndexToMetaDataIndex(int n, int total_tensors) { - if (kTensorOrdering == MklTfTensorOrdering::TENSORS_INTERLEAVED) { - // For interleaved ordering, Mkl tensor follows immediately after - // Tensorflow tensor. - return n + 1; - } else { - CHECK_EQ(kTensorOrdering, MklTfTensorOrdering::TENSORS_CONTIGUOUS); - // For contiguous ordering, Mkl tensor is n+total_tensors / 2 away. - return n + total_tensors / 2; - } +// Get index of MetaData tensor from index 'n' of Data tensor. +inline int DataIndexToMetaDataIndex(int n, int total_tensors) { + if (kTensorOrdering == MklTfTensorOrdering::TENSORS_INTERLEAVED) { + // For interleaved ordering, Mkl tensor follows immediately after + // Tensorflow tensor. + return n + 1; + } else { + CHECK_EQ(kTensorOrdering, MklTfTensorOrdering::TENSORS_CONTIGUOUS); + // For contiguous ordering, Mkl tensor is n+total_tensors / 2 away. + return n + total_tensors / 2; } +} - int inline GetTensorDataIndex(int n, int total_tensors) { - if (kTensorOrdering == MklTfTensorOrdering::TENSORS_INTERLEAVED) { - return 2 * n; // index corresponding to nth input/output tensor - } else { - CHECK_EQ(kTensorOrdering, MklTfTensorOrdering::TENSORS_CONTIGUOUS); - return n; - } - } +int inline GetTensorDataIndex(int n, int total_tensors) { + if (kTensorOrdering == MklTfTensorOrdering::TENSORS_INTERLEAVED) { + return 2 * n; // index corresponding to nth input/output tensor + } else { + CHECK_EQ(kTensorOrdering, MklTfTensorOrdering::TENSORS_CONTIGUOUS); + return n; + } +} - int inline GetTensorMetaDataIndex(int n, int total_tensors) { - // Get index for TensorData first and then use mapping function - // to get TensorMetaData index from TensorData index. - int tidx = GetTensorDataIndex(n, total_tensors); - return DataIndexToMetaDataIndex(tidx, total_tensors); - } +int inline GetTensorMetaDataIndex(int n, int total_tensors) { + // Get index for TensorData first and then use mapping function + // to get TensorMetaData index from TensorData index. + int tidx = GetTensorDataIndex(n, total_tensors); + return DataIndexToMetaDataIndex(tidx, total_tensors); +} namespace mkl_op_registry { - static const char* kMklOpLabel = "MklOp"; - static const char* kMklOpLabelPattern = "label='MklOp'"; - // Prefix that we add to Tensorflow op name to construct Mkl op name. - static const char* const kMklOpPrefix = "_Mkl"; +static const char* kMklOpLabel = "MklOp"; +static const char* kMklOpLabelPattern = "label='MklOp'"; +// Prefix that we add to Tensorflow op name to construct Mkl op name. +static const char* const kMklOpPrefix = "_Mkl"; - // Get the name of Mkl op from original TensorFlow op - // We prefix 'Mkl' to the original op to get Mkl op. - inline string GetMklOpName(const string& name) { - return string(kMklOpPrefix) + name; - } +// Get the name of Mkl op from original TensorFlow op +// We prefix 'Mkl' to the original op to get Mkl op. +inline string GetMklOpName(const string& name) { + return string(kMklOpPrefix) + name; +} - // Check whether opname with type T is registered as MKL-compliant. - // - // @input: name of the op - // @input: T datatype to be used for checking op - // @return: true if opname is registered as Mkl op; false otherwise - static inline bool IsMklOp(const std::string& op_name, DataType T) { - string kernel = KernelsRegisteredForOp(op_name); - bool result = - kernel.find(kMklOpLabelPattern) != string::npos && (T == DT_FLOAT); - return result; - } +// Check whether opname with type T is registered as MKL-compliant. +// +// @input: name of the op +// @input: T datatype to be used for checking op +// @return: true if opname is registered as Mkl op; false otherwise +static inline bool IsMklOp(const std::string& op_name, DataType T) { + string kernel = KernelsRegisteredForOp(op_name); + bool result = + kernel.find(kMklOpLabelPattern) != string::npos && (T == DT_FLOAT); + return result; +} - // Check whether opname with type T is registered as MKL-compliant and - // is element-wise. - // - // @input: name of the op - // @input: T datatype to be used for checking op - // @return: true if opname is registered as element-wise Mkl op; - // false otherwise - static inline bool IsMklElementWiseOp(const std::string& op_name, - DataType T) { - if (!IsMklOp(op_name, T)) { - return false; - } - bool result = (0 == op_name.compare(GetMklOpName("Add")) || - 0 == op_name.compare(GetMklOpName("Sub")) || - 0 == op_name.compare(GetMklOpName("Mul")) || - 0 == op_name.compare(GetMklOpName("Maximum")) || - 0 == op_name.compare(GetMklOpName("SquaredDifference"))); - - return result; +// Check whether opname with type T is registered as MKL-compliant and +// is element-wise. +// +// @input: name of the op +// @input: T datatype to be used for checking op +// @return: true if opname is registered as element-wise Mkl op; +// false otherwise +static inline bool IsMklElementWiseOp(const std::string& op_name, DataType T) { + if (!IsMklOp(op_name, T)) { + return false; } + bool result = (0 == op_name.compare(GetMklOpName("Add")) || + 0 == op_name.compare(GetMklOpName("Sub")) || + 0 == op_name.compare(GetMklOpName("Mul")) || + 0 == op_name.compare(GetMklOpName("Maximum")) || + 0 == op_name.compare(GetMklOpName("SquaredDifference"))); + + return result; +} } // namespace mkl_op_registry } // namespace tensorflow #endif // INTEL_MKL diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 55bc401b9d..68c3136019 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -37,8 +37,8 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/util/tensor_format.h" -#include "tensorflow/core/graph/mkl_layout_pass.h" #include "tensorflow/core/graph/mkl_graph_util.h" +#include "tensorflow/core/graph/mkl_layout_pass.h" namespace tensorflow { @@ -281,7 +281,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { csinfo_.mkl_conv2d_grad_filter = "_MklConv2DBackpropFilter"; csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias"; csinfo_.mkl_conv2d_with_bias_backprop_bias = - "_MklConv2DWithBiasBackpropBias"; + "_MklConv2DWithBiasBackpropBias"; csinfo_.relu = "Relu"; csinfo_.relu_grad = "ReluGrad"; csinfo_.reshape = "Reshape"; @@ -297,10 +297,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // End - element-wise ops. See note above. // NOTE: names are alphabetically sorted. - rinfo_.push_back({csinfo_.addn, mkl_op_registry::GetMklOpName(csinfo_.addn), CopyAttrsAddN, - AddNRewrite, nullptr}); - rinfo_.push_back({csinfo_.add, - mkl_op_registry::GetMklOpName(csinfo_.add), + rinfo_.push_back({csinfo_.addn, mkl_op_registry::GetMklOpName(csinfo_.addn), + CopyAttrsAddN, AddNRewrite, nullptr}); + rinfo_.push_back({csinfo_.add, mkl_op_registry::GetMklOpName(csinfo_.add), CopyAttrsDataType, AlwaysRewrite, nullptr}); rinfo_.push_back({csinfo_.avg_pool, mkl_op_registry::GetMklOpName(csinfo_.avg_pool), @@ -337,14 +336,14 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.fused_batch_norm, mkl_op_registry::GetMklOpName(csinfo_.fused_batch_norm), CopyAttrsFusedBatchNorm, AlwaysRewrite, nullptr}); - rinfo_.push_back({csinfo_.fused_batch_norm_grad, - mkl_op_registry::GetMklOpName(csinfo_.fused_batch_norm_grad), - CopyAttrsFusedBatchNorm, AlwaysRewrite, nullptr}); + rinfo_.push_back( + {csinfo_.fused_batch_norm_grad, + mkl_op_registry::GetMklOpName(csinfo_.fused_batch_norm_grad), + CopyAttrsFusedBatchNorm, AlwaysRewrite, nullptr}); rinfo_.push_back({csinfo_.identity, mkl_op_registry::GetMklOpName(csinfo_.identity), CopyAttrsIdentity, AlwaysRewrite, nullptr}); - rinfo_.push_back({csinfo_.lrn, - mkl_op_registry::GetMklOpName(csinfo_.lrn), + rinfo_.push_back({csinfo_.lrn, mkl_op_registry::GetMklOpName(csinfo_.lrn), CopyAttrsLRN, AlwaysRewrite, nullptr}); rinfo_.push_back({csinfo_.lrn_grad, mkl_op_registry::GetMklOpName(csinfo_.lrn_grad), @@ -358,11 +357,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.maximum, mkl_op_registry::GetMklOpName(csinfo_.maximum), CopyAttrsDataType, AlwaysRewrite, nullptr}); - rinfo_.push_back({csinfo_.mul, - mkl_op_registry::GetMklOpName(csinfo_.mul), + rinfo_.push_back({csinfo_.mul, mkl_op_registry::GetMklOpName(csinfo_.mul), CopyAttrsDataType, AlwaysRewrite, nullptr}); - rinfo_.push_back({csinfo_.relu, - mkl_op_registry::GetMklOpName(csinfo_.relu), + rinfo_.push_back({csinfo_.relu, mkl_op_registry::GetMklOpName(csinfo_.relu), CopyAttrsDataType, AlwaysRewrite, nullptr}); rinfo_.push_back({csinfo_.relu_grad, mkl_op_registry::GetMklOpName(csinfo_.relu_grad), @@ -373,8 +370,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.squared_difference, mkl_op_registry::GetMklOpName(csinfo_.squared_difference), CopyAttrsDataType, AlwaysRewrite, nullptr}); - rinfo_.push_back({csinfo_.sub, - mkl_op_registry::GetMklOpName(csinfo_.sub), + rinfo_.push_back({csinfo_.sub, mkl_op_registry::GetMklOpName(csinfo_.sub), CopyAttrsDataType, AlwaysRewrite, nullptr}); // Add info about which ops to add workspace edge to and the slots. @@ -388,9 +384,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { biasaddgrad_matmul_context_ = {csinfo_.bias_add_grad, csinfo_.matmul, IsBiasAddGradInMatMulContext}; - biasaddgrad_conv2dwithbias_context_ = {csinfo_.bias_add_grad, - csinfo_.mkl_conv2d_with_bias, - IsBiasAddGradInConv2DWithBiasContext}; + biasaddgrad_conv2dwithbias_context_ = { + csinfo_.bias_add_grad, csinfo_.mkl_conv2d_with_bias, + IsBiasAddGradInConv2DWithBiasContext}; cinfo_.push_back(&biasaddgrad_matmul_context_); cinfo_.push_back(&biasaddgrad_conv2dwithbias_context_); @@ -410,9 +406,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { /// Structure to specify the context information used in a node rewrite rule typedef struct { - string node; // Name of the node to be rewritten - string fwd; // Name of the node in the forward pass that this node - // corresponds to + string node; // Name of the node to be rewritten + string fwd; // Name of the node in the forward pass that this node + // corresponds to std::function context_match_fn; } ContextInfo; @@ -615,14 +611,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { std::vector ksize, strides; CHECK_EQ(GetNodeAttr(n->def(), "ksize", &ksize).ok(), true); CHECK_EQ(GetNodeAttr(n->def(), "strides", &strides).ok(), true); - CHECK_EQ(GetNodeAttr(n->def(), "data_format", &data_format_str).ok(), - true); + CHECK_EQ(GetNodeAttr(n->def(), "data_format", &data_format_str).ok(), true); CHECK_EQ(FormatFromString(data_format_str, &data_format), true); // Condition that specifies non-batch-wise and non-depth-wise pooling. - if (GetTensorDim(ksize, data_format, 'N') == 1 && + if (GetTensorDim(ksize, data_format, 'N') == 1 && GetTensorDim(strides, data_format, 'N') == 1 && - GetTensorDim(ksize, data_format, 'C') == 1 && + GetTensorDim(ksize, data_format, 'C') == 1 && GetTensorDim(strides, data_format, 'C') == 1) { return true; } @@ -785,8 +780,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { for (const Edge* fe : first_inp_of_filter->out_edges()) { if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias && fe->dst_input() == 0) { - VLOG(1) << "MklLayoutRewritePass: found " - << fe->dst()->DebugString() + VLOG(1) << "MklLayoutRewritePass: found " << fe->dst()->DebugString() << " as the forward node for matching context, backward" << " node is: " << n->DebugString(); *fwd_node = fe->dst(); @@ -803,13 +797,11 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // // @return - true (if BiasAddGrad is associated with MatMul); // false otherwise. - static bool IsBiasAddGradInMatMulContext(const Node* n, - const Node** fwd_node, + static bool IsBiasAddGradInMatMulContext(const Node* n, const Node** fwd_node, void* ci) { return (!IsBiasAddGradInConv2DWithBiasContext(n, fwd_node, ci)); } - // Rewrite rule that uses context-information for matching, // used in scenario 2. // @@ -880,10 +872,11 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // @output output_nodes - the list of new nodes creating Mkl tensors // // @return None - void GetNodesProducingMklTensorList(std::unique_ptr* g, - Node* orig_node, const gtl::InlinedVector, 4>& inputs, - int* input_idx, int list_length, - std::vector* output_nodes); + void GetNodesProducingMklTensorList( + std::unique_ptr* g, Node* orig_node, + const gtl::InlinedVector, 4>& inputs, + int* input_idx, int list_length, + std::vector* output_nodes); // Get a node that will feed an Mkl tensor to the new // node that we are constructing. The output node could be (1) 'n' @@ -900,7 +893,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // will feed the tensor // @return None void GetNodeProducingMklTensor(std::unique_ptr* g, Node* orig_node, - Node* n, int n_output_slot, Node** mkl_node, int* mkl_node_output_slot); + Node* n, int n_output_slot, Node** mkl_node, + int* mkl_node_output_slot); // Setup new inputs using old inputs 'inputs' for the rewritten node in 'nb' // in graph 'g'. Original node is input in 'old_node'. Inputs to 'nb' are @@ -970,9 +964,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { MklLayoutRewritePass::ConstStringsInfo MklLayoutRewritePass::csinfo_; MklLayoutRewritePass::ContextInfo - MklLayoutRewritePass::biasaddgrad_conv2dwithbias_context_; + MklLayoutRewritePass::biasaddgrad_conv2dwithbias_context_; MklLayoutRewritePass::ContextInfo - MklLayoutRewritePass::biasaddgrad_matmul_context_; + MklLayoutRewritePass::biasaddgrad_matmul_context_; std::vector MklLayoutRewritePass::cinfo_; // We register Mkl rewrite pass for phase 1 in post partitioning group. @@ -1041,13 +1035,13 @@ void MklLayoutRewritePass::GetDummyMklTensorNode(std::unique_ptr* g, TensorShape dummy_shape({8}); dummy_shape.AsProto(proto.mutable_tensor_shape()); TF_CHECK_OK(NodeBuilder((*g)->NewName("DMT"), "Const") - .Attr("value", proto) - .Attr("dtype", dt) - .Device(orig_node->def().device()) // We place this node on - // the same device as the - // device of the original - // node. - .Finalize(&**g, out)); + .Attr("value", proto) + .Attr("dtype", dt) + .Device(orig_node->def().device()) // We place this node on + // the same device as the + // device of the original + // node. + .Finalize(&**g, out)); // If number of inputs to the original node is > 0, then we add // control dependency between 1st input (index 0) of the original node and @@ -1060,8 +1054,8 @@ void MklLayoutRewritePass::GetDummyMklTensorNode(std::unique_ptr* g, // the same frame. if (orig_node->num_inputs() > 0) { Node* orig_input0 = nullptr; - TF_CHECK_OK(orig_node->input_node(0, - const_cast(&orig_input0))); + TF_CHECK_OK( + orig_node->input_node(0, const_cast(&orig_input0))); CHECK_NOTNULL((*g)->AddControlEdge(orig_input0, *out)); } @@ -1069,11 +1063,9 @@ void MklLayoutRewritePass::GetDummyMklTensorNode(std::unique_ptr* g, } void MklLayoutRewritePass::GetNodesProducingMklTensorList( - std::unique_ptr* g, - Node* orig_node, - const gtl::InlinedVector, 4>& inputs, - int* input_idx, int list_length, - std::vector* output_nodes) { + std::unique_ptr* g, Node* orig_node, + const gtl::InlinedVector, 4>& inputs, int* input_idx, + int list_length, std::vector* output_nodes) { CHECK_LT(*input_idx, inputs.size()); CHECK_GT(list_length, 0); CHECK_NOTNULL(output_nodes); @@ -1090,8 +1082,8 @@ void MklLayoutRewritePass::GetNodesProducingMklTensorList( int mkl_node_output_slot = 0; GetNodeProducingMklTensor(g, orig_node, n, slot, &mkl_node, &mkl_node_output_slot); - output_nodes->push_back(NodeBuilder::NodeOut(mkl_node, - mkl_node_output_slot)); + output_nodes->push_back( + NodeBuilder::NodeOut(mkl_node, mkl_node_output_slot)); (*input_idx)++; list_length--; } @@ -1101,9 +1093,9 @@ void MklLayoutRewritePass::GetNodesProducingMklTensorList( // node that we are constructing. An input node could be (1) 'n' // if it is Mkl layer, or (2) a dummy node producing dummy Mkl tensor // if 'n' is not an Mkl layer. -void MklLayoutRewritePass::GetNodeProducingMklTensor(std::unique_ptr* g, - Node* orig_node, Node* n, - int n_output_slot, Node** mkl_node, int* mkl_node_output_slot) { +void MklLayoutRewritePass::GetNodeProducingMklTensor( + std::unique_ptr* g, Node* orig_node, Node* n, int n_output_slot, + Node** mkl_node, int* mkl_node_output_slot) { CHECK_NOTNULL(n); CHECK_NOTNULL(mkl_node); CHECK_NOTNULL(mkl_node_output_slot); @@ -1234,8 +1226,8 @@ int MklLayoutRewritePass::SetUpContiguousInputs( if (ArgIsList(arg)) { std::vector new_node_inputs; int N = GetTensorListLength(arg, old_node); - GetNodesProducingMklTensorList(g, old_node, old_node_inputs, &iidx, - N, &new_node_inputs); + GetNodesProducingMklTensorList(g, old_node, old_node_inputs, &iidx, N, + &new_node_inputs); nb->Input(new_node_inputs); nn_slot_idx++; } else { @@ -1336,13 +1328,13 @@ void MklLayoutRewritePass::GetDummyWorkspaceTensorNode( TensorShape dummy_shape({1}); dummy_shape.AsProto(proto.mutable_tensor_shape()); TF_CHECK_OK(NodeBuilder((*g)->NewName("DMT"), "Const") - .Attr("value", proto) - .Attr("dtype", dt) - .Device(orig_node->def().device()) // We place this node on - // same the device as the - // device of the original - // node. - .Finalize(&**g, out)); + .Attr("value", proto) + .Attr("dtype", dt) + .Device(orig_node->def().device()) // We place this node on + // same the device as the + // device of the original + // node. + .Finalize(&**g, out)); // If number of inputs to the original node is > 0, then we add // control dependency between 1st input (index 0) of the original node and @@ -1355,8 +1347,8 @@ void MklLayoutRewritePass::GetDummyWorkspaceTensorNode( // the same frame. if (orig_node->num_inputs() > 0) { Node* orig_input0 = nullptr; - TF_CHECK_OK(orig_node->input_node(0, - const_cast(&orig_input0))); + TF_CHECK_OK( + orig_node->input_node(0, const_cast(&orig_input0))); CHECK_NOTNULL((*g)->AddControlEdge(orig_input0, *out)); } @@ -1374,7 +1366,8 @@ void MklLayoutRewritePass::AddWorkSpaceEdgeIfNeeded( TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &T)); for (auto ws : wsinfo_) { if (orig_node->type_string() == ws.fwd_op && - mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName(orig_node->type_string()), T)) { + mkl_op_registry::IsMklOp( + mkl_op_registry::GetMklOpName(orig_node->type_string()), T)) { // If this op is a fwd op, then we need to check if there is an // edge from this node's fwd_slot to bwdop's bwd_slot. If there is // an edge, then we just add an attribute on this node for setting @@ -1400,8 +1393,9 @@ void MklLayoutRewritePass::AddWorkSpaceEdgeIfNeeded( nb->Attr("workspace_enabled", false); } } else if (orig_node->type_string() == ws.bwd_op && - mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName(orig_node->type_string()), - T)) { + mkl_op_registry::IsMklOp( + mkl_op_registry::GetMklOpName(orig_node->type_string()), + T)) { // If this op is a bwd op, then we need to add workspace edge and // it's Mkl tensor edge between its corresponding fwd op and this // op. Corresponding fwd op is specified in 'fwd_op' field of @@ -1416,7 +1410,8 @@ void MklLayoutRewritePass::AddWorkSpaceEdgeIfNeeded( if (e->src_output() == ws.fwd_slot && // We would have rewritten the forward op, so we need to use // GetMklOpName call to get its Mkl name. - e->src()->type_string() == mkl_op_registry::GetMklOpName(ws.fwd_op) && + e->src()->type_string() == + mkl_op_registry::GetMklOpName(ws.fwd_op) && e->dst_input() == ws.bwd_slot) { nb->Attr("workspace_enabled", true); CHECK_NOTNULL(ws_tensors); @@ -1593,7 +1588,7 @@ void MklLayoutRewritePass::CopyAttrsDataType(const Node* orig_node, } void MklLayoutRewritePass::CopyAttrsReshape(const Node* orig_node, - NodeBuilder* nb) { + NodeBuilder* nb) { DataType T; DataType Tshape; @@ -1869,8 +1864,8 @@ Status MklLayoutRewritePass::MergeNode(std::unique_ptr* g, Node* succ, if (e->IsControlEdge()) { CHECK_NOTNULL((*g)->AddControlEdge(new_node, e->dst())); } else { - CHECK_NOTNULL((*g)->AddEdge(new_node, e->src_output(), e->dst(), - e->dst_input())); + CHECK_NOTNULL( + (*g)->AddEdge(new_node, e->src_output(), e->dst(), e->dst_input())); } } @@ -1941,9 +1936,9 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr* g, // and leave BiasAddGrad as it is. But we check for this condition // when we check for node rewrite rule. So we should not even come // here for MatMul. So we will fail now. - return Status( - error::Code::INVALID_ARGUMENT, - "No rewrite is required for BiasAddGrad for MatMul context."); + return Status( + error::Code::INVALID_ARGUMENT, + "No rewrite is required for BiasAddGrad for MatMul context."); } } @@ -2012,9 +2007,10 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr* g, if (e->IsControlEdge()) { CHECK_NOTNULL((*g)->AddControlEdge(new_node, e->dst())); } else { - CHECK_NOTNULL((*g)->AddEdge(new_node, GetTensorDataIndex(e->src_output(), - e->src()->num_outputs()), - e->dst(), e->dst_input())); + CHECK_NOTNULL((*g)->AddEdge( + new_node, + GetTensorDataIndex(e->src_output(), e->src()->num_outputs()), + e->dst(), e->dst_input())); } } @@ -2070,7 +2066,8 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { // BiasAddGrad is not an Mkl layer, so we make an exception for it. if (n->type_string() != csinfo_.bias_add_grad) { - if (!mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName(n->type_string()), T)) { + if (!mkl_op_registry::IsMklOp( + mkl_op_registry::GetMklOpName(n->type_string()), T)) { return nullptr; } } @@ -2186,8 +2183,7 @@ bool RunMklLayoutRewritePass(std::unique_ptr* g) { return MklLayoutRewritePass().RunPass(g); } -Status MklLayoutRewritePass::Run( - const GraphOptimizationPassOptions& options) { +Status MklLayoutRewritePass::Run(const GraphOptimizationPassOptions& options) { if (options.graph == nullptr && options.partition_graphs == nullptr) { return Status::OK(); } @@ -2215,7 +2211,7 @@ Status MklLayoutRewritePass::Run( return Status::OK(); } -#else // INTEL_MKL_DNN +#else // INTEL_MKL_DNN // This pass implements rewriting of graph to support following scenarios: // (A) Merging nodes in the graph @@ -2421,7 +2417,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { csinfo_.conv2d_grad_input = "Conv2DBackpropInput"; csinfo_.conv2d_grad_filter = "Conv2DBackpropFilter"; csinfo_.conv2d_grad_filter_with_bias = - "__MklDummyConv2DBackpropFilterWithBias"; + "__MklDummyConv2DBackpropFilterWithBias"; csinfo_.fused_batch_norm = "FusedBatchNorm"; csinfo_.fused_batch_norm_grad = "FusedBatchNormGrad"; csinfo_.identity = "Identity"; @@ -2435,11 +2431,11 @@ class MklLayoutRewritePass : public GraphOptimizationPass { csinfo_.mkl_conv2d_grad_filter = "_MklConv2DBackpropFilter"; csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias"; csinfo_.mkl_conv2d_grad_filter_with_bias = - "_MklConv2DBackpropFilterWithBias"; + "_MklConv2DBackpropFilterWithBias"; csinfo_.relu = "Relu"; csinfo_.relu_grad = "ReluGrad"; - csinfo_.tanh = "Tanh"; - csinfo_.tanh_grad = "TanhGrad"; + csinfo_.tanh = "Tanh"; + csinfo_.tanh_grad = "TanhGrad"; csinfo_.reshape = "Reshape"; csinfo_.softmax = "Softmax"; csinfo_.split = "Split"; @@ -2474,29 +2470,28 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.conv2d, mkl_op_registry::GetMklOpName(csinfo_.conv2d), CopyAttrsConv2D, AlwaysRewrite}); - rinfo_.push_back({csinfo_.conv2d_with_bias, - csinfo_.mkl_conv2d_with_bias, + rinfo_.push_back({csinfo_.conv2d_with_bias, csinfo_.mkl_conv2d_with_bias, CopyAttrsConv2D, AlwaysRewrite}); rinfo_.push_back({csinfo_.conv2d_grad_filter, mkl_op_registry::GetMklOpName(csinfo_.conv2d_grad_filter), CopyAttrsConv2D, AlwaysRewrite}); rinfo_.push_back({csinfo_.conv2d_grad_filter_with_bias, - csinfo_.mkl_conv2d_grad_filter_with_bias, - CopyAttrsConv2D, AlwaysRewrite}); + csinfo_.mkl_conv2d_grad_filter_with_bias, CopyAttrsConv2D, + AlwaysRewrite}); rinfo_.push_back({csinfo_.conv2d_grad_input, mkl_op_registry::GetMklOpName(csinfo_.conv2d_grad_input), CopyAttrsConv2D, AlwaysRewrite}); rinfo_.push_back({csinfo_.fused_batch_norm, mkl_op_registry::GetMklOpName(csinfo_.fused_batch_norm), CopyAttrsFusedBatchNorm, AlwaysRewrite}); - rinfo_.push_back({csinfo_.fused_batch_norm_grad, - mkl_op_registry::GetMklOpName(csinfo_.fused_batch_norm_grad), - CopyAttrsFusedBatchNorm, AlwaysRewrite}); + rinfo_.push_back( + {csinfo_.fused_batch_norm_grad, + mkl_op_registry::GetMklOpName(csinfo_.fused_batch_norm_grad), + CopyAttrsFusedBatchNorm, AlwaysRewrite}); rinfo_.push_back({csinfo_.identity, mkl_op_registry::GetMklOpName(csinfo_.identity), CopyAttrsDataType, AlwaysRewrite}); - rinfo_.push_back({csinfo_.lrn, - mkl_op_registry::GetMklOpName(csinfo_.lrn), + rinfo_.push_back({csinfo_.lrn, mkl_op_registry::GetMklOpName(csinfo_.lrn), CopyAttrsLRN, AlwaysRewrite}); rinfo_.push_back({csinfo_.lrn_grad, mkl_op_registry::GetMklOpName(csinfo_.lrn_grad), @@ -2515,8 +2510,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { mkl_op_registry::GetMklOpName(csinfo_.mul), CopyAttrsDataType, AlwaysRewrite}); */ - rinfo_.push_back({csinfo_.relu, - mkl_op_registry::GetMklOpName(csinfo_.relu), + rinfo_.push_back({csinfo_.relu, mkl_op_registry::GetMklOpName(csinfo_.relu), CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.relu_grad, mkl_op_registry::GetMklOpName(csinfo_.relu_grad), @@ -2550,8 +2544,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // Add a rule for merging nodes minfo_.push_back({csinfo_.conv2d, csinfo_.bias_add, - csinfo_.conv2d_with_bias, - GetConv2DOrBiasAdd}); + csinfo_.conv2d_with_bias, GetConv2DOrBiasAdd}); minfo_.push_back({csinfo_.conv2d_grad_filter, csinfo_.bias_add_grad, csinfo_.conv2d_grad_filter_with_bias, @@ -2846,9 +2839,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // Default rewrite rule to be used in scenario 1 for rewrite. // @return - true (since we want to always rewrite) - static bool AlwaysRewrite(const Node* n) { - return true; - } + static bool AlwaysRewrite(const Node* n) { return true; } // Check if we are performing pooling on depth or batch. If it is, then we // do not rewrite MaxPool node to Mkl version. @@ -2862,14 +2853,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { std::vector ksize, strides; CHECK_EQ(GetNodeAttr(n->def(), "ksize", &ksize).ok(), true); CHECK_EQ(GetNodeAttr(n->def(), "strides", &strides).ok(), true); - CHECK_EQ(GetNodeAttr(n->def(), "data_format", &data_format_str).ok(), - true); + CHECK_EQ(GetNodeAttr(n->def(), "data_format", &data_format_str).ok(), true); CHECK_EQ(FormatFromString(data_format_str, &data_format), true); // Condition that specifies non-batch-wise and non-depth-wise pooling. - if (GetTensorDim(ksize, data_format, 'N') == 1 && + if (GetTensorDim(ksize, data_format, 'N') == 1 && GetTensorDim(strides, data_format, 'N') == 1 && - GetTensorDim(ksize, data_format, 'C') == 1 && + GetTensorDim(ksize, data_format, 'C') == 1 && GetTensorDim(strides, data_format, 'C') == 1) { return true; } @@ -2941,10 +2931,11 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // @output output_nodes - the list of new nodes creating Mkl tensors // // @return None - void GetNodesProducingMklTensorList(std::unique_ptr* g, - Node* orig_node, const gtl::InlinedVector, 4>& inputs, - int* input_idx, int list_length, - std::vector* output_nodes); + void GetNodesProducingMklTensorList( + std::unique_ptr* g, Node* orig_node, + const gtl::InlinedVector, 4>& inputs, + int* input_idx, int list_length, + std::vector* output_nodes); // Get a node that will feed an Mkl tensor to the new // node that we are constructing. The output node could be (1) 'n' @@ -2961,7 +2952,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // will feed the tensor // @return None void GetNodeProducingMklTensor(std::unique_ptr* g, Node* orig_node, - Node* n, int n_output_slot, Node** mkl_node, int* mkl_node_output_slot); + Node* n, int n_output_slot, Node** mkl_node, + int* mkl_node_output_slot); // Setup new inputs using old inputs 'inputs' for the rewritten node in 'nb' // in graph 'g'. Original node is input in 'old_node'. Inputs to 'nb' are @@ -3096,13 +3088,13 @@ void MklLayoutRewritePass::GetDummyMklTensorNode(std::unique_ptr* g, TensorShape dummy_shape({8}); dummy_shape.AsProto(proto.mutable_tensor_shape()); TF_CHECK_OK(NodeBuilder((*g)->NewName("DMT"), "Const") - .Attr("value", proto) - .Attr("dtype", dt) - .Device(orig_node->def().device()) // We place this node on - // the same device as the - // device of the original - // node. - .Finalize(&**g, out)); + .Attr("value", proto) + .Attr("dtype", dt) + .Device(orig_node->def().device()) // We place this node on + // the same device as the + // device of the original + // node. + .Finalize(&**g, out)); // If number of inputs to the original node is > 0, then we add // control dependency between 1st input (index 0) of the original node and @@ -3115,8 +3107,8 @@ void MklLayoutRewritePass::GetDummyMklTensorNode(std::unique_ptr* g, // the same frame. if (orig_node->num_inputs() > 0) { Node* orig_input0 = nullptr; - TF_CHECK_OK(orig_node->input_node(0, - const_cast(&orig_input0))); + TF_CHECK_OK( + orig_node->input_node(0, const_cast(&orig_input0))); // Allow duplicate while adding control edge as it would fail (return // NULL) if we try to add duplicate edge. CHECK_NOTNULL((*g)->AddControlEdge(orig_input0, *out, true)); @@ -3126,11 +3118,9 @@ void MklLayoutRewritePass::GetDummyMklTensorNode(std::unique_ptr* g, } void MklLayoutRewritePass::GetNodesProducingMklTensorList( - std::unique_ptr* g, - Node* orig_node, - const gtl::InlinedVector, 4>& inputs, - int* input_idx, int list_length, - std::vector* output_nodes) { + std::unique_ptr* g, Node* orig_node, + const gtl::InlinedVector, 4>& inputs, int* input_idx, + int list_length, std::vector* output_nodes) { CHECK_LT(*input_idx, inputs.size()); CHECK_GT(list_length, 0); CHECK_NOTNULL(output_nodes); @@ -3147,8 +3137,8 @@ void MklLayoutRewritePass::GetNodesProducingMklTensorList( int mkl_node_output_slot = 0; GetNodeProducingMklTensor(g, orig_node, n, slot, &mkl_node, &mkl_node_output_slot); - output_nodes->push_back(NodeBuilder::NodeOut(mkl_node, - mkl_node_output_slot)); + output_nodes->push_back( + NodeBuilder::NodeOut(mkl_node, mkl_node_output_slot)); (*input_idx)++; list_length--; } @@ -3158,9 +3148,9 @@ void MklLayoutRewritePass::GetNodesProducingMklTensorList( // node that we are constructing. An input node could be (1) 'n' // if it is Mkl layer, or (2) a dummy node producing dummy Mkl tensor // if 'n' is not an Mkl layer. -void MklLayoutRewritePass::GetNodeProducingMklTensor(std::unique_ptr* g, - Node* orig_node, Node* n, - int n_output_slot, Node** mkl_node, int* mkl_node_output_slot) { +void MklLayoutRewritePass::GetNodeProducingMklTensor( + std::unique_ptr* g, Node* orig_node, Node* n, int n_output_slot, + Node** mkl_node, int* mkl_node_output_slot) { CHECK_NOTNULL(n); CHECK_NOTNULL(mkl_node); CHECK_NOTNULL(mkl_node_output_slot); @@ -3292,8 +3282,8 @@ int MklLayoutRewritePass::SetUpContiguousInputs( if (ArgIsList(arg)) { std::vector new_node_inputs; int N = GetTensorListLength(arg, old_node); - GetNodesProducingMklTensorList(g, old_node, old_node_inputs, &iidx, - N, &new_node_inputs); + GetNodesProducingMklTensorList(g, old_node, old_node_inputs, &iidx, N, + &new_node_inputs); nb->Input(new_node_inputs); nn_slot_idx++; } else { @@ -3394,13 +3384,13 @@ void MklLayoutRewritePass::GetDummyWorkspaceTensorNode( TensorShape dummy_shape({1}); dummy_shape.AsProto(proto.mutable_tensor_shape()); TF_CHECK_OK(NodeBuilder((*g)->NewName("DMT"), "Const") - .Attr("value", proto) - .Attr("dtype", dt) - .Device(orig_node->def().device()) // We place this node on - // same the device as the - // device of the original - // node. - .Finalize(&**g, out)); + .Attr("value", proto) + .Attr("dtype", dt) + .Device(orig_node->def().device()) // We place this node on + // same the device as the + // device of the original + // node. + .Finalize(&**g, out)); // If number of inputs to the original node is > 0, then we add // control dependency between 1st input (index 0) of the original node and @@ -3413,8 +3403,8 @@ void MklLayoutRewritePass::GetDummyWorkspaceTensorNode( // the same frame. if (orig_node->num_inputs() > 0) { Node* orig_input0 = nullptr; - TF_CHECK_OK(orig_node->input_node(0, - const_cast(&orig_input0))); + TF_CHECK_OK( + orig_node->input_node(0, const_cast(&orig_input0))); // Allow duplicate while adding control edge as it would fail (return // NULL) if we try to add duplicate edge. CHECK_NOTNULL((*g)->AddControlEdge(orig_input0, *out, true)); @@ -3434,8 +3424,8 @@ void MklLayoutRewritePass::AddWorkSpaceEdgeIfNeeded( TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &T)); for (auto ws : wsinfo_) { if (orig_node->type_string() == ws.fwd_op && - mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName( - orig_node->type_string()), T)) { + mkl_op_registry::IsMklOp( + mkl_op_registry::GetMklOpName(orig_node->type_string()), T)) { // If this op is a fwd op, then we need to check if there is an // edge from this node's fwd_slot to bwdop's bwd_slot. If there is // an edge, then we just add an attribute on this node for setting @@ -3461,8 +3451,9 @@ void MklLayoutRewritePass::AddWorkSpaceEdgeIfNeeded( nb->Attr("workspace_enabled", false); } } else if (orig_node->type_string() == ws.bwd_op && - mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName( - orig_node->type_string()), T)) { + mkl_op_registry::IsMklOp( + mkl_op_registry::GetMklOpName(orig_node->type_string()), + T)) { // If this op is a bwd op, then we need to add workspace edge and // it's Mkl tensor edge between its corresponding fwd op and this // op. Corresponding fwd op is specified in 'fwd_op' field of @@ -3477,8 +3468,8 @@ void MklLayoutRewritePass::AddWorkSpaceEdgeIfNeeded( if (e->src_output() == ws.fwd_slot && // We would have rewritten the forward op, so we need to use // GetMklOpName call to get its Mkl name. - e->src()->type_string() == mkl_op_registry::GetMklOpName( - ws.fwd_op) && + e->src()->type_string() == + mkl_op_registry::GetMklOpName(ws.fwd_op) && e->dst_input() == ws.bwd_slot) { nb->Attr("workspace_enabled", true); CHECK_NOTNULL(ws_tensors); @@ -3645,7 +3636,7 @@ void MklLayoutRewritePass::CopyAttrsDataType(const Node* orig_node, } void MklLayoutRewritePass::CopyAttrsReshape(const Node* orig_node, - NodeBuilder* nb) { + NodeBuilder* nb) { DataType T; DataType Tshape; @@ -3776,8 +3767,9 @@ Status MklLayoutRewritePass::MergeConv2DWithBiasAdd(std::unique_ptr* g, Node* m, Node* n) { CHECK_EQ(((m->type_string() == csinfo_.bias_add && n->type_string() == csinfo_.conv2d)) || - ((n->type_string() == csinfo_.bias_add && - m->type_string() == csinfo_.conv2d)), true); + ((n->type_string() == csinfo_.bias_add && + m->type_string() == csinfo_.conv2d)), + true); // If 'm' is BiasAdd, then 'n' is Conv2D. Since Conv2D feeds BiasAdd, // BiasAdd is successor node, and Conv2D predecessor node. @@ -3796,8 +3788,7 @@ Status MklLayoutRewritePass::MergeConv2DWithBiasAdd(std::unique_ptr* g, TF_CHECK_OK(GetNodeAttr(pred->def(), "strides", &strides)); TF_CHECK_OK(GetNodeAttr(pred->def(), "data_format", &data_format_pred)); TF_CHECK_OK(GetNodeAttr(succ->def(), "data_format", &data_format_succ)); - TF_CHECK_OK( - GetNodeAttr(pred->def(), "use_cudnn_on_gpu", &use_cudnn_on_gnu)); + TF_CHECK_OK(GetNodeAttr(pred->def(), "use_cudnn_on_gpu", &use_cudnn_on_gnu)); // We check to ensure that data formats of both succ and pred are same. // We expect them to be same, so we can enforce this as assert. // But assert can be too strict, so we enforce this as a check. @@ -3900,8 +3891,8 @@ Status MklLayoutRewritePass::MergeConv2DWithBiasAdd(std::unique_ptr* g, // BiasAdd has only 1 output (at slot 0) and merged node also has only 1 // output (at slot 0). const int kConv2DWithBiasOutputSlot = 0; - CHECK_NOTNULL((*g)->AddEdge(new_node, kConv2DWithBiasOutputSlot, - e->dst(), e->dst_input())); + CHECK_NOTNULL((*g)->AddEdge(new_node, kConv2DWithBiasOutputSlot, e->dst(), + e->dst_input())); } } @@ -3924,8 +3915,9 @@ Status MklLayoutRewritePass::MergeConv2DBackpropFilterWithBiasAddGrad( std::unique_ptr* g, Node* m, Node* n) { CHECK_EQ(((m->type_string() == csinfo_.bias_add_grad && n->type_string() == csinfo_.conv2d_grad_filter)) || - ((n->type_string() == csinfo_.bias_add_grad && - m->type_string() == csinfo_.conv2d_grad_filter)), true); + ((n->type_string() == csinfo_.bias_add_grad && + m->type_string() == csinfo_.conv2d_grad_filter)), + true); // If 'm' is BiasAddGrad, then 'n' is BackpropFilter. Node* badd = m->type_string() == csinfo_.bias_add_grad ? m : n; @@ -4132,9 +4124,10 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr* g, // NULL) if we try to add duplicate edge. CHECK_NOTNULL((*g)->AddControlEdge(new_node, e->dst(), true)); } else { - CHECK_NOTNULL((*g)->AddEdge(new_node, GetTensorDataIndex(e->src_output(), - e->src()->num_outputs()), - e->dst(), e->dst_input())); + CHECK_NOTNULL((*g)->AddEdge( + new_node, + GetTensorDataIndex(e->src_output(), e->src()->num_outputs()), + e->dst(), e->dst_input())); } } @@ -4166,9 +4159,9 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { // names. if (n->type_string() != csinfo_.conv2d_with_bias && n->type_string() != csinfo_.conv2d_grad_filter_with_bias && - !mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName( - n->type_string()), T)) { - return nullptr; + !mkl_op_registry::IsMklOp(mkl_op_registry::GetMklOpName(n->type_string()), + T)) { + return nullptr; } // For elementwise node, we reuse the Eigen implementation and pass the MKL @@ -4184,29 +4177,30 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { // eigen code to reduce cross-library dependency. VLOG(1) << "ELEMENTWISE: checking op: " << n->type_string(); if (mkl_op_registry::IsMklElementWiseOp( - mkl_op_registry::GetMklOpName(n->type_string()), T) || + mkl_op_registry::GetMklOpName(n->type_string()), T) || n->type_string().find("Identity") != string::npos) { VLOG(1) << "ELEMENTWISE: op is elementwise: " << n->type_string(); bool incoming_mkl_edge = false; int num_parent = 0; for (auto parent : n->in_edges()) { if (mkl_op_registry::IsMklOp(parent->src()->type_string(), T)) { - VLOG(1) << "ELEMENTWISE: parent " << num_parent++ << " is MKL op: " - << parent->src()->type_string(); + VLOG(1) << "ELEMENTWISE: parent " << num_parent++ + << " is MKL op: " << parent->src()->type_string(); incoming_mkl_edge = true; break; } else { - VLOG(1) << "ELEMENTWISE: parent " << num_parent++ << " is NON-MKL op: " - << parent->src()->type_string(); + VLOG(1) << "ELEMENTWISE: parent " << num_parent++ + << " is NON-MKL op: " << parent->src()->type_string(); } } if (incoming_mkl_edge == false) { - VLOG(1) << "ELEMENTWISE: Skipping replacement of elementwise node which has no MKL " + VLOG(1) << "ELEMENTWISE: Skipping replacement of elementwise node which " + "has no MKL " "parents."; return nullptr; } else { - VLOG(1) << "ELEMENTWISE: Replacing elementwise node " << n->type_string() << - " which has MKL parents"; + VLOG(1) << "ELEMENTWISE: Replacing elementwise node " << n->type_string() + << " which has MKL parents"; } } @@ -4214,8 +4208,7 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { // for this op, then we rewrite it to Mkl op. // Find matching RewriteInfo and then check that rewrite rule applies. for (auto ri = rinfo_.cbegin(); ri != rinfo_.cend(); ++ri) { - if (n->type_string().compare(ri->name) == 0 && - ri->rewrite_rule(n)) { + if (n->type_string().compare(ri->name) == 0 && ri->rewrite_rule(n)) { return &*ri; } } @@ -4297,8 +4290,7 @@ bool RunMklLayoutRewritePass(std::unique_ptr* g) { return MklLayoutRewritePass().RunPass(g); } -Status MklLayoutRewritePass::Run( - const GraphOptimizationPassOptions& options) { +Status MklLayoutRewritePass::Run(const GraphOptimizationPassOptions& options) { if (options.graph == nullptr && options.partition_graphs == nullptr) { return Status::OK(); } diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 75f7ca2d4d..320d5a48c7 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -125,8 +125,10 @@ REGISTER_OP("InputList").Output("o: N * float").Attr("N: int").SetIsStateful(); REGISTER_OP("HalfInput").Output("o: half").SetIsStateful(); REGISTER_OP("Int32Input").Output("o: int32").SetIsStateful(); REGISTER_OP("_MklInput").Output("o: uint8").SetIsStateful(); -REGISTER_OP("_MklInput2").Output("o: uint8") - .Output("o1: uint8").SetIsStateful(); +REGISTER_OP("_MklInput2") + .Output("o: uint8") + .Output("o1: uint8") + .SetIsStateful(); ///////////////////////////////////////////////////////////////////// // Unit tests related to node merge optiimization @@ -498,7 +500,6 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_Negative2) { "M->I:3;N->D:4;N->G:4;N->I:4;O->D:5;O->G:5;O->I:5"); } - // BiasAddGrad rewrite to BackpropBias in the presence of BackpropFilter only TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_BpropFilter_Positive) { InitGraph( @@ -874,11 +875,12 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Basic) { " input: ['A', 'B:0', 'B:1']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" " input: ['C', 'D'] }"); - EXPECT_EQ(DoMklLayoutOptimizationPass(), - "A(Const);B(InputList);C(Input);D(_MklConcat);DMT/_0(Const);" - "DMT/_1(Const);DMT/_2(Const);E(Zeta)|A->D;A:control->DMT/_0:control;" - "A:control->DMT/_1:control;A:control->DMT/_2:control;B->D:1;" - "B:1->D:2;C->E;D->E:1;DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); + EXPECT_EQ( + DoMklLayoutOptimizationPass(), + "A(Const);B(InputList);C(Input);D(_MklConcat);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Zeta)|A->D;A:control->DMT/_0:control;" + "A:control->DMT/_1:control;A:control->DMT/_2:control;B->D:1;" + "B:1->D:2;C->E;D->E:1;DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); } // Concat with 2 Mkl layers feeding it @@ -1273,7 +1275,8 @@ TEST_F(MklLayoutPassTest, MaxPoolLRN_Positive) { "node { name: 'H' op: 'Input'}" "node { name: 'I' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" " input: ['H', 'G'] }"); - EXPECT_EQ(DoMklLayoutOptimizationPass(), + EXPECT_EQ( + DoMklLayoutOptimizationPass(), "A(Input);B(_MklLRN);C(_MklMaxPool);D(Input);DMT/_0(Const);DMT/_1(Const);" "DMT/_2(Const);E(_MklMaxPoolGrad);F(Input);G(_MklLRNGrad);H(Input);" "I(Zeta)|A->B;A:control->DMT/_0:control;B->C;B->E;B->G:2;B:1->G:3;" @@ -1640,7 +1643,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_DeviceTest) { " attr { key: 'padding' value { s: 'SAME' } }" " input: ['A', 'B']}" "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['B', 'C'] }", kGPUDevice); + " input: ['B', 'C'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(Conv2D);D(Zeta)|A->C;B->C:1;B->D;C->D:1"); } @@ -1666,7 +1670,8 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_DeviceTest) { "node { name: 'F' op: 'BiasAddGrad'" " attr { key: 'T' value { type: DT_FLOAT } }" " attr { key: 'data_format' value { s: 'NCHW' } }" - " input: ['E'] }", kGPUDevice); + " input: ['E'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);" "E(Zeta);F(BiasAddGrad);M(_MklInput);N(_MklInput);" @@ -1687,7 +1692,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2DGradFilter_DeviceTest) { " attr { key: 'padding' value { s: 'SAME' } }" " input: ['A', 'B', 'C']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'D'] }", kGPUDevice); + " input: ['A', 'D'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Int32Input);C(Input);D(Conv2DBackpropFilter);E(Zeta)|" "A->D;A->E;B->D:1;C->D:2;D->E:1"); @@ -1700,7 +1706,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Relu_DeviceTest) { " attr { key: 'T' value { type: DT_FLOAT } }" " input: ['A'] }" "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'B'] }", kGPUDevice); + " input: ['A', 'B'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Relu);C(Zeta)|A->B;A->C;B->C:1"); } @@ -1713,7 +1720,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ReluGrad_DeviceTest) { " attr { key: 'T' value { type: DT_FLOAT } }" " input: ['A', 'B'] }" "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'C'] }", kGPUDevice); + " input: ['A', 'C'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(ReluGrad);D(Zeta)|A->C;A->D;B->C:1;C->D:1"); } @@ -1729,7 +1737,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_MaxPool_DeviceTest) { " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" " input: ['A'] }" "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'B'] }", kGPUDevice); + " input: ['A', 'B'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(MaxPool);C(Zeta)|A->B;A->C;B->C:1"); } @@ -1745,7 +1754,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_AvgPool_DeviceTest) { " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" " input: ['A'] }" "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'B'] }", kGPUDevice); + " input: ['A', 'B'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(AvgPool);C(Zeta)|A->B;A->C;B->C:1"); } @@ -1766,7 +1776,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_DeviceTest) { " attr { key: 'N' value { i: 2 } }" " input: ['A', 'B:0', 'B:1']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['C', 'D'] }", kGPUDevice); + " input: ['C', 'D'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Const);B(InputList);C(Input);D(Concat);E(Zeta)|A->D;" "B->D:1;B:1->D:2;C->E;D->E:1"); @@ -1788,7 +1799,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_DeviceTest) { " attr { key: 'N' value { i: 2 } }" " input: ['B:0', 'B:1', 'A']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['C', 'D'] }", kGPUDevice); + " input: ['C', 'D'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Const);B(InputList);C(Input);D(ConcatV2);E(Zeta)|" "A->D:2;B->D;B:1->D:1;C->E;D->E:1"); @@ -1808,7 +1820,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_FusedBatchNorm_DeviceTest) { " attr { key: 'is_training' value { b: true } }" " input: ['A', 'B', 'C', 'D', 'E'] }" "node { name: 'G' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'F'] }", kGPUDevice); + " input: ['A', 'F'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(Input);D(Input);E(Input);" "F(FusedBatchNorm);G(Zeta)|A->F;A->G;B->F:1;C->F:2;D->F:3;" @@ -1837,7 +1850,8 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_DeviceTest) { "node { name: 'Y' op: 'Input'}" "node { name: 'Z' op: 'Zeta'" " attr {key: 'T' value { type: DT_FLOAT } }" - " input: ['E', 'Y']}", kGPUDevice); + " input: ['E', 'Y']}", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(_MklConv2D);D(Input);E(BiasAdd);" "M(_MklInput);N(_MklInput);Y(Input);Z(Zeta)|A->C;" @@ -1972,8 +1986,10 @@ REGISTER_OP("InputList").Output("o: N * float").Attr("N: int").SetIsStateful(); REGISTER_OP("HalfInput").Output("o: half").SetIsStateful(); REGISTER_OP("Int32Input").Output("o: int32").SetIsStateful(); REGISTER_OP("_MklInput").Output("o: uint8").SetIsStateful(); -REGISTER_OP("_MklInput2").Output("o: uint8") - .Output("o1: uint8").SetIsStateful(); +REGISTER_OP("_MklInput2") + .Output("o: uint8") + .Output("o1: uint8") + .SetIsStateful(); ///////////////////////////////////////////////////////////////////// // Unit tests related to node merge optiimization @@ -2492,11 +2508,12 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_Basic) { " input: ['A', 'B:0', 'B:1']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" " input: ['C', 'D'] }"); - EXPECT_EQ(DoMklLayoutOptimizationPass(), - "A(Const);B(InputList);C(Input);D(_MklConcat);DMT/_0(Const);" - "DMT/_1(Const);DMT/_2(Const);E(Zeta)|A->D;A:control->DMT/_0:control;" - "A:control->DMT/_1:control;A:control->DMT/_2:control;B->D:1;" - "B:1->D:2;C->E;D->E:1;DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); + EXPECT_EQ( + DoMklLayoutOptimizationPass(), + "A(Const);B(InputList);C(Input);D(_MklConcat);DMT/_0(Const);" + "DMT/_1(Const);DMT/_2(Const);E(Zeta)|A->D;A:control->DMT/_0:control;" + "A:control->DMT/_1:control;A:control->DMT/_2:control;B->D:1;" + "B:1->D:2;C->E;D->E:1;DMT/_0->D:3;DMT/_1->D:4;DMT/_2->D:5"); } // Concat with 2 Mkl layers feeding it @@ -2891,7 +2908,8 @@ TEST_F(MklLayoutPassTest, MaxPoolLRN_Positive) { "node { name: 'H' op: 'Input'}" "node { name: 'I' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" " input: ['H', 'G'] }"); - EXPECT_EQ(DoMklLayoutOptimizationPass(), + EXPECT_EQ( + DoMklLayoutOptimizationPass(), "A(Input);B(_MklLRN);C(_MklMaxPool);D(Input);DMT/_0(Const);DMT/_1(Const);" "DMT/_2(Const);E(_MklMaxPoolGrad);F(Input);G(_MklLRNGrad);H(Input);" "I(Zeta)|A->B;A:control->DMT/_0:control;B->C;B->E;B->G:2;B:1->G:3;" @@ -3258,7 +3276,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2D_DeviceTest) { " attr { key: 'padding' value { s: 'SAME' } }" " input: ['A', 'B']}" "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['B', 'C'] }", kGPUDevice); + " input: ['B', 'C'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(Conv2D);D(Zeta)|A->C;B->C:1;B->D;C->D:1"); } @@ -3284,7 +3303,8 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DBackprop_DeviceTest) { "node { name: 'F' op: 'BiasAddGrad'" " attr { key: 'T' value { type: DT_FLOAT } }" " attr { key: 'data_format' value { s: 'NCHW' } }" - " input: ['E'] }", kGPUDevice); + " input: ['E'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(Input);D(_MklConv2DWithBias);" "E(Zeta);F(BiasAddGrad);M(_MklInput);N(_MklInput);" @@ -3305,7 +3325,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Conv2DGradFilter_DeviceTest) { " attr { key: 'padding' value { s: 'SAME' } }" " input: ['A', 'B', 'C']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'D'] }", kGPUDevice); + " input: ['A', 'D'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Int32Input);C(Input);D(Conv2DBackpropFilter);E(Zeta)|" "A->D;A->E;B->D:1;C->D:2;D->E:1"); @@ -3318,7 +3339,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Relu_DeviceTest) { " attr { key: 'T' value { type: DT_FLOAT } }" " input: ['A'] }" "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'B'] }", kGPUDevice); + " input: ['A', 'B'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Relu);C(Zeta)|A->B;A->C;B->C:1"); } @@ -3331,7 +3353,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ReluGrad_DeviceTest) { " attr { key: 'T' value { type: DT_FLOAT } }" " input: ['A', 'B'] }" "node { name: 'D' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'C'] }", kGPUDevice); + " input: ['A', 'C'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(ReluGrad);D(Zeta)|A->C;A->D;B->C:1;C->D:1"); } @@ -3347,7 +3370,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_MaxPool_DeviceTest) { " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" " input: ['A'] }" "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'B'] }", kGPUDevice); + " input: ['A', 'B'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(MaxPool);C(Zeta)|A->B;A->C;B->C:1"); } @@ -3363,7 +3387,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_AvgPool_DeviceTest) { " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" " input: ['A'] }" "node { name: 'C' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'B'] }", kGPUDevice); + " input: ['A', 'B'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(AvgPool);C(Zeta)|A->B;A->C;B->C:1"); } @@ -3384,7 +3409,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_Concat_DeviceTest) { " attr { key: 'N' value { i: 2 } }" " input: ['A', 'B:0', 'B:1']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['C', 'D'] }", kGPUDevice); + " input: ['C', 'D'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Const);B(InputList);C(Input);D(Concat);E(Zeta)|A->D;" "B->D:1;B:1->D:2;C->E;D->E:1"); @@ -3406,7 +3432,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_ConcatV2_DeviceTest) { " attr { key: 'N' value { i: 2 } }" " input: ['B:0', 'B:1', 'A']}" "node { name: 'E' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['C', 'D'] }", kGPUDevice); + " input: ['C', 'D'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Const);B(InputList);C(Input);D(ConcatV2);E(Zeta)|" "A->D:2;B->D;B:1->D:1;C->E;D->E:1"); @@ -3426,7 +3453,8 @@ TEST_F(MklLayoutPassTest, NodeRewrite_FusedBatchNorm_DeviceTest) { " attr { key: 'is_training' value { b: true } }" " input: ['A', 'B', 'C', 'D', 'E'] }" "node { name: 'G' op: 'Zeta' attr { key: 'T' value { type: DT_FLOAT } }" - " input: ['A', 'F'] }", kGPUDevice); + " input: ['A', 'F'] }", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(Input);D(Input);E(Input);" "F(FusedBatchNorm);G(Zeta)|A->F;A->G;B->F:1;C->F:2;D->F:3;" @@ -3455,7 +3483,8 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_DeviceTest) { "node { name: 'Y' op: 'Input'}" "node { name: 'Z' op: 'Zeta'" " attr {key: 'T' value { type: DT_FLOAT } }" - " input: ['E', 'Y']}", kGPUDevice); + " input: ['E', 'Y']}", + kGPUDevice); EXPECT_EQ(DoMklLayoutOptimizationPass(), "A(Input);B(Input);C(_MklConv2D);D(Input);E(BiasAdd);" "M(_MklInput);N(_MklInput);Y(Input);Z(Zeta)|A->C;" diff --git a/tensorflow/core/graph/mkl_tfconversion_pass.cc b/tensorflow/core/graph/mkl_tfconversion_pass.cc index 599bb88f01..5343e6802d 100644 --- a/tensorflow/core/graph/mkl_tfconversion_pass.cc +++ b/tensorflow/core/graph/mkl_tfconversion_pass.cc @@ -33,8 +33,8 @@ limitations under the License. #include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/platform/logging.h" -#include "tensorflow/core/graph/mkl_tfconversion_pass.h" #include "tensorflow/core/graph/mkl_graph_util.h" +#include "tensorflow/core/graph/mkl_tfconversion_pass.h" namespace tensorflow { @@ -152,12 +152,12 @@ Status MklToTfConversionPass::InsertConversionNodeOnEdge( string data_format; TF_CHECK_OK(GetNodeAttr(src->def(), "T", &src_datatype)); - bool dst_dtype_found = GetNodeAttr(dst->def(), "T", &dst_datatype) == - Status::OK(); + bool dst_dtype_found = + GetNodeAttr(dst->def(), "T", &dst_datatype) == Status::OK(); // We compare source and destination datatypes only when both are found. if (dst_dtype_found && (src_datatype != dst_datatype)) { - string err_msg = "T attribute of " + src->name() + " and " + - dst->name() + " do not match. Will not insert" + + string err_msg = "T attribute of " + src->name() + " and " + dst->name() + + " do not match. Will not insert" + " MklToTf node in such case."; return Status(error::Code::INVALID_ARGUMENT, err_msg.c_str()); } @@ -325,12 +325,12 @@ bool MklToTfConversionPass::RunPass(std::unique_ptr* g) { // may not be Mkl node. DataType src_datatype; DataType dst_datatype; - bool src_is_mkl_op = (GetNodeAttr(src->def(), "T", &src_datatype) == - Status::OK() && - IsMklSupportedOp(src->type_string(), src_datatype)); - bool dst_is_mkl_op = (GetNodeAttr(dst->def(), "T", &dst_datatype) == - Status::OK() && - IsMklSupportedOp(dst->type_string(), dst_datatype)); + bool src_is_mkl_op = + (GetNodeAttr(src->def(), "T", &src_datatype) == Status::OK() && + IsMklSupportedOp(src->type_string(), src_datatype)); + bool dst_is_mkl_op = + (GetNodeAttr(dst->def(), "T", &dst_datatype) == Status::OK() && + IsMklSupportedOp(dst->type_string(), dst_datatype)); // Check if src with is Mkl-compliant, while dst is not Mkl-compliant. if (src_is_mkl_op && !dst_is_mkl_op) { diff --git a/tensorflow/core/graph/testlib.cc b/tensorflow/core/graph/testlib.cc index 172471e34b..d5b026eae3 100644 --- a/tensorflow/core/graph/testlib.cc +++ b/tensorflow/core/graph/testlib.cc @@ -40,7 +40,7 @@ REGISTER_KERNEL_BUILDER( #ifdef TENSORFLOW_USE_SYCL REGISTER_KERNEL_BUILDER( Name("HostConst").Device(DEVICE_SYCL).HostMemory("output"), HostConstantOp); -#endif // TENSORFLOW_USE_SYCL +#endif // TENSORFLOW_USE_SYCL // Register the HostConst Op // Returns a constant tensor on the host. Useful for writing C++ tests diff --git a/tensorflow/core/kernels/batching_util/periodic_function.h b/tensorflow/core/kernels/batching_util/periodic_function.h index dbf1733dcc..36a4019002 100644 --- a/tensorflow/core/kernels/batching_util/periodic_function.h +++ b/tensorflow/core/kernels/batching_util/periodic_function.h @@ -114,7 +114,7 @@ class PeriodicFunction { void RunLoop(int64 start) LOCKS_EXCLUDED(mutex_); const std::function function_; // Actual client function - const int64 interval_micros_; // Interval between calls. + const int64 interval_micros_; // Interval between calls. const Options options_; // Protects state below. diff --git a/tensorflow/core/kernels/batching_util/shared_batch_scheduler_test.cc b/tensorflow/core/kernels/batching_util/shared_batch_scheduler_test.cc index d73dcf0fa0..d5ea2b648f 100644 --- a/tensorflow/core/kernels/batching_util/shared_batch_scheduler_test.cc +++ b/tensorflow/core/kernels/batching_util/shared_batch_scheduler_test.cc @@ -55,15 +55,14 @@ Status ScheduleTask(size_t task_size, BatchScheduler* scheduler) { // use the clock to be destroyed. std::unique_ptr CreateFakeClockAdvancerThread( test_util::FakeClockEnv* env, Notification* start, Notification* stop) { - return std::unique_ptr( - Env::Default()->StartThread({}, "FakeClockAdvancerThread", - [env, start, stop] { - start->WaitForNotification(); - while (!stop->HasBeenNotified()) { - env->AdvanceByMicroseconds(10); - Env::Default()->SleepForMicroseconds(10); - } - })); + return std::unique_ptr(Env::Default()->StartThread( + {}, "FakeClockAdvancerThread", [env, start, stop] { + start->WaitForNotification(); + while (!stop->HasBeenNotified()) { + env->AdvanceByMicroseconds(10); + Env::Default()->SleepForMicroseconds(10); + } + })); } TEST(SharedBatchSchedulerTest, Basic) { @@ -258,7 +257,7 @@ TEST(SharedBatchSchedulerTest, ObeysTimeout) { TEST(SharedBatchSchedulerTest, ObeysTimeoutWithRealClock) { Notification first_batch_processed, second_batch_processed; auto callback = [&first_batch_processed, &second_batch_processed]( - std::unique_ptr> batch) { + std::unique_ptr> batch) { ASSERT_TRUE(batch->IsClosed()); if (batch->size() == 1) { first_batch_processed.Notify(); @@ -301,7 +300,7 @@ TEST(SharedBatchSchedulerTest, { Notification first_batch_processed, second_batch_processed; auto callback = [&first_batch_processed, &second_batch_processed]( - std::unique_ptr> batch) { + std::unique_ptr> batch) { ASSERT_TRUE(batch->IsClosed()); if (batch->size() == 1) { first_batch_processed.Notify(); @@ -349,7 +348,7 @@ TEST(SharedBatchSchedulerTest, Fairness) { auto queue_0_callback = [&queue_0_first_batch_scheduled, &queue_0_first_batch_proceed, &queue_0_second_batch_scheduled]( - std::unique_ptr> batch) { + std::unique_ptr> batch) { if (!queue_0_first_batch_scheduled.HasBeenNotified()) { queue_0_first_batch_scheduled.Notify(); queue_0_first_batch_proceed.WaitForNotification(); @@ -467,7 +466,7 @@ TEST(SharedBatchSchedulerTest, ConstMethods) { TEST(SharedBatchSchedulerTest, OneFullQueueDoesntBlockOtherQueues) { Notification queue_0_processing, queue_0_proceed; auto queue_0_callback = [&queue_0_processing, &queue_0_proceed]( - std::unique_ptr> batch) { + std::unique_ptr> batch) { if (!queue_0_processing.HasBeenNotified()) { queue_0_processing.Notify(); queue_0_proceed.WaitForNotification(); diff --git a/tensorflow/core/kernels/data/batch_dataset_op.cc b/tensorflow/core/kernels/data/batch_dataset_op.cc index 2d6e06398f..0853362b26 100644 --- a/tensorflow/core/kernels/data/batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/batch_dataset_op.cc @@ -92,7 +92,6 @@ class BatchDatasetOp : public UnaryDatasetOpKernel { } private: - class Iterator : public DatasetIterator { public: explicit Iterator(const Params& params) diff --git a/tensorflow/core/kernels/data/captured_function.cc b/tensorflow/core/kernels/data/captured_function.cc index 1f6d32f8df..f3e4f1cd3f 100644 --- a/tensorflow/core/kernels/data/captured_function.cc +++ b/tensorflow/core/kernels/data/captured_function.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" #include "tensorflow/core/platform/notification.h" - namespace tensorflow { /* static */ @@ -185,8 +184,7 @@ Status CapturedFunction::MaybeInstantiate( return Status::OK(); } -Status CapturedFunction::Run(IteratorContext* ctx, - std::vector&& args, +Status CapturedFunction::Run(IteratorContext* ctx, std::vector&& args, std::vector* rets) { FunctionLibraryRuntime::Handle handle; TF_RETURN_IF_ERROR(MaybeInstantiate(ctx, &handle)); diff --git a/tensorflow/core/kernels/data/skip_dataset_op.cc b/tensorflow/core/kernels/data/skip_dataset_op.cc index 13c2501bbb..d636c37afe 100644 --- a/tensorflow/core/kernels/data/skip_dataset_op.cc +++ b/tensorflow/core/kernels/data/skip_dataset_op.cc @@ -128,8 +128,8 @@ class SkipDatasetOp : public UnaryDatasetOpKernel { while (i_ < dataset()->count_) { // Fetch and throw away Tensors. std::vector dummy_out_tensors; - TF_RETURN_IF_ERROR(input_impl_->GetNext(ctx, &dummy_out_tensors, - end_of_sequence)); + TF_RETURN_IF_ERROR( + input_impl_->GetNext(ctx, &dummy_out_tensors, end_of_sequence)); if (*end_of_sequence) { // We reached the end before the count was reached. input_impl_.reset(); @@ -140,8 +140,8 @@ class SkipDatasetOp : public UnaryDatasetOpKernel { } // Return GetNext() on the underlying iterator. - TF_RETURN_IF_ERROR(input_impl_->GetNext(ctx, out_tensors, - end_of_sequence)); + TF_RETURN_IF_ERROR( + input_impl_->GetNext(ctx, out_tensors, end_of_sequence)); if (*end_of_sequence) { input_impl_.reset(); } @@ -184,8 +184,7 @@ class SkipDatasetOp : public UnaryDatasetOpKernel { }; }; -REGISTER_KERNEL_BUILDER(Name("SkipDataset").Device(DEVICE_CPU), - SkipDatasetOp); +REGISTER_KERNEL_BUILDER(Name("SkipDataset").Device(DEVICE_CPU), SkipDatasetOp); } // namespace diff --git a/tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc index 6d4a9dfdef..37edd1ce0f 100644 --- a/tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/decode_base64_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc index b084a97204..f3b24b2341 100644 --- a/tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/decode_jpeg_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc index 9dd795b94e..e9ffad1786 100644 --- a/tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/decode_json_example_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc b/tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc index 4a68a5b580..020f18b189 100644 --- a/tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/decode_png_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc b/tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc index 2d6c82826c..a8f07f4bad 100644 --- a/tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/encode_base64_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc b/tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc index 81b6e49124..f5dd47a052 100644 --- a/tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/encode_jpeg_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc b/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc index d91a351c59..4d736a2160 100644 --- a/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/example_proto_fast_parsing_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/identity_fuzz.cc b/tensorflow/core/kernels/fuzzing/identity_fuzz.cc index ac3a12aa39..5c3fc4a279 100644 --- a/tensorflow/core/kernels/fuzzing/identity_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/identity_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc b/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc index 978fcd1028..c90ad2cfeb 100644 --- a/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/parse_tensor_op_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc b/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc index 7d1aa1fbf3..738d78e99a 100644 --- a/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/string_split_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc b/tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc index 94255d215e..e98363ffbf 100644 --- a/tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc +++ b/tensorflow/core/kernels/fuzzing/string_to_number_fuzz.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" #include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/kernels/fuzzing/fuzz_session.h" namespace tensorflow { namespace fuzzing { diff --git a/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc b/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc index f0d7c670a6..4040bf52bf 100644 --- a/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc +++ b/tensorflow/core/kernels/hexagon/graph_transfer_utils.cc @@ -46,7 +46,7 @@ GraphTransferUtils::GetTopNFloatResults(const float* const data, GetTopNFloatResults(data, labels, element_count); LOG(INFO) << "=== Dump ranking ==="; for (int i = 0; i < top_n; ++i) { - const std::tuple &entry = queue.top(); + const std::tuple& entry = queue.top(); LOG(INFO) << i << ": " << std::get<1>(entry) << ", " << std::get<2>(entry) << ", " << std::get<0>(entry); queue.pop(); diff --git a/tensorflow/core/kernels/hexagon/graph_transferer.h b/tensorflow/core/kernels/hexagon/graph_transferer.h index a360d188cc..0d43d028cd 100644 --- a/tensorflow/core/kernels/hexagon/graph_transferer.h +++ b/tensorflow/core/kernels/hexagon/graph_transferer.h @@ -181,8 +181,8 @@ class GraphTransferer { void AppendNodeInputParams(const int id, const Node& node, const std::vector& extra_inputs); - void AppendNodeOutputParams(const ShapeRefiner& shape_refiner, - const int id, const Node& node); + void AppendNodeOutputParams(const ShapeRefiner& shape_refiner, const int id, + const Node& node); static std::array BuildShapeArray( const shape_inference::ShapeHandle& shape_handle, diff --git a/tensorflow/core/kernels/hexagon/graph_transferer_test.cc b/tensorflow/core/kernels/hexagon/graph_transferer_test.cc index 536d295506..20b09f144b 100644 --- a/tensorflow/core/kernels/hexagon/graph_transferer_test.cc +++ b/tensorflow/core/kernels/hexagon/graph_transferer_test.cc @@ -42,8 +42,7 @@ constexpr float VALUE_TOLERANCE_FLOAT = 1e-8f; class GraphTransfererTest : public ::testing::Test { protected: - void SetUp() final { - } + void SetUp() final {} GraphTransferer gt_; }; @@ -61,7 +60,7 @@ class TestGraphTransferOpsDefinitions : public IRemoteFusedGraphOpsDefinitions { } } return -1; -} + } private: const std::vector op_types_{"INPUT", "OUTPUT", "Conv2D", diff --git a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc index 71bc4187b7..3f794dfb1a 100644 --- a/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc +++ b/tensorflow/core/kernels/hexagon/hexagon_graph_execution_test.cc @@ -420,7 +420,7 @@ TEST(GraphTransferer, false, // is_text_proto false, // shape_inference_for_unknown_shape true // dry_run_for_unknown_shape - ); + ); ASSERT_TRUE(status.ok()) << status; prof.Stop(); prof.DumpStatistics("LoadGraphFromProtoFile"); @@ -487,7 +487,7 @@ TEST(GraphTransferer, false, // is_text_proto true, // shape_inference_for_unknown_shape false // dry_run_for_unknown_shape - ); + ); ASSERT_TRUE(status.ok()) << status; prof.Stop(); prof.DumpStatistics("LoadGraphFromProtoFile"); @@ -556,7 +556,7 @@ TEST(GraphTransferer, DISABLED_CheckShapeInferencePerformance) { false, // is_text_proto false, // shape_inference_for_unknown_shape true // dry_run_for_unknown_shape - ); + ); const GraphTransferInfo& gfi0 = gt0.GetGraphTransferInfo(); ASSERT_TRUE(status.ok()); @@ -576,7 +576,7 @@ TEST(GraphTransferer, DISABLED_CheckShapeInferencePerformance) { false, // is_text_proto true, // shape_inference_for_unknown_shape false // dry_run_for_unknown_shape - ); + ); const GraphTransferInfo& gfi1 = gt1.GetGraphTransferInfo(); ASSERT_TRUE(status.ok()); diff --git a/tensorflow/core/kernels/neon/neon_depthwise_conv_op.cc b/tensorflow/core/kernels/neon/neon_depthwise_conv_op.cc index 17f2af550f..0e820bbb62 100644 --- a/tensorflow/core/kernels/neon/neon_depthwise_conv_op.cc +++ b/tensorflow/core/kernels/neon/neon_depthwise_conv_op.cc @@ -71,10 +71,10 @@ class NeonDepthwiseConv2dNativeOp : public BinaryOp { filter.shape().DebugString())); const int32 in_depth = input.dim_size(3); - OP_REQUIRES( - context, in_depth == filter.dim_size(2), - errors::InvalidArgument("input and filter must have the same depth: ", - in_depth, " vs ", filter.dim_size(2))); + OP_REQUIRES(context, in_depth == filter.dim_size(2), + errors::InvalidArgument( + "input and filter must have the same depth: ", in_depth, + " vs ", filter.dim_size(2))); const int32 batch = input.dim_size(0); const int32 input_rows = input.dim_size(1); const int32 input_cols = input.dim_size(2); diff --git a/tensorflow/core/lib/core/status.h b/tensorflow/core/lib/core/status.h index 58a50a70c2..49f74ff47f 100644 --- a/tensorflow/core/lib/core/status.h +++ b/tensorflow/core/lib/core/status.h @@ -131,7 +131,7 @@ inline tensorflow::string* TfCheckOpHelper(::tensorflow::Status v, while (auto _result = ::tensorflow::TfCheckOpHelper(val, #val)) \ LOG(level) << *(_result) -#define TF_CHECK_OK(val) TF_DO_CHECK_OK(val, FATAL) +#define TF_CHECK_OK(val) TF_DO_CHECK_OK(val, FATAL) #define TF_QCHECK_OK(val) TF_DO_CHECK_OK(val, QFATAL) // DEBUG only version of TF_CHECK_OK. Compiler still parses 'val' even in opt diff --git a/tensorflow/core/lib/core/threadpool.cc b/tensorflow/core/lib/core/threadpool.cc index 2b10ebeaf7..e55ed79d36 100644 --- a/tensorflow/core/lib/core/threadpool.cc +++ b/tensorflow/core/lib/core/threadpool.cc @@ -66,7 +66,9 @@ struct EigenEnvironment { } return Task{ std::unique_ptr(new TaskImpl{ - std::move(f), Context(ContextKind::kThread), id, + std::move(f), + Context(ContextKind::kThread), + id, }), }; } diff --git a/tensorflow/core/lib/core/threadpool_test.cc b/tensorflow/core/lib/core/threadpool_test.cc index 49ddb16645..627ef5a892 100644 --- a/tensorflow/core/lib/core/threadpool_test.cc +++ b/tensorflow/core/lib/core/threadpool_test.cc @@ -97,8 +97,8 @@ TEST(ThreadPool, ParallelForWithWorkerId) { } pool.ParallelForWithWorkerId( kWorkItems, kHugeCost, - [&threads_running, &work, num_threads]( - int64 begin, int64 end, int64 id) { + [&threads_running, &work, num_threads](int64 begin, int64 end, + int64 id) { // Store true for the current thread, and assert that another thread // is not running with the same id. ASSERT_LE(0, id); diff --git a/tensorflow/core/lib/db/sqlite.h b/tensorflow/core/lib/db/sqlite.h index 0faa458f1d..efe97f78d2 100644 --- a/tensorflow/core/lib/db/sqlite.h +++ b/tensorflow/core/lib/db/sqlite.h @@ -18,12 +18,12 @@ limitations under the License. #include #include "sqlite3.h" +#include "tensorflow/core/lib/core/refcount.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/thread_annotations.h" #include "tensorflow/core/platform/types.h" -#include "tensorflow/core/lib/core/refcount.h" /// TensorFlow SQLite Veneer /// @@ -121,10 +121,7 @@ class LOCKABLE Sqlite : public core::RefCounted { Sqlite(sqlite3* db, sqlite3_stmt* begin, sqlite3_stmt* commit, sqlite3_stmt* rollback) noexcept - : db_(db), - begin_(begin), - commit_(commit), - rollback_(rollback) {} + : db_(db), begin_(begin), commit_(commit), rollback_(rollback) {} sqlite3* const db_; sqlite3_stmt* const begin_; @@ -233,7 +230,8 @@ class SqliteStatement { /// freed until this statement is Reset() or finalized. void BindText(int parameter, const StringPiece& text) { Update(sqlite3_bind_text64(stmt_, parameter, text.data(), text.size(), - SQLITE_TRANSIENT, SQLITE_UTF8), parameter); + SQLITE_TRANSIENT, SQLITE_UTF8), + parameter); size_ += text.size(); } void BindText(const char* parameter, const StringPiece& text) { @@ -241,7 +239,8 @@ class SqliteStatement { } void BindTextUnsafe(int parameter, const StringPiece& text) { Update(sqlite3_bind_text64(stmt_, parameter, text.data(), text.size(), - SQLITE_STATIC, SQLITE_UTF8), parameter); + SQLITE_STATIC, SQLITE_UTF8), + parameter); size_ += text.size(); } void BindTextUnsafe(const char* parameter, const StringPiece& text) { @@ -254,7 +253,8 @@ class SqliteStatement { /// freed until this statement is Reset() or finalized. void BindBlob(int parameter, const StringPiece& blob) { Update(sqlite3_bind_blob64(stmt_, parameter, blob.data(), blob.size(), - SQLITE_TRANSIENT), parameter); + SQLITE_TRANSIENT), + parameter); size_ += blob.size(); } void BindBlob(const char* parameter, const StringPiece& blob) { @@ -262,7 +262,8 @@ class SqliteStatement { } void BindBlobUnsafe(int parameter, const StringPiece& blob) { Update(sqlite3_bind_blob64(stmt_, parameter, blob.data(), blob.size(), - SQLITE_STATIC), parameter); + SQLITE_STATIC), + parameter); size_ += blob.size(); } void BindBlobUnsafe(const char* parameter, const StringPiece& text) { @@ -320,9 +321,7 @@ class SqliteStatement { /// \brief Move constructor, after which is reset to empty. SqliteStatement(SqliteStatement&& other) noexcept - : db_(other.db_), - stmt_(other.stmt_), - bind_error_(other.bind_error_) { + : db_(other.db_), stmt_(other.stmt_), bind_error_(other.bind_error_) { other.db_ = nullptr; other.stmt_ = nullptr; other.bind_error_ = SQLITE_OK; diff --git a/tensorflow/core/lib/db/sqlite_test.cc b/tensorflow/core/lib/db/sqlite_test.cc index c9c76ea5f2..1e88323d01 100644 --- a/tensorflow/core/lib/db/sqlite_test.cc +++ b/tensorflow/core/lib/db/sqlite_test.cc @@ -33,9 +33,7 @@ class SqliteTest : public ::testing::Test { db_->PrepareOrDie("CREATE TABLE T (a BLOB, b BLOB)").StepAndResetOrDie(); } - void TearDown() override { - db_->Unref(); - } + void TearDown() override { db_->Unref(); } Sqlite* db_; bool is_done_; @@ -213,7 +211,7 @@ TEST_F(SqliteTest, BindFailed) { Status s = stmt.StepOnce(); EXPECT_NE(string::npos, s.error_message().find("INSERT INTO T (a) VALUES (123)")) - << s.error_message(); + << s.error_message(); } TEST_F(SqliteTest, SnappyExtension) { @@ -226,7 +224,7 @@ TEST_F(SqliteTest, SnappyBinaryCompatibility) { EXPECT_EQ( "today is the end of the republic", db_->PrepareOrDie("SELECT UNSNAP(X'03207C746F6461792069732074686520656E64" - "206F66207468652072657075626C6963')") + "206F66207468652072657075626C6963')") .StepOnceOrDie() .ColumnString(0)); } diff --git a/tensorflow/core/lib/gtl/cleanup.h b/tensorflow/core/lib/gtl/cleanup.h index 6053e98640..6bd60ca482 100644 --- a/tensorflow/core/lib/gtl/cleanup.h +++ b/tensorflow/core/lib/gtl/cleanup.h @@ -55,22 +55,21 @@ namespace gtl { template class Cleanup { public: - Cleanup() - : released_(true), f_() {} + Cleanup() : released_(true), f_() {} template - explicit Cleanup(G&& f) // NOLINT + explicit Cleanup(G&& f) // NOLINT : f_(std::forward(f)) {} // NOLINT(build/c++11) Cleanup(Cleanup&& src) // NOLINT - : released_(src.is_released()), f_(src.release()) { } + : released_(src.is_released()), f_(src.release()) {} // Implicitly move-constructible from any compatible Cleanup. // The source will be released as if src.release() were called. // A moved-from Cleanup can be safely destroyed or reassigned. template Cleanup(Cleanup&& src) // NOLINT - : released_(src.is_released()), f_(src.release()) { } + : released_(src.is_released()), f_(src.release()) {} // Assignment to a Cleanup object behaves like destroying it // and making a new one in its place, analogous to unique_ptr @@ -102,8 +101,8 @@ class Cleanup { F f_; }; -template ::type> +template ::type> TF_MUST_USE_RESULT Cleanup MakeCleanup(F&& f) { return Cleanup(std::forward(f)); } diff --git a/tensorflow/core/lib/gtl/cleanup_test.cc b/tensorflow/core/lib/gtl/cleanup_test.cc index bd151cb2ab..a86ffd5fe2 100644 --- a/tensorflow/core/lib/gtl/cleanup_test.cc +++ b/tensorflow/core/lib/gtl/cleanup_test.cc @@ -65,15 +65,14 @@ TEST(CleanupTest, Release) { TEST(FinallyTest, TypeErasedWithoutFactory) { string s = "active"; { - AnyCleanup s_cleaner([&s]{ s.append(" clean"); }); + AnyCleanup s_cleaner([&s] { s.append(" clean"); }); EXPECT_EQ("active", s); } EXPECT_EQ("active clean", s); } struct Appender { - Appender(string* s, const string& msg) - : s_(s), msg_(msg) {} + Appender(string* s, const string& msg) : s_(s), msg_(msg) {} void operator()() const { s_->append(msg_); } string* s_; string msg_; @@ -163,7 +162,12 @@ class CleanupReferenceTest : public ::testing::Test { int* i; F(int* cp, int* i) : cp(cp), i(i) {} F(const F& o) : cp(o.cp), i(o.i) { ++*cp; } - F& operator=(const F& o) { cp = o.cp; i = o.i; ++*cp; return *this; } + F& operator=(const F& o) { + cp = o.cp; + i = o.i; + ++*cp; + return *this; + } F(F&&) = default; F& operator=(F&&) = default; void operator()() const { ++*i; } @@ -279,7 +283,7 @@ BENCHMARK(BM_AnyCleanup); void BM_AnyCleanupNoFactory(int iters) { while (iters--) { - AnyCleanup fin([]{Incr();}); + AnyCleanup fin([] { Incr(); }); } } BENCHMARK(BM_AnyCleanupNoFactory); diff --git a/tensorflow/core/lib/gtl/inlined_vector.h b/tensorflow/core/lib/gtl/inlined_vector.h index d6e5d9effa..6e3cb2206d 100644 --- a/tensorflow/core/lib/gtl/inlined_vector.h +++ b/tensorflow/core/lib/gtl/inlined_vector.h @@ -31,12 +31,12 @@ limitations under the License. #ifndef TENSORFLOW_LIB_GTL_INLINED_VECTOR_H_ #define TENSORFLOW_LIB_GTL_INLINED_VECTOR_H_ -#include #include #include #include #include #include +#include #include #include #include @@ -407,7 +407,7 @@ class InlinedVector { }; // 2) Construct a T with args at not-yet-initialized memory pointed by dst. struct Construct { - template + template void operator()(T* dst, Args&&... args) const { new (dst) T(std::forward(args)...); } diff --git a/tensorflow/core/lib/gtl/int_type.h b/tensorflow/core/lib/gtl/int_type.h index 647fc81aa7..af3e50ad78 100644 --- a/tensorflow/core/lib/gtl/int_type.h +++ b/tensorflow/core/lib/gtl/int_type.h @@ -255,13 +255,13 @@ class IntType { value_ op arg_value; \ return *this; \ } - INT_TYPE_ASSIGNMENT_OP(+= ); - INT_TYPE_ASSIGNMENT_OP(-= ); - INT_TYPE_ASSIGNMENT_OP(*= ); - INT_TYPE_ASSIGNMENT_OP(/= ); - INT_TYPE_ASSIGNMENT_OP(<<= ); // NOLINT - INT_TYPE_ASSIGNMENT_OP(>>= ); // NOLINT - INT_TYPE_ASSIGNMENT_OP(%= ); + INT_TYPE_ASSIGNMENT_OP(+=); + INT_TYPE_ASSIGNMENT_OP(-=); + INT_TYPE_ASSIGNMENT_OP(*=); + INT_TYPE_ASSIGNMENT_OP(/=); + INT_TYPE_ASSIGNMENT_OP(<<=); // NOLINT + INT_TYPE_ASSIGNMENT_OP(>>=); // NOLINT + INT_TYPE_ASSIGNMENT_OP(%=); #undef INT_TYPE_ASSIGNMENT_OP ThisType& operator=(ValueType arg_value) { @@ -314,10 +314,10 @@ std::ostream& operator<<(std::ostream& os, // NOLINT INT_TYPE_ARITHMETIC_OP(+); INT_TYPE_ARITHMETIC_OP(-); INT_TYPE_ARITHMETIC_OP(*); -INT_TYPE_ARITHMETIC_OP(/ ); -INT_TYPE_ARITHMETIC_OP(<< ); // NOLINT -INT_TYPE_ARITHMETIC_OP(>> ); // NOLINT -INT_TYPE_ARITHMETIC_OP(% ); +INT_TYPE_ARITHMETIC_OP(/); +INT_TYPE_ARITHMETIC_OP(<<); // NOLINT +INT_TYPE_ARITHMETIC_OP(>>); // NOLINT +INT_TYPE_ARITHMETIC_OP(%); #undef INT_TYPE_ARITHMETIC_OP // -- NON-MEMBER COMPARISON OPERATORS ------------------------------------------ @@ -345,12 +345,12 @@ INT_TYPE_ARITHMETIC_OP(% ); IntType id) { \ return val op id.value(); \ } -INT_TYPE_COMPARISON_OP(== ); // NOLINT -INT_TYPE_COMPARISON_OP(!= ); // NOLINT -INT_TYPE_COMPARISON_OP(< ); // NOLINT -INT_TYPE_COMPARISON_OP(<= ); // NOLINT -INT_TYPE_COMPARISON_OP(> ); // NOLINT -INT_TYPE_COMPARISON_OP(>= ); // NOLINT +INT_TYPE_COMPARISON_OP(==); // NOLINT +INT_TYPE_COMPARISON_OP(!=); // NOLINT +INT_TYPE_COMPARISON_OP(<); // NOLINT +INT_TYPE_COMPARISON_OP(<=); // NOLINT +INT_TYPE_COMPARISON_OP(>); // NOLINT +INT_TYPE_COMPARISON_OP(>=); // NOLINT #undef INT_TYPE_COMPARISON_OP } // namespace gtl diff --git a/tensorflow/core/lib/gtl/int_type_test.cc b/tensorflow/core/lib/gtl/int_type_test.cc index d3c405d9ac..61d364017c 100644 --- a/tensorflow/core/lib/gtl/int_type_test.cc +++ b/tensorflow/core/lib/gtl/int_type_test.cc @@ -42,7 +42,8 @@ class IntTypeTest : public ::testing::Test { // All tests below will be executed on all supported IntTypes. typedef ::testing::Types SupportedIntTypes; + Int64_IT, UInt64_IT, Long_IT> + SupportedIntTypes; TYPED_TEST_CASE(IntTypeTest, SupportedIntTypes); @@ -232,7 +233,8 @@ TYPED_TEST(IntTypeTest, TestOperators) { TYPED_TEST(IntTypeTest, TestHashFunctor) { std::unordered_map map; + typename TestFixture::T::Hasher> + map; typename TestFixture::T a(0); map[a] = 'c'; EXPECT_EQ('c', map[a]); diff --git a/tensorflow/core/lib/gtl/optional.h b/tensorflow/core/lib/gtl/optional.h index 2ff8b9c7d1..fa33c24c0c 100644 --- a/tensorflow/core/lib/gtl/optional.h +++ b/tensorflow/core/lib/gtl/optional.h @@ -593,12 +593,12 @@ class optional : private internal_optional::optional_data, assert(this->engaged_); return this->pointer(); } - constexpr const T& operator*() const & { return reference(); } + constexpr const T& operator*() const& { return reference(); } T& operator*() & { assert(this->engaged_); return reference(); } - constexpr const T&& operator*() const && { return std::move(reference()); } + constexpr const T&& operator*() const&& { return std::move(reference()); } T&& operator*() && { assert(this->engaged_); return std::move(reference()); @@ -621,7 +621,7 @@ class optional : private internal_optional::optional_data, // Use `opt.value()` to get a reference to underlying value. The constness // and lvalue/rvalue-ness of `opt` is preserved to the view of the T // subobject. - const T& value() const & { + const T& value() const& { CHECK(*this) << "Bad optional access"; return reference(); } @@ -633,7 +633,7 @@ class optional : private internal_optional::optional_data, CHECK(*this) << "Bad optional access"; return std::move(reference()); } - const T&& value() const && { // NOLINT(build/c++11) + const T&& value() const&& { // NOLINT(build/c++11) CHECK(*this) << "Bad optional access"; return std::move(reference()); } @@ -641,7 +641,7 @@ class optional : private internal_optional::optional_data, // Use `opt.value_or(val)` to get either the value of T or the given default // `val` in the empty case. template - constexpr T value_or(U&& v) const & { + constexpr T value_or(U&& v) const& { return static_cast(*this) ? **this : static_cast(std::forward(v)); } @@ -656,8 +656,8 @@ class optional : private internal_optional::optional_data, constexpr const T& reference() const { return *this->pointer(); } T& reference() { return *(this->pointer()); } - // T constraint checks. You can't have an optional of nullopt_t, in_place_t or - // a reference. + // T constraint checks. You can't have an optional of nullopt_t, in_place_t + // or a reference. static_assert( !std::is_same::type>::value, "optional is not allowed."); diff --git a/tensorflow/core/lib/gtl/optional_test.cc b/tensorflow/core/lib/gtl/optional_test.cc index 547bee7b75..12b5bbc60b 100644 --- a/tensorflow/core/lib/gtl/optional_test.cc +++ b/tensorflow/core/lib/gtl/optional_test.cc @@ -24,17 +24,29 @@ limitations under the License. namespace tensorflow { namespace { -using tensorflow::gtl::optional; -using tensorflow::gtl::nullopt; -using tensorflow::gtl::nullopt_t; using tensorflow::gtl::in_place; using tensorflow::gtl::in_place_t; using tensorflow::gtl::make_optional; +using tensorflow::gtl::nullopt; +using tensorflow::gtl::nullopt_t; +using tensorflow::gtl::optional; -template string TypeQuals(T&) { return "&"; } -template string TypeQuals(T&&) { return "&&"; } -template string TypeQuals(const T&) { return "c&"; } -template string TypeQuals(const T&&) { return "c&&"; } +template +string TypeQuals(T&) { + return "&"; +} +template +string TypeQuals(T&&) { + return "&&"; +} +template +string TypeQuals(const T&) { + return "c&"; +} +template +string TypeQuals(const T&&) { + return "c&&"; +} struct StructorListener { int construct0 = 0; diff --git a/tensorflow/core/lib/gtl/top_n_test.cc b/tensorflow/core/lib/gtl/top_n_test.cc index fae85570dc..ba30c072a9 100644 --- a/tensorflow/core/lib/gtl/top_n_test.cc +++ b/tensorflow/core/lib/gtl/top_n_test.cc @@ -28,10 +28,10 @@ limitations under the License. namespace { +using tensorflow::string; using tensorflow::gtl::TopN; using tensorflow::random::PhiloxRandom; using tensorflow::random::SimplePhilox; -using tensorflow::string; // Move the contents from an owned raw pointer, returning by value. // Objects are easier to manage by value. diff --git a/tensorflow/core/lib/io/compression.cc b/tensorflow/core/lib/io/compression.cc index c12de98e40..0d25bca9ec 100644 --- a/tensorflow/core/lib/io/compression.cc +++ b/tensorflow/core/lib/io/compression.cc @@ -22,6 +22,6 @@ namespace compression { const char kNone[] = ""; const char kGzip[] = "GZIP"; -} -} -} +} // namespace compression +} // namespace io +} // namespace tensorflow diff --git a/tensorflow/core/lib/io/compression.h b/tensorflow/core/lib/io/compression.h index ef90c60a3a..4d8e7788ca 100644 --- a/tensorflow/core/lib/io/compression.h +++ b/tensorflow/core/lib/io/compression.h @@ -23,8 +23,8 @@ namespace compression { extern const char kNone[]; extern const char kGzip[]; -} -} -} +} // namespace compression +} // namespace io +} // namespace tensorflow #endif // TENSORFLOW_CORE_LIB_IO_COMPRESSION_H_ diff --git a/tensorflow/core/lib/io/record_reader.cc b/tensorflow/core/lib/io/record_reader.cc index 403c82818e..9cc6c4034f 100644 --- a/tensorflow/core/lib/io/record_reader.cc +++ b/tensorflow/core/lib/io/record_reader.cc @@ -207,7 +207,7 @@ Status RecordReader::SkipNBytes(uint64 offset) { } } return Status::OK(); -} +} // namespace io SequentialRecordReader::SequentialRecordReader( RandomAccessFile* file, const RecordReaderOptions& options) diff --git a/tensorflow/core/lib/io/recordio_test.cc b/tensorflow/core/lib/io/recordio_test.cc index 507c26a63f..b7e51256a2 100644 --- a/tensorflow/core/lib/io/recordio_test.cc +++ b/tensorflow/core/lib/io/recordio_test.cc @@ -218,8 +218,8 @@ TEST_F(RecordioTest, RandomRead) { // Tests of all the error paths in log_reader.cc follow: static void AssertHasSubstr(StringPiece s, StringPiece expected) { - EXPECT_TRUE(StringPiece(s).contains(expected)) << s << " does not contain " - << expected; + EXPECT_TRUE(StringPiece(s).contains(expected)) + << s << " does not contain " << expected; } TEST_F(RecordioTest, ReadError) { diff --git a/tensorflow/core/lib/png/png_io.cc b/tensorflow/core/lib/png/png_io.cc index 354c819b09..77a3414442 100644 --- a/tensorflow/core/lib/png/png_io.cc +++ b/tensorflow/core/lib/png/png_io.cc @@ -197,8 +197,8 @@ bool CommonInitDecode(StringPiece png_string, int desired_channels, int desired_channel_bits, DecodeContext* context) { CHECK(desired_channel_bits == 8 || desired_channel_bits == 16) << "desired_channel_bits = " << desired_channel_bits; - CHECK(0 <= desired_channels && desired_channels <= 4) << "desired_channels = " - << desired_channels; + CHECK(0 <= desired_channels && desired_channels <= 4) + << "desired_channels = " << desired_channels; context->error_condition = false; context->channels = desired_channels; context->png_ptr = png_create_read_struct(PNG_LIBPNG_VER_STRING, context, diff --git a/tensorflow/core/lib/random/philox_random_test_utils.h b/tensorflow/core/lib/random/philox_random_test_utils.h index f4bb087e10..6c29ae6b6a 100644 --- a/tensorflow/core/lib/random/philox_random_test_utils.h +++ b/tensorflow/core/lib/random/philox_random_test_utils.h @@ -35,8 +35,8 @@ void FillRandoms(PhiloxRandom gen, typename Distribution::ResultElementType* p, int64 size) { const int granularity = Distribution::kResultElementCount; - CHECK(size % granularity == 0) << " size: " << size - << " granularity: " << granularity; + CHECK(size % granularity == 0) + << " size: " << size << " granularity: " << granularity; Distribution dist; for (int i = 0; i < size; i += granularity) { diff --git a/tensorflow/core/lib/random/random_distributions.h b/tensorflow/core/lib/random/random_distributions.h index 0e281403f8..3fe1f9bc6c 100644 --- a/tensorflow/core/lib/random/random_distributions.h +++ b/tensorflow/core/lib/random/random_distributions.h @@ -17,8 +17,8 @@ limitations under the License. #define TENSORFLOW_LIB_RANDOM_RANDOM_DISTRIBUTIONS_H_ #define _USE_MATH_DEFINES -#include #include +#include #undef _USE_MATH_DEFINES #include @@ -27,7 +27,6 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/lib/random/philox_random.h" - namespace tensorflow { namespace random { diff --git a/tensorflow/core/lib/random/random_distributions_test.cc b/tensorflow/core/lib/random/random_distributions_test.cc index 90d0dba4a7..85d68f456e 100644 --- a/tensorflow/core/lib/random/random_distributions_test.cc +++ b/tensorflow/core/lib/random/random_distributions_test.cc @@ -45,8 +45,8 @@ void FillRandomsWithSingles(PhiloxRandom gen, int64 size) { int granularity = Distribution::kResultElementCount; - CHECK(size % granularity == 0) << " size: " << size - << " granularity: " << granularity; + CHECK(size % granularity == 0) + << " size: " << size << " granularity: " << granularity; SingleSampleAdapter single_samples(&gen); diff --git a/tensorflow/core/lib/strings/ordered_code.cc b/tensorflow/core/lib/strings/ordered_code.cc index af9a151259..ef90050b4f 100644 --- a/tensorflow/core/lib/strings/ordered_code.cc +++ b/tensorflow/core/lib/strings/ordered_code.cc @@ -472,7 +472,8 @@ void OrderedCode::WriteSignedNumIncreasing(string* dest, int64 val) { // buf = val in network byte order, sign extended to 10 bytes const char sign_byte = val < 0 ? '\xff' : '\0'; char buf[10] = { - sign_byte, sign_byte, + sign_byte, + sign_byte, }; StoreBigEndian64(buf + 2, val); static_assert(sizeof(buf) == kMaxSigned64Length, "max length size mismatch"); diff --git a/tensorflow/core/lib/strings/strcat.h b/tensorflow/core/lib/strings/strcat.h index 5835b0101d..2bc14945cd 100644 --- a/tensorflow/core/lib/strings/strcat.h +++ b/tensorflow/core/lib/strings/strcat.h @@ -126,7 +126,7 @@ class AlphaNum { : piece_(digits_, strlen(DoubleToBuffer(f, digits_))) {} AlphaNum(const Eigen::half &f); // NOLINT(runtime/explicit) - AlphaNum(Hex hex); // NOLINT(runtime/explicit) + AlphaNum(Hex hex); // NOLINT(runtime/explicit) AlphaNum(const char *c_str) : piece_(c_str) {} // NOLINT(runtime/explicit) AlphaNum(const StringPiece &pc) : piece_(pc) {} // NOLINT(runtime/explicit) diff --git a/tensorflow/core/ops/compat/backwards_compatibility_test.cc b/tensorflow/core/ops/compat/backwards_compatibility_test.cc index add05d6610..6e05ae4be4 100644 --- a/tensorflow/core/ops/compat/backwards_compatibility_test.cc +++ b/tensorflow/core/ops/compat/backwards_compatibility_test.cc @@ -25,8 +25,9 @@ namespace tensorflow { namespace { TEST(BackwardsCompatibilityTest, IsCompatible) { - OpCompatibilityLib compatibility( - "tensorflow/core/ops", strings::StrCat("v", TF_MAJOR_VERSION), nullptr); + OpCompatibilityLib compatibility("tensorflow/core/ops", + strings::StrCat("v", TF_MAJOR_VERSION), + nullptr); Env* env = Env::Default(); int changed_ops = 0; diff --git a/tensorflow/core/platform/cloud/gcs_dns_cache.cc b/tensorflow/core/platform/cloud/gcs_dns_cache.cc index 2b0e55bf37..4d9aff4d24 100644 --- a/tensorflow/core/platform/cloud/gcs_dns_cache.cc +++ b/tensorflow/core/platform/cloud/gcs_dns_cache.cc @@ -18,9 +18,9 @@ limitations under the License. #include #include #else +#include #include #include -#include #endif #include diff --git a/tensorflow/core/platform/cloud/http_request_fake.h b/tensorflow/core/platform/cloud/http_request_fake.h index 682b97f6ec..7711eaceb2 100644 --- a/tensorflow/core/platform/cloud/http_request_fake.h +++ b/tensorflow/core/platform/cloud/http_request_fake.h @@ -38,8 +38,7 @@ class FakeHttpRequest : public CurlHttpRequest { public: /// Return the response for the given request. FakeHttpRequest(const string& request, const string& response) - : FakeHttpRequest(request, response, Status::OK(), nullptr, {}, 200) { - } + : FakeHttpRequest(request, response, Status::OK(), nullptr, {}, 200) {} /// Return the response with headers for the given request. FakeHttpRequest(const string& request, const string& response, diff --git a/tensorflow/core/platform/cloud/oauth_client_test.cc b/tensorflow/core/platform/cloud/oauth_client_test.cc index 236259dbc1..ad569758cc 100644 --- a/tensorflow/core/platform/cloud/oauth_client_test.cc +++ b/tensorflow/core/platform/cloud/oauth_client_test.cc @@ -160,12 +160,12 @@ TEST(OAuthClientTest, GetTokenFromServiceAccountJson) { ASSERT_EQ(1, EVP_DigestVerifyInit(md_ctx, nullptr, md, nullptr, key)); ASSERT_EQ(1, EVP_DigestVerifyUpdate(md_ctx, header_dot_claim.c_str(), header_dot_claim.size())); - ASSERT_EQ( - 1, - EVP_DigestVerifyFinal( - md_ctx, const_cast( - reinterpret_cast(signature.data())), - signature.size())); + ASSERT_EQ(1, + EVP_DigestVerifyFinal( + md_ctx, + const_cast( + reinterpret_cast(signature.data())), + signature.size())); EVP_MD_CTX_cleanup(md_ctx); // Free all the crypto-related resources. diff --git a/tensorflow/core/platform/cloud/retrying_file_system.cc b/tensorflow/core/platform/cloud/retrying_file_system.cc index c3b6831361..870d935e11 100644 --- a/tensorflow/core/platform/cloud/retrying_file_system.cc +++ b/tensorflow/core/platform/cloud/retrying_file_system.cc @@ -25,7 +25,6 @@ namespace tensorflow { namespace { - class RetryingRandomAccessFile : public RandomAccessFile { public: RetryingRandomAccessFile(std::unique_ptr base_file, diff --git a/tensorflow/core/platform/cuda_libdevice_path_test.cc b/tensorflow/core/platform/cuda_libdevice_path_test.cc index 639f6804ea..2d34239a99 100644 --- a/tensorflow/core/platform/cuda_libdevice_path_test.cc +++ b/tensorflow/core/platform/cuda_libdevice_path_test.cc @@ -27,8 +27,7 @@ TEST(CudaLibdevicePathTest, LibdevicePath) { VLOG(2) << "Libdevice root = " << LibdeviceRoot(); std::vector libdevice_files; TF_EXPECT_OK(Env::Default()->GetMatchingPaths( - io::JoinPath(LibdeviceRoot(), "libdevice.*.bc"), - &libdevice_files)); + io::JoinPath(LibdeviceRoot(), "libdevice.*.bc"), &libdevice_files)); EXPECT_LT(0, libdevice_files.size()); } #endif diff --git a/tensorflow/core/platform/default/device_tracer.cc b/tensorflow/core/platform/default/device_tracer.cc index f4b0f16393..8e60a7f091 100644 --- a/tensorflow/core/platform/default/device_tracer.cc +++ b/tensorflow/core/platform/default/device_tracer.cc @@ -579,8 +579,10 @@ Status DeviceTracerImpl::Collect(StepStatsCollector *collector) { // TODO(pbar) Handle device IDs and prefix properly. const string prefix = ""; const int id = 0; - const string stream_device = strings::StrCat(prefix, "/device:GPU:", id, "/stream:"); - const string memcpy_device = strings::StrCat(prefix, "/device:GPU:", id, "/memcpy"); + const string stream_device = + strings::StrCat(prefix, "/device:GPU:", id, "/stream:"); + const string memcpy_device = + strings::StrCat(prefix, "/device:GPU:", id, "/memcpy"); mutex_lock l2(trace_mu_); for (const auto &rec : kernel_records_) { diff --git a/tensorflow/core/platform/default/logging.cc b/tensorflow/core/platform/default/logging.cc index 82bd69f9ca..2b874da198 100644 --- a/tensorflow/core/platform/default/logging.cc +++ b/tensorflow/core/platform/default/logging.cc @@ -83,15 +83,14 @@ void LogMessage::GenerateLogMessage() { const size_t time_buffer_size = 30; char time_buffer[time_buffer_size]; strftime(time_buffer, time_buffer_size, "%Y-%m-%d %H:%M:%S", - localtime(&now_seconds)); + localtime(&now_seconds)); // TODO(jeff,sanjay): Replace this with something that logs through the env. fprintf(stderr, "%s.%06d: %c %s:%d] %s\n", time_buffer, micros_remainder, - "IWEF"[severity_], fname_, line_, str().c_str()); + "IWEF"[severity_], fname_, line_, str().c_str()); } #endif - namespace { // Parse log level (int64) from environment variable (char*) diff --git a/tensorflow/core/platform/default/logging.h b/tensorflow/core/platform/default/logging.h index 40c260f236..f0efa31d55 100644 --- a/tensorflow/core/platform/default/logging.h +++ b/tensorflow/core/platform/default/logging.h @@ -19,8 +19,8 @@ limitations under the License. // IWYU pragma: private, include "third_party/tensorflow/core/platform/logging.h" // IWYU pragma: friend third_party/tensorflow/core/platform/logging.h -#include #include +#include #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/types.h" @@ -205,16 +205,18 @@ string* MakeCheckOpString(const T1& v1, const T2& v2, const char* exprtext) { inline string* name##Impl(int v1, int v2, const char* exprtext) { \ return name##Impl(v1, v2, exprtext); \ } \ - inline string* name##Impl(const size_t v1, const int v2, const char* exprtext) { \ + inline string* name##Impl(const size_t v1, const int v2, \ + const char* exprtext) { \ if (TF_PREDICT_FALSE(v2 < 0)) { \ - return ::tensorflow::internal::MakeCheckOpString(v1, v2, exprtext);\ + return ::tensorflow::internal::MakeCheckOpString(v1, v2, exprtext); \ } \ const size_t uval = (size_t)((unsigned)v1); \ return name##Impl(uval, v2, exprtext); \ } \ - inline string* name##Impl(const int v1, const size_t v2, const char* exprtext) { \ - if (TF_PREDICT_FALSE(v2 >= std::numeric_limits::max())) { \ - return ::tensorflow::internal::MakeCheckOpString(v1, v2, exprtext);\ + inline string* name##Impl(const int v1, const size_t v2, \ + const char* exprtext) { \ + if (TF_PREDICT_FALSE(v2 >= std::numeric_limits::max())) { \ + return ::tensorflow::internal::MakeCheckOpString(v1, v2, exprtext); \ } \ const size_t uval = (size_t)((unsigned)v2); \ return name##Impl(v1, uval, exprtext); \ @@ -225,12 +227,12 @@ string* MakeCheckOpString(const T1& v1, const T2& v2, const char* exprtext) { // This happens if, for example, those are used as token names in a // yacc grammar. TF_DEFINE_CHECK_OP_IMPL(Check_EQ, - == ) // Compilation error with CHECK_EQ(NULL, x)? -TF_DEFINE_CHECK_OP_IMPL(Check_NE, != ) // Use CHECK(x == NULL) instead. -TF_DEFINE_CHECK_OP_IMPL(Check_LE, <= ) -TF_DEFINE_CHECK_OP_IMPL(Check_LT, < ) -TF_DEFINE_CHECK_OP_IMPL(Check_GE, >= ) -TF_DEFINE_CHECK_OP_IMPL(Check_GT, > ) + ==) // Compilation error with CHECK_EQ(NULL, x)? +TF_DEFINE_CHECK_OP_IMPL(Check_NE, !=) // Use CHECK(x == NULL) instead. +TF_DEFINE_CHECK_OP_IMPL(Check_LE, <=) +TF_DEFINE_CHECK_OP_IMPL(Check_LT, <) +TF_DEFINE_CHECK_OP_IMPL(Check_GE, >=) +TF_DEFINE_CHECK_OP_IMPL(Check_GT, >) #undef TF_DEFINE_CHECK_OP_IMPL // In optimized mode, use CheckOpString to hint to compiler that diff --git a/tensorflow/core/platform/denormal.cc b/tensorflow/core/platform/denormal.cc index f13b0af2a7..e00dbdb4ae 100644 --- a/tensorflow/core/platform/denormal.cc +++ b/tensorflow/core/platform/denormal.cc @@ -41,8 +41,8 @@ namespace tensorflow { namespace port { ScopedFlushDenormal::ScopedFlushDenormal() { -// For now, we flush denormals only on SSE 3. Other architectures such as ARM -// can be added as needed. + // For now, we flush denormals only on SSE 3. Other architectures such as ARM + // can be added as needed. #ifdef DENORM_USE_INTRINSICS if (TestCPUFeature(SSE3)) { diff --git a/tensorflow/core/platform/device_tracer_test.cc b/tensorflow/core/platform/device_tracer_test.cc index c0c08dabac..89f14e905a 100644 --- a/tensorflow/core/platform/device_tracer_test.cc +++ b/tensorflow/core/platform/device_tracer_test.cc @@ -77,7 +77,8 @@ class DeviceTracerTest : public ::testing::Test { Node* y_neg = test::graph::Unary(&graph, "Neg", i); y_neg_ = y_neg->name(); - y_neg->set_assigned_device_name("/job:localhost/replica:0/task:0/device:GPU:0"); + y_neg->set_assigned_device_name( + "/job:localhost/replica:0/task:0/device:GPU:0"); test::graph::ToGraphDef(&graph, &def_); } diff --git a/tensorflow/core/platform/env.h b/tensorflow/core/platform/env.h index 557bfa87e5..34aaf3f78b 100644 --- a/tensorflow/core/platform/env.h +++ b/tensorflow/core/platform/env.h @@ -286,7 +286,7 @@ class Env { // "version" should be the version of the library or NULL // returns the name that LoadLibrary() can use virtual string FormatLibraryFileName(const string& name, - const string& version) = 0; + const string& version) = 0; private: // Returns a possible list of local temporary directories. @@ -353,6 +353,7 @@ class EnvWrapper : public Env { const string& version) override { return target_->FormatLibraryFileName(name, version); } + private: Env* target_; }; diff --git a/tensorflow/core/platform/file_system.cc b/tensorflow/core/platform/file_system.cc index 14755891fa..b9866cf641 100644 --- a/tensorflow/core/platform/file_system.cc +++ b/tensorflow/core/platform/file_system.cc @@ -131,18 +131,19 @@ Status FileSystem::GetMatchingPaths(const string& pattern, if (children.empty()) continue; // This IsDirectory call can be expensive for some FS. Parallelizing it. children_dir_status.resize(children.size()); - ForEach(0, children.size(), [this, ¤t_dir, &children, &fixed_prefix, - &children_dir_status](int i) { - const string child_path = io::JoinPath(current_dir, children[i]); - // In case the child_path doesn't start with the fixed_prefix then - // we don't need to explore this path. - if (!StringPiece(child_path).starts_with(fixed_prefix)) { - children_dir_status[i] = - Status(tensorflow::error::CANCELLED, "Operation not needed"); - } else { - children_dir_status[i] = IsDirectory(child_path); - } - }); + ForEach(0, children.size(), + [this, ¤t_dir, &children, &fixed_prefix, + &children_dir_status](int i) { + const string child_path = io::JoinPath(current_dir, children[i]); + // In case the child_path doesn't start with the fixed_prefix then + // we don't need to explore this path. + if (!StringPiece(child_path).starts_with(fixed_prefix)) { + children_dir_status[i] = Status(tensorflow::error::CANCELLED, + "Operation not needed"); + } else { + children_dir_status[i] = IsDirectory(child_path); + } + }); for (int i = 0; i < children.size(); ++i) { const string child_path = io::JoinPath(current_dir, children[i]); // If the IsDirectory call was cancelled we bail. diff --git a/tensorflow/core/platform/gif.h b/tensorflow/core/platform/gif.h index 9c72d34ff5..ab095a35c9 100644 --- a/tensorflow/core/platform/gif.h +++ b/tensorflow/core/platform/gif.h @@ -20,7 +20,8 @@ limitations under the License. #if defined(PLATFORM_GOOGLE) #include "tensorflow/core/platform/google/build_config/gif.h" -#elif defined(PLATFORM_POSIX)|| defined(PLATFORM_WINDOWS) ||defined(PLATFORM_POSIX_ANDROID) +#elif defined(PLATFORM_POSIX) || defined(PLATFORM_WINDOWS) || \ + defined(PLATFORM_POSIX_ANDROID) #include #else #error Define the appropriate PLATFORM_ macro for this platform diff --git a/tensorflow/core/platform/hadoop/hadoop_file_system.cc b/tensorflow/core/platform/hadoop/hadoop_file_system.cc index 0baeac0984..74863293a3 100644 --- a/tensorflow/core/platform/hadoop/hadoop_file_system.cc +++ b/tensorflow/core/platform/hadoop/hadoop_file_system.cc @@ -164,8 +164,9 @@ Status HadoopFileSystem::Connect(StringPiece fname, hdfsFS* fs) { } else { hdfs_->hdfsBuilderSetNameNode(builder, nn.c_str()); } - // KERB_TICKET_CACHE_PATH will be deleted in the future, Because KRB5CCNAME is the build in - // environment variable of Kerberos, so KERB_TICKET_CACHE_PATH and related code are unnecessary. + // KERB_TICKET_CACHE_PATH will be deleted in the future, Because KRB5CCNAME is + // the build in environment variable of Kerberos, so KERB_TICKET_CACHE_PATH + // and related code are unnecessary. char* ticket_cache_path = getenv("KERB_TICKET_CACHE_PATH"); if (ticket_cache_path != nullptr) { hdfs_->hdfsBuilderSetKerbTicketCachePath(builder, ticket_cache_path); diff --git a/tensorflow/core/platform/jpeg.h b/tensorflow/core/platform/jpeg.h index edbcbd960a..1b5e633f0a 100644 --- a/tensorflow/core/platform/jpeg.h +++ b/tensorflow/core/platform/jpeg.h @@ -20,7 +20,8 @@ limitations under the License. #if defined(PLATFORM_GOOGLE) #include "tensorflow/core/platform/google/build_config/jpeg.h" -#elif defined(PLATFORM_POSIX)|| defined(PLATFORM_WINDOWS) ||defined(PLATFORM_POSIX_ANDROID) +#elif defined(PLATFORM_POSIX) || defined(PLATFORM_WINDOWS) || \ + defined(PLATFORM_POSIX_ANDROID) #include #include #include diff --git a/tensorflow/core/platform/png.h b/tensorflow/core/platform/png.h index 5b0203c343..dad18d7219 100644 --- a/tensorflow/core/platform/png.h +++ b/tensorflow/core/platform/png.h @@ -20,7 +20,8 @@ limitations under the License. #if defined(PLATFORM_GOOGLE) #include "tensorflow/core/platform/google/build_config/png.h" -#elif defined(PLATFORM_POSIX)|| defined(PLATFORM_WINDOWS) ||defined(PLATFORM_POSIX_ANDROID) +#elif defined(PLATFORM_POSIX) || defined(PLATFORM_WINDOWS) || \ + defined(PLATFORM_POSIX_ANDROID) #include #else #error Define the appropriate PLATFORM_ macro for this platform diff --git a/tensorflow/core/platform/posix/error.cc b/tensorflow/core/platform/posix/error.cc index cda6d7d8f9..2bb9443fb3 100644 --- a/tensorflow/core/platform/posix/error.cc +++ b/tensorflow/core/platform/posix/error.cc @@ -73,19 +73,19 @@ error::Code ErrnoToCode(int err_number) { case ECHILD: // No child processes case EISCONN: // Socket is connected #if !defined(_WIN32) && !defined(__HAIKU__) - case ENOTBLK: // Block device required + case ENOTBLK: // Block device required #endif - case ENOTCONN: // The socket is not connected - case EPIPE: // Broken pipe + case ENOTCONN: // The socket is not connected + case EPIPE: // Broken pipe #if !defined(_WIN32) - case ESHUTDOWN: // Cannot send after transport endpoint shutdown + case ESHUTDOWN: // Cannot send after transport endpoint shutdown #endif - case ETXTBSY: // Text file busy + case ETXTBSY: // Text file busy code = error::FAILED_PRECONDITION; break; - case ENOSPC: // No space left on device + case ENOSPC: // No space left on device #if !defined(_WIN32) - case EDQUOT: // Disk quota exceeded + case EDQUOT: // Disk quota exceeded #endif case EMFILE: // Too many open files case EMLINK: // Too many links @@ -95,7 +95,7 @@ error::Code ErrnoToCode(int err_number) { case ENOMEM: // Not enough space case ENOSR: // No STREAM resources #if !defined(_WIN32) && !defined(__HAIKU__) - case EUSERS: // Too many users + case EUSERS: // Too many users #endif code = error::RESOURCE_EXHAUSTED; break; @@ -104,17 +104,17 @@ error::Code ErrnoToCode(int err_number) { case ERANGE: // Result too large code = error::OUT_OF_RANGE; break; - case ENOSYS: // Function not implemented - case ENOTSUP: // Operation not supported - case EAFNOSUPPORT: // Address family not supported + case ENOSYS: // Function not implemented + case ENOTSUP: // Operation not supported + case EAFNOSUPPORT: // Address family not supported #if !defined(_WIN32) - case EPFNOSUPPORT: // Protocol family not supported + case EPFNOSUPPORT: // Protocol family not supported #endif case EPROTONOSUPPORT: // Protocol not supported #if !defined(_WIN32) && !defined(__HAIKU__) case ESOCKTNOSUPPORT: // Socket type not supported #endif - case EXDEV: // Improper link + case EXDEV: // Improper link code = error::UNIMPLEMENTED; break; case EAGAIN: // Resource temporarily unavailable @@ -123,7 +123,7 @@ error::Code ErrnoToCode(int err_number) { case ECONNRESET: // Connection reset case EINTR: // Interrupted function call #if !defined(_WIN32) - case EHOSTDOWN: // Host is down + case EHOSTDOWN: // Host is down #endif case EHOSTUNREACH: // Host is unreachable case ENETDOWN: // Network is down @@ -139,7 +139,7 @@ error::Code ErrnoToCode(int err_number) { break; case EDEADLK: // Resource deadlock avoided #if !defined(_WIN32) - case ESTALE: // Stale file handle + case ESTALE: // Stale file handle #endif code = error::ABORTED; break; @@ -158,7 +158,7 @@ error::Code ErrnoToCode(int err_number) { case ENOMSG: // No message of the desired type case EPROTO: // Protocol error #if !defined(_WIN32) && !defined(__HAIKU__) - case EREMOTE: // Object is remote + case EREMOTE: // Object is remote #endif code = error::UNKNOWN; break; diff --git a/tensorflow/core/platform/profile_utils/android_armv7a_cpu_utils_helper.h b/tensorflow/core/platform/profile_utils/android_armv7a_cpu_utils_helper.h index 8604b01c53..ce2069b004 100644 --- a/tensorflow/core/platform/profile_utils/android_armv7a_cpu_utils_helper.h +++ b/tensorflow/core/platform/profile_utils/android_armv7a_cpu_utils_helper.h @@ -58,8 +58,8 @@ class AndroidArmV7ACpuUtilsHelper : public ICpuUtilsHelper { TF_DISALLOW_COPY_AND_ASSIGN(AndroidArmV7ACpuUtilsHelper); }; -} // profile_utils -} // tensorflow +} // namespace profile_utils +} // namespace tensorflow #endif // defined(__ANDROID__) && (__ANDROID_API__ >= 21) && // (defined(__ARM_ARCH_7A__) || defined(__aarch64__)) diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.cc b/tensorflow/core/platform/profile_utils/cpu_utils.cc index d3362690d7..02de7d1362 100644 --- a/tensorflow/core/platform/profile_utils/cpu_utils.cc +++ b/tensorflow/core/platform/profile_utils/cpu_utils.cc @@ -28,15 +28,17 @@ namespace profile_utils { static ICpuUtilsHelper* cpu_utils_helper_instance_ = nullptr; -#if (defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)) || (defined(__s390x__)) - /* static */ uint64 CpuUtils::GetCycleCounterFrequency() { - static const uint64 cpu_frequency = GetCycleCounterFrequencyImpl(); - return cpu_frequency; +#if (defined(__powerpc__) || \ + defined(__ppc__) && (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)) || \ + (defined(__s390x__)) +/* static */ uint64 CpuUtils::GetCycleCounterFrequency() { + static const uint64 cpu_frequency = GetCycleCounterFrequencyImpl(); + return cpu_frequency; } #else - /* static */ int64 CpuUtils::GetCycleCounterFrequency() { - static const int64 cpu_frequency = GetCycleCounterFrequencyImpl(); - return cpu_frequency; +/* static */ int64 CpuUtils::GetCycleCounterFrequency() { + static const int64 cpu_frequency = GetCycleCounterFrequencyImpl(); + return cpu_frequency; } #endif diff --git a/tensorflow/core/platform/profile_utils/cpu_utils.h b/tensorflow/core/platform/profile_utils/cpu_utils.h index 5d215b4804..2da20bb1b8 100644 --- a/tensorflow/core/platform/profile_utils/cpu_utils.h +++ b/tensorflow/core/platform/profile_utils/cpu_utils.h @@ -94,14 +94,16 @@ class CpuUtils { #endif } - // Return cycle counter frequency. - // As this method caches the cpu frequency internally, - // the first call will incur overhead, but not subsequent calls. - #if (defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)) || (defined(__s390x__)) - static uint64 GetCycleCounterFrequency(); - #else - static int64 GetCycleCounterFrequency(); - #endif +// Return cycle counter frequency. +// As this method caches the cpu frequency internally, +// the first call will incur overhead, but not subsequent calls. +#if (defined(__powerpc__) || \ + defined(__ppc__) && (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)) || \ + (defined(__s390x__)) + static uint64 GetCycleCounterFrequency(); +#else + static int64 GetCycleCounterFrequency(); +#endif // Return micro secound per each clock // As this method caches the cpu frequency internally, diff --git a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc index 5b11b684dd..eb8161fbfd 100644 --- a/tensorflow/core/platform/profile_utils/cpu_utils_test.cc +++ b/tensorflow/core/platform/profile_utils/cpu_utils_test.cc @@ -53,15 +53,17 @@ TEST_F(CpuUtilsTest, CheckGetCurrentClockCycle) { } TEST_F(CpuUtilsTest, CheckCycleCounterFrequency) { - #if (defined(__powerpc__) || defined(__ppc__) && ( __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)) || (defined(__s390x__)) - const uint64 cpu_frequency = CpuUtils::GetCycleCounterFrequency(); - CHECK_GT(cpu_frequency, 0); - CHECK_NE(cpu_frequency, unsigned(CpuUtils::INVALID_FREQUENCY)); - #else - const int64 cpu_frequency = CpuUtils::GetCycleCounterFrequency(); - CHECK_GT(cpu_frequency, 0); - CHECK_NE(cpu_frequency, CpuUtils::INVALID_FREQUENCY); - #endif +#if (defined(__powerpc__) || \ + defined(__ppc__) && (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)) || \ + (defined(__s390x__)) + const uint64 cpu_frequency = CpuUtils::GetCycleCounterFrequency(); + CHECK_GT(cpu_frequency, 0); + CHECK_NE(cpu_frequency, unsigned(CpuUtils::INVALID_FREQUENCY)); +#else + const int64 cpu_frequency = CpuUtils::GetCycleCounterFrequency(); + CHECK_GT(cpu_frequency, 0); + CHECK_NE(cpu_frequency, CpuUtils::INVALID_FREQUENCY); +#endif if (DBG) { LOG(INFO) << "Cpu frequency = " << cpu_frequency; } diff --git a/tensorflow/core/platform/profile_utils/i_cpu_utils_helper.h b/tensorflow/core/platform/profile_utils/i_cpu_utils_helper.h index 51c54d50d1..11b739c009 100644 --- a/tensorflow/core/platform/profile_utils/i_cpu_utils_helper.h +++ b/tensorflow/core/platform/profile_utils/i_cpu_utils_helper.h @@ -47,7 +47,7 @@ class ICpuUtilsHelper { TF_DISALLOW_COPY_AND_ASSIGN(ICpuUtilsHelper); }; -} // profile_utils -} // tensorflow +} // namespace profile_utils +} // namespace tensorflow #endif // TENSORFLOW_PLATFORM_PROFILEUTILS_I_CPU_UTILS_HELPER_H__ diff --git a/tensorflow/core/platform/protobuf_internal.h b/tensorflow/core/platform/protobuf_internal.h index 7d6e8f57a6..2f151a5aee 100644 --- a/tensorflow/core/platform/protobuf_internal.h +++ b/tensorflow/core/platform/protobuf_internal.h @@ -45,8 +45,8 @@ Status ParseAny(const google::protobuf::Any& any, T* message, #ifdef TENSORFLOW_LITE_PROTOS if (any.type_url() != strings::StrCat("type.googleapis.com/", type_name)) { return errors::FailedPrecondition( - "Expected Any type_url for: ", type_name, ". Got: ", - string(any.type_url().data(), any.type_url().size()), "."); + "Expected Any type_url for: ", type_name, + ". Got: ", string(any.type_url().data(), any.type_url().size()), "."); } if (!message->ParseFromString(any.value())) { return errors::FailedPrecondition("Failed to unpack: ", diff --git a/tensorflow/core/platform/s3/aws_logging.cc b/tensorflow/core/platform/s3/aws_logging.cc index fbca0acc36..44317f1a3e 100644 --- a/tensorflow/core/platform/s3/aws_logging.cc +++ b/tensorflow/core/platform/s3/aws_logging.cc @@ -96,7 +96,7 @@ Aws::Utils::Logging::LogLevel ParseLogLevelFromEnv() { return log_level; } -} +} // namespace static bool initialized = false; static mutex s3_logging_mutex(LINKER_INITIALIZED); diff --git a/tensorflow/core/platform/setround.cc b/tensorflow/core/platform/setround.cc index 0c66da09bb..592626bfa1 100644 --- a/tensorflow/core/platform/setround.cc +++ b/tensorflow/core/platform/setround.cc @@ -15,7 +15,6 @@ limitations under the License. #include "tensorflow/core/platform/setround.h" - namespace tensorflow { namespace port { diff --git a/tensorflow/core/platform/test_benchmark.h b/tensorflow/core/platform/test_benchmark.h index a6636225cc..327237dba9 100644 --- a/tensorflow/core/platform/test_benchmark.h +++ b/tensorflow/core/platform/test_benchmark.h @@ -60,7 +60,7 @@ class Benchmark { private: string name_; int num_args_; - std::vector> args_; + std::vector > args_; void (*fn0_)(int) = nullptr; void (*fn1_)(int, int) = nullptr; void (*fn2_)(int, int, int) = nullptr; diff --git a/tensorflow/core/platform/windows/env.cc b/tensorflow/core/platform/windows/env.cc index 788a4bf4b1..41b2644170 100644 --- a/tensorflow/core/platform/windows/env.cc +++ b/tensorflow/core/platform/windows/env.cc @@ -24,9 +24,9 @@ limitations under the License. #undef LoadLibrary #undef ERROR +#include #include #include -#include #include "tensorflow/core/lib/core/error_codes.pb.h" #include "tensorflow/core/platform/load_library.h" @@ -53,8 +53,7 @@ class StdThread : public Thread { class WindowsEnv : public Env { public: - WindowsEnv() - : GetSystemTimePreciseAsFileTime_(NULL) { + WindowsEnv() : GetSystemTimePreciseAsFileTime_(NULL) { // GetSystemTimePreciseAsFileTime function is only available in the latest // versions of Windows. For that reason, we try to look it up in // kernel32.dll at runtime and use an alternative option if the function @@ -72,8 +71,8 @@ class WindowsEnv : public Env { } bool MatchPath(const string& path, const string& pattern) override { - std::wstring ws_path(WindowsFileSystem::Utf8ToWideChar(path)); - std::wstring ws_pattern(WindowsFileSystem::Utf8ToWideChar(pattern)); + std::wstring ws_path(WindowsFileSystem::Utf8ToWideChar(path)); + std::wstring ws_pattern(WindowsFileSystem::Utf8ToWideChar(pattern)); return PathMatchSpecW(ws_path.c_str(), ws_pattern.c_str()) == TRUE; } @@ -122,14 +121,14 @@ class WindowsEnv : public Env { SetThreadpoolTimer(timer, &FileDueTime, 0, 0); } - Status LoadLibrary(const char *library_filename, void** handle) override { + Status LoadLibrary(const char* library_filename, void** handle) override { std::string file_name = library_filename; std::replace(file_name.begin(), file_name.end(), '/', '\\'); std::wstring ws_file_name(WindowsFileSystem::Utf8ToWideChar(file_name)); HMODULE hModule = LoadLibraryExW(ws_file_name.c_str(), NULL, - LOAD_WITH_ALTERED_SEARCH_PATH); + LOAD_WITH_ALTERED_SEARCH_PATH); if (!hModule) { return errors::NotFound(file_name + " not found"); } @@ -138,31 +137,30 @@ class WindowsEnv : public Env { } Status GetSymbolFromLibrary(void* handle, const char* symbol_name, - void** symbol) override { + void** symbol) override { FARPROC found_symbol; found_symbol = GetProcAddress((HMODULE)handle, symbol_name); if (found_symbol == NULL) { return errors::NotFound(std::string(symbol_name) + " not found"); } - *symbol = (void **)found_symbol; + *symbol = (void**)found_symbol; return Status::OK(); } - string FormatLibraryFileName(const string& name, const string& version) - override { + string FormatLibraryFileName(const string& name, + const string& version) override { string filename; if (version.size() == 0) { filename = name + ".dll"; - } - else { + } else { filename = name + version + ".dll"; } return filename; } private: - typedef VOID(WINAPI * FnGetSystemTimePreciseAsFileTime)(LPFILETIME); + typedef VOID(WINAPI* FnGetSystemTimePreciseAsFileTime)(LPFILETIME); FnGetSystemTimePreciseAsFileTime GetSystemTimePreciseAsFileTime_; }; diff --git a/tensorflow/core/platform/windows/error.cc b/tensorflow/core/platform/windows/error.cc index 39e941a383..291fc5003f 100644 --- a/tensorflow/core/platform/windows/error.cc +++ b/tensorflow/core/platform/windows/error.cc @@ -21,7 +21,7 @@ namespace internal { std::string GetWindowsErrorMessage(DWORD err) { LPSTR buffer = NULL; DWORD flags = FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | - FORMAT_MESSAGE_IGNORE_INSERTS; + FORMAT_MESSAGE_IGNORE_INSERTS; FormatMessageA(flags, NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), reinterpret_cast(&buffer), 0, NULL); std::string message = buffer; diff --git a/tensorflow/core/platform/windows/error.h b/tensorflow/core/platform/windows/error.h index 026e0d5aa9..ba643a0fa8 100644 --- a/tensorflow/core/platform/windows/error.h +++ b/tensorflow/core/platform/windows/error.h @@ -24,9 +24,7 @@ namespace tensorflow { namespace internal { std::string GetWindowsErrorMessage(DWORD err); - -} } +} // namespace tensorflow #endif // TENSORFLOW_CORE_PLATFORM_WINDOWS_ERROR_H_ - diff --git a/tensorflow/core/platform/windows/integral_types.h b/tensorflow/core/platform/windows/integral_types.h index 4970b8ca6a..46338a536d 100644 --- a/tensorflow/core/platform/windows/integral_types.h +++ b/tensorflow/core/platform/windows/integral_types.h @@ -1,18 +1,18 @@ - /* 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. - ==============================================================================*/ - +/* 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_PLATFORM_WINDOWS_INTEGRAL_TYPES_H_ #define TENSORFLOW_PLATFORM_WINDOWS_INTEGRAL_TYPES_H_ diff --git a/tensorflow/core/platform/windows/net.cc b/tensorflow/core/platform/windows/net.cc index 46eb072d42..2ab558ab95 100644 --- a/tensorflow/core/platform/windows/net.cc +++ b/tensorflow/core/platform/windows/net.cc @@ -26,7 +26,7 @@ limitations under the License. #undef ERROR -#pragma comment(lib,"Ws2_32.lib") +#pragma comment(lib, "Ws2_32.lib") namespace tensorflow { namespace internal { @@ -44,8 +44,8 @@ bool IsPortAvailable(int* port, bool is_tcp) { CHECK_GE(*port, 0); CHECK_LE(*port, 65535); if (sock == INVALID_SOCKET) { - LOG(ERROR) << "socket() failed: " << - GetWindowsErrorMessage(WSAGetLastError()); + LOG(ERROR) << "socket() failed: " + << GetWindowsErrorMessage(WSAGetLastError()); return false; } @@ -54,8 +54,8 @@ bool IsPortAvailable(int* port, bool is_tcp) { int result = setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&one), sizeof(one)); if (result == SOCKET_ERROR) { - LOG(ERROR) << "setsockopt() failed: " << - GetWindowsErrorMessage(WSAGetLastError()); + LOG(ERROR) << "setsockopt() failed: " + << GetWindowsErrorMessage(WSAGetLastError()); closesocket(sock); return false; } @@ -66,8 +66,8 @@ bool IsPortAvailable(int* port, bool is_tcp) { addr.sin_port = htons((uint16_t)*port); result = bind(sock, (struct sockaddr*)&addr, sizeof(addr)); if (result == SOCKET_ERROR) { - LOG(WARNING) << "bind(port=" << *port << ") failed: " << - GetWindowsErrorMessage(WSAGetLastError()); + LOG(WARNING) << "bind(port=" << *port + << ") failed: " << GetWindowsErrorMessage(WSAGetLastError()); closesocket(sock); return false; } @@ -75,8 +75,8 @@ bool IsPortAvailable(int* port, bool is_tcp) { // Get the bound port number. result = getsockname(sock, (struct sockaddr*)&addr, &addr_len); if (result == SOCKET_ERROR) { - LOG(WARNING) << "getsockname() failed: " << - GetWindowsErrorMessage(WSAGetLastError()); + LOG(WARNING) << "getsockname() failed: " + << GetWindowsErrorMessage(WSAGetLastError()); closesocket(sock); return false; } diff --git a/tensorflow/core/platform/windows/subprocess.h b/tensorflow/core/platform/windows/subprocess.h index b65313363e..66ec44885d 100644 --- a/tensorflow/core/platform/windows/subprocess.h +++ b/tensorflow/core/platform/windows/subprocess.h @@ -19,8 +19,7 @@ limitations under the License. namespace tensorflow { // SubProcess is not yet implemented for Windows. -class SubProcess { -}; +class SubProcess {}; } // namespace tensorflow diff --git a/tensorflow/core/platform/windows/test.cc b/tensorflow/core/platform/windows/test.cc index 0ffd02ff14..584acad91b 100644 --- a/tensorflow/core/platform/windows/test.cc +++ b/tensorflow/core/platform/windows/test.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/platform/net.h" #include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/net.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" diff --git a/tensorflow/core/platform/windows/windows_file_system.cc b/tensorflow/core/platform/windows/windows_file_system.cc index 604348fe03..b6b3722caa 100644 --- a/tensorflow/core/platform/windows/windows_file_system.cc +++ b/tensorflow/core/platform/windows/windows_file_system.cc @@ -13,12 +13,12 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include #include #include #include #include #include -#include #undef StrCat #include #include @@ -75,16 +75,16 @@ SSIZE_T pread(HANDLE hfile, char* src, size_t num_bytes, uint64_t offset) { if (TRUE == read_result) { result = bytes_read; } else if ((FALSE == read_result) && - ((last_error = GetLastError()) != ERROR_IO_PENDING)) { + ((last_error = GetLastError()) != ERROR_IO_PENDING)) { result = (last_error == ERROR_HANDLE_EOF) ? 0 : -1; } else { - if (ERROR_IO_PENDING == last_error) { // Otherwise bytes_read already has the result. - BOOL overlapped_result = ::GetOverlappedResult(hfile, &overlapped, - &bytes_read, TRUE); + if (ERROR_IO_PENDING == + last_error) { // Otherwise bytes_read already has the result. + BOOL overlapped_result = + ::GetOverlappedResult(hfile, &overlapped, &bytes_read, TRUE); if (FALSE == overlapped_result) { result = (::GetLastError() == ERROR_HANDLE_EOF) ? 0 : -1; - } - else { + } else { result = bytes_read; } } @@ -151,11 +151,11 @@ class WindowsWritableFile : public WritableFile { Status Append(const StringPiece& data) override { DWORD bytes_written = 0; DWORD data_size = static_cast(data.size()); - BOOL write_result = ::WriteFile(hfile_, data.data(), data_size, - &bytes_written, NULL); + BOOL write_result = + ::WriteFile(hfile_, data.data(), data_size, &bytes_written, NULL); if (FALSE == write_result) { - return IOErrorFromWindowsError( - "Failed to WriteFile: " + filename_, ::GetLastError()); + return IOErrorFromWindowsError("Failed to WriteFile: " + filename_, + ::GetLastError()); } assert(size_t(bytes_written) == data.size()); @@ -171,8 +171,8 @@ class WindowsWritableFile : public WritableFile { } if (FALSE == ::CloseHandle(hfile_)) { - return IOErrorFromWindowsError( - "CloseHandle failed for: " + filename_, ::GetLastError()); + return IOErrorFromWindowsError("CloseHandle failed for: " + filename_, + ::GetLastError()); } hfile_ = INVALID_HANDLE_VALUE; @@ -187,9 +187,7 @@ class WindowsWritableFile : public WritableFile { return Status::OK(); } - Status Sync() override { - return Flush(); - } + Status Sync() override { return Flush(); } }; class WinReadOnlyMemoryRegion : public ReadOnlyMemoryRegion { @@ -204,7 +202,10 @@ class WinReadOnlyMemoryRegion : public ReadOnlyMemoryRegion { public: WinReadOnlyMemoryRegion(const std::string& filename, HANDLE hfile, HANDLE hmap, const void* address, uint64 length) - : filename_(filename), hfile_(hfile), hmap_(hmap), address_(address), + : filename_(filename), + hfile_(hfile), + hmap_(hmap), + address_(address), length_(length) {} ~WinReadOnlyMemoryRegion() { @@ -238,9 +239,9 @@ Status WindowsFileSystem::NewRandomAccessFile( // almost all tests would work with a possible exception of fault_injection. DWORD share_mode = FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE; - HANDLE hfile = ::CreateFileW(ws_translated_fname.c_str(), GENERIC_READ, - share_mode, NULL, OPEN_EXISTING, file_flags, - NULL); + HANDLE hfile = + ::CreateFileW(ws_translated_fname.c_str(), GENERIC_READ, share_mode, NULL, + OPEN_EXISTING, file_flags, NULL); if (INVALID_HANDLE_VALUE == hfile) { string context = "NewRandomAccessFile failed to Create/Open: " + fname; @@ -258,9 +259,9 @@ Status WindowsFileSystem::NewWritableFile( result->reset(); DWORD share_mode = FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE; - HANDLE hfile = ::CreateFileW(ws_translated_fname.c_str(), GENERIC_WRITE, - share_mode, NULL, CREATE_ALWAYS, - FILE_ATTRIBUTE_NORMAL, NULL); + HANDLE hfile = + ::CreateFileW(ws_translated_fname.c_str(), GENERIC_WRITE, share_mode, + NULL, CREATE_ALWAYS, FILE_ATTRIBUTE_NORMAL, NULL); if (INVALID_HANDLE_VALUE == hfile) { string context = "Failed to create a NewWriteableFile: " + fname; @@ -278,9 +279,9 @@ Status WindowsFileSystem::NewAppendableFile( result->reset(); DWORD share_mode = FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE; - HANDLE hfile = ::CreateFileW(ws_translated_fname.c_str(), GENERIC_WRITE, - share_mode, NULL, OPEN_ALWAYS, - FILE_ATTRIBUTE_NORMAL, NULL); + HANDLE hfile = + ::CreateFileW(ws_translated_fname.c_str(), GENERIC_WRITE, share_mode, + NULL, OPEN_ALWAYS, FILE_ATTRIBUTE_NORMAL, NULL); if (INVALID_HANDLE_VALUE == hfile) { string context = "Failed to create a NewAppendableFile: " + fname; @@ -316,9 +317,9 @@ Status WindowsFileSystem::NewReadOnlyMemoryRegionFromFile( file_flags |= FILE_FLAG_OVERLAPPED; DWORD share_mode = FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE; - HANDLE hfile = ::CreateFileW(ws_translated_fname.c_str(), GENERIC_READ, - share_mode, NULL, OPEN_EXISTING, file_flags, - NULL); + HANDLE hfile = + ::CreateFileW(ws_translated_fname.c_str(), GENERIC_READ, share_mode, NULL, + OPEN_EXISTING, file_flags, NULL); if (INVALID_HANDLE_VALUE == hfile) { return IOErrorFromWindowsError( @@ -345,28 +346,32 @@ Status WindowsFileSystem::NewReadOnlyMemoryRegionFromFile( NULL); // Mapping name if (!hmap) { - string context = "Failed to create file mapping for " - "NewReadOnlyMemoryRegionFromFile: " + fname; + string context = + "Failed to create file mapping for " + "NewReadOnlyMemoryRegionFromFile: " + + fname; return IOErrorFromWindowsError(context, ::GetLastError()); } UniqueCloseHandlePtr map_guard(hmap, CloseHandleFunc); - const void* mapped_region = ::MapViewOfFileEx( - hmap, FILE_MAP_READ, - 0, // High DWORD of access start - 0, // Low DWORD - file_size, - NULL); // Let the OS choose the mapping + const void* mapped_region = + ::MapViewOfFileEx(hmap, FILE_MAP_READ, + 0, // High DWORD of access start + 0, // Low DWORD + file_size, + NULL); // Let the OS choose the mapping if (!mapped_region) { - string context = "Failed to MapViewOfFile for " - "NewReadOnlyMemoryRegionFromFile: " + fname; + string context = + "Failed to MapViewOfFile for " + "NewReadOnlyMemoryRegionFromFile: " + + fname; return IOErrorFromWindowsError(context, ::GetLastError()); } - result->reset(new WinReadOnlyMemoryRegion(fname, hfile, hmap, - mapped_region, file_size)); + result->reset(new WinReadOnlyMemoryRegion(fname, hfile, hmap, mapped_region, + file_size)); map_guard.release(); file_guard.release(); @@ -404,8 +409,8 @@ Status WindowsFileSystem::GetChildren(const string& dir, } do { - string file_name = WideCharToUtf8(find_data.cFileName); - const StringPiece basename = file_name; + string file_name = WideCharToUtf8(find_data.cFileName); + const StringPiece basename = file_name; if (basename != "." && basename != "..") { result->push_back(file_name); } @@ -457,8 +462,7 @@ Status WindowsFileSystem::GetFileSize(const string& fname, uint64* size) { file_size.HighPart = attrs.nFileSizeHigh; file_size.LowPart = attrs.nFileSizeLow; *size = file_size.QuadPart; - } - else { + } else { string context = "Can not get size for: " + fname; result = IOErrorFromWindowsError(context, ::GetLastError()); } @@ -472,7 +476,7 @@ Status WindowsFileSystem::RenameFile(const string& src, const string& target) { std::wstring ws_translated_src = Utf8ToWideChar(TranslateName(src)); std::wstring ws_translated_target = Utf8ToWideChar(TranslateName(target)); if (!::MoveFileExW(ws_translated_src.c_str(), ws_translated_target.c_str(), - MOVEFILE_REPLACE_EXISTING)) { + MOVEFILE_REPLACE_EXISTING)) { string context(strings::StrCat("Failed to rename: ", src, " to: ", target)); result = IOErrorFromWindowsError(context, ::GetLastError()); } diff --git a/tensorflow/core/platform/windows/windows_file_system.h b/tensorflow/core/platform/windows/windows_file_system.h index 8dcc153037..ba0302f0fd 100644 --- a/tensorflow/core/platform/windows/windows_file_system.h +++ b/tensorflow/core/platform/windows/windows_file_system.h @@ -63,33 +63,35 @@ class WindowsFileSystem : public FileSystem { Status RenameFile(const string& src, const string& target) override; - string TranslateName(const string& name) const override { - return name; - } + string TranslateName(const string& name) const override { return name; } static std::wstring Utf8ToWideChar(const string& utf8str) { - int size_required = MultiByteToWideChar(CP_UTF8, 0, utf8str.c_str(), (int)utf8str.size(), NULL, 0); - std::wstring ws_translated_str(size_required, 0); - MultiByteToWideChar(CP_UTF8, 0, utf8str.c_str(), (int)utf8str.size(), &ws_translated_str[0], size_required); - return ws_translated_str; + int size_required = MultiByteToWideChar(CP_UTF8, 0, utf8str.c_str(), + (int)utf8str.size(), NULL, 0); + std::wstring ws_translated_str(size_required, 0); + MultiByteToWideChar(CP_UTF8, 0, utf8str.c_str(), (int)utf8str.size(), + &ws_translated_str[0], size_required); + return ws_translated_str; } - static string WideCharToUtf8(const std::wstring &wstr) { - if (wstr.empty()) return std::string(); - int size_required = WideCharToMultiByte(CP_UTF8, 0, wstr.c_str(), (int)wstr.size(), NULL, 0, NULL, NULL); - string utf8_translated_str(size_required, 0); - WideCharToMultiByte(CP_UTF8, 0, wstr.c_str(), (int)wstr.size(), &utf8_translated_str[0], size_required, NULL, NULL); - return utf8_translated_str; + static string WideCharToUtf8(const std::wstring& wstr) { + if (wstr.empty()) return std::string(); + int size_required = WideCharToMultiByte( + CP_UTF8, 0, wstr.c_str(), (int)wstr.size(), NULL, 0, NULL, NULL); + string utf8_translated_str(size_required, 0); + WideCharToMultiByte(CP_UTF8, 0, wstr.c_str(), (int)wstr.size(), + &utf8_translated_str[0], size_required, NULL, NULL); + return utf8_translated_str; } }; class LocalWinFileSystem : public WindowsFileSystem { -public: - string TranslateName(const string& name) const override { - StringPiece scheme, host, path; - io::ParseURI(name, &scheme, &host, &path); - return path.ToString(); - } + public: + string TranslateName(const string& name) const override { + StringPiece scheme, host, path; + io::ParseURI(name, &scheme, &host, &path); + return path.ToString(); + } }; } // namespace tensorflow diff --git a/tensorflow/core/profiler/internal/advisor/tfprof_advisor_test.cc b/tensorflow/core/profiler/internal/advisor/tfprof_advisor_test.cc index d05143aff9..e968b9c97e 100644 --- a/tensorflow/core/profiler/internal/advisor/tfprof_advisor_test.cc +++ b/tensorflow/core/profiler/internal/advisor/tfprof_advisor_test.cc @@ -53,10 +53,13 @@ class TFProfAdvisorTest : public ::testing::Test { NodeExecStats node_stat; node_stat.set_all_start_micros(start_miros); node_stat.set_op_end_rel_micros(end_rel_micros); - node->AddStepStat(step, "/job:localhost/replica:0/task:0/device:GPU:0", node_stat); - node->AddStepStat(step, "/job:localhost/replica:0/task:0/device:GPU:0:stream:all", + node->AddStepStat(step, "/job:localhost/replica:0/task:0/device:GPU:0", node_stat); - node->AddStepStat(step, "/job:localhost/replica:0/task:0/device:GPU:0:stream:0", + node->AddStepStat(step, + "/job:localhost/replica:0/task:0/device:GPU:0:stream:all", + node_stat); + node->AddStepStat(step, + "/job:localhost/replica:0/task:0/device:GPU:0:stream:0", node_stat); return node; } diff --git a/tensorflow/core/profiler/internal/tfprof_op.cc b/tensorflow/core/profiler/internal/tfprof_op.cc index 5a8429d489..3dce1d85db 100644 --- a/tensorflow/core/profiler/internal/tfprof_op.cc +++ b/tensorflow/core/profiler/internal/tfprof_op.cc @@ -113,8 +113,9 @@ const ShowMultiNode* TFOp::ShowInternal(const Options& opts, root_->formatted_str = FormatNode(root_.get(), root_.get(), opts); } if (timeline) { - fprintf(stderr, "op view doesn't support timeline yet. " - "Consider graph/scope/code view.\n"); + fprintf(stderr, + "op view doesn't support timeline yet. " + "Consider graph/scope/code view.\n"); return root_.get(); } if (cnodes_map_.empty()) { @@ -265,9 +266,9 @@ string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const { double pct = 0.0; if (node->proto().total_parameters() > 0) { accu_pct = 100.0 * node->proto().total_parameters() / - root->proto().total_parameters(); - pct = 100.0 * node->proto().parameters() / - root->proto().total_parameters(); + root->proto().total_parameters(); + pct = + 100.0 * node->proto().parameters() / root->proto().total_parameters(); } attrs.push_back(strings::Printf( "%30s", @@ -282,9 +283,8 @@ string TFOp::FormatNode(OpNode* node, OpNode* root, const Options& opts) const { double pct = 0.0; if (node->proto().total_float_ops() > 0) { accu_pct = 100.0 * node->proto().total_float_ops() / - root->proto().total_float_ops(); - pct = 100.0 * node->proto().float_ops() / - root->proto().total_float_ops(); + root->proto().total_float_ops(); + pct = 100.0 * node->proto().float_ops() / root->proto().total_float_ops(); } attrs.push_back(strings::Printf( diff --git a/tensorflow/core/profiler/internal/tfprof_op.h b/tensorflow/core/profiler/internal/tfprof_op.h index fe1c3b2ae8..aa22182d36 100644 --- a/tensorflow/core/profiler/internal/tfprof_op.h +++ b/tensorflow/core/profiler/internal/tfprof_op.h @@ -41,8 +41,7 @@ namespace tfprof { // to input ops. class TFOp : public TFMultiShow { public: - explicit TFOp() - : TFMultiShow() {} + explicit TFOp() : TFMultiShow() {} ~TFOp() override {} void AddNode(TFGraphNode* node) override; @@ -51,7 +50,7 @@ class TFOp : public TFMultiShow { private: const ShowMultiNode* ShowInternal(const Options& opts, - Timeline* timeline) override; + Timeline* timeline) override; int64 SearchRoot(const std::vector nodes, const std::vector& regexes); diff --git a/tensorflow/core/profiler/internal/tfprof_show.h b/tensorflow/core/profiler/internal/tfprof_show.h index 4d6de06070..81b021549a 100644 --- a/tensorflow/core/profiler/internal/tfprof_show.h +++ b/tensorflow/core/profiler/internal/tfprof_show.h @@ -78,40 +78,43 @@ class TFShow { return nodes; } std::vector sorted_nodes = nodes; - std::sort(sorted_nodes.begin(), sorted_nodes.end(), [&opts](const T* n1, - const T* n2) { - if (n1->name() == kTFProfRoot) return true; - if (n2->name() == kTFProfRoot) return false; - bool name_cmp = n1->name() < n2->name(); - if (opts.order_by == kOrderBy[0]) { - return name_cmp; - } else if (opts.order_by == kOrderBy[1]) { - return n1->proto().total_requested_bytes() > - n2->proto().total_requested_bytes(); - } else if (opts.order_by == kOrderBy[2]) { - return n1->proto().total_peak_bytes() > n2->proto().total_peak_bytes(); - } else if (opts.order_by == kOrderBy[3]) { - return n1->proto().total_residual_bytes() > - n2->proto().total_residual_bytes(); - } else if (opts.order_by == kOrderBy[4]) { - return n1->proto().total_output_bytes() > - n2->proto().total_output_bytes(); - } else if (opts.order_by == kOrderBy[5]) { - return n1->proto().total_exec_micros() > - n2->proto().total_exec_micros(); - } else if (opts.order_by == kOrderBy[6]) { - return n1->proto().total_accelerator_exec_micros() > - n2->proto().total_accelerator_exec_micros(); - } else if (opts.order_by == kOrderBy[7]) { - return n1->proto().total_cpu_exec_micros() > - n2->proto().total_cpu_exec_micros(); - } else if (opts.order_by == kOrderBy[8]) { - return n1->proto().total_parameters() > n2->proto().total_parameters(); - } else if (opts.order_by == kOrderBy[9]) { - return n1->proto().total_float_ops() > n2->proto().total_float_ops(); - } - return name_cmp; - }); + std::sort(sorted_nodes.begin(), sorted_nodes.end(), + [&opts](const T* n1, const T* n2) { + if (n1->name() == kTFProfRoot) return true; + if (n2->name() == kTFProfRoot) return false; + bool name_cmp = n1->name() < n2->name(); + if (opts.order_by == kOrderBy[0]) { + return name_cmp; + } else if (opts.order_by == kOrderBy[1]) { + return n1->proto().total_requested_bytes() > + n2->proto().total_requested_bytes(); + } else if (opts.order_by == kOrderBy[2]) { + return n1->proto().total_peak_bytes() > + n2->proto().total_peak_bytes(); + } else if (opts.order_by == kOrderBy[3]) { + return n1->proto().total_residual_bytes() > + n2->proto().total_residual_bytes(); + } else if (opts.order_by == kOrderBy[4]) { + return n1->proto().total_output_bytes() > + n2->proto().total_output_bytes(); + } else if (opts.order_by == kOrderBy[5]) { + return n1->proto().total_exec_micros() > + n2->proto().total_exec_micros(); + } else if (opts.order_by == kOrderBy[6]) { + return n1->proto().total_accelerator_exec_micros() > + n2->proto().total_accelerator_exec_micros(); + } else if (opts.order_by == kOrderBy[7]) { + return n1->proto().total_cpu_exec_micros() > + n2->proto().total_cpu_exec_micros(); + } else if (opts.order_by == kOrderBy[8]) { + return n1->proto().total_parameters() > + n2->proto().total_parameters(); + } else if (opts.order_by == kOrderBy[9]) { + return n1->proto().total_float_ops() > + n2->proto().total_float_ops(); + } + return name_cmp; + }); return sorted_nodes; } diff --git a/tensorflow/core/profiler/internal/tfprof_show_multi.h b/tensorflow/core/profiler/internal/tfprof_show_multi.h index 2a2208d8e7..711d35f975 100644 --- a/tensorflow/core/profiler/internal/tfprof_show_multi.h +++ b/tensorflow/core/profiler/internal/tfprof_show_multi.h @@ -50,7 +50,7 @@ class TFMultiShow { protected: virtual const ShowMultiNode* ShowInternal(const Options& opts, - Timeline* timeline) = 0; + Timeline* timeline) = 0; bool LookUpCheckPoint(const string& name, std::unique_ptr* tensor); diff --git a/tensorflow/core/profiler/internal/tfprof_timeline.h b/tensorflow/core/profiler/internal/tfprof_timeline.h index 651ad3f0c1..baf3fb2bed 100644 --- a/tensorflow/core/profiler/internal/tfprof_timeline.h +++ b/tensorflow/core/profiler/internal/tfprof_timeline.h @@ -20,8 +20,8 @@ limitations under the License. #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/framework/step_stats.pb.h" #include "tensorflow/core/lib/strings/strcat.h" -#include "tensorflow/core/protobuf/config.pb.h" #include "tensorflow/core/profiler/internal/tfprof_node_show.h" +#include "tensorflow/core/protobuf/config.pb.h" namespace tensorflow { namespace tfprof { diff --git a/tensorflow/core/util/bcast.cc b/tensorflow/core/util/bcast.cc index 1eab7e3d02..3a5f1f83af 100644 --- a/tensorflow/core/util/bcast.cc +++ b/tensorflow/core/util/bcast.cc @@ -69,9 +69,9 @@ BCast::BCast(const Vec& sx, const Vec& sy, const bool fewer_dims_optimization) { State curr = UNKNOWN; const int64 x_i = x[i]; // i-th dimension of x. const int64 y_i = y[i]; // i-th dimension of y. - int64 o_i; // i-th dimension of the output. - int64 bx_i; // i-th broadcast for x. - int64 by_i; // i-th broadcast for y. + int64 o_i; // i-th dimension of the output. + int64 bx_i; // i-th broadcast for x. + int64 by_i; // i-th broadcast for y. // Invariant: // o_i = x_i * bx_i = y_i * by_i if (x_i == y_i) { diff --git a/tensorflow/core/util/ctc/ctc_loss_calculator.h b/tensorflow/core/util/ctc/ctc_loss_calculator.h index be00895b0d..dd1163310b 100644 --- a/tensorflow/core/util/ctc/ctc_loss_calculator.h +++ b/tensorflow/core/util/ctc/ctc_loss_calculator.h @@ -130,13 +130,13 @@ Status CTCLossCalculator::CalculateLoss( for (int t = 1; t < num_time_steps; ++t) { if (inputs[t].rows() != batch_size) { return errors::InvalidArgument("Expected batch size at t: ", t, - " to be: ", batch_size, " but got: ", - inputs[t].rows()); + " to be: ", batch_size, + " but got: ", inputs[t].rows()); } if (inputs[t].cols() != num_classes) { return errors::InvalidArgument("Expected class count at t: ", t, - " to be: ", num_classes, " but got: ", - inputs[t].cols()); + " to be: ", num_classes, + " but got: ", inputs[t].cols()); } } @@ -282,8 +282,8 @@ Status CTCLossCalculator::PopulateLPrimes( LabelSequences* l_primes) const { // labels is a Label array of size batch_size if (labels.size() != batch_size) { - return errors::InvalidArgument("labels.size() != batch_size: ", - labels.size(), " vs. ", batch_size); + return errors::InvalidArgument( + "labels.size() != batch_size: ", labels.size(), " vs. ", batch_size); } *max_u_prime = 0; // keep track of longest l' modified label sequence. @@ -325,12 +325,13 @@ Status CTCLossCalculator::PopulateLPrimes( for (int l_i : l) { if (l_i < 0) { return errors::InvalidArgument( - "All labels must be nonnegative integers, batch: ", b, " labels: ", - str_util::Join(l, ",")); + "All labels must be nonnegative integers, batch: ", b, + " labels: ", str_util::Join(l, ",")); } else if (l_i >= num_classes) { return errors::InvalidArgument( - "No label may be greater than num_classes. ", "num_classes: ", - num_classes, ", batch: ", b, " labels: ", str_util::Join(l, ",")); + "No label may be greater than num_classes. ", + "num_classes: ", num_classes, ", batch: ", b, + " labels: ", str_util::Join(l, ",")); } } if (!ignore_longer_outputs_than_inputs) { diff --git a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc index bd4c356ea0..732ed33ede 100644 --- a/tensorflow/core/util/cuda_kernel_helper_test.cu.cc +++ b/tensorflow/core/util/cuda_kernel_helper_test.cu.cc @@ -149,27 +149,27 @@ class CudaLaunchConfigTest : public ::testing::Test { TEST_F(CudaLaunchConfigTest, GetCudaLaunchConfig) { CudaLaunchConfig cfg; - // test valid inputs - #define TEST_LAUNCH_PARAMETER(work_element_count) \ - cfg = GetCudaLaunchConfig(bufsize, d); \ - SetOutbufZero<<>> \ - (cfg, outbuf); \ - CUDA_ASSERT_SUCCESS \ - cfg = GetCudaLaunchConfig(work_element_count, d); \ - Count1D<<>> ( \ - cfg, bufsize, outbuf); \ - CUDA_EXPECT_SUCCESS \ - EXPECT_EQ(work_element_count, std::accumulate(outbuf, outbuf + bufsize, 0));\ - \ - cfg = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \ - SetOutbufZero<<>> \ - (cfg, outbuf); \ - CUDA_ASSERT_SUCCESS \ - cfg = GetCudaLaunchConfig(work_element_count, d, Count1D, 0, 0); \ - Count1D<<>> ( \ - cfg, bufsize, outbuf); \ - CUDA_EXPECT_SUCCESS \ - EXPECT_EQ(work_element_count, std::accumulate(outbuf, outbuf + bufsize, 0)) +// test valid inputs +#define TEST_LAUNCH_PARAMETER(work_element_count) \ + cfg = GetCudaLaunchConfig(bufsize, d); \ + SetOutbufZero<<>>( \ + cfg, outbuf); \ + CUDA_ASSERT_SUCCESS \ + cfg = GetCudaLaunchConfig(work_element_count, d); \ + Count1D<<>>( \ + cfg, bufsize, outbuf); \ + CUDA_EXPECT_SUCCESS \ + EXPECT_EQ(work_element_count, std::accumulate(outbuf, outbuf + bufsize, 0)); \ + \ + cfg = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \ + SetOutbufZero<<>>( \ + cfg, outbuf); \ + CUDA_ASSERT_SUCCESS \ + cfg = GetCudaLaunchConfig(work_element_count, d, Count1D, 0, 0); \ + Count1D<<>>( \ + cfg, bufsize, outbuf); \ + CUDA_EXPECT_SUCCESS \ + EXPECT_EQ(work_element_count, std::accumulate(outbuf, outbuf + bufsize, 0)) TEST_LAUNCH_PARAMETER(128); TEST_LAUNCH_PARAMETER(129); @@ -181,7 +181,7 @@ TEST_F(CudaLaunchConfigTest, GetCudaLaunchConfig) { TEST_LAUNCH_PARAMETER(8192); TEST_LAUNCH_PARAMETER(123456); TEST_LAUNCH_PARAMETER(1 << 30); - #undef TEST_LAUNCH_PARAMETER +#undef TEST_LAUNCH_PARAMETER } bool operator==(const Cuda2DLaunchConfig& a, const Cuda2DLaunchConfig& b) { @@ -200,27 +200,27 @@ TEST_F(CudaLaunchConfigTest, GetCuda2DLaunchConfig) { Cuda2DLaunchConfig cfg; CudaLaunchConfig cfg1d; - // test valid inputs - #define TEST_LAUNCH_PARAMETER(dimx, dimy) \ - cfg1d = GetCudaLaunchConfig(bufsize, d); \ - SetOutbufZero<<>> \ - (cfg1d, outbuf);\ - CUDA_ASSERT_SUCCESS \ - cfg = GetCuda2DLaunchConfig(dimx, dimy, d); \ - Count2D<<>> ( \ - cfg, bufsize, outbuf); \ - CUDA_EXPECT_SUCCESS \ - EXPECT_EQ(dimx * dimy, std::accumulate(outbuf, outbuf + bufsize, 0)); \ - \ - cfg1d = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \ - SetOutbufZero<<>> \ - (cfg1d, outbuf);\ - CUDA_ASSERT_SUCCESS \ - cfg = GetCuda2DLaunchConfig(dimx, dimy, d, Count2D, 0, 0); \ - Count2D<<>> ( \ - cfg, bufsize, outbuf); \ - CUDA_EXPECT_SUCCESS \ - EXPECT_EQ(dimx * dimy, std::accumulate(outbuf, outbuf + bufsize, 0)) +// test valid inputs +#define TEST_LAUNCH_PARAMETER(dimx, dimy) \ + cfg1d = GetCudaLaunchConfig(bufsize, d); \ + SetOutbufZero<<>>( \ + cfg1d, outbuf); \ + CUDA_ASSERT_SUCCESS \ + cfg = GetCuda2DLaunchConfig(dimx, dimy, d); \ + Count2D<<>>( \ + cfg, bufsize, outbuf); \ + CUDA_EXPECT_SUCCESS \ + EXPECT_EQ(dimx* dimy, std::accumulate(outbuf, outbuf + bufsize, 0)); \ + \ + cfg1d = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \ + SetOutbufZero<<>>( \ + cfg1d, outbuf); \ + CUDA_ASSERT_SUCCESS \ + cfg = GetCuda2DLaunchConfig(dimx, dimy, d, Count2D, 0, 0); \ + Count2D<<>>( \ + cfg, bufsize, outbuf); \ + CUDA_EXPECT_SUCCESS \ + EXPECT_EQ(dimx* dimy, std::accumulate(outbuf, outbuf + bufsize, 0)) TEST_LAUNCH_PARAMETER(128, 128); TEST_LAUNCH_PARAMETER(129, 64); @@ -233,24 +233,24 @@ TEST_F(CudaLaunchConfigTest, GetCuda2DLaunchConfig) { TEST_LAUNCH_PARAMETER(123456, 12); TEST_LAUNCH_PARAMETER(1, 1 << 30); TEST_LAUNCH_PARAMETER(1 << 30, 1); - #undef TEST_LAUNCH_PARAMETER +#undef TEST_LAUNCH_PARAMETER } TEST_F(CudaLaunchConfigTest, GetCuda3DLaunchConfig) { Cuda3DLaunchConfig cfg; CudaLaunchConfig cfg1d; - // test valid inputs - #define TEST_LAUNCH_PARAMETER(dimx, dimy, dimz) \ - cfg1d = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \ - SetOutbufZero<<>> \ - (cfg1d, outbuf);\ - CUDA_ASSERT_SUCCESS \ - cfg = GetCuda3DLaunchConfig(dimx, dimy, dimz, d, Count3D, 0, 0); \ - Count3D<<>> ( \ - cfg, bufsize, outbuf); \ - CUDA_EXPECT_SUCCESS \ - EXPECT_EQ(dimx * dimy * dimz, std::accumulate(outbuf, outbuf + bufsize, 0)) +// test valid inputs +#define TEST_LAUNCH_PARAMETER(dimx, dimy, dimz) \ + cfg1d = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \ + SetOutbufZero<<>>( \ + cfg1d, outbuf); \ + CUDA_ASSERT_SUCCESS \ + cfg = GetCuda3DLaunchConfig(dimx, dimy, dimz, d, Count3D, 0, 0); \ + Count3D<<>>( \ + cfg, bufsize, outbuf); \ + CUDA_EXPECT_SUCCESS \ + EXPECT_EQ(dimx* dimy* dimz, std::accumulate(outbuf, outbuf + bufsize, 0)) TEST_LAUNCH_PARAMETER(128, 128, 128); TEST_LAUNCH_PARAMETER(129, 64, 1024); @@ -264,7 +264,7 @@ TEST_F(CudaLaunchConfigTest, GetCuda3DLaunchConfig) { TEST_LAUNCH_PARAMETER(1, 1, 1 << 30); TEST_LAUNCH_PARAMETER(1, 1 << 30, 1); TEST_LAUNCH_PARAMETER(1 << 30, 1, 1); - #undef TEST_LAUNCH_PARAMETER +#undef TEST_LAUNCH_PARAMETER } TEST(CudaDeviceFunctionsTest, ShuffleGetSrcLane) { diff --git a/tensorflow/core/util/example_proto_helper.cc b/tensorflow/core/util/example_proto_helper.cc index 41f56d2daa..e156a3bc8f 100644 --- a/tensorflow/core/util/example_proto_helper.cc +++ b/tensorflow/core/util/example_proto_helper.cc @@ -247,8 +247,9 @@ Status SingleExampleProtoToTensors( bool types_match; TF_RETURN_IF_ERROR(CheckTypesMatch(f, dtype, &types_match)); if (!types_match) { - return errors::InvalidArgument("Name: ", example_name, ", Feature: ", - key, ". Data types don't match. ", + return errors::InvalidArgument("Name: ", example_name, + ", Feature: ", key, + ". Data types don't match. ", "Expected type: ", DataTypeString(dtype), " Feature is: ", ProtoDebugString(f)); } @@ -278,8 +279,9 @@ Status SingleExampleProtoToTensors( bool types_match; TF_RETURN_IF_ERROR(CheckTypesMatch(f, dtype, &types_match)); if (!types_match) { - return errors::InvalidArgument("Name: ", example_name, ", Feature: ", - key, ". Data types don't match. ", + return errors::InvalidArgument("Name: ", example_name, + ", Feature: ", key, + ". Data types don't match. ", "Expected type: ", DataTypeString(dtype), " Feature is: ", ProtoDebugString(f)); } diff --git a/tensorflow/core/util/memmapped_file_system_test.cc b/tensorflow/core/util/memmapped_file_system_test.cc index 616eb5dac3..504d2d353f 100644 --- a/tensorflow/core/util/memmapped_file_system_test.cc +++ b/tensorflow/core/util/memmapped_file_system_test.cc @@ -144,8 +144,8 @@ TEST(MemmappedFileSystemTest, ProxyToDefault) { TF_ASSERT_OK(memmapped_env.NewAppendableFile(filename, &writable_file_temp)); // Making sure to clean up after the test finishes. const auto adh = [&memmapped_env, &filename](WritableFile* f) { - delete f; - TF_CHECK_OK(memmapped_env.DeleteFile(filename)); + delete f; + TF_CHECK_OK(memmapped_env.DeleteFile(filename)); }; std::unique_ptr writable_file( writable_file_temp.release(), adh); diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index 2caf5fc56d..864e7e39c2 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -210,31 +210,32 @@ class MklShape { CHECK_EQ(dnnDelete_F32(convert), E_SUCCESS); } -// The following methods are used for serializing and de-serializing the -// contents of the mklshape object. -// The data is serialized in this order -// isMklTensor_ -// dimension_ -// sizes_ -// strides_ -// mklLayout_ -// tfLayout_ -// tf_to_mkl_dim_map_ + // The following methods are used for serializing and de-serializing the + // contents of the mklshape object. + // The data is serialized in this order + // isMklTensor_ + // dimension_ + // sizes_ + // strides_ + // mklLayout_ + // tfLayout_ + // tf_to_mkl_dim_map_ #define SIZE_OF_MKL_DNN_BUF \ (dnnLayoutSerializationBufferSize_F32()) // Size of buffer needed to // serialize dnn_layout pointer -// Size of buffer to hold the serialized object, the size is computed as follows -// sizeof(isMklTensor_) + sizeof(dimension_) + sizeof(sizes_) + sizeof(strides_) -// + sizeof(mklLayout_ buffer) + sizeof(tfLayout_ buffer) -// + sizeof(tf_to_mkl_dim_map_) + // Size of buffer to hold the serialized object, the size is computed as + // follows sizeof(isMklTensor_) + sizeof(dimension_) + sizeof(sizes_) + + // sizeof(strides_) + // + sizeof(mklLayout_ buffer) + sizeof(tfLayout_ buffer) + // + sizeof(tf_to_mkl_dim_map_) #define SIZE_OF_MKL_SERIAL_DATA(dims) \ (2 * sizeof(size_t) + 3 * dims * sizeof(size_t) + 2 * SIZE_OF_MKL_DNN_BUF) -// First we need to define some macro for offsets into the serial buffer where -// different elements of Mklshape is written/read from + // First we need to define some macro for offsets into the serial buffer where + // different elements of Mklshape is written/read from #define IS_MKL_TENSOR_OFFSET 0 // Location from start of buffer where isMklTensor_ is serialized @@ -388,7 +389,7 @@ class MklDnnShape { /// Equality function for MklDnnShape objects /// @return true if both are equal; false otherwise. - inline bool operator == (const MklDnnShape& input_shape) const { + inline bool operator==(const MklDnnShape& input_shape) const { if (this->IsMklTensor() != input_shape.IsMklTensor()) { return false; } @@ -406,7 +407,7 @@ class MklDnnShape { /// Equality operator for MklDnnShape and TFShape. /// Returns: true if TF shapes for both are the same, false otherwise - inline bool operator == (const TensorShape& input_shape) const { + inline bool operator==(const TensorShape& input_shape) const { if (!this->IsMklTensor()) { return false; } @@ -425,7 +426,7 @@ class MklDnnShape { inline size_t GetDimension(char dimension) const { int index = GetMklDnnTensorDimIndex(dimension); CHECK(index >= 0 && index < this->GetDimension()) - << "Invalid index from the dimension: " << index << ", " << dimension; + << "Invalid index from the dimension: " << index << ", " << dimension; return this->DimSize(index); } @@ -705,8 +706,8 @@ inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, Tensor output_tensor; TensorShape output_shape; - TF_CHECK_OK(Status(error::Code::UNIMPLEMENTED, - "Unimplemented conversion function")); + TF_CHECK_OK( + Status(error::Code::UNIMPLEMENTED, "Unimplemented conversion function")); return output_tensor; } @@ -748,7 +749,6 @@ inline void GetMklInputList(OpKernelContext* ctext, StringPiece name, ctext->input_list(name, input_tensors); } - #ifndef INTEL_MKL_DNN inline void GetMklShapeList(OpKernelContext* ctext, StringPiece name, @@ -973,8 +973,8 @@ inline int64 GetMklTensorDim(const MklShape& mkl_shape, char dimension) { return mkl_shape.dim_size(index); } -inline void CopyMklTensorInToOut(OpKernelContext* context, - int idx_in, int idx_out) { +inline void CopyMklTensorInToOut(OpKernelContext* context, int idx_in, + int idx_out) { int num_inputs = context->num_inputs(); int num_outputs = context->num_outputs(); int idx_data_in = GetTensorDataIndex(idx_in, num_inputs); @@ -995,8 +995,8 @@ inline void CopyMklTensorInToOut(OpKernelContext* context, } #ifndef INTEL_MKL_DNN -inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, - int idx_in, int idx_out, +inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, int idx_in, + int idx_out, const TensorShape& shape) { int num_inputs = context->num_inputs(); int num_outputs = context->num_outputs(); @@ -1013,8 +1013,8 @@ inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, context->set_output(idx_data_out, output); } #else -inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, - int idx_in, int idx_out, +inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, int idx_in, + int idx_out, const TensorShape& shape) { int num_inputs = context->num_inputs(); int num_outputs = context->num_outputs(); @@ -1034,8 +1034,8 @@ inline void CopyTfTensorInToOutWithShape(OpKernelContext* context, #ifndef INTEL_MKL_DNN -inline void ForwardTfTensorInToOut(OpKernelContext* context, - int idx_in, int idx_out) { +inline void ForwardTfTensorInToOut(OpKernelContext* context, int idx_in, + int idx_out) { int num_inputs = context->num_inputs(); int num_outputs = context->num_outputs(); int idx_data_in = GetTensorDataIndex(idx_in, num_inputs); @@ -1053,8 +1053,8 @@ inline void ForwardTfTensorInToOut(OpKernelContext* context, #else -inline void ForwardTfTensorInToOut(OpKernelContext* context, - int idx_in, int idx_out) { +inline void ForwardTfTensorInToOut(OpKernelContext* context, int idx_in, + int idx_out) { int num_inputs = context->num_inputs(); int num_outputs = context->num_outputs(); int idx_data_in = GetTensorDataIndex(idx_in, num_inputs); @@ -1072,8 +1072,8 @@ inline void ForwardTfTensorInToOut(OpKernelContext* context, #endif -inline void ForwardMklTensorInToOut(OpKernelContext* context, - int idx_in, int idx_out) { +inline void ForwardMklTensorInToOut(OpKernelContext* context, int idx_in, + int idx_out) { int num_inputs = context->num_inputs(); int num_outputs = context->num_outputs(); int idx_data_in = GetTensorDataIndex(idx_in, num_inputs); @@ -1092,8 +1092,8 @@ inline void ForwardMklTensorInToOut(OpKernelContext* context, #ifdef INTEL_MKL_DNN inline void ForwardMklTensorInToOutWithMklShape(OpKernelContext* context, - int idx_in, int idx_out, - const MklDnnShape& mkl_shape) { + int idx_in, int idx_out, + const MklDnnShape& mkl_shape) { int num_inputs = context->num_inputs(); int num_outputs = context->num_outputs(); int idx_data_in = GetTensorDataIndex(idx_in, num_inputs); @@ -1216,11 +1216,11 @@ inline void MklNHWCToNCHW(const Tensor& input, Tensor** output) { int64 H = input.dim_size(1); int64 W = input.dim_size(2); int64 C = input.dim_size(3); - int64 stride_n = H*W*C; -# pragma omp parallel for num_threads(16) + int64 stride_n = H * W * C; +#pragma omp parallel for num_threads(16) for (int64 n = 0; n < N; ++n) { - mkl_somatcopy('R', 'T', H*W, C, 1, buf_in + n*stride_n, C, - buf_out + n*stride_n, H*W); + mkl_somatcopy('R', 'T', H * W, C, 1, buf_in + n * stride_n, C, + buf_out + n * stride_n, H * W); } } @@ -1232,11 +1232,11 @@ inline void MklNCHWToNHWC(const Tensor& input, Tensor** output) { int64 H = (*output)->dim_size(1); int64 W = (*output)->dim_size(2); int64 C = (*output)->dim_size(3); - int64 stride_n = H*W*C; -# pragma omp parallel for num_threads(16) + int64 stride_n = H * W * C; +#pragma omp parallel for num_threads(16) for (int64 n = 0; n < N; ++n) { - mkl_somatcopy('R', 'T', C, H*W, 1, buf_in + n*stride_n, H*W, - buf_out + n*stride_n, C); + mkl_somatcopy('R', 'T', C, H * W, 1, buf_in + n * stride_n, H * W, + buf_out + n * stride_n, C); } } @@ -1279,10 +1279,11 @@ inline memory::format TFDataFormatToMklDnnDataFormat(TensorFormat format) { /// @return: Tensorflow data format corresponding to memory::format /// Fails with an error if invalid data format. inline TensorFormat MklDnnDataFormatToTFDataFormat(memory::format format) { - if (format == memory::format::nhwc) return FORMAT_NHWC; - else if (format == memory::format::nchw) return FORMAT_NCHW; - TF_CHECK_OK(Status(error::Code::INVALID_ARGUMENT, - "Unsupported data format")); + if (format == memory::format::nhwc) + return FORMAT_NHWC; + else if (format == memory::format::nchw) + return FORMAT_NCHW; + TF_CHECK_OK(Status(error::Code::INVALID_ARGUMENT, "Unsupported data format")); // Return to prevent compiler warnings, otherwise TF_CHECK_OK will ensure // that we don't come here. @@ -1425,7 +1426,6 @@ inline memory::desc CreateBlockedMemDescHelper(const memory::dims& dim, return memory::desc(md); } - /* * Class to represent all the resources corresponding to a tensor in TensorFlow * that are required to execute an operation (such as Convolution). @@ -1494,7 +1494,7 @@ class MklDnnData { /// @return: memory::desc object corresponding to blocked memory format /// for given dimensions and strides. static inline memory::desc CreateBlockedMemDesc(const memory::dims& dim, - const memory::dims& strides) { + const memory::dims& strides) { return CreateBlockedMemDescHelper(dim, strides, MklDnnType()); } @@ -1563,7 +1563,6 @@ class MklDnnData { return user_memory_->get_primitive_desc(); } - /// Get function for descriptor of user memory. inline memory::desc GetUsrMemDesc() { // This is ugly. Why MKL-DNN does not provide desc() method of const type?? @@ -1634,7 +1633,8 @@ class MklDnnData { /// @return: true in case reorder of input is needed; false, otherwise. inline bool IsReorderNeeded(const memory::format& target_format) const { CHECK_NOTNULL(user_memory_); - return target_format != user_memory_->get_primitive_desc().desc().data.format; + return target_format != + user_memory_->get_primitive_desc().desc().data.format; } /// Function to create a reorder from memory pointed by from to memory pointed diff --git a/tensorflow/core/util/presized_cuckoo_map.h b/tensorflow/core/util/presized_cuckoo_map.h index e7dab830f0..f88ad2faaf 100644 --- a/tensorflow/core/util/presized_cuckoo_map.h +++ b/tensorflow/core/util/presized_cuckoo_map.h @@ -67,7 +67,7 @@ inline uint64 multiply_high_u64(uint64 x, uint64 y) { return prod_hi + (prod_mid1 >> 32) + (prod_mid2 >> 32) + carry; #endif } -} +} // namespace presized_cuckoo_map template class PresizedCuckooMap { diff --git a/tensorflow/core/util/reporter_test.cc b/tensorflow/core/util/reporter_test.cc index 1cb07718fe..575c27d4ef 100644 --- a/tensorflow/core/util/reporter_test.cc +++ b/tensorflow/core/util/reporter_test.cc @@ -29,8 +29,8 @@ namespace { // Tests of all the error paths in log_reader.cc follow: static void ExpectHasSubstr(StringPiece s, StringPiece expected) { - EXPECT_TRUE(StringPiece(s).contains(expected)) << s << " does not contain " - << expected; + EXPECT_TRUE(StringPiece(s).contains(expected)) + << s << " does not contain " << expected; } TEST(TestReporter, NoLogging) { diff --git a/tensorflow/core/util/sparse/sparse_tensor.h b/tensorflow/core/util/sparse/sparse_tensor.h index f2401a0af4..258ee418c1 100644 --- a/tensorflow/core/util/sparse/sparse_tensor.h +++ b/tensorflow/core/util/sparse/sparse_tensor.h @@ -20,6 +20,7 @@ limitations under the License. #include #include +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/framework/types.h" @@ -31,7 +32,6 @@ limitations under the License. #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/sparse/dim_comparator.h" #include "tensorflow/core/util/sparse/group_iterator.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { namespace sparse { @@ -59,8 +59,8 @@ class SparseTensor { shape_(shape.begin(), shape.end()), order_(order.begin(), order.end()), dims_(GetDimsFromIx(ix)) { - CHECK_EQ(ix.dtype(), DT_INT64) << "indices must be type int64 but got: " - << ix.dtype(); + CHECK_EQ(ix.dtype(), DT_INT64) + << "indices must be type int64 but got: " << ix.dtype(); CHECK(TensorShapeUtils::IsVector(vals.shape())) << "vals must be a vec, but got: " << vals.shape().DebugString(); CHECK_EQ(ix.shape().dim_size(0), vals.shape().dim_size(0)) diff --git a/tensorflow/core/util/sparse/sparse_tensor_test.cc b/tensorflow/core/util/sparse/sparse_tensor_test.cc index efdd97fd3d..85de032085 100644 --- a/tensorflow/core/util/sparse/sparse_tensor_test.cc +++ b/tensorflow/core/util/sparse/sparse_tensor_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_types.h" #include "tensorflow/core/lib/core/status_test_util.h" @@ -25,7 +26,6 @@ limitations under the License. #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/platform/test_benchmark.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" namespace tensorflow { namespace sparse { diff --git a/tensorflow/core/util/stream_executor_util.h b/tensorflow/core/util/stream_executor_util.h index 6a5ddec04c..f7767ace71 100644 --- a/tensorflow/core/util/stream_executor_util.h +++ b/tensorflow/core/util/stream_executor_util.h @@ -41,9 +41,10 @@ class StreamExecutorUtil { // This assumes that the error codes between the two implementations // match. static Status ConvertStatus(const perftools::gputools::port::Status& s) { - return s.ok() ? Status::OK() : Status(static_cast( - static_cast(s.code())), - s.error_message()); + return s.ok() ? Status::OK() + : Status(static_cast( + static_cast(s.code())), + s.error_message()); } }; diff --git a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc index 579b70ab51..462b420976 100644 --- a/tensorflow/core/util/tensor_bundle/tensor_bundle.cc +++ b/tensorflow/core/util/tensor_bundle/tensor_bundle.cc @@ -913,8 +913,8 @@ Status BundleReader::LookupSlice(StringPiece full_tensor_key, Status BundleReader::GetSliceValue(StringPiece full_tensor_key, const BundleEntryProto& full_tensor_entry, const TensorSlice& slice_spec, Tensor* val) { - using checkpoint::TensorSliceSet; using checkpoint::RegisterTensorSlice; + using checkpoint::TensorSliceSet; DCHECK_GE(full_tensor_entry.slices_size(), 0); const TensorShape full_shape(TensorShape(full_tensor_entry.shape())); diff --git a/tensorflow/core/util/tensor_slice_reader_cache.cc b/tensorflow/core/util/tensor_slice_reader_cache.cc index 0f009d7de5..424f8098a9 100644 --- a/tensorflow/core/util/tensor_slice_reader_cache.cc +++ b/tensorflow/core/util/tensor_slice_reader_cache.cc @@ -55,7 +55,7 @@ const TensorSliceReader* TensorSliceReaderCache::GetReader( TensorSliceReader::OpenTableFunction open_function, int preferred_shard) { mutex_lock l(mu_); -#if defined(__GXX_RTTI) || defined(_CPPRTTI) +#if defined(__GXX_RTTI) || defined(_CPPRTTI) // Get the function pointer from the open_function value. TensorSliceReaderCache::OpenFuncType* func_ptr = open_function.target(); diff --git a/tensorflow/core/util/tensor_slice_set.cc b/tensorflow/core/util/tensor_slice_set.cc index 4217df90ca..7c1d325c0a 100644 --- a/tensorflow/core/util/tensor_slice_set.cc +++ b/tensorflow/core/util/tensor_slice_set.cc @@ -188,9 +188,9 @@ Status RegisterTensorSlice( } if (type != tss->type()) { return errors::Internal("Incompatible tensor types detected for tensor ", - name, ": existing = ", - DataTypeString(tss->type()), ", new = ", - DataTypeString(type)); + name, + ": existing = ", DataTypeString(tss->type()), + ", new = ", DataTypeString(type)); } } // Register the tensor slices without the actual data. diff --git a/tensorflow/core/util/tensor_slice_util.h b/tensorflow/core/util/tensor_slice_util.h index c7edae66b2..8f5a6f1d93 100644 --- a/tensorflow/core/util/tensor_slice_util.h +++ b/tensorflow/core/util/tensor_slice_util.h @@ -139,9 +139,9 @@ static bool CopyDataFromTensorSliceToTensorSlice(const TensorShape& shape, const TensorSlice& slice_d, const SrcT* ptr_s, DstT* ptr_d) { - CHECK_LE(shape.dims(), kTensorSliceMaxRank) << "Only tensors of size up to " - << kTensorSliceMaxRank - << " are supported"; + CHECK_LE(shape.dims(), kTensorSliceMaxRank) + << "Only tensors of size up to " << kTensorSliceMaxRank + << " are supported"; // We need to compute the intersection of the two slices. TensorSlice inter; if (!slice_s.Intersect(slice_d, &inter)) { diff --git a/tensorflow/core/util/tensor_slice_writer.h b/tensorflow/core/util/tensor_slice_writer.h index bdb4921e1b..2888c66d10 100644 --- a/tensorflow/core/util/tensor_slice_writer.h +++ b/tensorflow/core/util/tensor_slice_writer.h @@ -101,8 +101,8 @@ Status TensorSliceWriter::Add(const string& name, const TensorShape& shape, // The tensor and the slice have to be compatible if (shape.dims() != slice.dims()) { return errors::Internal("Incompatible tensor shape and slice: ", "shape = ", - shape.DebugString(), ", slice = ", - slice.DebugString()); + shape.DebugString(), + ", slice = ", slice.DebugString()); } DataType dt = DataTypeToEnum::value; // We need to add an entry for "name" if there isn't an entry already. @@ -114,9 +114,9 @@ Status TensorSliceWriter::Add(const string& name, const TensorShape& shape, CHECK_EQ(name, ssm.name()) << ProtoShortDebugString(ssm); TensorShape ssm_shape(ssm.shape()); if (!shape.IsSameSize(ssm_shape)) { - return errors::Internal("Mismatching shapes: existing tensor = ", - ssm_shape.DebugString(), ", trying to add name ", - name, ", shape = ", shape.DebugString()); + return errors::Internal( + "Mismatching shapes: existing tensor = ", ssm_shape.DebugString(), + ", trying to add name ", name, ", shape = ", shape.DebugString()); } if (dt != ssm.type()) { return errors::Internal( -- GitLab From 8f0e7207774279f4fe50f4d6c4fbd576e2941463 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 10:18:36 -0800 Subject: [PATCH 265/423] Prepare variance to be exported for serving with the servo library. PiperOrigin-RevId: 183851026 --- .../tensor_forest/client/random_forest.py | 41 +++++++++++++++---- 1 file changed, 34 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/tensor_forest/client/random_forest.py b/tensorflow/contrib/tensor_forest/client/random_forest.py index a998ac1e11..4abcc20ed3 100644 --- a/tensorflow/contrib/tensor_forest/client/random_forest.py +++ b/tensorflow/contrib/tensor_forest/client/random_forest.py @@ -18,7 +18,7 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib import layers - +from tensorflow.contrib.learn.python.learn.estimators import constants from tensorflow.contrib.learn.python.learn.estimators import estimator from tensorflow.contrib.learn.python.learn.estimators import head as head_lib from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib @@ -43,8 +43,8 @@ from tensorflow.python.training import training_util KEYS_NAME = 'keys' LOSS_NAME = 'rf_training_loss' TREE_PATHS_PREDICTION_KEY = 'tree_paths' -VARIANCE_PREDICTION_KEY = 'regression_variance' - +VARIANCE_PREDICTION_KEY = 'prediction_variance' +ALL_SERVING_KEY = 'tensorforest_all' EPSILON = 0.000001 @@ -134,7 +134,8 @@ def get_model_fn(params, trainer_id=0, report_feature_importances=False, local_eval=False, - head_scope=None): + head_scope=None, + include_all_in_serving=False): """Return a model function given a way to construct a graph builder.""" if model_head is None: model_head = get_default_head(params, weights_name) @@ -238,7 +239,13 @@ def get_model_fn(params, model_ops.predictions[TREE_PATHS_PREDICTION_KEY] = tree_paths model_ops.predictions[VARIANCE_PREDICTION_KEY] = regression_variance - + if include_all_in_serving: + # In order to serve the variance we need to add the prediction dict + # to output_alternatives dict. + if not model_ops.output_alternatives: + model_ops.output_alternatives = {} + model_ops.output_alternatives[ALL_SERVING_KEY] = ( + constants.ProblemType.UNSPECIFIED, model_ops.predictions) return model_ops return _model_fn @@ -293,7 +300,8 @@ class TensorForestEstimator(estimator.Estimator): report_feature_importances=False, local_eval=False, version=None, - head=None): + head=None, + include_all_in_serving=False): """Initializes a TensorForestEstimator instance. Args: @@ -339,6 +347,23 @@ class TensorForestEstimator(estimator.Estimator): version: Unused. head: A heads_lib.Head object that calculates losses and such. If None, one will be automatically created based on params. + include_all_in_serving: if True, allow preparation of the complete + prediction dict including the variance to be exported for serving with + the Servo lib; and it also requires calling export_savedmodel with + default_output_alternative_key=ALL_SERVING_KEY, i.e. + estimator.export_savedmodel(export_dir_base=your_export_dir, + serving_input_fn=your_export_input_fn, + default_output_alternative_key=ALL_SERVING_KEY) + if False, resort to default behavior, i.e. export scores and + probabilities but no variances. In this case + default_output_alternative_key should be None while calling + export_savedmodel(). + Note, that due to backward compatibility we cannot always set + include_all_in_serving to True because in this case calling + export_saved_model() without + default_output_alternative_key=ALL_SERVING_KEY (legacy behavior) the + saved_model_export_utils.get_output_alternatives() would raise + ValueError. Returns: A `TensorForestEstimator` instance. @@ -357,7 +382,9 @@ class TensorForestEstimator(estimator.Estimator): num_trainers=num_trainers, trainer_id=trainer_id, report_feature_importances=report_feature_importances, - local_eval=local_eval), + local_eval=local_eval, + include_all_in_serving=include_all_in_serving, + ), model_dir=model_dir, config=config, feature_engineering_fn=feature_engineering_fn) -- GitLab From 4463d105a8a4a83642b9709ba79310e8f4ddf577 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 10:43:03 -0800 Subject: [PATCH 266/423] Cleanup: Ran clang-format on all *.{cc,h} files in tensorflow/contrib/.../*.{hh,c}. PiperOrigin-RevId: 183855242 --- .../contrib/android/jni/run_stats_jni.cc | 2 +- .../boosted_trees/kernels/model_ops.cc | 5 +- .../boosted_trees/kernels/prediction_ops.cc | 2 +- .../boosted_trees/kernels/quantile_ops.cc | 4 +- .../kernels/split_handler_ops.cc | 7 +- .../kernels/stats_accumulator_ops.cc | 30 +-- .../learner/common/stats/node-stats_test.cc | 2 +- .../lib/quantiles/weighted_quantiles_stream.h | 2 +- .../lib/testutil/random_tree_gen.cc | 2 +- .../lib/utils/batch_features_test.cc | 2 +- .../boosted_trees/lib/utils/dropout_utils.cc | 2 +- .../lib/utils/dropout_utils_test.cc | 2 +- .../contrib/boosted_trees/ops/quantile_ops.cc | 2 +- .../boosted_trees/ops/split_handler_ops.cc | 2 +- .../ops/stats_accumulator_ops.cc | 2 +- .../bigquery_table_accessor_test_data.h | 2 +- .../contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc | 8 +- .../kernels/masked_matmul_ops.cc | 25 +- .../contrib/ffmpeg/default/ffmpeg_lib.cc | 41 ++-- .../contrib/ffmpeg/default/ffmpeg_lib_test.cc | 8 +- .../ffmpeg/default/ffmpeg_lib_utility_test.cc | 2 - .../framework/kernels/zero_initializer_op.cc | 6 +- .../framework/kernels/zero_initializer_op.h | 4 +- .../contrib/framework/ops/variable_ops.cc | 4 +- tensorflow/contrib/gdr/gdr_memory_manager.cc | 13 +- tensorflow/contrib/image/kernels/image_ops.cc | 13 +- ...single_image_random_dot_stereograms_ops.cc | 4 +- ...single_image_random_dot_stereograms_ops.cc | 4 +- .../kernels/input_pipeline_kernels.cc | 5 +- .../kernels/sparse_feature_cross_kernel.cc | 5 +- tensorflow/contrib/lite/interpreter.h | 2 +- .../contrib/lite/kernels/activations.cc | 11 +- tensorflow/contrib/lite/kernels/add.cc | 8 +- tensorflow/contrib/lite/kernels/basic_rnn.cc | 6 +- .../contrib/lite/kernels/basic_rnn_test.cc | 5 +- .../kernels/embedding_lookup_sparse_test.cc | 22 +- .../contrib/lite/kernels/gather_test.cc | 9 +- .../lite/kernels/hashtable_lookup_test.cc | 5 +- .../kernels/internal/optimized/cpu_check.h | 8 +- .../internal/optimized/depthwiseconv_float.h | 10 +- .../optimized/eigen_spatial_convolutions.h | 14 +- .../lite/kernels/optional_tensor_test.cc | 2 - tensorflow/contrib/lite/kernels/pad.cc | 4 +- tensorflow/contrib/lite/kernels/svdf.cc | 2 +- tensorflow/contrib/lite/kernels/svdf_test.cc | 2 +- .../kernels/unidirectional_sequence_rnn.cc | 6 +- .../unidirectional_sequence_rnn_test.cc | 5 +- .../contrib/lite/toco/tensorflow_util.cc | 6 +- tensorflow/contrib/lite/toco/tflite/export.cc | 6 +- .../contrib/lite/toco/tflite/operator.cc | 12 +- .../memory_stats/kernels/memory_stats_ops.cc | 8 +- tensorflow/contrib/mpi/mpi_rendezvous_mgr.cc | 218 ++++++++--------- tensorflow/contrib/mpi/mpi_rendezvous_mgr.h | 11 +- tensorflow/contrib/mpi/mpi_server_lib.cc | 2 +- tensorflow/contrib/mpi/mpi_utils.h | 2 +- .../mpi_collectives/kernels/mpi_ops.cc | 2 +- .../kernels/hyperplane_lsh_probes.cc | 24 +- .../kernels/periodic_resample_op.cc | 5 +- .../kernels/periodic_resample_op.h | 6 +- .../contrib/pi_examples/camera/camera.cc | 10 +- .../pi_examples/label_image/label_image.cc | 89 +++---- .../kernels/reduce_slice_ops.cc | 2 +- .../kernels/reduce_slice_ops.h | 12 +- .../kernels/reduce_slice_ops_gpu.cu.cc | 2 +- .../resampler/kernels/resampler_ops.cc | 223 +++++++----------- .../contrib/resampler/kernels/resampler_ops.h | 39 +-- .../resampler/kernels/resampler_ops_gpu.cu.cc | 188 +++++++-------- tensorflow/contrib/rnn/kernels/blas_gemm.cc | 9 +- tensorflow/contrib/rnn/kernels/gru_ops.cc | 110 ++++----- tensorflow/contrib/rnn/kernels/lstm_ops.cc | 193 ++++++++------- tensorflow/contrib/rnn/kernels/lstm_ops.h | 1 - tensorflow/contrib/rnn/ops/lstm_ops_test.cc | 5 +- .../session_bundle/bundle_shim_test.cc | 14 +- .../contrib/session_bundle/signature.cc | 14 +- .../core/ops/hard_routing_function_op.cc | 27 +-- .../hybrid/core/ops/k_feature_gradient_op.cc | 56 ++--- .../core/ops/k_feature_routing_function_op.cc | 49 ++-- .../hybrid/core/ops/routing_function_op.cc | 29 +-- .../stochastic_hard_routing_function_op.cc | 38 ++- .../stochastic_hard_routing_gradient_op.cc | 18 +- .../hybrid/core/ops/unpack_path_op.cc | 10 +- .../tensor_forest/hybrid/core/ops/utils.cc | 11 +- .../tensor_forest/hybrid/core/ops/utils.h | 13 +- .../kernels/reinterpret_string_to_float_op.cc | 15 +- .../kernels/scatter_add_ndim_op.cc | 19 +- .../tensor_forest/kernels/tree_utils.cc | 78 +++--- .../tensor_forest/kernels/tree_utils.h | 25 +- .../tensor_forest/kernels/tree_utils_test.cc | 128 +++++----- .../kernels/v4/candidate_graph_runner.cc | 13 +- .../kernels/v4/decision-tree-resource.h | 13 +- .../kernels/v4/decision_node_evaluator.h | 1 - .../v4/decision_node_evaluator_test.cc | 5 +- .../kernels/v4/fertile-stats-resource.h | 9 +- .../tensor_forest/kernels/v4/grow_stats.cc | 36 ++- .../tensor_forest/kernels/v4/grow_stats.h | 25 +- .../kernels/v4/grow_stats_test.cc | 47 ++-- .../tensor_forest/kernels/v4/input_data.cc | 8 +- .../tensor_forest/kernels/v4/input_data.h | 4 +- .../tensor_forest/kernels/v4/input_target.h | 4 +- .../kernels/v4/leaf_model_operators.cc | 1 - .../kernels/v4/leaf_model_operators_test.cc | 24 +- .../contrib/tensor_forest/kernels/v4/params.h | 1 - .../tensor_forest/kernels/v4/params_test.cc | 2 - .../kernels/v4/split_collection_operators.cc | 7 +- .../kernels/v4/split_collection_operators.h | 4 +- .../tensor_forest/kernels/v4/stat_utils.cc | 22 +- .../tensor_forest/kernels/v4/test_utils.h | 5 +- .../convert_graphdef_memmapped_format_lib.cc | 6 +- tensorflow/contrib/util/inspect_checkpoint.cc | 2 +- tensorflow/contrib/verbs/verbs_server_lib.cc | 4 +- 110 files changed, 1029 insertions(+), 1276 deletions(-) diff --git a/tensorflow/contrib/android/jni/run_stats_jni.cc b/tensorflow/contrib/android/jni/run_stats_jni.cc index 119fa9cd2c..707853b59b 100644 --- a/tensorflow/contrib/android/jni/run_stats_jni.cc +++ b/tensorflow/contrib/android/jni/run_stats_jni.cc @@ -21,8 +21,8 @@ limitations under the License. #include "tensorflow/core/protobuf/config.pb.h" #include "tensorflow/core/util/stat_summarizer.h" -using tensorflow::StatSummarizer; using tensorflow::RunMetadata; +using tensorflow::StatSummarizer; namespace { StatSummarizer* requireHandle(JNIEnv* env, jlong handle) { diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc index 4b5d5ba0de..754b7bc327 100644 --- a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc @@ -48,8 +48,9 @@ class CreateTreeEnsembleVariableOp : public OpKernel { if (!result->InitFromSerialized(tree_ensemble_config_t->scalar()(), stamp_token)) { result->Unref(); - OP_REQUIRES(context, false, errors::InvalidArgument( - "Unable to parse tree ensemble config.")); + OP_REQUIRES( + context, false, + errors::InvalidArgument("Unable to parse tree ensemble config.")); } // Only create one, if one does not exist already. Report status for all diff --git a/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc index f8086b0c2b..b3fe38614e 100644 --- a/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/prediction_ops.cc @@ -47,8 +47,8 @@ namespace boosted_trees { using boosted_trees::learner::LearnerConfig; using boosted_trees::learner::LearningRateConfig; using boosted_trees::learner::LearningRateDropoutDrivenConfig; -using boosted_trees::models::MultipleAdditiveTrees; using boosted_trees::models::DecisionTreeEnsembleResource; +using boosted_trees::models::MultipleAdditiveTrees; using boosted_trees::utils::DropoutUtils; using boosted_trees::utils::TensorUtils; diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc index e91232bf10..0f4c2298f5 100644 --- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc @@ -36,8 +36,8 @@ namespace tensorflow { using ::boosted_trees::QuantileConfig; -using boosted_trees::utils::TensorUtils; using boosted_trees::QuantileStreamResource; +using boosted_trees::utils::TensorUtils; namespace { const char* const kExampleWeightsName = "example_weights"; @@ -384,7 +384,7 @@ class MakeQuantileSummariesOp : public OpKernel { protobuf::Arena arena; ::boosted_trees::QuantileSummaryState* summary_proto = protobuf::Arena::CreateMessage< - ::boosted_trees::QuantileSummaryState>(&arena); + ::boosted_trees::QuantileSummaryState>(&arena); const auto& summary = stream.GetFinalSummary(); CopySummaryToProto(summary, summary_proto); // Output to tensor. diff --git a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc index 18b4abd654..44a8ffaf4b 100644 --- a/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/split_handler_ops.cc @@ -34,10 +34,10 @@ namespace tensorflow { +using boosted_trees::learner::LearnerConfig_MultiClassStrategy; using boosted_trees::learner::SplitInfo; using boosted_trees::learner::stochastic::GradientStats; using boosted_trees::learner::stochastic::NodeStats; -using boosted_trees::learner::LearnerConfig_MultiClassStrategy; namespace { const int32 DUMMY_FEATURE_DIMENSION = -1; @@ -47,9 +47,8 @@ class BaseBuildSplitOp : public OpKernel { public: explicit BaseBuildSplitOp(OpKernelConstruction* const context) : OpKernel(context) { - OP_REQUIRES_OK( - context, - context->GetAttr("feature_column_group_id", &feature_column_group_id_)); + OP_REQUIRES_OK(context, context->GetAttr("feature_column_group_id", + &feature_column_group_id_)); OP_REQUIRES_OK(context, context->GetAttr("l1_regularization", &l1_regularization_)); OP_REQUIRES_OK(context, diff --git a/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc b/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc index a9a229c8ae..90a0655201 100644 --- a/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/stats_accumulator_ops.cc @@ -134,10 +134,9 @@ void SerializeScalarAccumulatorToOutput( OpKernelContext* context) { int64 num_slots = accumulator_resource.values().size(); Tensor* partition_ids_t = nullptr; - OP_REQUIRES_OK( - context, - context->allocate_output("output_partition_ids", TensorShape({num_slots}), - &partition_ids_t)); + OP_REQUIRES_OK(context, context->allocate_output("output_partition_ids", + TensorShape({num_slots}), + &partition_ids_t)); auto partition_ids = partition_ids_t->vec(); // Feature ids tensor has ids of feature columns and their dimensions. @@ -149,15 +148,14 @@ void SerializeScalarAccumulatorToOutput( Tensor* gradients_t = nullptr; OP_REQUIRES_OK( - context, - context->allocate_output("output_gradients", TensorShape({num_slots}), - &gradients_t)); + context, context->allocate_output( + "output_gradients", TensorShape({num_slots}), &gradients_t)); auto gradients = gradients_t->vec(); Tensor* hessians_t = nullptr; - OP_REQUIRES_OK(context, - context->allocate_output( - "output_hessians", TensorShape({num_slots}), &hessians_t)); + OP_REQUIRES_OK( + context, context->allocate_output("output_hessians", + TensorShape({num_slots}), &hessians_t)); auto hessians = hessians_t->vec(); int i = 0; @@ -177,10 +175,9 @@ void SerializeTensorAccumulatorToOutput( OpKernelContext* context) { int64 num_slots = accumulator_resource.values().size(); Tensor* partition_ids_t = nullptr; - OP_REQUIRES_OK( - context, - context->allocate_output("output_partition_ids", TensorShape({num_slots}), - &partition_ids_t)); + OP_REQUIRES_OK(context, context->allocate_output("output_partition_ids", + TensorShape({num_slots}), + &partition_ids_t)); auto partition_ids = partition_ids_t->vec(); Tensor* feature_ids_t = nullptr; @@ -202,9 +199,8 @@ void SerializeTensorAccumulatorToOutput( int64 num_hessian_elements = hessian_shape.num_elements(); hessian_shape.InsertDim(0, num_slots); Tensor* hessians_t = nullptr; - OP_REQUIRES_OK( - context, - context->allocate_output("output_hessians", hessian_shape, &hessians_t)); + OP_REQUIRES_OK(context, context->allocate_output("output_hessians", + hessian_shape, &hessians_t)); auto hessians = hessians_t->flat_outer_dims(); int i = 0; diff --git a/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats_test.cc b/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats_test.cc index f867e77d3e..8bca132acf 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats_test.cc +++ b/tensorflow/contrib/boosted_trees/lib/learner/common/stats/node-stats_test.cc @@ -17,8 +17,8 @@ #include "tensorflow/core/framework/tensor_testutil.h" #include "tensorflow/core/platform/test.h" -using tensorflow::test::AsTensor; using std::vector; +using tensorflow::test::AsTensor; namespace tensorflow { namespace boosted_trees { 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 1c4181f1b1..8ad97fedc9 100644 --- a/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h +++ b/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h @@ -15,9 +15,9 @@ #ifndef TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_QUANTILES_WEIGHTED_QUANTILES_STREAM_H_ #define TENSORFLOW_CONTRIB_BOOSTED_TREES_LIB_QUANTILES_WEIGHTED_QUANTILES_STREAM_H_ +#include #include #include -#include #include "tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_buffer.h" #include "tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_summary.h" diff --git a/tensorflow/contrib/boosted_trees/lib/testutil/random_tree_gen.cc b/tensorflow/contrib/boosted_trees/lib/testutil/random_tree_gen.cc index cbe26ba918..705b65e9db 100644 --- a/tensorflow/contrib/boosted_trees/lib/testutil/random_tree_gen.cc +++ b/tensorflow/contrib/boosted_trees/lib/testutil/random_tree_gen.cc @@ -22,9 +22,9 @@ namespace tensorflow { namespace boosted_trees { namespace testutil { +using boosted_trees::trees::DenseFloatBinarySplit; using tensorflow::boosted_trees::trees::DecisionTreeConfig; using tensorflow::boosted_trees::trees::TreeNode; -using boosted_trees::trees::DenseFloatBinarySplit; namespace { diff --git a/tensorflow/contrib/boosted_trees/lib/utils/batch_features_test.cc b/tensorflow/contrib/boosted_trees/lib/utils/batch_features_test.cc index 9de3e32b09..609519e8b1 100644 --- a/tensorflow/contrib/boosted_trees/lib/utils/batch_features_test.cc +++ b/tensorflow/contrib/boosted_trees/lib/utils/batch_features_test.cc @@ -25,8 +25,8 @@ namespace boosted_trees { namespace utils { namespace { -using test::AsTensor; using errors::InvalidArgument; +using test::AsTensor; class BatchFeaturesTest : public ::testing::Test {}; diff --git a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.cc b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.cc index 38f0151255..db34db998a 100644 --- a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.cc +++ b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils.cc @@ -23,10 +23,10 @@ #include "tensorflow/core/lib/random/simple_philox.h" #include "tensorflow/core/platform/logging.h" +using tensorflow::Status; using tensorflow::boosted_trees::learner::LearningRateDropoutDrivenConfig; using tensorflow::random::PhiloxRandom; using tensorflow::random::SimplePhilox; -using tensorflow::Status; namespace tensorflow { namespace boosted_trees { diff --git a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc index ce7632e589..02f972c8e0 100644 --- a/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc +++ b/tensorflow/contrib/boosted_trees/lib/utils/dropout_utils_test.cc @@ -26,9 +26,9 @@ #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/env.h" +using std::unordered_set; using tensorflow::boosted_trees::learner::LearningRateDropoutDrivenConfig; using tensorflow::boosted_trees::trees::DecisionTreeEnsembleConfig; -using std::unordered_set; namespace tensorflow { namespace boosted_trees { diff --git a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc index bb57dcf8ae..ae99d53a2c 100644 --- a/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc +++ b/tensorflow/contrib/boosted_trees/ops/quantile_ops.cc @@ -19,8 +19,8 @@ namespace tensorflow { namespace boosted_trees { -using shape_inference::InferenceContext; using shape_inference::DimensionHandle; +using shape_inference::InferenceContext; using shape_inference::ShapeHandle; REGISTER_RESOURCE_HANDLE_OP(QuantileStreamResource); diff --git a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc index 0d27ddaf3a..5d0ebbf73c 100644 --- a/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc +++ b/tensorflow/contrib/boosted_trees/ops/split_handler_ops.cc @@ -18,9 +18,9 @@ namespace tensorflow { +using shape_inference::DimensionHandle; using shape_inference::InferenceContext; using shape_inference::ShapeHandle; -using shape_inference::DimensionHandle; REGISTER_OP("BuildDenseInequalitySplits") .Attr("feature_column_group_id: int") diff --git a/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc index 0354f7853c..179505eef0 100644 --- a/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc +++ b/tensorflow/contrib/boosted_trees/ops/stats_accumulator_ops.cc @@ -19,9 +19,9 @@ namespace tensorflow { namespace boosted_trees { +using shape_inference::DimensionHandle; using shape_inference::InferenceContext; using shape_inference::ShapeHandle; -using shape_inference::DimensionHandle; REGISTER_RESOURCE_HANDLE_OP(StatsAccumulatorScalarResource); diff --git a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test_data.h b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test_data.h index 59f2333298..fea6b15640 100644 --- a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test_data.h +++ b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test_data.h @@ -399,6 +399,6 @@ const string kTestEmptyRow = R"({ }]}]})"; } // namespace -} // namepsace tensorflow +} // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_CLOUD_BIGQUERY_TABLE_ACCESSOR_TEST_DATA_H_ diff --git a/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc b/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc index 9e41e67857..1a79bf066c 100644 --- a/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc +++ b/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc @@ -251,9 +251,8 @@ REGISTER_OP("CudnnRNNParamsToCanonical") TF_RETURN_IF_ERROR(c->GetAttr("num_params", &num_params)); // Set shape for weight matrices for (int i = 0; i < num_params; i++) { - c->set_output(i, - c->Matrix(InferenceContext::kUnknownDim, - InferenceContext::kUnknownDim)); + c->set_output(i, c->Matrix(InferenceContext::kUnknownDim, + InferenceContext::kUnknownDim)); } // Set shape for bias vectors for (int i = 0; i < num_params; i++) { @@ -300,6 +299,7 @@ upcoming training or inferences. num_params: number of parameter sets for all layers. Each layer may contain multiple parameter sets, with each set consisting of a weight matrix and a bias vector. -)doc", kCudnnRNNCommonAttrs)); +)doc", + kCudnnRNNCommonAttrs)); } // namespace tensorflow diff --git a/tensorflow/contrib/factorization/kernels/masked_matmul_ops.cc b/tensorflow/contrib/factorization/kernels/masked_matmul_ops.cc index 31d08bfb65..a8c5d0763c 100644 --- a/tensorflow/contrib/factorization/kernels/masked_matmul_ops.cc +++ b/tensorflow/contrib/factorization/kernels/masked_matmul_ops.cc @@ -57,11 +57,11 @@ typedef Eigen::Map< class MaskedMatmulOp : public OpKernel { public: - explicit MaskedMatmulOp(OpKernelConstruction* context) - : OpKernel(context) { - OP_REQUIRES_OK(context, context->MatchSignature( - {DT_FLOAT, DT_FLOAT, DT_INT64, DT_BOOL, DT_BOOL}, - {DT_FLOAT})); + explicit MaskedMatmulOp(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK( + context, + context->MatchSignature( + {DT_FLOAT, DT_FLOAT, DT_INT64, DT_BOOL, DT_BOOL}, {DT_FLOAT})); } void Compute(OpKernelContext* context) override { @@ -110,12 +110,11 @@ class MaskedMatmulOp : public OpKernel { num_nonzero_elements, 2); Tensor* prod_values_tensor; - OP_REQUIRES_OK(context, - context->allocate_output( - 0, TensorShape({num_nonzero_elements}), - &prod_values_tensor)); - EigenMatFloatMap prod_values(prod_values_tensor->vec().data(), - 1, num_nonzero_elements); + OP_REQUIRES_OK(context, context->allocate_output( + 0, TensorShape({num_nonzero_elements}), + &prod_values_tensor)); + EigenMatFloatMap prod_values(prod_values_tensor->vec().data(), 1, + num_nonzero_elements); auto get_a_index = [&indices_mat, &a_dim_0](int64 i) { int64 a_index = internal::SubtleMustCopy(indices_mat(i, 0)); @@ -182,8 +181,8 @@ class MaskedMatmulOp : public OpKernel { } }; // Shard the work. - worker_threads.workers->ParallelFor( - num_nonzero_elements, cost_per_unit, work); + worker_threads.workers->ParallelFor(num_nonzero_elements, cost_per_unit, + work); } }; REGISTER_KERNEL_BUILDER(Name("MaskedMatmul").Device(DEVICE_CPU), diff --git a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc index c85b1837ab..e61221a6b0 100644 --- a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc +++ b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc @@ -47,20 +47,19 @@ std::vector FfmpegAudioCommandLine(const string& input_filename, int32 channel_count, const string& stream) { std::vector command({ - "-nostats", // No additional progress display. - "-nostdin", // No interactive commands accepted. - "-f", input_format_id, // eg: "mp3" - "-probesize", StrCat(kDefaultProbeSize), "-i", input_filename, - "-loglevel", "error", // Print errors only. - "-hide_banner", // Skip printing build options, version, etc. - "-map_metadata", "-1", // Copy global metadata from input to output. - "-vn", // No video recording. - "-ac:a:0", StrCat(channel_count), "-ar:a:0", - StrCat(samples_per_second), - // Output set (in several ways) to signed 16-bit little-endian ints. - "-codec:a:0", "pcm_s16le", "-sample_fmt", "s16", "-f", "s16le", - "-sn", // No subtitle recording. - "-y" // Overwrite output file. + "-nostats", // No additional progress display. + "-nostdin", // No interactive commands accepted. + "-f", input_format_id, // eg: "mp3" + "-probesize", StrCat(kDefaultProbeSize), "-i", input_filename, + "-loglevel", "error", // Print errors only. + "-hide_banner", // Skip printing build options, version, etc. + "-map_metadata", "-1", // Copy global metadata from input to output. + "-vn", // No video recording. + "-ac:a:0", StrCat(channel_count), "-ar:a:0", StrCat(samples_per_second), + // Output set (in several ways) to signed 16-bit little-endian ints. + "-codec:a:0", "pcm_s16le", "-sample_fmt", "s16", "-f", "s16le", + "-sn", // No subtitle recording. + "-y" // Overwrite output file. }); if (!stream.empty()) { command.emplace_back("-map"); @@ -75,21 +74,13 @@ std::vector FfmpegVideoCommandLine(const string& input_filename, const string& output_filename) { return {"-nostats", // No additional progress display. "-nostdin", // No interactive commands accepted. - "-i", - input_filename, - "-f", - "image2pipe", - "-probesize", - StrCat(kDefaultProbeSize), - "-loglevel", + "-i", input_filename, "-f", "image2pipe", "-probesize", + StrCat(kDefaultProbeSize), "-loglevel", // Info is needed to get the information about stream, etc. // It is generated to a separate file, not stdout/stderr. "info", "-hide_banner", // Skip printing build options, version, etc. - "-vcodec", - "rawvideo", - "-pix_fmt", - "rgb24", + "-vcodec", "rawvideo", "-pix_fmt", "rgb24", "-y", // Overwrite output file. StrCat(output_filename)}; } diff --git a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_test.cc b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_test.cc index 85b61b2616..05728b3d37 100644 --- a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_test.cc +++ b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_test.cc @@ -32,10 +32,8 @@ namespace tensorflow { namespace ffmpeg { namespace { -const char kTestWavFilename[] = - "contrib/ffmpeg/testdata/mono_10khz.wav"; -const char kTestMp3Filename[] = - "contrib/ffmpeg/testdata/test_sound1.mp3"; +const char kTestWavFilename[] = "contrib/ffmpeg/testdata/mono_10khz.wav"; +const char kTestMp3Filename[] = "contrib/ffmpeg/testdata/test_sound1.mp3"; // Set to true via a command line flag iff the test is expected to have FFmpeg // installed. @@ -139,7 +137,7 @@ TEST(FfmpegLibTest, TestRoundTripWav) { } // namespace ffmpeg } // namespace tensorflow -int main(int argc, char **argv) { +int main(int argc, char** argv) { tensorflow::string usage = tensorflow::ffmpeg::ParseTestFlags(&argc, argv); testing::InitGoogleTest(&argc, argv); if (argc != 1) { diff --git a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_utility_test.cc b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_utility_test.cc index 36fc71794b..d6c885a324 100644 --- a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_utility_test.cc +++ b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib_utility_test.cc @@ -20,8 +20,6 @@ #include #include - -#include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/lib/core/threadpool.h" #include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/platform/env.h" diff --git a/tensorflow/contrib/framework/kernels/zero_initializer_op.cc b/tensorflow/contrib/framework/kernels/zero_initializer_op.cc index 6677dca752..5bf6b67529 100644 --- a/tensorflow/contrib/framework/kernels/zero_initializer_op.cc +++ b/tensorflow/contrib/framework/kernels/zero_initializer_op.cc @@ -21,8 +21,8 @@ limitations under the License. #include "tensorflow/contrib/framework/kernels/zero_initializer_op.h" -#include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" namespace tensorflow { @@ -81,8 +81,8 @@ TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPEC); #define REGISTER_GPU_KERNELS(T) REGISTER_KERNELS(GPU, T); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GPU_KERNELS); #undef REGISTER_GPU_KERNELS -#endif // GOOGLE_CUDA +#endif // GOOGLE_CUDA #undef REGISTER_KERNELS -} // namespace tensorflow +} // namespace tensorflow diff --git a/tensorflow/contrib/framework/kernels/zero_initializer_op.h b/tensorflow/contrib/framework/kernels/zero_initializer_op.h index 14c9268efa..99389a5ab6 100644 --- a/tensorflow/contrib/framework/kernels/zero_initializer_op.h +++ b/tensorflow/contrib/framework/kernels/zero_initializer_op.h @@ -29,5 +29,5 @@ struct TensorSetZero { }; } // namespace functor -} // end namespace tensorflow -#endif // TENSORFLOW_CONTRIB_FRAMEWORK_KERNELS_ZERO_INITIALIZER_OP_H_ +} // end namespace tensorflow +#endif // TENSORFLOW_CONTRIB_FRAMEWORK_KERNELS_ZERO_INITIALIZER_OP_H_ diff --git a/tensorflow/contrib/framework/ops/variable_ops.cc b/tensorflow/contrib/framework/ops/variable_ops.cc index 1ee8e1498c..706134ba9a 100644 --- a/tensorflow/contrib/framework/ops/variable_ops.cc +++ b/tensorflow/contrib/framework/ops/variable_ops.cc @@ -26,8 +26,8 @@ REGISTER_OP("ZeroInitializer") .Attr("T: realnumbertype") .SetAllowsUninitializedInput() .SetShapeFn([](InferenceContext* c) { - c->set_output(0, c->input(0)); - return Status::OK(); + c->set_output(0, c->input(0)); + return Status::OK(); }) .Doc(R"doc( Initialize 'ref' with all zeros. This op requires that the tensor is not diff --git a/tensorflow/contrib/gdr/gdr_memory_manager.cc b/tensorflow/contrib/gdr/gdr_memory_manager.cc index 5c7ac74428..81e70ae30a 100644 --- a/tensorflow/contrib/gdr/gdr_memory_manager.cc +++ b/tensorflow/contrib/gdr/gdr_memory_manager.cc @@ -86,8 +86,9 @@ int TryToReadNumaNode(ibv_device* device) { if (strings::safe_strto32(content, &value)) { if (value < 0) { LOG(INFO) << "Successful NUMA node read from SysFS had negative value (" - << value << "), but there must be at least one NUMA node" - ", so returning NUMA node zero"; + << value + << "), but there must be at least one NUMA node" + ", so returning NUMA node zero"; return 0; } LOG(INFO) << "NUMA node for device: " << device->name << " is " << value; @@ -290,8 +291,8 @@ Status GdrMemoryManager::Init() { // Host memory allocators for (Allocator* allocator : allocators) { auto* visitable_allocator = dynamic_cast(allocator); - CHECK(visitable_allocator) << "is not visitable for instrumentation" - << allocator->Name(); + CHECK(visitable_allocator) + << "is not visitable for instrumentation" << allocator->Name(); // Make sure we don't instrument the same allocator twice if (instrumented_.find(allocator) == std::end(instrumented_)) { visitable_allocator->AddAllocVisitor(alloc_visitor); @@ -635,8 +636,8 @@ void GdrMemoryManager::TensorFromTransportOptions( } else { checksum = GPUUtil::Checksum(*tensor); } - CHECK(checksum == remote_mr.checksum()) << "Checksum mismatch: " << checksum - << "!=" << remote_mr.checksum(); + CHECK(checksum == remote_mr.checksum()) + << "Checksum mismatch: " << checksum << "!=" << remote_mr.checksum(); #endif } done(Status::OK()); diff --git a/tensorflow/contrib/image/kernels/image_ops.cc b/tensorflow/contrib/image/kernels/image_ops.cc index 6adf837ca0..c2e32da133 100644 --- a/tensorflow/contrib/image/kernels/image_ops.cc +++ b/tensorflow/contrib/image/kernels/image_ops.cc @@ -43,9 +43,9 @@ template struct FillProjectiveTransform; typedef Eigen::ThreadPoolDevice CPUDevice; using functor::FillProjectiveTransform; +using generator::Interpolation; using generator::INTERPOLATION_BILINEAR; using generator::INTERPOLATION_NEAREST; -using generator::Interpolation; using generator::ProjectiveGenerator; template @@ -72,11 +72,12 @@ class ImageProjectiveTransform : public OpKernel { const Tensor& transform_t = ctx->input(1); OP_REQUIRES(ctx, images_t.shape().dims() == 4, errors::InvalidArgument("Input images must have rank 4")); - OP_REQUIRES(ctx, (TensorShapeUtils::IsMatrix(transform_t.shape()) && - (transform_t.dim_size(0) == images_t.dim_size(0) || - transform_t.dim_size(0) == 1) && - transform_t.dim_size(1) == - ProjectiveGenerator::kNumParameters), + OP_REQUIRES(ctx, + (TensorShapeUtils::IsMatrix(transform_t.shape()) && + (transform_t.dim_size(0) == images_t.dim_size(0) || + transform_t.dim_size(0) == 1) && + transform_t.dim_size(1) == + ProjectiveGenerator::kNumParameters), errors::InvalidArgument( "Input transform should be num_images x 8 or 1 x 8")); auto images = images_t.tensor(); diff --git a/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc b/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc index 9f0bf37aed..8f9a5c2803 100755 --- a/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc +++ b/tensorflow/contrib/image/kernels/single_image_random_dot_stereograms_ops.cc @@ -143,8 +143,8 @@ class SingleImageRandomDotStereogramsOp : public OpKernel { } data_box_left = deltaX_border_image / 2; // Center DATA in X dimension - data_box_width = data_Xwindow; // width of scan line - data_box_height = data_Ywindow; // hight of image + data_box_width = data_Xwindow; // width of scan line + data_box_height = data_Ywindow; // hight of image const T* inputZ = input_tensor.flat().data(); // Flatten input Z buffer diff --git a/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc b/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc index 1f41f243f2..8139d4272d 100755 --- a/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc +++ b/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc @@ -58,7 +58,9 @@ REGISTER_OP("SingleImageRandomDotStereograms") int colors; TF_RETURN_IF_ERROR(c->GetAttr("number_colors", &colors)); - c->set_output(0, c->MakeShape({y_dim, x_dim, colors > 256? c->MakeDim(3) : c->MakeDim(1)})); + c->set_output( + 0, c->MakeShape( + {y_dim, x_dim, colors > 256 ? c->MakeDim(3) : c->MakeDim(1)})); return Status::OK(); }) .Doc(R"doc( diff --git a/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc b/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc index ca288c1f73..886f679815 100644 --- a/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc +++ b/tensorflow/contrib/input_pipeline/kernels/input_pipeline_kernels.cc @@ -34,9 +34,8 @@ class ObtainNextOp : public OpKernel { // Allocate output. Tensor* output_tensor = nullptr; - OP_REQUIRES_OK( - ctx, - ctx->allocate_output("out_element", TensorShape({}), &output_tensor)); + OP_REQUIRES_OK(ctx, ctx->allocate_output("out_element", TensorShape({}), + &output_tensor)); // Obtain mutex for the "counter" tensor. mutex* mu; diff --git a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc index 932c5ab992..01893d6061 100644 --- a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc +++ b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc @@ -423,8 +423,9 @@ class SparseFeatureCrossOp : public OpKernel { "Input values should be a std::vector but received shape ", values_list_in[i].shape().DebugString(), " at position ", i)); OP_REQUIRES( - context, indices_list_in[i].shape().dim_size(0) == - values_list_in[i].shape().dim_size(0), + context, + indices_list_in[i].shape().dim_size(0) == + values_list_in[i].shape().dim_size(0), errors::InvalidArgument( "Expected size of values to be ", indices_list_in[i].shape().dim_size(0), " got ", diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 9dc864ead8..52e52df1b6 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -171,7 +171,7 @@ class Interpreter { // read/write access to structure TfLiteTensor* tensor(int tensor_index) { if (tensor_index >= context_.tensors_size || tensor_index < 0) - return nullptr; + return nullptr; return &context_.tensors[tensor_index]; } diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index 8ac93bc8c8..3c5c77815d 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -15,8 +15,8 @@ limitations under the License. #include #include #include -#include #include +#include #include #include @@ -134,8 +134,7 @@ TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) { float* out = output->data.f; for (; in < in_end; in++, out++) *out = std::max(0.f, *in); return kTfLiteOk; - } - break; + } break; default: context->ReportError(context, "Only float32 supported currently."); return kTfLiteError; @@ -173,8 +172,7 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) { float* out = output->data.f; for (; in < in_end; in++, out++) *out = std::min(std::max(0.f, *in), 6.f); return kTfLiteOk; - } - break; + } break; default: context->ReportError(context, "Only float32 supported currently."); return kTfLiteError; @@ -192,8 +190,7 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { float* out = output->data.f; for (; in < in_end; in++, out++) *out = std::tanh(*in); return kTfLiteOk; - } - break; + } break; default: context->ReportError(context, "Only float32 supported currently."); return kTfLiteError; diff --git a/tensorflow/contrib/lite/kernels/add.cc b/tensorflow/contrib/lite/kernels/add.cc index 0e10a249ab..fb5764f280 100644 --- a/tensorflow/contrib/lite/kernels/add.cc +++ b/tensorflow/contrib/lite/kernels/add.cc @@ -70,10 +70,10 @@ void EvalAddFloat(TfLiteContext* context, TfLiteNode* node, GetTensorData(input2), GetTensorDims(input2), \ output_activation_min, output_activation_max, \ GetTensorData(output), GetTensorDims(output)) - if (kernel_type == kReference) { - TF_LITE_ADD(reference_ops); - } else { - TF_LITE_ADD(optimized_ops); + if (kernel_type == kReference) { + TF_LITE_ADD(reference_ops); + } else { + TF_LITE_ADD(optimized_ops); } #undef TF_LITE_ADD } diff --git a/tensorflow/contrib/lite/kernels/basic_rnn.cc b/tensorflow/contrib/lite/kernels/basic_rnn.cc index 3cee43c68b..a0391e030f 100644 --- a/tensorflow/contrib/lite/kernels/basic_rnn.cc +++ b/tensorflow/contrib/lite/kernels/basic_rnn.cc @@ -15,8 +15,8 @@ limitations under the License. #include #include #include -#include #include +#include #include #include @@ -76,8 +76,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteIntArray* output_size_array = TfLiteIntArrayCreate(2); output_size_array->data[0] = batch_size; output_size_array->data[1] = num_units; - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output, - output_size_array)); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, output, output_size_array)); return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/kernels/basic_rnn_test.cc b/tensorflow/contrib/lite/kernels/basic_rnn_test.cc index 5ecccb985e..fa7ef525db 100644 --- a/tensorflow/contrib/lite/kernels/basic_rnn_test.cc +++ b/tensorflow/contrib/lite/kernels/basic_rnn_test.cc @@ -14,8 +14,8 @@ limitations under the License. ==============================================================================*/ // Unit test for TFLite RNN op. -#include #include +#include #include #include @@ -120,8 +120,7 @@ static float rnn_golden_output[] = { 0.415153, 0.210318, 0, 0, 0, 0, 0, 2.02616, 0, 0.728256, 0.84183, 0.0907453, - 0.628881, 3.58099, 1.49974, 0 -}; + 0.628881, 3.58099, 1.49974, 0}; class RNNOpModel : public SingleOpModel { public: diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup_sparse_test.cc b/tensorflow/contrib/lite/kernels/embedding_lookup_sparse_test.cc index dcdc5fffad..ef2b542225 100644 --- a/tensorflow/contrib/lite/kernels/embedding_lookup_sparse_test.cc +++ b/tensorflow/contrib/lite/kernels/embedding_lookup_sparse_test.cc @@ -123,18 +123,16 @@ TEST(EmbeddingLookupOpTest, SimpleTestSqrtn) { [](int i, int j, int k) { return i + j / 10.0f + k / 100.0f; }); m.Invoke(); - EXPECT_THAT( - m.GetOutput(), - ElementsAreArray(ArrayFloatNear({ - 1.00, 1.01, 1.10, 1.11, 1.20, 1.21, // Row 1 - 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, // - - 6.00f / std::sqrt(20.0f), 6.06f / std::sqrt(20.0f), - 6.60f / std::sqrt(20.0f), 6.66f / std::sqrt(20.0f), - 7.20f / std::sqrt(20.0f), - 7.26f / - std::sqrt( - 20.0f), // 2 * Row 3 + 4 * Row 0, // 2 * Row 3 + 4 * Row 0 - }))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({ + 1.00, 1.01, 1.10, 1.11, 1.20, 1.21, // Row 1 + 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, // - + 6.00f / std::sqrt(20.0f), 6.06f / std::sqrt(20.0f), + 6.60f / std::sqrt(20.0f), 6.66f / std::sqrt(20.0f), + 7.20f / std::sqrt(20.0f), + 7.26f / std::sqrt(20.0f), // 2 * Row 3 + 4 * Row 0, // 2 * + // Row 3 + 4 * Row 0 + }))); } TEST(EmbeddingLookupOpTest, Indices3DTest) { diff --git a/tensorflow/contrib/lite/kernels/gather_test.cc b/tensorflow/contrib/lite/kernels/gather_test.cc index 658d977b8d..cdadbeda18 100644 --- a/tensorflow/contrib/lite/kernels/gather_test.cc +++ b/tensorflow/contrib/lite/kernels/gather_test.cc @@ -81,10 +81,8 @@ TEST(GatherOpTest, Test0DIndex) { m.SetInputFloat({-2.0, 0.2, 0.7, 0.8}); m.SetPositions({1}); m.Invoke(); - EXPECT_THAT(m.GetOutputFloat(), - ElementsAreArray(ArrayFloatNear({0.7, 0.8}))); - EXPECT_THAT(m.GetOutputShape(), - ElementsAreArray({2})); + EXPECT_THAT(m.GetOutputFloat(), ElementsAreArray(ArrayFloatNear({0.7, 0.8}))); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); } TEST(GatherOpTest, Test0DIndexWith0DResult) { @@ -94,8 +92,7 @@ TEST(GatherOpTest, Test0DIndexWith0DResult) { m.SetInputFloat({1.0, 2.0, 3.0}); m.SetPositions({1}); m.Invoke(); - EXPECT_THAT(m.GetOutputFloat(), - ElementsAreArray(ArrayFloatNear({2.0}))); + EXPECT_THAT(m.GetOutputFloat(), ElementsAreArray(ArrayFloatNear({2.0}))); EXPECT_TRUE(m.GetOutputShape().empty()); } diff --git a/tensorflow/contrib/lite/kernels/hashtable_lookup_test.cc b/tensorflow/contrib/lite/kernels/hashtable_lookup_test.cc index cb6038f900..ba0ed5ce06 100644 --- a/tensorflow/contrib/lite/kernels/hashtable_lookup_test.cc +++ b/tensorflow/contrib/lite/kernels/hashtable_lookup_test.cc @@ -116,7 +116,10 @@ TEST(HashtableLookupOpTest, Test2DInput) { 1.0, 1.1, // 1-st item }))); EXPECT_THAT(m.GetHit(), ElementsAreArray({ - 1, 0, 1, 1, + 1, + 0, + 1, + 1, })); } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h index dea46cc120..6cb556bf45 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/cpu_check.h @@ -36,15 +36,11 @@ inline bool TestCPUFeatureNeon() { #elif __ARM_NEON -inline bool TestCPUFeatureNeon() { - return true; -} +inline bool TestCPUFeatureNeon() { return true; } #else -inline bool TestCPUFeatureNeon() { - return false; -} +inline bool TestCPUFeatureNeon() { return false; } #endif diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h index 81796e295d..e2c87df80b 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h @@ -992,11 +992,11 @@ inline void DepthwiseConv(const float* input_data, const Dims<4>& input_dims, for (int k = 0; k < 4; k++) { acc[k] = vld1q_f32(acc_buffer + i + 4 * k); } - for (int k = 0; k < 4; k++) { - acc[k] = vmaxq_f32( - vdupq_n_f32(output_activation_min), - vminq_f32(vdupq_n_f32(output_activation_max), acc[k])); - } + for (int k = 0; k < 4; k++) { + acc[k] = vmaxq_f32( + vdupq_n_f32(output_activation_min), + vminq_f32(vdupq_n_f32(output_activation_max), acc[k])); + } for (int k = 0; k < 4; k++) { vst1q_f32(output_ptr + 4 * k, acc[k]); } diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/eigen_spatial_convolutions.h b/tensorflow/contrib/lite/kernels/internal/optimized/eigen_spatial_convolutions.h index f21fbf532a..ce3cde7699 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/eigen_spatial_convolutions.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/eigen_spatial_convolutions.h @@ -39,7 +39,6 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #endif - namespace Eigen { /** SpatialConvolution @@ -215,13 +214,12 @@ EIGEN_DEVICE_FUNC } // TODO(yangke): choose() is defined in TensorContraction.h -- consider // moving it to somewhere more "common". - return - input - .extract_image_patches(kernelRows, kernelCols, row_stride, col_stride, - row_in_stride, col_in_stride, padding_type) - .reshape(pre_contract_dims) - .contract(kernel.reshape(kernel_dims), contract_dims) - .reshape(post_contract_dims); + return input + .extract_image_patches(kernelRows, kernelCols, row_stride, col_stride, + row_in_stride, col_in_stride, padding_type) + .reshape(pre_contract_dims) + .contract(kernel.reshape(kernel_dims), contract_dims) + .reshape(post_contract_dims); } } // end namespace Eigen diff --git a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc index 17166715ca..cee3ec6197 100644 --- a/tensorflow/contrib/lite/kernels/optional_tensor_test.cc +++ b/tensorflow/contrib/lite/kernels/optional_tensor_test.cc @@ -243,7 +243,6 @@ class LSTMOpModel : public SingleOpModel { int n_output_; }; - TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { const int n_batch = 1; const int n_input = 2; @@ -282,7 +281,6 @@ TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { {0}, // projection_bias tensor }); - lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781, 0.04717243, 0.48944736, -0.38535351, -0.17212132}); diff --git a/tensorflow/contrib/lite/kernels/pad.cc b/tensorflow/contrib/lite/kernels/pad.cc index 4003ed10df..48114e5a40 100644 --- a/tensorflow/contrib/lite/kernels/pad.cc +++ b/tensorflow/contrib/lite/kernels/pad.cc @@ -177,9 +177,7 @@ TfLiteRegistration* Register_PAD_GENERIC_OPT() { return &r; } -TfLiteRegistration* Register_PAD() { - return Register_PAD_GENERIC_OPT(); -} +TfLiteRegistration* Register_PAD() { return Register_PAD_GENERIC_OPT(); } } // namespace builtin } // namespace ops diff --git a/tensorflow/contrib/lite/kernels/svdf.cc b/tensorflow/contrib/lite/kernels/svdf.cc index 72f705fe42..c69755447d 100644 --- a/tensorflow/contrib/lite/kernels/svdf.cc +++ b/tensorflow/contrib/lite/kernels/svdf.cc @@ -15,8 +15,8 @@ limitations under the License. #include #include #include -#include #include +#include #include #include diff --git a/tensorflow/contrib/lite/kernels/svdf_test.cc b/tensorflow/contrib/lite/kernels/svdf_test.cc index 4de2ceaf05..0f166dc69b 100644 --- a/tensorflow/contrib/lite/kernels/svdf_test.cc +++ b/tensorflow/contrib/lite/kernels/svdf_test.cc @@ -14,8 +14,8 @@ limitations under the License. ==============================================================================*/ // Unit test for TFLite SVDF op. -#include #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 f5f1ec2cf3..7ce87e4deb 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc @@ -15,8 +15,8 @@ limitations under the License. #include #include #include -#include #include +#include #include #include @@ -82,8 +82,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { output_size_array->data[0] = (time_major) ? max_time : batch_size; output_size_array->data[1] = (time_major) ? batch_size : max_time; output_size_array->data[2] = num_units; - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, output, - output_size_array)); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, output, output_size_array)); return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc index 82c680ec3d..7e32969763 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn_test.cc @@ -14,8 +14,8 @@ limitations under the License. ==============================================================================*/ // Unit test for TFLite Sequential RNN op. -#include #include +#include #include #include @@ -120,8 +120,7 @@ static float rnn_golden_output[] = { 0.415153, 0.210318, 0, 0, 0, 0, 0, 2.02616, 0, 0.728256, 0.84183, 0.0907453, - 0.628881, 3.58099, 1.49974, 0 -}; + 0.628881, 3.58099, 1.49974, 0}; class UnidirectionalRNNOpModel : public SingleOpModel { public: diff --git a/tensorflow/contrib/lite/toco/tensorflow_util.cc b/tensorflow/contrib/lite/toco/tensorflow_util.cc index 82e2800ca2..0e7e9c41a0 100644 --- a/tensorflow/contrib/lite/toco/tensorflow_util.cc +++ b/tensorflow/contrib/lite/toco/tensorflow_util.cc @@ -51,7 +51,8 @@ void LogDumpGraphDef(int log_level, const string& message, BEGIN DUMP OF TENSORFLOW GRAPHDEF (%s) There are %d nodes. There are %zu different op types: -)MSG", message, tf_graph.node_size(), ops.size()); +)MSG", + message, tf_graph.node_size(), ops.size()); for (const auto& op : ops) { toco::port::AppendF(&dump, " %s\n", op); } @@ -63,7 +64,8 @@ PROTO DUMP BEGIN NODE: name = %s op = %s inputs = [ -)MSG", node.name(), node.op()); +)MSG", + node.name(), node.op()); for (const auto& input : node.input()) { toco::port::AppendF(&dump, " %s\n", input); } diff --git a/tensorflow/contrib/lite/toco/tflite/export.cc b/tensorflow/contrib/lite/toco/tflite/export.cc index 391ef87029..2771959970 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.cc +++ b/tensorflow/contrib/lite/toco/tflite/export.cc @@ -26,6 +26,9 @@ namespace toco { namespace tflite { +using flatbuffers::FlatBufferBuilder; +using flatbuffers::Offset; +using flatbuffers::Vector; using ::tflite::Buffer; using ::tflite::BuiltinOperator; using ::tflite::BuiltinOperator_CUSTOM; @@ -39,9 +42,6 @@ using ::tflite::Operator; using ::tflite::OperatorCode; using ::tflite::SubGraph; using ::tflite::Tensor; -using flatbuffers::FlatBufferBuilder; -using flatbuffers::Offset; -using flatbuffers::Vector; namespace { diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index e2162e1493..461494fd99 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -144,8 +144,7 @@ class SpaceToBatchND } void ReadOptions(const TfLiteOptions& options, - TocoOperator* op) const override { - } + TocoOperator* op) const override {} }; class Sub : public BuiltinOperator { @@ -452,8 +450,7 @@ class Pad : public BuiltinOperator #include -#include "tensorflow/core/distributed_runtime/tensor_coding.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/device_mgr.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" #include "tensorflow/core/distributed_runtime/session_mgr.h" +#include "tensorflow/core/distributed_runtime/tensor_coding.h" namespace tensorflow { @@ -62,7 +62,6 @@ BaseRemoteRendezvous* MPIRendezvousMgr::Create(int64 step_id, void MPIRemoteRendezvous::RecvFromRemoteAsync( const Rendezvous::ParsedKey& parsed, const Rendezvous::Args& recv_args, DoneCallback done) { - Status s = Status::OK(); MPIRequestTensorCall* rendezvous_call = new MPIRequestTensorCall(); @@ -103,37 +102,37 @@ void MPIRemoteRendezvous::RecvFromRemoteAsync( // Create the function which is called when the Tensor is send by remote const int64 temp1 = step_id_; rendezvous_call->recv_call_ = - [this, parsed, recv_args, done, dst, temp1, rendezvous_call]( - MPIRecvTensorResponse mpi_response) { - Status s; - Device* dst_device; - if (s.ok()) { - s = env_->device_mgr->LookupDevice(parsed.dst_device, &dst_device); - CHECK(s.ok()) << "Device lookup failed"; - } - - VLOG(3) << "MPI Received tensor " << parsed.FullKey() - << " @ step: " << temp1 - << " single-send: " << mpi_response.singlesend(); - - Tensor val; - if (mpi_response.singlesend()) { - dst_device->MakeTensorFromProto(mpi_response.response().tensor(), - recv_args.alloc_attrs, &val); - } else { - TensorResponse tr; - tr.InitAlloc(dst_device, recv_args.alloc_attrs); - tr.InitPartial(mpi_response.response()); - const size_t nBytes = tr.tensor().TotalBytes(); - void* data = const_cast(DMAHelper::base(&tr.tensor())); - MPI_Status status; - MPI_CHECK(MPI_Recv(data, static_cast(nBytes), MPI_BYTE, dst, - TAG_SENDTENSOR2, MPI_COMM_WORLD, &status)); - val = std::move(tr.tensor()); - } - - done(s, Args(), recv_args, val, mpi_response.response().is_dead()); - }; + [this, parsed, recv_args, done, dst, temp1, + rendezvous_call](MPIRecvTensorResponse mpi_response) { + Status s; + Device* dst_device; + if (s.ok()) { + s = env_->device_mgr->LookupDevice(parsed.dst_device, &dst_device); + CHECK(s.ok()) << "Device lookup failed"; + } + + VLOG(3) << "MPI Received tensor " << parsed.FullKey() + << " @ step: " << temp1 + << " single-send: " << mpi_response.singlesend(); + + Tensor val; + if (mpi_response.singlesend()) { + dst_device->MakeTensorFromProto(mpi_response.response().tensor(), + recv_args.alloc_attrs, &val); + } else { + TensorResponse tr; + tr.InitAlloc(dst_device, recv_args.alloc_attrs); + tr.InitPartial(mpi_response.response()); + const size_t nBytes = tr.tensor().TotalBytes(); + void* data = const_cast(DMAHelper::base(&tr.tensor())); + MPI_Status status; + MPI_CHECK(MPI_Recv(data, static_cast(nBytes), MPI_BYTE, dst, + TAG_SENDTENSOR2, MPI_COMM_WORLD, &status)); + val = std::move(tr.tensor()); + } + + done(s, Args(), recv_args, val, mpi_response.response().is_dead()); + }; MPIRendezvousMgr* mgr = reinterpret_cast(this->rendezvous_mgr_); @@ -159,9 +158,11 @@ void MPIRendezvousMgr::AddRequest(RecvTensorRequest request, TF_CHECK_OK(Rendezvous::ParseKey(key, &parsed)); MPIRecvTensorCallBack send_cb = [this, mpi_dst, parsed]( - const Status& status, const Rendezvous::Args& send_args, - const Rendezvous::Args& recv_args, const Tensor& val, bool is_dead, - MPISendTensorCall* mpi_send_call) { + const Status& status, + const Rendezvous::Args& send_args, + const Rendezvous::Args& recv_args, + const Tensor& val, bool is_dead, + MPISendTensorCall* mpi_send_call) { // TODO(jbedorf) this should be a loop over max size CHECK(mpi_send_call->mRes_.ByteSize() < INT_MAX) << "Buffer too large for single transfer"; @@ -194,74 +195,78 @@ void MPIRendezvousMgr::AddRequest(RecvTensorRequest request, }; // Wrapper around the read callback to place the callback on our queue - Rendezvous::DoneCallback done_cb = [this, parsed, step_id, send_cb]( - const Status& status, const Rendezvous::Args& send_args, - const Rendezvous::Args& recv_args, const Tensor& val, bool is_dead) { - if (!status.ok()) { - CHECK(status.ok()) << "RecvLocalAsync was not ok, key: " - << parsed.FullKey() << " step: " << step_id - << " error message: " << status.error_message(); - return; - } - - VLOG(3) << "MPI Sending tensor " << parsed.FullKey() - << " @ step: " << step_id << std::endl; - - auto mpi_send_call = new MPISendTensorCall(); - mpi_send_call->Init(parsed, step_id, is_dead); - - Device* src_dev = nullptr; - Status s = this->worker_env_2->device_mgr->LookupDevice(parsed.src_device, - &src_dev); - CHECK(s.ok()) << "src device not found"; - - // Control if shape and data should be send together or if we can optimize - // it in two different transfers, thereby reducing memory copies - bool doOptimalTransfer = true; - if (!DataTypeCanUseMemcpy(val.dtype())) doOptimalTransfer = false; - if (val.TotalBytes() < 1024) doOptimalTransfer = false; - - doOptimalTransfer = doOptimalTransfer && use_optimal_transfer_; - - if (doOptimalTransfer) { - // First send the Tensor description and in a follow up transfer the data - mpi_send_call->mRes_.mutable_response()->mutable_tensor()->set_dtype( - val.dtype()); - val.shape().AsProto(mpi_send_call->mRes_.mutable_response() - ->mutable_tensor() - ->mutable_tensor_shape()); - mpi_send_call->mRes_.set_singlesend(false); - } else { - // Send the Tensor description and data in a single transfer - if (src_dev->tensorflow_gpu_device_info() && - (!send_args.alloc_attrs.on_host())) { - Notification n; - GPUUtil::SetProtoFromGPU( - val, src_dev, send_args.device_context, - mpi_send_call->mRes_.mutable_response()->mutable_tensor(), is_dead, - [&n, &s](const Status& s_) { - s = s_; - n.Notify(); - }); - n.WaitForNotification(); - } else { - val.AsProtoTensorContent( - mpi_send_call->mRes_.mutable_response()->mutable_tensor()); - } - } - - std::function res = std::bind( - send_cb, status, send_args, recv_args, val, is_dead, mpi_send_call); - - SendQueueEntry req(parsed.FullKey().ToString().c_str(), std::move(res)); - - this->QueueSendRequest(req); - - // Wait for the notification that indicates the tensor has been - // successfully transmitted to the remote process. Only needed if we - // have not parsed the tensor to proto - if (doOptimalTransfer) mpi_send_call->n_.WaitForNotification(); - }; // done_cb + Rendezvous::DoneCallback done_cb = + [this, parsed, step_id, send_cb]( + const Status& status, const Rendezvous::Args& send_args, + const Rendezvous::Args& recv_args, const Tensor& val, bool is_dead) { + if (!status.ok()) { + CHECK(status.ok()) + << "RecvLocalAsync was not ok, key: " << parsed.FullKey() + << " step: " << step_id + << " error message: " << status.error_message(); + return; + } + + VLOG(3) << "MPI Sending tensor " << parsed.FullKey() + << " @ step: " << step_id << std::endl; + + auto mpi_send_call = new MPISendTensorCall(); + mpi_send_call->Init(parsed, step_id, is_dead); + + Device* src_dev = nullptr; + Status s = this->worker_env_2->device_mgr->LookupDevice( + parsed.src_device, &src_dev); + CHECK(s.ok()) << "src device not found"; + + // Control if shape and data should be send together or if we can + // optimize it in two different transfers, thereby reducing memory + // copies + bool doOptimalTransfer = true; + if (!DataTypeCanUseMemcpy(val.dtype())) doOptimalTransfer = false; + if (val.TotalBytes() < 1024) doOptimalTransfer = false; + + doOptimalTransfer = doOptimalTransfer && use_optimal_transfer_; + + if (doOptimalTransfer) { + // First send the Tensor description and in a follow up transfer the + // data + mpi_send_call->mRes_.mutable_response()->mutable_tensor()->set_dtype( + val.dtype()); + val.shape().AsProto(mpi_send_call->mRes_.mutable_response() + ->mutable_tensor() + ->mutable_tensor_shape()); + mpi_send_call->mRes_.set_singlesend(false); + } else { + // Send the Tensor description and data in a single transfer + if (src_dev->tensorflow_gpu_device_info() && + (!send_args.alloc_attrs.on_host())) { + Notification n; + GPUUtil::SetProtoFromGPU( + val, src_dev, send_args.device_context, + mpi_send_call->mRes_.mutable_response()->mutable_tensor(), + is_dead, [&n, &s](const Status& s_) { + s = s_; + n.Notify(); + }); + n.WaitForNotification(); + } else { + val.AsProtoTensorContent( + mpi_send_call->mRes_.mutable_response()->mutable_tensor()); + } + } + + std::function res = std::bind( + send_cb, status, send_args, recv_args, val, is_dead, mpi_send_call); + + SendQueueEntry req(parsed.FullKey().ToString().c_str(), std::move(res)); + + this->QueueSendRequest(req); + + // Wait for the notification that indicates the tensor has been + // successfully transmitted to the remote process. Only needed if we + // have not parsed the tensor to proto + if (doOptimalTransfer) mpi_send_call->n_.WaitForNotification(); + }; // done_cb worker_env_2->compute_pool->Schedule([this, step_id, parsed, done_cb]() { this->RecvLocalAsync(step_id, parsed, done_cb); @@ -293,9 +298,8 @@ void MPIRendezvousMgr::MPIBackgroundThread() { } // Remove sends that have been completed - active_sends.remove_if([](std::unique_ptr& i) { - return i->IsFinished(); - }); + active_sends.remove_if( + [](std::unique_ptr& i) { return i->IsFinished(); }); // send a Tensor request RequestQueueEntry req; diff --git a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h index ca42ee2f6d..e665922135 100644 --- a/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h +++ b/tensorflow/contrib/mpi/mpi_rendezvous_mgr.h @@ -18,12 +18,12 @@ limitations under the License. #ifdef TENSORFLOW_USE_MPI -#include -#include #include -#include -#include #include +#include +#include +#include +#include #include #include #include @@ -160,7 +160,8 @@ class MPIRendezvousMgr : public BaseRendezvousMgr { private: typedef std::function MPIRecvTensorCallBack; + const Tensor&, const bool, MPISendTensorCall*)> + MPIRecvTensorCallBack; typedef std::pair> RequestQueueEntry; typedef std::pair> diff --git a/tensorflow/contrib/mpi/mpi_server_lib.cc b/tensorflow/contrib/mpi/mpi_server_lib.cc index d585c0565e..a31fa9ce0b 100644 --- a/tensorflow/contrib/mpi/mpi_server_lib.cc +++ b/tensorflow/contrib/mpi/mpi_server_lib.cc @@ -22,8 +22,8 @@ limitations under the License. #include "grpc/support/alloc.h" -#include "tensorflow/core/distributed_runtime/server_lib.h" #include "tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h" +#include "tensorflow/core/distributed_runtime/server_lib.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/env.h" diff --git a/tensorflow/contrib/mpi/mpi_utils.h b/tensorflow/contrib/mpi/mpi_utils.h index 45e21f2b25..fa297c28cb 100644 --- a/tensorflow/contrib/mpi/mpi_utils.h +++ b/tensorflow/contrib/mpi/mpi_utils.h @@ -18,8 +18,8 @@ limitations under the License. #ifdef TENSORFLOW_USE_MPI -#include #include +#include #include #include "tensorflow/core/lib/strings/str_util.h" diff --git a/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc b/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc index 2d5b98022c..8dca90a1e3 100644 --- a/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc +++ b/tensorflow/contrib/mpi_collectives/kernels/mpi_ops.cc @@ -35,8 +35,8 @@ limitations under the License. #define OMPI_SKIP_MPICXX #include "third_party/mpi/mpi.h" -#include "tensorflow/contrib/mpi_collectives/mpi_message.pb.h" #include "tensorflow/contrib/mpi_collectives/kernels/ring.h" +#include "tensorflow/contrib/mpi_collectives/mpi_message.pb.h" /* * MPI Allreduce and Allgather Ops for TensorFlow. diff --git a/tensorflow/contrib/nearest_neighbor/kernels/hyperplane_lsh_probes.cc b/tensorflow/contrib/nearest_neighbor/kernels/hyperplane_lsh_probes.cc index 2b412fac9a..13db6f62f5 100644 --- a/tensorflow/contrib/nearest_neighbor/kernels/hyperplane_lsh_probes.cc +++ b/tensorflow/contrib/nearest_neighbor/kernels/hyperplane_lsh_probes.cc @@ -75,7 +75,8 @@ class HyperplaneLSHProbesOp : public OpKernel { num_hyperplanes_per_table, ".")); OP_REQUIRES(context, num_hyperplanes_per_table <= 30, InvalidArgument("Need num_hyperplanes_per_table <= 30, got ", - num_hyperplanes_per_table, ". " + num_hyperplanes_per_table, + ". " "If you need more hyperplanes, change this Op" " to work for larger integer types (int64).")); @@ -88,12 +89,13 @@ class HyperplaneLSHProbesOp : public OpKernel { InvalidArgument("num_probes must be at least 1.")); int expected_num_hyperplanes = num_tables * num_hyperplanes_per_table; - OP_REQUIRES( - context, products_tensor.dim_size(1) == expected_num_hyperplanes, - InvalidArgument("Expected number of hyperplanes is ", - expected_num_hyperplanes, " but received ", - products_tensor.dim_size(1), " inner products per " - "point.")); + OP_REQUIRES(context, + products_tensor.dim_size(1) == expected_num_hyperplanes, + InvalidArgument("Expected number of hyperplanes is ", + expected_num_hyperplanes, " but received ", + products_tensor.dim_size(1), + " inner products per " + "point.")); auto products_eigen_tensor = products_tensor.matrix(); ConstMatrixMap products_matrix(products_eigen_tensor.data(), @@ -116,13 +118,11 @@ class HyperplaneLSHProbesOp : public OpKernel { // lschmidt's workstation. int64 cost_per_unit = 21 * num_hyperplanes_per_table * num_tables; if (num_probes > num_tables) { - cost_per_unit += 110 * num_hyperplanes_per_table - * (num_probes - num_tables); + cost_per_unit += + 110 * num_hyperplanes_per_table * (num_probes - num_tables); } context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor( - batch_size, - cost_per_unit, - [&](int64 start, int64 end) { + batch_size, cost_per_unit, [&](int64 start, int64 end) { HyperplaneMultiprobe multiprobe( num_hyperplanes_per_table, num_tables); diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc index 9cee405cef..e18923c8aa 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc @@ -14,13 +14,12 @@ // limitations under the License. // ============================================================================= -#include "tensorflow/core/framework/register_types.h" #include "tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h" +#include "tensorflow/core/framework/register_types.h" namespace tensorflow { -REGISTER_KERNEL_BUILDER(Name("PeriodicResample") - .Device(DEVICE_CPU), +REGISTER_KERNEL_BUILDER(Name("PeriodicResample").Device(DEVICE_CPU), PeriodicResampleOp); } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h index ba410f025d..3ab588c458 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h @@ -118,9 +118,9 @@ template #include -#include #include #include #include #include #include #include -#include -#include #include +#include +#include +#include #include #include "tensorflow/core/framework/graph.pb.h" @@ -46,10 +46,10 @@ limitations under the License. // These are all common classes it's handy to reference with no namespace. using tensorflow::Flag; -using tensorflow::Tensor; +using tensorflow::int32; using tensorflow::Status; using tensorflow::string; -using tensorflow::int32; +using tensorflow::Tensor; // Used to store the memory-mapped buffers we use for capture. struct CameraBuffer { diff --git a/tensorflow/contrib/pi_examples/label_image/label_image.cc b/tensorflow/contrib/pi_examples/label_image/label_image.cc index 0b18045789..c6935a093f 100644 --- a/tensorflow/contrib/pi_examples/label_image/label_image.cc +++ b/tensorflow/contrib/pi_examples/label_image/label_image.cc @@ -23,9 +23,9 @@ limitations under the License. // // Full build instructions are at tensorflow/contrib/pi_examples/README.md. -#include #include #include +#include #include #include @@ -46,10 +46,10 @@ limitations under the License. // These are all common classes it's handy to reference with no namespace. using tensorflow::Flag; -using tensorflow::Tensor; +using tensorflow::int32; using tensorflow::Status; using tensorflow::string; -using tensorflow::int32; +using tensorflow::Tensor; // Takes a file name, and loads a list of labels from it, one per line, and // returns a vector of the strings. It pads with empty strings so the length @@ -77,23 +77,22 @@ Status ReadLabelsFile(string file_name, std::vector* result, // Error handling for JPEG decoding. void CatchError(j_common_ptr cinfo) { (*cinfo->err->output_message)(cinfo); - jmp_buf *jpeg_jmpbuf = reinterpret_cast(cinfo->client_data); + jmp_buf* jpeg_jmpbuf = reinterpret_cast(cinfo->client_data); jpeg_destroy(cinfo); longjmp(*jpeg_jmpbuf, 1); } // Decompresses a JPEG file from disk. Status LoadJpegFile(string file_name, std::vector* data, - int* width, int* height, int* channels) { + int* width, int* height, int* channels) { struct jpeg_decompress_struct cinfo; - FILE * infile; + FILE* infile; JSAMPARRAY buffer; int row_stride; if ((infile = fopen(file_name.c_str(), "rb")) == NULL) { LOG(ERROR) << "Can't open " << file_name; - return tensorflow::errors::NotFound("JPEG file ", file_name, - " not found"); + return tensorflow::errors::NotFound("JPEG file ", file_name, " not found"); } struct jpeg_error_mgr jerr; @@ -116,10 +115,11 @@ Status LoadJpegFile(string file_name, std::vector* data, data->resize((*height) * (*width) * (*channels)); row_stride = cinfo.output_width * cinfo.output_components; - buffer = (*cinfo.mem->alloc_sarray) - ((j_common_ptr) &cinfo, JPOOL_IMAGE, row_stride, 1); + buffer = (*cinfo.mem->alloc_sarray)((j_common_ptr)&cinfo, JPOOL_IMAGE, + row_stride, 1); while (cinfo.output_scanline < cinfo.output_height) { - tensorflow::uint8* row_address = &((*data)[cinfo.output_scanline * row_stride]); + tensorflow::uint8* row_address = + &((*data)[cinfo.output_scanline * row_stride]); jpeg_read_scanlines(&cinfo, buffer, 1); memcpy(row_address, buffer[0], row_stride); } @@ -141,24 +141,25 @@ Status ReadTensorFromImageFile(string file_name, const int wanted_height, int image_height; int image_channels; TF_RETURN_IF_ERROR(LoadJpegFile(file_name, &image_data, &image_width, - &image_height, &image_channels)); - LOG(INFO) << "Loaded JPEG: " << image_width << "x" << image_height - << "x" << image_channels; + &image_height, &image_channels)); + LOG(INFO) << "Loaded JPEG: " << image_width << "x" << image_height << "x" + << image_channels; const int wanted_channels = 3; if (image_channels < wanted_channels) { - return tensorflow::errors::FailedPrecondition("Image needs to have at least ", - wanted_channels, " but only has ", - image_channels); + return tensorflow::errors::FailedPrecondition( + "Image needs to have at least ", wanted_channels, " but only has ", + image_channels); } - // In these loops, we convert the eight-bit data in the image into float, resize - // it using bilinear filtering, and scale it numerically to the float range that - // the model expects (given by input_mean and input_std). + // In these loops, we convert the eight-bit data in the image into float, + // resize it using bilinear filtering, and scale it numerically to the float + // range that the model expects (given by input_mean and input_std). tensorflow::Tensor image_tensor( - tensorflow::DT_FLOAT, tensorflow::TensorShape( - {1, wanted_height, wanted_width, wanted_channels})); + tensorflow::DT_FLOAT, + tensorflow::TensorShape( + {1, wanted_height, wanted_width, wanted_channels})); auto image_tensor_mapped = image_tensor.tensor(); tensorflow::uint8* in = image_data.data(); - float *out = image_tensor_mapped.data(); + float* out = image_tensor_mapped.data(); const size_t image_rowlen = image_width * image_channels; const float width_scale = static_cast(image_width) / wanted_width; const float height_scale = static_cast(image_height) / wanted_height; @@ -166,35 +167,37 @@ Status ReadTensorFromImageFile(string file_name, const int wanted_height, const float in_y = y * height_scale; const int top_y_index = static_cast(floorf(in_y)); const int bottom_y_index = - std::min(static_cast(ceilf(in_y)), (image_height - 1)); + std::min(static_cast(ceilf(in_y)), (image_height - 1)); const float y_lerp = in_y - top_y_index; tensorflow::uint8* in_top_row = in + (top_y_index * image_rowlen); tensorflow::uint8* in_bottom_row = in + (bottom_y_index * image_rowlen); - float *out_row = out + (y * wanted_width * wanted_channels); + float* out_row = out + (y * wanted_width * wanted_channels); for (int x = 0; x < wanted_width; ++x) { const float in_x = x * width_scale; const int left_x_index = static_cast(floorf(in_x)); const int right_x_index = - std::min(static_cast(ceilf(in_x)), (image_width - 1)); + std::min(static_cast(ceilf(in_x)), (image_width - 1)); tensorflow::uint8* in_top_left_pixel = - in_top_row + (left_x_index * wanted_channels); + in_top_row + (left_x_index * wanted_channels); tensorflow::uint8* in_top_right_pixel = - in_top_row + (right_x_index * wanted_channels); + in_top_row + (right_x_index * wanted_channels); tensorflow::uint8* in_bottom_left_pixel = - in_bottom_row + (left_x_index * wanted_channels); + in_bottom_row + (left_x_index * wanted_channels); tensorflow::uint8* in_bottom_right_pixel = - in_bottom_row + (right_x_index * wanted_channels); + in_bottom_row + (right_x_index * wanted_channels); const float x_lerp = in_x - left_x_index; - float *out_pixel = out_row + (x * wanted_channels); + float* out_pixel = out_row + (x * wanted_channels); for (int c = 0; c < wanted_channels; ++c) { - const float top_left((in_top_left_pixel[c] - input_mean) / input_std); - const float top_right((in_top_right_pixel[c] - input_mean) / input_std); - const float bottom_left((in_bottom_left_pixel[c] - input_mean) / input_std); - const float bottom_right((in_bottom_right_pixel[c] - input_mean) / input_std); - const float top = top_left + (top_right - top_left) * x_lerp; - const float bottom = - bottom_left + (bottom_right - bottom_left) * x_lerp; - out_pixel[c] = top + (bottom - top) * y_lerp; + const float top_left((in_top_left_pixel[c] - input_mean) / input_std); + const float top_right((in_top_right_pixel[c] - input_mean) / input_std); + const float bottom_left((in_bottom_left_pixel[c] - input_mean) / + input_std); + const float bottom_right((in_bottom_right_pixel[c] - input_mean) / + input_std); + const float top = top_left + (top_right - top_left) * x_lerp; + const float bottom = + bottom_left + (bottom_right - bottom_left) * x_lerp; + out_pixel[c] = top + (bottom - top) * y_lerp; } } } @@ -233,10 +236,10 @@ Status GetTopLabels(const std::vector& outputs, int how_many_labels, scores.push_back(std::pair({i, unsorted_scores_flat(i)})); } std::sort(scores.begin(), scores.end(), - [](const std::pair &left, - const std::pair &right) { - return left.second > right.second; - }); + [](const std::pair& left, + const std::pair& right) { + return left.second > right.second; + }); scores.resize(how_many_labels); Tensor sorted_indices(tensorflow::DT_INT32, {scores.size()}); Tensor sorted_scores(tensorflow::DT_FLOAT, {scores.size()}); diff --git a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.cc b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.cc index c33804906f..2def4f3f17 100644 --- a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.cc +++ b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.cc @@ -15,8 +15,8 @@ limitations under the License. #define EIGEN_USE_THREADS -#include #include "tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h" +#include #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h index 9bb1724a2c..d8c0a0631d 100644 --- a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h +++ b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h @@ -16,10 +16,10 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_PARTIAL_REDUCTION_OPS_H_ #define TENSORFLOW_CORE_KERNELS_PARTIAL_REDUCTION_OPS_H_ +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/tensor_types.h" -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #define Sum(a, b) ((a) + (b)) #define Prod(a, b) ((a) * (b)) @@ -58,11 +58,11 @@ inline T negative_infinity() { } // namespace reduce_functions -#define CALL_ALL_REDUCEOPS(func, ...) \ - func(Sum, functor::reduce_functions::zero, ##__VA_ARGS__) \ - func(Prod, functor::reduce_functions::one, ##__VA_ARGS__) \ - func(Max, functor::reduce_functions::negative_infinity, ##__VA_ARGS__) \ - func(Min, functor::reduce_functions::infinity, ##__VA_ARGS__) +#define CALL_ALL_REDUCEOPS(func, ...) \ + func(Sum, functor::reduce_functions::zero, ##__VA_ARGS__) \ + func(Prod, functor::reduce_functions::one, ##__VA_ARGS__) func( \ + Max, functor::reduce_functions::negative_infinity, ##__VA_ARGS__) \ + func(Min, functor::reduce_functions::infinity, ##__VA_ARGS__) #define ReduceSliceFunctorReduceop(reduceop, dummy) \ template \ diff --git a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc index 501cddb8c8..9f2be03d71 100644 --- a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc +++ b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc @@ -17,10 +17,10 @@ limitations under the License. #define EIGEN_USE_GPU +#include "tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" -#include "tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h" #include "tensorflow/core/util/cuda_kernel_helper.h" namespace tensorflow { diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops.cc b/tensorflow/contrib/resampler/kernels/resampler_ops.cc index e02c1b6a2b..63c72836d7 100644 --- a/tensorflow/contrib/resampler/kernels/resampler_ops.cc +++ b/tensorflow/contrib/resampler/kernels/resampler_ops.cc @@ -36,17 +36,12 @@ using GPUDevice = Eigen::GpuDevice; namespace functor { template -struct Resampler2DFunctor{ - void operator ()(::tensorflow::OpKernelContext* ctx, - const CPUDevice& d, - const T* __restrict__ data, - const T* __restrict__ warp, - T* __restrict__ output, - const int batch_size, - const int data_height, - const int data_width, - const int data_channels, - const int num_sampling_points){ +struct Resampler2DFunctor { + void operator()(::tensorflow::OpKernelContext* ctx, const CPUDevice& d, + const T* __restrict__ data, const T* __restrict__ warp, + T* __restrict__ output, const int batch_size, + const int data_height, const int data_width, + const int data_channels, const int num_sampling_points) { const int warp_batch_stride = num_sampling_points * 2; const int data_batch_stride = data_height * data_width * data_channels; const int output_batch_stride = num_sampling_points * data_channels; @@ -59,24 +54,19 @@ struct Resampler2DFunctor{ // The functions take care of performing the relevant pointer // arithmetics abstracting away the low level details in the // main loop over samples. Note that data is stored in NHWC format. - auto set_output = [&](const int sample_id, - const int channel, + auto set_output = [&](const int sample_id, const int channel, const T value) { - output[batch_id * output_batch_stride + - sample_id * data_channels + + output[batch_id * output_batch_stride + sample_id * data_channels + channel] = value; }; - auto get_data_point = [&](const int x, - const int y, - const int chan) { + auto get_data_point = [&](const int x, const int y, const int chan) { const bool point_is_in_range = (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1); return point_is_in_range - ? data[batch_id * data_batch_stride + - data_channels * (y * data_width + x) + - chan] - : zero; + ? data[batch_id * data_batch_stride + + data_channels * (y * data_width + x) + chan] + : zero; }; for (int sample_id = 0; sample_id < num_sampling_points; ++sample_id) { @@ -89,8 +79,7 @@ struct Resampler2DFunctor{ // The effect is that the sampled signal smoothly goes to 0 outside // the original input domain, rather than presenting a jump // discontinuity at the image boundaries. - if (x > static_cast(-1.0) && - y > static_cast(-1.0) && + if (x > static_cast(-1.0) && y > static_cast(-1.0) && x < static_cast(data_width) && y < static_cast(data_height)) { // Precompute floor (f) and ceil (c) values for x and y. @@ -103,12 +92,10 @@ struct Resampler2DFunctor{ for (int chan = 0; chan < data_channels; ++chan) { const T img_fxfy = dx * dy * get_data_point(fx, fy, chan); - const T img_cxcy = (one - dx) * (one - dy) * - get_data_point(cx, cy, chan); - const T img_fxcy = dx * (one - dy) * - get_data_point(fx, cy, chan); - const T img_cxfy = (one - dx) * dy * - get_data_point(cx, fy, chan); + const T img_cxcy = + (one - dx) * (one - dy) * get_data_point(cx, cy, chan); + const T img_fxcy = dx * (one - dy) * get_data_point(fx, cy, chan); + const T img_cxfy = (one - dx) * dy * get_data_point(cx, fy, chan); set_output(sample_id, chan, img_fxfy + img_cxcy + img_fxcy + img_cxfy); } @@ -125,8 +112,8 @@ struct Resampler2DFunctor{ // estimate of the cost of each work unit is needed to correctly shard the // workload. Shard assumes each cost unit is 1ns, minimum cost per shard // being 10us. - const int64 cost = static_cast(num_sampling_points) * - data_channels * 1000; + const int64 cost = + static_cast(num_sampling_points) * data_channels * 1000; auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads()); ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers, batch_size, cost, resample_batches); @@ -138,8 +125,8 @@ struct Resampler2DFunctor{ template class ResamplerOp : public ::tensorflow::OpKernel { public: - explicit ResamplerOp(::tensorflow::OpKernelConstruction* context) : - ::tensorflow::OpKernel(context) {} + explicit ResamplerOp(::tensorflow::OpKernelConstruction* context) + : ::tensorflow::OpKernel(context) {} void Compute(::tensorflow::OpKernelContext* ctx) override { const ::tensorflow::Tensor& data = ctx->input(0); @@ -158,16 +145,17 @@ class ResamplerOp : public ::tensorflow::OpKernel { ::tensorflow::errors::InvalidArgument( "warp should be at least a matrix, got shape ", warp_shape.DebugString())); - OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims()-1) == 2, + OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims() - 1) == 2, ::tensorflow::errors::Unimplemented( "Only bilinear interpolation is supported, warping " "coordinates must be 2D; warp shape last entry should be " - "2, but shape vector is: ", warp_shape.DebugString())); + "2, but shape vector is: ", + warp_shape.DebugString())); OP_REQUIRES(ctx, data_shape.dim_size(0) == warp_shape.dim_size(0), ::tensorflow::errors::InvalidArgument( "Batch size of data and warp tensor must be the same, but " - "input shapes are: ", data_shape.DebugString(), ", ", - warp_shape.DebugString())); + "input shapes are: ", + data_shape.DebugString(), ", ", warp_shape.DebugString())); const int batch_size = data_shape.dim_size(0); const int data_height = data_shape.dim_size(1); const int data_width = data_shape.dim_size(2); @@ -180,16 +168,10 @@ class ResamplerOp : public ::tensorflow::OpKernel { // Execute kernel only for nonempty output; otherwise Eigen crashes on GPU. if (num_sampling_points > 0) { - functor::Resampler2DFunctor()(ctx, - ctx->eigen_device(), - data.flat().data(), - warp.flat().data(), - output->flat().data(), - batch_size, - data_height, - data_width, - data_channels, - num_sampling_points); + functor::Resampler2DFunctor()( + ctx, ctx->eigen_device(), data.flat().data(), + warp.flat().data(), output->flat().data(), batch_size, + data_height, data_width, data_channels, num_sampling_points); } } @@ -197,12 +179,9 @@ class ResamplerOp : public ::tensorflow::OpKernel { TF_DISALLOW_COPY_AND_ASSIGN(ResamplerOp); }; - -#define REGISTER(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("Resampler") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ +#define REGISTER(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("Resampler").Device(DEVICE_CPU).TypeConstraint("T"), \ ResamplerOp); TF_CALL_half(REGISTER); @@ -211,40 +190,32 @@ TF_CALL_double(REGISTER); #undef REGISTER #if GOOGLE_CUDA -#define REGISTER(TYPE) \ - REGISTER_KERNEL_BUILDER(Name("Resampler") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T"), \ - ResamplerOp) +#define REGISTER(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("Resampler").Device(DEVICE_GPU).TypeConstraint("T"), \ + ResamplerOp) TF_CALL_float(REGISTER); TF_CALL_double(REGISTER); #undef REGISTER #endif // GOOGLE_CUDA - namespace functor { template -struct ResamplerGrad2DFunctor{ - void operator ()(::tensorflow::OpKernelContext* ctx, - const CPUDevice& d, - const T* __restrict__ data, - const T* __restrict__ warp, - const T* __restrict__ grad_output, - T* __restrict__ grad_data, - T* __restrict__ grad_warp, - const int batch_size, - const int data_height, - const int data_width, - const int data_channels, - const int num_sampling_points){ +struct ResamplerGrad2DFunctor { + void operator()(::tensorflow::OpKernelContext* ctx, const CPUDevice& d, + const T* __restrict__ data, const T* __restrict__ warp, + const T* __restrict__ grad_output, T* __restrict__ grad_data, + T* __restrict__ grad_warp, const int batch_size, + const int data_height, const int data_width, + const int data_channels, const int num_sampling_points) { // Set gradients to 0, because the kernel incrementally updates the // tensor entries by adding partial contributions. - const int resampler_output_size = batch_size * num_sampling_points * - data_channels; + const int resampler_output_size = + batch_size * num_sampling_points * data_channels; const int grad_warp_size = resampler_output_size / data_channels * 2; - const int grad_data_size = data_height * data_width * data_channels * - batch_size; + const int grad_data_size = + data_height * data_width * data_channels * batch_size; memset(grad_data, 0, sizeof(T) * grad_data_size); memset(grad_warp, 0, sizeof(T) * grad_warp_size); @@ -260,35 +231,29 @@ struct ResamplerGrad2DFunctor{ // The functions take care of performing the relevant pointer // arithmetics abstracting away the low level details in the // main loop over samples. Note that data is stored in NHWC format. - auto get_data_point = [&](const int x, - const int y, - const int chan) { + auto get_data_point = [&](const int x, const int y, const int chan) { const bool point_is_in_range = - (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1); + (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1); return point_is_in_range - ? data[batch_id * data_batch_stride + - data_channels * (y * data_width + x) + - chan] - : zero; + ? data[batch_id * data_batch_stride + + data_channels * (y * data_width + x) + chan] + : zero; }; auto update_grad_data = [&](const int x, const int y, const int chan, const T value) { const bool point_is_in_range = (x >= 0 && y >= 0 && x <= data_width - 1 && y <= data_height - 1); - if (point_is_in_range){ + if (point_is_in_range) { grad_data[batch_id * data_batch_stride + - data_channels * (y * data_width + x) + - chan] += value; + data_channels * (y * data_width + x) + chan] += value; } }; - auto update_grad_warp = [&](const int sample_id, - const int channel, + auto update_grad_warp = [&](const int sample_id, const int channel, const T value) { - grad_warp[batch_id * warp_batch_stride + - sample_id * 2 + - channel] += value; + grad_warp[batch_id * warp_batch_stride + sample_id * 2 + channel] += + value; }; for (int sample_id = 0; sample_id < num_sampling_points; ++sample_id) { @@ -301,8 +266,7 @@ struct ResamplerGrad2DFunctor{ // The effect is that the sampled signal smoothly goes to 0 outside // the original input domain, rather than presenting a jump // discontinuity at the image boundaries. - if (x > static_cast(-1.0) && - y > static_cast(-1.0) && + if (x > static_cast(-1.0) && y > static_cast(-1.0) && x < static_cast(data_width) && y < static_cast(data_height)) { // Precompute floor (f) and ceil (c) values for x and y. @@ -316,27 +280,25 @@ struct ResamplerGrad2DFunctor{ for (int chan = 0; chan < data_channels; ++chan) { const T grad_output_value = grad_output[batch_id * output_batch_stride + - sample_id * data_channels + - chan]; + sample_id * data_channels + chan]; const T img_fxfy = get_data_point(fx, fy, chan); const T img_cxcy = get_data_point(cx, cy, chan); const T img_fxcy = get_data_point(fx, cy, chan); const T img_cxfy = get_data_point(cx, fy, chan); // Update partial gradients wrt relevant warp field entries - update_grad_warp(sample_id, 0, - grad_output_value * - ((one - dy) * (img_cxcy - img_fxcy) + - dy * (img_cxfy - img_fxfy))); + update_grad_warp( + sample_id, 0, + grad_output_value * ((one - dy) * (img_cxcy - img_fxcy) + + dy * (img_cxfy - img_fxfy))); - update_grad_warp(sample_id, 1, - grad_output_value * - ((one - dx) * (img_cxcy - img_cxfy) + - dx * (img_fxcy - img_fxfy))); + update_grad_warp( + sample_id, 1, + grad_output_value * ((one - dx) * (img_cxcy - img_cxfy) + + dx * (img_fxcy - img_fxfy))); // Update partial gradients wrt sampled data - update_grad_data(fx, fy, chan, - grad_output_value * dx * dy); + update_grad_data(fx, fy, chan, grad_output_value * dx * dy); update_grad_data(cx, cy, chan, grad_output_value * (one - dx) * (one - dy)); update_grad_data(fx, cy, chan, @@ -355,8 +317,8 @@ struct ResamplerGrad2DFunctor{ // being 10us. // TODO(fviola): Check out if there is a better way of doing this. auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads()); - const int64 cost = static_cast(num_sampling_points) * - data_channels * 1000; + const int64 cost = + static_cast(num_sampling_points) * data_channels * 1000; ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers, batch_size, cost, update_grads_for_batches); } @@ -364,12 +326,11 @@ struct ResamplerGrad2DFunctor{ } // namespace functor - template class ResamplerGradOp : public ::tensorflow::OpKernel { public: - explicit ResamplerGradOp(::tensorflow::OpKernelConstruction* context) : - ::tensorflow::OpKernel(context) {} + explicit ResamplerGradOp(::tensorflow::OpKernelConstruction* context) + : ::tensorflow::OpKernel(context) {} void Compute(::tensorflow::OpKernelContext* ctx) override { const ::tensorflow::Tensor& data = ctx->input(0); @@ -383,7 +344,7 @@ class ResamplerGradOp : public ::tensorflow::OpKernel { "tensor must be a batch of 2d data; data shape should have " "4 entries corresponding to [batch_size, data_height, " "data_width, data_channels], but is: ", - data_shape.DebugString())); + data_shape.DebugString())); const int batch_size = data_shape.dim_size(0); const int data_height = data_shape.dim_size(1); const int data_width = data_shape.dim_size(2); @@ -394,7 +355,7 @@ class ResamplerGradOp : public ::tensorflow::OpKernel { ::tensorflow::errors::InvalidArgument( "warp should be at least a matrix, got shape ", warp_shape.DebugString())); - OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims()-1) == 2, + OP_REQUIRES(ctx, warp_shape.dim_size(warp_shape.dims() - 1) == 2, ::tensorflow::errors::Unimplemented( "Only bilinear interpolation is supported, warping " "coordinates must be 2D; warp shape last entry should be " @@ -417,18 +378,11 @@ class ResamplerGradOp : public ::tensorflow::OpKernel { OP_REQUIRES_OK(ctx, ctx->allocate_output(1, warp.shape(), &grad_warp)); // Execute kernel only for nonempty output; otherwise Eigen crashes on GPU. if (num_sampling_points > 0) { - functor::ResamplerGrad2DFunctor()(ctx, - ctx->eigen_device(), - data.flat().data(), - warp.flat().data(), - grad_output.flat().data(), - grad_data->flat().data(), - grad_warp->flat().data(), - batch_size, - data_height, - data_width, - data_channels, - num_sampling_points); + functor::ResamplerGrad2DFunctor()( + ctx, ctx->eigen_device(), data.flat().data(), + warp.flat().data(), grad_output.flat().data(), + grad_data->flat().data(), grad_warp->flat().data(), batch_size, + data_height, data_width, data_channels, num_sampling_points); } } @@ -436,11 +390,9 @@ class ResamplerGradOp : public ::tensorflow::OpKernel { TF_DISALLOW_COPY_AND_ASSIGN(ResamplerGradOp); }; -#define REGISTER(TYPE) \ - REGISTER_KERNEL_BUILDER( \ - Name("ResamplerGrad") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ +#define REGISTER(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("ResamplerGrad").Device(DEVICE_CPU).TypeConstraint("T"), \ ResamplerGradOp); TF_CALL_half(REGISTER); @@ -449,11 +401,10 @@ TF_CALL_double(REGISTER); #undef REGISTER #if GOOGLE_CUDA -#define REGISTER(TYPE) \ - REGISTER_KERNEL_BUILDER(Name("ResamplerGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T"), \ - ResamplerGradOp) +#define REGISTER(TYPE) \ + REGISTER_KERNEL_BUILDER( \ + Name("ResamplerGrad").Device(DEVICE_GPU).TypeConstraint("T"), \ + ResamplerGradOp) // Disable half and double precision since atomicAdds are not supported // TF_CALL_half(REGISTER); // TF_CALL_double(REGISTER); diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops.h b/tensorflow/contrib/resampler/kernels/resampler_ops.h index 85d3676efa..7fe3b9c0df 100644 --- a/tensorflow/contrib/resampler/kernels/resampler_ops.h +++ b/tensorflow/contrib/resampler/kernels/resampler_ops.h @@ -29,38 +29,25 @@ namespace functor { // Helper functor for the Resampler Op in 2D template -struct Resampler2DFunctor{ - void operator ()(::tensorflow::OpKernelContext* ctx, - const Device& d, - const T* __restrict__ data, - const T* __restrict__ warp, - T* __restrict__ output, - const int batch_size, - const int data_height, - const int data_width, - const int data_channels, - const int num_sampling_points); +struct Resampler2DFunctor { + void operator()(::tensorflow::OpKernelContext* ctx, const Device& d, + const T* __restrict__ data, const T* __restrict__ warp, + T* __restrict__ output, const int batch_size, + const int data_height, const int data_width, + const int data_channels, const int num_sampling_points); }; - // Helper functor for the Resampler Gradient Op in 2D template -struct ResamplerGrad2DFunctor{ - void operator ()(::tensorflow::OpKernelContext* ctx, - const Device& d, - const T* __restrict__ data, - const T* __restrict__ warp, - const T* __restrict__ grad_output, - T* __restrict__ grad_data, - T* __restrict__ grad_warp, - const int batch_size, - const int data_height, - const int data_width, - const int data_channels, - const int num_sampling_points); +struct ResamplerGrad2DFunctor { + void operator()(::tensorflow::OpKernelContext* ctx, const Device& d, + const T* __restrict__ data, const T* __restrict__ warp, + const T* __restrict__ grad_output, T* __restrict__ grad_data, + T* __restrict__ grad_warp, const int batch_size, + const int data_height, const int data_width, + const int data_channels, const int num_sampling_points); }; - } // namespace functor } // namespace tensorflow diff --git a/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc b/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc index 636847a212..3c07051f68 100644 --- a/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc +++ b/tensorflow/contrib/resampler/kernels/resampler_ops_gpu.cu.cc @@ -31,18 +31,15 @@ using GPUDevice = Eigen::GpuDevice; namespace { -#define GET_DATA_POINT(x, y) \ - data[batch_id * data_batch_stride + \ - data_channels * (y * data_width + x) + \ +#define GET_DATA_POINT(x, y) \ + data[batch_id * data_batch_stride + data_channels * (y * data_width + x) + \ chan] template __global__ void Resampler2DKernel(const T* __restrict__ data, const T* __restrict__ warp, - T* __restrict__ output, - const int batch_size, - const int data_height, - const int data_width, + T* __restrict__ output, const int batch_size, + const int data_height, const int data_width, const int data_channels, const int num_sampling_points) { const int output_data_size = batch_size * num_sampling_points * data_channels; @@ -75,10 +72,8 @@ __global__ void Resampler2DKernel(const T* __restrict__ data, // The effect is that the sampled signal smoothly goes to 0 outside // the original input domain, rather than presenting a jump // discontinuity at the image boundaries. - if (x > static_cast(-1.0) && - y > static_cast(-1.0) && - x < static_cast(data_width) && - y < static_cast(data_height)) { + if (x > static_cast(-1.0) && y > static_cast(-1.0) && + x < static_cast(data_width) && y < static_cast(data_height)) { // Precompute floor (f) and ceil (c) values for x and y. const int fx = std::floor(static_cast(x)); const int fy = std::floor(static_cast(y)); @@ -87,21 +82,20 @@ __global__ void Resampler2DKernel(const T* __restrict__ data, const T dx = static_cast(cx) - x; const T dy = static_cast(cy) - y; - const T img_fxfy = (fx >= 0 && fy >= 0) - ? dx * dy * GET_DATA_POINT(fx, fy) - : zero; + const T img_fxfy = + (fx >= 0 && fy >= 0) ? dx * dy * GET_DATA_POINT(fx, fy) : zero; const T img_cxcy = (cx <= data_width - 1 && cy <= data_height - 1) - ? (one - dx) * (one - dy) * GET_DATA_POINT(cx, cy) - : zero; + ? (one - dx) * (one - dy) * GET_DATA_POINT(cx, cy) + : zero; const T img_fxcy = (fx >= 0 && cy <= data_height - 1) - ? dx * (one - dy) * GET_DATA_POINT(fx, cy) - : zero; + ? dx * (one - dy) * GET_DATA_POINT(fx, cy) + : zero; const T img_cxfy = (cx <= data_width - 1 && fy >= 0) - ? (one - dx) * dy * GET_DATA_POINT(cx, fy) - : zero; + ? (one - dx) * dy * GET_DATA_POINT(cx, fy) + : zero; output[out_index] = img_fxfy + img_cxcy + img_fxcy + img_cxfy; } else { @@ -115,24 +109,20 @@ __global__ void Resampler2DKernel(const T* __restrict__ data, namespace functor { template -struct Resampler2DFunctor{ - void operator ()(::tensorflow::OpKernelContext* ctx, - const GPUDevice& d, - const T* __restrict__ data, - const T* __restrict__ warp, - T* __restrict__ output, - const int batch_size, - const int data_height, - const int data_width, - const int data_channels, - const int num_sampling_points) { - const int output_data_size = batch_size * num_sampling_points * data_channels; - ::tensorflow::CudaLaunchConfig config = - ::tensorflow::GetCudaLaunchConfig(output_data_size, d); - Resampler2DKernel - <<>>( - data, warp, output, batch_size, data_height, data_width, - data_channels, num_sampling_points); +struct Resampler2DFunctor { + void operator()(::tensorflow::OpKernelContext* ctx, const GPUDevice& d, + const T* __restrict__ data, const T* __restrict__ warp, + T* __restrict__ output, const int batch_size, + const int data_height, const int data_width, + const int data_channels, const int num_sampling_points) { + const int output_data_size = + batch_size * num_sampling_points * data_channels; + ::tensorflow::CudaLaunchConfig config = + ::tensorflow::GetCudaLaunchConfig(output_data_size, d); + Resampler2DKernel + <<>>( + data, warp, output, batch_size, data_height, data_width, + data_channels, num_sampling_points); } }; @@ -145,26 +135,20 @@ template struct Resampler2DFunctor; namespace { -#define UPDATE_GRAD_DATA_POINT(x, y, v) \ - atomicAdd(grad_data + (batch_id * data_batch_stride + \ - data_channels * (y * data_width + x) + \ - chan), \ +#define UPDATE_GRAD_DATA_POINT(x, y, v) \ + atomicAdd(grad_data + (batch_id * data_batch_stride + \ + data_channels * (y * data_width + x) + chan), \ v) - template -__global__ void ResamplerGrad2DKernel(const T* __restrict__ data, - const T* __restrict__ warp, - const T* __restrict__ grad_output, - T* __restrict__ grad_data, - T* __restrict__ grad_warp, - const int batch_size, - const int data_height, - const int data_width, - const int data_channels, - const int num_sampling_points) { - const int resampler_output_size = batch_size * num_sampling_points * - data_channels; +__global__ void ResamplerGrad2DKernel( + const T* __restrict__ data, const T* __restrict__ warp, + const T* __restrict__ grad_output, T* __restrict__ grad_data, + T* __restrict__ grad_warp, const int batch_size, const int data_height, + const int data_width, const int data_channels, + const int num_sampling_points) { + const int resampler_output_size = + batch_size * num_sampling_points * data_channels; CUDA_1D_KERNEL_LOOP(index, resampler_output_size) { const int out_index = index; @@ -199,10 +183,8 @@ __global__ void ResamplerGrad2DKernel(const T* __restrict__ data, // The effect is that the sampled signal smoothly goes to 0 outside // the original input domain, rather than presenting a jump // discontinuity at the image boundaries. - if (x > static_cast(-1.0) && - y > static_cast(-1.0) && - x < static_cast(data_width) && - y < static_cast(data_height)) { + if (x > static_cast(-1.0) && y > static_cast(-1.0) && + x < static_cast(data_width) && y < static_cast(data_height)) { // Precompute floor (f) and ceil (c) values for x and y. const int fx = std::floor(static_cast(x)); const int fy = std::floor(static_cast(y)); @@ -211,21 +193,17 @@ __global__ void ResamplerGrad2DKernel(const T* __restrict__ data, const T dx = static_cast(cx) - x; const T dy = static_cast(cy) - y; - const T img_fxfy = (fx >= 0 && fy >= 0) - ? GET_DATA_POINT(fx, fy) - : zero; + const T img_fxfy = (fx >= 0 && fy >= 0) ? GET_DATA_POINT(fx, fy) : zero; const T img_cxcy = (cx <= data_width - 1 && cy <= data_height - 1) - ? GET_DATA_POINT(cx, cy) - : zero; + ? GET_DATA_POINT(cx, cy) + : zero; - const T img_fxcy = (fx >= 0 && cy <= data_height - 1) - ? GET_DATA_POINT(fx, cy) - : zero; + const T img_fxcy = + (fx >= 0 && cy <= data_height - 1) ? GET_DATA_POINT(fx, cy) : zero; - const T img_cxfy = (cx <= data_width - 1 && fy >= 0) - ? GET_DATA_POINT(cx, fy) - : zero; + const T img_cxfy = + (cx <= data_width - 1 && fy >= 0) ? GET_DATA_POINT(cx, fy) : zero; // Update partial gradients wrt relevant warp field entries atomicAdd(grad_warp + warp_id_x, @@ -241,7 +219,7 @@ __global__ void ResamplerGrad2DKernel(const T* __restrict__ data, } if (cx <= data_width - 1 && cy <= data_height - 1) { UPDATE_GRAD_DATA_POINT(cx, cy, - grad_output_value * (one - dx) * (one - dy)); + grad_output_value * (one - dx) * (one - dy)); } if (fx >= 0 && cy <= data_height - 1) { UPDATE_GRAD_DATA_POINT(fx, cy, grad_output_value * dx * (one - dy)); @@ -261,43 +239,37 @@ __global__ void ResamplerGrad2DKernel(const T* __restrict__ data, namespace functor { template -struct ResamplerGrad2DFunctor{ - void operator ()(::tensorflow::OpKernelContext* ctx, - const GPUDevice& d, - const T* __restrict__ data, - const T* __restrict__ warp, - const T* __restrict__ grad_output, - T* __restrict__ grad_data, - T* __restrict__ grad_warp, - const int batch_size, - const int data_height, - const int data_width, - const int data_channels, - const int num_sampling_points) { - // Set gradients to 0, because the kernel incrementally updates the - // tensor entries by adding partial contributions. - const int grad_warp_size = batch_size * num_sampling_points * 2; - const int grad_data_size = batch_size * data_height * data_width * - data_channels; - - ::tensorflow::CudaLaunchConfig config = - ::tensorflow::GetCudaLaunchConfig(grad_warp_size, d); - ::tensorflow::SetZero - <<>>( - grad_warp_size, grad_warp); - - config = ::tensorflow::GetCudaLaunchConfig(grad_data_size, d); - ::tensorflow::SetZero - <<>>( - grad_data_size, grad_data); - - const int resampler_output_size = batch_size * num_sampling_points * - data_channels; - config = ::tensorflow::GetCudaLaunchConfig(resampler_output_size, d); - ResamplerGrad2DKernel - <<>>( - data, warp, grad_output, grad_data, grad_warp, batch_size, - data_height, data_width, data_channels, num_sampling_points); +struct ResamplerGrad2DFunctor { + void operator()(::tensorflow::OpKernelContext* ctx, const GPUDevice& d, + const T* __restrict__ data, const T* __restrict__ warp, + const T* __restrict__ grad_output, T* __restrict__ grad_data, + T* __restrict__ grad_warp, const int batch_size, + const int data_height, const int data_width, + const int data_channels, const int num_sampling_points) { + // Set gradients to 0, because the kernel incrementally updates the + // tensor entries by adding partial contributions. + const int grad_warp_size = batch_size * num_sampling_points * 2; + const int grad_data_size = + batch_size * data_height * data_width * data_channels; + + ::tensorflow::CudaLaunchConfig config = + ::tensorflow::GetCudaLaunchConfig(grad_warp_size, d); + ::tensorflow:: + SetZero<<>>( + grad_warp_size, grad_warp); + + config = ::tensorflow::GetCudaLaunchConfig(grad_data_size, d); + ::tensorflow:: + SetZero<<>>( + grad_data_size, grad_data); + + const int resampler_output_size = + batch_size * num_sampling_points * data_channels; + config = ::tensorflow::GetCudaLaunchConfig(resampler_output_size, d); + ResamplerGrad2DKernel + <<>>( + data, warp, grad_output, grad_data, grad_warp, batch_size, + data_height, data_width, data_channels, num_sampling_points); } }; diff --git a/tensorflow/contrib/rnn/kernels/blas_gemm.cc b/tensorflow/contrib/rnn/kernels/blas_gemm.cc index e62501e9b1..03006dab32 100644 --- a/tensorflow/contrib/rnn/kernels/blas_gemm.cc +++ b/tensorflow/contrib/rnn/kernels/blas_gemm.cc @@ -36,11 +36,10 @@ perftools::gputools::DeviceMemory AsDeviceMemory(const T* cuda_memory) { namespace functor { template -void TensorCuBlasGemm::operator()(OpKernelContext* ctx, - bool transa, bool transb, uint64 m, - uint64 n, uint64 k, T alpha, const T* a, - int lda, const T* b, int ldb, T beta, T* c, - int ldc) { +void TensorCuBlasGemm::operator()(OpKernelContext* ctx, bool transa, + bool transb, uint64 m, uint64 n, uint64 k, + T alpha, const T* a, int lda, const T* b, + int ldb, T beta, T* c, int ldc) { #if GOOGLE_CUDA perftools::gputools::blas::Transpose trans[] = { perftools::gputools::blas::Transpose::kNoTranspose, diff --git a/tensorflow/contrib/rnn/kernels/gru_ops.cc b/tensorflow/contrib/rnn/kernels/gru_ops.cc index 0796f82b21..bd3d898fb0 100644 --- a/tensorflow/contrib/rnn/kernels/gru_ops.cc +++ b/tensorflow/contrib/rnn/kernels/gru_ops.cc @@ -15,8 +15,8 @@ limitations under the License. #define EIGEN_USE_THREADS -#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/contrib/rnn/kernels/gru_ops.h" +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { @@ -61,9 +61,9 @@ class GRUCellBlockOp : public OpKernel { h_prev_tensor->dim_size(0), " vs. ", batch_size)); OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("h_prev.dims(1) != cell_size: ", - h_prev_tensor->dim_size(1), " vs. ", - cell_size)); + errors::InvalidArgument( + "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1), + " vs. ", cell_size)); // Shape of 'w_ru' must be [input_size+cell_size, 2*cell_size] OP_REQUIRES(ctx, w_ru_tensor->dim_size(0) == input_size + cell_size, @@ -82,10 +82,10 @@ class GRUCellBlockOp : public OpKernel { "w_c.dim_size(0) != input_size + cell_size: ", w_c_tensor->dim_size(0), " vs. ", input_size + cell_size)); - OP_REQUIRES( - ctx, w_c_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("w_c.dim_size(1) != cell_size: ", - w_c_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, w_c_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "w_c.dim_size(1) != cell_size: ", w_c_tensor->dim_size(1), + " vs. ", cell_size)); // Shape of 'b_ru' must be [2*cell_size] OP_REQUIRES(ctx, b_ru_tensor->dim_size(0) == cell_size * 2, @@ -97,10 +97,10 @@ class GRUCellBlockOp : public OpKernel { errors::InvalidArgument("Rank of b_ru must be 1", b_ru_tensor->dims(), " vs. 1", 1)); // Shape of 'b_c' must be [cell_size] - OP_REQUIRES( - ctx, b_c_tensor->dim_size(0) == cell_size, - errors::InvalidArgument("b_c.dim_size(0) != cell_size: ", - b_c_tensor->dim_size(0), " vs. ", cell_size)); + OP_REQUIRES(ctx, b_c_tensor->dim_size(0) == cell_size, + errors::InvalidArgument( + "b_c.dim_size(0) != cell_size: ", b_c_tensor->dim_size(0), + " vs. ", cell_size)); OP_REQUIRES(ctx, b_c_tensor->dims() == 1, errors::InvalidArgument("Rank of b_c must be 1", b_c_tensor->dims(), " vs. 1")); @@ -216,9 +216,9 @@ class GRUBlockCellGradOp : public OpKernel { h_prev_tensor->dim_size(0), " vs. ", batch_size)); OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("h_prev.dims(1) != cell_size: ", - h_prev_tensor->dim_size(1), " vs. ", - cell_size)); + errors::InvalidArgument( + "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1), + " vs. ", cell_size)); // Shape of 'w_ru' must be [input_size+cell_size, 2*cell_size] OP_REQUIRES(ctx, w_ru_tensor->dim_size(0) == input_size + cell_size, @@ -237,10 +237,10 @@ class GRUBlockCellGradOp : public OpKernel { "w_c.dim_size(0) != input_size + cell_size: ", w_c_tensor->dim_size(0), " vs. ", input_size + cell_size)); - OP_REQUIRES( - ctx, w_c_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("w_c.dim_size(1) != cell_size: ", - w_c_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, w_c_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "w_c.dim_size(1) != cell_size: ", w_c_tensor->dim_size(1), + " vs. ", cell_size)); // Shape of 'b_ru' must be [2*cell_size] OP_REQUIRES(ctx, b_ru_tensor->dim_size(0) == cell_size * 2, @@ -253,54 +253,54 @@ class GRUBlockCellGradOp : public OpKernel { b_ru_tensor->dims(), " vs. 1")); // Shape of 'b_c' must be [cell_size] - OP_REQUIRES( - ctx, b_c_tensor->dim_size(0) == cell_size, - errors::InvalidArgument("b_c.dim_size(0) != cell_size: ", - b_c_tensor->dim_size(0), " vs. ", cell_size)); + OP_REQUIRES(ctx, b_c_tensor->dim_size(0) == cell_size, + errors::InvalidArgument( + "b_c.dim_size(0) != cell_size: ", b_c_tensor->dim_size(0), + " vs. ", cell_size)); OP_REQUIRES(ctx, b_c_tensor->dims() == 1, errors::InvalidArgument("Rank of b_c must be 1 ", b_c_tensor->dims(), " vs. 1")); // Shape of 'r' must be [batch_size, cell_size] - OP_REQUIRES( - ctx, r_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("r.dims(0) != batch_size: ", - r_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, r_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("r.dims(1) != cell_size: ", - r_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, r_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "r.dims(0) != batch_size: ", r_tensor->dim_size(0), " vs. ", + batch_size)); + OP_REQUIRES(ctx, r_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "r.dims(1) != cell_size: ", r_tensor->dim_size(1), " vs. ", + cell_size)); // Shape of 'u' must be [batch_size, cell_size] - OP_REQUIRES( - ctx, u_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("u.dims(0) != batch_size: ", - u_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, u_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("u.dims(1) != cell_size: ", - u_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, u_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "u.dims(0) != batch_size: ", u_tensor->dim_size(0), " vs. ", + batch_size)); + OP_REQUIRES(ctx, u_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "u.dims(1) != cell_size: ", u_tensor->dim_size(1), " vs. ", + cell_size)); // Shape of 'c' must be [batch_size, cell_size] - OP_REQUIRES( - ctx, c_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("c.dims(0) != batch_size: ", - c_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, c_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("c.dims(1) != cell_size: ", - c_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, c_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "c.dims(0) != batch_size: ", c_tensor->dim_size(0), " vs. ", + batch_size)); + OP_REQUIRES(ctx, c_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "c.dims(1) != cell_size: ", c_tensor->dim_size(1), " vs. ", + cell_size)); // Shape of 'd_h' must be [batch_size, cell_size] - OP_REQUIRES( - ctx, d_h_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("d_h.dims(0) != batch_size: ", - d_h_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, d_h_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("d_h.dims(1) != cell_size: ", - d_h_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, d_h_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "d_h.dims(0) != batch_size: ", d_h_tensor->dim_size(0), + " vs. ", batch_size)); + OP_REQUIRES(ctx, d_h_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "d_h.dims(1) != cell_size: ", d_h_tensor->dim_size(1), + " vs. ", cell_size)); // Create output tensors. Tensor* d_x_tensor = nullptr; diff --git a/tensorflow/contrib/rnn/kernels/lstm_ops.cc b/tensorflow/contrib/rnn/kernels/lstm_ops.cc index 941a457fd3..5e7cf0ce84 100644 --- a/tensorflow/contrib/rnn/kernels/lstm_ops.cc +++ b/tensorflow/contrib/rnn/kernels/lstm_ops.cc @@ -281,23 +281,23 @@ class LSTMBlockCellOp : public OpKernel { h_prev_tensor->dim_size(0), " vs. ", batch_size)); OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("h_prev.dims(1) != cell_size: ", - h_prev_tensor->dim_size(1), " vs. ", - cell_size)); + errors::InvalidArgument( + "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1), + " vs. ", cell_size)); OP_REQUIRES(ctx, w_tensor->dim_size(0) == input_size + cell_size, errors::InvalidArgument( "w.dim_size(0) != input_size + cell_size: ", w_tensor->dim_size(0), " vs. ", input_size + cell_size)); - OP_REQUIRES( - ctx, w_tensor->dim_size(1) == cell_size * 4, - errors::InvalidArgument("w.dim_size(1) != cell_size * 4: ", - w_tensor->dim_size(1), " vs. ", cell_size * 4)); + OP_REQUIRES(ctx, w_tensor->dim_size(1) == cell_size * 4, + errors::InvalidArgument( + "w.dim_size(1) != cell_size * 4: ", w_tensor->dim_size(1), + " vs. ", cell_size * 4)); - OP_REQUIRES( - ctx, b_tensor->dim_size(0) == cell_size * 4, - errors::InvalidArgument("b.dim_size(0) != cell_size * 4: ", - b_tensor->dim_size(0), " vs. ", cell_size * 4)); + OP_REQUIRES(ctx, b_tensor->dim_size(0) == cell_size * 4, + errors::InvalidArgument( + "b.dim_size(0) != cell_size * 4: ", b_tensor->dim_size(0), + " vs. ", cell_size * 4)); // Allocate our output tensors. Tensor* i_tensor = nullptr; @@ -484,77 +484,77 @@ class LSTMBlockCellGradOp : public OpKernel { h_prev_tensor->dim_size(0), " vs. ", batch_size)); OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("h_prev.dims(1) != cell_size: ", - h_prev_tensor->dim_size(1), " vs. ", - cell_size)); + errors::InvalidArgument( + "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1), + " vs. ", cell_size)); OP_REQUIRES(ctx, w_tensor->dim_size(0) == input_size + cell_size, errors::InvalidArgument( "w.dim_size(0) != input_size + cell_size: ", w_tensor->dim_size(0), " vs. ", input_size + cell_size)); - OP_REQUIRES( - ctx, w_tensor->dim_size(1) == cell_size * 4, - errors::InvalidArgument("w.dim_size(1) != cell_size * 4: ", - w_tensor->dim_size(1), " vs. ", cell_size * 4)); + OP_REQUIRES(ctx, w_tensor->dim_size(1) == cell_size * 4, + errors::InvalidArgument( + "w.dim_size(1) != cell_size * 4: ", w_tensor->dim_size(1), + " vs. ", cell_size * 4)); - OP_REQUIRES( - ctx, b_tensor->dim_size(0) == cell_size * 4, - errors::InvalidArgument("b.dim_size(0) != cell_size * 4: ", - b_tensor->dim_size(0), " vs. ", cell_size * 4)); + OP_REQUIRES(ctx, b_tensor->dim_size(0) == cell_size * 4, + errors::InvalidArgument( + "b.dim_size(0) != cell_size * 4: ", b_tensor->dim_size(0), + " vs. ", cell_size * 4)); - OP_REQUIRES( - ctx, i_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("i.dim_size(0) != batch_size: ", - i_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, i_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("i.dim_size(1) != cell_size: ", - i_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, i_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "i.dim_size(0) != batch_size: ", i_tensor->dim_size(0), + " vs. ", batch_size)); + OP_REQUIRES(ctx, i_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "i.dim_size(1) != cell_size: ", i_tensor->dim_size(1), + " vs. ", cell_size)); - OP_REQUIRES( - ctx, cs_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("cs.dim_size(0) != batch_size: ", - cs_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, cs_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("cs.dim_size(1) != cell_size: ", - cs_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, cs_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "cs.dim_size(0) != batch_size: ", cs_tensor->dim_size(0), + " vs. ", batch_size)); + OP_REQUIRES(ctx, cs_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "cs.dim_size(1) != cell_size: ", cs_tensor->dim_size(1), + " vs. ", cell_size)); - OP_REQUIRES( - ctx, f_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("f.dim_size(0) != batch_size: ", - f_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, f_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("i.dim_size(1) != cell_size: ", - f_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, f_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "f.dim_size(0) != batch_size: ", f_tensor->dim_size(0), + " vs. ", batch_size)); + OP_REQUIRES(ctx, f_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "i.dim_size(1) != cell_size: ", f_tensor->dim_size(1), + " vs. ", cell_size)); - OP_REQUIRES( - ctx, o_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("o.dim_size(0) != batch_size: ", - o_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, o_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("o.dim_size(1) != cell_size: ", - o_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, o_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "o.dim_size(0) != batch_size: ", o_tensor->dim_size(0), + " vs. ", batch_size)); + OP_REQUIRES(ctx, o_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "o.dim_size(1) != cell_size: ", o_tensor->dim_size(1), + " vs. ", cell_size)); - OP_REQUIRES( - ctx, ci_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("ci.dim_size(0) != batch_size: ", - ci_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, ci_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("ci.dim_size(1) != cell_size: ", - ci_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, ci_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "ci.dim_size(0) != batch_size: ", ci_tensor->dim_size(0), + " vs. ", batch_size)); + OP_REQUIRES(ctx, ci_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "ci.dim_size(1) != cell_size: ", ci_tensor->dim_size(1), + " vs. ", cell_size)); - OP_REQUIRES( - ctx, co_tensor->dim_size(0) == batch_size, - errors::InvalidArgument("co.dim_size(0) != batch_size: ", - co_tensor->dim_size(0), " vs. ", batch_size)); - OP_REQUIRES( - ctx, co_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("co.dim_size(1) != cell_size: ", - co_tensor->dim_size(1), " vs. ", cell_size)); + OP_REQUIRES(ctx, co_tensor->dim_size(0) == batch_size, + errors::InvalidArgument( + "co.dim_size(0) != batch_size: ", co_tensor->dim_size(0), + " vs. ", batch_size)); + OP_REQUIRES(ctx, co_tensor->dim_size(1) == cell_size, + errors::InvalidArgument( + "co.dim_size(1) != cell_size: ", co_tensor->dim_size(1), + " vs. ", cell_size)); OP_REQUIRES(ctx, cs_grad_tensor->dim_size(0) == batch_size, errors::InvalidArgument( @@ -860,9 +860,9 @@ class BlockLSTMOp : public OpKernel { h_prev_tensor->dim_size(0), " vs. ", batch_size)); OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size, - errors::InvalidArgument("h_prev.dims(1) != cell_size: ", - h_prev_tensor->dim_size(1), " vs. ", - cell_size)); + errors::InvalidArgument( + "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1), + " vs. ", cell_size)); const Tensor* w_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("w", &w_tensor)); @@ -872,46 +872,46 @@ class BlockLSTMOp : public OpKernel { errors::InvalidArgument( "w.dim_size(0) != input_size + cell_size: ", w_tensor->dim_size(0), " vs. ", input_size + cell_size)); - OP_REQUIRES( - ctx, w_tensor->dim_size(1) == cell_size * 4, - errors::InvalidArgument("w.dim_size(1) != cell_size * 4: ", - w_tensor->dim_size(1), " vs. ", cell_size * 4)); + OP_REQUIRES(ctx, w_tensor->dim_size(1) == cell_size * 4, + errors::InvalidArgument( + "w.dim_size(1) != cell_size * 4: ", w_tensor->dim_size(1), + " vs. ", cell_size * 4)); const Tensor* wci_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("wci", &wci_tensor)); OP_REQUIRES(ctx, wci_tensor->dims() == 1, errors::InvalidArgument("wci must be 1D")); - OP_REQUIRES( - ctx, wci_tensor->dim_size(0) == cell_size, - errors::InvalidArgument("wci.dim_size(0) != cell_size: ", - wci_tensor->dim_size(0), " vs. ", cell_size)); + OP_REQUIRES(ctx, wci_tensor->dim_size(0) == cell_size, + errors::InvalidArgument( + "wci.dim_size(0) != cell_size: ", wci_tensor->dim_size(0), + " vs. ", cell_size)); const Tensor* wcf_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("wcf", &wcf_tensor)); OP_REQUIRES(ctx, wcf_tensor->dims() == 1, errors::InvalidArgument("wcf must be 1D")); - OP_REQUIRES( - ctx, wcf_tensor->dim_size(0) == cell_size, - errors::InvalidArgument("wcf.dim_size(0) != cell_size: ", - wcf_tensor->dim_size(0), " vs. ", cell_size)); + OP_REQUIRES(ctx, wcf_tensor->dim_size(0) == cell_size, + errors::InvalidArgument( + "wcf.dim_size(0) != cell_size: ", wcf_tensor->dim_size(0), + " vs. ", cell_size)); const Tensor* wco_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("wco", &wco_tensor)); OP_REQUIRES(ctx, wco_tensor->dims() == 1, errors::InvalidArgument("wco must be 1D")); - OP_REQUIRES( - ctx, wco_tensor->dim_size(0) == cell_size, - errors::InvalidArgument("wco.dim_size(0) != cell_size: ", - wco_tensor->dim_size(0), " vs. ", cell_size)); + OP_REQUIRES(ctx, wco_tensor->dim_size(0) == cell_size, + errors::InvalidArgument( + "wco.dim_size(0) != cell_size: ", wco_tensor->dim_size(0), + " vs. ", cell_size)); const Tensor* b_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("b", &b_tensor)); OP_REQUIRES(ctx, b_tensor->dims() == 1, errors::InvalidArgument("b must be 1D")); - OP_REQUIRES( - ctx, b_tensor->dim_size(0) == cell_size * 4, - errors::InvalidArgument("b.dim_size(0) != cell_size * 4: ", - b_tensor->dim_size(0), " vs. ", cell_size * 4)); + OP_REQUIRES(ctx, b_tensor->dim_size(0) == cell_size * 4, + errors::InvalidArgument( + "b.dim_size(0) != cell_size * 4: ", b_tensor->dim_size(0), + " vs. ", cell_size * 4)); TensorShape batch_cell_shape({timelen, batch_size, cell_size}); Tensor* i_out; @@ -1065,9 +1065,9 @@ class BlockLSTMGradOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("w", &w_tensor)); const int64 cell_size = w_tensor->dim_size(1) / 4; OP_REQUIRES(ctx, input_size + cell_size == w_tensor->dim_size(0), - errors::InvalidArgument("w matrix rows don't match: ", - input_size + cell_size, " vs. ", - w_tensor->dim_size(0))); + errors::InvalidArgument( + "w matrix rows don't match: ", input_size + cell_size, + " vs. ", w_tensor->dim_size(0))); const Tensor* wci_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("wci", &wci_tensor)); @@ -1193,7 +1193,6 @@ class BlockLSTMGradOp : public OpKernel { OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum::v(), batch_cell_shape, &h_grad_tensor)); - const Device& device = ctx->eigen_device(); functor::TensorZero()(device, cs_grad_tensor.flat()); diff --git a/tensorflow/contrib/rnn/kernels/lstm_ops.h b/tensorflow/contrib/rnn/kernels/lstm_ops.h index bc6b85f3f1..d23cedc234 100644 --- a/tensorflow/contrib/rnn/kernels/lstm_ops.h +++ b/tensorflow/contrib/rnn/kernels/lstm_ops.h @@ -92,7 +92,6 @@ struct TensorZeroPadding { } }; - struct LSTMBlockCell { LSTMBlockCell(const int batch_size, const int input_size, const int cell_size) : batch_size_(batch_size), diff --git a/tensorflow/contrib/rnn/ops/lstm_ops_test.cc b/tensorflow/contrib/rnn/ops/lstm_ops_test.cc index 544cd163c5..68184b643e 100644 --- a/tensorflow/contrib/rnn/ops/lstm_ops_test.cc +++ b/tensorflow/contrib/rnn/ops/lstm_ops_test.cc @@ -149,8 +149,9 @@ TEST_F(LSTMOpsTest, BlockLSTMGrad_ShapeFn) { INFER_ERROR("must be rank 1", op, "?;?;?;?;?;?;?;?;[1,?]" + suffix); // Output with all input knowns makes known rank outputs. - INFER_OK(op, JoinedCopies("?", 18), "[?,?,?];" + JoinedCopies("[?,?]", 3) + - ";" + JoinedCopies("[?]", 4)); + INFER_OK( + op, JoinedCopies("?", 18), + "[?,?,?];" + JoinedCopies("[?,?]", 3) + ";" + JoinedCopies("[?]", 4)); // Output with copies input shapes to output. string input = strings::StrCat("?;[?,?,?];", JoinedCopies("[?,?]", 3), ";", diff --git a/tensorflow/contrib/session_bundle/bundle_shim_test.cc b/tensorflow/contrib/session_bundle/bundle_shim_test.cc index 72f32a0f55..9a1dd9303f 100644 --- a/tensorflow/contrib/session_bundle/bundle_shim_test.cc +++ b/tensorflow/contrib/session_bundle/bundle_shim_test.cc @@ -493,17 +493,15 @@ TEST(BundleShimTest, DefaultAndNamedSignatureWithPredict) { ASSERT_FALSE( actual_signature_def_predict->second.inputs().find("foo-input") == actual_signature_def_predict->second.inputs().end()); - EXPECT_EQ("foo-input", - actual_signature_def_predict->second.inputs() - .find("foo-input") - ->second.name()); + EXPECT_EQ("foo-input", actual_signature_def_predict->second.inputs() + .find("foo-input") + ->second.name()); ASSERT_FALSE( actual_signature_def_predict->second.outputs().find("foo-output") == actual_signature_def_predict->second.outputs().end()); - EXPECT_EQ("foo-output", - actual_signature_def_predict->second.outputs() - .find("foo-output") - ->second.name()); + EXPECT_EQ("foo-output", actual_signature_def_predict->second.outputs() + .find("foo-output") + ->second.name()); EXPECT_EQ(kPredictMethodName, actual_signature_def_predict->second.method_name()); } diff --git a/tensorflow/contrib/session_bundle/signature.cc b/tensorflow/contrib/session_bundle/signature.cc index 7133875ad5..ed70a5b91b 100644 --- a/tensorflow/contrib/session_bundle/signature.cc +++ b/tensorflow/contrib/session_bundle/signature.cc @@ -38,9 +38,9 @@ namespace { Status BatchSizesMatch(const Tensor& input, const Tensor& output) { // Ensure the number of outputs match the number of inputs. if (input.dim_size(0) != output.dim_size(0)) { - return errors::Internal( - strings::StrCat("Input batch size did not match output batch size: ", - input.dim_size(0), " vs. ", output.dim_size(0))); + return errors::Internal(strings::StrCat( + "Input batch size did not match output batch size: ", input.dim_size(0), + " vs. ", output.dim_size(0))); } return Status::OK(); } @@ -100,8 +100,8 @@ Status GetNamedClassificationSignature( const auto& it = signatures.named_signatures().find(name); if (it == signatures.named_signatures().end()) { return errors::NotFound( - strings::StrCat("Missing signature named \"", name, "\" in: ", - DebugStringIfAvailable(signatures))); + strings::StrCat("Missing signature named \"", name, + "\" in: ", DebugStringIfAvailable(signatures))); } if (!it->second.has_classification_signature()) { return errors::FailedPrecondition( @@ -232,8 +232,8 @@ Status GetNamedSignature(const string& name, const auto& it = signatures.named_signatures().find(name); if (it == signatures.named_signatures().end()) { return errors::NotFound( - strings::StrCat("Missing signature named \"", name, "\" in: ", - DebugStringIfAvailable(signatures))); + strings::StrCat("Missing signature named \"", name, + "\" in: ", DebugStringIfAvailable(signatures))); } *signature = it->second; return Status::OK(); diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/hard_routing_function_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/hard_routing_function_op.cc index 76cfb4c9ca..cf0db788a4 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/hard_routing_function_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/hard_routing_function_op.cc @@ -99,18 +99,17 @@ class HardRoutingFunction : public OpKernel { const Tensor& tree_biases_tensor = context->input(2); if (input_data.shape().dim_size(0) > 0) { - OP_REQUIRES(context, input_data.shape().dims() == 2, - errors::InvalidArgument( - "input_data should be two-dimensional")); + OP_REQUIRES( + context, input_data.shape().dims() == 2, + errors::InvalidArgument("input_data should be two-dimensional")); } // Check tensor bounds. if (!CheckTensorBounds(context, input_data)) return; - const int32 num_data = static_cast( - input_data.shape().dim_size(0)); - const int32 num_features = static_cast( - input_data.shape().dim_size(1)); + const int32 num_data = static_cast(input_data.shape().dim_size(0)); + const int32 num_features = + static_cast(input_data.shape().dim_size(1)); Tensor* output_probability = nullptr; TensorShape output_probability_shape; @@ -125,9 +124,8 @@ class HardRoutingFunction : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(0, output_probability_shape, &output_probability)); - OP_REQUIRES_OK(context, - context->allocate_output(1, output_path_shape, - &output_path)); + OP_REQUIRES_OK( + context, context->allocate_output(1, output_path_shape, &output_path)); auto out_probability = output_probability->tensor(); auto out_path = output_path->tensor(); @@ -144,12 +142,11 @@ class HardRoutingFunction : public OpKernel { out_probability(i, 0) = 1.0; out_path(i, 0) = 0; for (int j = 0; j < tree_depth_ - 1; j++) { - float left_prob = LeftProbability(point, - tree_parameters_tensor.Slice(j, j+1), - tree_biases(j), - num_features); + float left_prob = + LeftProbability(point, tree_parameters_tensor.Slice(j, j + 1), + tree_biases(j), num_features); - int32 left_child = 2*node + 1; + int32 left_child = 2 * node + 1; int32 right_child = left_child + 1; float dot_product = 0.0; diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_gradient_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_gradient_op.cc index 28f50f1a32..f64155fa55 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_gradient_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_gradient_op.cc @@ -85,12 +85,9 @@ REGISTER_OP("KFeatureGradient") class KFeatureGradient : public OpKernel { public: - explicit KFeatureGradient(OpKernelConstruction* context) - : OpKernel(context) { - OP_REQUIRES_OK(context, context->GetAttr("layer_num", - &layer_num_)); - OP_REQUIRES_OK(context, context->GetAttr("random_seed", - &random_seed_)); + explicit KFeatureGradient(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("layer_num", &layer_num_)); + OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_)); } void Compute(OpKernelContext* context) override { @@ -101,14 +98,14 @@ class KFeatureGradient : public OpKernel { const Tensor& routing_tensor = context->input(3); // Extract dimensions from input tensors. - const int32 num_data = static_cast( - input_data_tensor.shape().dim_size(0)); - const int32 num_features = static_cast( - input_data_tensor.shape().dim_size(1)); - const int32 num_nodes = static_cast( - tree_parameters_tensor.shape().dim_size(0)); - const int32 num_features_per_node = static_cast( - tree_parameters_tensor.shape().dim_size(1)); + const int32 num_data = + static_cast(input_data_tensor.shape().dim_size(0)); + const int32 num_features = + static_cast(input_data_tensor.shape().dim_size(1)); + const int32 num_nodes = + static_cast(tree_parameters_tensor.shape().dim_size(0)); + const int32 num_features_per_node = + static_cast(tree_parameters_tensor.shape().dim_size(1)); // Construct output tensors. Tensor* out_routes = nullptr; @@ -127,12 +124,12 @@ class KFeatureGradient : public OpKernel { out_weights_shape.AddDim(num_nodes); out_weights_shape.AddDim(num_features_per_node); - OP_REQUIRES_OK(context, context->allocate_output( - 0, out_routes_shape, &out_routes)); - OP_REQUIRES_OK(context, context->allocate_output( - 1, out_data_shape, &out_data)); - OP_REQUIRES_OK(context, context->allocate_output( - 2, out_weights_shape, &out_weights)); + OP_REQUIRES_OK(context, + context->allocate_output(0, out_routes_shape, &out_routes)); + OP_REQUIRES_OK(context, + context->allocate_output(1, out_data_shape, &out_data)); + OP_REQUIRES_OK( + context, context->allocate_output(2, out_weights_shape, &out_weights)); tensorforest::Initialize(*out_data, 0.0f); @@ -148,18 +145,13 @@ class KFeatureGradient : public OpKernel { std::vector feature_set; for (int i = 0; i < num_data; i++) { - const Tensor point = input_data_tensor.Slice(i, i+1); + const Tensor point = input_data_tensor.Slice(i, i + 1); feature_set.clear(); // Traverse the tree from the bottom up. for (int j = num_nodes - 1; j >= 0; j--) { - tensorforest::GetFeatureSet( - layer_num_, - j, - random_seed_, - num_features, - num_features_per_node, - &feature_set); + tensorforest::GetFeatureSet(layer_num_, j, random_seed_, num_features, + num_features_per_node, &feature_set); // Compute routing gradient. // j is a leaf node. @@ -170,12 +162,8 @@ class KFeatureGradient : public OpKernel { int32 right_child = left_child + 1; float left_prob = LeftProbabilityK( - point, - feature_set, - tree_parameters_tensor.Slice(j, j+1), - tree_biases(j), - num_features, - num_features_per_node); + point, feature_set, tree_parameters_tensor.Slice(j, j + 1), + tree_biases(j), num_features, num_features_per_node); float right_prob = 1.0f - left_prob; diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_routing_function_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_routing_function_op.cc index 9bc42eb61f..e7cafb144d 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_routing_function_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/k_feature_routing_function_op.cc @@ -43,7 +43,6 @@ using shape_inference::ShapeHandle; using tensorforest::CheckTensorBounds; using tensorforest::LeftProbabilityK; - // The term 'routing function' is synonymous with 'the probability // that an instance is routed to each leaf node.' It is defined in // 'Deep Neural Decision Forests' by Kontschieder et al. @@ -96,10 +95,8 @@ class KFeatureRoutingFunction : public OpKernel { OP_REQUIRES_OK(context, context->GetAttr("max_nodes", &max_nodes_)); OP_REQUIRES_OK(context, context->GetAttr("num_features_per_node", &num_features_per_node_)); - OP_REQUIRES_OK(context, context->GetAttr("layer_num", - &layer_num_)); - OP_REQUIRES_OK(context, context->GetAttr("random_seed", - &random_seed_)); + OP_REQUIRES_OK(context, context->GetAttr("layer_num", &layer_num_)); + OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_)); } void Compute(OpKernelContext* context) override { @@ -108,27 +105,25 @@ class KFeatureRoutingFunction : public OpKernel { const Tensor& tree_biases_tensor = context->input(2); if (input_data.shape().dim_size(0) > 0) { - OP_REQUIRES(context, input_data.shape().dims() == 2, - errors::InvalidArgument( - "input_data should be two-dimensional")); + OP_REQUIRES( + context, input_data.shape().dims() == 2, + errors::InvalidArgument("input_data should be two-dimensional")); } // Check tensor bounds. if (!CheckTensorBounds(context, input_data)) return; - const int32 num_data = static_cast( - input_data.shape().dim_size(0)); - const int32 num_features = static_cast( - input_data.shape().dim_size(1)); + const int32 num_data = static_cast(input_data.shape().dim_size(0)); + const int32 num_features = + static_cast(input_data.shape().dim_size(1)); Tensor* output_probabilities = nullptr; TensorShape output_shape; output_shape.AddDim(num_data); output_shape.AddDim(max_nodes_); - OP_REQUIRES_OK(context, - context->allocate_output(0, output_shape, - &output_probabilities)); + OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, + &output_probabilities)); auto out_probs = output_probabilities->tensor(); const auto tree_biases = tree_biases_tensor.tensor(); @@ -136,30 +131,22 @@ class KFeatureRoutingFunction : public OpKernel { // Iteratively compute the probability of reaching each leaf. std::vector feature_set; for (int i = 0; i < num_data; i++) { - const Tensor point = input_data.Slice(i, i+1); + const Tensor point = input_data.Slice(i, i + 1); out_probs(i, 0) = 1.0f; for (int j = 0; j < max_nodes_ / 2; j++) { feature_set.clear(); - tensorforest::GetFeatureSet( - layer_num_, - i, - random_seed_, - num_features, - num_features_per_node_, - &feature_set); - - int32 left_child = 2*j + 1; + tensorforest::GetFeatureSet(layer_num_, i, random_seed_, num_features, + num_features_per_node_, &feature_set); + + int32 left_child = 2 * j + 1; int32 right_child = left_child + 1; float prob = out_probs(i, j); - float left_prob = LeftProbabilityK(point, - feature_set, - tree_parameters_tensor.Slice(j, j+1), - tree_biases(j), - num_features, - num_features_per_node_); + float left_prob = LeftProbabilityK( + point, feature_set, tree_parameters_tensor.Slice(j, j + 1), + tree_biases(j), num_features, num_features_per_node_); out_probs(i, left_child) = prob * left_prob; out_probs(i, right_child) = prob * (1.0f - left_prob); diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/routing_function_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/routing_function_op.cc index 4027e732b3..0c2eaabe8f 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/routing_function_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/routing_function_op.cc @@ -90,46 +90,43 @@ class RoutingFunction : public OpKernel { const Tensor& tree_biases_tensor = context->input(2); if (input_data.shape().dim_size(0) > 0) { - OP_REQUIRES(context, input_data.shape().dims() == 2, - errors::InvalidArgument( - "input_data should be two-dimensional")); + OP_REQUIRES( + context, input_data.shape().dims() == 2, + errors::InvalidArgument("input_data should be two-dimensional")); } // Check tensor bounds. if (!CheckTensorBounds(context, input_data)) return; - const int32 num_data = static_cast( - input_data.shape().dim_size(0)); - const int32 num_features = static_cast( - input_data.shape().dim_size(1)); + const int32 num_data = static_cast(input_data.shape().dim_size(0)); + const int32 num_features = + static_cast(input_data.shape().dim_size(1)); Tensor* output_probabilities = nullptr; TensorShape output_shape; output_shape.AddDim(num_data); output_shape.AddDim(max_nodes_); - OP_REQUIRES_OK(context, - context->allocate_output(0, output_shape, - &output_probabilities)); + OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, + &output_probabilities)); auto out_probs = output_probabilities->tensor(); const auto tree_biases = tree_biases_tensor.tensor(); // Iteratively compute the probability of reaching each leaf. for (int i = 0; i < num_data; i++) { - const Tensor point = input_data.Slice(i, i+1); + const Tensor point = input_data.Slice(i, i + 1); out_probs(i, 0) = 1.0; for (int j = 0; j < max_nodes_ / 2; j++) { - int32 left_child = 2*j + 1; + int32 left_child = 2 * j + 1; int32 right_child = left_child + 1; float prob = out_probs(i, j); - float left_prob = LeftProbability(point, - tree_parameters_tensor.Slice(j, j+1), - tree_biases(j), - num_features); + float left_prob = + LeftProbability(point, tree_parameters_tensor.Slice(j, j + 1), + tree_biases(j), num_features); out_probs(i, left_child) = prob * left_prob; out_probs(i, right_child) = prob * (1.0 - left_prob); diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc index 66aa293dc1..c9df09bfda 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc @@ -96,10 +96,9 @@ class StochasticHardRoutingFunction : public OpKernel { explicit StochasticHardRoutingFunction(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("tree_depth", &tree_depth_)); - OP_REQUIRES_OK(context, context->GetAttr("random_seed", - &random_seed_)); + OP_REQUIRES_OK(context, context->GetAttr("random_seed", &random_seed_)); single_rand_ = std::unique_ptr( - new random::PhiloxRandom(random_seed_)); + new random::PhiloxRandom(random_seed_)); rng_ = std::unique_ptr( new random::SimplePhilox(single_rand_.get())); } @@ -111,20 +110,19 @@ class StochasticHardRoutingFunction : public OpKernel { const Tensor& tree_biases_tensor = context->input(2); if (input_data.shape().dim_size(0) > 0) { - OP_REQUIRES(context, input_data.shape().dims() == 2, - errors::InvalidArgument( - "input_data should be two-dimensional")); + OP_REQUIRES( + context, input_data.shape().dims() == 2, + errors::InvalidArgument("input_data should be two-dimensional")); } // Check tensor bounds. if (!CheckTensorBounds(context, input_data)) return; - const int32 num_data = static_cast( - input_data.shape().dim_size(0)); - const int32 num_features = static_cast( - input_data.shape().dim_size(1)); - const int32 num_nodes = static_cast( - tree_parameters_tensor.shape().dim_size(0)); + const int32 num_data = static_cast(input_data.shape().dim_size(0)); + const int32 num_features = + static_cast(input_data.shape().dim_size(1)); + const int32 num_nodes = + static_cast(tree_parameters_tensor.shape().dim_size(0)); Tensor* output_probability = nullptr; TensorShape output_probability_shape; @@ -139,9 +137,8 @@ class StochasticHardRoutingFunction : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(0, output_probability_shape, &output_probability)); - OP_REQUIRES_OK(context, - context->allocate_output(1, output_path_shape, - &output_path)); + OP_REQUIRES_OK( + context, context->allocate_output(1, output_path_shape, &output_path)); auto out_probability = output_probability->tensor(); auto out_path = output_path->tensor(); @@ -150,19 +147,18 @@ class StochasticHardRoutingFunction : public OpKernel { // Stochastically traverse the tree to a leaf. for (int i = 0; i < num_data; i++) { - const Tensor point = input_data.Slice(i, i+1); + const Tensor point = input_data.Slice(i, i + 1); int32 node = 0; out_probability(i, 0) = 1.0; out_path(i, 0) = 0; for (int j = 0; j < tree_depth_ - 1; j++) { - int32 left_child = 2*node + 1; + int32 left_child = 2 * node + 1; int32 right_child = left_child + 1; - float left_prob = LeftProbability(point, - tree_parameters_tensor.Slice(j, j+1), - tree_biases(j), - num_features); + float left_prob = + LeftProbability(point, tree_parameters_tensor.Slice(j, j + 1), + tree_biases(j), num_features); if (left_prob < rng_->RandFloat()) { CHECK_LT(i, num_data); diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_gradient_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_gradient_op.cc index 0b5afe464f..b0d8b832b5 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_gradient_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_gradient_op.cc @@ -149,14 +149,14 @@ class StochasticHardRoutingGradient : public OpKernel { TensorShape output_bias_shape; output_bias_shape.AddDim(num_data); - OP_REQUIRES_OK(context, context->allocate_output( - 0, output_routing_shape, &output_routing)); - OP_REQUIRES_OK(context, context->allocate_output( - 1, output_data_shape, &output_data)); - OP_REQUIRES_OK(context, context->allocate_output( - 2, output_parameters_shape, &output_parameters)); - OP_REQUIRES_OK(context, context->allocate_output( - 3, output_bias_shape, &output_bias)); + OP_REQUIRES_OK(context, context->allocate_output(0, output_routing_shape, + &output_routing)); + OP_REQUIRES_OK( + context, context->allocate_output(1, output_data_shape, &output_data)); + OP_REQUIRES_OK(context, context->allocate_output(2, output_parameters_shape, + &output_parameters)); + OP_REQUIRES_OK( + context, context->allocate_output(3, output_bias_shape, &output_bias)); tensorforest::Initialize(*output_routing, 0.0); tensorforest::Initialize(*output_data, 0.0); @@ -178,7 +178,7 @@ class StochasticHardRoutingGradient : public OpKernel { const Tensor point = input_data.Slice(i, i + 1); // Traverses the tree from the bottom up. - for (int j = tree_depth_-1; j > -1; j--) { + for (int j = tree_depth_ - 1; j > -1; j--) { int32 node = path(i, j); CHECK_LT(node, num_nodes); diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc index cacad03e27..25825a78a1 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc @@ -64,8 +64,7 @@ REGISTER_OP("UnpackPath") class UnpackPath : public OpKernel { public: - explicit UnpackPath(OpKernelConstruction* context) - : OpKernel(context) {} + explicit UnpackPath(OpKernelConstruction* context) : OpKernel(context) {} void Compute(OpKernelContext* context) override { VLOG(1) << "unpack start"; @@ -73,8 +72,8 @@ class UnpackPath : public OpKernel { const Tensor& path_values_tensor = context->input(1); const int32 num_data = static_cast(path_tensor.shape().dim_size(0)); - const int32 tree_depth = static_cast( - path_tensor.shape().dim_size(1)); + const int32 tree_depth = + static_cast(path_tensor.shape().dim_size(1)); const int32 num_nodes = MathUtil::IPow(2, tree_depth) - 1; @@ -107,7 +106,6 @@ class UnpackPath : public OpKernel { } }; -REGISTER_KERNEL_BUILDER(Name("UnpackPath").Device(DEVICE_CPU), - UnpackPath); +REGISTER_KERNEL_BUILDER(Name("UnpackPath").Device(DEVICE_CPU), UnpackPath); } // namespace tensorflow diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.cc index c091a73c4e..34388fe1aa 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.cc @@ -25,9 +25,7 @@ namespace tensorforest { using tensorflow::Tensor; -float LeftProbability(const Tensor& point, - const Tensor& weight, - float bias, +float LeftProbability(const Tensor& point, const Tensor& weight, float bias, int num_features) { const auto p = point.unaligned_flat(); const auto w = weight.unaligned_flat(); @@ -41,11 +39,8 @@ float LeftProbability(const Tensor& point, return 1.0 / (1.0 + exp(-dot_product + bias)); } -float LeftProbabilityK(const Tensor& point, - std::vector feature_set, - const Tensor& weight, - float bias, - int num_features, +float LeftProbabilityK(const Tensor& point, std::vector feature_set, + const Tensor& weight, float bias, int num_features, int k) { const auto p = point.unaligned_flat(); const auto w = weight.unaligned_flat(); diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.h b/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.h index c5902184f9..69a0143a4e 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.h +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.h @@ -24,16 +24,11 @@ namespace tensorflow { namespace tensorforest { // Returns the probability that the point falls to the left. -float LeftProbability(const Tensor& point, - const Tensor& weight, - float bias, +float LeftProbability(const Tensor& point, const Tensor& weight, float bias, int num_features); -float LeftProbabilityK(const Tensor& point, - std::vector feature_set, - const Tensor& weight, - float bias, - int num_features, +float LeftProbabilityK(const Tensor& point, std::vector feature_set, + const Tensor& weight, float bias, int num_features, int k); // Returns a random set of num_features_to_pick features in the @@ -49,5 +44,3 @@ void GetFeatureSet(int32 tree_num, int32 node_num, int32 random_seed, } // namespace tensorflow #endif // LEARNING_LIB_TENSOR_FOREST_HYBRID_CORE_OPS_UTILS_H_ - - diff --git a/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc b/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc index 47b49a379c..b21a917977 100644 --- a/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc +++ b/tensorflow/contrib/tensor_forest/kernels/reinterpret_string_to_float_op.cc @@ -30,15 +30,13 @@ namespace tensorflow { using tensorforest::CheckTensorBounds; - float Convert(const string& in) { const std::size_t intval = std::hash()(in); return static_cast(intval); } - -void Evaluate(const Tensor& input_data, Tensor output_data, - int32 start, int32 end) { +void Evaluate(const Tensor& input_data, Tensor output_data, int32 start, + int32 end) { auto out_data = output_data.unaligned_flat(); const auto in_data = input_data.unaligned_flat(); @@ -59,9 +57,8 @@ class ReinterpretStringToFloat : public OpKernel { if (!CheckTensorBounds(context, input_data)) return; Tensor* output_data = nullptr; - OP_REQUIRES_OK(context, - context->allocate_output(0, input_data.shape(), - &output_data)); + OP_REQUIRES_OK( + context, context->allocate_output(0, input_data.shape(), &output_data)); // Evaluate input data in parallel. const int32 num_data = static_cast(input_data.NumElements()); @@ -73,8 +70,8 @@ class ReinterpretStringToFloat : public OpKernel { auto work = [&input_data, output_data, num_data](int64 start, int64 end) { CHECK(start <= end); CHECK(end <= num_data); - Evaluate(input_data, *output_data, - static_cast(start), static_cast(end)); + Evaluate(input_data, *output_data, static_cast(start), + static_cast(end)); }; Shard(num_threads, worker_threads->workers, num_data, 100, work); } diff --git a/tensorflow/contrib/tensor_forest/kernels/scatter_add_ndim_op.cc b/tensorflow/contrib/tensor_forest/kernels/scatter_add_ndim_op.cc index dd2a98b08c..60740c2be3 100644 --- a/tensorflow/contrib/tensor_forest/kernels/scatter_add_ndim_op.cc +++ b/tensorflow/contrib/tensor_forest/kernels/scatter_add_ndim_op.cc @@ -22,7 +22,6 @@ #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/platform/logging.h" - namespace tensorflow { using tensorforest::CheckTensorBounds; @@ -38,20 +37,19 @@ class ScatterAddNdim : public OpKernel { if (indices_tensor.shape().dim_size(0) > 0) { OP_REQUIRES(context, indices_tensor.shape().dims() == 2, - errors::InvalidArgument( - "indices should be two-dimensional")); + errors::InvalidArgument("indices should be two-dimensional")); const int32 delta_dims = deltas_tensor.shape().dims(); OP_REQUIRES( context, indices_tensor.shape().dim_size(1) + delta_dims == - input_tensor.shape().dims() + 1, + input_tensor.shape().dims() + 1, errors::InvalidArgument( "Number of indices dimensions should be the same as input " "rank.")); OP_REQUIRES( context, indices_tensor.shape().dim_size(0) == - deltas_tensor.shape().dim_size(0), + deltas_tensor.shape().dim_size(0), errors::InvalidArgument( "Number of updates should be same as number of indices.")); } else { @@ -68,8 +66,8 @@ class ScatterAddNdim : public OpKernel { const auto indices = indices_tensor.tensor(); const auto deltas = deltas_tensor.unaligned_flat(); - const int32 num_dims = static_cast( - indices_tensor.shape().dim_size(1)); + const int32 num_dims = + static_cast(indices_tensor.shape().dim_size(1)); // Figure out if indices don't specify a complete position in the // input tensor. @@ -80,10 +78,9 @@ class ScatterAddNdim : public OpKernel { // Calculate index multipliers. std::vector multipliers; - OP_REQUIRES( - context, input.size() < std::numeric_limits::max(), - errors::InvalidArgument( - "Input must contain less than 2^31 total elements")); + OP_REQUIRES(context, input.size() < std::numeric_limits::max(), + errors::InvalidArgument( + "Input must contain less than 2^31 total elements")); int32 last_size = static_cast(input.size()); for (int32 j = 0; j < num_dims; j++) { diff --git a/tensorflow/contrib/tensor_forest/kernels/tree_utils.cc b/tensorflow/contrib/tensor_forest/kernels/tree_utils.cc index 94e12cea5a..44997ec5d6 100644 --- a/tensorflow/contrib/tensor_forest/kernels/tree_utils.cc +++ b/tensorflow/contrib/tensor_forest/kernels/tree_utils.cc @@ -65,8 +65,8 @@ void GetTwoBest(int max, const std::function& score_fn, float ClassificationSplitScore( const Eigen::Tensor& splits, - const Eigen::Tensor& rights, - int32 num_classes, int i) { + const Eigen::Tensor& rights, int32 num_classes, + int i) { Eigen::array offsets; // Class counts are stored with the total in [0], so the length of each // count vector is num_classes + 1. @@ -74,7 +74,7 @@ float ClassificationSplitScore( Eigen::array extents; extents[0] = num_classes; return WeightedGiniImpurity(splits.slice(offsets, extents)) + - WeightedGiniImpurity(rights.slice(offsets, extents)); + WeightedGiniImpurity(rights.slice(offsets, extents)); } void GetTwoBestClassification(const Tensor& total_counts, @@ -90,29 +90,28 @@ void GetTwoBestClassification(const Tensor& total_counts, // in seg faults, so we have to go with flat views of these tensors. However, // it is still pretty efficient because we put off evaluation until the // score is actually returned. - const auto tc = total_counts.Slice( - accumulator, accumulator + 1).unaligned_flat(); + const auto tc = + total_counts.Slice(accumulator, accumulator + 1).unaligned_flat(); // TODO(gilberth): See if we can delay evaluation here by templating the // arguments to ClassificationSplitScore. - const Eigen::Tensor splits = split_counts.Slice( - accumulator, accumulator + 1).unaligned_flat(); + const Eigen::Tensor splits = + split_counts.Slice(accumulator, accumulator + 1).unaligned_flat(); Eigen::array bcast; bcast[0] = num_splits; const Eigen::Tensor rights = tc.broadcast(bcast) - splits; - std::function score_fn = std::bind( - ClassificationSplitScore, splits, rights, num_classes, - std::placeholders::_1); + std::function score_fn = + std::bind(ClassificationSplitScore, splits, rights, num_classes, + std::placeholders::_1); GetTwoBest(num_splits, score_fn, best_score, best_index, second_best_score, second_best_index); } -int32 BestFeatureClassification( - const Tensor& total_counts, const Tensor& split_counts, - int32 accumulator) { +int32 BestFeatureClassification(const Tensor& total_counts, + const Tensor& split_counts, int32 accumulator) { float best_score; float second_best_score; int best_feature_index; @@ -130,8 +129,7 @@ float RegressionSplitScore( const Eigen::Tensor& splits_square, const Eigen::Tensor& right_sums, const Eigen::Tensor& right_squares, - int32 accumulator, - int32 num_regression_dims, int i) { + int32 accumulator, int32 num_regression_dims, int i) { Eigen::array offsets = {i * num_regression_dims + 1}; Eigen::array extents = {num_regression_dims - 1}; float left_count = splits_count_accessor(accumulator, i, 0); @@ -141,15 +139,15 @@ float RegressionSplitScore( // Guard against divide-by-zero. if (left_count > 0) { - score += WeightedVariance( - splits_sum.slice(offsets, extents), - splits_square.slice(offsets, extents), left_count); + score += + WeightedVariance(splits_sum.slice(offsets, extents), + splits_square.slice(offsets, extents), left_count); } if (right_count > 0) { - score += WeightedVariance(right_sums.slice(offsets, extents), - right_squares.slice(offsets, extents), - right_count); + score += + WeightedVariance(right_sums.slice(offsets, extents), + right_squares.slice(offsets, extents), right_count); } return score; } @@ -159,20 +157,20 @@ void GetTwoBestRegression(const Tensor& total_sums, const Tensor& total_squares, int32 accumulator, float* best_score, int* best_index, float* second_best_score, int* second_best_index) { const int32 num_splits = static_cast(split_sums.shape().dim_size(1)); - const int32 num_regression_dims = static_cast( - split_sums.shape().dim_size(2)); + const int32 num_regression_dims = + static_cast(split_sums.shape().dim_size(2)); // Ideally, Eigen::Tensor::chip would be best to use here but it results // in seg faults, so we have to go with flat views of these tensors. However, // it is still pretty efficient because we put off evaluation until the // score is actually returned. - const auto tc_sum = total_sums.Slice( - accumulator, accumulator + 1).unaligned_flat(); - const auto tc_square = total_squares.Slice( - accumulator, accumulator + 1).unaligned_flat(); - const auto splits_sum = split_sums.Slice( - accumulator, accumulator + 1).unaligned_flat(); - const auto splits_square = split_squares.Slice( - accumulator, accumulator + 1).unaligned_flat(); + const auto tc_sum = + total_sums.Slice(accumulator, accumulator + 1).unaligned_flat(); + const auto tc_square = + total_squares.Slice(accumulator, accumulator + 1).unaligned_flat(); + const auto splits_sum = + split_sums.Slice(accumulator, accumulator + 1).unaligned_flat(); + const auto splits_square = + split_squares.Slice(accumulator, accumulator + 1).unaligned_flat(); // Eigen is infuriating to work with, usually resulting in all kinds of // unhelpful compiler errors when trying something that seems sane. This // helps us do a simple thing like access the first element (the counts) @@ -193,10 +191,10 @@ void GetTwoBestRegression(const Tensor& total_sums, const Tensor& total_squares, best_score, best_index, second_best_score, second_best_index); } -int32 BestFeatureRegression( - const Tensor& total_sums, const Tensor& total_squares, - const Tensor& split_sums, const Tensor& split_squares, - int32 accumulator) { +int32 BestFeatureRegression(const Tensor& total_sums, + const Tensor& total_squares, + const Tensor& split_sums, + const Tensor& split_squares, int32 accumulator) { float best_score; float second_best_score; int best_feature_index; @@ -207,10 +205,11 @@ int32 BestFeatureRegression( return best_feature_index; } -bool BestSplitDominatesRegression( - const Tensor& total_sums, const Tensor& total_squares, - const Tensor& split_sums, const Tensor& split_squares, - int32 accumulator) { +bool BestSplitDominatesRegression(const Tensor& total_sums, + const Tensor& total_squares, + const Tensor& split_sums, + const Tensor& split_squares, + int32 accumulator) { // TODO(thomaswc): Implement this, probably as part of v3. return false; } @@ -599,7 +598,6 @@ bool Decide(float value, float bias, DataColumnTypes type) { } } - void GetParentWeightedMean(float leaf_sum, const float* leaf_data, float parent_sum, const float* parent_data, float valid_leaf_threshold, int num_outputs, diff --git a/tensorflow/contrib/tensor_forest/kernels/tree_utils.h b/tensorflow/contrib/tensor_forest/kernels/tree_utils.h index dad9df4898..edbac67006 100644 --- a/tensorflow/contrib/tensor_forest/kernels/tree_utils.h +++ b/tensorflow/contrib/tensor_forest/kernels/tree_utils.h @@ -45,13 +45,10 @@ const int32 LEAF_NODE = -1; const int32 FREE_NODE = -2; // Used to indicate column types, e.g. categorical vs. float -enum DataColumnTypes { - kDataFloat = 0, - kDataCategorical = 1 -}; +enum DataColumnTypes { kDataFloat = 0, kDataCategorical = 1 }; // Calculates the sum of a tensor. -template +template T Sum(Tensor counts) { Eigen::Tensor count_sum = counts.unaligned_flat().sum(); @@ -97,7 +94,7 @@ float WeightedGiniImpurity(const T& counts) { return RawWeightedGiniImpurity(smoothed); } -template +template float WeightedVariance(const T1& sums, const T2& squares, float count) { const auto e_x = sums / count; const auto e_x2 = squares / count; @@ -120,10 +117,11 @@ int32 BestFeatureRegression(const Tensor& total_sums, // Returns true if the best split's variance is sufficiently smaller than // that of the next best split. -bool BestSplitDominatesRegression( - const Tensor& total_sums, const Tensor& total_squares, - const Tensor& split_sums, const Tensor& split_squares, - int32 accumulator); +bool BestSplitDominatesRegression(const Tensor& total_sums, + const Tensor& total_squares, + const Tensor& split_sums, + const Tensor& split_squares, + int32 accumulator); // Performs booststrap_samples bootstrap samples of the best split's class // counts and the second best splits's class counts, and returns true if at @@ -178,10 +176,8 @@ bool DecideNode(const GetFeatureFnType& get_dense, // isn't present in sparse_input_indices. sparse_input_indices is assumed // to be sorted. template -float FindSparseValue( - const T1& sparse_input_indices, - const T2& sparse_input_values, - int32 i, int32 j) { +float FindSparseValue(const T1& sparse_input_indices, + const T2& sparse_input_values, int32 i, int32 j) { int32 low = 0; int32 high = sparse_input_values.dimension(0); while (low < high) { @@ -273,7 +269,6 @@ int32 GetNumSparseFeatures(const T1& indices, int32 input_index, // categorical data, it is value != bias. bool Decide(float value, float bias, DataColumnTypes type = kDataFloat); - // Returns true if all the splits are initialized. Since they get initialized // in order, we can simply infer this from the last split. // This should only be called for a single allocator's candidate features diff --git a/tensorflow/contrib/tensor_forest/kernels/tree_utils_test.cc b/tensorflow/contrib/tensor_forest/kernels/tree_utils_test.cc index 7485a695df..0855354550 100644 --- a/tensorflow/contrib/tensor_forest/kernels/tree_utils_test.cc +++ b/tensorflow/contrib/tensor_forest/kernels/tree_utils_test.cc @@ -44,11 +44,13 @@ TEST(TestWeightedVariance, Basic) { Tensor squares = test::AsTensor({29, 12}, {2}); EXPECT_FLOAT_EQ(WeightedVariance(sums.unaligned_flat(), - squares.unaligned_flat(), 3), 2.0); + squares.unaligned_flat(), 3), + 2.0); Tensor zero = test::AsTensor({0}, {1}); EXPECT_FLOAT_EQ(WeightedVariance(zero.unaligned_flat(), - zero.unaligned_flat(), 1), 0); + zero.unaligned_flat(), 1), + 0); } TEST(TestInitialize, Basic) { @@ -94,17 +96,16 @@ TEST(BestFeatureClassification, Basic) { const int32 num_accumulators = 4; const int32 num_splits = 3; const int32 num_classes = 4; - Tensor totals = test::AsTensor({1, 5, 6, 7, - 0, 0, 0, 0, - 30, 10, 10, 10, // this one - -1, -1, -1, -1}, - {num_accumulators, num_classes}); - Tensor splits = test::AsTensor( - {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 30, 10, 10, 10, 10, 0, 0, 10, 19, 5, 6, 8, // this one - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, - {num_accumulators, num_splits, num_classes}); + Tensor totals = test::AsTensor( + {1, 5, 6, 7, 0, 0, 0, 0, 30, 10, 10, 10, // this one + -1, -1, -1, -1}, + {num_accumulators, num_classes}); + Tensor splits = + test::AsTensor({1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 10, + 10, 10, 10, 0, 0, 10, 19, 5, 6, 8, // this one + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, + {num_accumulators, num_splits, num_classes}); EXPECT_EQ(BestFeatureClassification(totals, splits, 2), 1); } @@ -114,17 +115,16 @@ TEST(BestFeatureClassification, NoWinner) { const int32 num_splits = 3; const int32 num_classes = 4; // When counts are all the same, the most reasonable thing to do is pick 0. - Tensor totals = test::AsTensor({1, 5, 6, 7, - 0, 0, 0, 0, - 18, 6, 6, 6, // this one - -1, -1, -1, -1}, - {num_accumulators, num_classes}); - Tensor splits = test::AsTensor( - {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 9, 3, 3, 3, 9, 3, 3, 3, 9, 3, 3, 3, // this one - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, - {num_accumulators, num_splits, num_classes}); + Tensor totals = + test::AsTensor({1, 5, 6, 7, 0, 0, 0, 0, 18, 6, 6, 6, // this one + -1, -1, -1, -1}, + {num_accumulators, num_classes}); + Tensor splits = + test::AsTensor({1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 3, + 3, 3, 9, 3, 3, 3, 9, 3, 3, 3, // this one + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, + {num_accumulators, num_splits, num_classes}); EXPECT_EQ(BestFeatureClassification(totals, splits, 2), 0); } @@ -133,36 +133,34 @@ TEST(BestFeatureRegression, Basic) { const int32 num_accumulators = 4; const int32 num_splits = 3; const int32 num_classes = 4; - Tensor total_sums = test::AsTensor( - {1, 5, 6, 7, - 0, 0, 0, 0, - 10, 8, 6, 9, // this one - -1, -1, -1, -1}, - {num_accumulators, num_classes}); + Tensor total_sums = + test::AsTensor({1, 5, 6, 7, 0, 0, 0, 0, 10, 8, 6, 9, // this one + -1, -1, -1, -1}, + {num_accumulators, num_classes}); Tensor total_squares = test::AsTensor( - {1, 5, 6, 7, - 0, 0, 0, 0, - 100, 50, 40, 45, // this one + {1, 5, 6, 7, 0, 0, 0, 0, 100, 50, 40, 45, // this one -1, -1, -1, -1}, {num_accumulators, num_classes}); - Tensor split_sums = test::AsTensor( - {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 10, 8, 6, 9, 9, 8, 5, 9, 0, 0, 0, 0, // this one - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, - {num_accumulators, num_splits, num_classes}); + Tensor split_sums = + test::AsTensor({1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 8, + 6, 9, 9, 8, 5, 9, 0, 0, 0, 0, // this one + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, + {num_accumulators, num_splits, num_classes}); // lower the variance by lowering one of the squares just a little. - Tensor split_squares = test::AsTensor( - {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 100, 50, 40, 45, 100, 50, 40, 43, 0, 0, 0, 0, // this one - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, - {num_accumulators, num_splits, num_classes}); + Tensor split_squares = + test::AsTensor( + {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 100, 50, 40, 45, 100, 50, 40, 43, 0, 0, 0, 0, // this one + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, + {num_accumulators, num_splits, num_classes}); EXPECT_EQ(BestFeatureRegression(total_sums, total_squares, split_sums, - split_squares, 2), 1); + split_squares, 2), + 1); } TEST(BestFeatureRegression, NoWinner) { @@ -170,37 +168,33 @@ TEST(BestFeatureRegression, NoWinner) { const int32 num_splits = 3; const int32 num_classes = 4; // when counts are all the same, the most reasonable thing to do is pick 0. - Tensor total_sums = test::AsTensor( - {1, 5, 6, 7, - 0, 0, 0, 0, - 10, 8, 6, 9, // this one - -1, -1, -1, -1}, - {num_accumulators, num_classes}); + Tensor total_sums = + test::AsTensor({1, 5, 6, 7, 0, 0, 0, 0, 10, 8, 6, 9, // this one + -1, -1, -1, -1}, + {num_accumulators, num_classes}); Tensor total_squares = test::AsTensor( - {1, 5, 6, 7, - 0, 0, 0, 0, - 100, 50, 40, 45, // this one + {1, 5, 6, 7, 0, 0, 0, 0, 100, 50, 40, 45, // this one -1, -1, -1, -1}, {num_accumulators, num_classes}); - Tensor split_sums = test::AsTensor( - {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 10, 8, 6, 9, 10, 8, 6, 9, 10, 8, 6, 9, // this one - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, - {num_accumulators, num_splits, num_classes}); + Tensor split_sums = + test::AsTensor({1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 8, + 6, 9, 10, 8, 6, 9, 10, 8, 6, 9, // this one + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, + {num_accumulators, num_splits, num_classes}); Tensor split_squares = test::AsTensor( - {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 100, 50, 40, 45, 100, 50, 40, 45, 100, 50, 40, 45, // this one - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, + {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 100, 50, 40, 45, 100, 50, 40, 45, 100, 50, 40, 45, // this one + -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, {num_accumulators, num_splits, num_classes}); EXPECT_EQ(BestFeatureRegression(total_sums, total_squares, split_sums, - split_squares, 2), 0); + split_squares, 2), + 0); } } // namespace tensorforest } // namespace tensorflow - diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc index 81e2a1b2a1..f4a7058ddb 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.cc @@ -14,8 +14,8 @@ // ============================================================================= #include "tensorflow/contrib/tensor_forest/kernels/v4/candidate_graph_runner.h" -#include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/framework/graph.pb.h" +#include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/platform/env.h" namespace tensorflow { @@ -58,8 +58,7 @@ CandidateGraphRunner::CandidateGraphRunner( // Features don't change, store them in a tensor. const auto& oblique = split.inequality_left_child_test().oblique(); const int32 feat_size = oblique.features_size(); - features_.reset( - new Tensor(tensorflow::DT_INT32, TensorShape({feat_size}))); + features_.reset(new Tensor(tensorflow::DT_INT32, TensorShape({feat_size}))); auto feat = features_->flat(); int i = 0; for (const auto& id : oblique.features()) { @@ -67,10 +66,10 @@ CandidateGraphRunner::CandidateGraphRunner( } } -void CandidateGraphRunner::RunOp( - const string& name, const TensorNameValueList& inputs, - const std::vector& output_tensor_names, - std::vector* outputs) { +void CandidateGraphRunner::RunOp(const string& name, + const TensorNameValueList& inputs, + const std::vector& output_tensor_names, + std::vector* outputs) { std::vector op_name; if (name != kNoOp) { op_name.push_back(name); diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h index cced26b903..328af28725 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision-tree-resource.h @@ -26,7 +26,6 @@ namespace tensorflow { namespace tensorforest { - // Keep a tree ensemble in memory for efficient evaluation and mutation. class DecisionTreeResource : public ResourceBase { public: @@ -35,15 +34,12 @@ class DecisionTreeResource : public ResourceBase { string DebugString() override { return strings::StrCat("DecisionTree[size=", - decision_tree_->decision_tree().nodes_size(), - "]"); + decision_tree_->decision_tree().nodes_size(), "]"); } void MaybeInitialize(); - const decision_trees::Model& decision_tree() const { - return *decision_tree_; - } + const decision_trees::Model& decision_tree() const { return *decision_tree_; } decision_trees::Model* mutable_decision_tree() { return decision_tree_.get(); @@ -59,9 +55,7 @@ class DecisionTreeResource : public ResourceBase { // Resets the resource and frees the proto. // Caller needs to hold the mutex lock while calling this. - void Reset() { - decision_tree_.reset(new decision_trees::Model()); - } + void Reset() { decision_tree_.reset(new decision_trees::Model()); } mutex* get_mutex() { return &mu_; } @@ -84,7 +78,6 @@ class DecisionTreeResource : public ResourceBase { std::vector> node_evaluators_; }; - } // namespace tensorforest } // namespace tensorflow diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h index 85ce7b825b..bf2b2aaa3c 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator.h @@ -22,7 +22,6 @@ namespace tensorflow { namespace tensorforest { - // Base class for evaluators of decision nodes that effectively copy proto // contents into C++ structures for faster execution. class DecisionNodeEvaluator { diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc index 5c49b87443..af5cf72a3c 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/decision_node_evaluator_test.cc @@ -20,11 +20,11 @@ namespace tensorflow { namespace { +using tensorflow::decision_trees::InequalityTest; +using tensorflow::decision_trees::MatchingValuesTest; using tensorflow::tensorforest::InequalityDecisionNodeEvaluator; using tensorflow::tensorforest::MatchingValuesDecisionNodeEvaluator; using tensorflow::tensorforest::ObliqueInequalityDecisionNodeEvaluator; -using tensorflow::decision_trees::InequalityTest; -using tensorflow::decision_trees::MatchingValuesTest; TEST(InequalityDecisionNodeEvaluatorTest, TestLessOrEqual) { InequalityTest test; @@ -124,4 +124,3 @@ TEST(ObliqueDecisionNodeEvaluatorTest, Basic) { } // namespace } // namespace tensorflow - diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h index 0d6712e9e5..eea0be27ca 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/fertile-stats-resource.h @@ -40,9 +40,7 @@ class FertileStatsResource : public ResourceBase { model_op_ = LeafModelOperatorFactory::CreateLeafModelOperator(params_); } - string DebugString() override { - return "FertileStats"; - } + string DebugString() override { return "FertileStats"; } void ExtractFromProto(const FertileStats& stats); @@ -50,8 +48,7 @@ class FertileStatsResource : public ResourceBase { // Resets the resource and frees the proto. // Caller needs to hold the mutex lock while calling this. - void Reset() { - } + void Reset() {} // Reset the stats for a node, but leave the leaf_stats intact. void ResetSplitStats(int32 node_id, int32 depth) { @@ -84,7 +81,6 @@ class FertileStatsResource : public ResourceBase { // was found. bool BestSplit(int32 node_id, SplitCandidate* best, int32* depth); - private: mutex mu_; std::shared_ptr model_op_; @@ -94,7 +90,6 @@ class FertileStatsResource : public ResourceBase { void AllocateNode(int32 node_id, int32 depth); }; - } // namespace tensorforest } // namespace tensorflow diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc index 3ce630e3a9..da600d34ea 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.cc @@ -20,7 +20,6 @@ #include "tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.h" #include "tensorflow/core/lib/random/distribution_sampler.h" - namespace tensorflow { namespace tensorforest { @@ -454,14 +453,14 @@ void DenseClassificationGrowStats::PackToProto(FertileSlot* slot) const { class_stats->add_value()->set_float_value(total_counts_[i]); } - for (int split_num = 0; split_num < num_splits(); ++split_num) { + for (int split_num = 0; split_num < num_splits(); ++split_num) { auto* cand = slot->add_candidates(); *cand->mutable_split() = splits_[split_num]; auto* left_stats = cand->mutable_left_stats() ->mutable_classification() ->mutable_dense_counts(); for (int i = 0; i < num_outputs_; ++i) { - left_stats->add_value()->set_float_value(left_count(split_num, i)); + left_stats->add_value()->set_float_value(left_count(split_num, i)); } } } @@ -546,7 +545,7 @@ void SparseClassificationGrowStats::PackToProto(FertileSlot* slot) const { (*class_stats)[entry.first] = val; } - for (int split_num = 0; split_num < num_splits(); ++split_num) { + for (int split_num = 0; split_num < num_splits(); ++split_num) { auto* cand = slot->add_candidates(); *cand->mutable_split() = splits_[split_num]; auto* left_stats = cand->mutable_left_stats() @@ -561,8 +560,8 @@ void SparseClassificationGrowStats::PackToProto(FertileSlot* slot) const { } } -float SparseClassificationGrowStats::GiniScore( - int split, float* left_sum, float* right_sum) const { +float SparseClassificationGrowStats::GiniScore(int split, float* left_sum, + float* right_sum) const { float left_square = 0, right_square = 0; *left_sum = 0; *right_sum = 0; @@ -844,12 +843,11 @@ void LeastSquaresRegressionGrowStats::PackToProto(FertileSlot* slot) const { total_squares->add_value()->set_float_value(total_sum_squares_[i]); } - for (int split_num = 0; split_num < num_splits(); ++split_num) { + for (int split_num = 0; split_num < num_splits(); ++split_num) { auto* cand = slot->add_candidates(); *cand->mutable_split() = splits_[split_num]; - auto* sums = cand->mutable_left_stats() - ->mutable_regression() - ->mutable_mean_output(); + auto* sums = + cand->mutable_left_stats()->mutable_regression()->mutable_mean_output(); auto* squares = cand->mutable_left_stats() ->mutable_regression() ->mutable_mean_output_squares(); @@ -891,20 +889,17 @@ float LeastSquaresRegressionGrowStats::SplitVariance(int split) const { float total_variance = 0; for (int i = 0; i < params_.num_outputs(); ++i) { // Left side - const float le_x = - left_sum(split, i) / left_counts_[split]; + const float le_x = left_sum(split, i) / left_counts_[split]; - const float le_x2 = - left_square(split, i) / left_counts_[split]; + const float le_x2 = left_square(split, i) / left_counts_[split]; total_variance += le_x2 - le_x * le_x; // Right side const float re_x = (total_sum_[i] - left_sum(split, i)) / (weight_sum_ - left_counts_[split]); - const float re_x2 = - (total_sum_squares_[i] - left_square(split, i)) / - (weight_sum_ - left_counts_[split]); + const float re_x2 = (total_sum_squares_[i] - left_square(split, i)) / + (weight_sum_ - left_counts_[split]); total_variance += re_x2 - re_x * re_x; } return total_variance; @@ -937,8 +932,7 @@ bool LeastSquaresRegressionGrowStats::BestSplit(SplitCandidate* best) const { left->set_weight_sum(left_counts_[best_index]); auto* left_output_sum = left_reg_stats->mutable_mean_output(); for (int i = 0; i < num_outputs; ++i) { - left_output_sum->add_value()->set_float_value( - left_sum(best_index, i)); + left_output_sum->add_value()->set_float_value(left_sum(best_index, i)); } // Right @@ -947,8 +941,8 @@ bool LeastSquaresRegressionGrowStats::BestSplit(SplitCandidate* best) const { right->set_weight_sum(weight_sum_ - left_counts_[best_index]); auto* right_output_sum = right_reg_stats->mutable_mean_output(); for (int i = 0; i < num_outputs; ++i) { - right_output_sum->add_value()->set_float_value( - total_sum_[i] - left_sum(best_index, i)); + right_output_sum->add_value()->set_float_value(total_sum_[i] - + left_sum(best_index, i)); } return true; } diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h index 02c0fc687f..04e6b0a735 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h @@ -73,21 +73,15 @@ class GrowStats { const InputTarget* target, int example) {} void RemoveSplit(int split_num); - int num_splits() const { - return splits_.size(); - } + int num_splits() const { return splits_.size(); } - float weight_sum() const { - return weight_sum_; - } + float weight_sum() const { return weight_sum_; } virtual bool IsInitialized() const { return weight_sum_ > 0 || splits_.size() == num_splits_to_consider_; } - int32 depth() const { - return depth_; - } + int32 depth() const { return depth_; } protected: GrowStats(const TensorForestParams& params, int32 depth); @@ -206,8 +200,8 @@ class ClassificationStats : public GrowStats { virtual float left_count(int split, int class_num) const = 0; virtual float right_count(int split, int class_num) const = 0; - virtual void ClassificationAddLeftExample( - int split, int64 int_label, float weight) = 0; + virtual void ClassificationAddLeftExample(int split, int64 int_label, + float weight) = 0; virtual void ClassificationAddRightExample(int split, int64 int_label, float weight) { // Does nothing by default, but sub-classes can override. @@ -375,9 +369,7 @@ class SparseClassificationGrowStats : public ClassificationStats { SparseClassificationGrowStats(const TensorForestParams& params, int32 depth) : ClassificationStats(params, depth) {} - void Initialize() override { - Clear(); - } + void Initialize() override { Clear(); } void ExtractFromProto(const FertileSlot& slot) override; void PackToProto(FertileSlot* slot) const override; @@ -562,9 +554,9 @@ class LeastSquaresRegressionGrowStats : public GrowStats { } void RemoveSplitStats(int split_num) override { left_sums_.erase(left_sums_.begin() + num_outputs_ * split_num, - left_sums_.begin() + num_outputs_ * (split_num + 1)); + left_sums_.begin() + num_outputs_ * (split_num + 1)); left_squares_.erase(left_squares_.begin() + num_outputs_ * split_num, - left_squares_.begin() + num_outputs_ * (split_num + 1)); + left_squares_.begin() + num_outputs_ * (split_num + 1)); left_counts_.erase(left_counts_.begin() + split_num, left_counts_.begin() + (split_num + 1)); } @@ -605,7 +597,6 @@ class LeastSquaresRegressionGrowStats : public GrowStats { std::vector left_counts_; }; - } // namespace tensorforest } // namespace tensorflow diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc index ceb58d2ead..26e989928e 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats_test.cc @@ -24,21 +24,21 @@ namespace tensorflow { namespace { -using tensorflow::tensorforest::GrowStats; -using tensorflow::tensorforest::TestableInputTarget; -using tensorflow::tensorforest::FertileSlot; +using tensorflow::decision_trees::BinaryNode; +using tensorflow::decision_trees::FeatureId; +using tensorflow::decision_trees::InequalityTest; using tensorflow::tensorforest::DenseClassificationGrowStats; -using tensorflow::tensorforest::SparseClassificationGrowStats; +using tensorflow::tensorforest::FertileSlot; using tensorflow::tensorforest::FixedSizeClassStats; using tensorflow::tensorforest::FixedSizeSparseClassificationGrowStats; +using tensorflow::tensorforest::GrowStats; using tensorflow::tensorforest::LeastSquaresRegressionGrowStats; -using tensorflow::tensorforest::TensorForestParams; +using tensorflow::tensorforest::SparseClassificationGrowStats; using tensorflow::tensorforest::SPLIT_FINISH_BASIC; using tensorflow::tensorforest::SPLIT_FINISH_DOMINATE_HOEFFDING; using tensorflow::tensorforest::SPLIT_PRUNE_HOEFFDING; -using tensorflow::decision_trees::BinaryNode; -using tensorflow::decision_trees::InequalityTest; -using tensorflow::decision_trees::FeatureId; +using tensorflow::tensorforest::TensorForestParams; +using tensorflow::tensorforest::TestableInputTarget; BinaryNode MakeSplit(const string& feat, float val) { BinaryNode split; @@ -52,8 +52,7 @@ BinaryNode MakeSplit(const string& feat, float val) { return split; } -void RunBatch(GrowStats* stats, - const TestableInputTarget* target) { +void RunBatch(GrowStats* stats, const TestableInputTarget* target) { std::unique_ptr dataset( new tensorflow::tensorforest::TestableDataSet( {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, 2)); @@ -102,18 +101,10 @@ class TestableRunningStats : public DenseClassificationGrowStats { TestableRunningStats(const TensorForestParams& params, int32 depth) : DenseClassificationGrowStats(params, depth) {} - float test_left_sum(int split) { - return get_left_gini()->sum(split); - } - float test_left_square(int split) { - return get_left_gini()->square(split); - } - float test_right_sum(int split) { - return get_right_gini()->sum(split); - } - float test_right_square(int split) { - return get_right_gini()->square(split); - } + float test_left_sum(int split) { return get_left_gini()->sum(split); } + float test_left_square(int split) { return get_left_gini()->square(split); } + float test_right_sum(int split) { return get_right_gini()->sum(split); } + float test_right_square(int split) { return get_right_gini()->square(split); } }; TEST(GrowStatsDenseClassificationTest, BasicRunningStats) { @@ -166,9 +157,7 @@ class TestableFinishEarly : public DenseClassificationGrowStats { int num_times_called_; protected: - void CheckFinishEarlyHoeffding() override { - ++num_times_called_; - } + void CheckFinishEarlyHoeffding() override { ++num_times_called_; } }; TEST(GrowStatsDenseClassificationTest, TestFinishEarly) { @@ -212,7 +201,6 @@ TEST(GrowStatsDenseClassificationTest, TestFinishEarly) { ASSERT_EQ(stat->num_times_called_, 9); } - TEST(GrowStatsDenseClassificationTest, TestCheckPruneHoeffding) { TensorForestParams params; params.set_num_outputs(2); @@ -224,7 +212,8 @@ TEST(GrowStatsDenseClassificationTest, TestCheckPruneHoeffding) { finish->set_type(SPLIT_FINISH_BASIC); finish->mutable_check_every_steps()->set_constant_value(100); params.mutable_pruning_type()->set_type(SPLIT_PRUNE_HOEFFDING); - params.mutable_pruning_type()->mutable_prune_every_samples() + params.mutable_pruning_type() + ->mutable_prune_every_samples() ->set_constant_value(1); // On each iteration, we add two examples, one of class 0 and one @@ -234,8 +223,8 @@ TEST(GrowStatsDenseClassificationTest, TestCheckPruneHoeffding) { std::vector weights = {1, 1}; TestableInputTarget target(labels, weights, 1); std::unique_ptr dataset( - new tensorflow::tensorforest::TestableDataSet( - {-1.0, -1.0, 1.0, -1.0}, 2)); + new tensorflow::tensorforest::TestableDataSet({-1.0, -1.0, 1.0, -1.0}, + 2)); DenseClassificationGrowStats stats(params, 1); stats.Initialize(); diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc index bf0fb92450..d43884481a 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc @@ -109,10 +109,10 @@ void TensorDataSet::set_input_tensors(const Tensor& dense, dense_data_.reset(new DenseStorageType(dense.tensor())); } if (sparse_indices.shape().dims() == 2) { - sparse_indices_.reset(new SparseIndicesStorageType( - sparse_indices.tensor())); - sparse_values_.reset(new SparseValuesStorageType( - sparse_values.tensor())); + sparse_indices_.reset( + new SparseIndicesStorageType(sparse_indices.tensor())); + sparse_values_.reset( + new SparseValuesStorageType(sparse_values.tensor())); sparse_batch_size_ = sparse_shape.tensor()(0); } original_dense_tensor_ = dense; diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h index eafad6b591..c544a8c75e 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.h @@ -93,9 +93,7 @@ class TensorDataSet { // an int32 you can avoid the atoi32. virtual float GetExampleValue(int example, int32 feature_id) const; - int num_features() { - return available_features_.size(); - } + int num_features() { return available_features_.size(); } const Tensor& original_tensor() const { return original_dense_tensor_; } diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h index 44ec09c50e..d4402b6055 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_target.h @@ -79,9 +79,7 @@ class TensorInputTarget : public StoredInputTarget { return (*target_)(example_index * num_targets_ + target_index); } - const Tensor& original_tensor() const { - return original_tensor_; - } + const Tensor& original_tensor() const { return original_tensor_; } protected: Tensor original_tensor_; diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc index d43c068e46..83614a2531 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators.cc @@ -160,6 +160,5 @@ void RegressionLeafModelOperator::ExportModel( } } - } // namespace tensorforest } // namespace tensorflow diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc index ffd92c01f9..ab4191809b 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/leaf_model_operators_test.cc @@ -26,19 +26,19 @@ namespace { using tensorflow::decision_trees::Leaf; using tensorflow::tensorforest::DenseClassificationLeafModelOperator; using tensorflow::tensorforest::LeafModelOperator; -using tensorflow::tensorforest::SparseClassificationLeafModelOperator; -using tensorflow::tensorforest::SparseOrDenseClassificationLeafModelOperator; using tensorflow::tensorforest::LeafStat; using tensorflow::tensorforest::RegressionLeafModelOperator; -using tensorflow::tensorforest::TestableInputTarget; +using tensorflow::tensorforest::SparseClassificationLeafModelOperator; +using tensorflow::tensorforest::SparseOrDenseClassificationLeafModelOperator; using tensorflow::tensorforest::TensorForestParams; +using tensorflow::tensorforest::TestableInputTarget; const int32 kNumClasses = 3; constexpr char kRegressionStatProto[] = - "weight_sum: 3 " - "regression { " - "mean_output { " + "weight_sum: 3 " + "regression { " + "mean_output { " "value { " " float_value: 27 " "} " @@ -48,8 +48,8 @@ constexpr char kRegressionStatProto[] = "value { " " float_value: 10 " "} " - "} " - "mean_output_squares { " + "} " + "mean_output_squares { " "value {" " float_value: 245" "}" @@ -59,8 +59,8 @@ constexpr char kRegressionStatProto[] = "value {" " float_value: 46" "}" - "}" -"}"; + "}" + "}"; void TestClassificationNormalUse(const std::unique_ptr& op) { Leaf l; @@ -83,7 +83,6 @@ void TestClassificationNormalUse(const std::unique_ptr& op) { EXPECT_FLOAT_EQ(op->GetOutputValue(l, 1), 3.4); } - TEST(DenseLeafModelOperatorsTest, NormalUse) { TensorForestParams params; params.set_num_outputs(kNumClasses); @@ -182,7 +181,7 @@ TEST(SparseLeafModelOperatorsTest, InitWithExisting) { std::unique_ptr leaf(new Leaf); - op->ExportModel( *stat, leaf.get()); + op->ExportModel(*stat, leaf.get()); // Make sure it was initialized correctly. EXPECT_FLOAT_EQ(op->GetOutputValue(*leaf, 0), 1.1); @@ -194,7 +193,6 @@ TEST(SparseLeafModelOperatorsTest, InitWithExisting) { EXPECT_EQ(leaf->sparse_vector().sparse_value().size(), kNumClasses); } - TEST(RegressionLeafModelOperatorsTest, NormalUse) { TensorForestParams params; params.set_num_outputs(kNumClasses); diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/params.h b/tensorflow/contrib/tensor_forest/kernels/v4/params.h index b0ed949424..7583e3d040 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/params.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/params.h @@ -24,7 +24,6 @@ namespace tensorforest { // Return the value of the given depth-dependent parameter given a leaf's depth. float ResolveParam(const DepthDependentParam& param, int32 depth); - } // namespace tensorforest } // namespace tensorflow diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc b/tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc index 801881af13..4010a71006 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/params_test.cc @@ -71,5 +71,3 @@ TEST(ParamsTest, TestThreshold) { } } // namespace - - diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc index cdb1d80a4b..b7b60d0ab8 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.cc @@ -52,8 +52,8 @@ std::unique_ptr SplitCollectionOperator::CreateGrowStats( new SparseClassificationGrowStats(params_, depth)); case STATS_LEAST_SQUARES_REGRESSION: - return std::unique_ptr(new LeastSquaresRegressionGrowStats( - params_, depth)); + return std::unique_ptr( + new LeastSquaresRegressionGrowStats(params_, depth)); case STATS_FIXED_SIZE_SPARSE_GINI: return std::unique_ptr( @@ -136,8 +136,7 @@ void SplitCollectionOperator::CreateAndInitializeCandidateWithExample( stats_.at(node_id)->AddSplit(split, input_data, target, example); } -bool SplitCollectionOperator::BestSplit(int32 node_id, - SplitCandidate* best, +bool SplitCollectionOperator::BestSplit(int32 node_id, SplitCandidate* best, int32* depth) const { auto* slot = stats_.at(node_id).get(); *depth = slot->depth(); diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h index ad52f89fad..c606ff98c6 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/split_collection_operators.h @@ -71,9 +71,7 @@ class SplitCollectionOperator { } // Perform any necessary cleanup for any tracked state for the slot. - virtual void ClearSlot(int32 node_id) { - stats_.erase(node_id); - } + virtual void ClearSlot(int32 node_id) { stats_.erase(node_id); } // Return true if slot is fully initialized. virtual bool IsInitialized(int32 node_id) const; diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc b/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc index 0bec198e97..c749fbe69e 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc +++ b/tensorflow/contrib/tensor_forest/kernels/v4/stat_utils.cc @@ -32,9 +32,9 @@ namespace tensorforest { // smoothed_sum = stats.sum() + #_classes float GiniImpurity(const LeafStat& stats, int32 num_classes) { const float smoothed_sum = num_classes + stats.weight_sum(); - return 1.0 - ( - (stats.classification().gini().square() - + 2 * stats.weight_sum() + num_classes) / (smoothed_sum * smoothed_sum)); + return 1.0 - ((stats.classification().gini().square() + + 2 * stats.weight_sum() + num_classes) / + (smoothed_sum * smoothed_sum)); } float WeightedGiniImpurity(const LeafStat& stats, int32 num_classes) { @@ -46,21 +46,20 @@ void UpdateGini(LeafStat* stats, float old_val, float weight) { // Equivalent to stats->square() - old_val * old_val + new_val * new_val, // (for new_val = old_val + weight), but more numerically stable. stats->mutable_classification()->mutable_gini()->set_square( - stats->classification().gini().square() - + weight * weight + 2 * old_val * weight); + stats->classification().gini().square() + weight * weight + + 2 * old_val * weight); } - float Variance(const LeafStat& stats, int output) { if (stats.weight_sum() == 0) { return 0; } const float e_x = - stats.regression().mean_output().value(output).float_value() - / stats.weight_sum(); + stats.regression().mean_output().value(output).float_value() / + stats.weight_sum(); const auto e_x2 = - stats.regression().mean_output_squares().value(output).float_value() - / stats.weight_sum(); + stats.regression().mean_output_squares().value(output).float_value() / + stats.weight_sum(); return e_x2 - e_x * e_x; } @@ -75,8 +74,7 @@ float TotalVariance(const LeafStat& stats) { float SmoothedGini(float sum, float square, int num_classes) { // See comments for GiniImpurity above. const float smoothed_sum = num_classes + sum; - return 1.0 - - (square + 2 * sum + num_classes) / (smoothed_sum * smoothed_sum); + return 1.0 - (square + 2 * sum + num_classes) / (smoothed_sum * smoothed_sum); } float WeightedSmoothedGini(float sum, float square, int num_classes) { diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h b/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h index 289c81e9d5..38deb3e3cd 100644 --- a/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h +++ b/tensorflow/contrib/tensor_forest/kernels/v4/test_utils.h @@ -27,9 +27,7 @@ class TestableInputTarget : public StoredInputTarget> { : StoredInputTarget(new std::vector(t), new std::vector(w), num_t) {} - int NumItems() const { - return target_->size(); - } + int NumItems() const { return target_->size(); } int32 GetTargetAsClassIndex(int example_index, int target_index) const override { @@ -51,7 +49,6 @@ class TestableInputTarget : public StoredInputTarget> { } }; - class TestableDataSet : public TensorDataSet { public: TestableDataSet(const std::vector& data, int num_features) diff --git a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc index 2992a61ea8..9675428e56 100644 --- a/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc +++ b/tensorflow/contrib/util/convert_graphdef_memmapped_format_lib.cc @@ -142,9 +142,9 @@ Status ConvertConstantsToImmutable(const string& in_graph_filename, const auto load_graph_status = ReadBinaryProto(default_env, in_graph_filename, &graph_def); if (!load_graph_status.ok()) { - return tensorflow::errors::NotFound("Failed to load graph at '", - in_graph_filename, "' : ", - load_graph_status.error_message()); + return tensorflow::errors::NotFound( + "Failed to load graph at '", in_graph_filename, + "' : ", load_graph_status.error_message()); } NodeConverter node_converter; diff --git a/tensorflow/contrib/util/inspect_checkpoint.cc b/tensorflow/contrib/util/inspect_checkpoint.cc index 39088aeaad..9b578ceb07 100644 --- a/tensorflow/contrib/util/inspect_checkpoint.cc +++ b/tensorflow/contrib/util/inspect_checkpoint.cc @@ -13,10 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/core/platform/init_main.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/init_main.h" #include "tensorflow/core/util/tensor_slice_reader.h" namespace tensorflow { diff --git a/tensorflow/contrib/verbs/verbs_server_lib.cc b/tensorflow/contrib/verbs/verbs_server_lib.cc index 47ed83f521..1a0b5028fe 100644 --- a/tensorflow/contrib/verbs/verbs_server_lib.cc +++ b/tensorflow/contrib/verbs/verbs_server_lib.cc @@ -49,8 +49,8 @@ VerbsServer::~VerbsServer() { Status VerbsServer::ChannelCacheFactory(const ServerDef& server_def, GrpcChannelCache** channel_cache) { string name_prefix = - strings::StrCat("/job:", server_def.job_name(), "/replica:0", "/task:", - server_def.task_index()); + strings::StrCat("/job:", server_def.job_name(), "/replica:0", + "/task:", server_def.task_index()); GrpcChannelSpec channel_spec; TF_RETURN_IF_ERROR(ParseChannelSpec(server_def, &channel_spec)); -- GitLab From b970652bf714cbf676fdd84256cb128afc2b1306 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 11:09:06 -0800 Subject: [PATCH 267/423] Add py2tf to contrib_py. PiperOrigin-RevId: 183860192 --- tensorflow/contrib/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index f1e54432fa..efb6449bb0 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -76,6 +76,7 @@ py_library( "//tensorflow/contrib/predictor", "//tensorflow/contrib/quantization:quantization_py", "//tensorflow/contrib/quantize:quantize_graph", + "//tensorflow/contrib/py2tf", "//tensorflow/contrib/receptive_field:receptive_field_py", "//tensorflow/contrib/reduce_slice_ops:reduce_slice_ops_py", "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py", -- GitLab From 29d24237b0e29d83478182ad219da478ee2135c1 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Tue, 30 Jan 2018 11:18:03 -0800 Subject: [PATCH 268/423] Make loss_ops_test.py work with C API enabled. PiperOrigin-RevId: 183861779 --- tensorflow/contrib/losses/python/losses/loss_ops_test.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/losses/python/losses/loss_ops_test.py b/tensorflow/contrib/losses/python/losses/loss_ops_test.py index 9d0f95e6f3..1417772e04 100644 --- a/tensorflow/contrib/losses/python/losses/loss_ops_test.py +++ b/tensorflow/contrib/losses/python/losses/loss_ops_test.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops @@ -274,6 +275,7 @@ class SoftmaxCrossEntropyLossTest(test.TestCase): self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3) +@test_util.with_c_api class SparseSoftmaxCrossEntropyLossTest(test.TestCase): def testNoneWeightRaisesValueError(self): @@ -471,7 +473,11 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): labels = constant_op.constant([[0, 1], [2, 3]]) weights = constant_op.constant([1.2, 3.4, 5.6, 7.8]) - with self.assertRaises(errors_impl.InvalidArgumentError): + if ops._USE_C_API: + error_type = ValueError + else: + error_type = errors_impl.InvalidArgumentError + with self.assertRaises(error_type): loss_ops.sparse_softmax_cross_entropy( logits, labels, weights=weights).eval() -- GitLab From a694f0ca2682f53f89a75707ad1f6c2ddffeacde Mon Sep 17 00:00:00 2001 From: Chris Leary Date: Tue, 30 Jan 2018 11:22:12 -0800 Subject: [PATCH 269/423] [XLA] Fix tools broken by cl/183837856 PiperOrigin-RevId: 183862522 --- tensorflow/compiler/xla/BUILD | 4 ---- .../compiler/xla/executable_run_options.cc | 17 ----------------- .../compiler/xla/executable_run_options.h | 7 ------- .../dumped_computation_to_operation_list.cc | 8 +++++--- .../xla/tools/dumped_computation_to_text.cc | 9 ++++++--- 5 files changed, 11 insertions(+), 34 deletions(-) diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 38e39afdc0..c22fd37129 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -444,10 +444,6 @@ cc_library( srcs = ["executable_run_options.cc"], hdrs = ["executable_run_options.h"], visibility = ["//visibility:public"], - deps = [ - ":types", - "//tensorflow/core:lib", - ], ) cc_library( diff --git a/tensorflow/compiler/xla/executable_run_options.cc b/tensorflow/compiler/xla/executable_run_options.cc index f8bb8e52c7..392ad9010a 100644 --- a/tensorflow/compiler/xla/executable_run_options.cc +++ b/tensorflow/compiler/xla/executable_run_options.cc @@ -15,8 +15,6 @@ limitations under the License. #include "tensorflow/compiler/xla/executable_run_options.h" -#include "tensorflow/core/lib/strings/stringprintf.h" - namespace xla { ExecutableRunOptions& ExecutableRunOptions::set_device_ordinal( @@ -89,19 +87,4 @@ const DeviceAssignment* ExecutableRunOptions::device_assignment() const { return device_assignment_; } -string ExecutableRunOptions::ToString() const { - return tensorflow::strings::Printf( - "ExecutableRunOptions{allocator=%p, device_ordinal=%d, " - "device_assignment=%p, stream=%p, inter_op_thread_pool=%p, " - "intra_op_thread_pool=%p, execution_profile=%p}", - allocator_, device_ordinal_, device_assignment_, stream_, - inter_op_thread_pool_, intra_op_thread_pool_, execution_profile_); -} - -std::ostream& operator<<(std::ostream& out, - const ExecutableRunOptions& options) { - out << options.ToString(); - return out; -} - } // namespace xla diff --git a/tensorflow/compiler/xla/executable_run_options.h b/tensorflow/compiler/xla/executable_run_options.h index c7a20bb33c..d4fcbf0493 100644 --- a/tensorflow/compiler/xla/executable_run_options.h +++ b/tensorflow/compiler/xla/executable_run_options.h @@ -16,8 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ #define TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ -#include "tensorflow/compiler/xla/types.h" - // Intentionally forward declared so that ExecutableRunOptions can be linked // into an XLA-compiled binary without having to link all of the pointed-to // objects (e.g., for an ahead-of-time compiled CPU binary, the gpu tools don't @@ -86,8 +84,6 @@ class ExecutableRunOptions { DeviceAssignment* device_assignment); const DeviceAssignment* device_assignment() const; - string ToString() const; - private: DeviceMemoryAllocator* allocator_ = nullptr; int device_ordinal_ = -1; @@ -98,9 +94,6 @@ class ExecutableRunOptions { ExecutionProfile* execution_profile_ = nullptr; }; -std::ostream& operator<<(std::ostream& out, - const ExecutableRunOptions& options); - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_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 4ad356d045..b82f1c81c8 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc @@ -85,10 +85,12 @@ void RealMain(tensorflow::gtl::ArraySlice args) { for (int i = 0; i < program_shape->parameters_size(); ++i) { layouts.push_back(&program_shape->parameters(i)); } + ExecutableBuildOptions build_options; + build_options.set_device_ordinal(0); + build_options.set_result_layout(program_shape->result()); StatusOr> executable = - local_service->CompileExecutable( - computation.handle(), layouts, &program_shape->result(), - /*device_ordinal=*/0, /*device_allocator=*/nullptr); + local_service->CompileExecutable(computation.handle(), layouts, + build_options); const HloModule& module = executable.ValueOrDie()->module(); diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc index 5ebb75a31c..05c0fdf97d 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc @@ -60,10 +60,13 @@ void RealMain(tensorflow::gtl::ArraySlice args, bool compile) { for (int i = 0; i < program_shape->parameters_size(); ++i) { layouts.push_back(&program_shape->parameters(i)); } + + ExecutableBuildOptions build_options; + build_options.set_device_ordinal(0); + build_options.set_result_layout(program_shape->result()); StatusOr> executable = - local_service->CompileExecutable( - computation.handle(), layouts, &program_shape->result(), - /*device_ordinal=*/0, /*device_allocator=*/nullptr); + local_service->CompileExecutable(computation.handle(), layouts, + build_options); const HloModule& module = executable.ValueOrDie()->module(); -- GitLab From 979f139dd94e2bf5fba4794536715973b55373c1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 11:09:06 -0800 Subject: [PATCH 270/423] Add py2tf to contrib_py. PiperOrigin-RevId: 183860192 --- tensorflow/compiler/xla/BUILD | 4 ++++ .../compiler/xla/executable_run_options.cc | 17 +++++++++++++++++ .../compiler/xla/executable_run_options.h | 7 +++++++ .../dumped_computation_to_operation_list.cc | 8 +++----- .../xla/tools/dumped_computation_to_text.cc | 9 +++------ .../losses/python/losses/loss_ops_test.py | 8 +------- 6 files changed, 35 insertions(+), 18 deletions(-) diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index c22fd37129..38e39afdc0 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -444,6 +444,10 @@ cc_library( srcs = ["executable_run_options.cc"], hdrs = ["executable_run_options.h"], visibility = ["//visibility:public"], + deps = [ + ":types", + "//tensorflow/core:lib", + ], ) cc_library( diff --git a/tensorflow/compiler/xla/executable_run_options.cc b/tensorflow/compiler/xla/executable_run_options.cc index 392ad9010a..f8bb8e52c7 100644 --- a/tensorflow/compiler/xla/executable_run_options.cc +++ b/tensorflow/compiler/xla/executable_run_options.cc @@ -15,6 +15,8 @@ limitations under the License. #include "tensorflow/compiler/xla/executable_run_options.h" +#include "tensorflow/core/lib/strings/stringprintf.h" + namespace xla { ExecutableRunOptions& ExecutableRunOptions::set_device_ordinal( @@ -87,4 +89,19 @@ const DeviceAssignment* ExecutableRunOptions::device_assignment() const { return device_assignment_; } +string ExecutableRunOptions::ToString() const { + return tensorflow::strings::Printf( + "ExecutableRunOptions{allocator=%p, device_ordinal=%d, " + "device_assignment=%p, stream=%p, inter_op_thread_pool=%p, " + "intra_op_thread_pool=%p, execution_profile=%p}", + allocator_, device_ordinal_, device_assignment_, stream_, + inter_op_thread_pool_, intra_op_thread_pool_, execution_profile_); +} + +std::ostream& operator<<(std::ostream& out, + const ExecutableRunOptions& options) { + out << options.ToString(); + return out; +} + } // namespace xla diff --git a/tensorflow/compiler/xla/executable_run_options.h b/tensorflow/compiler/xla/executable_run_options.h index d4fcbf0493..c7a20bb33c 100644 --- a/tensorflow/compiler/xla/executable_run_options.h +++ b/tensorflow/compiler/xla/executable_run_options.h @@ -16,6 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ #define TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ +#include "tensorflow/compiler/xla/types.h" + // Intentionally forward declared so that ExecutableRunOptions can be linked // into an XLA-compiled binary without having to link all of the pointed-to // objects (e.g., for an ahead-of-time compiled CPU binary, the gpu tools don't @@ -84,6 +86,8 @@ class ExecutableRunOptions { DeviceAssignment* device_assignment); const DeviceAssignment* device_assignment() const; + string ToString() const; + private: DeviceMemoryAllocator* allocator_ = nullptr; int device_ordinal_ = -1; @@ -94,6 +98,9 @@ class ExecutableRunOptions { ExecutionProfile* execution_profile_ = nullptr; }; +std::ostream& operator<<(std::ostream& out, + const ExecutableRunOptions& options); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_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 b82f1c81c8..4ad356d045 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc @@ -85,12 +85,10 @@ void RealMain(tensorflow::gtl::ArraySlice args) { for (int i = 0; i < program_shape->parameters_size(); ++i) { layouts.push_back(&program_shape->parameters(i)); } - ExecutableBuildOptions build_options; - build_options.set_device_ordinal(0); - build_options.set_result_layout(program_shape->result()); StatusOr> executable = - local_service->CompileExecutable(computation.handle(), layouts, - build_options); + local_service->CompileExecutable( + computation.handle(), layouts, &program_shape->result(), + /*device_ordinal=*/0, /*device_allocator=*/nullptr); const HloModule& module = executable.ValueOrDie()->module(); diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc index 05c0fdf97d..5ebb75a31c 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc @@ -60,13 +60,10 @@ void RealMain(tensorflow::gtl::ArraySlice args, bool compile) { for (int i = 0; i < program_shape->parameters_size(); ++i) { layouts.push_back(&program_shape->parameters(i)); } - - ExecutableBuildOptions build_options; - build_options.set_device_ordinal(0); - build_options.set_result_layout(program_shape->result()); StatusOr> executable = - local_service->CompileExecutable(computation.handle(), layouts, - build_options); + local_service->CompileExecutable( + computation.handle(), layouts, &program_shape->result(), + /*device_ordinal=*/0, /*device_allocator=*/nullptr); const HloModule& module = executable.ValueOrDie()->module(); diff --git a/tensorflow/contrib/losses/python/losses/loss_ops_test.py b/tensorflow/contrib/losses/python/losses/loss_ops_test.py index 1417772e04..9d0f95e6f3 100644 --- a/tensorflow/contrib/losses/python/losses/loss_ops_test.py +++ b/tensorflow/contrib/losses/python/losses/loss_ops_test.py @@ -27,7 +27,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed -from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops @@ -275,7 +274,6 @@ class SoftmaxCrossEntropyLossTest(test.TestCase): self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3) -@test_util.with_c_api class SparseSoftmaxCrossEntropyLossTest(test.TestCase): def testNoneWeightRaisesValueError(self): @@ -473,11 +471,7 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): labels = constant_op.constant([[0, 1], [2, 3]]) weights = constant_op.constant([1.2, 3.4, 5.6, 7.8]) - if ops._USE_C_API: - error_type = ValueError - else: - error_type = errors_impl.InvalidArgumentError - with self.assertRaises(error_type): + with self.assertRaises(errors_impl.InvalidArgumentError): loss_ops.sparse_softmax_cross_entropy( logits, labels, weights=weights).eval() -- GitLab From 4da9f21f03f1031e02fcda27734ace1c552d2d2d Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Tue, 30 Jan 2018 11:18:03 -0800 Subject: [PATCH 271/423] Make loss_ops_test.py work with C API enabled. PiperOrigin-RevId: 183861779 --- tensorflow/contrib/losses/python/losses/loss_ops_test.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/losses/python/losses/loss_ops_test.py b/tensorflow/contrib/losses/python/losses/loss_ops_test.py index 9d0f95e6f3..1417772e04 100644 --- a/tensorflow/contrib/losses/python/losses/loss_ops_test.py +++ b/tensorflow/contrib/losses/python/losses/loss_ops_test.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops @@ -274,6 +275,7 @@ class SoftmaxCrossEntropyLossTest(test.TestCase): self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3) +@test_util.with_c_api class SparseSoftmaxCrossEntropyLossTest(test.TestCase): def testNoneWeightRaisesValueError(self): @@ -471,7 +473,11 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): labels = constant_op.constant([[0, 1], [2, 3]]) weights = constant_op.constant([1.2, 3.4, 5.6, 7.8]) - with self.assertRaises(errors_impl.InvalidArgumentError): + if ops._USE_C_API: + error_type = ValueError + else: + error_type = errors_impl.InvalidArgumentError + with self.assertRaises(error_type): loss_ops.sparse_softmax_cross_entropy( logits, labels, weights=weights).eval() -- GitLab From ca15793a33a9d0e6baaf94eecb8f2edd117488a1 Mon Sep 17 00:00:00 2001 From: Chris Leary Date: Tue, 30 Jan 2018 11:22:12 -0800 Subject: [PATCH 272/423] [XLA] Fix tools broken by cl/183837856 PiperOrigin-RevId: 183862522 --- tensorflow/compiler/xla/BUILD | 4 ---- .../compiler/xla/executable_run_options.cc | 17 ----------------- .../compiler/xla/executable_run_options.h | 7 ------- .../dumped_computation_to_operation_list.cc | 8 +++++--- .../xla/tools/dumped_computation_to_text.cc | 9 ++++++--- 5 files changed, 11 insertions(+), 34 deletions(-) diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 38e39afdc0..c22fd37129 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -444,10 +444,6 @@ cc_library( srcs = ["executable_run_options.cc"], hdrs = ["executable_run_options.h"], visibility = ["//visibility:public"], - deps = [ - ":types", - "//tensorflow/core:lib", - ], ) cc_library( diff --git a/tensorflow/compiler/xla/executable_run_options.cc b/tensorflow/compiler/xla/executable_run_options.cc index f8bb8e52c7..392ad9010a 100644 --- a/tensorflow/compiler/xla/executable_run_options.cc +++ b/tensorflow/compiler/xla/executable_run_options.cc @@ -15,8 +15,6 @@ limitations under the License. #include "tensorflow/compiler/xla/executable_run_options.h" -#include "tensorflow/core/lib/strings/stringprintf.h" - namespace xla { ExecutableRunOptions& ExecutableRunOptions::set_device_ordinal( @@ -89,19 +87,4 @@ const DeviceAssignment* ExecutableRunOptions::device_assignment() const { return device_assignment_; } -string ExecutableRunOptions::ToString() const { - return tensorflow::strings::Printf( - "ExecutableRunOptions{allocator=%p, device_ordinal=%d, " - "device_assignment=%p, stream=%p, inter_op_thread_pool=%p, " - "intra_op_thread_pool=%p, execution_profile=%p}", - allocator_, device_ordinal_, device_assignment_, stream_, - inter_op_thread_pool_, intra_op_thread_pool_, execution_profile_); -} - -std::ostream& operator<<(std::ostream& out, - const ExecutableRunOptions& options) { - out << options.ToString(); - return out; -} - } // namespace xla diff --git a/tensorflow/compiler/xla/executable_run_options.h b/tensorflow/compiler/xla/executable_run_options.h index c7a20bb33c..d4fcbf0493 100644 --- a/tensorflow/compiler/xla/executable_run_options.h +++ b/tensorflow/compiler/xla/executable_run_options.h @@ -16,8 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ #define TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_H_ -#include "tensorflow/compiler/xla/types.h" - // Intentionally forward declared so that ExecutableRunOptions can be linked // into an XLA-compiled binary without having to link all of the pointed-to // objects (e.g., for an ahead-of-time compiled CPU binary, the gpu tools don't @@ -86,8 +84,6 @@ class ExecutableRunOptions { DeviceAssignment* device_assignment); const DeviceAssignment* device_assignment() const; - string ToString() const; - private: DeviceMemoryAllocator* allocator_ = nullptr; int device_ordinal_ = -1; @@ -98,9 +94,6 @@ class ExecutableRunOptions { ExecutionProfile* execution_profile_ = nullptr; }; -std::ostream& operator<<(std::ostream& out, - const ExecutableRunOptions& options); - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_EXECUTABLE_RUN_OPTIONS_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 4ad356d045..b82f1c81c8 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc @@ -85,10 +85,12 @@ void RealMain(tensorflow::gtl::ArraySlice args) { for (int i = 0; i < program_shape->parameters_size(); ++i) { layouts.push_back(&program_shape->parameters(i)); } + ExecutableBuildOptions build_options; + build_options.set_device_ordinal(0); + build_options.set_result_layout(program_shape->result()); StatusOr> executable = - local_service->CompileExecutable( - computation.handle(), layouts, &program_shape->result(), - /*device_ordinal=*/0, /*device_allocator=*/nullptr); + local_service->CompileExecutable(computation.handle(), layouts, + build_options); const HloModule& module = executable.ValueOrDie()->module(); diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc index 5ebb75a31c..05c0fdf97d 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc @@ -60,10 +60,13 @@ void RealMain(tensorflow::gtl::ArraySlice args, bool compile) { for (int i = 0; i < program_shape->parameters_size(); ++i) { layouts.push_back(&program_shape->parameters(i)); } + + ExecutableBuildOptions build_options; + build_options.set_device_ordinal(0); + build_options.set_result_layout(program_shape->result()); StatusOr> executable = - local_service->CompileExecutable( - computation.handle(), layouts, &program_shape->result(), - /*device_ordinal=*/0, /*device_allocator=*/nullptr); + local_service->CompileExecutable(computation.handle(), layouts, + build_options); const HloModule& module = executable.ValueOrDie()->module(); -- GitLab From 4c30f9676915704f1e0c31fa7a0729e375cb8412 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Tue, 30 Jan 2018 11:42:31 -0800 Subject: [PATCH 273/423] Delete dead code in Layer. PiperOrigin-RevId: 183866106 --- tensorflow/python/layers/base.py | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index 5d9feb07b4..04b6056ace 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -139,9 +139,6 @@ class Layer(object): self._init_set_name(name) - # Holds functions for creating regularizer ops. - self._regularizer_factories = [] - # Determine variable scope. scope = kwargs.get('_scope') if scope: @@ -306,22 +303,6 @@ class Layer(object): inputs_hash = None return self._per_input_updates.get(inputs_hash, []) - def _get_regularizer_factories(self): - try: - # Some subclasses of Layer do not use its constructor. - return self._regularizer_factories - except AttributeError: - self._regularizer_factories = [] - return self._regularizer_factories - - def _maybe_create_variable_regularizers(self): - """Creates added but uninstantiated regularizers.""" - factories = self._get_regularizer_factories() - if factories: - for factory in factories: - factory() - factories[:] = [] - @property def losses(self): """Losses which are associated with this `Layer`. @@ -333,7 +314,6 @@ class Layer(object): Returns: A list of tensors. """ - self._maybe_create_variable_regularizers() if context.in_eager_mode(): # _losses may only contain variable regularization losses when executing # eagerly, and they have been saved as lambdas to be executed when @@ -417,7 +397,6 @@ class Layer(object): inputs_hash = layers_util.object_list_uid(inputs) else: inputs_hash = None - self._maybe_create_variable_regularizers() return self._per_input_losses.get(inputs_hash, []) def build(self, _): -- GitLab From 6558e37454de83652b5a9c5beb8f9230faecc7be Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 11:47:43 -0800 Subject: [PATCH 274/423] K-FAC: expose set_global_constants() for tf.contrib.kfac.utils PiperOrigin-RevId: 183867014 --- tensorflow/contrib/kfac/examples/mlp.py | 5 ++++- tensorflow/contrib/kfac/python/ops/utils_lib.py | 1 + 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/kfac/examples/mlp.py b/tensorflow/contrib/kfac/examples/mlp.py index 0f0dbb53f4..87eed03888 100644 --- a/tensorflow/contrib/kfac/examples/mlp.py +++ b/tensorflow/contrib/kfac/examples/mlp.py @@ -317,7 +317,10 @@ def train_mnist_estimator(data_dir, num_epochs, use_fake_data=False): return tf.estimator.EstimatorSpec( mode=mode, loss=loss, train_op=train_op, training_hooks=hooks) + run_config = tf.estimator.RunConfig( + model_dir="/tmp/mnist", save_checkpoints_steps=1, keep_checkpoint_max=100) + # Train until input_fn() is empty with Estimator. This is a prerequisite for # TPU compatibility. - estimator = tf.estimator.Estimator(model_fn=model_fn) + estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config) estimator.train(input_fn=input_fn) diff --git a/tensorflow/contrib/kfac/python/ops/utils_lib.py b/tensorflow/contrib/kfac/python/ops/utils_lib.py index cc48e3c69f..fe8e39c212 100644 --- a/tensorflow/contrib/kfac/python/ops/utils_lib.py +++ b/tensorflow/contrib/kfac/python/ops/utils_lib.py @@ -24,6 +24,7 @@ from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import _allowed_symbols = [ + "set_global_constants", "SequenceDict", "tensors_to_column", "column_to_tensors", -- GitLab From 8a8fdb667bc77120e6ae47a88ab14e52cdd2cf07 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 30 Jan 2018 11:53:26 -0800 Subject: [PATCH 275/423] [TF:XLA] Bump open source llvm revision to r323761 PiperOrigin-RevId: 183868087 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 357b04d75f..0a669eeccd 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -472,11 +472,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/674f4d74284e9c431c7b69e30f6e10fc4fcbc9ef.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/674f4d74284e9c431c7b69e30f6e10fc4fcbc9ef.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/36a30fc7c9ee6fdfe5157190ad15c1801b1ab2de.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/36a30fc7c9ee6fdfe5157190ad15c1801b1ab2de.tar.gz", ], - sha256 = "3330c50efc9fc5d742e227dc810c2f586c7e36a60ecacd8251fafd2ea591e404", - strip_prefix = "llvm-674f4d74284e9c431c7b69e30f6e10fc4fcbc9ef", + sha256 = "3b74ecd8f59c712b4daf715a4da15c43ebdd40edcd4c30737bffef62f6a2bc9d", + strip_prefix = "llvm-36a30fc7c9ee6fdfe5157190ad15c1801b1ab2de", build_file = str(Label("//third_party/llvm:llvm.BUILD")), ) -- GitLab From fcbfead019e064ac796a19b9d8b05325d26b3115 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 12:00:04 -0800 Subject: [PATCH 276/423] Add static_sample flag to Mixture, permitting calls to `sample` to not rely on dynamic tensor indexing. This allows for some static graph compilation optimizations, but at the expense of sampling all underlying distributions in the mixture. PiperOrigin-RevId: 183869189 --- .../kernel_tests/distribution_util_test.py | 40 +++++ .../python/kernel_tests/mixture_test.py | 142 ++++++++++++------ .../python/ops/distribution_util.py | 39 +++++ .../distributions/python/ops/mixture.py | 35 +++++ .../python/ops/mixture_same_family.py | 45 ++---- 5 files changed, 229 insertions(+), 72 deletions(-) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py index a255d4fc89..31d24aa9ea 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py @@ -23,10 +23,15 @@ import itertools import numpy as np from tensorflow.contrib.distributions.python.ops import distribution_util +from tensorflow.contrib.distributions.python.ops import mixture +from tensorflow.contrib.distributions.python.ops import mixture_same_family +from tensorflow.contrib.distributions.python.ops import mvn_diag from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops +from tensorflow.python.ops.distributions import categorical +from tensorflow.python.ops.distributions import normal from tensorflow.python.ops.linalg import linear_operator_diag import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test @@ -395,6 +400,41 @@ class MixtureStddevTest(test.TestCase): self.assertAllClose(actual_devs, expected_devs) +class PadMixtureDimensionsTest(test.TestCase): + + def test_pad_mixture_dimensions_mixture(self): + with self.test_session() as sess: + gm = mixture.Mixture( + cat=categorical.Categorical(probs=[[0.3, 0.7]]), + components=[ + normal.Normal(loc=[-1.0], scale=[1.0]), + normal.Normal(loc=[1.0], scale=[0.5]) + ]) + + x = array_ops.constant([[1.0, 2.0], [3.0, 4.0]]) + x_pad = distribution_util.pad_mixture_dimensions( + x, gm, gm.cat, gm.event_shape.ndims) + x_out, x_pad_out = sess.run([x, x_pad]) + + self.assertAllEqual(x_pad_out.shape, [2, 2]) + self.assertAllEqual(x_out.reshape([-1]), x_pad_out.reshape([-1])) + + def test_pad_mixture_dimensions_mixture_same_family(self): + with self.test_session() as sess: + gm = mixture_same_family.MixtureSameFamily( + mixture_distribution=categorical.Categorical(probs=[0.3, 0.7]), + components_distribution=mvn_diag.MultivariateNormalDiag( + loc=[[-1., 1], [1, -1]], scale_identity_multiplier=[1.0, 0.5])) + + x = array_ops.constant([[1.0, 2.0], [3.0, 4.0]]) + x_pad = distribution_util.pad_mixture_dimensions( + x, gm, gm.mixture_distribution, gm.event_shape.ndims) + x_out, x_pad_out = sess.run([x, x_pad]) + + self.assertAllEqual(x_pad_out.shape, [2, 2, 1]) + self.assertAllEqual(x_out.reshape([-1]), x_pad_out.reshape([-1])) + + class _PadTest(object): def testNegAxisCorrectness(self): diff --git a/tensorflow/contrib/distributions/python/kernel_tests/mixture_test.py b/tensorflow/contrib/distributions/python/kernel_tests/mixture_test.py index 1e514fe0ff..0206489175 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/mixture_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/mixture_test.py @@ -107,7 +107,7 @@ def _test_capture_normal_sample_outputs(): ds.Normal._call_sample_n = true_normal_call_sample_n -def make_univariate_mixture(batch_shape, num_components): +def make_univariate_mixture(batch_shape, num_components, use_static_graph): batch_shape = ops.convert_to_tensor(batch_shape, dtypes.int32) logits = random_ops.random_uniform( array_ops.concat((batch_shape, [num_components]), axis=0), @@ -119,11 +119,11 @@ def make_univariate_mixture(batch_shape, num_components): for _ in range(num_components) ] cat = ds.Categorical(logits, dtype=dtypes.int32) - return ds.Mixture(cat, components) + return ds.Mixture(cat, components, use_static_graph=use_static_graph) def make_multivariate_mixture(batch_shape, num_components, event_shape, - batch_shape_tensor=None): + use_static_graph, batch_shape_tensor=None): if batch_shape_tensor is None: batch_shape_tensor = batch_shape batch_shape_tensor = ops.convert_to_tensor(batch_shape_tensor, dtypes.int32) @@ -145,15 +145,17 @@ def make_multivariate_mixture(batch_shape, num_components, event_shape, loc=loc, scale_diag=scale_diag) components = [create_component() for _ in range(num_components)] cat = ds.Categorical(logits, dtype=dtypes.int32) - return ds.Mixture(cat, components) + return ds.Mixture(cat, components, use_static_graph=use_static_graph) class MixtureTest(test.TestCase): + use_static_graph = False def testShapes(self): with self.test_session(): for batch_shape in ([], [1], [2, 3, 4]): - dist = make_univariate_mixture(batch_shape, num_components=10) + dist = make_univariate_mixture(batch_shape, num_components=10, + use_static_graph=self.use_static_graph) self.assertAllEqual(batch_shape, dist.batch_shape) self.assertAllEqual(batch_shape, dist.batch_shape_tensor().eval()) self.assertAllEqual([], dist.event_shape) @@ -161,7 +163,8 @@ class MixtureTest(test.TestCase): for event_shape in ([1], [2]): dist = make_multivariate_mixture( - batch_shape, num_components=10, event_shape=event_shape) + batch_shape, num_components=10, event_shape=event_shape, + use_static_graph=self.use_static_graph) self.assertAllEqual(batch_shape, dist.batch_shape) self.assertAllEqual(batch_shape, dist.batch_shape_tensor().eval()) self.assertAllEqual(event_shape, dist.event_shape) @@ -172,7 +175,8 @@ class MixtureTest(test.TestCase): r"cat.num_classes != len"): ds.Mixture( ds.Categorical([0.1, 0.5]), # 2 classes - [ds.Normal(loc=1.0, scale=2.0)]) + [ds.Normal(loc=1.0, scale=2.0)], + use_static_graph=self.use_static_graph) with self.assertRaisesWithPredicateMatch( ValueError, r"\(\) and \(2,\) are not compatible"): # The value error is raised because the batch shapes of the @@ -185,13 +189,15 @@ class MixtureTest(test.TestCase): loc=1.0, scale=2.0), # scalar dist ds.Normal( loc=[1.0, 1.0], scale=[2.0, 2.0]) - ]) + ], + use_static_graph=self.use_static_graph) with self.assertRaisesWithPredicateMatch(ValueError, r"Could not infer"): cat_logits = array_ops.placeholder(shape=[1, None], dtype=dtypes.float32) ds.Mixture( ds.Categorical(cat_logits), [ds.Normal( - loc=[1.0], scale=[2.0])]) + loc=[1.0], scale=[2.0])], + use_static_graph=self.use_static_graph) def testBrokenShapesDynamic(self): with self.test_session(): @@ -203,29 +209,37 @@ class MixtureTest(test.TestCase): loc=d0_param, scale=d0_param), ds.Normal( loc=d1_param, scale=d1_param) ], - validate_args=True) - with self.assertRaisesOpError(r"batch shape must match"): + validate_args=True, + use_static_graph=self.use_static_graph) + + if self.use_static_graph: + error_string = r"Shapes of all inputs must match" + else: + error_string = r"batch shape must match" + + with self.assertRaisesOpError(error_string): d.sample().eval(feed_dict={d0_param: [2.0, 3.0], d1_param: [1.0]}) - with self.assertRaisesOpError(r"batch shape must match"): + with self.assertRaisesOpError(error_string): d.sample().eval(feed_dict={d0_param: [2.0, 3.0], d1_param: 1.0}) def testBrokenTypes(self): with self.assertRaisesWithPredicateMatch(TypeError, "Categorical"): - ds.Mixture(None, []) + ds.Mixture(None, [], use_static_graph=self.use_static_graph) cat = ds.Categorical([0.3, 0.2]) # components must be a list of distributions with self.assertRaisesWithPredicateMatch( TypeError, "all .* must be Distribution instances"): - ds.Mixture(cat, [None]) + ds.Mixture(cat, [None], use_static_graph=self.use_static_graph) with self.assertRaisesWithPredicateMatch(TypeError, "same dtype"): ds.Mixture( cat, [ ds.Normal(loc=[1.0], scale=[2.0]), ds.Normal(loc=[np.float16(1.0)], scale=[np.float16(2.0)]), - ]) + ], use_static_graph=self.use_static_graph) with self.assertRaisesWithPredicateMatch(ValueError, "non-empty list"): - ds.Mixture(ds.Categorical([0.3, 0.2]), None) + ds.Mixture(ds.Categorical([0.3, 0.2]), None, + use_static_graph=self.use_static_graph) # TODO(ebrevdo): once distribution Domains have been added, add a # test to ensure that the domains of the distributions in a @@ -235,7 +249,8 @@ class MixtureTest(test.TestCase): with self.test_session() as sess: for batch_shape in ((), (2,), (2, 3)): dist = make_univariate_mixture( - batch_shape=batch_shape, num_components=2) + batch_shape=batch_shape, num_components=2, + use_static_graph=self.use_static_graph) mean = dist.mean() self.assertEqual(batch_shape, mean.get_shape()) @@ -256,7 +271,8 @@ class MixtureTest(test.TestCase): with self.test_session() as sess: for batch_shape in ((), (2,), (2, 3)): dist = make_multivariate_mixture( - batch_shape=batch_shape, num_components=2, event_shape=(4,)) + batch_shape=batch_shape, num_components=2, event_shape=(4,), + use_static_graph=self.use_static_graph) mean = dist.mean() self.assertEqual(batch_shape + (4,), mean.get_shape()) @@ -283,7 +299,8 @@ class MixtureTest(test.TestCase): with self.test_session() as sess: for batch_shape in ((), (2,), (2, 3)): dist = make_univariate_mixture( - batch_shape=batch_shape, num_components=num_components) + batch_shape=batch_shape, num_components=num_components, + use_static_graph=self.use_static_graph) dev = dist.stddev() self.assertEqual(batch_shape, dev.get_shape()) @@ -325,7 +342,8 @@ class MixtureTest(test.TestCase): dist = make_multivariate_mixture( batch_shape=batch_shape, num_components=num_components, - event_shape=(4,)) + event_shape=(4,), + use_static_graph=self.use_static_graph) dev = dist.stddev() self.assertEqual(batch_shape + (4,), dev.get_shape()) @@ -371,7 +389,8 @@ class MixtureTest(test.TestCase): scale=component_devs[0]), ds.Normal(loc=component_means[1], scale=component_devs[1]), - ]) + ], + use_static_graph=self.use_static_graph) mix_dev = mixture_dist.stddev() with self.test_session() as sess: actual_stddev = sess.run(mix_dev) @@ -379,7 +398,8 @@ class MixtureTest(test.TestCase): def testProbScalarUnivariate(self): with self.test_session() as sess: - dist = make_univariate_mixture(batch_shape=[], num_components=2) + dist = make_univariate_mixture(batch_shape=[], num_components=2, + use_static_graph=self.use_static_graph) for x in [ np.array( [1.0, 2.0], dtype=np.float32), np.array( @@ -405,7 +425,8 @@ class MixtureTest(test.TestCase): def testProbScalarMultivariate(self): with self.test_session() as sess: dist = make_multivariate_mixture( - batch_shape=[], num_components=2, event_shape=[3]) + batch_shape=[], num_components=2, event_shape=[3], + use_static_graph=self.use_static_graph) for x in [ np.array( [[-1.0, 0.0, 1.0], [0.5, 1.0, -0.3]], dtype=np.float32), np.array( @@ -432,7 +453,8 @@ class MixtureTest(test.TestCase): def testProbBatchUnivariate(self): with self.test_session() as sess: - dist = make_univariate_mixture(batch_shape=[2, 3], num_components=2) + dist = make_univariate_mixture(batch_shape=[2, 3], num_components=2, + use_static_graph=self.use_static_graph) for x in [ np.random.randn(2, 3).astype(np.float32), @@ -459,7 +481,8 @@ class MixtureTest(test.TestCase): def testProbBatchMultivariate(self): with self.test_session() as sess: dist = make_multivariate_mixture( - batch_shape=[2, 3], num_components=2, event_shape=[4]) + batch_shape=[2, 3], num_components=2, event_shape=[4], + use_static_graph=self.use_static_graph) for x in [ np.random.randn(2, 3, 4).astype(np.float32), @@ -487,7 +510,8 @@ class MixtureTest(test.TestCase): num_components = 3 batch_shape = [] dist = make_univariate_mixture( - batch_shape=batch_shape, num_components=num_components) + batch_shape=batch_shape, num_components=num_components, + use_static_graph=self.use_static_graph) n = 4 with _test_capture_normal_sample_outputs() as component_samples: samples = dist.sample(n, seed=123) @@ -502,7 +526,10 @@ class MixtureTest(test.TestCase): which_c = np.where(cat_sample_values == c)[0] size_c = which_c.size # Scalar Batch univariate case: batch_size == 1, rank 1 - which_dist_samples = dist_sample_values[c][:size_c] + if self.use_static_graph: + which_dist_samples = dist_sample_values[c][which_c] + else: + which_dist_samples = dist_sample_values[c][:size_c] self.assertAllClose(which_dist_samples, sample_values[which_c]) # Test that sampling with the same seed twice gives the same results. @@ -522,7 +549,8 @@ class MixtureTest(test.TestCase): ] cat = ds.Categorical( logits, dtype=dtypes.int32, name="cat1") - dist1 = ds.Mixture(cat, components, name="mixture1") + dist1 = ds.Mixture(cat, components, name="mixture1", + use_static_graph=self.use_static_graph) samples1 = dist1.sample(n, seed=123456).eval() random_seed.set_random_seed(654321) @@ -532,7 +560,8 @@ class MixtureTest(test.TestCase): ] cat2 = ds.Categorical( logits, dtype=dtypes.int32, name="cat2") - dist2 = ds.Mixture(cat2, components2, name="mixture2") + dist2 = ds.Mixture(cat2, components2, name="mixture2", + use_static_graph=self.use_static_graph) samples2 = dist2.sample(n, seed=123456).eval() self.assertAllClose(samples1, samples2) @@ -541,7 +570,8 @@ class MixtureTest(test.TestCase): with self.test_session() as sess: num_components = 3 dist = make_multivariate_mixture( - batch_shape=[], num_components=num_components, event_shape=[2]) + batch_shape=[], num_components=num_components, event_shape=[2], + use_static_graph=self.use_static_graph) n = 4 with _test_capture_mvndiag_sample_outputs() as component_samples: samples = dist.sample(n, seed=123) @@ -555,14 +585,18 @@ class MixtureTest(test.TestCase): which_c = np.where(cat_sample_values == c)[0] size_c = which_c.size # Scalar Batch multivariate case: batch_size == 1, rank 2 - which_dist_samples = dist_sample_values[c][:size_c, :] + if self.use_static_graph: + which_dist_samples = dist_sample_values[c][which_c, :] + else: + which_dist_samples = dist_sample_values[c][:size_c, :] self.assertAllClose(which_dist_samples, sample_values[which_c, :]) def testSampleBatchUnivariate(self): with self.test_session() as sess: num_components = 3 dist = make_univariate_mixture( - batch_shape=[2, 3], num_components=num_components) + batch_shape=[2, 3], num_components=num_components, + use_static_graph=self.use_static_graph) n = 4 with _test_capture_normal_sample_outputs() as component_samples: samples = dist.sample(n, seed=123) @@ -576,8 +610,12 @@ class MixtureTest(test.TestCase): which_c_s, which_c_b0, which_c_b1 = np.where(cat_sample_values == c) size_c = which_c_s.size # Batch univariate case: batch_size == [2, 3], rank 3 - which_dist_samples = dist_sample_values[c][range(size_c), which_c_b0, - which_c_b1] + if self.use_static_graph: + which_dist_samples = dist_sample_values[c][which_c_s, which_c_b0, + which_c_b1] + else: + which_dist_samples = dist_sample_values[c][range(size_c), which_c_b0, + which_c_b1] self.assertAllClose(which_dist_samples, sample_values[which_c_s, which_c_b0, which_c_b1]) @@ -594,7 +632,8 @@ class MixtureTest(test.TestCase): dist = make_multivariate_mixture( batch_shape=batch_shape, num_components=num_components, event_shape=[4], - batch_shape_tensor=batch_shape_tensor) + batch_shape_tensor=batch_shape_tensor, + use_static_graph=self.use_static_graph) n = 5 with _test_capture_mvndiag_sample_outputs() as component_samples: samples = dist.sample(n, seed=123) @@ -617,8 +656,12 @@ class MixtureTest(test.TestCase): which_c_s, which_c_b0, which_c_b1 = np.where(cat_sample_values == c) size_c = which_c_s.size # Batch univariate case: batch_size == [2, 3], rank 4 (multivariate) - which_dist_samples = dist_sample_values[c][range(size_c), which_c_b0, - which_c_b1, :] + if self.use_static_graph: + which_dist_samples = dist_sample_values[c][which_c_s, which_c_b0, + which_c_b1, :] + else: + which_dist_samples = dist_sample_values[c][range(size_c), which_c_b0, + which_c_b1, :] self.assertAllClose(which_dist_samples, sample_values[which_c_s, which_c_b0, which_c_b1, :]) @@ -632,7 +675,8 @@ class MixtureTest(test.TestCase): with self.test_session() as sess: for batch_shape in ((), (2,), (2, 3)): dist = make_multivariate_mixture( - batch_shape=batch_shape, num_components=2, event_shape=(4,)) + batch_shape=batch_shape, num_components=2, event_shape=(4,), + use_static_graph=self.use_static_graph) entropy_lower_bound = dist.entropy_lower_bound() self.assertEqual(batch_shape, entropy_lower_bound.get_shape()) @@ -673,7 +717,8 @@ class MixtureTest(test.TestCase): cat_tf = ds.Categorical(probs=mixture_weights) components_tf = [ds.Normal(loc=mu, scale=sigma) for (mu, sigma) in zip(means, sigmas)] - mixture_tf = ds.Mixture(cat=cat_tf, components=components_tf) + mixture_tf = ds.Mixture(cat=cat_tf, components=components_tf, + use_static_graph=self.use_static_graph) x_tensor = array_ops.placeholder(shape=(), dtype=dtypes.float32) @@ -721,7 +766,8 @@ class MixtureTest(test.TestCase): cat_tf = ds.Categorical(probs=mixture_weights) components_tf = [ds.Normal(loc=mu, scale=sigma) for (mu, sigma) in zip(means, sigmas)] - mixture_tf = ds.Mixture(cat=cat_tf, components=components_tf) + mixture_tf = ds.Mixture(cat=cat_tf, components=components_tf, + use_static_graph=self.use_static_graph) x_tensor = array_ops.placeholder(shape=psize, dtype=dtypes.float32) xs_to_check = [ @@ -760,12 +806,18 @@ class MixtureTest(test.TestCase): gm = ds.Mixture( cat=ds.Categorical(probs=[.3, .7]), components=[ds.Gamma(1., 2.), - ds.Gamma(2., 1.)]) + ds.Gamma(2., 1.)], + use_static_graph=self.use_static_graph) x_ = gm.sample().eval() self.assertAllEqual([], x_.shape) +class MixtureStaticSampleTest(MixtureTest): + use_static_graph = True + + class MixtureBenchmark(test.Benchmark): + use_static_graph = False def _runSamplingBenchmark(self, name, create_distribution, use_gpu, num_components, batch_size, num_features, @@ -811,7 +863,7 @@ class MixtureBenchmark(test.Benchmark): components = list( ds.MultivariateNormalDiag( loc=mu, scale_diag=sigma) for (mu, sigma) in zip(mus, sigmas)) - return ds.Mixture(cat, components) + return ds.Mixture(cat, components, use_static_graph=self.use_static_graph) for use_gpu in False, True: if use_gpu and not test.is_gpu_available(): @@ -853,7 +905,7 @@ class MixtureBenchmark(test.Benchmark): ds.MultivariateNormalTriL( loc=mu, scale_tril=linalg_ops.cholesky(sigma)) for (mu, sigma) in zip(mus, sigmas)) - return ds.Mixture(cat, components) + return ds.Mixture(cat, components, use_static_graph=self.use_static_graph) for use_gpu in False, True: if use_gpu and not test.is_gpu_available(): @@ -872,5 +924,9 @@ class MixtureBenchmark(test.Benchmark): sample_size=sample_size) +class MixtureStaticSampleBenchmark(MixtureBenchmark): + use_static_graph = True + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distributions/python/ops/distribution_util.py b/tensorflow/contrib/distributions/python/ops/distribution_util.py index a4d249d41e..289e1d50e1 100644 --- a/tensorflow/contrib/distributions/python/ops/distribution_util.py +++ b/tensorflow/contrib/distributions/python/ops/distribution_util.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib import linalg +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops @@ -442,6 +443,44 @@ def maybe_check_scalar_distribution( return assertions +def pad_mixture_dimensions(x, mixture_distribution, categorical_distribution, + event_ndims): + """Pad dimensions of event tensors for mixture distributions. + + See `Mixture._sample_n` and `MixtureSameFamily._sample_n` for usage examples. + + Args: + x: event tensor to pad. + mixture_distribution: Base distribution of the mixture. + categorical_distribution: `Categorical` distribution that mixes the base + distribution. + event_ndims: Integer specifying the number of event dimensions in the event + tensor. + + Returns: + A padded version of `x` that can broadcast with `categorical_distribution`. + """ + with ops.name_scope("pad_mix_dims", values=[x]): + def _get_ndims(d): + if d.batch_shape.ndims is not None: + return d.batch_shape.ndims + return array_ops.shape(d.batch_shape_tensor())[0] + dist_batch_ndims = _get_ndims(mixture_distribution) + cat_batch_ndims = _get_ndims(categorical_distribution) + pad_ndims = array_ops.where( + categorical_distribution.is_scalar_batch(), + dist_batch_ndims, + dist_batch_ndims - cat_batch_ndims) + s = array_ops.shape(x) + x = array_ops.reshape(x, shape=array_ops.concat([ + s[:-1], + array_ops.ones([pad_ndims], dtype=dtypes.int32), + s[-1:], + array_ops.ones([event_ndims], dtype=dtypes.int32), + ], axis=0)) + return x + + def static_value(x): """Returns the static value of a `Tensor` or `None`.""" return tensor_util.constant_value(ops.convert_to_tensor(x)) diff --git a/tensorflow/contrib/distributions/python/ops/mixture.py b/tensorflow/contrib/distributions/python/ops/mixture.py index f2d492f548..cef6a143fc 100644 --- a/tensorflow/contrib/distributions/python/ops/mixture.py +++ b/tensorflow/contrib/distributions/python/ops/mixture.py @@ -71,6 +71,7 @@ class Mixture(distribution.Distribution): components, validate_args=False, allow_nan_stats=True, + use_static_graph=False, name="Mixture"): """Initialize a Mixture distribution. @@ -96,6 +97,11 @@ class Mixture(distribution.Distribution): exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. If `True`, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. + use_static_graph: Calls to `sample` will not rely on dynamic tensor + indexing, allowing for some static graph compilation optimizations, but + at the expense of sampling all underlying distributions in the mixture. + (Possibly useful when running on TPUs). + Default value: `False` (i.e., use dynamic indexing). name: A name for this distribution (optional). Raises: @@ -178,6 +184,10 @@ class Mixture(distribution.Distribution): self._static_event_shape = static_event_shape self._static_batch_shape = static_batch_shape + self._use_static_graph = use_static_graph + if use_static_graph and static_num_components is None: + raise ValueError("Number of categories must be known statically when " + "`static_sample=True`.") # We let the Mixture distribution access _graph_parents since its arguably # more like a baseclass. graph_parents = self._cat._graph_parents # pylint: disable=protected-access @@ -292,6 +302,31 @@ class Mixture(distribution.Distribution): return mixture_log_cdf def _sample_n(self, n, seed=None): + if self._use_static_graph: + # This sampling approach is almost the same as the approach used by + # `MixtureSameFamily`. The differences are due to having a list of + # `Distribution` objects rather than a single object, and maintaining + # random seed management that is consistent with the non-static code path. + samples = [] + cat_samples = self.cat.sample(n, seed=seed) + for c in range(self.num_components): + seed = distribution_util.gen_new_seed(seed, "mixture") + samples.append(self.components[c].sample(n, seed=seed)) + x = array_ops.stack( + samples, -self._static_event_shape.ndims - 1) # [n, B, k, E] + npdt = x.dtype.as_numpy_dtype + mask = array_ops.one_hot( + indices=cat_samples, # [n, B] + depth=self._num_components, # == k + on_value=np.ones([], dtype=npdt), + off_value=np.zeros([], dtype=npdt)) # [n, B, k] + mask = distribution_utils.pad_mixture_dimensions( + mask, self, self._cat, + self._static_event_shape.ndims) # [n, B, k, [1]*e] + return math_ops.reduce_sum( + x * mask, + axis=-1 - self._static_event_shape.ndims) # [n, B, E] + with ops.control_dependencies(self._assertions): n = ops.convert_to_tensor(n, name="n") static_n = tensor_util.constant_value(n) diff --git a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py index 49afbea7f0..b93bdc5ab4 100644 --- a/tensorflow/contrib/distributions/python/ops/mixture_same_family.py +++ b/tensorflow/contrib/distributions/python/ops/mixture_same_family.py @@ -20,7 +20,7 @@ from __future__ import print_function import numpy as np -from tensorflow.python.framework import dtypes +from tensorflow.contrib.distributions.python.ops import distribution_util as distribution_utils from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -239,7 +239,9 @@ class MixtureSameFamily(distribution.Distribution): depth=self._num_components, # == k on_value=np.ones([], dtype=npdt), off_value=np.zeros([], dtype=npdt)) # [n, B, k] - mask = self._pad_mix_dims(mask) # [n, B, k, [1]*e] + mask = distribution_utils.pad_mixture_dimensions( + mask, self, self.mixture_distribution, + self._event_shape().ndims) # [n, B, k, [1]*e] return math_ops.reduce_sum( x * mask, axis=-1 - self._event_ndims) # [n, B, E] @@ -254,8 +256,9 @@ class MixtureSameFamily(distribution.Distribution): def _mean(self): with ops.control_dependencies(self._runtime_assertions): - probs = self._pad_mix_dims( - self.mixture_distribution.probs) # [B, k, [1]*e] + probs = distribution_utils.pad_mixture_dimensions( + self.mixture_distribution.probs, self, self.mixture_distribution, + self._event_shape().ndims) # [B, k, [1]*e] return math_ops.reduce_sum( probs * self.components_distribution.mean(), axis=-1 - self._event_ndims) # [B, E] @@ -271,8 +274,9 @@ class MixtureSameFamily(distribution.Distribution): def _variance(self): with ops.control_dependencies(self._runtime_assertions): # Law of total variance: Var(Y) = E[Var(Y|X)] + Var(E[Y|X]) - probs = self._pad_mix_dims( - self.mixture_distribution.probs) # [B, k, [1]*e] + probs = distribution_utils.pad_mixture_dimensions( + self.mixture_distribution.probs, self, self.mixture_distribution, + self._event_shape().ndims) # [B, k, [1]*e] mean_cond_var = math_ops.reduce_sum( probs * self.components_distribution.variance(), axis=-1 - self._event_ndims) # [B, E] @@ -291,8 +295,12 @@ class MixtureSameFamily(distribution.Distribution): with ops.control_dependencies(self._runtime_assertions): # Law of total variance: Var(Y) = E[Var(Y|X)] + Var(E[Y|X]) - probs = self._pad_mix_dims(self._pad_mix_dims( - self.mixture_distribution.probs)) # [B, k, 1, 1] + probs = distribution_utils.pad_mixture_dimensions( + distribution_utils.pad_mixture_dimensions( + self.mixture_distribution.probs, self, self.mixture_distribution, + self._event_shape().ndims), + self, self.mixture_distribution, + self._event_shape().ndims) # [B, k, 1, 1] mean_cond_var = math_ops.reduce_sum( probs * self.components_distribution.covariance(), axis=-3) # [B, e, e] @@ -312,27 +320,6 @@ class MixtureSameFamily(distribution.Distribution): shape[:d], [1], shape[d:]], axis=0)) return x - def _pad_mix_dims(self, x): - with ops.name_scope("pad_mix_dims", values=[x]): - def _get_ndims(d): - if d.batch_shape.ndims is not None: - return d.batch_shape.ndims - return array_ops.shape(d.batch_shape_tensor())[0] - dist_batch_ndims = _get_ndims(self) - cat_batch_ndims = _get_ndims(self.mixture_distribution) - pad_ndims = array_ops.where( - self.mixture_distribution.is_scalar_batch(), - dist_batch_ndims, - dist_batch_ndims - cat_batch_ndims) - s = array_ops.shape(x) - x = array_ops.reshape(x, shape=array_ops.concat([ - s[:-1], - array_ops.ones([pad_ndims], dtype=dtypes.int32), - s[-1:], - array_ops.ones([self._event_ndims], dtype=dtypes.int32), - ], axis=0)) - return x - def _outer_squared_difference(x, y): """Convenience function analogous to tf.squared_difference.""" -- GitLab From 9eea6fa72a90b0b16b554ff23185e0365c2a6f48 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 12:01:02 -0800 Subject: [PATCH 277/423] RetryingFileSystem::FlushCaches() calls the base FileSystem's FlushCaches(). PiperOrigin-RevId: 183869325 --- .../platform/cloud/retrying_file_system.cc | 2 ++ .../platform/cloud/retrying_file_system.h | 2 ++ .../cloud/retrying_file_system_test.cc | 19 ++++++++++++++++++- 3 files changed, 22 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/platform/cloud/retrying_file_system.cc b/tensorflow/core/platform/cloud/retrying_file_system.cc index 870d935e11..be9ebe67b1 100644 --- a/tensorflow/core/platform/cloud/retrying_file_system.cc +++ b/tensorflow/core/platform/cloud/retrying_file_system.cc @@ -202,4 +202,6 @@ Status RetryingFileSystem::DeleteRecursively(const string& dirname, initial_delay_microseconds_); } +void RetryingFileSystem::FlushCaches() { base_file_system_->FlushCaches(); } + } // namespace tensorflow diff --git a/tensorflow/core/platform/cloud/retrying_file_system.h b/tensorflow/core/platform/cloud/retrying_file_system.h index d9d8ea6b00..a262a5fd94 100644 --- a/tensorflow/core/platform/cloud/retrying_file_system.h +++ b/tensorflow/core/platform/cloud/retrying_file_system.h @@ -69,6 +69,8 @@ class RetryingFileSystem : public FileSystem { Status DeleteRecursively(const string& dirname, int64* undeleted_files, int64* undeleted_dirs) override; + void FlushCaches() override; + private: std::unique_ptr base_file_system_; const int64 initial_delay_microseconds_; diff --git a/tensorflow/core/platform/cloud/retrying_file_system_test.cc b/tensorflow/core/platform/cloud/retrying_file_system_test.cc index 232dcb3e71..d3f763bb3c 100644 --- a/tensorflow/core/platform/cloud/retrying_file_system_test.cc +++ b/tensorflow/core/platform/cloud/retrying_file_system_test.cc @@ -84,7 +84,8 @@ class MockWritableFile : public WritableFile { class MockFileSystem : public FileSystem { public: - explicit MockFileSystem(const ExpectedCalls& calls) : calls_(calls) {} + explicit MockFileSystem(const ExpectedCalls& calls, bool* flushed = nullptr) + : calls_(calls), flushed_(flushed) {} Status NewRandomAccessFile( const string& fname, std::unique_ptr* result) override { @@ -156,11 +157,18 @@ class MockFileSystem : public FileSystem { return calls_.ConsumeNextCall("DeleteRecursively"); } + void FlushCaches() override { + if (flushed_) { + *flushed_ = true; + } + } + std::unique_ptr writable_file_to_return; std::unique_ptr random_access_file_to_return; private: MockCallSequence calls_; + bool* flushed_ = nullptr; }; TEST(RetryingFileSystemTest, NewRandomAccessFile_ImmediateSuccess) { @@ -702,5 +710,14 @@ TEST(RetryingFileSystemTest, DeleteRecursively_AllRetriesFailed) { << status; } +TEST(RetryingFileSystemTest, FlushCaches) { + ExpectedCalls none; + bool flushed = false; + std::unique_ptr base_fs(new MockFileSystem(none, &flushed)); + RetryingFileSystem fs(std::move(base_fs), 0); + fs.FlushCaches(); + EXPECT_TRUE(flushed); +} + } // namespace } // namespace tensorflow -- GitLab From c48431588e7cf8aff61d4c299231e3e925144df8 Mon Sep 17 00:00:00 2001 From: "David G. Andersen" Date: Tue, 30 Jan 2018 12:06:40 -0800 Subject: [PATCH 278/423] Eliminate crash on a 'no error' return from DecodeGif when parsing an invalid gif. (Previous code tried to strcat a null). PiperOrigin-RevId: 183870288 --- tensorflow/core/lib/gif/gif_io.cc | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/lib/gif/gif_io.cc b/tensorflow/core/lib/gif/gif_io.cc index 0f6999c88f..e5deb2b873 100644 --- a/tensorflow/core/lib/gif/gif_io.cc +++ b/tensorflow/core/lib/gif/gif_io.cc @@ -44,6 +44,14 @@ int input_callback(GifFileType* gif_file, GifByteType* buf, int size) { return 0; } +static const char* GifErrorStringNonNull(int error_code) { + const char* error_string = GifErrorString(error_code); + if (error_string == nullptr) { + return "Unknown error"; + } + return error_string; +} + uint8* Decode(const void* srcdata, int datasize, const std::function& allocate_output, string* error_string) { @@ -55,17 +63,17 @@ uint8* Decode(const void* srcdata, int datasize, int error_code = D_GIF_SUCCEEDED; if (gif_file && DGifCloseFile(gif_file, &error_code) != GIF_OK) { LOG(WARNING) << "Fail to close gif file, reason: " - << GifErrorString(error_code); + << GifErrorStringNonNull(error_code); } }); if (error_code != D_GIF_SUCCEEDED) { *error_string = strings::StrCat("failed to open gif file: ", - GifErrorString(error_code)); + GifErrorStringNonNull(error_code)); return nullptr; } if (DGifSlurp(gif_file) != GIF_OK) { *error_string = strings::StrCat("failed to slurp gif file: ", - GifErrorString(gif_file->Error)); + GifErrorStringNonNull(gif_file->Error)); return nullptr; } if (gif_file->ImageCount <= 0) { -- GitLab From 77b22b38f03e5d9b52909d444f08592ffbe0334d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 12:13:58 -0800 Subject: [PATCH 279/423] Add check macro for not-equal. PiperOrigin-RevId: 183871336 --- tensorflow/contrib/lite/kernels/internal/compatibility.h | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tensorflow/contrib/lite/kernels/internal/compatibility.h b/tensorflow/contrib/lite/kernels/internal/compatibility.h index 1d963afb7e..51426bb1c5 100644 --- a/tensorflow/contrib/lite/kernels/internal/compatibility.h +++ b/tensorflow/contrib/lite/kernels/internal/compatibility.h @@ -27,6 +27,10 @@ limitations under the License. #define TFLITE_DCHECK_EQ(x, y) ((x) == (y)) ? (void)0 : assert(false) #endif +#ifndef TFLITE_DCHECK_NE +#define TFLITE_DCHECK_NE(x, y) ((x) != (y)) ? (void)0 : assert(false) +#endif + #ifndef TFLITE_DCHECK_GE #define TFLITE_DCHECK_GE(x, y) ((x) >= (y)) ? (void)0 : assert(false) #endif @@ -52,6 +56,10 @@ limitations under the License. #define TFLITE_CHECK_EQ(x, y) ((x) == (y)) ? (void)0 : abort() #endif +#ifndef TFLITE_CHECK_NE +#define TFLITE_CHECK_NE(x, y) ((x) != (y)) ? (void)0 : abort() +#endif + #ifndef TFLITE_CHECK_GE #define TFLITE_CHECK_GE(x, y) ((x) >= (y)) ? (void)0 : abort() #endif -- GitLab From 7115e52f599724dfe7ab12d15e0dc193600c880f Mon Sep 17 00:00:00 2001 From: Martin Wicke Date: Tue, 30 Jan 2018 12:32:30 -0800 Subject: [PATCH 280/423] Fix bad logging call in warmstarting_utils.py. PiperOrigin-RevId: 183873925 --- tensorflow/python/estimator/warm_starting_util.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/estimator/warm_starting_util.py b/tensorflow/python/estimator/warm_starting_util.py index ad95c71234..48110ef57f 100644 --- a/tensorflow/python/estimator/warm_starting_util.py +++ b/tensorflow/python/estimator/warm_starting_util.py @@ -415,8 +415,8 @@ def _warm_start(warm_start_settings): a stronger check for variable configuration than relying on users to examine the logs. """ - logging.info("Warm-starting from: ", - warm_start_settings.ckpt_to_initialize_from) + logging.info("Warm-starting from: %s", + (warm_start_settings.ckpt_to_initialize_from,)) # We have to deal with partitioned variables, since get_collection flattens # out the list. grouped_variables = {} -- GitLab From 5e9a946b0fbab8e36e4e2dd20480f0982f540890 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 12:36:51 -0800 Subject: [PATCH 281/423] Automated g4 rollback of changelist 183846994 PiperOrigin-RevId: 183874527 --- tensorflow/cc/saved_model/loader.cc | 4 +--- tensorflow/cc/saved_model/loader_test.cc | 18 ------------------ 2 files changed, 1 insertion(+), 21 deletions(-) diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index faa1e378d0..acef098c7d 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -96,9 +96,7 @@ Status FindMetaGraphDefToLoad(const SavedModel& saved_model_proto, Status LoadMetaGraphIntoSession(const MetaGraphDef& meta_graph_def, const SessionOptions& session_options, std::unique_ptr* session) { - Session* session_p = nullptr; - TF_RETURN_IF_ERROR(NewSession(session_options, &session_p)); - session->reset(session_p); + session->reset(NewSession(session_options)); return (*session)->Create(meta_graph_def.graph_def()); } diff --git a/tensorflow/cc/saved_model/loader_test.cc b/tensorflow/cc/saved_model/loader_test.cc index 4c64d2cfe3..0ad6b33bba 100644 --- a/tensorflow/cc/saved_model/loader_test.cc +++ b/tensorflow/cc/saved_model/loader_test.cc @@ -155,24 +155,6 @@ TEST_F(LoaderTest, NoTagMatchMultiple) { << st.error_message(); } -TEST_F(LoaderTest, SessionCreationFailure) { - SavedModelBundle bundle; - // Use invalid SessionOptions to cause session creation to fail. Default - // options work, so provide an invalid value for the target field. - SessionOptions session_options; - constexpr char kInvalidTarget[] = "invalid target"; - session_options.target = kInvalidTarget; - RunOptions run_options; - - const string export_dir = - io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataSharded); - Status st = LoadSavedModel(session_options, run_options, export_dir, - {kSavedModelTagServe}, &bundle); - EXPECT_FALSE(st.ok()); - EXPECT_TRUE(StringPiece(st.error_message()).contains(kInvalidTarget)) - << st.error_message(); -} - TEST_F(LoaderTest, PbtxtFormat) { SavedModelBundle bundle; SessionOptions session_options; -- GitLab From 92b3200b220e236cdaec6bf7c9a1a99192794347 Mon Sep 17 00:00:00 2001 From: Jonathan Hseu Date: Tue, 30 Jan 2018 12:49:18 -0800 Subject: [PATCH 282/423] Support outfeed host calls in TPUEstimator. That implicitly allows us to support tf.contrib.summary (see the unit test). We may change the recommended way to use summaries later. PiperOrigin-RevId: 183876356 --- tensorflow/contrib/tpu/BUILD | 1 + .../contrib/tpu/python/tpu/tpu_estimator.py | 397 ++++++++++-------- 2 files changed, 232 insertions(+), 166 deletions(-) diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index 0199313bc8..a7d54d8a0c 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -43,6 +43,7 @@ py_library( deps = [ ":tpu_lib", ":tpu_py", + "//tensorflow/contrib/summary:summary_ops", "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:control_flow_ops", diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 7907d0baa5..9d715bb236 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -30,6 +30,7 @@ import six from six.moves import queue as Queue # pylint: disable=redefined-builtin from six.moves import xrange # pylint: disable=redefined-builtin +from tensorflow.contrib.summary import summary_ops as contrib_summary from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.contrib.tpu.python.tpu import tpu_config @@ -385,7 +386,8 @@ class TPUEstimatorSpec( 'train_op', 'eval_metrics', 'export_outputs', - 'scaffold_fn' + 'scaffold_fn', + 'host_call' ])): """Ops and objects returned from a `model_fn` and passed to `TPUEstimator`. @@ -411,6 +413,16 @@ class TPUEstimatorSpec( `scaffold_fn` is a function running on CPU to generate the `Scaffold`. This function should not capture any Tensors in `model_fn`. + + `host_call` is a tuple of a `function` and a list or dictionary of `tensors` + to pass to that function. `host_call` currently works for train() and + evaluate(). The function's graph is executed on the CPU on every step, so + there is communication overhead when sending tensors from TPU to CPU. To + reduce the overhead, try reducing the size of the tensors. The `tensors` are + concatenated along their major (batch) dimension, and so must be >= rank 1. + The `host_call` is useful for writing summaries with + @{tf.contrib.summary.create_file_writer}. Note that `host_call` does not + currently work if `use_tpu` is set to False. """ def __new__(cls, @@ -420,10 +432,15 @@ class TPUEstimatorSpec( train_op=None, eval_metrics=None, export_outputs=None, - scaffold_fn=None): + scaffold_fn=None, + host_call=None): """Creates a validated `TPUEstimatorSpec` instance.""" + host_calls = {} if eval_metrics is not None: - _EvalMetrics.validate(eval_metrics) + host_calls['eval_metrics'] = eval_metrics + if host_call is not None: + host_calls['host_call'] = host_call + _OutfeedHostCall.validate(host_calls) return super(TPUEstimatorSpec, cls).__new__( cls, mode=mode, @@ -432,12 +449,15 @@ class TPUEstimatorSpec( train_op=train_op, eval_metrics=eval_metrics, export_outputs=export_outputs, - scaffold_fn=scaffold_fn) + scaffold_fn=scaffold_fn, + host_call=host_call) def as_estimator_spec(self): """Creates an equivalent `EstimatorSpec` used by CPU train/eval.""" - eval_metric_ops = _EvalMetrics.to_metric_metric_ops_for_cpu( - self.eval_metrics) + eval_metric_ops = None + if self.eval_metrics is not None: + eval_metric_ops = _OutfeedHostCall.create_cpu_hostcall( + {'eval_metrics': self.eval_metrics})['eval_metrics'] scaffold = self.scaffold_fn() if self.scaffold_fn else None return model_fn_lib.EstimatorSpec( mode=self.mode, @@ -490,7 +510,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): dequeue. """ - def __init__(self, ctx, enqueue_ops, dequeue_ops=None): + def __init__(self, ctx, enqueue_ops, dequeue_ops): self._master_job = ctx.master_job self._enqueue_ops = enqueue_ops self._dequeue_ops = dequeue_ops @@ -504,8 +524,15 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): def begin(self): logging.info('TPU job name %s', self._master_job) self._iterations_per_loop_var = _create_or_get_iterations_per_loop() - self._init_op = [tpu.initialize_system(job=self._master_job)] - self._finalize_op = [tpu.shutdown_system(job=self._master_job)] + self._init_ops = [tpu.initialize_system(job=self._master_job)] + self._finalize_ops = [tpu.shutdown_system(job=self._master_job)] + + summary_writer_init_ops = contrib_summary.summary_writer_initializer_op() + self._init_ops.extend(summary_writer_init_ops) + # Get all the writer resources from the initializer, so we know what to + # flush. + 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. @@ -517,8 +544,9 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): emitting a stack trace for the infeed. Args: - session: `tf.Session`, session to be terminated - error: exception that triggered logging. + session: `tf.Session`, session to be terminated error: exception that + triggered logging. + error: the Exception to log. """ logging.warning( '\n\n' @@ -594,18 +622,16 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): def after_create_session(self, session, coord): logging.info('Init TPU system') - session.run( - self._init_op, - options=config_pb2.RunOptions(timeout_in_ms=5 * 60 * 1000)) + session.run(self._init_ops, + options=config_pb2.RunOptions(timeout_in_ms=5 * 60 * 1000)) logging.info('Start infeed thread controller') self._infeed_controller = _OpQueueContext( name='InfeedController', target=self._run_infeed, args=(session,)) - if self._dequeue_ops is not None: - logging.info('Start outfeed thread controller') - self._outfeed_controller = _OpQueueContext( - name='OutfeedController', target=self._run_outfeed, args=(session,)) + logging.info('Start outfeed thread controller') + self._outfeed_controller = _OpQueueContext( + name='OutfeedController', target=self._run_outfeed, args=(session,)) def before_run(self, run_context): if self._feed_error: @@ -618,11 +644,10 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): logging.info('Enqueue next (%d) batch(es) of data to infeed.', iterations) self._infeed_controller.send_next_batch_signal(iterations) - if self._dequeue_ops is not None: - # TODO(xiejw): Refactor the outfeed dequeue into tf.while_loop. - logging.info('Dequeue next (%d) batch(es) of data from outfeed.', - iterations) - self._outfeed_controller.send_next_batch_signal(iterations) + # TODO(xiejw): Refactor the outfeed dequeue into tf.while_loop. + logging.info('Dequeue next (%d) batch(es) of data from outfeed.', + iterations) + self._outfeed_controller.send_next_batch_signal(iterations) def end(self, session): if self._session_cancel_timer: @@ -633,12 +658,11 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): logging.info('Stop infeed thread controller') self._infeed_controller.join() - if self._dequeue_ops is not None: - logging.info('Stop output thread controller') - self._outfeed_controller.join() + logging.info('Stop output thread controller') + self._outfeed_controller.join() logging.info('Shutdown TPU system.') - session.run(self._finalize_op) + session.run(self._finalize_ops) class _TPUStopAtStepHook(session_run_hook.SessionRunHook): @@ -1080,6 +1104,7 @@ class _ModelFnWrapper(object): A Fn representing the train step for TPU. """ + host_call = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() def train_step(loss): @@ -1091,15 +1116,19 @@ class _ModelFnWrapper(object): self._call_model_fn(features, labels)) loss, train_op = estimator_spec.loss, estimator_spec.train_op + host_call_outfeed_ops = [] if isinstance(estimator_spec, TPUEstimatorSpec): captured_scaffold_fn.capture(estimator_spec.scaffold_fn) + if estimator_spec.host_call is not None: + host_call.record({'host_call': estimator_spec.host_call}) + host_call_outfeed_ops = host_call.create_enqueue_op() else: captured_scaffold_fn.capture(None) - with ops.control_dependencies([train_op]): + with ops.control_dependencies([train_op] + host_call_outfeed_ops): return array_ops.identity(loss) - return train_step, captured_scaffold_fn + return train_step, host_call, captured_scaffold_fn def convert_to_single_tpu_eval_step(self, dequeue_fn): """Converts user provided model_fn` as a single eval step on TPU. @@ -1125,9 +1154,9 @@ class _ModelFnWrapper(object): Returns: A tuple of eval_fn and eval_metrics. The eval_fn representing the eval - step for TPU. and eval_metrics is an `_EvalMetrics` instance. + step for TPU. and eval_metrics is an `_OutfeedHostCall` instance. """ - eval_metrics = _EvalMetrics(self._ctx) + host_calls = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() def eval_step(total_loss): @@ -1142,13 +1171,16 @@ class _ModelFnWrapper(object): loss = tpu_estimator_spec.loss captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn) - eval_metrics.record(tpu_estimator_spec) - outfeed_ops = tpu_ops.outfeed_enqueue_tuple(eval_metrics.outfeed_tensors) + to_record = {} + to_record['eval_metrics'] = tpu_estimator_spec.eval_metrics + if tpu_estimator_spec.host_call is not None: + to_record['host_call'] = tpu_estimator_spec.host_call + host_calls.record(to_record) - with ops.control_dependencies([outfeed_ops]): + with ops.control_dependencies(host_calls.create_enqueue_op()): return math_ops.add(total_loss, loss) - return eval_step, eval_metrics, captured_scaffold_fn + return eval_step, host_calls, captured_scaffold_fn def _call_model_fn(self, features, labels): """Calls the model_fn with required parameters.""" @@ -1208,158 +1240,178 @@ class _ModelFnWrapper(object): return estimator_spec -class _EvalMetrics(object): - """Class wraps TPUEstimator.eval_metrics.""" +class _OutfeedHostCall(object): + """Support for `eval_metrics` and `host_call` in TPUEstimatorSpec.""" def __init__(self, ctx): self._ctx = ctx - self._metric_fn = None - self._is_dict = False - self._tensor_keys = [] - self._tensors = [] - self._tensor_dtypes = [] - self._tensor_shapes = [] - self._recorded = False + self._names = [] + # All of these are dictionaries of lists keyed on the name. + self._host_fns = {} + self._tensor_keys = collections.defaultdict(list) + self._tensors = collections.defaultdict(list) + self._tensor_dtypes = collections.defaultdict(list) + self._tensor_shapes = collections.defaultdict(list) @staticmethod - def validate(eval_metrics): - """Validates the `eval_metrics` in `TPUEstimatorSpec`.""" - - if not isinstance(eval_metrics, (tuple, list)): - raise ValueError('eval_metrics should be tuple or list') - if len(eval_metrics) != 2: - raise ValueError('eval_metrics should have two elements.') - if not callable(eval_metrics[0]): - raise TypeError('eval_metrics[0] should be callable.') - if not isinstance(eval_metrics[1], (tuple, list, dict)): - raise ValueError('eval_metrics[1] should be tuple or list, or dict.') - - if isinstance(eval_metrics[1], (tuple, list)): - fn_args = util.fn_args(eval_metrics[0]) - if len(eval_metrics[1]) != len(fn_args): - raise RuntimeError( - 'In TPUEstimatorSpec.eval_metrics, length of tensors does not ' - 'match method args of metric_fn.') + def validate(host_calls): + """Validates the `eval_metrics` and `host_call` in `TPUEstimatorSpec`.""" + + for name, host_call in host_calls.items(): + if not isinstance(host_call, (tuple, list)): + raise ValueError('{} should be tuple or list'.format(name)) + if len(host_call) != 2: + raise ValueError('{} should have two elements.'.format(name)) + if not callable(host_call[0]): + raise TypeError('{}[0] should be callable.'.format(name)) + if not isinstance(host_call[1], (tuple, list, dict)): + raise ValueError('{}[1] should be tuple or list, or dict.'.format(name)) + + if isinstance(host_call[1], (tuple, list)): + fn_args = util.fn_args(host_call[0]) + if len(host_call[1]) != len(fn_args): + raise RuntimeError( + 'In TPUEstimatorSpec.{}, length of tensors does not ' + 'match method args of metric_fn.'.format(name)) @staticmethod - def to_metric_metric_ops_for_cpu(eval_metrics): - """Converts `TPUEstimatorSpec.eval_metrics` to `eval_metric_ops` for CPU.""" - if not eval_metrics: - return None - - _EvalMetrics.validate(eval_metrics) - - metric_fn, tensors = eval_metrics + def create_cpu_hostcall(host_calls): + """Runs on the host_call on CPU instead of TPU when use_tpu=False.""" + + _OutfeedHostCall.validate(host_calls) + ret = {} + for name, host_call in host_calls.items(): + host_fn, tensors = host_call + if isinstance(tensors, (tuple, list)): + ret[name] = host_fn(*tensors) + else: + # Must be dict. + try: + ret[name] = host_fn(**tensors) + except TypeError as e: + logging.warning( + 'Exception while calling %s: %s. It is likely the tensors ' + '(%s[1]) do not match the ' + 'function\'s arguments', name, e, name) + raise e + return ret + + def record(self, host_calls): + """Records the host_call structure.""" + + for name, host_call in host_calls.items(): + host_fn, tensor_list_or_dict = host_call + self._names.append(name) + self._host_fns[name] = host_fn + + if isinstance(tensor_list_or_dict, dict): + for (key, tensor) in six.iteritems(tensor_list_or_dict): + self._tensor_keys[name].append(key) + self._tensors[name].append(tensor) + self._tensor_dtypes[name].append(tensor.dtype) + self._tensor_shapes[name].append(tensor.shape) + else: + # List or tuple. + self._tensor_keys[name] = None + for tensor in tensor_list_or_dict: + self._tensors[name].append(tensor) + self._tensor_dtypes[name].append(tensor.dtype) + self._tensor_shapes[name].append(tensor.shape) - if isinstance(tensors, (tuple, list)): - return metric_fn(*tensors) - else: - # Must be dict. - try: - return metric_fn(**tensors) - except TypeError as e: - logging.warning( - 'Exception while calling metric_fn for evalution: %s. ' - 'It is likely the tensors (eval_metrics[1]) do not match the ' - 'metric_fn arguments', e) - raise e - - def record(self, spec): - """Records the eval_metrics structure in `spec`.""" - if self._recorded: - raise RuntimeError('Eval metrics have been recorded already.') - - self._metric_fn, tensor_list_or_dict = spec.eval_metrics - - if isinstance(tensor_list_or_dict, dict): - self._is_dict = True - for (key, tensor) in six.iteritems(tensor_list_or_dict): - self._tensor_keys.append(key) - self._tensors.append(tensor) - self._tensor_dtypes.append(tensor.dtype) - self._tensor_shapes.append(tensor.shape) - else: - # List or tuple. - self._is_dict = False - self._tensors = tensor_list_or_dict - for tensor in tensor_list_or_dict: - self._tensor_dtypes.append(tensor.dtype) - self._tensor_shapes.append(tensor.shape) - self._recorded = True + def create_enqueue_op(self): + """Create the op to enqueue the recorded host_calls. - @property - def outfeed_tensors(self): - if not self._recorded: - raise RuntimeError('Eval metrics have not been recorded yet') - return self._tensors + Returns: + A list of enqueue ops, which is empty if there are no host calls. + """ + if not self._names: + return [] - def to_metric_metric_ops_for_tpu(self, dummy_update_op): - """Creates the eval_metric_ops now based on the TPU outfeed. + tensors = [] + # TODO(jhseu): Consider deduping tensors. + for name in self._names: + tensors.extend(self._tensors[name]) + return [tpu_ops.outfeed_enqueue_tuple(tensors)] - `eval_metric_ops` is defined in `EstimatorSpec`. From all shards, tensors - are dequeued from outfeed and then concatenated (along batch size dimension) - to form global-like tensors. All global-like tensors are passed to the - metric fn. + def create_tpu_hostcall(self): + """Sends the tensors through outfeed and runs the host_fn on CPU. - Args: - dummy_update_op: A dummy update op. + The tensors are concatenated along dimension 0 to form a global tensor + across all shards. The concatenated function is passed to the host_fn and + executed on the first host. Returns: - A tuple of (`eval_metric_ops` and `update_ops`), where `update_ops` should - be invoked in Outfeed dequeue thread, which drive the outfeed dequeue and - update the state of metrics. + A dictionary mapping name to the return type of the host_call by that + name. Raises: RuntimeError: If outfeed tensor is scalar. """ + if not self._names: + return [] - num_cores = self._ctx.num_cores - + ret = {} # For each i, dequeue_ops[i] is a list containing the tensors from all # shards. This list is concatenated later. dequeue_ops = [] - for i in xrange(len(self._tensors)): - dequeue_ops.append([]) - - # Outfeed ops execute on each JF node. + tensor_dtypes = [] + tensor_shapes = [] + for name in self._names: + for _ in self._tensors[name]: + dequeue_ops.append([]) + for dtype in self._tensor_dtypes[name]: + tensor_dtypes.append(dtype) + for shape in self._tensor_shapes[name]: + tensor_shapes.append(shape) + + # Outfeed ops execute on each JF node. Note: we must constraint it such that + # we have at most one outfeed dequeue and enqueue. tpu_device_placement_fn = self._ctx.tpu_device_placement_function - for i in xrange(num_cores): + for i in xrange(self._ctx.num_cores): with ops.device(tpu_device_placement_fn(i)): outfeed_tensors = tpu_ops.outfeed_dequeue_tuple( - dtypes=self._tensor_dtypes, shapes=self._tensor_shapes) + dtypes=tensor_dtypes, shapes=tensor_shapes) for j, item in enumerate(outfeed_tensors): dequeue_ops[j].append(item) - # It is assumed evaluation always happends on single host TPU system. So, + # Deconstruct dequeue ops. + dequeue_ops_by_name = {} + pos = 0 + for name in self._names: + dequeue_ops_by_name[name] = dequeue_ops[pos:pos+len(self._tensors[name])] + pos += len(self._tensors[name]) + + # It is assumed evaluation always happens on single host TPU system. So, # place all ops on tpu host if possible. + # + # TODO(jhseu): Evaluate whether this is right for summaries. with ops.device(self._ctx.tpu_host_placement_function(core_id=0)): - for i, item in enumerate(dequeue_ops): - if dequeue_ops[i][0].shape.ndims == 0: - raise RuntimeError( - 'All tensors outfed from TPU should preseve batch size ' - 'dimension, but got scalar {}'.format(dequeue_ops[i][0])) - # TODO(xiejw): Allow users to specify the axis for batch size dimension. - dequeue_ops[i] = array_ops.concat(dequeue_ops[i], axis=0) - - if self._is_dict: - dequeue_ops = dict(zip(self._tensor_keys, dequeue_ops)) - try: - eval_metric_ops = self._metric_fn(**dequeue_ops) - except TypeError as e: - logging.warning( - 'Exception while calling metric_fn for evalution: %s. ' - 'It is likely the tensors (eval_metrics[1]) do not match the ' - 'metric_fn arguments', e) - raise e - else: - eval_metric_ops = self._metric_fn(*dequeue_ops) - - eval_update_ops = [] - for k, v in eval_metric_ops.items(): - eval_metric_ops[k] = (v[0], dummy_update_op) - eval_update_ops.append(v[1]) + for name in self._names: + dequeue_ops = dequeue_ops_by_name[name] + for i, item in enumerate(dequeue_ops): + if dequeue_ops[i][0].shape.ndims == 0: + raise RuntimeError( + 'All tensors outfed from TPU should preserve batch size ' + 'dimension, but got scalar {}'.format(dequeue_ops[i][0])) + # TODO(xiejw): Allow users to specify the axis for batch size + # dimension. + dequeue_ops[i] = array_ops.concat(dequeue_ops[i], axis=0) + + 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) + except TypeError as e: + logging.warning( + 'Exception while calling %s: %s. It is likely the tensors ' + '(%s[1]) do not match the ' + 'function\'s arguments', name, e, name) + raise e + else: + ret[name] = self._host_fns[name](*dequeue_ops) - return eval_metric_ops, eval_update_ops + return ret class ExamplesPerSecondHook(basic_session_run_hooks.StepCounterHook): @@ -1717,10 +1769,13 @@ class TPUEstimator(estimator_lib.Estimator): input_holders.generate_infeed_enqueue_ops_and_dequeue_fn()) if mode == model_fn_lib.ModeKeys.TRAIN: - loss, scaffold = ( + loss, host_call, scaffold = ( _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn)) + host_ops = host_call.create_tpu_hostcall() + if host_ops is None: + host_ops = [] hooks = [ - TPUInfeedOutfeedSessionHook(ctx, enqueue_ops), + TPUInfeedOutfeedSessionHook(ctx, enqueue_ops, host_ops), ExamplesPerSecondHook(ctx.global_batch_size), InstallSignalHandlerHook(), training.LoggingTensorHook( @@ -1745,7 +1800,7 @@ class TPUEstimator(estimator_lib.Estimator): scaffold=scaffold) # Now eval. - total_loss, eval_metric_ops, scaffold = _eval_on_tpu_system( + total_loss, host_calls, scaffold = _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, @@ -1767,10 +1822,20 @@ class TPUEstimator(estimator_lib.Estimator): with ops.control_dependencies(internal_ops_to_run): dummy_update_op = control_flow_ops.no_op() - eval_metric_ops, eval_update_ops = ( - eval_metric_ops.to_metric_metric_ops_for_tpu(dummy_update_op)) + host_call_ret = host_calls.create_tpu_hostcall() + eval_metric_ops = {} + eval_update_ops = [] + for k, v in host_call_ret['eval_metrics'].items(): + eval_metric_ops[k] = (v[0], dummy_update_op) + eval_update_ops.append(v[1]) + + if 'host_call' not in host_call_ret: + host_ops = [] + else: + host_ops = host_call_ret['host_call'] hooks = [ - TPUInfeedOutfeedSessionHook(ctx, enqueue_ops, eval_update_ops), + TPUInfeedOutfeedSessionHook(ctx, enqueue_ops, + eval_update_ops + host_ops), ] return model_fn_lib.EstimatorSpec( @@ -1788,7 +1853,7 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): num_cores = ctx.num_cores iterations_per_loop_var = _create_or_get_iterations_per_loop() - single_tpu_eval_step, eval_metric_ops, captured_scaffold_fn = ( + single_tpu_eval_step, host_calls, captured_scaffold_fn = ( model_fn_wrapper.convert_to_single_tpu_eval_step(dequeue_fn)) def multi_tpu_eval_steps_on_single_shard(): @@ -1804,7 +1869,7 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): outputs_from_all_shards=False) scaffold = _get_scaffold(captured_scaffold_fn) - return loss, eval_metric_ops, scaffold + return loss, host_calls, scaffold def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): @@ -1812,7 +1877,7 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): num_cores = ctx.num_cores iterations_per_loop_var = _create_or_get_iterations_per_loop() - single_tpu_train_step, captured_scaffold_fn = ( + single_tpu_train_step, host_call, captured_scaffold_fn = ( model_fn_wrapper.convert_to_single_tpu_train_step(dequeue_fn)) def multi_tpu_train_steps_on_single_shard(): @@ -1828,7 +1893,7 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): outputs_from_all_shards=False) scaffold = _get_scaffold(captured_scaffold_fn) - return loss, scaffold + return loss, host_call, scaffold def _wrap_computation_in_while_loop(device, op_fn): -- GitLab From d56883617c07125eb9d488bc73baccaacd55f48b Mon Sep 17 00:00:00 2001 From: Russell Power Date: Tue, 30 Jan 2018 13:02:25 -0800 Subject: [PATCH 283/423] Enable bulk restoration by default. This enables loading multiple tensors in single call, allowing for better buffering and reduced load on distributed filesystems. PiperOrigin-RevId: 183878169 --- tensorflow/python/training/saver.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 2c59b82ebe..abc700b810 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1229,7 +1229,7 @@ class Saver(object): The `saver_def` proto should be the one returned by the `as_saver_def()` call of the `Saver` that was created for that `Graph`. builder: Optional `SaverBuilder` to use if a `saver_def` was not provided. - Defaults to `BaseSaverBuilder()`. + Defaults to `BulkSaverBuilder()`. defer_build: If `True`, defer adding the save and restore ops to the `build()` call. In that case `build()` should be called before finalizing the graph or using the saver. @@ -1309,7 +1309,7 @@ class Saver(object): if not self.saver_def or context.in_eager_mode(): if self._builder is None: - self._builder = BaseSaverBuilder(self._write_version) + self._builder = BulkSaverBuilder(self._write_version) if self._var_list is None: # pylint: disable=protected-access -- GitLab From 21cd5e381769b388b35437e0143af23235c4b5bf Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 13:10:54 -0800 Subject: [PATCH 284/423] Add missing dependency. PiperOrigin-RevId: 183879566 --- tensorflow/core/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 455da05738..7a0c0ea3fe 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1898,6 +1898,7 @@ cc_library( tf_cuda_library( name = "cuda_device_functions", hdrs = ["util/cuda_device_functions.h"], + cuda_deps = ["//third_party_gpus/cuda:cuda_headers"], visibility = ["//visibility:public"], deps = [":framework_lite"], ) -- GitLab From d52e0e0269b72624c1443e8eb78467dd99844558 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Tue, 30 Jan 2018 13:20:13 -0800 Subject: [PATCH 285/423] Eager: Update documentation to reflect that you can use TensorFlow 1.5 PiperOrigin-RevId: 183880991 --- tensorflow/contrib/eager/README.md | 24 ++------------ .../contrib/eager/python/g3doc/guide.md | 33 +++++++++++-------- 2 files changed, 21 insertions(+), 36 deletions(-) diff --git a/tensorflow/contrib/eager/README.md b/tensorflow/contrib/eager/README.md index 09242ee47d..9d2ca07c3a 100644 --- a/tensorflow/contrib/eager/README.md +++ b/tensorflow/contrib/eager/README.md @@ -41,28 +41,8 @@ support for distributed and multi-GPU training and CPU performance. ## Installation -Since eager execution is not yet part of a TensorFlow release, using it requires -either [building from source](https://www.tensorflow.org/install/install_sources) -or the latest nightly builds. The nightly builds are available as: - -- [`pip` packages](https://github.com/tensorflow/tensorflow/blob/master/README.md#installation) and - -- [docker](https://hub.docker.com/r/tensorflow/tensorflow/) images. - -For example, to run the latest nightly docker image: - -```sh -# If you have a GPU, use https://github.com/NVIDIA/nvidia-docker -nvidia-docker pull tensorflow/tensorflow:nightly-gpu -nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:nightly-gpu - -# If you do not have a GPU, use the CPU-only image -docker pull tensorflow/tensorflow:nightly -docker run -it -p 8888:8888 tensorflow/tensorflow:nightly -``` - -And then visit http://localhost:8888 in your browser for a Jupyter notebook -environment. Try out the notebooks below. +Eager execution is included in TensorFlow versions 1.5 and above. +Installation instructions at https://www.tensorflow.org/install/ ## Documentation diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md index 7eea93ce1f..ffc1d0332e 100644 --- a/tensorflow/contrib/eager/python/g3doc/guide.md +++ b/tensorflow/contrib/eager/python/g3doc/guide.md @@ -19,29 +19,34 @@ to models defined without using eager execution. ## Installation -Eager execution is **not** included in the latest release (version 1.4) of -TensorFlow. To use it, you will need to [build TensorFlow from -source](https://www.tensorflow.org/install/install_sources) or install the -nightly builds. +Eager execution is included in TensorFlow versions 1.5 and above. +Installation instructions at https://www.tensorflow.org/install/ -For example, the nightly builds can be installed using `pip`: +The contents of this guide are compatible with TensorFlow 1.5. +However, if you run into bugs that are fixed in source but not the +release, you may want to either either [building from +source](https://www.tensorflow.org/install/install_sources) +or the try latest nightly builds. The nightly builds are available as: -- `pip install tf-nightly` (for CPU-only TensorFlow) -- `pip install tf-nightly-gpu` (for GPU-enabled TensorFlow) +- [`pip` packages](https://github.com/tensorflow/tensorflow/blob/master/README.md#installation) and -Or using `docker`, with [Jupyter Notebook](http://jupyter.org/) support: +- [docker](https://hub.docker.com/r/tensorflow/tensorflow/) images. + +For example, to run the latest nightly docker image: ```sh -# For CPU-only TensorFlow +# If you have a GPU, use https://github.com/NVIDIA/nvidia-docker +docker pull tensorflow/tensorflow:nightly-gpu +docker run --runtime=nvidia -it -p 8888:8888 tensorflow/tensorflow:nightly-gpu + +# If you do not have a GPU, use the CPU-only image docker pull tensorflow/tensorflow:nightly docker run -it -p 8888:8888 tensorflow/tensorflow:nightly - -# For GPU-enabled TensorFlow: -# (Requires https://github.com/NVIDIA/nvidia-docker) -nvidia-docker pull tensorflow/tensorflow:nightly-gpu -nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:nightly-gpu ``` +And then visit http://localhost:8888 in your browser for a Jupyter notebook +environment. + ## Getting Started With TensorFlow installed, eager execution is enabled via a single call: -- GitLab From 548df15375488fc06ff663670f88734f3ece4814 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Tue, 30 Jan 2018 13:26:51 -0800 Subject: [PATCH 286/423] [tf.data] Add `IteratorContext::allocator()`. This enables the various iterator implementations to use the actual allocator for the device on which they are running, rather than defaulting to `cpu_allocator()` (which is typically a plain malloc). In future, this will enable allocating iterator outputs in CUDA-pinned memory (and GPU memory). PERFORMANCE NOTE: In sessions where `ConfigProto.force_gpu_compatible == True`, this change has the effect of allocating all input pipeline tensors in CUDA-pinned memory. Previous if this flag was set, only the tensors allocated during function execution would be allocated in this space, and other tensors (e.g. the result of a `Dataset.batch()` would be allocated using `cpu_allocator()` (i.e. `malloc()`). This change should lead to more efficient communication between a host-side input pipeline and GPUs, but it may also create more pressure on the CUDA host allocator (whose default maximum size is 64GB). The "TF_CUDA_HOST_MEM_LIMIT_IN_MB" environment variable can be used to override this value. This change is a starting point for working on issue #13610. PiperOrigin-RevId: 183881907 --- tensorflow/core/kernels/data/BUILD | 1 + tensorflow/core/kernels/data/batch_dataset_op.cc | 2 +- tensorflow/core/kernels/data/dataset.cc | 5 +++++ tensorflow/core/kernels/data/dataset.h | 5 +++++ .../kernels/data/dense_to_sparse_batch_dataset_op.cc | 6 +++--- .../core/kernels/data/map_and_batch_dataset_op.cc | 11 ++++++----- .../core/kernels/data/padded_batch_dataset_op.cc | 2 +- tensorflow/core/kernels/data/random_dataset_op.cc | 2 +- tensorflow/core/kernels/data/range_dataset_op.cc | 2 +- tensorflow/core/kernels/data/reader_dataset_ops.cc | 6 +++--- tensorflow/core/kernels/data/sql/BUILD | 1 + tensorflow/core/kernels/data/sql/query_connection.h | 4 +++- .../core/kernels/data/sql/sqlite_query_connection.cc | 7 +++++-- .../core/kernels/data/sql/sqlite_query_connection.h | 2 +- tensorflow/core/kernels/data/sql_dataset_ops.cc | 4 ++-- .../core/kernels/data/tensor_slice_dataset_op.cc | 2 +- 16 files changed, 40 insertions(+), 22 deletions(-) diff --git a/tensorflow/core/kernels/data/BUILD b/tensorflow/core/kernels/data/BUILD index 45505ef716..cdb4023861 100644 --- a/tensorflow/core/kernels/data/BUILD +++ b/tensorflow/core/kernels/data/BUILD @@ -49,6 +49,7 @@ cc_library( srcs = ["dataset.cc"], hdrs = ["dataset.h"], deps = [ + "//tensorflow/core:core_cpu", "//tensorflow/core:framework", "//tensorflow/core:graph", "//tensorflow/core:lib", diff --git a/tensorflow/core/kernels/data/batch_dataset_op.cc b/tensorflow/core/kernels/data/batch_dataset_op.cc index 0853362b26..7fa67efb9e 100644 --- a/tensorflow/core/kernels/data/batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/batch_dataset_op.cc @@ -144,7 +144,7 @@ class BatchDatasetOp : public UnaryDatasetOpKernel { const Tensor& first_element = batch_elements[0][component_index]; TensorShape batch_component_shape({num_batch_elements}); batch_component_shape.AppendShape(first_element.shape()); - Tensor batch_component(cpu_allocator(), first_element.dtype(), + Tensor batch_component(ctx->allocator({}), first_element.dtype(), batch_component_shape); // Build the output tuple component by copying one slice // from each input element in the batch. diff --git a/tensorflow/core/kernels/data/dataset.cc b/tensorflow/core/kernels/data/dataset.cc index 2ea6875567..d18cb16018 100644 --- a/tensorflow/core/kernels/data/dataset.cc +++ b/tensorflow/core/kernels/data/dataset.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/core/kernels/data/dataset.h" +#include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/graph/graph_def_builder.h" #include "tensorflow/core/graph/node_builder.h" @@ -264,6 +265,10 @@ void BinaryDatasetOpKernel::MakeDataset(OpKernelContext* ctx, MakeDataset(ctx, input, another_input, output); } +Allocator* IteratorContext::allocator(AllocatorAttributes attrs) { + return params_.lib->device()->GetAllocator(attrs); +} + const char GraphDatasetBase::kDatasetGraphKey[] = "_DATASET_GRAPH"; const char GraphDatasetBase::kDatasetGraphOutputNodeKey[] = "_DATASET_GRAPH_OUTPUT_NODE"; diff --git a/tensorflow/core/kernels/data/dataset.h b/tensorflow/core/kernels/data/dataset.h index 2ef31ddfaa..08c3ca82ea 100644 --- a/tensorflow/core/kernels/data/dataset.h +++ b/tensorflow/core/kernels/data/dataset.h @@ -272,6 +272,9 @@ class IteratorContext { // The FunctionLibraryRuntime object to be used to make function calls. FunctionLibraryRuntime* lib = nullptr; std::shared_ptr function_library = nullptr; + + // The Allocator to be used to allocate the output of an iterator. + Allocator* allocator = nullptr; }; explicit IteratorContext(Params params) : params_(std::move(params)) {} @@ -298,6 +301,8 @@ class IteratorContext { void set_lib(FunctionLibraryRuntime* lib) { params_.lib = lib; } + Allocator* allocator(AllocatorAttributes attrs); + private: Params params_; }; diff --git a/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc b/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc index e7224bb547..132808a5f1 100644 --- a/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc @@ -155,7 +155,7 @@ class DenseToSparseBatchDatasetOp : public UnaryDatasetOpKernel { // Determine the size of the output tensors: // * dense_shape will be [`row_shape + 1`]. - Tensor dense_shape(cpu_allocator(), DT_INT64, {row_ndims + 1}); + Tensor dense_shape(ctx->allocator({}), DT_INT64, {row_ndims + 1}); auto dense_shape_vec = dense_shape.vec(); for (size_t i = 0; i < row_ndims; ++i) { if (row_shape.dim_size(i) == -1) { @@ -215,10 +215,10 @@ class DenseToSparseBatchDatasetOp : public UnaryDatasetOpKernel { // * indices will be [`total_elements`, `row_shape + 1`]. // * values will be [`total_elements`]. - Tensor indices(cpu_allocator(), DT_INT64, + Tensor indices(ctx->allocator({}), DT_INT64, {total_elements, row_ndims + 1}); Tensor values( - cpu_allocator(), + ctx->allocator({}), DatasetIterator>::dataset()->input_->output_dtypes()[0], {total_elements}); auto indices_matrix = indices.matrix(); diff --git a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc index c529f671f2..9ce263732f 100644 --- a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc @@ -183,7 +183,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { TensorShape component_shape( batch_results_[current_batch_index_].output[i].shape()); component_shape.set_dim(0, num_elements); - Tensor component(cpu_allocator(), output[i].dtype(), + Tensor component(ctx->allocator({}), output[i].dtype(), component_shape); TF_RETURN_IF_ERROR( CopyPartialBatch(&component, output[i], num_elements)); @@ -244,7 +244,8 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { return Status::OK(); } - void EnsureOutputAllocated(BatchResult* batch_result, + void EnsureOutputAllocated(IteratorContext* ctx, + BatchResult* batch_result, const std::vector& return_values) { mutex_lock l(batch_result->mu); if (batch_result->output_allocated) { @@ -254,7 +255,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { for (size_t i = 0; i < num_components; ++i) { TensorShape component_shape({dataset()->batch_size_}); component_shape.AppendShape(return_values[i].shape()); - Tensor component(cpu_allocator(), return_values[i].dtype(), + Tensor component(ctx->allocator({}), return_values[i].dtype(), component_shape); batch_result->output.emplace_back(std::move(component)); } @@ -285,10 +286,9 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { dataset()->captured_func_->RunAsync( ctx, std::move(input_element), &result->return_values, [this, ctx, result, batch_result, offset](Status ret_status) { - delete ctx; result->status.Update(ret_status); if (ret_status.ok()) { - EnsureOutputAllocated(batch_result, + EnsureOutputAllocated(ctx, batch_result, result->return_values); const size_t num_components = result->return_values.size(); @@ -318,6 +318,7 @@ class MapAndBatchDatasetOp : public UnaryDatasetOpKernel { } } } + delete ctx; // NOTE(mrry): We clear the return values here to release // any memory associated with them and to paralellize the // destruction of the tensors (which can be surprisingly diff --git a/tensorflow/core/kernels/data/padded_batch_dataset_op.cc b/tensorflow/core/kernels/data/padded_batch_dataset_op.cc index 346eca0bb2..4fe4e8e294 100644 --- a/tensorflow/core/kernels/data/padded_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/padded_batch_dataset_op.cc @@ -376,7 +376,7 @@ class PaddedBatchDatasetOp : public UnaryDatasetOpKernel { // 2. Copy each batch element to the appropriate location in // the output component tensor. - Tensor batch_component(cpu_allocator(), + Tensor batch_component(ctx->allocator({}), output_dtypes()[component_index], batch_component_shape); TF_RETURN_IF_ERROR(SetElementZero( diff --git a/tensorflow/core/kernels/data/random_dataset_op.cc b/tensorflow/core/kernels/data/random_dataset_op.cc index bc638864b0..210b9ad1b8 100644 --- a/tensorflow/core/kernels/data/random_dataset_op.cc +++ b/tensorflow/core/kernels/data/random_dataset_op.cc @@ -99,7 +99,7 @@ class RandomDatasetOp : public DatasetOpKernel { std::vector* out_tensors, bool* end_of_sequence) override { mutex_lock l(mu_); - Tensor value_tensor(cpu_allocator(), DT_INT64, {}); + Tensor value_tensor(ctx->allocator({}), DT_INT64, {}); value_tensor.scalar()() = Random(); out_tensors->emplace_back(std::move(value_tensor)); *end_of_sequence = false; diff --git a/tensorflow/core/kernels/data/range_dataset_op.cc b/tensorflow/core/kernels/data/range_dataset_op.cc index d0bc61acd9..b57518e678 100644 --- a/tensorflow/core/kernels/data/range_dataset_op.cc +++ b/tensorflow/core/kernels/data/range_dataset_op.cc @@ -100,7 +100,7 @@ class RangeDatasetOp : public DatasetOpKernel { *end_of_sequence = true; return Status::OK(); } - Tensor value_tensor(cpu_allocator(), DT_INT64, {}); + Tensor value_tensor(ctx->allocator({}), DT_INT64, {}); value_tensor.scalar()() = next_; out_tensors->emplace_back(std::move(value_tensor)); *end_of_sequence = false; diff --git a/tensorflow/core/kernels/data/reader_dataset_ops.cc b/tensorflow/core/kernels/data/reader_dataset_ops.cc index aa39fffc2e..34d7d9f914 100644 --- a/tensorflow/core/kernels/data/reader_dataset_ops.cc +++ b/tensorflow/core/kernels/data/reader_dataset_ops.cc @@ -141,7 +141,7 @@ class TextLineDatasetOp : public DatasetOpKernel { if (s.ok()) { // Produce the line as output. - Tensor line_tensor(cpu_allocator(), DT_STRING, {}); + Tensor line_tensor(ctx->allocator({}), DT_STRING, {}); line_tensor.scalar()() = line_contents; out_tensors->emplace_back(std::move(line_tensor)); *end_of_sequence = false; @@ -384,7 +384,7 @@ class FixedLengthRecordDatasetOp : public DatasetOpKernel { TF_RETURN_IF_ERROR( input_buffer_->ReadNBytes(dataset()->record_bytes_, &record)); // Produce the record as output. - Tensor record_tensor(cpu_allocator(), DT_STRING, {}); + Tensor record_tensor(ctx->allocator({}), DT_STRING, {}); record_tensor.scalar()() = record; out_tensors->emplace_back(std::move(record_tensor)); *end_of_sequence = false; @@ -589,7 +589,7 @@ class TFRecordDatasetOp : public DatasetOpKernel { do { // We are currently processing a file, so try to read the next record. if (reader_) { - Tensor result_tensor(cpu_allocator(), DT_STRING, {}); + Tensor result_tensor(ctx->allocator({}), DT_STRING, {}); Status s = reader_->ReadRecord(&result_tensor.scalar()()); if (s.ok()) { out_tensors->emplace_back(std::move(result_tensor)); diff --git a/tensorflow/core/kernels/data/sql/BUILD b/tensorflow/core/kernels/data/sql/BUILD index 0286825af3..f4698bdaf7 100644 --- a/tensorflow/core/kernels/data/sql/BUILD +++ b/tensorflow/core/kernels/data/sql/BUILD @@ -33,6 +33,7 @@ cc_library( deps = [ "//tensorflow/core:framework", "//tensorflow/core:lib", + "//tensorflow/core/kernels/data:dataset", "//tensorflow/core/lib/db:sqlite", ], ) diff --git a/tensorflow/core/kernels/data/sql/query_connection.h b/tensorflow/core/kernels/data/sql/query_connection.h index f31017bd19..e9ffca202f 100644 --- a/tensorflow/core/kernels/data/sql/query_connection.h +++ b/tensorflow/core/kernels/data/sql/query_connection.h @@ -19,6 +19,8 @@ limitations under the License. namespace tensorflow { +class IteratorContext; + namespace sql { // This interface allows a user to connect to a database, execute a query, and // iterate over the result set, putting the results into an output tensor. @@ -56,7 +58,7 @@ class QueryConnection { // If there are no more rows in the result set, then instead `true` will be // stored in `*end_of_sequence`, and the content of `*out_tensors` will be // undefined. - virtual Status GetNext(std::vector* out_tensors, + virtual Status GetNext(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) = 0; }; diff --git a/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc b/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc index 029a0aab97..7cd07bd8ec 100644 --- a/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc +++ b/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/sql/sqlite_query_connection.h" #include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/kernels/data/dataset.h" #include "tensorflow/core/lib/strings/stringprintf.h" namespace tensorflow { @@ -48,14 +49,16 @@ Status SqliteQueryConnection::Close() { return Status::OK(); } -Status SqliteQueryConnection::GetNext(std::vector* out_tensors, +Status SqliteQueryConnection::GetNext(IteratorContext* ctx, + std::vector* out_tensors, bool* end_of_sequence) { if (!stmt_) TF_RETURN_IF_ERROR(PrepareQuery()); TF_RETURN_IF_ERROR(stmt_.Step(end_of_sequence)); if (!*end_of_sequence) { for (int i = 0; i < column_count_; i++) { DataType dt = output_types_[i]; - Tensor tensor(cpu_allocator(), dt, {}); + // TODO(mrry): Pass in the `IteratorContext::allocator()`. + Tensor tensor(ctx->allocator({}), dt, {}); FillTensorWithResultSetEntry(dt, i, &tensor); out_tensors->emplace_back(std::move(tensor)); } diff --git a/tensorflow/core/kernels/data/sql/sqlite_query_connection.h b/tensorflow/core/kernels/data/sql/sqlite_query_connection.h index 787c17d6c0..81b19530b7 100644 --- a/tensorflow/core/kernels/data/sql/sqlite_query_connection.h +++ b/tensorflow/core/kernels/data/sql/sqlite_query_connection.h @@ -32,7 +32,7 @@ class SqliteQueryConnection : public QueryConnection { Status Open(const string& data_source_name, const string& query, const DataTypeVector& output_types) override; Status Close() override; - Status GetNext(std::vector* out_tensors, + Status GetNext(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) override; private: diff --git a/tensorflow/core/kernels/data/sql_dataset_ops.cc b/tensorflow/core/kernels/data/sql_dataset_ops.cc index 7230219080..d50e9c9cf9 100644 --- a/tensorflow/core/kernels/data/sql_dataset_ops.cc +++ b/tensorflow/core/kernels/data/sql_dataset_ops.cc @@ -116,7 +116,7 @@ class SqlDatasetOp : public DatasetOpKernel { } } - Status GetNextInternal(IteratorContext* /*ctx*/, + Status GetNextInternal(IteratorContext* ctx, std::vector* out_tensors, bool* end_of_sequence) override { mutex_lock l(mu_); @@ -132,7 +132,7 @@ class SqlDatasetOp : public DatasetOpKernel { return s; } } - return query_connection_->GetNext(out_tensors, end_of_sequence); + return query_connection_->GetNext(ctx, out_tensors, end_of_sequence); } private: diff --git a/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc b/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc index 18adae1ea3..d5be4c7780 100644 --- a/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc +++ b/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc @@ -117,7 +117,7 @@ class TensorSliceDatasetOp : public DatasetOpKernel { out_tensors->reserve(dataset()->tensors_.size()); for (int i = 0; i < dataset()->tensors_.size(); ++i) { const Tensor& t = dataset()->tensors_[i]; - Tensor t_slice(cpu_allocator(), t.dtype(), + Tensor t_slice(ctx->allocator({}), t.dtype(), TensorShape(dataset()->shapes_[i].dim_sizes())); TF_RETURN_IF_ERROR(batch_util::CopySliceToElement(t, &t_slice, i_)); out_tensors->emplace_back(std::move(t_slice)); -- GitLab From 36f3a3b31ea9c32c64f1f4af543c75692d338876 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 13:58:05 -0800 Subject: [PATCH 287/423] Add and Mul support broadcasting. PiperOrigin-RevId: 183886920 --- tensorflow/contrib/lite/kernels/add.cc | 102 ++++++++++++------ tensorflow/contrib/lite/kernels/add_test.cc | 54 ++++++++-- tensorflow/contrib/lite/kernels/mul.cc | 99 +++++++++++------ tensorflow/contrib/lite/kernels/mul_test.cc | 59 ++++++++-- .../testing/generated_examples_zip_test.cc | 4 +- 5 files changed, 235 insertions(+), 83 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/add.cc b/tensorflow/contrib/lite/kernels/add.cc index fb5764f280..63ea89df56 100644 --- a/tensorflow/contrib/lite/kernels/add.cc +++ b/tensorflow/contrib/lite/kernels/add.cc @@ -37,7 +37,23 @@ constexpr int kInputTensor1 = 0; constexpr int kInputTensor2 = 1; constexpr int kOutputTensor = 0; +struct OpData { + bool requires_broadcast; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* data = new OpData; + data->requires_broadcast = false; + return data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); @@ -45,43 +61,56 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TF_LITE_ENSURE_EQ(context, NumDimensions(input1), NumDimensions(input2)); - for (int i = 0; i < NumDimensions(input1); ++i) { - TF_LITE_ENSURE_EQ(context, SizeOfDimension(input1, i), - SizeOfDimension(input2, i)); - } + TF_LITE_ENSURE_EQ(context, input1->type, input2->type); + output->type = input2->type; + + data->requires_broadcast = !HaveSameShapes(input1, input2); - TF_LITE_ENSURE_EQ(context, input1->type, output->type); - TF_LITE_ENSURE_EQ(context, input2->type, output->type); + TfLiteIntArray* output_size = nullptr; + if (data->requires_broadcast) { + TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast( + context, input1, input2, &output_size)); + } else { + output_size = TfLiteIntArrayCopy(input1->dims); + } - TfLiteIntArray* output_size = TfLiteIntArrayCopy(input1->dims); return context->ResizeTensor(context, output, output_size); } template void EvalAddFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteAddParams* params, TfLiteTensor* input1, - TfLiteTensor* input2, TfLiteTensor* output) { + TfLiteAddParams* params, const OpData* data, + TfLiteTensor* input1, TfLiteTensor* input2, + TfLiteTensor* output) { float output_activation_min, output_activation_max; CalculateActivationRangeFloat(params->activation, &output_activation_min, &output_activation_max); -#define TF_LITE_ADD(type) \ - type::Add(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_ADD(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + output_activation_min, output_activation_max, \ + GetTensorData(output), GetTensorDims(output)) if (kernel_type == kReference) { - TF_LITE_ADD(reference_ops); + if (data->requires_broadcast) { + TF_LITE_ADD(reference_ops, BroadcastAdd); + } else { + TF_LITE_ADD(reference_ops, Add); + } } else { - TF_LITE_ADD(optimized_ops); + if (data->requires_broadcast) { + TF_LITE_ADD(optimized_ops, BroadcastAdd); + } else { + TF_LITE_ADD(optimized_ops, Add); + } } #undef TF_LITE_ADD } template void EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteAddParams* params, TfLiteTensor* input1, - TfLiteTensor* input2, TfLiteTensor* output) { + TfLiteAddParams* params, const OpData* data, + TfLiteTensor* input1, TfLiteTensor* input2, + TfLiteTensor* output) { auto input1_offset = -input1->params.zero_point; auto input2_offset = -input2->params.zero_point; auto output_offset = output->params.zero_point; @@ -112,19 +141,20 @@ void EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, CalculateActivationRangeUint8(params->activation, output, &output_activation_min, &output_activation_max); -#define TF_LITE_ADD(type) \ - type::BroadcastAdd( \ - 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_ADD(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)); + // The quantized version of Add doesn't support activations, so we + // always use BroadcastAdd. if (kernel_type == kReference) { - TF_LITE_ADD(reference_ops); + TF_LITE_ADD(reference_ops, BroadcastAdd); } else { - TF_LITE_ADD(optimized_ops); + TF_LITE_ADD(optimized_ops, BroadcastAdd); } #undef TF_LITE_ADD } @@ -132,15 +162,17 @@ void EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, template TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { auto* params = reinterpret_cast(node->builtin_data); + OpData* data = reinterpret_cast(node->user_data); TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); if (output->type == kTfLiteFloat32) { - EvalAddFloat(context, node, params, input1, input2, output); + EvalAddFloat(context, node, params, data, input1, input2, + output); } else if (output->type == kTfLiteUInt8) { - EvalAddQuantized(context, node, params, input1, input2, + EvalAddQuantized(context, node, params, data, input1, input2, output); } else { context->ReportError(context, @@ -154,19 +186,19 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace add TfLiteRegistration* Register_ADD_REF() { - static TfLiteRegistration r = {nullptr, nullptr, add::Prepare, + static TfLiteRegistration r = {add::Init, add::Free, add::Prepare, add::Eval}; return &r; } TfLiteRegistration* Register_ADD_GENERIC_OPT() { - static TfLiteRegistration r = {nullptr, nullptr, add::Prepare, + static TfLiteRegistration r = {add::Init, add::Free, add::Prepare, add::Eval}; return &r; } TfLiteRegistration* Register_ADD_NEON_OPT() { - static TfLiteRegistration r = {nullptr, nullptr, add::Prepare, + static TfLiteRegistration r = {add::Init, add::Free, add::Prepare, add::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/add_test.cc b/tensorflow/contrib/lite/kernels/add_test.cc index 306dfc3e80..956d05bed5 100644 --- a/tensorflow/contrib/lite/kernels/add_test.cc +++ b/tensorflow/contrib/lite/kernels/add_test.cc @@ -25,10 +25,11 @@ using ::testing::ElementsAreArray; class BaseAddOpModel : public SingleOpModel { public: - BaseAddOpModel(const TensorData& input, const TensorData& output, + BaseAddOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, ActivationFunctionType activation_type) { - input1_ = AddInput(input); - input2_ = AddInput(input); + input1_ = AddInput(input1); + input2_ = AddInput(input2); output_ = AddOutput(output); SetBuiltinOp(BuiltinOperator_ADD, BuiltinOptions_AddOptions, CreateAddOptions(builder_, activation_type).Union()); @@ -70,6 +71,7 @@ float GetTolerance(int min, int max) { TEST(FloatAddOpModel, NoActivation) { FloatAddOpModel 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}); @@ -78,9 +80,9 @@ TEST(FloatAddOpModel, NoActivation) { } TEST(FloatAddOpModel, ActivationRELU_N1_TO_1) { - FloatAddOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, - {TensorType_FLOAT32, {}}, - ActivationFunctionType_RELU_N1_TO_1); + FloatAddOpModel m( + {TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_RELU_N1_TO_1); 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(); @@ -92,6 +94,7 @@ TEST(FloatAddOpModel, VariousInputShapes) { {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; for (int i = 0; i < test_shapes.size(); ++i) { FloatAddOpModel m({TensorType_FLOAT32, test_shapes[i]}, + {TensorType_FLOAT32, test_shapes[i]}, {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); m.PopulateTensor(m.input2(), {0.1, 0.2, 0.3, 0.5, 1.1, 0.1}); @@ -102,6 +105,23 @@ TEST(FloatAddOpModel, VariousInputShapes) { } } +TEST(FloatAddOpModel, WithBroadcast) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + FloatAddOpModel m({TensorType_FLOAT32, test_shapes[i]}, + {TensorType_FLOAT32, {}}, // always a scalar + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); + m.PopulateTensor(m.input2(), {0.1}); + m.Invoke(); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-1.9, 0.3, 0.8, 0.9, 1.2, 2.1}))) + << "With shape number " << i; + } +} + TEST(QuantizedAddOpModel, QuantizedTestsNoActivation) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); std::vector> inputs1 = { @@ -112,6 +132,7 @@ TEST(QuantizedAddOpModel, QuantizedTestsNoActivation) { {0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.1, 0.8}}; for (int i = 0; i < inputs1.size(); ++i) { QuantizedAddOpModel m({TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, + {TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, {TensorType_UINT8, {}, -1.0, 1.0}, ActivationFunctionType_NONE); m.QuantizeAndPopulate(m.input1(), inputs1[i]); @@ -133,6 +154,7 @@ TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1) { {-0.2, 0.6, -0.1, 0.8}}; for (int i = 0; i < inputs1.size(); ++i) { QuantizedAddOpModel m({TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, + {TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, {TensorType_UINT8, {}, -1.0, 1.0}, ActivationFunctionType_RELU_N1_TO_1); m.QuantizeAndPopulate(m.input1(), inputs1[i]); @@ -150,6 +172,7 @@ TEST(QuantizedAddOpModel, QuantizedVariousInputShapes) { {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; for (int i = 0; i < test_shapes.size(); ++i) { QuantizedAddOpModel m({TensorType_UINT8, test_shapes[i], -3.0, 3.0}, + {TensorType_UINT8, test_shapes[i], -3.0, 3.0}, {TensorType_UINT8, {}, -3.0, 3.0}, ActivationFunctionType_NONE); m.QuantizeAndPopulate(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); @@ -162,6 +185,25 @@ TEST(QuantizedAddOpModel, QuantizedVariousInputShapes) { } } +TEST(QuantizedAddOpModel, QuantizedWithBroadcast) { + float kQuantizedTolerance = GetTolerance(-3.0, 3.0); + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + QuantizedAddOpModel m({TensorType_UINT8, test_shapes[i], -3.0, 3.0}, + {TensorType_UINT8, {}, -3.0, 3.0}, + {TensorType_UINT8, {}, -3.0, 3.0}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); + m.QuantizeAndPopulate(m.input2(), {0.1}); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({-1.9, 0.3, 0.8, 0.9, 1.2, 2.1}, + kQuantizedTolerance))) + << "With shape number " << i; + } +} + } // namespace } // namespace tflite int main(int argc, char** argv) { diff --git a/tensorflow/contrib/lite/kernels/mul.cc b/tensorflow/contrib/lite/kernels/mul.cc index 81c73f2523..54575019de 100644 --- a/tensorflow/contrib/lite/kernels/mul.cc +++ b/tensorflow/contrib/lite/kernels/mul.cc @@ -37,7 +37,23 @@ constexpr int kInputTensor1 = 0; constexpr int kInputTensor2 = 1; constexpr int kOutputTensor = 0; +struct OpData { + bool requires_broadcast; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* data = new OpData; + data->requires_broadcast = false; + return data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + OpData* data = reinterpret_cast(node->user_data); + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); @@ -45,43 +61,56 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TF_LITE_ENSURE_EQ(context, NumDimensions(input1), NumDimensions(input2)); - for (int i = 0; i < NumDimensions(input1); ++i) { - TF_LITE_ENSURE_EQ(context, SizeOfDimension(input1, i), - SizeOfDimension(input2, i)); - } + TF_LITE_ENSURE_EQ(context, input1->type, input2->type); + output->type = input2->type; + + data->requires_broadcast = !HaveSameShapes(input1, input2); - TF_LITE_ENSURE_EQ(context, input1->type, output->type); - TF_LITE_ENSURE_EQ(context, input2->type, output->type); + TfLiteIntArray* output_size = nullptr; + if (data->requires_broadcast) { + TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast( + context, input1, input2, &output_size)); + } else { + output_size = TfLiteIntArrayCopy(input1->dims); + } - TfLiteIntArray* output_size = TfLiteIntArrayCopy(input1->dims); return context->ResizeTensor(context, output, output_size); } template void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, TfLiteTensor* input1, - TfLiteTensor* input2, TfLiteTensor* output) { + TfLiteMulParams* params, const OpData* data, + TfLiteTensor* input1, TfLiteTensor* input2, + TfLiteTensor* output) { float output_activation_min, output_activation_max; CalculateActivationRangeFloat(params->activation, &output_activation_min, &output_activation_max); -#define TF_LITE_MUL(type) \ - type::Mul(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + output_activation_min, output_activation_max, \ + GetTensorData(output), GetTensorDims(output)) if (kernel_type == kReference) { - TF_LITE_MUL(reference_ops); + if (data->requires_broadcast) { + TF_LITE_MUL(reference_ops, BroadcastMul); + } else { + TF_LITE_MUL(reference_ops, Mul); + } } else { - TF_LITE_MUL(optimized_ops); + if (data->requires_broadcast) { + TF_LITE_MUL(optimized_ops, BroadcastMul); + } else { + TF_LITE_MUL(optimized_ops, Mul); + } } #undef TF_LITE_MUL } template void EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, TfLiteTensor* input1, - TfLiteTensor* input2, TfLiteTensor* output) { + TfLiteMulParams* params, const OpData* data, + TfLiteTensor* input1, TfLiteTensor* input2, + TfLiteTensor* output) { auto input1_offset = -input1->params.zero_point; auto input2_offset = -input2->params.zero_point; auto output_offset = output->params.zero_point; @@ -98,17 +127,19 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node, CalculateActivationRangeUint8(params->activation, output, &output_activation_min, &output_activation_max); -#define TF_LITE_MUL(type) \ - type::BroadcastMul(GetTensorData(input1), GetTensorDims(input1), \ - input1_offset, GetTensorData(input2), \ - GetTensorDims(input2), input2_offset, output_offset, \ - output_multiplier, output_shift, output_activation_min, \ - output_activation_max, GetTensorData(output), \ - GetTensorDims(output)); +#define TF_LITE_MUL(type, opname) \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + input1_offset, GetTensorData(input2), \ + GetTensorDims(input2), input2_offset, output_offset, \ + output_multiplier, output_shift, output_activation_min, \ + output_activation_max, GetTensorData(output), \ + GetTensorDims(output)); + // The quantized version of Mul doesn't support activations, so we + // always use BroadcastMul. if (kernel_type == kReference) { - TF_LITE_MUL(reference_ops); + TF_LITE_MUL(reference_ops, BroadcastMul); } else { - TF_LITE_MUL(optimized_ops); + TF_LITE_MUL(optimized_ops, BroadcastMul); } #undef TF_LITE_MUL } @@ -116,15 +147,17 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node, template TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { auto* params = reinterpret_cast(node->builtin_data); + OpData* data = reinterpret_cast(node->user_data); TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); if (output->type == kTfLiteFloat32) { - EvalFloat(context, node, params, input1, input2, output); + EvalFloat(context, node, params, data, input1, input2, output); } else if (output->type == kTfLiteUInt8) { - EvalQuantized(context, node, params, input1, input2, output); + EvalQuantized(context, node, params, data, input1, input2, + output); } else { context->ReportError(context, "Mul only supports FLOAT32 and quantized UINT8 now."); @@ -137,19 +170,19 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace mul TfLiteRegistration* Register_MUL_REF() { - static TfLiteRegistration r = {nullptr, nullptr, mul::Prepare, + static TfLiteRegistration r = {mul::Init, mul::Free, mul::Prepare, mul::Eval}; return &r; } TfLiteRegistration* Register_MUL_GENERIC_OPT() { - static TfLiteRegistration r = {nullptr, nullptr, mul::Prepare, + static TfLiteRegistration r = {mul::Init, mul::Free, mul::Prepare, mul::Eval}; return &r; } TfLiteRegistration* Register_MUL_NEON_OPT() { - static TfLiteRegistration r = {nullptr, nullptr, mul::Prepare, + static TfLiteRegistration r = {mul::Init, mul::Free, mul::Prepare, mul::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/mul_test.cc b/tensorflow/contrib/lite/kernels/mul_test.cc index 8838b300c0..f1a30f8263 100644 --- a/tensorflow/contrib/lite/kernels/mul_test.cc +++ b/tensorflow/contrib/lite/kernels/mul_test.cc @@ -25,10 +25,11 @@ using ::testing::ElementsAreArray; class BaseMulOpModel : public SingleOpModel { public: - BaseMulOpModel(TensorData input, TensorData output, + BaseMulOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, ActivationFunctionType activation_type) { - input1_ = AddInput(input); - input2_ = AddInput(input); + input1_ = AddInput(input1); + input2_ = AddInput(input2); output_ = AddOutput(output); SetBuiltinOp(BuiltinOperator_MUL, BuiltinOptions_MulOptions, CreateMulOptions(builder_, activation_type).Union()); @@ -70,6 +71,7 @@ class QuantizedMulOpModel : public BaseMulOpModel { TEST(FloatMulOpTest, NoActivation) { FloatMulOpModel 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}); @@ -79,9 +81,9 @@ TEST(FloatMulOpTest, NoActivation) { } TEST(FloatMulOpTest, ActivationRELU_N1_TO_1) { - FloatMulOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, - {TensorType_FLOAT32, {}}, - ActivationFunctionType_RELU_N1_TO_1); + FloatMulOpModel m( + {TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_RELU_N1_TO_1); m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8}); m.PopulateTensor(m.input2(), {0.1, 0.2, 0.3, 5}); m.Invoke(); @@ -94,6 +96,7 @@ TEST(FloatMulOpTest, VariousInputShapes) { {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; for (int i = 0; i < test_shapes.size(); ++i) { FloatMulOpModel m({TensorType_FLOAT32, test_shapes[i]}, + {TensorType_FLOAT32, test_shapes[i]}, {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); m.PopulateTensor(m.input2(), {0.1, 0.2, 0.3, 0.5, 1.1, 0.1}); @@ -105,8 +108,26 @@ TEST(FloatMulOpTest, VariousInputShapes) { } } +TEST(FloatMulOpTest, WithBroadcast) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + FloatMulOpModel m({TensorType_FLOAT32, test_shapes[i]}, + {TensorType_FLOAT32, {}}, // always a scalar + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); + m.PopulateTensor(m.input2(), {0.1}); + m.Invoke(); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-0.2, 0.02, 0.07, 0.08, 0.11, 0.2}))) + << "With shape number " << i; + } +} + TEST(QuantizedMulOpTest, NoActivation) { QuantizedMulOpModel m({TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, + {TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, {TensorType_UINT8, {}, -1.0, 1.0}, ActivationFunctionType_NONE); m.QuantizeAndPopulate(m.input1(), {-0.8, 0.2, 0.9, 0.7}); @@ -117,6 +138,32 @@ TEST(QuantizedMulOpTest, NoActivation) { kQuantizedTolerance))); } +// for quantized Mul, the error shouldn't exceed 2*step +float GetTolerance(int min, int max) { + float kQuantizedStep = (max - min) / 255.0; + float kQuantizedTolerance = 2.0 * kQuantizedStep; + return kQuantizedTolerance; +} + +TEST(QuantizedMulOpTest, WithBroadcast) { + float kQuantizedTolerance = GetTolerance(-3.0, 3.0); + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + QuantizedMulOpModel m({TensorType_UINT8, test_shapes[i], -3.0, 3.0}, + {TensorType_UINT8, {}, -3.0, 3.0}, // always a scalar + {TensorType_UINT8, {}, -3.0, 3.0}, + ActivationFunctionType_NONE); + m.QuantizeAndPopulate(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0}); + m.QuantizeAndPopulate(m.input2(), {0.1}); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + {-0.2, 0.02, 0.07, 0.08, 0.11, 0.2}, kQuantizedTolerance))) + << "With shape number " << i; + } +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index d73c9937ce..e8b425a592 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -47,9 +47,7 @@ tensorflow::Env* env = tensorflow::Env::Default(); // Key is a substring of the test name and value is a bug number. // TODO(ahentz): make sure we clean this list up frequently. std::map kBrokenTests = { - // Add doesn't support broadcasting. - {R"(^\/adda.*input_shape_1=\[1,3,4,3\],input_shape_2=\[3\])", "68500195"}, - {R"(^\/mula.*input_shape_1=\[1,3,4,3\],input_shape_2=\[3\])", "68500195"}, + // Sub and Div don't support broadcasting. {R"(^\/diva.*input_shape_1=\[1,3,4,3\],input_shape_2=\[3\])", "68500195"}, {R"(^\/suba.*input_shape_1=\[1,3,4,3\],input_shape_2=\[3\])", "68500195"}, -- GitLab From 5b39f0c75e15f33834a9e68c0b7654944a743476 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Tue, 30 Jan 2018 14:06:07 -0800 Subject: [PATCH 288/423] Remove unnecessary dependency from xla:types PiperOrigin-RevId: 183888495 --- tensorflow/compiler/xla/BUILD | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index c22fd37129..34e733bc8d 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -88,7 +88,6 @@ cc_library( visibility = [":friends"], deps = [ "//tensorflow/core:framework_lite", - "//tensorflow/core:lib", "//third_party/eigen3", ], ) -- GitLab From 8301d4b3c6c05f837122736b06d1ae1f20849b0a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 14:07:26 -0800 Subject: [PATCH 289/423] Add `tf.contrib.distributions.Kumaraswamy`. PiperOrigin-RevId: 183888726 --- .../python/kernel_tests/kumaraswamy_test.py | 388 ++++++++++++++++++ .../distributions/python/ops/kumaraswamy.py | 258 ++++++++++++ 2 files changed, 646 insertions(+) create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py create mode 100644 tensorflow/contrib/distributions/python/ops/kumaraswamy.py diff --git a/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py new file mode 100644 index 0000000000..ea3c86b5c0 --- /dev/null +++ b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py @@ -0,0 +1,388 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import importlib + +import numpy as np + +from tensorflow.contrib.distributions.python.ops import kumaraswamy as kumaraswamy_lib +from tensorflow.python.client import session +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import random_seed +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test +from tensorflow.python.platform import tf_logging + + +def try_import(name): # pylint: disable=invalid-name + module = None + try: + module = importlib.import_module(name) + except ImportError as e: + tf_logging.warning("Could not import %s: %s" % (name, str(e))) + return module + + +special = try_import("scipy.special") +stats = try_import("scipy.stats") + + +def _kumaraswamy_mode(a, b): + a = np.asarray(a) + b = np.asarray(b) + return ((a - 1) / (a * b - 1))**(1 / a) + + +def _kumaraswamy_moment(a, b, n): + a = np.asarray(a) + b = np.asarray(b) + return b * special.beta(1.0 + n / a, b) + + +def _harmonic_number(b): + b = np.asarray(b) + return special.psi(b + 1) - special.psi(1) + + +def _kumaraswamy_cdf(a, b, x): + a = np.asarray(a) + b = np.asarray(b) + x = np.asarray(x) + return 1 - (1 - x**a)**b + + +def _kumaraswamy_pdf(a, b, x): + a = np.asarray(a) + b = np.asarray(b) + x = np.asarray(x) + return a * b * x ** (a - 1) * (1 - x ** a) ** (b - 1) + + +class KumaraswamyTest(test.TestCase): + + def testSimpleShapes(self): + with self.test_session(): + a = np.random.rand(3) + b = np.random.rand(3) + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertAllEqual([], dist.event_shape_tensor().eval()) + self.assertAllEqual([3], dist.batch_shape_tensor().eval()) + self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape) + self.assertEqual(tensor_shape.TensorShape([3]), dist.batch_shape) + + def testComplexShapes(self): + with self.test_session(): + a = np.random.rand(3, 2, 2) + b = np.random.rand(3, 2, 2) + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertAllEqual([], dist.event_shape_tensor().eval()) + self.assertAllEqual([3, 2, 2], dist.batch_shape_tensor().eval()) + self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape) + self.assertEqual(tensor_shape.TensorShape([3, 2, 2]), dist.batch_shape) + + def testComplexShapesBroadcast(self): + with self.test_session(): + a = np.random.rand(3, 2, 2) + b = np.random.rand(2, 2) + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertAllEqual([], dist.event_shape_tensor().eval()) + self.assertAllEqual([3, 2, 2], dist.batch_shape_tensor().eval()) + self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape) + self.assertEqual(tensor_shape.TensorShape([3, 2, 2]), dist.batch_shape) + + def testAProperty(self): + a = [[1., 2, 3]] + b = [[2., 4, 3]] + with self.test_session(): + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertEqual([1, 3], dist.concentration1.get_shape()) + self.assertAllClose(a, dist.concentration1.eval()) + + def testBProperty(self): + a = [[1., 2, 3]] + b = [[2., 4, 3]] + with self.test_session(): + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertEqual([1, 3], dist.concentration0.get_shape()) + self.assertAllClose(b, dist.concentration0.eval()) + + def testPdfXProper(self): + a = [[1., 2, 3]] + b = [[2., 4, 3]] + with self.test_session(): + dist = kumaraswamy_lib.Kumaraswamy(a, b, validate_args=True) + dist.prob([.1, .3, .6]).eval() + dist.prob([.2, .3, .5]).eval() + # Either condition can trigger. + with self.assertRaisesOpError("sample must be positive"): + dist.prob([-1., 0.1, 0.5]).eval() + with self.assertRaisesOpError("sample must be positive"): + dist.prob([0., 0.1, 0.5]).eval() + with self.assertRaisesOpError("sample must be no larger than `1`"): + dist.prob([.1, .2, 1.2]).eval() + + def testPdfTwoBatches(self): + with self.test_session(): + a = [1., 2] + b = [1., 2] + x = [.5, .5] + dist = kumaraswamy_lib.Kumaraswamy(a, b) + pdf = dist.prob(x) + expected_pdf = _kumaraswamy_pdf(a, b, x) + self.assertAllClose(expected_pdf, pdf.eval()) + self.assertEqual((2,), pdf.get_shape()) + + def testPdfTwoBatchesNontrivialX(self): + with self.test_session(): + a = [1., 2] + b = [1., 2] + x = [.3, .7] + dist = kumaraswamy_lib.Kumaraswamy(a, b) + pdf = dist.prob(x) + expected_pdf = _kumaraswamy_pdf(a, b, x) + self.assertAllClose(expected_pdf, pdf.eval()) + self.assertEqual((2,), pdf.get_shape()) + + def testPdfUniformZeroBatch(self): + with self.test_session(): + # This is equivalent to a uniform distribution + a = 1. + b = 1. + x = np.array([.1, .2, .3, .5, .8], dtype=np.float32) + dist = kumaraswamy_lib.Kumaraswamy(a, b) + pdf = dist.prob(x) + expected_pdf = _kumaraswamy_pdf(a, b, x) + self.assertAllClose(expected_pdf, pdf.eval()) + self.assertEqual((5,), pdf.get_shape()) + + def testPdfAStretchedInBroadcastWhenSameRank(self): + with self.test_session(): + a = [[1., 2]] + b = [[1., 2]] + x = [[.5, .5], [.3, .7]] + dist = kumaraswamy_lib.Kumaraswamy(a, b) + pdf = dist.prob(x) + expected_pdf = _kumaraswamy_pdf(a, b, x) + self.assertAllClose(expected_pdf, pdf.eval()) + self.assertEqual((2, 2), pdf.get_shape()) + + def testPdfAStretchedInBroadcastWhenLowerRank(self): + with self.test_session(): + a = [1., 2] + b = [1., 2] + x = [[.5, .5], [.2, .8]] + pdf = kumaraswamy_lib.Kumaraswamy(a, b).prob(x) + expected_pdf = _kumaraswamy_pdf(a, b, x) + self.assertAllClose(expected_pdf, pdf.eval()) + self.assertEqual((2, 2), pdf.get_shape()) + + def testPdfXStretchedInBroadcastWhenSameRank(self): + with self.test_session(): + a = [[1., 2], [2., 3]] + b = [[1., 2], [2., 3]] + x = [[.5, .5]] + pdf = kumaraswamy_lib.Kumaraswamy(a, b).prob(x) + expected_pdf = _kumaraswamy_pdf(a, b, x) + self.assertAllClose(expected_pdf, pdf.eval()) + self.assertEqual((2, 2), pdf.get_shape()) + + def testPdfXStretchedInBroadcastWhenLowerRank(self): + with self.test_session(): + a = [[1., 2], [2., 3]] + b = [[1., 2], [2., 3]] + x = [.5, .5] + pdf = kumaraswamy_lib.Kumaraswamy(a, b).prob(x) + expected_pdf = _kumaraswamy_pdf(a, b, x) + self.assertAllClose(expected_pdf, pdf.eval()) + self.assertEqual((2, 2), pdf.get_shape()) + + def testKumaraswamyMean(self): + with session.Session(): + a = [1., 2, 3] + b = [2., 4, 1.2] + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertEqual(dist.mean().get_shape(), (3,)) + if not stats: + return + expected_mean = _kumaraswamy_moment(a, b, 1) + self.assertAllClose(expected_mean, dist.mean().eval()) + + def testKumaraswamyVariance(self): + with session.Session(): + a = [1., 2, 3] + b = [2., 4, 1.2] + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertEqual(dist.variance().get_shape(), (3,)) + if not stats: + return + expected_variance = _kumaraswamy_moment(a, b, 2) - _kumaraswamy_moment( + a, b, 1)**2 + self.assertAllClose(expected_variance, dist.variance().eval()) + + def testKumaraswamyMode(self): + with session.Session(): + a = np.array([1.1, 2, 3]) + b = np.array([2., 4, 1.2]) + expected_mode = _kumaraswamy_mode(a, b) + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertEqual(dist.mode().get_shape(), (3,)) + self.assertAllClose(expected_mode, dist.mode().eval()) + + def testKumaraswamyModeInvalid(self): + with session.Session(): + a = np.array([1., 2, 3]) + b = np.array([2., 4, 1.2]) + dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False) + with self.assertRaisesOpError("Condition x < y.*"): + dist.mode().eval() + + a = np.array([2., 2, 3]) + b = np.array([1., 4, 1.2]) + dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False) + with self.assertRaisesOpError("Condition x < y.*"): + dist.mode().eval() + + def testKumaraswamyModeEnableAllowNanStats(self): + with session.Session(): + a = np.array([1., 2, 3]) + b = np.array([2., 4, 1.2]) + dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=True) + + expected_mode = _kumaraswamy_mode(a, b) + expected_mode[0] = np.nan + self.assertEqual((3,), dist.mode().get_shape()) + self.assertAllClose(expected_mode, dist.mode().eval()) + + a = np.array([2., 2, 3]) + b = np.array([1., 4, 1.2]) + dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=True) + + expected_mode = _kumaraswamy_mode(a, b) + expected_mode[0] = np.nan + self.assertEqual((3,), dist.mode().get_shape()) + self.assertAllClose(expected_mode, dist.mode().eval()) + + def testKumaraswamyEntropy(self): + with session.Session(): + a = np.array([1., 2, 3]) + b = np.array([2., 4, 1.2]) + dist = kumaraswamy_lib.Kumaraswamy(a, b) + self.assertEqual(dist.entropy().get_shape(), (3,)) + if not stats: + return + expected_entropy = (1 - 1. / a) + ( + 1 - 1. / b) * _harmonic_number(b) + np.log(a * b) + self.assertAllClose(expected_entropy, dist.entropy().eval()) + + def testKumaraswamySample(self): + with self.test_session(): + a = 1. + b = 2. + kumaraswamy = kumaraswamy_lib.Kumaraswamy(a, b) + n = constant_op.constant(100000) + samples = kumaraswamy.sample(n) + sample_values = samples.eval() + self.assertEqual(sample_values.shape, (100000,)) + self.assertFalse(np.any(sample_values < 0.0)) + if not stats: + return + self.assertLess( + stats.kstest( + # Kumaraswamy is a univariate distribution. + sample_values, + lambda x: _kumaraswamy_cdf(1., 2., x))[0], + 0.01) + # The standard error of the sample mean is 1 / (sqrt(18 * n)) + expected_mean = _kumaraswamy_moment(a, b, 1) + self.assertAllClose(sample_values.mean(axis=0), expected_mean, atol=1e-2) + expected_variance = _kumaraswamy_moment(a, b, 2) - _kumaraswamy_moment( + a, b, 1)**2 + self.assertAllClose( + np.cov(sample_values, rowvar=0), expected_variance, atol=1e-1) + + # Test that sampling with the same seed twice gives the same results. + def testKumaraswamySampleMultipleTimes(self): + with self.test_session(): + a_val = 1. + b_val = 2. + n_val = 100 + + random_seed.set_random_seed(654321) + kumaraswamy1 = kumaraswamy_lib.Kumaraswamy( + concentration1=a_val, concentration0=b_val, name="kumaraswamy1") + samples1 = kumaraswamy1.sample(n_val, seed=123456).eval() + + random_seed.set_random_seed(654321) + kumaraswamy2 = kumaraswamy_lib.Kumaraswamy( + concentration1=a_val, concentration0=b_val, name="kumaraswamy2") + samples2 = kumaraswamy2.sample(n_val, seed=123456).eval() + + self.assertAllClose(samples1, samples2) + + def testKumaraswamySampleMultidimensional(self): + with self.test_session(): + a = np.random.rand(3, 2, 2).astype(np.float32) + b = np.random.rand(3, 2, 2).astype(np.float32) + kumaraswamy = kumaraswamy_lib.Kumaraswamy(a, b) + n = constant_op.constant(100000) + samples = kumaraswamy.sample(n) + sample_values = samples.eval() + self.assertEqual(sample_values.shape, (100000, 3, 2, 2)) + self.assertFalse(np.any(sample_values < 0.0)) + if not stats: + return + self.assertAllClose( + sample_values[:, 1, :].mean(axis=0), + _kumaraswamy_moment(a, b, 1)[1, :], + atol=1e-1) + + def testKumaraswamyCdf(self): + with self.test_session(): + shape = (30, 40, 50) + for dt in (np.float32, np.float64): + a = 10. * np.random.random(shape).astype(dt) + b = 10. * np.random.random(shape).astype(dt) + x = np.random.random(shape).astype(dt) + actual = kumaraswamy_lib.Kumaraswamy(a, b).cdf(x).eval() + self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x) + self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x) + if not stats: + return + self.assertAllClose( + _kumaraswamy_cdf(a, b, x), actual, rtol=1e-4, atol=0) + + def testKumaraswamyLogCdf(self): + with self.test_session(): + shape = (30, 40, 50) + for dt in (np.float32, np.float64): + a = 10. * np.random.random(shape).astype(dt) + b = 10. * np.random.random(shape).astype(dt) + x = np.random.random(shape).astype(dt) + actual = math_ops.exp(kumaraswamy_lib.Kumaraswamy(a, + b).log_cdf(x)).eval() + self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x) + self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x) + if not stats: + return + self.assertAllClose( + _kumaraswamy_cdf(a, b, x), actual, rtol=1e-4, atol=0) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py new file mode 100644 index 0000000000..74d5d8773c --- /dev/null +++ b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py @@ -0,0 +1,258 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""The Kumaraswamy distribution class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import special_math_ops +from tensorflow.python.ops.distributions import beta +from tensorflow.python.ops.distributions import distribution +from tensorflow.python.ops.distributions import util as distribution_util +from tensorflow.python.util.tf_export import tf_export + +__all__ = [ + "Kumaraswamy", +] + +_kumaraswamy_sample_note = """Note: `x` must have dtype `self.dtype` and be in +`[0, 1].` It must have a shape compatible with `self.batch_shape()`.""" + + +def _harmonic_number(x): + """Compute the harmonic number from its analytic continuation. + + Derivation from [1] and Euler's constant [2]. + [1] - + https://en.wikipedia.org/wiki/Digamma_function#Relation_to_harmonic_numbers + [2] - https://en.wikipedia.org/wiki/Euler%E2%80%93Mascheroni_constant + + + Args: + x: input float. + + Returns: + z: The analytic continuation of the harmonic number for the input. + + """ + one = array_ops.ones([], dtype=x.dtype) + return math_ops.digamma(x + one) - math_ops.digamma(one) + + +@tf_export("distributions.Kumaraswamy") +class Kumaraswamy(beta.Beta): + """Kumaraswamy distribution. + + The Kumaraswamy distribution is defined over the `(0, 1)` interval using + parameters + `concentration1` (aka "alpha") and `concentration0` (aka "beta"). It has a + shape similar to the Beta distribution, but is reparameterizeable. + + #### Mathematical Details + + The probability density function (pdf) is, + + ```none + pdf(x; alpha, beta) = alpha * beta * x**(alpha - 1) * (1 - x**alpha)**(beta - + 1) + ``` + + where: + + * `concentration1 = alpha`, + * `concentration0 = beta`, + + Distribution parameters are automatically broadcast in all functions; see + examples for details. + + #### Examples + + ```python + # Create a batch of three Kumaraswamy distributions. + alpha = [1, 2, 3] + beta = [1, 2, 3] + dist = Kumaraswamy(alpha, beta) + + dist.sample([4, 5]) # Shape [4, 5, 3] + + # `x` has three batch entries, each with two samples. + x = [[.1, .4, .5], + [.2, .3, .5]] + # Calculate the probability of each pair of samples under the corresponding + # distribution in `dist`. + dist.prob(x) # Shape [2, 3] + ``` + + ```python + # Create batch_shape=[2, 3] via parameter broadcast: + alpha = [[1.], [2]] # Shape [2, 1] + beta = [3., 4, 5] # Shape [3] + dist = Kumaraswamy(alpha, beta) + + # alpha broadcast as: [[1., 1, 1,], + # [2, 2, 2]] + # beta broadcast as: [[3., 4, 5], + # [3, 4, 5]] + # batch_Shape [2, 3] + dist.sample([4, 5]) # Shape [4, 5, 2, 3] + + x = [.2, .3, .5] + # x will be broadcast as [[.2, .3, .5], + # [.2, .3, .5]], + # thus matching batch_shape [2, 3]. + dist.prob(x) # Shape [2, 3] + ``` + + """ + + def __init__(self, + concentration1=None, + concentration0=None, + validate_args=False, + allow_nan_stats=True, + name="Kumaraswamy"): + """Initialize a batch of Kumaraswamy distributions. + + Args: + concentration1: Positive floating-point `Tensor` indicating mean + number of successes; aka "alpha". Implies `self.dtype` and + `self.batch_shape`, i.e., + `concentration1.shape = [N1, N2, ..., Nm] = self.batch_shape`. + concentration0: Positive floating-point `Tensor` indicating mean + number of failures; aka "beta". Otherwise has same semantics as + `concentration1`. + validate_args: Python `bool`, default `False`. When `True` distribution + parameters are checked for validity despite possibly degrading runtime + performance. When `False` invalid inputs may silently render incorrect + outputs. + allow_nan_stats: Python `bool`, default `True`. When `True`, statistics + (e.g., mean, mode, variance) use the value "`NaN`" to indicate the + result is undefined. When `False`, an exception is raised if one or + more of the statistic's batch members are undefined. + name: Python `str` name prefixed to Ops created by this class. + """ + super(Kumaraswamy, self).__init__( + concentration1=concentration1, + concentration0=concentration0, + validate_args=validate_args, + allow_nan_stats=allow_nan_stats, + name=name) + self._reparameterization_type = distribution.FULLY_REPARAMETERIZED + + def _sample_n(self, n, seed=None): + expanded_concentration1 = array_ops.ones_like( + self.total_concentration, dtype=self.dtype) * self.concentration1 + expanded_concentration0 = array_ops.ones_like( + self.total_concentration, dtype=self.dtype) * self.concentration0 + shape = array_ops.concat([[n], self.batch_shape_tensor()], 0) + uniform_sample = random_ops.random_uniform( + shape=shape, minval=0.0, maxval=1.0, dtype=self.dtype, seed=seed) + + kumaraswamy_sample = (1 - uniform_sample**(1. / expanded_concentration0))**( + 1. / expanded_concentration1) + return kumaraswamy_sample + + @distribution_util.AppendDocstring(_kumaraswamy_sample_note) + def _log_cdf(self, x): + a = self.concentration1 + b = self.concentration0 + return math_ops.log1p(-(1 - x**a)**b) + + @distribution_util.AppendDocstring(_kumaraswamy_sample_note) + def _cdf(self, x): + a = self.concentration1 + b = self.concentration0 + return 1 - (1 - x**a)**b + + def _survival_function(self, x): + a = self.concentration1 + b = self.concentration0 + return (1 - x**a)**b + + def _log_survival_function(self, x): + a = self.concentration1 + b = self.concentration0 + return b * math_ops.log1p(-x**a) + + def _log_unnormalized_prob(self, x): + x = self._maybe_assert_valid_sample(x) + a = self.concentration1 + b = self.concentration0 + return (a - 1) * math_ops.log(x) + (b - 1) * math_ops.log1p(-x**a) + + def _log_normalization(self): + a = self.concentration1 + b = self.concentration0 + return -(math_ops.log(a) + math_ops.log(b)) + + def _entropy(self): + a = self.concentration1 + b = self.concentration0 + return (1 - 1. / a) + ( + 1 - 1. / b) * _harmonic_number(b) + math_ops.log(a) + math_ops.log(b) + + def _moment(self, n): + """Compute the n'th (uncentered) moment.""" + expanded_concentration1 = array_ops.ones_like( + self.total_concentration, dtype=self.dtype) * self.concentration1 + expanded_concentration0 = array_ops.ones_like( + self.total_concentration, dtype=self.dtype) * self.concentration0 + beta_arg0 = 1 + n / expanded_concentration1 + beta_arg = array_ops.stack([beta_arg0, expanded_concentration0], -1) + log_moment = math_ops.log(expanded_concentration0) + special_math_ops.lbeta( + beta_arg) + return math_ops.exp(log_moment) + + def _mean(self): + return self._moment(1) + + def _variance(self): + # TODO(b/72696533): Investigate a more numerically stable version. + return self._moment(2) - math_ops.square(self._moment(1)) + + @distribution_util.AppendDocstring( + """Note: The mode is undefined when `concentration1 <= 1` or + `concentration0 <= 1`. If `self.allow_nan_stats` is `True`, `NaN` + is used for undefined modes. If `self.allow_nan_stats` is `False` an + exception is raised when one or more modes are undefined.""") + def _mode(self): + a = self.concentration1 + b = self.concentration0 + mode = ((a - 1) / (a * b - 1))**(1. / a) + if self.allow_nan_stats: + nan = array_ops.fill( + self.batch_shape_tensor(), + np.array(np.nan, dtype=self.dtype.as_numpy_dtype), + name="nan") + is_defined = (self.concentration1 > 1.) & (self.concentration0 > 1.) + return array_ops.where(is_defined, mode, nan) + return control_flow_ops.with_dependencies([ + check_ops.assert_less( + array_ops.ones([], dtype=self.dtype), + self.concentration1, + message="Mode undefined for concentration1 <= 1."), + check_ops.assert_less( + array_ops.ones([], dtype=self.dtype), + self.concentration0, + message="Mode undefined for concentration0 <= 1.") + ], mode) -- GitLab From 23b9ffc522353baa1c06245b96e4b5ecc955d359 Mon Sep 17 00:00:00 2001 From: David Majnemer Date: Tue, 30 Jan 2018 14:28:06 -0800 Subject: [PATCH 290/423] [XLA] Add a test for scalar kRoundNearestAfz PiperOrigin-RevId: 183892483 --- tensorflow/compiler/xla/tests/scalar_computations_test.cc | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index debf2d2d31..43e4d891a1 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -852,5 +852,12 @@ XLA_TEST_F(ScalarComputationsTest, SqrtF320) { ComputeAndCompareR0(&builder, 0.0f, {zero_data.get()}, error_spec_); } +XLA_TEST_F(ScalarComputationsTest, RoundScalar) { + ComputationBuilder builder(client_, TestName()); + builder.Round(builder.ConstantR0(1.4f)); + + ComputeAndCompareR0(&builder, 1.0f, {}, error_spec_); +} + } // namespace } // namespace xla -- GitLab From 58f0be0179a3adf7a47db95a1be57af53c3ee601 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 14:47:11 -0800 Subject: [PATCH 291/423] Expose the decorator in the main API. Move the top-level implementation files into a submodule, to avoid problems around the remove_undocumented call. PiperOrigin-RevId: 183896273 --- tensorflow/BUILD | 1 + tensorflow/contrib/py2tf/BUILD | 59 +---------------- tensorflow/contrib/py2tf/__init__.py | 9 +-- tensorflow/contrib/py2tf/impl/BUILD | 65 +++++++++++++++++++ tensorflow/contrib/py2tf/{ => impl}/api.py | 4 +- .../contrib/py2tf/{ => impl}/api_test.py | 4 +- tensorflow/contrib/py2tf/{ => impl}/config.py | 3 +- .../contrib/py2tf/{ => impl}/conversion.py | 4 +- .../py2tf/{ => impl}/conversion_test.py | 2 +- tensorflow/contrib/py2tf/{ => impl}/naming.py | 0 .../contrib/py2tf/{ => impl}/naming_test.py | 2 +- tensorflow/tools/pip_package/BUILD | 3 +- .../tools/pip_package/pip_smoke_test.py | 1 - 13 files changed, 84 insertions(+), 73 deletions(-) create mode 100644 tensorflow/contrib/py2tf/impl/BUILD rename tensorflow/contrib/py2tf/{ => impl}/api.py (98%) rename tensorflow/contrib/py2tf/{ => impl}/api_test.py (98%) rename tensorflow/contrib/py2tf/{ => impl}/config.py (89%) rename tensorflow/contrib/py2tf/{ => impl}/conversion.py (99%) rename tensorflow/contrib/py2tf/{ => impl}/conversion_test.py (97%) rename tensorflow/contrib/py2tf/{ => impl}/naming.py (100%) rename tensorflow/contrib/py2tf/{ => impl}/naming_test.py (98%) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 3d2411a266..66a2ecd8b5 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -536,6 +536,7 @@ filegroup( "//tensorflow/contrib/predictor:all_files", "//tensorflow/contrib/py2tf:all_files", "//tensorflow/contrib/py2tf/converters:all_files", + "//tensorflow/contrib/py2tf/impl:all_files", "//tensorflow/contrib/py2tf/pyct:all_files", "//tensorflow/contrib/py2tf/pyct/static_analysis:all_files", "//tensorflow/contrib/quantize:all_files", diff --git a/tensorflow/contrib/py2tf/BUILD b/tensorflow/contrib/py2tf/BUILD index 3e846aefeb..cea3738499 100644 --- a/tensorflow/contrib/py2tf/BUILD +++ b/tensorflow/contrib/py2tf/BUILD @@ -18,69 +18,12 @@ py_library( name = "py2tf", srcs = [ "__init__.py", - "api.py", - "config.py", - "conversion.py", - "naming.py", ], srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ - "//tensorflow/contrib/py2tf/converters", - "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", + "//tensorflow/contrib/py2tf/impl", "@gast_archive//:gast", "@six_archive//:six", ], ) - -# Separate target that allows access to internal symbols for testing. -py_library( - name = "py2tf_internal", - srcs = [ - "api.py", - "config.py", - "conversion.py", - "naming.py", - ], - srcs_version = "PY2AND3", - visibility = ["//tensorflow:__subpackages__"], - deps = [ - "//tensorflow/contrib/py2tf/converters", - "//tensorflow/contrib/py2tf/pyct", - "//tensorflow/contrib/py2tf/pyct/static_analysis", - "@gast_archive//:gast", - "@six_archive//:six", - ], -) - -py_test( - name = "api_test", - srcs = ["api_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":py2tf_internal", - "//tensorflow/python:client_testlib", - ], -) - -py_test( - name = "conversion_test", - srcs = ["conversion_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":py2tf_internal", - "//tensorflow/python:client_testlib", - "@gast_archive//:gast", - ], -) - -py_test( - name = "naming_test", - srcs = ["naming_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":py2tf_internal", - "//tensorflow/python:client_testlib", - ], -) diff --git a/tensorflow/contrib/py2tf/__init__.py b/tensorflow/contrib/py2tf/__init__.py index d187da99e0..878941b3a3 100644 --- a/tensorflow/contrib/py2tf/__init__.py +++ b/tensorflow/contrib/py2tf/__init__.py @@ -21,11 +21,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf.api import to_code -from tensorflow.contrib.py2tf.api import to_graph +from tensorflow.contrib.py2tf.impl.api import convert +from tensorflow.contrib.py2tf.impl.api import graph_ready +from tensorflow.contrib.py2tf.impl.api import to_code +from tensorflow.contrib.py2tf.impl.api import to_graph from tensorflow.python.util.all_util import remove_undocumented - -_allowed_symbols = ['to_graph', 'to_code'] +_allowed_symbols = ['to_graph', 'to_code', 'convert', 'graph_ready'] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/py2tf/impl/BUILD b/tensorflow/contrib/py2tf/impl/BUILD new file mode 100644 index 0000000000..22f0c25cab --- /dev/null +++ b/tensorflow/contrib/py2tf/impl/BUILD @@ -0,0 +1,65 @@ +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "impl", + srcs = [ + "api.py", + "config.py", + "conversion.py", + "naming.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + "//tensorflow/contrib/py2tf/converters", + "//tensorflow/contrib/py2tf/pyct", + "//tensorflow/contrib/py2tf/pyct/static_analysis", + "@gast_archive//:gast", + "@six_archive//:six", + ], +) + +py_test( + name = "api_test", + srcs = ["api_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":impl", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "conversion_test", + srcs = ["conversion_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":impl", + "//tensorflow/python:client_testlib", + "@gast_archive//:gast", + ], +) + +py_test( + name = "naming_test", + srcs = ["naming_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":impl", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/py2tf/api.py b/tensorflow/contrib/py2tf/impl/api.py similarity index 98% rename from tensorflow/contrib/py2tf/api.py rename to tensorflow/contrib/py2tf/impl/api.py index 1f250d5f57..8ff6618912 100644 --- a/tensorflow/contrib/py2tf/api.py +++ b/tensorflow/contrib/py2tf/impl/api.py @@ -23,8 +23,8 @@ from functools import wraps import gast import six -from tensorflow.contrib.py2tf import config -from tensorflow.contrib.py2tf import conversion +from tensorflow.contrib.py2tf.impl import config +from tensorflow.contrib.py2tf.impl import conversion from tensorflow.contrib.py2tf.pyct import compiler from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.util import tf_inspect diff --git a/tensorflow/contrib/py2tf/api_test.py b/tensorflow/contrib/py2tf/impl/api_test.py similarity index 98% rename from tensorflow/contrib/py2tf/api_test.py rename to tensorflow/contrib/py2tf/impl/api_test.py index 2384447708..dbd079a3ca 100644 --- a/tensorflow/contrib/py2tf/api_test.py +++ b/tensorflow/contrib/py2tf/impl/api_test.py @@ -18,8 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf import api -from tensorflow.contrib.py2tf import config +from tensorflow.contrib.py2tf.impl import api +from tensorflow.contrib.py2tf.impl import config from tensorflow.contrib.py2tf.pyct import parser from tensorflow.python.framework import constant_op from tensorflow.python.ops import math_ops diff --git a/tensorflow/contrib/py2tf/config.py b/tensorflow/contrib/py2tf/impl/config.py similarity index 89% rename from tensorflow/contrib/py2tf/config.py rename to tensorflow/contrib/py2tf/impl/config.py index 8c502a7a9e..0892241983 100644 --- a/tensorflow/contrib/py2tf/config.py +++ b/tensorflow/contrib/py2tf/impl/config.py @@ -32,7 +32,8 @@ DEFAULT_UNCOMPILED_MODULES = set(( NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',)) # TODO(mdan): Also allow controlling the generated names (for testability). +# TODO(mdan): Verify that these names are not hidden by generated code. +# TODO(mdan): Make sure copybara renames the reference below. COMPILED_IMPORT_STATEMENTS = ( - 'from contextlib import contextmanager', 'import tensorflow as tf', ) diff --git a/tensorflow/contrib/py2tf/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py similarity index 99% rename from tensorflow/contrib/py2tf/conversion.py rename to tensorflow/contrib/py2tf/impl/conversion.py index 67ca52d194..ed71ff5c06 100644 --- a/tensorflow/contrib/py2tf/conversion.py +++ b/tensorflow/contrib/py2tf/impl/conversion.py @@ -21,8 +21,6 @@ from __future__ import print_function import gast import six -from tensorflow.contrib.py2tf import config -from tensorflow.contrib.py2tf import naming from tensorflow.contrib.py2tf.converters import asserts from tensorflow.contrib.py2tf.converters import break_canonicalization from tensorflow.contrib.py2tf.converters import builtin_functions @@ -34,6 +32,8 @@ from tensorflow.contrib.py2tf.converters import for_canonicalization from tensorflow.contrib.py2tf.converters import logical_expressions from tensorflow.contrib.py2tf.converters import print_functions from tensorflow.contrib.py2tf.converters import side_effect_guards +from tensorflow.contrib.py2tf.impl import config +from tensorflow.contrib.py2tf.impl import naming from tensorflow.contrib.py2tf.pyct import context from tensorflow.contrib.py2tf.pyct import parser from tensorflow.contrib.py2tf.pyct.static_analysis import access diff --git a/tensorflow/contrib/py2tf/conversion_test.py b/tensorflow/contrib/py2tf/impl/conversion_test.py similarity index 97% rename from tensorflow/contrib/py2tf/conversion_test.py rename to tensorflow/contrib/py2tf/impl/conversion_test.py index 26f915f4f4..3888958f19 100644 --- a/tensorflow/contrib/py2tf/conversion_test.py +++ b/tensorflow/contrib/py2tf/impl/conversion_test.py @@ -20,7 +20,7 @@ from __future__ import print_function import gast -from tensorflow.contrib.py2tf import conversion +from tensorflow.contrib.py2tf.impl import conversion from tensorflow.python.platform import test diff --git a/tensorflow/contrib/py2tf/naming.py b/tensorflow/contrib/py2tf/impl/naming.py similarity index 100% rename from tensorflow/contrib/py2tf/naming.py rename to tensorflow/contrib/py2tf/impl/naming.py diff --git a/tensorflow/contrib/py2tf/naming_test.py b/tensorflow/contrib/py2tf/impl/naming_test.py similarity index 98% rename from tensorflow/contrib/py2tf/naming_test.py rename to tensorflow/contrib/py2tf/impl/naming_test.py index 5cf0a3da2c..beb4e54937 100644 --- a/tensorflow/contrib/py2tf/naming_test.py +++ b/tensorflow/contrib/py2tf/impl/naming_test.py @@ -18,7 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.py2tf import naming +from tensorflow.contrib.py2tf.impl import naming from tensorflow.python.platform import test diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 598080ed27..e4fa6694d8 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -151,9 +151,10 @@ sh_binary( "//tensorflow/contrib/ndlstm:ndlstm", "//tensorflow/contrib/nn:nn_py", "//tensorflow/contrib/predictor:predictor_pip", - "//tensorflow/contrib/py2tf:py2tf_internal", + "//tensorflow/contrib/py2tf:py2tf", "//tensorflow/contrib/py2tf/converters:converters", "//tensorflow/contrib/py2tf/converters:test_lib", + "//tensorflow/contrib/py2tf/impl:impl", "//tensorflow/contrib/py2tf/pyct:pyct", "//tensorflow/contrib/py2tf/pyct/static_analysis:static_analysis", "//tensorflow/contrib/receptive_field:receptive_field_pip", diff --git a/tensorflow/tools/pip_package/pip_smoke_test.py b/tensorflow/tools/pip_package/pip_smoke_test.py index 38a9007387..73d759eb13 100644 --- a/tensorflow/tools/pip_package/pip_smoke_test.py +++ b/tensorflow/tools/pip_package/pip_smoke_test.py @@ -65,7 +65,6 @@ BLACKLIST = [ "//tensorflow/contrib/framework:checkpoint_ops_testdata", "//tensorflow/contrib/bayesflow:reinforce_simple_example", "//tensorflow/contrib/bayesflow:examples/reinforce_simple/reinforce_simple_example.py", # pylint:disable=line-too-long - "//tensorflow/contrib/py2tf:py2tf_internal", "//tensorflow/contrib/timeseries/examples:predict", "//tensorflow/contrib/timeseries/examples:multivariate", "//tensorflow/contrib/timeseries/examples:known_anomaly", -- GitLab From 0181aa321c8656cb574506476ca5f749061c0e87 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Tue, 30 Jan 2018 15:00:10 -0800 Subject: [PATCH 292/423] Assign*VariableOp: Documentation fix. These ops have no outputs. The way to ensure ordering is by adding dependencies via control inputs. Helps with #16464 PiperOrigin-RevId: 183898409 --- .../api_def/base_api/api_def_AssignAddVariableOp.pbtxt | 7 ++----- .../api_def/base_api/api_def_AssignSubVariableOp.pbtxt | 7 ++----- 2 files changed, 4 insertions(+), 10 deletions(-) diff --git a/tensorflow/core/api_def/base_api/api_def_AssignAddVariableOp.pbtxt b/tensorflow/core/api_def/base_api/api_def_AssignAddVariableOp.pbtxt index 5d21d7bab6..ac05b54eea 100644 --- a/tensorflow/core/api_def/base_api/api_def_AssignAddVariableOp.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_AssignAddVariableOp.pbtxt @@ -20,10 +20,7 @@ END } summary: "Adds a value to the current value of a variable." description: < Date: Tue, 30 Jan 2018 15:04:14 -0800 Subject: [PATCH 293/423] Optimize memory allocation of TFLite StridedSlice Op for constant tensors. PiperOrigin-RevId: 183899156 --- .../contrib/lite/kernels/strided_slice.cc | 183 +++++++++--------- .../contrib/lite/testing/generate_examples.py | 72 ++++--- .../propagate_fixed_sizes.cc | 3 +- 3 files changed, 133 insertions(+), 125 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/strided_slice.cc b/tensorflow/contrib/lite/kernels/strided_slice.cc index c510ee3b9f..c4ffdf79d3 100644 --- a/tensorflow/contrib/lite/kernels/strided_slice.cc +++ b/tensorflow/contrib/lite/kernels/strided_slice.cc @@ -57,63 +57,6 @@ struct StridedSliceContext { int dims; }; -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TF_LITE_ENSURE_EQ(context, NumInputs(node), 4); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - StridedSliceContext op_context(context, node); - - // Ensure validity of input tensor and its dimension - TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.begin), 1); - TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.end), 1); - TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.strides), 1); - TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); - // Only INT32 begin/end/strides are supported - // TODO(soroosh) add support for INT64 - TF_LITE_ENSURE_EQ(context, op_context.begin->type, kTfLiteInt32); - TF_LITE_ENSURE_EQ(context, op_context.end->type, kTfLiteInt32); - TF_LITE_ENSURE_EQ(context, op_context.strides->type, kTfLiteInt32); - TF_LITE_ENSURE_MSG(context, op_context.dims <= 4, - "StridedSlice op only supports 1D-4D input arrays."); - - // TODO(soroosh): add the following missing functionalities - TF_LITE_ENSURE_MSG(context, op_context.params->ellipsis_mask == 0, - "ellipsis_mask is not implemented yet."); - TF_LITE_ENSURE_MSG(context, op_context.params->new_axis_mask == 0, - "new_axis_mask is not implemented yet."); - - // TODO(soroosh): optimize for constant tensors to do allocation in Prepare - op_context.output->allocation_type = kTfLiteDynamic; - return kTfLiteOk; -} // namespace strided_slice - -// TODO(soroosh): consolidate with BytesRequired in interpreter.h -TfLiteStatus BytesRequired(TfLiteContext* context, TfLiteType type, - const int* dims, int dims_size, size_t* bytes) { - // TODO(aselle): Check for overflow here using overflow.h in TensorFlow - // MultiplyWithoutOverflow. - TF_LITE_ENSURE(context, bytes != nullptr); - size_t count = 1; - for (int k = 0; k < dims_size; k++) count *= dims[k]; - switch (type) { - case kTfLiteFloat32: - *bytes = sizeof(float) * count; - break; - case kTfLiteInt32: - *bytes = sizeof(int32_t) * count; - break; - case kTfLiteUInt8: - *bytes = sizeof(uint8_t) * count; - break; - case kTfLiteInt64: - *bytes = sizeof(int64_t) * count; - break; - default: - return kTfLiteError; - } - return kTfLiteOk; -} - // Reverse order of bits in the mask to match the expected order in kernel inline int ReverseMaskBits(int mask, int num_dimensions) { int out = 0; @@ -144,43 +87,44 @@ inline int32_t ClampedIndex(int32_t index, int dim, bool pos_stride) { std::min(std::max(index, -dim), dim - 1), dim)); } -template -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - StridedSliceContext op_context(context, node); +inline int32_t GetBeginValueAtIndex(StridedSliceContext* op_context, int idx) { + const int dim = op_context->input->dims->data[idx]; + const bool pos_stride = GetTensorData(op_context->strides)[idx] > 0; + return op_context->params->begin_mask & (1 << idx) + ? pos_stride ? 0 : dim - 1 + : ClampedIndex(GetTensorData(op_context->begin)[idx], dim, + pos_stride); +} - std::vector starts; - std::vector stops; - std::vector strides; +inline int32_t GetEndValueAtIndex(StridedSliceContext* op_context, int idx) { + const int dim = op_context->input->dims->data[idx]; + const bool pos_stride = GetTensorData(op_context->strides)[idx] > 0; + return op_context->params->end_mask & (1 << idx) + ? pos_stride ? dim : -1 + : ClampedIndex(GetTensorData(op_context->end)[idx], dim, + pos_stride); +} + +// Processes the indexing tensors (begin, end and strides) to resize the +// output tensor. This function is callable from both Prepare() and Eval() as +// long as the caller ensures the indexing tensors are present. +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + StridedSliceContext* op_context) { std::vector output_shape_vector; - for (int idx = op_context.dims - 1; idx >= 0; --idx) { - int dim = op_context.input->dims->data[idx]; - int32_t stride = GetTensorData(op_context.strides)[idx]; + for (int idx = op_context->dims - 1; idx >= 0; --idx) { + int32_t stride = GetTensorData(op_context->strides)[idx]; TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero"); - bool pos_stride = stride > 0; - - int32_t begin = - op_context.params->begin_mask & (1 << idx) - ? pos_stride ? 0 : dim - 1 - : ClampedIndex(GetTensorData(op_context.begin)[idx], dim, - pos_stride); - int32_t end = - op_context.params->end_mask & (1 << idx) - ? pos_stride ? dim : -1 - : ClampedIndex(GetTensorData(op_context.end)[idx], dim, - pos_stride); + + int32_t begin = GetBeginValueAtIndex(op_context, idx); + int32_t end = GetEndValueAtIndex(op_context, idx); // This is valid for both positive and negative strides int32_t dim_shape = ceil((end - begin) / static_cast(stride)); dim_shape = dim_shape < 0 ? 0 : dim_shape; - - if (!(op_context.params->shrink_axis_mask & (1 << idx))) { + if (!(op_context->params->shrink_axis_mask & (1 << idx))) { output_shape_vector.push_back(dim_shape); } - - starts.emplace_back(begin); - stops.emplace_back(end); - strides.emplace_back(stride); } TfLiteIntArray* output_shape = @@ -189,22 +133,73 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { std::reverse_copy(output_shape_vector.begin(), output_shape_vector.end(), output_shape->data); + TF_LITE_ENSURE_STATUS( + context->ResizeTensor(context, op_context->output, output_shape)); + + return kTfLiteOk; +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 4); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + StridedSliceContext op_context(context, node); + + // Ensure validity of input tensor and its dimension + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.begin), 1); + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.end), 1); + TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.strides), 1); + TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type); + // Only INT32 begin/end/strides are supported + // TODO(soroosh) add support for INT64 + TF_LITE_ENSURE_EQ(context, op_context.begin->type, kTfLiteInt32); + TF_LITE_ENSURE_EQ(context, op_context.end->type, kTfLiteInt32); + TF_LITE_ENSURE_EQ(context, op_context.strides->type, kTfLiteInt32); + TF_LITE_ENSURE_MSG(context, op_context.dims <= 4, + "StridedSlice op only supports 1D-4D input arrays."); + + // TODO(soroosh): add the following missing functionalities + TF_LITE_ENSURE_MSG(context, op_context.params->ellipsis_mask == 0, + "ellipsis_mask is not implemented yet."); + TF_LITE_ENSURE_MSG(context, op_context.params->new_axis_mask == 0, + "new_axis_mask is not implemented yet."); + + // Postpone allocation of output if any of the indexing tensors is not + // constant + if (!(IsConstantTensor(op_context.begin) && + IsConstantTensor(op_context.end) && + IsConstantTensor(op_context.strides))) { + SetTensorToDynamic(op_context.output); + return kTfLiteOk; + } + return ResizeOutputTensor(context, &op_context); +} + +template +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + StridedSliceContext op_context(context, node); + + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + TfLiteTensorRealloc(op_context.output->bytes, op_context.output); + } + + std::vector starts; + std::vector stops; + std::vector strides; + + for (int idx = op_context.dims - 1; idx >= 0; --idx) { + starts.emplace_back(GetBeginValueAtIndex(&op_context, idx)); + stops.emplace_back(GetEndValueAtIndex(&op_context, idx)); + strides.emplace_back(GetTensorData(op_context.strides)[idx]); + } + for (int i = op_context.dims; i < kMaxDim; i++) { starts.emplace_back(0); stops.emplace_back(1); strides.emplace_back(1); } - TF_LITE_ENSURE_STATUS( - context->ResizeTensor(context, op_context.output, output_shape)); - - size_t required_bytes; - TF_LITE_ENSURE_OK( - context, - BytesRequired(context, op_context.output->type, output_shape->data, - output_shape->size, &required_bytes)); - TfLiteTensorRealloc(required_bytes, op_context.output); - op_context.params->begin_mask = ReverseMaskBits(op_context.params->begin_mask, op_context.dims); op_context.params->end_mask = diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index e7606eecc4..b2227a7c98 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -1560,10 +1560,11 @@ def make_strided_slice_tests(zip_path): "input_shape": [[12, 2, 2, 5]], "begin": [[0, 0, 0, 0], [1, 0, 1, 0]], "end": [[8, 2, 2, 3], [12, 2, 2, 5]], - "strides": [None, [1, 1, 1, 1], [2, 1, 3, 1]], - "begin_mask": [None, 1, 2, 8], - "end_mask": [None, 1, 2, 8], - "shrink_axis_mask": [None, 1, 2, 4, 8, 11, 15, -1], + "strides": [None, [2, 1, 3, 1]], + "begin_mask": [None, 1, 8], + "end_mask": [None, 1, 8], + "shrink_axis_mask": [None, 1, 8, 11, 15, -1], + "constant_indices": [False, True], }, # 2-D { @@ -1572,10 +1573,11 @@ def make_strided_slice_tests(zip_path): "input_shape": [[2, 3]], "begin": [[0, 0], [1, 0]], "end": [[2, 3], [2, 2]], - "strides": [None, [1, 1], [2, 2]], + "strides": [None, [2, 2]], "begin_mask": [None, 1, 2], "end_mask": [None, 1, 2], "shrink_axis_mask": [None, 1, 2, 3, -1], + "constant_indices": [False, True], }, # Negative strides { @@ -1588,6 +1590,7 @@ def make_strided_slice_tests(zip_path): "begin_mask": [None, 1, 2], "end_mask": [None, 1, 2], "shrink_axis_mask": [None, 1, 2, 3, -1], + "constant_indices": [False], }, ] @@ -1597,23 +1600,29 @@ def make_strided_slice_tests(zip_path): dtype=parameters["dtype"], name="input", shape=parameters["input_shape"]) - begin = tf.placeholder( - dtype=parameters["index_type"], - name="begin", - shape=[len(parameters["input_shape"])]) - end = tf.placeholder( - dtype=parameters["index_type"], - name="end", - shape=[len(parameters["input_shape"])]) - strides = ( - tf.placeholder( - dtype=parameters["index_type"], - name="strides", - shape=[len(parameters["input_shape"])]) - if parameters["strides"] is not None else None) - tensors = [input_tensor, begin, end] - if strides is not None: - tensors.append(strides) + if parameters["constant_indices"]: + begin = parameters["begin"] + end = parameters["end"] + strides = parameters["strides"] + tensors = [input_tensor] + else: + begin = tf.placeholder( + dtype=parameters["index_type"], + name="begin", + shape=[len(parameters["input_shape"])]) + end = tf.placeholder( + dtype=parameters["index_type"], + name="end", + shape=[len(parameters["input_shape"])]) + strides = ( + tf.placeholder( + dtype=parameters["index_type"], + name="strides", + shape=[len(parameters["input_shape"])]) + if parameters["strides"] is not None else None) + tensors = [input_tensor, begin, end] + if strides is not None: + tensors.append(strides) out = tf.strided_slice( input_tensor, begin, @@ -1628,14 +1637,17 @@ def make_strided_slice_tests(zip_path): input_values = create_tensor_data(parameters["dtype"], parameters["input_shape"]) index_type = _TF_TYPE_INFO[parameters["index_type"]][0] - begin_values = np.array(parameters["begin"]).astype(index_type) - end_values = np.array(parameters["end"]).astype(index_type) - stride_values = ( - np.array(parameters["strides"]).astype(index_type) - if parameters["strides"] is not None else None) - values = [input_values, begin_values, end_values] - if stride_values is not None: - values.append(stride_values) + values = [input_values] + if not parameters["constant_indices"]: + begin_values = np.array(parameters["begin"]).astype(index_type) + end_values = np.array(parameters["end"]).astype(index_type) + stride_values = ( + np.array(parameters["strides"]).astype(index_type) + if parameters["strides"] is not None else None) + values.append(begin_values) + values.append(end_values) + if stride_values is not None: + values.append(stride_values) return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) 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 4fb3b6ae7a..7f26884bc1 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -1120,7 +1120,8 @@ void ProcessStridedSliceOperator(Model* model, StridedSliceOperator* op) { stop += input_array.shape().dims(i); } - int dim_size = (stop - start) / op->strides[i]; + int dim_size = ceil((stop - start) / static_cast(op->strides[i])); + dim_size = dim_size < 0 ? 0 : dim_size; if (op->shrink_axis_mask & mask) { CHECK_EQ(dim_size, 1) << "Output size for an axis must compute to 1 when " "shrinking that axis"; -- GitLab From b0ac7c553db40d42e699baa86442289b1314a2e1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 15:05:49 -0800 Subject: [PATCH 294/423] Always evaluate() on TPU in TPUEstimator unless use_tpu is set to False. PiperOrigin-RevId: 183899481 --- .../contrib/tpu/python/tpu/tpu_config.py | 3 +- .../contrib/tpu/python/tpu/tpu_estimator.py | 70 ++++++++----------- 2 files changed, 33 insertions(+), 40 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py index 0c2580211a..188db6e2f0 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_config.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py @@ -53,7 +53,8 @@ class TPUConfig( num_shards: The number of TPU shards in the system. per_host_input_for_training: If `True`, `input_fn` is invoked Per-Host rather than Per-Core. With Per-Host input pipeline deployment, `input_fn` - is invoked once on each host. To be precise, with a global batch size + is invoked once on each host. With Per-Core input pipeline deployment, it + is invoked once for each core. To be precise, with a global batch size `train_batch_size` in `TPUEstimator` constructor, the batch size for each shard is `train_batch_size` // #hosts. With Per-Core input pipeline deployment, the shard batch size is `train_batch_size` // #cores. diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 9d715bb236..bc55dbcb50 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -215,16 +215,11 @@ class _TPUContext(object): def is_running_on_cpu(self): """Determines whether the input_fn and model_fn should be invoked on CPU.""" mode = self._assert_mode() - return ((not self._use_tpu) or mode == model_fn_lib.ModeKeys.PREDICT or - (mode == model_fn_lib.ModeKeys.EVAL and - self._eval_batch_size is None)) + return (not self._use_tpu) or mode == model_fn_lib.ModeKeys.PREDICT @property def global_batch_size(self): mode = self._assert_mode() - if mode == model_fn_lib.ModeKeys.EVAL and self._eval_batch_size is None: - raise RuntimeError('Internal error, EVAL on TPU is not enabled, but ' - '`global_batch_size` is called.') return (self._train_batch_size if mode == model_fn_lib.ModeKeys.TRAIN else self._eval_batch_size) @@ -232,9 +227,6 @@ class _TPUContext(object): def batch_size_for_input_fn(self): """Returns the shard batch size for `input_fn`.""" mode = self._assert_mode() - # Special case for eval. - if mode == model_fn_lib.ModeKeys.EVAL and self._eval_batch_size is None: - return None if self.is_running_on_cpu(): if mode == model_fn_lib.ModeKeys.TRAIN: return self._train_batch_size @@ -255,9 +247,6 @@ class _TPUContext(object): def batch_size_for_model_fn(self): """Returns the shard batch size for `model_fn`.""" mode = self._assert_mode() - # Special case for eval. - if mode == model_fn_lib.ModeKeys.EVAL and self._eval_batch_size is None: - return None if self.is_running_on_cpu(): if mode == model_fn_lib.ModeKeys.TRAIN: return self._train_batch_size @@ -1464,30 +1453,28 @@ class TPUEstimator(estimator_lib.Estimator): replicating inputs and models for each core, and returning to host periodically to run hooks. - If `use_tpu` is false, all training, evaluation, and predict are executed on - CPU. - - For training, TPUEstimator transforms a global batch size in params to a - per-shard batch size when calling the `input_fn` and `model_fn`. Users should - specify `train_batch_size` in constructor, and then get the batch size for - each shard in `input_fn` and `model_fn` by `params['batch_size']`. If - `TPUConfig.per_host_input_for_training` is `True`, `input_fn` is invoked per - host rather than per core. In this case, a global batch size is transformed a - per-host batch size in params for `input_fn`, but `model_fn` still gets - per-core batch size. - - For evaluation, if `eval_batch_size` is None, it is executed on CPU, even if - `use_tpu` is `True`. If `eval_batch_size` is not `None`, it is executed on - TPU, which is an experimental feature. In this case, `model_fn` should return - `TPUEstimatorSpec` instead of `EstimatorSpec`, which expects the - `eval_metrics` for TPU evaluation. - + TPUEstimator transforms a global batch size in params to a per-shard batch + size when calling the `input_fn` and `model_fn`. Users should specify + global batch size in constructor, and then get the batch size for each shard + in `input_fn` and `model_fn` by `params['batch_size']`. + For training, `model_fn` gets per-core batch size; `input_fn` may get + per-core or per-host batch size depending on + `per_host_input_for_training` in `TPUConfig`. + For evaluation, `model_fn` gets per-core batch size and `input_fn` get + per-host batch size. + + `model_fn` should return `TPUEstimatorSpec`, which expects the `eval_metrics` + for TPU evaluation. `TPUEstimatorSpec.eval_metrics` is a tuple of `metric_fn` and `tensors`, where `tensors` could be a list of `Tensor`s or dict of names to `Tensor`s. (See `TPUEstimatorSpec` for details). `metric_fn` takes the `tensors` and returns a dict from metric string name to the result of calling a metric function, namely a `(metric_tensor, update_op)` tuple. + One can set `use_tpu` to `False` for testing. All training, evaluation, and + predict will be executed on CPU. `input_fn` and `model_fn` will receive + `train_batch_size` or `eval_batch_size` unmodified as `params['batch_size']`. + Current limitations: 1. TPU evaluation only works on single host. @@ -1560,8 +1547,7 @@ class TPUEstimator(estimator_lib.Estimator): basic python types. There are reserved keys for `TPUEstimator`, including 'batch_size'. use_tpu: A bool indicating whether TPU support is enabled. Currently, - - TPU training respects this bit. - - If true, see `eval_batch_size` for evaluate support. + - TPU training and evaluation respect this bit. - Predict still happens on CPU. train_batch_size: An int representing the global training batch size. TPUEstimator transforms this global batch size to a per-shard batch @@ -1569,9 +1555,7 @@ class TPUEstimator(estimator_lib.Estimator): Cannot be `None` if `use_tpu` is `True`. Must be divisible by `config.tpu_config.num_shards`. eval_batch_size: An int representing the global training batch size. - Currently, if `None`, evaluation is still executed on CPU (even when - `use_tpu` is True). In near future, `use_tpu` will be the only option to - switch between TPU/CPU evaluation. + Must be divisible by `config.tpu_config.num_shards`. batch_axis: A python tuple of int values describing how each tensor produced by the Estimator `input_fn` should be split across the TPU compute shards. For example, if your input_fn produced (images, labels) @@ -1611,10 +1595,10 @@ class TPUEstimator(estimator_lib.Estimator): .format(train_batch_size, config.tpu_config.num_shards)) if eval_batch_size is not None: - if config.tpu_config.num_shards > 8: - raise NotImplementedError( - 'TPU evaluation is only supported with one host.') - + if not isinstance(eval_batch_size, int): + raise ValueError('`eval_batch_size` must be an int') + if eval_batch_size < 1: + raise ValueError('`eval_batch_size` must be positive') if eval_batch_size % config.tpu_config.num_shards != 0: raise ValueError( 'eval batch size {} must be divisible by number of shards {}' @@ -1687,6 +1671,14 @@ class TPUEstimator(estimator_lib.Estimator): util_lib.check_positive_integer(steps, 'Eval steps') + if self._config.tpu_config.num_shards > 8: + raise NotImplementedError( + 'TPU evaluation is only supported with one host.') + + if self._ctx._eval_batch_size is None: # pylint: disable=protected-access + raise ValueError('`eval_batch_size` cannot be `None`' + 'if evaluate() is called on TPU.') + return [ evaluation._StopAfterNEvalsHook( # pylint: disable=protected-access num_evals=steps), -- GitLab From ac7167794f7c22d42c14a8d2f47b9533e118d73f Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Tue, 30 Jan 2018 15:07:03 -0800 Subject: [PATCH 295/423] Supporting paths for build_server script. PiperOrigin-RevId: 183899675 --- tensorflow/tools/dist_test/build_server.sh | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/tensorflow/tools/dist_test/build_server.sh b/tensorflow/tools/dist_test/build_server.sh index 878fabd248..225c034741 100755 --- a/tensorflow/tools/dist_test/build_server.sh +++ b/tensorflow/tools/dist_test/build_server.sh @@ -16,14 +16,15 @@ # # Builds the test server for distributed (GRPC) TensorFlow # -# Usage: build_server.sh [--test] +# Usage: build_server.sh [--test] # # Arguments: # docker_image_name: Name of the docker image to build. # E.g.: tensorflow/tf_grpc_test_server:0.11.0rc1 # -# whl_url: URL from which the TensorFlow whl file will be downloaded. +# whl_file_location: URL from which the TensorFlow whl file will be downloaded. # E.g.: https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.11.0rc1-cp27-none-linux_x86_64.whl +# E.g.: /path/to/folder/tensorflow-0.11.0rc1-cp27-none-linux_x86_64.whl # # The optional flag --test lets the script to use the Dockerfile for the # testing GRPC server. Without the flag, the script will build the non-test @@ -41,11 +42,11 @@ die() { # Check arguments if [[ $# -lt 2 ]]; then - die "Usage: $0 [--test]" + die "Usage: $0 [--test]" fi DOCKER_IMG_NAME=$1 -WHL_URL=$2 +WHL_FILE_LOCATION=$2 shift 2 # Current script directory @@ -53,7 +54,7 @@ DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" BUILD_DIR=$(mktemp -d) echo "" -echo "Using whl file URL: ${WHL_URL}" +echo "Using whl file URL: ${WHL_FILE_LOCATION}" echo "Building in temporary directory: ${BUILD_DIR}" cp -r ${DIR}/* "${BUILD_DIR}"/ || \ @@ -65,9 +66,15 @@ if [[ $1 == "--test" ]]; then fi echo "Using Docker file: ${DOCKER_FILE}" +if [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then + # Download whl file into the build context directory. + wget -P "${BUILD_DIR}" "${WHL_FILE_LOCATION}" || \ + die "Failed to download tensorflow whl file from URL: ${WHL_FILE_LOCATION}" +else + cp "${WHL_FILE_LOCATION}" "${BUILD_DIR}" +fi + # Download whl file into the build context directory. -wget -P "${BUILD_DIR}" ${WHL_URL} || \ - die "Failed to download tensorflow whl file from URL: ${WHL_URL}" if [[ ! -f "${DOCKER_FILE}" ]]; then die "ERROR: Unable to find dockerfile: ${DOCKER_FILE}" -- GitLab From e7df91c5f65e8b10535d1a09c817aa4e900e4001 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Tue, 30 Jan 2018 15:11:33 -0800 Subject: [PATCH 296/423] Internal change. PiperOrigin-RevId: 183900332 --- .../contrib/lite/kernels/batch_to_space_nd.cc | 9 ++++++++- .../contrib/lite/kernels/batch_to_space_nd_test.cc | 13 +++++++++++++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc index d84a77039b..889239f932 100644 --- a/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc +++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd.cc @@ -57,6 +57,7 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, BatchToSpaceNDContext* op_context) { TfLiteIntArray* input_size = op_context->input->dims; const int* block_shape = GetTensorData(op_context->block_shape); + const int* crops = GetTensorData(op_context->crops); TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->block_shape), kBlockSizeDimensionNum); @@ -65,7 +66,13 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->crops), kSpatialDimensionNum); - // TODO(ycling): Add crops as part of calculation. + // TODO(ycling): Add crops as part of calculation. Remove check for a crops + // containing all zeroes. + TF_LITE_ENSURE_EQ(context, crops[0], 0); + TF_LITE_ENSURE_EQ(context, crops[1], 0); + TF_LITE_ENSURE_EQ(context, crops[2], 0); + TF_LITE_ENSURE_EQ(context, crops[3], 0); + // Number of batch must be multiple of (block_shape[0] * block_shape[1]). TF_LITE_ENSURE_EQ(context, input_size->data[0] % (block_shape[0] * block_shape[1]), 0); diff --git a/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc b/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc index c9152bf967..8485cde1b4 100644 --- a/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc +++ b/tensorflow/contrib/lite/kernels/batch_to_space_nd_test.cc @@ -119,6 +119,19 @@ TEST(BatchToSpaceNDOpTest, InvalidShapeTest) { "Cannot allocate tensors"); } +TEST(BatchToSpaceNDOpTest, InvalidCropsConstTest) { + EXPECT_DEATH(BatchToSpaceNDOpConstModel({3, 2, 2, 1}, {2, 2}, {0, 0, 0, 1}), + "1 != 0"); +} + +TEST(BatchToSpaceNDOpTest, InvalidCropsDynamicTest) { + BatchToSpaceNDOpDynamicModel m({4, 2, 2, 1}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + m.SetBlockShape({2, 2}); + m.SetCrops({0, 0, 1, 0}); + EXPECT_DEATH(m.Invoke(), "1 != 0"); +} + } // namespace } // namespace tflite -- GitLab From b76f013900afcf679d27cf03fbd21c4e5035e9a2 Mon Sep 17 00:00:00 2001 From: Anna R Date: Tue, 30 Jan 2018 15:36:08 -0800 Subject: [PATCH 297/423] Internal change. PiperOrigin-RevId: 183904042 --- tensorflow/python/eager/BUILD | 23 ------ tensorflow/python/eager/gen_op.bzl | 65 ----------------- .../python/eager/python_eager_op_gen_main.cc | 72 ------------------- 3 files changed, 160 deletions(-) delete mode 100644 tensorflow/python/eager/gen_op.bzl delete mode 100644 tensorflow/python/eager/python_eager_op_gen_main.cc diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index 9e3382d4f3..ab81d40148 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -206,29 +206,6 @@ cc_library( ], ) -cc_library( - name = "python_eager_op_gen_main", - srcs = [ - "python_eager_op_gen_main.cc", - ], - visibility = ["//visibility:public"], - deps = [ - ":python_eager_op_gen", - "//tensorflow/core:framework", - "//tensorflow/core:lib", - "//tensorflow/core:op_gen_lib", - "//tensorflow/core:protos_all_cc", - ], -) - -tf_cc_binary( - name = "python_eager_op_gen_demo", - deps = [ - ":python_eager_op_gen_main", - "//tensorflow/core:ops", - ], -) - py_library( name = "custom_gradient", srcs = ["custom_gradient.py"], diff --git a/tensorflow/python/eager/gen_op.bzl b/tensorflow/python/eager/gen_op.bzl deleted file mode 100644 index 8bc1d6c10a..0000000000 --- a/tensorflow/python/eager/gen_op.bzl +++ /dev/null @@ -1,65 +0,0 @@ -"""For eager-mode Python.""" - -load("//tensorflow:tensorflow.bzl", - "clean_dep", - "tf_binary_additional_srcs", - "tf_copts", - "tf_cc_binary") - -def tfe_gen_op_wrapper_py(name, - out=None, - visibility=None, - deps=[], - generated_target_name=None, - # ApiDefs will be loaded in the order specified in this list. - api_def_srcs=[]): - """Generate an eager-mode Python op wrapper for an op library.""" - # Construct a cc_binary containing the specified ops. - tool_name = "gen_" + name + "_py_wrappers_cc" - if not deps: - deps = [str(Label("//tensorflow/core:" + name + "_op_lib"))] - tf_cc_binary( - name=tool_name, - linkopts=["-lm"], - copts=tf_copts(), - linkstatic=1, - deps=([ - clean_dep("//tensorflow/python/eager:python_eager_op_gen_main") - ] + deps), - visibility=[clean_dep("//visibility:public")],) - - # Invoke the previous cc_binary to generate a python file. - if not out: - out = "gen_" + name + ".py" - - if not api_def_srcs: - api_def_args_str = "," - else: - api_def_args = [] - for api_def_src in api_def_srcs: - # Add directory of the first ApiDef source to args. - # We are assuming all ApiDefs in a single api_def_src are in the - # same directory. - api_def_args.append( - "$$(dirname $$(echo $(locations " + api_def_src + - ") | cut -d\" \" -f1))") - api_def_args_str = ",".join(api_def_args) - - native.genrule( - name=name + "_pygenrule", - outs=[out], - srcs=api_def_srcs, - tools=[tool_name] + tf_binary_additional_srcs(), - cmd=("$(location " + tool_name + ") " + api_def_args_str + " > $@")) - - # Make a py_library out of the generated python file. - if not generated_target_name: - generated_target_name = name - native.py_library( - name=generated_target_name, - srcs=[out], - srcs_version="PY2AND3", - visibility=visibility, - deps=[ - clean_dep("//tensorflow/python/eager:framework_for_generated_wrappers"), - ],) diff --git a/tensorflow/python/eager/python_eager_op_gen_main.cc b/tensorflow/python/eager/python_eager_op_gen_main.cc deleted file mode 100644 index 05351bd8b1..0000000000 --- a/tensorflow/python/eager/python_eager_op_gen_main.cc +++ /dev/null @@ -1,72 +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/python/eager/python_eager_op_gen.h" - -#include -#include -#include - -#include "tensorflow/core/framework/op.h" -#include "tensorflow/core/framework/op_def.pb.h" -#include "tensorflow/core/framework/op_gen_lib.h" -#include "tensorflow/core/lib/io/path.h" -#include "tensorflow/core/lib/strings/str_util.h" -#include "tensorflow/core/platform/env.h" -#include "tensorflow/core/platform/init_main.h" - -namespace tensorflow { -namespace { - -void PrintAllPythonOps(const std::vector& hidden_ops, - const std::vector& api_def_dirs) { - OpList ops; - OpRegistry::Global()->Export(false, &ops); - - ApiDefMap api_def_map(ops); - if (!api_def_dirs.empty()) { - Env* env = Env::Default(); - - for (const auto& api_def_dir : api_def_dirs) { - std::vector api_files; - TF_CHECK_OK(env->GetMatchingPaths(io::JoinPath(api_def_dir, "*.pbtxt"), - &api_files)); - TF_CHECK_OK(api_def_map.LoadFileList(env, api_files)); - } - api_def_map.UpdateDocs(); - } - - PrintEagerPythonOps(ops, api_def_map, hidden_ops, true /* require_shapes */); -} - -} // namespace -} // namespace tensorflow - -int main(int argc, char* argv[]) { - tensorflow::port::InitMain(argv[0], &argc, &argv); - - // Usage: - // python_eager_op_gen_main api_def_dir1,api_def_dir2,... - if (argc == 1) { - tensorflow::PrintAllPythonOps({}, {}); - } else if (argc == 2) { - const std::vector api_def_dirs = - tensorflow::str_util::Split(argv[1], ",", - tensorflow::str_util::SkipEmpty()); - tensorflow::PrintAllPythonOps({}, api_def_dirs); - } else { - return -1; - } - return 0; -} -- GitLab From 1f89285655e201f89c7fdb027cf16cb3eeeb8ade Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 15:53:55 -0800 Subject: [PATCH 298/423] [XLA] Add Clamp and Round to the local Python XLA client. PiperOrigin-RevId: 183906722 --- .../xla/python/local_computation_builder.cc | 17 +++++++++----- .../xla/python/local_computation_builder.h | 14 +++++++---- .../xla/python/local_computation_builder.i | 2 ++ tensorflow/compiler/xla/python/xla_client.py | 8 +++++++ .../compiler/xla/python/xla_client_test.py | 23 +++++++++++++++++++ 5 files changed, 54 insertions(+), 10 deletions(-) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 5772532b84..67a73bc33d 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -367,12 +367,6 @@ LocalComputationBuilder::SelectAndScatterWithGeneralPadding( source, init_value, scatter.computation()); } -ComputationDataHandle LocalComputationBuilder::Select( - const ComputationDataHandle& pred, const ComputationDataHandle& on_true, - const ComputationDataHandle& on_false) { - return builder_.Select(pred, on_true, on_false); -} - ComputationDataHandle LocalComputationBuilder::Tuple( tensorflow::gtl::ArraySlice elements) { return builder_.Tuple(elements); @@ -487,6 +481,15 @@ ComputationDataHandle LocalComputationBuilder::While( tensorflow::gtl::ArraySlice broadcast_dimensions), \ (lhs, rhs, broadcast_dimensions)) +#define _FORWARD_TRIOP(method_name) \ + _FORWARD( \ + method_name, ComputationDataHandle, \ + (const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, \ + const ComputationDataHandle& ehs), \ + (lhs, rhs, ehs)) + +_FORWARD_TRIOP(Select) +_FORWARD_TRIOP(Clamp) _FORWARD_BINOP(Eq) _FORWARD_BINOP(Ne) _FORWARD_BINOP(Ge) @@ -507,6 +510,7 @@ _FORWARD_UNOP(Abs) _FORWARD_UNOP(Exp) _FORWARD_UNOP(Floor) _FORWARD_UNOP(Ceil) +_FORWARD_UNOP(Round) _FORWARD_UNOP(Log) _FORWARD_UNOP(Sign) _FORWARD_UNOP(Cos) @@ -523,6 +527,7 @@ _FORWARD_UNOP(Sort) #undef _FORWARD #undef _FORWARD_UNOP #undef _FORWARD_BINOP +#undef _FORWARD_TRIOP void DeleteLocalShapedBuffer(LocalShapedBuffer* local_shaped_buffer) { delete local_shaped_buffer; diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 6851c2644d..d5c4c58040 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -174,10 +174,6 @@ class LocalComputationBuilder { const ComputationDataHandle& source, const ComputationDataHandle& init_value, const LocalComputation& scatter); - ComputationDataHandle Select(const ComputationDataHandle& pred, - const ComputationDataHandle& on_true, - const ComputationDataHandle& on_false); - ComputationDataHandle Tuple( tensorflow::gtl::ArraySlice elements); @@ -254,6 +250,14 @@ class LocalComputationBuilder { (const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, \ tensorflow::gtl::ArraySlice broadcast_dimensions)) +#define _FORWARD_TRIOP(method_name) \ + _FORWARD( \ + method_name, ComputationDataHandle, \ + (const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, \ + const ComputationDataHandle& ehs)) + + _FORWARD_TRIOP(Select) + _FORWARD_TRIOP(Clamp) _FORWARD_BINOP(Eq) _FORWARD_BINOP(Ne) _FORWARD_BINOP(Ge) @@ -274,6 +278,7 @@ class LocalComputationBuilder { _FORWARD_UNOP(Exp) _FORWARD_UNOP(Floor) _FORWARD_UNOP(Ceil) + _FORWARD_UNOP(Round) _FORWARD_UNOP(Log) _FORWARD_UNOP(Sign) _FORWARD_UNOP(Cos) @@ -290,6 +295,7 @@ class LocalComputationBuilder { #undef _FORWARD #undef _FORWARD_UNOP #undef _FORWARD_BINOP +#undef _FORWARD_TRIOP private: ComputationBuilder builder_; diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index 6a52a088dd..89f8385501 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -701,6 +701,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Call; %unignore xla::swig::LocalComputationBuilder::Transpose; %unignore xla::swig::LocalComputationBuilder::Rev; +%unignore xla::swig::LocalComputationBuilder::Clamp; %unignore xla::swig::LocalComputationBuilder::Map; %unignore xla::swig::LocalComputationBuilder::Reduce; %unignore xla::swig::LocalComputationBuilder::ReduceWindowWithGeneralPadding; @@ -730,6 +731,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Exp; %unignore xla::swig::LocalComputationBuilder::Floor; %unignore xla::swig::LocalComputationBuilder::Ceil; +%unignore xla::swig::LocalComputationBuilder::Round; %unignore xla::swig::LocalComputationBuilder::Log; %unignore xla::swig::LocalComputationBuilder::Sign; %unignore xla::swig::LocalComputationBuilder::Cos; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index a89e2643c8..7ee5febc09 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -89,6 +89,7 @@ _UNARY_OPS = [ 'Abs', 'Exp', 'Floor', + 'Round', 'Ceil', 'Log', 'Sign', @@ -619,6 +620,13 @@ class ComputationBuilder(object): return _wrap_data_handle( self._client.Rev(_unwrap_data_handle(operand), dimensions)) + def Clamp(self, min, operand, max): # pylint: disable=redefined-builtin + """Clamp op.""" + return _wrap_data_handle( + self._client.Clamp(_unwrap_data_handle(min), + _unwrap_data_handle(operand), + _unwrap_data_handle(max))) + def SelectAndScatter(self, operand, select, window_dimensions, window_strides, padding, source, init_value, scatter): """Select and scatter op, used by the gradient of ReduceWindow. diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index c0413b9bbc..3b5bbfd786 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -496,6 +496,12 @@ class SingleOpTest(LocalComputationTest): c.Exp(c.Constant(arr)) self._ExecuteAndCompareClose(c, expected=np.exp(arr)) + def testRound(self): + c = self._NewComputation() + arr = NumpyArrayF32([3.3, 12.1]) + c.Round(c.Constant(arr)) + self._ExecuteAndCompareClose(c, expected=np.round(arr)) + def testLog(self): c = self._NewComputation() arr = NumpyArrayF32([3.3, 12.1]) @@ -699,6 +705,23 @@ class SingleOpTest(LocalComputationTest): self._ExecuteAndCompareExact( c, expected=[[[6, 5], [8, 7]], [[2, 1], [4, 3]]]) + def testClampF32(self): + c = self._NewComputation() + c.Clamp( + c.Constant(NumpyArrayF32(-1)), + c.Constant(NumpyArrayF32([-2, -1, 0, 1, 2, 3])), + c.Constant(NumpyArrayF32(2))) + self._ExecuteAndCompareExact(c, expected=[-1, -1, 0, 1, 2, 2]) + + # TODO(b/72689392): re-enable when bug S32 resolved + def DISABLED_testClampS32(self): + c = self._NewComputation() + c.Clamp( + c.Constant(NumpyArrayS32(-1)), + c.Constant(NumpyArrayS32([-2, -1, 0, 1, 2, 3])), + c.Constant(NumpyArrayS32(2))) + self._ExecuteAndCompareExact(c, expected=[-1, 0, 1, 2, 2]) + def testSelect(self): c = self._NewComputation() c.Select( -- GitLab From 6ed47cc06b0d7f445b322fb15bd08d28aa7791b4 Mon Sep 17 00:00:00 2001 From: Anjali Sridhar Date: Tue, 30 Jan 2018 16:12:32 -0800 Subject: [PATCH 299/423] Fix _standardize_input_data function to allow users to use list of ints/floats and list of lists as training and validation data input. PiperOrigin-RevId: 183909545 --- .../keras/_impl/keras/engine/training.py | 21 +++++++++++-------- .../keras/_impl/keras/engine/training_test.py | 18 ++++++++++++++++ 2 files changed, 30 insertions(+), 9 deletions(-) diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py index 699ae2edf0..6885ef5724 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training.py +++ b/tensorflow/python/keras/_impl/keras/engine/training.py @@ -82,21 +82,24 @@ def _standardize_input_data(data, if data[x].__class__.__name__ == 'DataFrame' else data[x] for x in names ] - data = [np.expand_dims(x, 1) if x.ndim == 1 else x for x in data] except KeyError as e: raise ValueError('No data provided for "' + e.args[0] + '". Need data ' 'for each key in: ' + str(names)) elif isinstance(data, list): - data = [ - x.values if x.__class__.__name__ == 'DataFrame' else x for x in data - ] - data = [ - np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x - for x in data - ] + if isinstance(data[0], list): + data = [np.asarray(d) for d in data] + elif len(names) == 1 and isinstance(data[0], (float, int)): + data = [np.asarray(data)] + else: + data = [ + x.values if x.__class__.__name__ == 'DataFrame' else x for x in data + ] else: data = data.values if data.__class__.__name__ == 'DataFrame' else data - data = [np.expand_dims(data, 1)] if data.ndim == 1 else [data] + data = [data] + data = [ + np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data + ] if len(data) != len(names): if data and hasattr(data[0], 'shape'): diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py index 5a033a04ad..b380238e4e 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training_test.py +++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py @@ -78,6 +78,14 @@ class TrainingTest(test.TestCase): verbose=2) model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np]) + # Test model with input data as a list of lists + model.fit( + [np.ndarray.tolist(input_a_np), np.ndarray.tolist(input_b_np)], + [output_d_np, output_e_np], + epochs=2, + batch_size=5, + verbose=2) + # Test with validation data model.fit( [input_a_np, input_b_np], [output_d_np, output_e_np], @@ -205,6 +213,16 @@ class TrainingTest(test.TestCase): with self.assertRaises(ValueError): model.fit([input_a_np, input_a_np], output_d_np, epochs=1) + # Test model on a list of floats + input_a_np = np.random.random((10, 3)) + input_b_np = np.random.random((10, 4)) + + model.fit([np.ndarray.tolist(input_a_np)], + [np.ndarray.tolist(input_b_np)], + epochs=2, + batch_size=5, + verbose=2) + def test_evaluate_predict_on_arrays(self): with self.test_session(): a = keras.layers.Input(shape=(3,), name='input_a') -- GitLab From 600c7a5fb8ed91060d3dae07a2fdd655af703244 Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Tue, 30 Jan 2018 16:14:17 -0800 Subject: [PATCH 300/423] [tf.data] Native support for `tf.SparseTensor` and serialization tests for the `tf.data` methods that can produce `tf.SparseTensor` elements. PiperOrigin-RevId: 183909820 --- .../kernel_tests/batch_dataset_op_test.py | 17 +++++++++++ .../dataset_serialization_test_base.py | 27 +++++++++++++---- .../kernel_tests/filter_dataset_op_test.py | 4 +++ .../kernel_tests/flat_map_dataset_op_test.py | 15 ++++++++++ .../interleave_dataset_op_test.py | 16 ++++++++++ .../kernel_tests/map_dataset_op_test.py | 29 +++++++++++++++---- 6 files changed, 97 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py index 015f69c567..0c2827b1e4 100644 --- a/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/batch_dataset_op_test.py @@ -744,6 +744,23 @@ class BatchDatasetSerializationTest( lambda: self._build_dataset_dense_to_sparse(diff_comp), num_outputs) + def _sparse(self, i): + return sparse_tensor.SparseTensorValue( + indices=[[0]], values=(i * [1]), dense_shape=[1]) + + def _build_dataset_sparse(self, batch_size=5): + return dataset_ops.Dataset.range(10).map(self._sparse).batch(batch_size) + + def testSparseCore(self): + self.run_core_tests(self._build_dataset_sparse, + lambda: self._build_dataset_sparse(2), 2) + + def _build_dataset_nested_sparse(self): + return dataset_ops.Dataset.range(10).map(self._sparse).batch(5).batch(2) + + def testNestedSparseCore(self): + self.run_core_tests(self._build_dataset_nested_sparse, None, 1) + class PaddedBatchDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py index 701fc8247e..4574a625a3 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py +++ b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py @@ -41,8 +41,8 @@ class DatasetSerializationTestBase(test.TestCase): def tearDown(self): self._delete_ckpt() - # TODO(b/70988345): Support native `tf.SparseTensor` objects and get rid of - # `sparse_tensors` argument. + # TODO(b/72657739): Remove sparse_tensor argument to test the (deprecated) + # `from_sparse_tensor_slices()` API once the API and tests are deleted. def run_core_tests(self, ds_fn1, ds_fn2, num_outputs, sparse_tensors=False): """Runs the core tests. @@ -559,13 +559,16 @@ class DatasetSerializationTestBase(test.TestCase): get_next = sparse_tensor.SparseTensor(*iterator.get_next()) else: get_next = iterator.get_next() - self._add_iterator_ops_to_collection(init_op, get_next, sparse_tensors) + self._add_iterator_ops_to_collection(init_op, get_next, ds_fn, + sparse_tensors) saver = saver_lib.Saver(allow_empty=True) return init_op, get_next, saver def _build_empty_graph(self, ds_fn, sparse_tensors=False): iterator = iterator_ops.Iterator.from_structure( - self._get_output_types(ds_fn), self._get_output_shapes(ds_fn)) + self._get_output_types(ds_fn), + output_shapes=self._get_output_shapes(ds_fn), + output_classes=self._get_output_classes(ds_fn)) saveable = contrib_iterator_ops.make_saveable_from_iterator(iterator) ops.add_to_collection(ops.GraphKeys.SAVEABLE_OBJECTS, saveable) if sparse_tensors: @@ -578,12 +581,19 @@ class DatasetSerializationTestBase(test.TestCase): def _add_iterator_ops_to_collection(self, init_op, get_next, + ds_fn, sparse_tensors=False): ops.add_to_collection("iterator_ops", init_op) # `get_next` may be a tuple e.g. in TensorSliceDataset. Since Collections # do not support tuples we flatten the tensors and restore the shape in # `_get_iterator_ops_from_collection`. - if sparse_tensors: + + # TODO(shivaniagrwal): `output_classes` is a nested structure of classes, + # this base class is specific to current test cases. Update when test are + # added with `output_classes` as a nested structure with at least one of the + # component being `tf.SparseTensor`. + if (sparse_tensors or + self._get_output_classes(ds_fn) is sparse_tensor.SparseTensor): ops.add_to_collection("iterator_ops", get_next.indices) ops.add_to_collection("iterator_ops", get_next.values) ops.add_to_collection("iterator_ops", get_next.dense_shape) @@ -593,7 +603,8 @@ class DatasetSerializationTestBase(test.TestCase): def _get_iterator_ops_from_collection(self, ds_fn, sparse_tensors=False): all_ops = ops.get_collection("iterator_ops") - if sparse_tensors: + if (sparse_tensors or + self._get_output_classes(ds_fn) is sparse_tensor.SparseTensor): init_op, indices, values, dense_shape = all_ops return init_op, sparse_tensor.SparseTensor(indices, values, dense_shape) else: @@ -608,6 +619,10 @@ class DatasetSerializationTestBase(test.TestCase): with ops.Graph().as_default(): return ds_fn().output_shapes + def _get_output_classes(self, ds_fn): + with ops.Graph().as_default(): + return ds_fn().output_classes + def _ckpt_path(self): return os.path.join(self.get_temp_dir(), "iterator") diff --git a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py index 5921be2ae8..06883934d0 100644 --- a/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/filter_dataset_op_test.py @@ -194,6 +194,10 @@ class FilterDatasetSerializationTest( return dataset_ops.Dataset.range(10).map(_map_fn).filter(_filter_fn).map( lambda x, i: x) + def testSparseCore(self): + num_outputs = 5 + self.run_core_tests(self._build_sparse_filter, None, num_outputs) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py index d4fbaa5cdc..86d69495ef 100644 --- a/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/flat_map_dataset_op_test.py @@ -225,6 +225,21 @@ class FlatMapDatasetSerializationTest( self.verify_error_on_save(build_ds, 500, errors.InvalidArgumentError) + def testSparseCore(self): + + def _map_fn(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) + + def _flat_map_fn(x): + return dataset_ops.Dataset.from_tensor_slices( + sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) + + def _build_ds(): + return dataset_ops.Dataset.range(10).map(_map_fn).flat_map(_flat_map_fn) + + self.run_core_tests(_build_ds, None, 20) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py index b1937c08f3..db8429512b 100644 --- a/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/interleave_dataset_op_test.py @@ -252,6 +252,22 @@ class InterleaveDatasetSeriazationTest( None, num_outputs) # pylint: enable=g-long-lambda + def testSparseCore(self): + + def _map_fn(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 1]], values=(i * [1, -1]), dense_shape=[2, 2]) + + def _interleave_fn(x): + return dataset_ops.Dataset.from_tensor_slices( + sparse_ops.sparse_to_dense(x.indices, x.dense_shape, x.values)) + + def _build_dataset(): + return dataset_ops.Dataset.range(10).map(_map_fn).interleave( + _interleave_fn, cycle_length=1) + + self.run_core_tests(_build_dataset, None, 20) + class ParallelInterleaveDatasetTest(test.TestCase): 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 dd8247bfd4..d3ce89298b 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 @@ -805,6 +805,21 @@ class MapDatasetSerializationTest( self.run_core_tests(_build_ds, None, num_outputs) + def testSparseCore(self): + + def _sparse(i): + return sparse_tensor.SparseTensorValue( + indices=np.array([[0, 0]]), + values=(i * np.array([1])), + dense_shape=np.array([1, 1])) + + def _build_ds(num_outputs): + return contrib_dataset_ops.Dataset.range(num_outputs).map(_sparse) + + num_outputs = 10 + self.run_core_tests(lambda: _build_ds(num_outputs), + lambda: _build_ds(int(num_outputs / 2)), num_outputs) + class ParallelMapDatasetSerializationTest( dataset_serialization_test_base.DatasetSerializationTestBase): @@ -851,7 +866,8 @@ class ParallelMapDatasetSerializationTest( return random_ops.random_uniform( (), 0, 10, dtype=dtypes.int32) * math_ops.to_int32(x) - return contrib_dataset_ops.Dataset.range(100).map(_map_fn) + return contrib_dataset_ops.Dataset.range(100).map( + _map_fn, num_parallel_calls=2).prefetch(2) self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) @@ -861,7 +877,8 @@ class ParallelMapDatasetSerializationTest( counter_var = variable_scope.get_variable( "counter", (), dtypes.int32, use_resource=True) return (contrib_dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda _: counter_var.assign_add(1))) + lambda _: counter_var.assign_add(1), + num_parallel_calls=2).prefetch(2)) self.verify_error_on_save(_build_ds, 15, errors.InvalidArgumentError) @@ -870,7 +887,7 @@ class ParallelMapDatasetSerializationTest( def _build_ds(): constant_var = constant_op.constant(5) return (contrib_dataset_ops.Dataset.from_tensors(0).repeat(10).map( - lambda x: x + constant_var)) + lambda x: x + constant_var, num_parallel_calls=2).prefetch(2)) self.run_core_tests(_build_ds, None, 10) @@ -883,7 +900,8 @@ class ParallelMapDatasetSerializationTest( def defun_fn(x): return constant_op.constant(1000) + math_ops.to_int32(x) - return contrib_dataset_ops.Dataset.range(num_outputs).map(defun_fn) + return contrib_dataset_ops.Dataset.range(num_outputs).map( + defun_fn, num_parallel_calls=2).prefetch(2) self.run_core_tests(_build_ds, None, num_outputs) @@ -901,7 +919,8 @@ class ParallelMapDatasetSerializationTest( return constant_op.constant(11000) + defun_fn_deep(math_ops.to_int32(x)) - return contrib_dataset_ops.Dataset.range(num_outputs).map(defun_fn) + return contrib_dataset_ops.Dataset.range(num_outputs).map( + defun_fn, num_parallel_calls=2).prefetch(2) self.run_core_tests(_build_ds, None, num_outputs) -- GitLab From f8f9163449c3b1ec60db7d05d68f652a0ca9257a Mon Sep 17 00:00:00 2001 From: joel-shor Date: Tue, 30 Jan 2018 19:06:48 -0800 Subject: [PATCH 301/423] Simplify loader_impl.py logic around main Op Tensor. --- tensorflow/python/saved_model/loader_impl.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index 5ff954fd9f..6e85df0cbf 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -232,13 +232,9 @@ def load(sess, tags, export_dir, **saver_kwargs): asset_tensors_dictionary = _get_asset_tensors(export_dir, meta_graph_def_to_load) - main_op_tensor = _get_main_op_tensor(meta_graph_def_to_load) + main_op_tensor = (_get_main_op_tensor(meta_graph_def_to_load) or + (_get_legacy_init_op_tensor(meta_graph_def_to_load))) if main_op_tensor is not None: sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) - else: - legacy_init_op_tensor = _get_legacy_init_op_tensor(meta_graph_def_to_load) - if legacy_init_op_tensor is not None: - sess.run( - fetches=[legacy_init_op_tensor], feed_dict=asset_tensors_dictionary) return meta_graph_def_to_load -- GitLab From 00c4febdac23cade71d6fdcf10c9ccb982e2a582 Mon Sep 17 00:00:00 2001 From: Koan-Sin Tan Date: Wed, 31 Jan 2018 13:22:54 +0800 Subject: [PATCH 302/423] add brackets and new line Add curly brackets and new line to address the format problem mentioned in review. --- .../contrib/lite/examples/label_image/bitmap_helpers_impl.h | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h index 942906e269..33ea695dda 100644 --- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h +++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h @@ -67,7 +67,9 @@ void resize(T* out, uint8_t* in, int image_height, int image_width, // fill input image // in[] are integers, cannot do memcpy() directly auto input = interpreter->typed_tensor(0); - for (int i = 0; i < number_of_pixels; i++) input[i] = in[i]; + for (int i = 0; i < number_of_pixels; i++) { + input[i] = in[i]; + } // fill new_sizes interpreter->typed_tensor(1)[0] = wanted_height; -- GitLab From dfb983c07237542264002ef90084d06a1ef9134c Mon Sep 17 00:00:00 2001 From: Pete Warden Date: Wed, 31 Jan 2018 11:11:58 -0800 Subject: [PATCH 303/423] Fixed iOS build script for all architectures, fixes #12904 (#16559) --- tensorflow/contrib/makefile/build_all_ios.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/makefile/build_all_ios.sh b/tensorflow/contrib/makefile/build_all_ios.sh index a18df256f9..2d99791839 100755 --- a/tensorflow/contrib/makefile/build_all_ios.sh +++ b/tensorflow/contrib/makefile/build_all_ios.sh @@ -96,7 +96,7 @@ if [[ "${ONLY_MAKE_TENSORFLOW}" != "true" ]]; then if [[ -z "${BUILD_ARCH}" ]]; then # Compile protobuf for the target iOS device architectures. - tensorflow/contrib/makefile/compile_ios_protobuf.sh -a ${DEFAULT_ARCH} + tensorflow/contrib/makefile/compile_ios_protobuf.sh else # Compile protobuf for the target iOS device architectures. tensorflow/contrib/makefile/compile_ios_protobuf.sh -a ${BUILD_ARCH} -- GitLab From 6afe900f543e0005ce69b3152330f1b7b16cb286 Mon Sep 17 00:00:00 2001 From: yegord Date: Thu, 1 Feb 2018 00:02:25 +0100 Subject: [PATCH 304/423] optimize_for_inference_lib.fold_batch_norms() preserves data_format (#16075) Fixes https://github.com/tensorflow/tensorflow/issues/15034 --- .../tools/optimize_for_inference_lib.py | 1 + .../tools/optimize_for_inference_test.py | 89 ++++++++++--------- 2 files changed, 48 insertions(+), 42 deletions(-) diff --git a/tensorflow/python/tools/optimize_for_inference_lib.py b/tensorflow/python/tools/optimize_for_inference_lib.py index c2687bf557..9c19271222 100644 --- a/tensorflow/python/tools/optimize_for_inference_lib.py +++ b/tensorflow/python/tools/optimize_for_inference_lib.py @@ -349,6 +349,7 @@ def fold_batch_norms(input_graph_def): bias_add_op.op = "BiasAdd" bias_add_op.name = node.name bias_add_op.attr["T"].CopyFrom(conv_op.attr["T"]) + bias_add_op.attr["data_format"].CopyFrom(conv_op.attr["data_format"]) bias_add_op.input.extend([new_conv_op.name, offset_op.name]) new_ops.extend([scaled_weights_op, new_conv_op, offset_op, bias_add_op]) diff --git a/tensorflow/python/tools/optimize_for_inference_test.py b/tensorflow/python/tools/optimize_for_inference_test.py index 7686bb0f14..2ef612473b 100644 --- a/tensorflow/python/tools/optimize_for_inference_test.py +++ b/tensorflow/python/tools/optimize_for_inference_test.py @@ -173,48 +173,53 @@ class OptimizeForInferenceTest(test.TestCase): self.assertNotEqual("BatchNormWithGlobalNormalization", node.op) def testFoldFusedBatchNorms(self): - with self.test_session() as sess: - inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6] - input_op = constant_op.constant( - np.array(inputs), shape=[1, 1, 6, 2], dtype=dtypes.float32) - weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4] - weights_op = constant_op.constant( - np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32) - conv_op = nn_ops.conv2d( - input_op, weights_op, [1, 1, 1, 1], padding="SAME", name="conv_op") - mean_op = constant_op.constant( - np.array([10, 20]), shape=[2], dtype=dtypes.float32) - variance_op = constant_op.constant( - np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32) - beta_op = constant_op.constant( - np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32) - gamma_op = constant_op.constant( - np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32) - ops.get_default_graph().graph_def_versions.producer = 9 - gen_nn_ops._fused_batch_norm( - conv_op, - gamma_op, - beta_op, - mean_op, - variance_op, - 0.00001, - is_training=False, - name="output") - original_graph_def = sess.graph_def - original_result = sess.run(["output:0"]) - optimized_graph_def = optimize_for_inference_lib.fold_batch_norms( - original_graph_def) - - with self.test_session() as sess: - _ = importer.import_graph_def( - optimized_graph_def, input_map={}, name="optimized") - optimized_result = sess.run(["optimized/output:0"]) - - self.assertAllClose( - original_result, optimized_result, rtol=1e-04, atol=1e-06) - - for node in optimized_graph_def.node: - self.assertNotEqual("FusedBatchNorm", node.op) + for data_format, use_gpu in [("NHWC", False), ("NCHW", True)]: + with self.test_session(use_gpu=use_gpu) as sess: + inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6] + input_op = constant_op.constant( + np.array(inputs), + shape=[1, 1, 6, 2] if data_format == "NHWC" else [1, 2, 1, 6], + dtype=dtypes.float32) + weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4] + weights_op = constant_op.constant( + np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32) + conv_op = nn_ops.conv2d( + input_op, weights_op, [1, 1, 1, 1], padding="SAME", + data_format=data_format, name="conv_op") + mean_op = constant_op.constant( + np.array([10, 20]), shape=[2], dtype=dtypes.float32) + variance_op = constant_op.constant( + np.array([0.25, 0.5]), shape=[2], dtype=dtypes.float32) + beta_op = constant_op.constant( + np.array([0.1, 0.6]), shape=[2], dtype=dtypes.float32) + gamma_op = constant_op.constant( + np.array([1.0, 2.0]), shape=[2], dtype=dtypes.float32) + ops.get_default_graph().graph_def_versions.producer = 9 + gen_nn_ops._fused_batch_norm( + conv_op, + gamma_op, + beta_op, + mean_op, + variance_op, + 0.00001, + is_training=False, + data_format=data_format, + name="output") + original_graph_def = sess.graph_def + original_result = sess.run(["output:0"]) + optimized_graph_def = optimize_for_inference_lib.fold_batch_norms( + original_graph_def) + + with self.test_session(use_gpu=use_gpu) as sess: + _ = importer.import_graph_def( + optimized_graph_def, input_map={}, name="optimized") + optimized_result = sess.run(["optimized/output:0"]) + + self.assertAllClose( + original_result, optimized_result, rtol=1e-04, atol=1e-06) + + for node in optimized_graph_def.node: + self.assertNotEqual("FusedBatchNorm", node.op) def testFuseResizePadAndConv(self): with self.test_session() as sess: -- GitLab From 1ec4b3568ec7d387ba0d672562cb6d6898b17311 Mon Sep 17 00:00:00 2001 From: Suharsh Sivakumar Date: Tue, 30 Jan 2018 17:03:49 -0800 Subject: [PATCH 305/423] Update graph_matcher to include OneofPattern. PiperOrigin-RevId: 183916547 --- .../contrib/quantize/python/graph_matcher.py | 111 +++++++++++++----- .../quantize/python/graph_matcher_test.py | 40 ++++++- 2 files changed, 120 insertions(+), 31 deletions(-) diff --git a/tensorflow/contrib/quantize/python/graph_matcher.py b/tensorflow/contrib/quantize/python/graph_matcher.py index e3581cc559..b458f039df 100644 --- a/tensorflow/contrib/quantize/python/graph_matcher.py +++ b/tensorflow/contrib/quantize/python/graph_matcher.py @@ -18,8 +18,19 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import abc -class OpTypePattern(object): + +class Pattern(object): + """The parent class of all patterns (e.g. OpTypePattern and OneofPattern).""" + + @abc.abstractmethod + def match(self, op, tensor): + """Returns the result of matching op/tensor against this pattern.""" + raise NotImplementedError('Method "match" not implemented.') + + +class OpTypePattern(Pattern): """A tree pattern that matches TF expressions with certain op types.""" def __init__(self, op_type, name=None, inputs=None): @@ -34,7 +45,7 @@ class OpTypePattern(object): similar TF op types. name: Optional string. The name of the pattern that can be looked up in MatchResult. - inputs: Optional list of `OpTypePattern`s or strings that specify the + inputs: Optional list of `Pattern`s or strings that specify the patterns for the inputs of a matching op. If None, this pattern accepts any inputs of a matching op. """ @@ -43,22 +54,51 @@ class OpTypePattern(object): if inputs is None: inputs = [] self._inputs = [ - input_pattern if isinstance(input_pattern, OpTypePattern) else - OpTypePattern(input_pattern) for input_pattern in inputs + input_pattern + if isinstance(input_pattern, Pattern) else OpTypePattern(input_pattern) + for input_pattern in inputs ] - @property - def op_type(self): - return self._op_type - - @property - def inputs(self): - return self._inputs - @property def name(self): return self._name + def match(self, op, tensor): + if self._op_type != '*': + if op.type not in self._op_type.split('|'): + return None + + match_result = MatchResult() + match_result.add(self, op, tensor) + + if not self._inputs: + # If pattern.inputs is empty, skips the rest and accepts all the inputs. + return match_result + + if len(op.inputs) != len(self._inputs): + return None + + for input_tensor, input_pattern in zip(op.inputs, self._inputs): + input_match_result = input_pattern.match(input_tensor.op, input_tensor) + if input_match_result is None: + return None + match_result.merge_from(input_match_result) + return match_result + + +class OneofPattern(Pattern): + """Matches one of the given sub-patterns.""" + + def __init__(self, sub_patterns): + self._sub_patterns = sub_patterns + + def match(self, op, tensor): + for sub_pattern in self._sub_patterns: + match_result = sub_pattern.match(op, tensor) + if match_result is not None: + return match_result + return None + class MatchResult(object): r"""Encapsulates the result of a match done by GraphMatcher. @@ -102,16 +142,36 @@ class MatchResult(object): return pattern_or_name if isinstance(pattern_or_name, str): + if pattern_or_name not in self._name_to_pattern: + return None return self._name_to_pattern[pattern_or_name] raise ValueError('pattern_or_name has type %s. Expect OpTypePattern or str.' % type(pattern_or_name)) + def _get_op_tensor(self, pattern_or_name): + pattern = self._to_pattern(pattern_or_name) + if pattern is None: + return None + + if pattern not in self._pattern_to_op_tensor: + return None + + return self._pattern_to_op_tensor[pattern] + def get_op(self, pattern_or_name): - return self._pattern_to_op_tensor[self._to_pattern(pattern_or_name)][0] + op_tensor = self._get_op_tensor(pattern_or_name) + return op_tensor[0] if op_tensor else None def get_tensor(self, pattern_or_name): - return self._pattern_to_op_tensor[self._to_pattern(pattern_or_name)][1] + op_tensor = self._get_op_tensor(pattern_or_name) + return op_tensor[1] if op_tensor else None + + def merge_from(self, other_match_result): + # pylint: disable=protected-access + self._pattern_to_op_tensor.update(other_match_result._pattern_to_op_tensor) + self._name_to_pattern.update(other_match_result._name_to_pattern) + # pylint: enable=protected-access class GraphMatcher(object): @@ -121,7 +181,7 @@ class GraphMatcher(object): """Initializes a GraphMatcher. Args: - pattern: The `OpTypePattern` against which `GraphMatcher` matches + pattern: The `Pattern` against which `GraphMatcher` matches subgraphs. """ self._pattern = pattern @@ -133,7 +193,7 @@ class GraphMatcher(object): with key `pattern`. Args: - pattern: An `OpTypePattern`. + pattern: An `Pattern`. op: A `tf.Operation` to match against the pattern. tensor: the output `tf.Tensor` of `op` that is used by the matching op of `pattern`'s parent. Can be None if `pattern` is already the root of the @@ -142,20 +202,11 @@ class GraphMatcher(object): Returns: True if an TF expression rooted at `op` matches `pattern`. """ - if pattern.op_type != '*': - if op.type not in pattern.op_type.split('|'): - return False - - self._match_result.add(pattern, op, tensor) - - if not pattern.inputs: - # If pattern.inputs is empty, skips the rest and accepts all the inputs. - return True - - return len(op.inputs) == len(pattern.inputs) and all([ - self._match_pattern(input_pattern, input_tensor.op, input_tensor) - for input_tensor, input_pattern in zip(op.inputs, pattern.inputs) - ]) + match_result = pattern.match(op, tensor) + if match_result is None: + return False + self._match_result.merge_from(match_result) + return True def match_op(self, op): """Matches `op` against `self._pattern`. diff --git a/tensorflow/contrib/quantize/python/graph_matcher_test.py b/tensorflow/contrib/quantize/python/graph_matcher_test.py index e1572865e4..6d58757218 100644 --- a/tensorflow/contrib/quantize/python/graph_matcher_test.py +++ b/tensorflow/contrib/quantize/python/graph_matcher_test.py @@ -105,7 +105,7 @@ class GraphMatcherTest(test_util.TensorFlowTestCase): self.assertEqual(match_result.get_op(y1_pattern), y1.op) self.assertEqual(match_result.get_tensor(y1_pattern), y1) - def test_oneof_pattern(self): + def test_oneof_type_pattern(self): # - + # / \ / \ # x y z @@ -125,6 +125,44 @@ class GraphMatcherTest(test_util.TensorFlowTestCase): for match_result in matcher.match_graph(g) ], [plus.op, minus.op]) + def test_oneof_pattern(self): + reshape_pattern = graph_matcher.OpTypePattern('Reshape') + transpose_pattern = graph_matcher.OneofPattern([ + graph_matcher.OpTypePattern( + 'Transpose', + name='transpose', + inputs=[ + graph_matcher.OpTypePattern( + 'Slice', name='slice', inputs=[reshape_pattern, '*', '*']), + '*' + ]), + graph_matcher.OpTypePattern( + 'Transpose', name='transpose', inputs=[reshape_pattern, '*']) + ]) + + matcher = graph_matcher.GraphMatcher(transpose_pattern) + + g = ops.Graph() + with g.as_default(): + inputs = array_ops.placeholder(dtypes.float32, shape=[6]) + reshape = array_ops.reshape(inputs, [2, 3]) + transpose = array_ops.transpose(reshape) + [match_result] = list(matcher.match_graph(g)) + self.assertEqual(match_result.get_tensor(reshape_pattern), reshape) + self.assertEqual(match_result.get_tensor('slice'), None) + self.assertEqual(match_result.get_op('transpose'), transpose.op) + + g = ops.Graph() + with g.as_default(): + inputs = array_ops.placeholder(dtypes.float32, shape=[6]) + reshape = array_ops.reshape(inputs, [2, 3]) + slicing = array_ops.slice(reshape, [0, 0], [-1, -1]) + transpose = array_ops.transpose(slicing) + [match_result] = list(matcher.match_graph(g)) + self.assertEqual(match_result.get_tensor(reshape_pattern), reshape) + self.assertEqual(match_result.get_tensor('slice'), slicing) + self.assertEqual(match_result.get_op('transpose'), transpose.op) + if __name__ == '__main__': googletest.main() -- GitLab From 9d91cc94bbcc4f4e27167edc419579af4b9be0c8 Mon Sep 17 00:00:00 2001 From: Jonathan Hseu Date: Tue, 30 Jan 2018 17:28:42 -0800 Subject: [PATCH 306/423] Give tf.contrib.summary a default suffix of .v2. This is so that it doesn't conflict with v1 summaries that are being created at the same time. By default, MonitoredTrainingSession and Estimator will create v1 summaries, so using both currently creates a race condition, and the last one to create the event file writer wins. PiperOrigin-RevId: 183919988 --- tensorflow/contrib/summary/summary_ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/summary/summary_ops.py b/tensorflow/contrib/summary/summary_ops.py index ee661dfdc1..a6968d8b2a 100644 --- a/tensorflow/contrib/summary/summary_ops.py +++ b/tensorflow/contrib/summary/summary_ops.py @@ -202,7 +202,7 @@ def create_file_writer(logdir, if flush_millis is None: flush_millis = constant_op.constant(2 * 60 * 1000) if filename_suffix is None: - filename_suffix = constant_op.constant("") + filename_suffix = constant_op.constant(".v2") return _make_summary_writer( name, gen_summary_ops.create_summary_file_writer, -- GitLab From f6d3f0a5506ff221ba52c4f800d57c8d6b47643d Mon Sep 17 00:00:00 2001 From: Max Galkin Date: Tue, 30 Jan 2018 17:41:00 -0800 Subject: [PATCH 307/423] Add a CLIF wrapper for DeviceProperties. PiperOrigin-RevId: 183921389 --- tensorflow/core/BUILD | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 7a0c0ea3fe..1fcdfa73ad 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1351,6 +1351,13 @@ tf_pyclif_proto_library( visibility = ["//visibility:public"], ) +tf_pyclif_proto_library( + name = "protobuf/device_properties_pyclif", + proto_lib = ":protos_all_cc", + proto_srcfile = "protobuf/device_properties.proto", + visibility = ["//visibility:public"], +) + # ----------------------------------------------------------------------------- # Internal targets -- GitLab From 4c2ee0464a7c50afa1db3973215b2c7df8ade0e4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 17:58:28 -0800 Subject: [PATCH 308/423] K-FAC: Wrap extract_image_patches() for compatibility with XLA. PiperOrigin-RevId: 183923073 --- .../contrib/kfac/python/ops/fisher_factors.py | 63 ++++++++++++++++--- 1 file changed, 55 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/kfac/python/ops/fisher_factors.py b/tensorflow/contrib/kfac/python/ops/fisher_factors.py index f59168cbc0..bcba18ae14 100644 --- a/tensorflow/contrib/kfac/python/ops/fisher_factors.py +++ b/tensorflow/contrib/kfac/python/ops/fisher_factors.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn from tensorflow.python.ops import special_math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables @@ -111,6 +112,54 @@ def diagonal_covariance_initializer(shape, dtype, partition_info): # pylint: di return array_ops.ones(shape, dtype) +def extract_image_patches(image, ksizes, strides, padding, name=None): + """Extracts image patches for an N-dimensional convolution. + + This function is a compatibility wrapper over tf.extract_image_patches(), as + ExtractImagePatches isn't yet implemented in XLA. + + Args: + image: Tensor of shape [batch, in_x, in_y, ..., in_channels]. Input images. + All dimensions except 'batch' must be defined. + ksizes: [filter_x, filter_y, ...]. Spatial shape of filter in each + dimension. + strides: [stride_x, stride_y, ...]. Spatial stride for filter in each + dimension. + padding: str. "VALID" or "SAME". + name: str or None. name of Op. + + Returns: + result: [batch, out_x, out_y, ..., filter_x, filter_y, ..., in_channels]. + Contains image patches to which conv kernel would be applied for each + output location. [out_x, out_y, ...] depends on padding. + """ + if not utils.on_tpu(): + return array_ops.extract_image_patches( + image, + ksizes=([1] + list(ksizes) + [1]), + strides=([1] + list(strides) + [1]), + rates=[1, 1, 1, 1], + padding=padding, + name=name) + + with tf_ops.name_scope(name, "extract_image_patches", + [image, ksizes, strides, padding]): + batch = image.shape.as_list()[0] + in_channels = image.shape.as_list()[-1] + + # Map each input feature to a location in the output. + out_channels = np.prod(ksizes) * in_channels + filters = linalg_ops.eye(out_channels), + filters = array_ops.reshape(filters, ksizes + [in_channels, out_channels]) + + result = nn.convolution(image, filters, padding, strides=strides) + out_spatial = result.shape.as_list()[1:-1] + result = array_ops.reshape( + result, [batch or -1] + out_spatial + ksizes + [in_channels]) + + return result + + def compute_cov(tensor, tensor_right=None, normalizer=None): """Compute the empirical second moment of the rows of a 2D Tensor. @@ -668,11 +717,10 @@ class ConvDiagonalFactor(DiagonalFactor): # TODO(b/64144716): there is potential here for a big savings in terms # of memory use. - patches = array_ops.extract_image_patches( + patches = extract_image_patches( self._inputs, - ksizes=[1, filter_height, filter_width, 1], - strides=self._strides, - rates=[1, 1, 1, 1], + ksizes=[filter_height, filter_width], + strides=self._strides[1:-1], padding=self._padding) if self._has_bias: @@ -816,11 +864,10 @@ class ConvInputKroneckerFactor(InverseProvidingFactor): # TODO(b/64144716): there is potential here for a big savings in terms of # memory use. - patches = array_ops.extract_image_patches( + patches = extract_image_patches( self._inputs, - ksizes=[1, filter_height, filter_width, 1], - strides=self._strides, - rates=[1, 1, 1, 1], + ksizes=[filter_height, filter_width], + strides=self._strides[1:-1], padding=self._padding) flatten_size = (filter_height * filter_width * in_channels) -- GitLab From a667b5da18f28c50857adb211de15a883f85fd65 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 18:03:31 -0800 Subject: [PATCH 309/423] Removed unused variable doReverseChannels. PiperOrigin-RevId: 183923876 --- .../examples/ios/camera/CameraExampleViewController.mm | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm b/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm index 10f31bb6f1..d74e275f04 100644 --- a/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm +++ b/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm @@ -225,14 +225,8 @@ static void GetTopN(const uint8_t* prediction, const int prediction_size, const assert(pixelBuffer != NULL); OSType sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer); - int doReverseChannels; - if (kCVPixelFormatType_32ARGB == sourcePixelFormat) { - doReverseChannels = 1; - } else if (kCVPixelFormatType_32BGRA == sourcePixelFormat) { - doReverseChannels = 0; - } else { - assert(false); // Unknown source format - } + assert(sourcePixelFormat == kCVPixelFormatType_32ARGB || + sourcePixelFormat == kCVPixelFormatType_32BGRA); const int sourceRowBytes = (int)CVPixelBufferGetBytesPerRow(pixelBuffer); const int image_width = (int)CVPixelBufferGetWidth(pixelBuffer); -- GitLab From 7788bcaf4b48b22eaadee9a9cc1b0aff04cc5907 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 18:08:52 -0800 Subject: [PATCH 310/423] internal change PiperOrigin-RevId: 183924520 --- .../tpu/profiler/capture_tpu_profile.cc | 28 +++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc index 6a05a2abf6..9ac9e3cb28 100644 --- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/contrib/tpu/profiler/version.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_util.h" #include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/platform/init_main.h" #include "tensorflow/core/util/command_line_flags.h" @@ -47,6 +48,19 @@ string GetCurrentTimeStampAsString() { return s; } +Status ValidateHostPortPair(const string& host_port) { + uint32 port; + std::vector parts = str_util::Split(host_port, ':'); + // Must be host:port, port must be a number, host must not contain a '/', + // host also must not be empty. + if (parts.size() != 2 || !strings::safe_strtou32(parts[1], &port) || + parts[0].find("/") != string::npos || parts[0].empty()) { + return errors::InvalidArgument("Could not interpret \"", host_port, + "\" as a host-port pair."); + } + return Status::OK(); +} + ProfileResponse Profile(const string& service_addr, int duration_ms, const ProfileOptions& opts) { ProfileRequest request; @@ -60,11 +74,14 @@ ProfileResponse Profile(const string& service_addr, int duration_ms, ::grpc::ClientContext context; ::grpc::ChannelArguments channel_args; // TODO(ioeric): use `SetMaxReceiveMessageSize` instead once it's available. + // TODO(qiuminxu): use `NewHostPortGrpcChannel` instead once their + // `ValidateHostPortPair` checks for empty host string case. channel_args.SetInt(GRPC_ARG_MAX_MESSAGE_LENGTH, std::numeric_limits::max()); std::unique_ptr stub = TPUProfiler::NewStub(::grpc::CreateCustomChannel( - service_addr, ::grpc::InsecureChannelCredentials(), channel_args)); + "dns:///" + service_addr, ::grpc::InsecureChannelCredentials(), + channel_args)); ProfileResponse response; TF_QCHECK_OK(FromGrpcStatus(stub->Profile(&context, request, &response))); return response; @@ -101,7 +118,14 @@ int main(int argc, char** argv) { tensorflow::string usage = tensorflow::Flags::Usage(argv[0], flag_list); bool parse_ok = tensorflow::Flags::Parse(&argc, argv, flag_list); if (!parse_ok || FLAGS_service_addr.empty() || FLAGS_logdir.empty()) { - std::printf("%s", usage.c_str()); + std::cout << usage.c_str() << std::endl; + return 2; + } + tensorflow::Status status = + tensorflow::tpu::ValidateHostPortPair(FLAGS_service_addr); + if (!status.ok()) { + std::cout << status.error_message() << std::endl; + std::cout << usage.c_str() << std::endl; return 2; } tensorflow::port::InitMain(argv[0], &argc, &argv); -- GitLab From 811785eec2d24743804d1128d1cce900aedad3d0 Mon Sep 17 00:00:00 2001 From: Anjali Sridhar Date: Tue, 30 Jan 2018 18:50:56 -0800 Subject: [PATCH 311/423] Eager support for tf.Keras. PiperOrigin-RevId: 183928850 --- tensorflow/python/keras/BUILD | 14 + .../python/keras/_impl/keras/backend.py | 14 +- .../keras/_impl/keras/engine/topology.py | 4 +- .../keras/_impl/keras/engine/training.py | 225 ++++-- .../_impl/keras/engine/training_eager.py | 666 +++++++++++++++ .../_impl/keras/engine/training_eager_test.py | 755 ++++++++++++++++++ .../python/keras/_impl/keras/layers/core.py | 4 +- .../keras/_impl/keras/layers/normalization.py | 3 +- .../python/keras/_impl/keras/optimizers.py | 10 +- tensorflow/python/layers/base.py | 10 +- tensorflow/python/layers/network.py | 19 +- 11 files changed, 1651 insertions(+), 73 deletions(-) create mode 100644 tensorflow/python/keras/_impl/keras/engine/training_eager.py create mode 100644 tensorflow/python/keras/_impl/keras/engine/training_eager_test.py diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 6125755775..a9dd8d8e9d 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -39,6 +39,7 @@ py_library( "_impl/keras/engine/__init__.py", "_impl/keras/engine/topology.py", "_impl/keras/engine/training.py", + "_impl/keras/engine/training_eager.py", "_impl/keras/estimator.py", "_impl/keras/initializers.py", "_impl/keras/layers/__init__.py", @@ -719,6 +720,19 @@ py_test( ], ) +py_test( + name = "training_eager_test", + size = "medium", + srcs = ["_impl/keras/engine/training_eager_test.py"], + srcs_version = "PY2AND3", + tags = ["notsan"], + deps = [ + ":keras", + "//tensorflow/python:client_testlib", + "//third_party/py/numpy", + ], +) + py_test( name = "topology_test", size = "small", diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py index 460c0dc5f3..098ea063f9 100644 --- a/tensorflow/python/keras/_impl/keras/backend.py +++ b/tensorflow/python/keras/_impl/keras/backend.py @@ -29,6 +29,7 @@ import numpy as np from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session as session_module +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes as dtypes_module from tensorflow.python.framework import ops @@ -326,7 +327,15 @@ def learning_phase(): Returns: Learning phase (scalar integer tensor or Python integer). + + Raises: + ValueError: If called when Eager execution is enabled. """ + if context.in_eager_mode(): + if 'eager' not in _GRAPH_LEARNING_PHASES: + raise ValueError('No learning phase set in Eager mode.') + return _GRAPH_LEARNING_PHASES['eager'] + graph = ops.get_default_graph() if graph not in _GRAPH_LEARNING_PHASES: phase = array_ops.placeholder_with_default( @@ -347,7 +356,10 @@ def set_learning_phase(value): global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned if value not in {0, 1}: raise ValueError('Expected learning phase to be ' '0 or 1.') - _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = value + if context.in_eager_mode(): + _GRAPH_LEARNING_PHASES['eager'] = value + else: + _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = value def get_session(): diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/topology.py index 64aa868f38..8354a2b8fd 100644 --- a/tensorflow/python/keras/_impl/keras/engine/topology.py +++ b/tensorflow/python/keras/_impl/keras/engine/topology.py @@ -708,8 +708,10 @@ class Network(tf_network.GraphNetwork, Layer): self.input_names.append(layer.name) if layer.is_placeholder: self._feed_input_names.append(layer.name) - self._feed_inputs.append(layer.input) self._feed_input_shapes.append(K.int_shape(self.inputs[i])) + # layer.input gives an error in eager mode + if context.in_graph_mode(): + self._feed_inputs.append(layer.input) for layer in self._output_layers: self.output_names.append(layer.name) diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py index 6885ef5724..43d95b1f19 100644 --- a/tensorflow/python/keras/_impl/keras/engine/training.py +++ b/tensorflow/python/keras/_impl/keras/engine/training.py @@ -22,17 +22,21 @@ import copy import numpy as np +from tensorflow.python.eager import context +from tensorflow.python.framework import ops from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import callbacks as cbks from tensorflow.python.keras._impl.keras import losses from tensorflow.python.keras._impl.keras import metrics as metrics_module from tensorflow.python.keras._impl.keras import optimizers +from tensorflow.python.keras._impl.keras.engine import training_eager from tensorflow.python.keras._impl.keras.engine.topology import Network from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import optimizer as tf_optimizer_module try: from scipy.sparse import issparse # pylint: disable=g-import-not-at-top @@ -621,9 +625,15 @@ class Model(Network): `optimizer`, `loss`, `metrics` or `sample_weight_mode`. """ loss = loss or {} + if context.in_eager_mode() and not isinstance( + optimizer, tf_optimizer_module.Optimizer): + raise ValueError('Only TF native optimizers are supported in Eager mode.') + self.optimizer = optimizers.get(optimizer) self.loss = loss self.loss_weights = loss_weights + if context.in_eager_mode() and sample_weight_mode is not None: + raise ValueError('sample_weight_mode is not supported in Eager mode.') self.sample_weight_mode = sample_weight_mode # Prepare loss functions. @@ -654,6 +664,7 @@ class Model(Network): loss_function = losses.get(loss) loss_functions = [loss_function for _ in range(len(self.outputs))] self.loss_functions = loss_functions + weighted_losses = [_weighted_masked_objective(fn) for fn in loss_functions] skip_target_indices = [] skip_target_weighing_indices = [] @@ -667,11 +678,12 @@ class Model(Network): skip_target_weighing_indices.append(i) # Prepare output masks. - masks = self.compute_mask(self.inputs, mask=None) - if masks is None: - masks = [None for _ in self.outputs] - if not isinstance(masks, list): - masks = [masks] + if context.in_graph_mode(): + masks = self.compute_mask(self.inputs, mask=None) + if masks is None: + masks = [None for _ in self.outputs] + if not isinstance(masks, list): + masks = [masks] # Prepare loss weights. if loss_weights is None: @@ -697,6 +709,32 @@ class Model(Network): else: raise TypeError('Could not interpret loss_weights argument: ' + str(loss_weights) + ' - expected a list of dicts.') + self.loss_weights_list = loss_weights_list + + # initialization for Eager mode execution + if context.in_eager_mode(): + if target_tensors is not None: + raise ValueError('target_tensors are not currently supported in Eager' + 'mode.') + self.total_loss = None + self.metrics = metrics + self.weighted_metrics = weighted_metrics + self.metrics_tensors = [] + self.metrics_names = ['loss'] + for i in range(len(self.outputs)): + if len(self.outputs) > 1: + self.metrics_names.append(self.output_names[i] + '_loss') + self.nested_metrics = _collect_metrics(metrics, self.output_names) + self._feed_sample_weight_modes = [] + for i in range(len(self.outputs)): + self._feed_sample_weight_modes.append(None) + self.sample_weights = [] + self.targets = [] + self._collected_trainable_weights = self.trainable_weights + for i in range(len(self.outputs)): + self._feed_output_names.append(self.output_names[i]) + + return # Prepare targets of model. self.targets = [] @@ -723,6 +761,7 @@ class Model(Network): else: raise TypeError('Expected `target_tensors` to be ' 'a list or dict, but got:', target_tensors) + for i in range(len(self.outputs)): if i in skip_target_indices: self.targets.append(None) @@ -772,7 +811,7 @@ class Model(Network): weight = K.placeholder(ndim=2, name=name + '_sample_weights') sample_weight_modes.append('temporal') else: - weight = K.placeholder(ndim=1, name=name + '_sample_weights') + weight = K.placeholder(ndim=1, name=name + 'sample_weights') sample_weight_modes.append(None) sample_weights.append(weight) elif isinstance(sample_weight_mode, list): @@ -932,7 +971,7 @@ class Model(Network): self._feed_sample_weights = [] for i in range(len(self.sample_weights)): if i not in skip_target_weighing_indices: - self._feed_sample_weights.append(sample_weights[i]) + self._feed_sample_weights.append(self.sample_weights[i]) # Functions for train, test and predict will # be compiled lazily when required. @@ -981,6 +1020,7 @@ class Model(Network): with K.name_scope(self.optimizer.__class__.__name__): training_updates = self.optimizer.get_updates( params=self._collected_trainable_weights, loss=self.total_loss) + updates = self.updates + training_updates # Gets loss and metrics. Updates weights at each call. self.train_function = K.function( @@ -1159,6 +1199,7 @@ class Model(Network): callback_model = self callbacks.set_model(callback_model) + callbacks.set_params({ 'batch_size': batch_size, 'epochs': epochs, @@ -1219,6 +1260,7 @@ class Model(Network): np.random.shuffle(index_array) batches = _make_batches(num_train_samples, batch_size) + for batch_index, (batch_start, batch_end) in enumerate(batches): batch_ids = index_array[batch_start:batch_end] try: @@ -1413,6 +1455,7 @@ class Model(Network): ins_batch[i] = ins_batch[i].toarray() batch_outs = f(ins_batch) + if isinstance(batch_outs, list): if batch_index == 0: for batch_out in enumerate(batch_outs): @@ -1423,7 +1466,6 @@ class Model(Network): if batch_index == 0: outs.append(0.) outs[0] += batch_outs * len(batch_ids) - if verbose == 1: progbar.update(batch_end) for i in range(len(outs)): @@ -1639,6 +1681,7 @@ class Model(Network): batch_size=batch_size) # Prepare validation data. do_validation = False + val_ins = [] if validation_data: do_validation = True if len(validation_data) == 2: @@ -1689,39 +1732,65 @@ class Model(Network): ins = x + y + sample_weights + [1.] else: ins = x + y + sample_weights - self._make_train_function() - f = self.train_function # Prepare display labels. out_labels = self._get_deduped_metrics_names() - if do_validation: - self._make_test_function() - val_f = self.test_function - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] + if context.in_eager_mode(): + if do_validation: + callback_metrics = copy.copy(out_labels) + [ + 'val_' + n for n in out_labels + ] + else: + callback_metrics = copy.copy(out_labels) + + return training_eager.fit_loop( + self, + ins, + out_labels=out_labels, + batch_size=batch_size, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + val_ins=val_ins, + shuffle=shuffle, + callback_metrics=callback_metrics, + initial_epoch=initial_epoch, + steps_per_epoch=steps_per_epoch, + validation_steps=validation_steps) else: - callback_metrics = copy.copy(out_labels) - val_f = None - val_ins = [] - - # Delegate logic to `_fit_loop`. - return self._fit_loop( - f, - ins, - out_labels=out_labels, - batch_size=batch_size, - epochs=epochs, - verbose=verbose, - callbacks=callbacks, - val_f=val_f, - val_ins=val_ins, - shuffle=shuffle, - callback_metrics=callback_metrics, - initial_epoch=initial_epoch, - steps_per_epoch=steps_per_epoch, - validation_steps=validation_steps) + self._make_train_function() + f = self.train_function + + if do_validation: + if context.in_graph_mode(): + self._make_test_function() + val_f = self.test_function + else: + val_f = None + callback_metrics = copy.copy(out_labels) + [ + 'val_' + n for n in out_labels + ] + else: + val_f = None + callback_metrics = copy.copy(out_labels) + + # Delegate logic to `_fit_loop`. + return self._fit_loop( + f, + ins, + out_labels=out_labels, + batch_size=batch_size, + epochs=epochs, + verbose=verbose, + callbacks=callbacks, + val_f=val_f, + val_ins=val_ins, + shuffle=shuffle, + callback_metrics=callback_metrics, + initial_epoch=initial_epoch, + steps_per_epoch=steps_per_epoch, + validation_steps=validation_steps) def evaluate(self, x=None, @@ -1797,10 +1866,15 @@ class Model(Network): ins = x + y + sample_weights + [0.] else: ins = x + y + sample_weights - self._make_test_function() - f = self.test_function - return self._test_loop( - f, ins, batch_size=batch_size, verbose=verbose, steps=steps) + + if context.in_eager_mode(): + return training_eager.test_loop( + self, ins, batch_size=batch_size, verbose=verbose, steps=steps) + else: + self._make_test_function() + f = self.test_function + return self._test_loop( + f, ins, batch_size=batch_size, verbose=verbose, steps=steps) def predict(self, x, batch_size=None, verbose=0, steps=None): """Generates output predictions for the input samples. @@ -1852,10 +1926,16 @@ class Model(Network): ins = x + [0.] else: ins = x - self._make_predict_function() - f = self.predict_function - return self._predict_loop( - f, ins, batch_size=batch_size, verbose=verbose, steps=steps) + + if context.in_eager_mode(): + return training_eager.predict_loop( + self, ins, batch_size=batch_size, verbose=verbose, steps=steps) + else: + self._make_predict_function() + f = self.predict_function + + return self._predict_loop( + f, ins, batch_size=batch_size, verbose=verbose, steps=steps) def train_on_batch(self, x, y, sample_weight=None, class_weight=None): """Runs a single gradient update on a single batch of data. @@ -1891,6 +1971,7 @@ class Model(Network): or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. + """ x, y, sample_weights = self._standardize_user_data( x, @@ -1902,11 +1983,16 @@ class Model(Network): ins = x + y + sample_weights + [1.] else: ins = x + y + sample_weights - self._make_train_function() - outputs = self.train_function(ins) - if len(outputs) == 1: - return outputs[0] - return outputs + + if context.in_eager_mode(): + return training_eager.train_on_batch(self, ins) + + if context.in_graph_mode(): + self._make_train_function() + outputs = self.train_function(ins) + if len(outputs) == 1: + return outputs[0] + return outputs def test_on_batch(self, x, y, sample_weight=None): """Test the model on a single batch of samples. @@ -1945,11 +2031,16 @@ class Model(Network): ins = x + y + sample_weights + [0.] else: ins = x + y + sample_weights - self._make_test_function() - outputs = self.test_function(ins) - if len(outputs) == 1: - return outputs[0] - return outputs + + if context.in_eager_mode(): + return training_eager.test_on_batch(self, ins) + + if context.in_graph_mode(): + self._make_test_function() + outputs = self.test_function(ins) + if len(outputs) == 1: + return outputs[0] + return outputs def predict_on_batch(self, x): """Returns predictions for a single batch of samples. @@ -1959,6 +2050,7 @@ class Model(Network): Returns: Numpy array(s) of predictions. + """ x = _standardize_input_data(x, self._feed_input_names, self._feed_input_shapes) @@ -1966,11 +2058,25 @@ class Model(Network): ins = x + [0.] else: ins = x - self._make_predict_function() - outputs = self.predict_function(ins) - if len(outputs) == 1: - return outputs[0] - return outputs + + if context.in_eager_mode(): + ins_batch_converted = [] + for ib in ins: + ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + + eager_model_inputs = [] + for i in range(len(self.inputs)): + eager_model_inputs.append(ins_batch_converted[i]) + + outs = self(eager_model_inputs) # pylint: disable=not-callable + return outs + + if context.in_graph_mode(): + self._make_predict_function() + outputs = self.predict_function(ins) + if len(outputs) == 1: + return outputs[0] + return outputs def fit_generator(self, generator, @@ -2075,7 +2181,6 @@ class Model(Network): model.fit_generator(generate_arrays_from_file('/my_file.txt'), steps_per_epoch=10000, epochs=10) ``` - Raises: ValueError: In case the generator yields data in an invalid format. diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py new file mode 100644 index 0000000000..0a115969ca --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py @@ -0,0 +1,666 @@ +# 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. +# ============================================================================== +"""Keras training and evaluation routines. +""" +# pylint: disable=protected-access +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import numpy as np +from tensorflow.python.eager.backprop import GradientTape +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util +from tensorflow.python.keras._impl.keras import backend as K +from tensorflow.python.keras._impl.keras import callbacks as cbks +from tensorflow.python.keras._impl.keras import losses +from tensorflow.python.keras._impl.keras import metrics as metrics_module +from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar + + +def _make_batches(size, batch_size): + """Returns a list of batch indices (tuples of indices). + + Arguments: + size: Integer, total size of the data to slice into batches. + batch_size: Integer, batch size. + + Returns: + A list of tuples of array indices. + """ + num_batches = int(np.ceil(size / float(batch_size))) + return [(i * batch_size, min(size, (i + 1) * batch_size)) + for i in range(0, num_batches)] + + +def _slice_arrays(arrays, start=None, stop=None): + """Slice an array or list of arrays. + + This takes an array-like, or a list of + array-likes, and outputs: + - arrays[start:stop] if `arrays` is an array-like + - [x[start:stop] for x in arrays] if `arrays` is a list + + Can also work on list/array of indices: `_slice_arrays(x, indices)` + + Arguments: + arrays: Single array or list of arrays. + start: can be an integer index (start index) + or a list/array of indices + stop: integer (stop index); should be None if + `start` was a list. + + Returns: + A slice of the array(s). + + Raises: + ValueError: If the value of start is a list and stop is not None. + """ + if arrays is None: + return [None] + if isinstance(start, list) and stop is not None: + raise ValueError('The stop argument has to be None if the value of start is' + 'a list.') + elif isinstance(arrays, list): + if hasattr(start, '__len__'): + # hdf5 datasets only support list objects as indices + if hasattr(start, 'shape'): + start = start.tolist() + return [None if x is None else x[start] for x in arrays] + else: + return [None if x is None else x[start:stop] for x in arrays] + else: + if hasattr(start, '__len__'): + if hasattr(start, 'shape'): + start = start.tolist() + return arrays[start] + elif hasattr(start, '__getitem__'): + return arrays[start:stop] + else: + return [None] + + +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 K.name_scope(output_name + '_loss'): + loss = loss_fn(targets, outputs) + return loss + + +def _eager_metrics_fn(model, outputs, targets): + """Calculates the metrics for each output of the given model. + + Arguments: + model: The model on which metrics are being calculated. + outputs: The outputs of the given model. + targets: The predictions or targets of the given model. + + Returns: + Returns the metric names and metric results for each output of the model. + """ + metric_names = [] + metric_results = [] + if not isinstance(outputs, list): + outputs = [outputs] + + if not isinstance(targets, list): + targets = [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( + nested_output_metric, model._internal_output_shapes[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) + + with K.name_scope(metric_name): + metric_result = metric_fn(outputs[i], targets[i]) + metric_names.append(metric_name) + metric_results.append(K.mean(metric_result)) + + return metric_names, metric_results + + +def _model_loss(model, inputs, targets): + """Calculates the loss for a given model. + + Arguments: + model: The model on which metrics are being calculated. + inputs: The inputs of the given model. This is typically the mini batch of + data that is fed to the model. + targets: The predictions or targets of the given model. + + Returns: + Returns the model output, total loss and loss value calculated using the + specified loss function. The total loss includes regularization losses and + applies masking and sample weighting to the loss value. + """ + total_loss = 0 + outs = model(inputs) + if not isinstance(outs, list): + outs = [outs] + + if not isinstance(targets, list): + targets = [targets] + + loss_metrics = [] + with K.name_scope('loss'): + for i, loss_fn in enumerate(model.loss_functions): + # compute the loss + output_loss = _eager_loss_fn(outs[i], targets[i], loss_fn, + model.output_names[i]) + loss_metrics.append(K.mean(output_loss)) + + mask = outs[i]._keras_mask + # adapted from weighted_loss_fn + if mask is not None: + # mask should have the same shape as output_loss + output_loss *= mask + # the loss per batch should be proportional + # to the number of unmasked samples. + output_loss /= K.mean(mask) + + # adapted from weighted_loss_fn + # apply sample weighting + if model.sample_weights: + # reduce score_array to same ndim as weight array + ndim = K.ndim(output_loss) + weight_ndim = K.ndim(model.sample_weights) + output_loss = K.mean(output_loss, axis=list(range(weight_ndim, ndim))) + output_loss *= model.sample_weights + output_loss /= K.mean(K.cast(K.not_equal(model.sample_weights, 0), + K.floatx())) + output_loss = K.mean(output_loss) + + loss_weight = model.loss_weights_list[i] + if total_loss is None: + total_loss = loss_weight * output_loss + else: + total_loss += loss_weight * output_loss + + total_loss = K.mean(total_loss) + # Add regularization losses + custom_losses = [] + for layer in model.layers: + if layer.losses: + custom_losses += layer.losses + + if custom_losses: + total_loss += sum(custom_losses) + + return outs, total_loss, loss_metrics + + +def _process_single_batch(eager_model_inputs, eager_model_outputs, model, + training=True): + """Calculate the loss and gradient for one input batch. + + The model weights are updated if training is set to True. + + Arguments: + eager_model_inputs: Input batch data. + eager_model_outputs: Output batch data. + model: Model whose loss has to be calculated. + training: The boolean represents if the weights of the model are updated. + 'fit' methods will set this to True while 'evaluate' methods will + set this to False. + + Returns: + output of the model, total loss and the loss associated with each output. + + Raises: + ValueError: If the model loss is 0 or if the trainable weights list is + empty when the trainable parameter is set to True. + """ + K.set_learning_phase(training) + with GradientTape() as tape: + outs, loss, loss_metrics = _model_loss(model, eager_model_inputs, + eager_model_outputs) + if loss is None: + raise ValueError('The model cannot be run ' + 'because it has no loss to optimize.') + if training: + if not model._collected_trainable_weights: + raise ValueError('The list of trainable weights is empty. Make sure that ' + 'you are not setting model.trainable to False before ' + 'compiling the model.') + grads = tape.gradient(loss, model._collected_trainable_weights) + model.optimizer.apply_gradients(zip(grads, + model._collected_trainable_weights)) + return outs, loss, loss_metrics + + +def train_on_batch(model, ins): + """Calculates the loss and gradient updates for one input batch. + + Arguments: + model: Given model on which loss and gradients are calculated. + ins: Input and output batch numpy arrays. + + Returns: + total loss and the loss associated with each output. + """ + ins_batch_converted = [] + for ib in ins: + ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + eager_model_inputs = [] + eager_model_outputs = [] + for i in range(len(model.inputs)): + eager_model_inputs.append(ins_batch_converted[i]) + for i in range(len(model.inputs), len(ins_batch_converted)): + eager_model_outputs.append(ins_batch_converted[i]) + outs, loss, _ = _process_single_batch( + eager_model_inputs, eager_model_outputs, model) + if not isinstance(outs, list): + outs = [outs] + _, metrics_results = _eager_metrics_fn( + model, outs, eager_model_outputs) + if not isinstance(loss, list): + loss = [loss] + return loss + metrics_results + + +def test_on_batch(model, ins): + """Calculates the loss for one input batch. + + Arguments: + model: Given model on which loss is calculated. + ins: Input and output batch numpy arrays. + + Returns: + total loss, loss and metrics associated with each output. + """ + ins_batch_converted = [] + for ib in ins: + ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + eager_model_inputs = [] + eager_model_outputs = [] + for i in range(len(model.inputs)): + eager_model_inputs.append(ins_batch_converted[i]) + for i in range(len(model.inputs), len(ins_batch_converted)): + eager_model_outputs.append(ins_batch_converted[i]) + outs, loss, loss_metrics = _process_single_batch( + eager_model_inputs, eager_model_outputs, model, training=False) + if not isinstance(outs, list): + outs = [outs] + metric_names, metrics_results = _eager_metrics_fn( + model, outs, eager_model_outputs) + model.metrics_names.append(metric_names) + if not isinstance(loss, list): + loss = [loss] + return loss + loss_metrics + metrics_results + + +def fit_loop( + model, + ins, + out_labels=None, + batch_size=None, + epochs=100, + verbose=1, + callbacks=None, + val_ins=None, + shuffle=True, + callback_metrics=None, + initial_epoch=0, + steps_per_epoch=None, + validation_steps=None): + """Abstract fit function for `f(ins)`. + + Assume that f returns a list, labeled by out_labels. + + Arguments: + model: Instance of the model that is being executed in Eager mode. + ins: List of tensors to be fed to `f` + out_labels: List of strings, display names of + the outputs of `f` + batch_size: Integer batch size or None if unknown. + epochs: Number of times to iterate over the data + verbose: Verbosity mode, 0, 1 or 2 + callbacks: List of callbacks to be called during training + val_ins: List of tensors to be fed to `val_f` + shuffle: Whether to shuffle the data at the beginning of each epoch + callback_metrics: List of strings, the display names of the metrics + passed to the callbacks. They should be the + concatenation of list the display names of the outputs of + `f` and the list of display names of the outputs of `f_val`. + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run) + steps_per_epoch: Total number of steps (batches of samples) + before declaring one epoch finished and starting the + next epoch. Ignored with the default value of `None`. + validation_steps: Number of steps to run validation for (only if doing + validation from data tensors). Ignored with default value of `None`. + + Returns: + `History` object. + + Raises: + ValueError: In case of invalid argument values. + """ + # Required for Eager mode + K.set_learning_phase(True) + + do_validation = False + if val_ins: + do_validation = True + if (verbose and ins and hasattr(ins[0], 'shape') and + hasattr(val_ins[0], 'shape')): + print('Train on %d samples, validate on %d samples' % + (ins[0].shape[0], val_ins[0].shape[0])) + if validation_steps: + if steps_per_epoch is None: + raise ValueError('Can only use `validation_steps` when doing step-wise ' + 'training, i.e. `steps_per_epoch` must be set.') + do_validation = True + + num_train_samples = model._check_num_samples( + ins, batch_size, steps_per_epoch, 'steps_per_epoch') + + if num_train_samples is not None: + index_array = np.arange(num_train_samples) + + model.history = cbks.History() + callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] + if verbose: + if steps_per_epoch is not None: + count_mode = 'steps' + else: + count_mode = 'samples' + callbacks += [cbks.ProgbarLogger(count_mode)] + callbacks = cbks.CallbackList(callbacks) + out_labels = out_labels or [] + + # it's possible to callback a different model than self + # (used by Sequential models) + if hasattr(model, 'callback_model') and model.callback_model: + callback_model = model.callback_model + else: + callback_model = model + + callbacks.set_model(callback_model) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + callbacks.on_train_begin() + callback_model.stop_training = False + for cbk in callbacks: + cbk.validation_data = val_ins + + for epoch in range(initial_epoch, epochs): + callbacks.on_epoch_begin(epoch) + epoch_logs = {} + if shuffle == 'batch': + index_array = model._batch_shuffle(index_array, batch_size) + elif shuffle: + np.random.shuffle(index_array) + + batches = _make_batches(num_train_samples, batch_size) + + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + try: + if isinstance(ins[-1], float): + # Do not slice the training phase flag. + ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = _slice_arrays(ins, batch_ids) + except TypeError: + raise TypeError('TypeError while preparing batch. ' + 'If using HDF5 input data, ' + 'pass shuffle="batch".') + batch_logs = {} + batch_logs['batch'] = batch_index + batch_logs['size'] = len(batch_ids) + + callbacks.on_batch_begin(batch_index, batch_logs) + + ins_batch_converted = [] + for ib in ins_batch: + ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + eager_model_inputs = [] + eager_model_outputs = [] + for i in range(len(model.inputs)): + eager_model_inputs.append(ins_batch_converted[i]) + + for i in range(len(model.inputs), len(ins_batch_converted)): + eager_model_outputs.append(ins_batch_converted[i]) + + outs, loss, loss_metrics = _process_single_batch(eager_model_inputs, + eager_model_outputs, + model) + + if not isinstance(outs, list): + outs = [outs] + + for l, o in zip(out_labels, outs): + batch_logs[l] = o + # Required for Eager mode + metrics_names, metrics_results = _eager_metrics_fn(model, outs, + eager_model_outputs) + batch_logs['loss'] = tensor_util.constant_value(K.mean(loss)) + + # TODO(anjalisridhar): Move this to compile to avoid duplicate code. + # In graph mode we set the metric names in compile. However in + # Eager mode we calculate the metrics for each batch in fit_loop. + # We could calculate the metric names and functions in compile. + # This would avoid setting the callback parameters separately. + # We need to do this for the first iteration alone + for m in metrics_names: + if m not in callback_metrics: + callback_metrics.append(m) + + callbacks.set_params({ + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': num_train_samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics or [], + }) + + for k, v in zip(model.metrics_names, + [K.mean(loss)] + loss_metrics + metrics_results): + batch_logs[k] = tensor_util.constant_value(v) + + callbacks.on_batch_end(batch_index, batch_logs) + if callback_model.stop_training: + break + + if batch_index == len(batches) - 1: # Last batch. + if do_validation: + val_outs = test_loop( + model, val_ins, batch_size=batch_size, verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o + callbacks.on_epoch_end(epoch, epoch_logs) + if callback_model.stop_training: + break + callbacks.on_train_end() + return model.history + + +def test_loop(model, ins, batch_size=None, verbose=0, steps=None): + """Abstract method to loop over some data in batches. + + Arguments: + model: Model instance that is being evaluated in Eager mode. + ins: list of tensors to be fed to `f`. + batch_size: integer batch size or `None`. + verbose: verbosity mode. + steps: Total number of steps (batches of samples) + before declaring predictions finished. + Ignored with the default value of `None`. + + Returns: + Scalar loss (if the model has a single output and no metrics) + or list of scalars (if the model has multiple outputs + and/or metrics). The attribute `model.metrics_names` will give you + the display labels for the scalar outputs. + """ + K.set_learning_phase(False) + num_samples = model._check_num_samples(ins, batch_size, steps, 'steps') + outs = [] + if verbose == 1: + progbar = Progbar(target=num_samples) + batches = _make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + if isinstance(ins[-1], float): + # Do not slice the training phase flag. + ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = _slice_arrays(ins, batch_ids) + + ins_batch_converted = [] + for ib in ins_batch: + ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + + eager_model_inputs = [] + eager_model_outputs = [] + for i in range(len(model.inputs)): + eager_model_inputs.append(ins_batch_converted[i]) + + for i in range(len(model.inputs), len(ins_batch_converted)): + eager_model_outputs.append(ins_batch_converted[i]) + + loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs, + eager_model_outputs) + _, metrics_results = _eager_metrics_fn(model, loss_outs, + eager_model_outputs) + batch_outs = [] + for _, v in zip(model.metrics_names, + [K.mean(loss)] + loss_metrics + metrics_results): + batch_outs.append(tensor_util.constant_value(v)) + + if isinstance(batch_outs, list): + if batch_index == 0: + for batch_out in enumerate(batch_outs): + outs.append(0.) + for i, batch_out in enumerate(batch_outs): + outs[i] += batch_out * len(batch_ids) + else: + if batch_index == 0: + outs.append(0.) + outs[0] += batch_outs * len(batch_ids) + + if verbose == 1: + progbar.update(batch_end) + for i in range(len(outs)): + outs[i] /= num_samples + if len(outs) == 1: + return outs[0] + return outs + + +def predict_loop(model, ins, batch_size=32, verbose=0, steps=None): + """Abstract method to loop over some data in batches. + + Arguments: + model: + ins: list of tensors to be fed to `f`. + batch_size: integer batch size. + verbose: verbosity mode. + steps: Total number of steps (batches of samples) + before declaring `_predict_loop` finished. + Ignored with the default value of `None`. + + Returns: + Array of predictions (if the model has a single output) + or list of arrays of predictions + (if the model has multiple outputs). + """ + K.set_learning_phase(False) + num_samples = model._check_num_samples(ins, batch_size, steps, 'steps') + if verbose == 1: + if steps is not None: + progbar = Progbar(target=steps) + else: + progbar = Progbar(target=num_samples) + + outs = [] + batches = _make_batches(num_samples, batch_size) + index_array = np.arange(num_samples) + for batch_index, (batch_start, batch_end) in enumerate(batches): + batch_ids = index_array[batch_start:batch_end] + if ins and isinstance(ins[-1], float): + # Do not slice the training phase flag. + ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] + else: + ins_batch = _slice_arrays(ins, batch_ids) + + ins_batch_converted = [] + for ib in ins_batch: + ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx())) + + eager_model_inputs = [] + for i in range(len(model.inputs)): + eager_model_inputs.append(ins_batch_converted[i]) + + batch_outs = model(eager_model_inputs) + + if not isinstance(batch_outs, list): + batch_outs = [batch_outs] + if batch_index == 0: + # Pre-allocate the results arrays. + for batch_out in batch_outs: + dims = batch_out.shape[1:].dims + dims_list = [d.value for d in dims] + shape = (num_samples,) + tuple(dims_list) + outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype)) + for i, batch_out in enumerate(batch_outs): + outs[i][batch_start:batch_end] = batch_out + if verbose == 1: + progbar.update(batch_end) + if len(outs) == 1: + return outs[0] + return outs diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py new file mode 100644 index 0000000000..81e2f7a514 --- /dev/null +++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py @@ -0,0 +1,755 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for training routines.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import numpy as np + +from tensorflow.python.framework import ops +from tensorflow.python.keras._impl import keras +from tensorflow.python.keras._impl.keras import testing_utils +from tensorflow.python.platform import test +from tensorflow.python.training.rmsprop import RMSPropOptimizer + + +class TrainingTest(test.TestCase): + + def test_fit_on_arrays(self): + a = keras.layers.Input(shape=(3,), name='input_a') + b = keras.layers.Input(shape=(3,), name='input_b') + + dense = keras.layers.Dense(4, name='dense') + c = dense(a) + d = dense(b) + e = keras.layers.Dropout(0.5, name='dropout')(c) + + model = keras.models.Model([a, b], [d, e]) + + optimizer = RMSPropOptimizer(learning_rate=0.001) + loss = 'mse' + loss_weights = [1., 0.5] + metrics = ['mae'] + model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights) + + input_a_np = np.random.random((10, 3)) + input_b_np = np.random.random((10, 3)) + + output_d_np = np.random.random((10, 4)) + output_e_np = np.random.random((10, 4)) + + # Test fit at different verbosity + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + epochs=1, + batch_size=5, + verbose=0) + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + epochs=1, + batch_size=5, + verbose=1) + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + epochs=2, + batch_size=5, + verbose=2) + + # Test with validation data + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + validation_data=([input_a_np, input_b_np], [output_d_np, + output_e_np]), + epochs=1, + batch_size=5, + verbose=0) + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + validation_data=([input_a_np, input_b_np], [output_d_np, + output_e_np]), + epochs=2, + batch_size=5, + verbose=1) + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + validation_data=([input_a_np, input_b_np], [output_d_np, + output_e_np]), + epochs=2, + batch_size=5, + verbose=2) + model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np]) + + # Test with validation split + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + epochs=2, + batch_size=5, + verbose=0, + validation_split=0.2) + + # Test with dictionary inputs + model.fit( + { + 'input_a': input_a_np, + 'input_b': input_b_np + }, {'dense': output_d_np, + 'dropout': output_e_np}, + epochs=1, + batch_size=5, + verbose=0) + model.fit( + { + 'input_a': input_a_np, + 'input_b': input_b_np + }, {'dense': output_d_np, + 'dropout': output_e_np}, + epochs=1, + batch_size=5, + verbose=1) + model.fit( + { + 'input_a': input_a_np, + 'input_b': input_b_np + }, {'dense': output_d_np, + 'dropout': output_e_np}, + validation_data=({'input_a': input_a_np, + 'input_b': input_b_np + }, + { + 'dense': output_d_np, + 'dropout': output_e_np + }), + epochs=1, + batch_size=5, + verbose=0) + model.train_on_batch({ + 'input_a': input_a_np, + 'input_b': input_b_np + }, {'dense': output_d_np, + 'dropout': output_e_np}) + # Test with lists for loss, metrics + loss = ['mae', 'mse'] + metrics = ['acc', 'mae'] + model.compile(optimizer, loss, metrics=metrics) + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + epochs=1, + batch_size=5, + verbose=0) + + # Test with dictionaries for loss, metrics, loss weights + loss = {'dense': 'mse', 'dropout': 'mae'} + loss_weights = {'dense': 1., 'dropout': 0.5} + metrics = {'dense': 'mse', 'dropout': 'mae'} + model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights) + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + epochs=1, + batch_size=5, + verbose=0) + + # Invalid use cases + with self.assertRaises(AttributeError): + model.fit( + [input_a_np, input_b_np], [output_d_np, output_e_np], + epochs=1, + validation_data=([input_a_np, input_b_np], 0, 0), + verbose=0) + with self.assertRaises(ValueError): + model.train_on_batch({'input_a': input_a_np}, + [output_d_np, output_e_np]) + with self.assertRaises(ValueError): + model.train_on_batch([input_a_np], [output_d_np, output_e_np]) + with self.assertRaises(AttributeError): + model.train_on_batch(1, [output_d_np, output_e_np]) + with self.assertRaises(ValueError): + model.train_on_batch(input_a_np, [output_d_np, output_e_np]) + with self.assertRaises(ValueError): + bad_input = np.random.random((11, 3)) + model.train_on_batch([bad_input, input_b_np], + [output_d_np, output_e_np]) + with self.assertRaises(ValueError): + bad_target = np.random.random((11, 4)) + model.train_on_batch([input_a_np, input_b_np], + [bad_target, output_e_np]) + + # Build single-input model + x = keras.layers.Input(shape=(3,), name='input_a') + y = keras.layers.Dense(4)(x) + model = keras.models.Model(x, y) + model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse') + # This will work + model.fit([input_a_np], output_d_np, epochs=1) + with self.assertRaises(ValueError): + model.fit([input_a_np, input_a_np], output_d_np, epochs=1) + + def test_evaluate_predict_on_arrays(self): + a = keras.layers.Input(shape=(3,), name='input_a') + b = keras.layers.Input(shape=(3,), name='input_b') + + dense = keras.layers.Dense(4, name='dense') + c = dense(a) + d = dense(b) + e = keras.layers.Dropout(0.5, name='dropout')(c) + + model = keras.models.Model([a, b], [d, e]) + + optimizer = RMSPropOptimizer(learning_rate=0.001) + loss = 'mse' + loss_weights = [1., 0.5] + metrics = ['mae'] + model.compile( + optimizer, + loss, + metrics=metrics, + loss_weights=loss_weights, + sample_weight_mode=None) + + input_a_np = np.random.random((10, 3)) + input_b_np = np.random.random((10, 3)) + + output_d_np = np.random.random((10, 4)) + output_e_np = np.random.random((10, 4)) + + # Test evaluate at different verbosity + out = model.evaluate( + [input_a_np, input_b_np], [output_d_np, output_e_np], + batch_size=5, + verbose=0) + self.assertEqual(len(out), 5) + out = model.evaluate( + [input_a_np, input_b_np], [output_d_np, output_e_np], + batch_size=5, + verbose=1) + self.assertEqual(len(out), 5) + out = model.evaluate( + [input_a_np, input_b_np], [output_d_np, output_e_np], + batch_size=5, + verbose=2) + self.assertEqual(len(out), 5) + out = model.test_on_batch([input_a_np, input_b_np], + [output_d_np, output_e_np]) + self.assertEqual(len(out), 5) + + # Test evaluate with dictionary inputs + model.evaluate( + { + 'input_a': input_a_np, + 'input_b': input_b_np + }, {'dense': output_d_np, + 'dropout': output_e_np}, + batch_size=5, + verbose=0) + model.evaluate( + { + 'input_a': input_a_np, + 'input_b': input_b_np + }, {'dense': output_d_np, + 'dropout': output_e_np}, + batch_size=5, + verbose=1) + + # Test predict + out = model.predict([input_a_np, input_b_np], batch_size=5) + self.assertEqual(len(out), 2) + out = model.predict({'input_a': input_a_np, 'input_b': input_b_np}) + self.assertEqual(len(out), 2) + out = model.predict_on_batch({ + 'input_a': input_a_np, + 'input_b': input_b_np + }) + self.assertEqual(len(out), 2) + + def test_invalid_loss_or_metrics(self): + num_classes = 5 + train_samples = 1000 + test_samples = 1000 + input_dim = 5 + + model = keras.models.Sequential() + model.add(keras.layers.Dense(10, input_shape=(input_dim,))) + model.add(keras.layers.Activation('relu')) + model.add(keras.layers.Dense(num_classes)) + model.add(keras.layers.Activation('softmax')) + model.compile(loss='categorical_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001)) + np.random.seed(1337) + + (x_train, y_train), (_, _) = testing_utils.get_test_data( + train_samples=train_samples, + test_samples=test_samples, + input_shape=(input_dim,), + num_classes=num_classes) + + with self.assertRaises(ValueError): + model.fit(x_train, np.concatenate([y_train, y_train], axis=-1)) + + with self.assertRaises(TypeError): + model.compile(loss='categorical_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=set(0)) + + with self.assertRaises(ValueError): + model.compile(loss=None, + optimizer='rms') + + +class LossWeightingTest(test.TestCase): + + def test_class_weights(self): + num_classes = 5 + batch_size = 5 + epochs = 5 + weighted_class = 3 + train_samples = 3000 + test_samples = 3000 + input_dim = 5 + + model = keras.models.Sequential() + model.add(keras.layers.Dense(10, input_shape=(input_dim,))) + model.add(keras.layers.Activation('relu')) + model.add(keras.layers.Dense(num_classes)) + model.add(keras.layers.Activation('softmax')) + model.compile(loss='categorical_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001)) + + np.random.seed(1337) + (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data( + train_samples=train_samples, + test_samples=test_samples, + input_shape=(input_dim,), + num_classes=num_classes) + int_y_test = y_test.copy() + int_y_train = y_train.copy() + # convert class vectors to binary class matrices + y_train = keras.utils.to_categorical(y_train, num_classes) + y_test = keras.utils.to_categorical(y_test, num_classes) + test_ids = np.where(int_y_test == np.array(weighted_class))[0] + + class_weight = dict([(i, 1.) for i in range(num_classes)]) + class_weight[weighted_class] = 2. + + sample_weight = np.ones((y_train.shape[0])) + sample_weight[int_y_train == weighted_class] = 2. + + model.fit( + x_train, + y_train, + batch_size=batch_size, + epochs=epochs // 3, + verbose=0, + class_weight=class_weight, + validation_data=(x_train, y_train, sample_weight)) + model.fit( + x_train, + y_train, + batch_size=batch_size, + epochs=epochs // 2, + verbose=0, + class_weight=class_weight) + model.fit( + x_train, + y_train, + batch_size=batch_size, + epochs=epochs // 2, + verbose=0, + class_weight=class_weight, + validation_split=0.1) + + model.train_on_batch( + x_train[:batch_size], y_train[:batch_size], class_weight=class_weight) + ref_score = model.evaluate(x_test, y_test, verbose=0) + score = model.evaluate( + x_test[test_ids, :], y_test[test_ids, :], verbose=0) + self.assertLess(score, ref_score) + + def test_sample_weights(self): + num_classes = 5 + batch_size = 5 + epochs = 5 + weighted_class = 3 + train_samples = 3000 + test_samples = 3000 + input_dim = 5 + + model = keras.models.Sequential() + model.add(keras.layers.Dense(10, input_shape=(input_dim,))) + model.add(keras.layers.Activation('relu')) + model.add(keras.layers.Dense(num_classes)) + model.add(keras.layers.Activation('softmax')) + model.compile(loss='categorical_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001)) + + np.random.seed(43) + (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data( + train_samples=train_samples, + test_samples=test_samples, + input_shape=(input_dim,), + num_classes=num_classes) + int_y_test = y_test.copy() + int_y_train = y_train.copy() + # convert class vectors to binary class matrices + y_train = keras.utils.to_categorical(y_train, num_classes) + y_test = keras.utils.to_categorical(y_test, num_classes) + test_ids = np.where(int_y_test == np.array(weighted_class))[0] + + class_weight = dict([(i, 1.) for i in range(num_classes)]) + class_weight[weighted_class] = 2. + + sample_weight = np.ones((y_train.shape[0])) + sample_weight[int_y_train == weighted_class] = 2. + + model.fit( + x_train, + y_train, + batch_size=batch_size, + epochs=epochs // 3, + verbose=0, + sample_weight=sample_weight) + model.fit( + x_train, + y_train, + batch_size=batch_size, + epochs=epochs // 3, + verbose=0, + sample_weight=sample_weight, + validation_split=0.1) + model.train_on_batch( + x_train[:batch_size], + y_train[:batch_size], + sample_weight=sample_weight[:batch_size]) + model.test_on_batch( + x_train[:batch_size], + y_train[:batch_size], + sample_weight=sample_weight[:batch_size]) + + def test_temporal_sample_weights(self): + num_classes = 5 + weighted_class = 3 + train_samples = 1000 + test_samples = 1000 + input_dim = 5 + timesteps = 3 + + model = keras.models.Sequential() + model.add( + keras.layers.TimeDistributed( + keras.layers.Dense(num_classes), + input_shape=(timesteps, input_dim))) + model.add(keras.layers.Activation('softmax')) + + np.random.seed(1337) + (_, y_train), _ = testing_utils.get_test_data( + train_samples=train_samples, + test_samples=test_samples, + input_shape=(input_dim,), + num_classes=num_classes) + int_y_train = y_train.copy() + # convert class vectors to binary class matrices + y_train = keras.utils.to_categorical(y_train, num_classes) + + class_weight = dict([(i, 1.) for i in range(num_classes)]) + class_weight[weighted_class] = 2. + + sample_weight = np.ones((y_train.shape[0])) + sample_weight[int_y_train == weighted_class] = 2. + with self.assertRaises(ValueError): + model.compile( + loss='binary_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001), + sample_weight_mode='temporal') + + def test_class_weight_invalid_use_case(self): + num_classes = 5 + train_samples = 1000 + test_samples = 1000 + input_dim = 5 + timesteps = 3 + + model = keras.models.Sequential() + model.add( + keras.layers.TimeDistributed( + keras.layers.Dense(num_classes), + input_shape=(timesteps, input_dim))) + model.add(keras.layers.Activation('softmax')) + model.compile( + loss='binary_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001)) + + (x_train, y_train), _ = testing_utils.get_test_data( + train_samples=train_samples, + test_samples=test_samples, + input_shape=(input_dim,), + num_classes=num_classes) + # convert class vectors to binary class matrices + y_train = keras.utils.to_categorical(y_train, num_classes) + class_weight = dict([(i, 1.) for i in range(num_classes)]) + + del class_weight[1] + with self.assertRaises(ValueError): + model.fit(x_train, y_train, + epochs=0, verbose=0, class_weight=class_weight) + + with self.assertRaises(ValueError): + model.compile( + loss='binary_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001), + sample_weight_mode=[]) + + # Build multi-output model + x = keras.Input((3,)) + y1 = keras.layers.Dense(4, name='1')(x) + y2 = keras.layers.Dense(4, name='2')(x) + model = keras.models.Model(x, [y1, y2]) + model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse') + x_np = np.random.random((10, 3)) + y_np = np.random.random((10, 4)) + w_np = np.random.random((10,)) + # This will work + model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': w_np}) + # These will not + with self.assertRaises(ValueError): + model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=[w_np]) + with self.assertRaises(TypeError): + model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=w_np) + with self.assertRaises(ValueError): + bad_w_np = np.random.random((11,)) + model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np}) + with self.assertRaises(ValueError): + bad_w_np = np.random.random((10, 2)) + model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np}) + with self.assertRaises(ValueError): + bad_w_np = np.random.random((10, 2, 2)) + model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np}) + + +class TestDynamicTrainability(test.TestCase): + + def test_trainable_warning(self): + x = np.random.random((5, 3)) + y = np.random.random((5, 2)) + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_dim=3)) + model.trainable = False + model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') + model.trainable = True + with self.assertRaises(ValueError): + model.train_on_batch(x, y) + + def test_trainable_argument(self): + x = np.random.random((5, 3)) + y = np.random.random((5, 2)) + + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_dim=3, trainable=False)) + model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') + out = model.predict(x) + with self.assertRaises(ValueError): + model.train_on_batch(x, y) + out_2 = model.predict(x) + self.assertAllClose(out, out_2) + + # test with nesting + inputs = keras.layers.Input(shape=(3,)) + output = model(inputs) + model = keras.models.Model(inputs, output) + model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse') + out = model.predict(x) + with self.assertRaises(ValueError): + model.train_on_batch(x, y) + out_2 = model.predict(x) + self.assertAllClose(out, out_2) + + def test_layer_trainability_switch(self): + # with constructor argument, in Sequential + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, trainable=False, input_dim=1)) + self.assertListEqual(model.trainable_weights, []) + + # by setting the `trainable` argument, in Sequential + model = keras.models.Sequential() + layer = keras.layers.Dense(2, input_dim=1) + model.add(layer) + self.assertListEqual(model.trainable_weights, layer.trainable_weights) + layer.trainable = False + self.assertListEqual(model.trainable_weights, []) + + # with constructor argument, in Model + x = keras.layers.Input(shape=(1,)) + y = keras.layers.Dense(2, trainable=False)(x) + model = keras.models.Model(x, y) + self.assertListEqual(model.trainable_weights, []) + + # by setting the `trainable` argument, in Model + x = keras.layers.Input(shape=(1,)) + layer = keras.layers.Dense(2) + y = layer(x) + model = keras.models.Model(x, y) + self.assertListEqual(model.trainable_weights, layer.trainable_weights) + layer.trainable = False + self.assertListEqual(model.trainable_weights, []) + + def test_model_trainability_switch(self): + # a non-trainable model has no trainable weights + x = keras.layers.Input(shape=(1,)) + y = keras.layers.Dense(2)(x) + model = keras.models.Model(x, y) + model.trainable = False + self.assertListEqual(model.trainable_weights, []) + + # same for Sequential + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_dim=1)) + model.trainable = False + self.assertListEqual(model.trainable_weights, []) + + def test_nested_model_trainability(self): + + # a Sequential inside a Model + inner_model = keras.models.Sequential() + inner_model.add(keras.layers.Dense(2, input_dim=1)) + + x = keras.layers.Input(shape=(1,)) + y = inner_model(x) + outer_model = keras.models.Model(x, y) + self.assertListEqual(outer_model.trainable_weights, + inner_model.trainable_weights) + inner_model.trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + inner_model.trainable = True + inner_model.layers[-1].trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + + # a Sequential inside a Sequential + inner_model = keras.models.Sequential() + inner_model.add(keras.layers.Dense(2, input_dim=1)) + outer_model = keras.models.Sequential() + outer_model.add(inner_model) + self.assertListEqual(outer_model.trainable_weights, + inner_model.trainable_weights) + inner_model.trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + inner_model.trainable = True + inner_model.layers[-1].trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + + # a Model inside a Model + x = keras.layers.Input(shape=(1,)) + y = keras.layers.Dense(2)(x) + inner_model = keras.models.Model(x, y) + x = keras.layers.Input(shape=(1,)) + y = inner_model(x) + outer_model = keras.models.Model(x, y) + self.assertListEqual(outer_model.trainable_weights, + inner_model.trainable_weights) + inner_model.trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + inner_model.trainable = True + inner_model.layers[-1].trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + + # a Model inside a Sequential + x = keras.layers.Input(shape=(1,)) + y = keras.layers.Dense(2)(x) + inner_model = keras.models.Model(x, y) + outer_model = keras.models.Sequential() + outer_model.add(inner_model) + self.assertListEqual(outer_model.trainable_weights, + inner_model.trainable_weights) + inner_model.trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + inner_model.trainable = True + inner_model.layers[-1].trainable = False + self.assertListEqual(outer_model.trainable_weights, []) + + +class TestTrainingUtils(test.TestCase): + + def test_check_array_lengths(self): + keras.engine.training._check_array_lengths(None, None, None) + a_np = np.random.random((4, 3, 3)) + keras.engine.training._check_array_lengths(a_np, a_np, a_np) + keras.engine.training._check_array_lengths( + [a_np, a_np], [a_np, a_np], [a_np, a_np]) + keras.engine.training._check_array_lengths([None], [None], [None]) + + b_np = np.random.random((3, 4)) + with self.assertRaises(ValueError): + keras.engine.training._check_array_lengths(a_np, None, None) + with self.assertRaises(ValueError): + keras.engine.training._check_array_lengths(a_np, a_np, None) + with self.assertRaises(ValueError): + keras.engine.training._check_array_lengths([a_np], [None], None) + with self.assertRaises(ValueError): + keras.engine.training._check_array_lengths([a_np], [b_np], None) + with self.assertRaises(ValueError): + keras.engine.training._check_array_lengths([a_np], None, [b_np]) + + def test_slice_arrays(self): + input_a = np.random.random((10, 3)) + keras.engine.training._slice_arrays(None) + keras.engine.training._slice_arrays(input_a, 0) + keras.engine.training._slice_arrays(input_a, 0, 1) + keras.engine.training._slice_arrays(input_a, stop=2) + input_a = [None, [1, 1], None, [1, 1]] + keras.engine.training._slice_arrays(input_a, 0) + keras.engine.training._slice_arrays(input_a, 0, 1) + keras.engine.training._slice_arrays(input_a, stop=2) + input_a = [None] + keras.engine.training._slice_arrays(input_a, 0) + keras.engine.training._slice_arrays(input_a, 0, 1) + keras.engine.training._slice_arrays(input_a, stop=2) + input_a = None + keras.engine.training._slice_arrays(input_a, 0) + keras.engine.training._slice_arrays(input_a, 0, 1) + keras.engine.training._slice_arrays(input_a, stop=2) + + def test_fit_with_BatchNorm(self): + model = keras.models.Sequential() + model.add(keras.layers.Dense(10, input_dim=4)) + model.add(keras.layers.BatchNormalization()) + model.add(keras.layers.Activation('tanh')) + model.add(keras.layers.Dropout(0.2)) + + input_a_np = np.random.random((10, 4)) + output_b_np = np.random.random((10, 10)) + + model.compile(loss='binary_crossentropy', optimizer=RMSPropOptimizer(0.001)) + model.fit(input_a_np, output_b_np, epochs=1, batch_size=5, verbose=0) + + def test_fit_with_regularization(self): + model = keras.models.Sequential() + with self.assertRaises(ValueError): + model.add( + keras.layers.Dense(4, input_dim=3, + kernel_regularizer=keras.regularizers.l2(0.01), + activity_regularizer=keras.regularizers.l1(0.01))) + + +if __name__ == '__main__': + # Bazel sets these environment variables to very long paths. + # Tempfile uses them to create long paths, and in turn multiprocessing + # library tries to create sockets named after paths. Delete whatever bazel + # writes to these to avoid tests failing due to socket addresses being too + # long. + for var in ('TMPDIR', 'TMP', 'TEMP'): + if var in os.environ: + del os.environ[var] + + ops.enable_eager_execution() + test.main() diff --git a/tensorflow/python/keras/_impl/keras/layers/core.py b/tensorflow/python/keras/_impl/keras/layers/core.py index 6ee3fb48b2..ea2d3f2f04 100644 --- a/tensorflow/python/keras/_impl/keras/layers/core.py +++ b/tensorflow/python/keras/_impl/keras/layers/core.py @@ -23,6 +23,7 @@ import types as python_types import numpy as np +from tensorflow.python.eager import context from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import activations from tensorflow.python.keras._impl.keras import backend as K @@ -119,7 +120,8 @@ class Dropout(tf_core_layers.Dropout, Layer): if training is None: training = K.learning_phase() output = super(Dropout, self).call(inputs, training=training) - if training is K.learning_phase(): + # EagerTensor object has no attribute _uses_learning_phase + if not context.in_eager_mode() and training is K.learning_phase(): output._uses_learning_phase = True # pylint: disable=protected-access return output diff --git a/tensorflow/python/keras/_impl/keras/layers/normalization.py b/tensorflow/python/keras/_impl/keras/layers/normalization.py index 965ef70e6e..eecb14ceaa 100644 --- a/tensorflow/python/keras/_impl/keras/layers/normalization.py +++ b/tensorflow/python/keras/_impl/keras/layers/normalization.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.eager import context from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers @@ -108,7 +109,7 @@ class BatchNormalization(tf_normalization_layers.BatchNormalization, Layer): if training is None: training = K.learning_phase() output = super(BatchNormalization, self).call(inputs, training=training) - if training is K.learning_phase(): + if context.in_graph_mode() and training is K.learning_phase(): output._uses_learning_phase = True # pylint: disable=protected-access return output diff --git a/tensorflow/python/keras/_impl/keras/optimizers.py b/tensorflow/python/keras/_impl/keras/optimizers.py index e47987aadc..a55a5e39a6 100644 --- a/tensorflow/python/keras/_impl/keras/optimizers.py +++ b/tensorflow/python/keras/_impl/keras/optimizers.py @@ -24,6 +24,7 @@ import copy import six from six.moves import zip # pylint: disable=redefined-builtin +from tensorflow.python.eager import context from tensorflow.python.framework import dtypes as dtypes_module from tensorflow.python.framework import ops from tensorflow.python.keras._impl.keras import backend as K @@ -680,7 +681,14 @@ class TFOptimizer(Optimizer): def __init__(self, optimizer): # pylint: disable=super-init-not-called self.optimizer = optimizer with K.name_scope(self.__class__.__name__): - self.iterations = K.variable(0, dtype='int64', name='iterations') + if context.in_graph_mode(): + self.iterations = K.variable(0, dtype='int64', name='iterations') + + def apply_gradients(self, grads): + self.optimizer.apply_gradients(grads) + + def get_grads(self, loss, params): + return self.optimizer.compute_gradients(loss, params) def get_updates(self, loss, params): grads = self.optimizer.compute_gradients(loss, params) diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index 04b6056ace..5dea732cba 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -31,6 +31,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.layers import utils as layers_util +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.ops import variables as tf_variables @@ -649,6 +650,7 @@ class Layer(object): else: scope_context_manager = vs.variable_scope( self._scope, reuse=self._reuse, auxiliary_name_scope=False) + input_shapes = None with scope_context_manager as scope: with ops.name_scope(self._name_scope_name(scope)): if not self.built: @@ -698,6 +700,9 @@ class Layer(object): else: # Deferred mode behavior: use `compute_output_shape` to # infer the number of outputs of the layer and their shapes. + if input_shapes is None: + input_shapes = nest.map_structure(lambda x: x.get_shape(), inputs) + output_shapes = self.compute_output_shape(input_shapes) output_shapes = nest.flatten(output_shapes) outputs = [ @@ -1393,7 +1398,10 @@ class _DeferredTensor(object): def __init__(self, shape, dtype, name=None): self.shape = tensor_shape.TensorShape(shape) - self.dtype = dtypes.as_dtype(dtype) + if dtype is None: + self.dtype = dtypes.as_dtype(np.float32) + else: + self.dtype = dtypes.as_dtype(dtype) self.name = name def get_shape(self): diff --git a/tensorflow/python/layers/network.py b/tensorflow/python/layers/network.py index 0a5dd57621..745843975c 100644 --- a/tensorflow/python/layers/network.py +++ b/tensorflow/python/layers/network.py @@ -621,6 +621,11 @@ class GraphNetwork(base.Layer): A list of loss tensors. """ losses = [] + if context.in_eager_mode(): + for layer in self.layers: + losses += layer.losses + return losses + # Retrieve losses for all internal layers. for layer in self.layers: if hasattr(layer, 'losses'): @@ -853,7 +858,6 @@ class GraphNetwork(base.Layer): for node in nodes: # This is always a single layer, never a list. layer = node.outbound_layer - reference_input_tensors = node.input_tensors reference_output_tensors = node.output_tensors @@ -901,12 +905,13 @@ class GraphNetwork(base.Layer): else: output_masks = [None for _ in range(len(output_tensors))] - # Apply activity regularizer if any: - if layer.activity_regularizer is not None: - regularization_losses = [ - layer.activity_regularizer(x) for x in computed_tensors - ] - layer.add_loss(regularization_losses, computed_tensors) + if context.in_graph_mode(): + if layer.activity_regularizer is not None: + regularization_losses = [ + layer.activity_regularizer(x) for x in computed_tensors + ] + # Apply activity regularizer if any: + layer.add_loss(regularization_losses, computed_tensors) if context.in_graph_mode(): # Update model updates and losses: -- GitLab From 33f537e5275d477771d68eb9b6654ddeeffd2f15 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 30 Jan 2018 19:30:18 -0800 Subject: [PATCH 312/423] internal change PiperOrigin-RevId: 183932015 --- .../contrib/tpu/profiler/capture_tpu_profile.cc | 2 ++ tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc | 11 ++++------- tensorflow/contrib/tpu/profiler/dump_tpu_profile.h | 2 ++ 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc index 9ac9e3cb28..b1ef9fde37 100644 --- a/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/capture_tpu_profile.cc @@ -154,6 +154,8 @@ int main(int argc, char** argv) { << std::endl << "Tip: increase number of attempts with --num_tracing_attempts." << std::endl; + // Don't dump profile data if no trace is collected. + return 0; } // Use the current timestamp as the run name. diff --git a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc index 64e4e6275d..ebd6185faa 100644 --- a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc +++ b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.cc @@ -151,8 +151,7 @@ Status WriteTensorboardTPUProfile(const string& logdir, const string& run, TF_RETURN_IF_ERROR(Env::Default()->RecursivelyCreateDir(profile_run_dir)); // Ignore computation_graph for now. - const bool empty_trace = response.encoded_trace().empty(); - if (!empty_trace) { + if (!response.encoded_trace().empty()) { LOG(INFO) << "Converting trace events to TraceViewer JSON."; TF_RETURN_IF_ERROR( DumpTraceToLogDirectory(profile_run_dir, response.encoded_trace(), os)); @@ -163,11 +162,9 @@ Status WriteTensorboardTPUProfile(const string& logdir, const string& run, TF_RETURN_IF_ERROR(DumpOpProfileToLogDirectory(profile_run_dir, response.op_profile(), os)); } - if (!empty_trace && !response.tool_data().empty()) { - for (const auto& tool_data : response.tool_data()) { - TF_RETURN_IF_ERROR( - DumpToolDataToLogDirectory(profile_run_dir, tool_data, os)); - } + for (const auto& tool_data : response.tool_data()) { + TF_RETURN_IF_ERROR( + DumpToolDataToLogDirectory(profile_run_dir, tool_data, os)); } return Status::OK(); diff --git a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h index 2f8656a37b..29ef977bac 100644 --- a/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h +++ b/tensorflow/contrib/tpu/profiler/dump_tpu_profile.h @@ -29,6 +29,8 @@ namespace tpu { // - Op profile // - Input pipeline analyzer // - Overview page +// Note: this function creates a directory even when all fields in +// ProfileResponse are unset/empty. Status WriteTensorboardTPUProfile(const string& logdir, const string& run, const ProfileResponse& response, std::ostream* os); -- GitLab From 2a01e3f2ee1ec5b1cf212dd949c1072129e4770a Mon Sep 17 00:00:00 2001 From: Bjarke Hammersholt Roune Date: Tue, 30 Jan 2018 20:12:22 -0800 Subject: [PATCH 313/423] Remove Google-internal bug numbers from XLA error messages. PiperOrigin-RevId: 183934860 --- .../compiler/xla/service/cpu/ir_emitter.cc | 26 +++++++++---------- .../xla/service/elemental_ir_emitter.cc | 7 +++-- .../compiler/xla/service/gpu/ir_emitter.cc | 9 ++++--- .../xla/service/gpu/ir_emitter_unnested.cc | 3 +-- 4 files changed, 23 insertions(+), 22 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 71e8133189..0b2d3d4746 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -479,7 +479,7 @@ Status IrEmitter::HandleOutfeed(HloInstruction* outfeed) { Status IrEmitter::HandleSort(HloInstruction* sort) { // TODO(b/26783907): Implement sort on CPU. - return Unimplemented("Sort is not supported on CPU (b/26783907)."); + return Unimplemented("Sort is not implemented on CPU."); } Status IrEmitter::HandleTuple(HloInstruction* tuple) { @@ -522,7 +522,7 @@ Status IrEmitter::HandleReduceWindow(HloInstruction* reduce_window) { // TODO(b/31410564): Implement dilation for reduce-window. if (window_util::HasDilation(window)) { return Unimplemented( - "Dilation for reduce-window not implemented on CPU. See b/31410564."); + "Dilation for ReduceWindow is not implemented on CPU."); } // The called computation should have been emitted previously. @@ -625,8 +625,7 @@ Status IrEmitter::HandleSelectAndScatter(HloInstruction* select_and_scatter) { // TODO(b/31410564): Implement dilation for select-and-scatter. if (window_util::HasDilation(window)) { return Unimplemented( - "Dilation for select-and-scatter not implemented on CPU. " - "See b/31410564."); + "Dilation for SelectAndScatter is not implemented on CPU. "); } // The select and scatter computations should have been emitted previously. @@ -1196,8 +1195,7 @@ Status IrEmitter::HandleCrossReplicaSum(HloInstruction* crs) { } // TODO(b/33011107): Support cross replica sum on CPU. - return Unimplemented( - "Cross replica sum is not implemented on CPU. See b/33011107."); + return Unimplemented("CrossReplicaSum is not implemented on CPU."); } // Fills up the free variables in 'index_with_free_var' with values from @@ -1811,12 +1809,12 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { Status IrEmitter::HandleSend(HloInstruction* send) { // TODO(b/33942983): Support Send/Recv on CPU. - return Unimplemented("Send is not implemented on CPU. See b/33942983."); + return Unimplemented("Send is not implemented on CPU."); } Status IrEmitter::HandleSendDone(HloInstruction* send_done) { // TODO(b/33942983): Support Send/Recv on CPU. - return Unimplemented("Send-done is not implemented on CPU. See b/33942983."); + return Unimplemented("Send-done is not implemented on CPU."); } Status IrEmitter::HandleSlice(HloInstruction* slice) { @@ -1981,12 +1979,12 @@ Status IrEmitter::HandleDynamicUpdateSlice( Status IrEmitter::HandleRecv(HloInstruction* recv) { // TODO(b/33942983): Support Send/Recv on CPU. - return Unimplemented("Recv is not implemented on CPU. See b/33942983."); + return Unimplemented("Recv is not implemented on CPU."); } Status IrEmitter::HandleRecvDone(HloInstruction* recv_done) { // TODO(b/33942983): Support Send/Recv on CPU. - return Unimplemented("Recv-done is not implemented on CPU. See b/33942983."); + return Unimplemented("Recv-done is not implemented on CPU."); } Status IrEmitter::HandlePad(HloInstruction* pad) { @@ -1995,10 +1993,10 @@ Status IrEmitter::HandlePad(HloInstruction* pad) { for (auto& padding_dimension : pad->padding_config().dimensions()) { if (padding_dimension.edge_padding_low() < 0 || padding_dimension.edge_padding_high() < 0) { - return Unimplemented( - "Negative padding not supported in the CPU backend (b/34628603); " - "this should have been eliminated at the HLO level: %s", - pad->padding_config().ShortDebugString().c_str()); + return InternalErrorStrCat( + "Encountered negative padding in IrEmitter on CPU. " + "This should have been eliminated at the HLO level. ", + pad->ToString()); } } diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 9780bac16e..28cd425309 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -428,7 +428,7 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( llvm::Intrinsic::round, {operand_value}, {operand_value->getType()}, ir_builder_); case HloOpcode::kSign: { - // TODO(b/32151903): Ensure consistent sign behavior for -0.0 + // 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); @@ -870,7 +870,10 @@ llvm::Value* ElementalIrEmitter::EmitFloatMin(llvm::Value* lhs_value, StatusOr ElementalIrEmitter::EmitErfInv(PrimitiveType prim_type, llvm::Value* x) const { if (prim_type != F32) { - return Unimplemented("inverse erf only implemented for F32 (b/34339814)"); + // TODO(b/34339814): Implement inverse erf for F64. + return Unimplemented( + "Inverse erf is only implemented for element " + "type F32."); } auto getFloat = [&](const float f) { return llvm::ConstantFP::get(ir_builder_->getFloatTy(), f); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 23b72c3f71..affd2ffa8e 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -615,8 +615,7 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { Status IrEmitter::HandleCrossReplicaSum(HloInstruction* crs) { // TODO(b/33011107): Support cross replica sum on GPU. - return Unimplemented( - "Cross replica sum not implemented on GPU. See b/33011107."); + return Unimplemented("CrossReplicaSum is not implemented on GPU."); } Status IrEmitter::HandleParameter(HloInstruction* parameter) { @@ -710,11 +709,13 @@ Status IrEmitter::HandleCustomCall(HloInstruction*) { } Status IrEmitter::HandleInfeed(HloInstruction*) { - return Unimplemented("Infeed is not supported on GPU (b/30467474)."); + // TODO(b/30467474): Implement infeed on GPU. + return Unimplemented("Infeed is not supported on GPU."); } Status IrEmitter::HandleOutfeed(HloInstruction*) { - return Unimplemented("Outfeed is not supported on GPU (b/34359662)."); + // TODO(b/34359662): Implement outfeed on GPU. + return Unimplemented("Outfeed is not supported on GPU."); } Status IrEmitter::HandleRng(HloInstruction* random) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index fc8783e753..bd428f8028 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -1658,8 +1658,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // TODO(b/31410564): Implement dilation rate for select-and-scatter. if (window_util::HasDilation(window)) { return Unimplemented( - "Dilation for select-and-scatter not implemented on GPU. " - "See b/31410564."); + "Dilation for SelectAndScatter not implemented on GPU."); } // kSelectAndScatter is implemented as two kernel launches: the first launch -- GitLab From e9d4d3d06c0fb211f7488f868fefb477f07df4f8 Mon Sep 17 00:00:00 2001 From: Anna R Date: Tue, 30 Jan 2018 20:28:38 -0800 Subject: [PATCH 314/423] Adding tf_export decorators/calls to TensorFlow functions and constants. PiperOrigin-RevId: 183936100 --- tensorflow/python/training/adadelta.py | 2 ++ tensorflow/python/training/adagrad.py | 2 ++ tensorflow/python/training/adagrad_da.py | 2 ++ tensorflow/python/training/adam.py | 2 ++ tensorflow/python/training/basic_loops.py | 2 ++ .../python/training/basic_session_run_hooks.py | 14 ++++++++++++++ tensorflow/python/training/checkpoint_utils.py | 5 +++++ tensorflow/python/training/coordinator.py | 3 +++ tensorflow/python/training/device_setter.py | 2 ++ tensorflow/python/training/ftrl.py | 3 ++- tensorflow/python/training/gradient_descent.py | 2 ++ tensorflow/python/training/input.py | 15 +++++++++++++++ tensorflow/python/training/learning_rate_decay.py | 10 ++++++++++ tensorflow/python/training/momentum.py | 2 ++ tensorflow/python/training/monitored_session.py | 8 ++++++++ tensorflow/python/training/moving_averages.py | 2 ++ tensorflow/python/training/optimizer.py | 2 ++ tensorflow/python/training/proximal_adagrad.py | 2 ++ .../python/training/proximal_gradient_descent.py | 2 ++ tensorflow/python/training/queue_runner_impl.py | 5 +++++ tensorflow/python/training/rmsprop.py | 2 ++ tensorflow/python/training/saver.py | 10 ++++++++++ tensorflow/python/training/server_lib.py | 3 +++ tensorflow/python/training/session_manager.py | 2 ++ tensorflow/python/training/session_run_hook.py | 5 +++++ tensorflow/python/training/supervisor.py | 2 ++ .../python/training/sync_replicas_optimizer.py | 2 ++ tensorflow/python/training/training_util.py | 6 ++++++ tensorflow/tools/api/generator/BUILD | 1 + 29 files changed, 119 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/training/adadelta.py b/tensorflow/python/training/adadelta.py index 13c07cfd7b..c08e3cca00 100644 --- a/tensorflow/python/training/adadelta.py +++ b/tensorflow/python/training/adadelta.py @@ -22,8 +22,10 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.AdadeltaOptimizer") class AdadeltaOptimizer(optimizer.Optimizer): """Optimizer that implements the Adadelta algorithm. diff --git a/tensorflow/python/training/adagrad.py b/tensorflow/python/training/adagrad.py index afa192f7cc..deb4e6f546 100644 --- a/tensorflow/python/training/adagrad.py +++ b/tensorflow/python/training/adagrad.py @@ -25,8 +25,10 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.AdagradOptimizer") class AdagradOptimizer(optimizer.Optimizer): """Optimizer that implements the Adagrad algorithm. diff --git a/tensorflow/python/training/adagrad_da.py b/tensorflow/python/training/adagrad_da.py index b3f9ea323c..5ba403554f 100644 --- a/tensorflow/python/training/adagrad_da.py +++ b/tensorflow/python/training/adagrad_da.py @@ -23,8 +23,10 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.AdagradDAOptimizer") class AdagradDAOptimizer(optimizer.Optimizer): """Adagrad Dual Averaging algorithm for sparse linear models. diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py index 0c69f8bf39..c92f6fc301 100644 --- a/tensorflow/python/training/adam.py +++ b/tensorflow/python/training/adam.py @@ -26,8 +26,10 @@ from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.AdamOptimizer") class AdamOptimizer(optimizer.Optimizer): """Optimizer that implements the Adam algorithm. diff --git a/tensorflow/python/training/basic_loops.py b/tensorflow/python/training/basic_loops.py index 52b0f42106..7af821c819 100644 --- a/tensorflow/python/training/basic_loops.py +++ b/tensorflow/python/training/basic_loops.py @@ -18,8 +18,10 @@ from __future__ import division from __future__ import print_function from tensorflow.python.framework import errors +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.basic_train_loop") def basic_train_loop(supervisor, train_step_fn, args=None, kwargs=None, master=""): """Basic loop to train a model. diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index 752d585cd1..17e07e171a 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -47,6 +47,7 @@ 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 class _HookTimer(object): @@ -85,6 +86,7 @@ class _HookTimer(object): raise NotImplementedError +@tf_export("train.SecondOrStepTimer") class SecondOrStepTimer(_HookTimer): """Timer that triggers at most once every N seconds or once every N steps. """ @@ -164,6 +166,7 @@ class NeverTriggerTimer(_HookTimer): return None +@tf_export("train.LoggingTensorHook") class LoggingTensorHook(session_run_hook.SessionRunHook): """Prints the given tensors every N local steps, every N seconds, or at end. @@ -262,6 +265,7 @@ class LoggingTensorHook(session_run_hook.SessionRunHook): self._log_tensors(values) +@tf_export("train.StopAtStepHook") class StopAtStepHook(session_run_hook.SessionRunHook): """Hook that requests stop at a specified step.""" @@ -317,6 +321,7 @@ class StopAtStepHook(session_run_hook.SessionRunHook): run_context.request_stop() +@tf_export("train.CheckpointSaverListener") class CheckpointSaverListener(object): """Interface for listeners that take action before or after checkpoint save. @@ -375,6 +380,7 @@ class CheckpointSaverListener(object): pass +@tf_export("train.CheckpointSaverHook") class CheckpointSaverHook(session_run_hook.SessionRunHook): """Saves checkpoints every N steps or seconds.""" @@ -497,6 +503,7 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): return savers[0] +@tf_export("train.StepCounterHook") class StepCounterHook(session_run_hook.SessionRunHook): """Hook that counts steps per second.""" @@ -575,12 +582,14 @@ class StepCounterHook(session_run_hook.SessionRunHook): self._last_global_step = stale_global_step +@tf_export("train.NanLossDuringTrainingError") class NanLossDuringTrainingError(RuntimeError): def __str__(self): return "NaN loss during training." +@tf_export("train.NanTensorHook") class NanTensorHook(session_run_hook.SessionRunHook): """Monitors the loss tensor and stops training if loss is NaN. @@ -612,6 +621,7 @@ class NanTensorHook(session_run_hook.SessionRunHook): run_context.request_stop() +@tf_export("train.SummarySaverHook") class SummarySaverHook(session_run_hook.SessionRunHook): """Saves summaries every N steps.""" @@ -720,6 +730,7 @@ class SummarySaverHook(session_run_hook.SessionRunHook): return summary_op +@tf_export("train.GlobalStepWaiterHook") class GlobalStepWaiterHook(session_run_hook.SessionRunHook): """Delays execution until global step reaches `wait_until_step`. @@ -767,6 +778,7 @@ class GlobalStepWaiterHook(session_run_hook.SessionRunHook): time.sleep(0.5) +@tf_export("train.FinalOpsHook") class FinalOpsHook(session_run_hook.SessionRunHook): """A hook which evaluates `Tensors` at the end of a session.""" @@ -793,6 +805,7 @@ class FinalOpsHook(session_run_hook.SessionRunHook): feed_dict=self._final_ops_feed_dict) +@tf_export("train.FeedFnHook") class FeedFnHook(session_run_hook.SessionRunHook): """Runs `feed_fn` and sets the `feed_dict` accordingly.""" @@ -810,6 +823,7 @@ class FeedFnHook(session_run_hook.SessionRunHook): fetches=None, feed_dict=self.feed_fn()) +@tf_export("train.ProfilerHook") class ProfilerHook(session_run_hook.SessionRunHook): """Captures CPU/GPU profiling information every N steps or seconds. diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index 63235a1454..fa3de6fad2 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver +from tensorflow.python.util.tf_export import tf_export __all__ = [ @@ -36,6 +37,7 @@ __all__ = [ ] +@tf_export("train.load_checkpoint") def load_checkpoint(ckpt_dir_or_file): """Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`. @@ -60,6 +62,7 @@ def load_checkpoint(ckpt_dir_or_file): return pywrap_tensorflow.NewCheckpointReader(filename) +@tf_export("train.load_variable") def load_variable(ckpt_dir_or_file, name): """Returns the tensor value of the given variable in the checkpoint. @@ -77,6 +80,7 @@ def load_variable(ckpt_dir_or_file, name): return reader.get_tensor(name) +@tf_export("train.list_variables") def list_variables(ckpt_dir_or_file): """Returns list of all variables in the checkpoint. @@ -95,6 +99,7 @@ def list_variables(ckpt_dir_or_file): return result +@tf_export("train.init_from_checkpoint") def init_from_checkpoint(ckpt_dir_or_file, assignment_map): """Initializes current variables with tensors loaded from given checkpoint. diff --git a/tensorflow/python/training/coordinator.py b/tensorflow/python/training/coordinator.py index 0e31255b74..0ff97d85e3 100644 --- a/tensorflow/python/training/coordinator.py +++ b/tensorflow/python/training/coordinator.py @@ -27,8 +27,10 @@ import six from tensorflow.python.framework import errors from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.Coordinator") class Coordinator(object): """A coordinator for threads. @@ -406,6 +408,7 @@ class Coordinator(object): # Threads for the standard services. +@tf_export("train.LooperThread") class LooperThread(threading.Thread): """A thread that runs code repeatedly, optionally on a timer. diff --git a/tensorflow/python/training/device_setter.py b/tensorflow/python/training/device_setter.py index 37ab625779..689088bb41 100644 --- a/tensorflow/python/training/device_setter.py +++ b/tensorflow/python/training/device_setter.py @@ -23,6 +23,7 @@ from tensorflow.core.framework import node_def_pb2 from tensorflow.python.framework import device as pydev from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import server_lib +from tensorflow.python.util.tf_export import tf_export class _RoundRobinStrategy(object): @@ -121,6 +122,7 @@ class _ReplicaDeviceChooser(object): return worker_device.to_string() +@tf_export("train.replica_device_setter") def replica_device_setter(ps_tasks=0, ps_device="/job:ps", worker_device="/job:worker", merge_devices=True, cluster=None, ps_ops=None, ps_strategy=None): diff --git a/tensorflow/python/training/ftrl.py b/tensorflow/python/training/ftrl.py index c64a1b3f79..9d02e694db 100644 --- a/tensorflow/python/training/ftrl.py +++ b/tensorflow/python/training/ftrl.py @@ -22,8 +22,10 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.FtrlOptimizer") class FtrlOptimizer(optimizer.Optimizer): """Optimizer that implements the FTRL algorithm. @@ -265,4 +267,3 @@ class FtrlOptimizer(optimizer.Optimizer): grad.dtype), math_ops.cast(self._learning_rate_power_tensor, grad.dtype), use_locking=self._use_locking) - diff --git a/tensorflow/python/training/gradient_descent.py b/tensorflow/python/training/gradient_descent.py index 5a536e2729..380e14e024 100644 --- a/tensorflow/python/training/gradient_descent.py +++ b/tensorflow/python/training/gradient_descent.py @@ -23,8 +23,10 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.GradientDescentOptimizer") class GradientDescentOptimizer(optimizer.Optimizer): """Optimizer that implements the gradient descent algorithm. """ diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py index 331a51e8bc..992184ec9e 100644 --- a/tensorflow/python/training/input.py +++ b/tensorflow/python/training/input.py @@ -44,6 +44,7 @@ from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.summary import summary from tensorflow.python.training import queue_runner +from tensorflow.python.util.tf_export import tf_export # pylint: disable=protected-access @@ -53,6 +54,7 @@ _restore_sparse = sparse_ops._take_many_sparse_from_tensors_map # pylint: enable=protected-access +@tf_export("train.match_filenames_once") def match_filenames_once(pattern, name=None): """Save the list of files matching pattern, so it is only computed once. @@ -70,6 +72,7 @@ def match_filenames_once(pattern, name=None): collections=[ops.GraphKeys.LOCAL_VARIABLES]) +@tf_export("train.limit_epochs") def limit_epochs(tensor, num_epochs=None, name=None): """Returns tensor `num_epochs` times and then raises an `OutOfRange` error. @@ -102,6 +105,7 @@ def limit_epochs(tensor, num_epochs=None, name=None): return array_ops.identity(tensor, name=name) +@tf_export("train.input_producer") def input_producer(input_tensor, element_shape=None, num_epochs=None, @@ -184,6 +188,7 @@ def input_producer(input_tensor, return q +@tf_export("train.string_input_producer") def string_input_producer(string_tensor, num_epochs=None, shuffle=True, @@ -253,6 +258,7 @@ def string_input_producer(string_tensor, cancel_op=cancel_op) +@tf_export("train.range_input_producer") def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None): """Produces the integers from 0 to limit-1 in a queue. @@ -290,6 +296,7 @@ def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, shared_name, "fraction_of_%d_full" % capacity, name) +@tf_export("train.slice_input_producer") def slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None): """Produces a slice of each `Tensor` in `tensor_list`. @@ -885,6 +892,7 @@ def _shuffle_batch_join(tensors_list, batch_size, capacity, # Batching functions ---------------------------------------------------------- +@tf_export("train.batch") def batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -979,6 +987,7 @@ def batch(tensors, batch_size, num_threads=1, capacity=32, name=name) +@tf_export("train.maybe_batch") def maybe_batch(tensors, keep_input, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -1031,6 +1040,7 @@ def maybe_batch(tensors, keep_input, batch_size, num_threads=1, capacity=32, name=name) +@tf_export("train.batch_join") def batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -1136,6 +1146,7 @@ def batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, name=name) +@tf_export("train.maybe_batch_join") def maybe_batch_join(tensors_list, keep_input, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, @@ -1188,6 +1199,7 @@ def maybe_batch_join(tensors_list, keep_input, batch_size, capacity=32, name=name) +@tf_export("train.shuffle_batch") def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -1287,6 +1299,7 @@ def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, name=name) +@tf_export("train.maybe_shuffle_batch") def maybe_shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, keep_input, num_threads=1, seed=None, enqueue_many=False, shapes=None, @@ -1346,6 +1359,7 @@ def maybe_shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, name=name) +@tf_export("train.shuffle_batch_join") def shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, @@ -1439,6 +1453,7 @@ def shuffle_batch_join(tensors_list, batch_size, capacity, name=name) +@tf_export("train.maybe_shuffle_batch_join") def maybe_shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, keep_input, seed=None, enqueue_many=False, shapes=None, diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index 343a49cded..10ab4c1137 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -25,8 +25,10 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.exponential_decay") def exponential_decay(learning_rate, global_step, decay_steps, @@ -103,6 +105,7 @@ def exponential_decay(learning_rate, learning_rate, math_ops.pow(decay_rate, p), name=name) +@tf_export("train.piecewise_constant") def piecewise_constant(x, boundaries, values, name=None): """Piecewise constant from boundaries and interval values. @@ -182,6 +185,7 @@ def piecewise_constant(x, boundaries, values, name=None): return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) +@tf_export("train.polynomial_decay") def polynomial_decay(learning_rate, global_step, decay_steps, @@ -291,6 +295,7 @@ def polynomial_decay(learning_rate, name=name) +@tf_export("train.natural_exp_decay") def natural_exp_decay(learning_rate, global_step, decay_steps, @@ -362,6 +367,7 @@ def natural_exp_decay(learning_rate, return math_ops.multiply(learning_rate, exponent, name=name) +@tf_export("train.inverse_time_decay") def inverse_time_decay(learning_rate, global_step, decay_steps, @@ -444,6 +450,7 @@ def inverse_time_decay(learning_rate, return math_ops.div(learning_rate, denom, name=name) +@tf_export("train.cosine_decay") def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): """Applies cosine decay to the learning rate. @@ -503,6 +510,7 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): return math_ops.multiply(learning_rate, decayed) +@tf_export("train.cosine_decay_restarts") def cosine_decay_restarts(learning_rate, global_step, first_decay_steps, @@ -596,6 +604,7 @@ def cosine_decay_restarts(learning_rate, return math_ops.multiply(learning_rate, decayed, name=name) +@tf_export("train.linear_cosine_decay") def linear_cosine_decay(learning_rate, global_step, decay_steps, @@ -679,6 +688,7 @@ def linear_cosine_decay(learning_rate, return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) +@tf_export("train.noisy_linear_cosine_decay") def noisy_linear_cosine_decay(learning_rate, global_step, decay_steps, diff --git a/tensorflow/python/training/momentum.py b/tensorflow/python/training/momentum.py index cf9530d87c..bd9fa79d8f 100644 --- a/tensorflow/python/training/momentum.py +++ b/tensorflow/python/training/momentum.py @@ -22,8 +22,10 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.MomentumOptimizer") class MomentumOptimizer(optimizer.Optimizer): """Optimizer that implements the Momentum algorithm. diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py index fa3517db27..6c5c9e01a7 100644 --- a/tensorflow/python/training/monitored_session.py +++ b/tensorflow/python/training/monitored_session.py @@ -41,6 +41,7 @@ from tensorflow.python.training import queue_runner from tensorflow.python.training import saver as training_saver from tensorflow.python.training import session_manager as sm from tensorflow.python.training import session_run_hook +from tensorflow.python.util.tf_export import tf_export # The list of exceptions that we should recover from. Exceptions not in this @@ -52,6 +53,7 @@ _PREEMPTION_ERRORS = (errors.AbortedError, errors.UnavailableError) USE_DEFAULT = object() +@tf_export('train.Scaffold') class Scaffold(object): """Structure to create or gather pieces commonly needed to train a model. @@ -272,6 +274,7 @@ class Scaffold(object): resources.initialize_resources(resources.local_resources())) +@tf_export('train.MonitoredTrainingSession') def MonitoredTrainingSession(master='', # pylint: disable=invalid-name is_chief=True, checkpoint_dir=None, @@ -381,6 +384,7 @@ def MonitoredTrainingSession(master='', # pylint: disable=invalid-name stop_grace_period_secs=stop_grace_period_secs) +@tf_export('train.SessionCreator') class SessionCreator(object): """A factory for tf.Session.""" @@ -390,6 +394,7 @@ class SessionCreator(object): 'create_session is not implemented for {}.'.format(self)) +@tf_export('train.ChiefSessionCreator') class ChiefSessionCreator(SessionCreator): """Creates a tf.Session for a chief.""" @@ -441,6 +446,7 @@ class ChiefSessionCreator(SessionCreator): init_fn=self._scaffold.init_fn) +@tf_export('train.WorkerSessionCreator') class WorkerSessionCreator(SessionCreator): """Creates a tf.Session for a worker.""" @@ -706,6 +712,7 @@ class _MonitoredSession(object): return self._coordinated_creator.tf_sess +@tf_export('train.MonitoredSession') class MonitoredSession(_MonitoredSession): """Session-like object that handles initialization, recovery and hooks. @@ -788,6 +795,7 @@ class MonitoredSession(_MonitoredSession): stop_grace_period_secs=stop_grace_period_secs) +@tf_export('train.SingularMonitoredSession') class SingularMonitoredSession(_MonitoredSession): """Session-like object that handles initialization, restoring, and hooks. diff --git a/tensorflow/python/training/moving_averages.py b/tensorflow/python/training/moving_averages.py index 43ed1ac170..2d89082ad7 100644 --- a/tensorflow/python/training/moving_averages.py +++ b/tensorflow/python/training/moving_averages.py @@ -26,6 +26,7 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import slot_creator +from tensorflow.python.util.tf_export import tf_export # TODO(touts): switch to variables.Variable. @@ -230,6 +231,7 @@ def _zero_debias(unbiased_var, value, decay): return unbiased_ema_delta +@tf_export("train.ExponentialMovingAverage") class ExponentialMovingAverage(object): """Maintains moving averages of variables by employing an exponential decay. diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index a06b3eada6..425dbd8313 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -36,6 +36,7 @@ from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import slot_creator from tensorflow.python.util import nest +from tensorflow.python.util.tf_export import tf_export def _get_variable_for(v): @@ -187,6 +188,7 @@ def _get_processor(v): raise NotImplementedError("Trying to optimize unsupported type ", v) +@tf_export("train.Optimizer") class Optimizer(object): """Base class for optimizers. diff --git a/tensorflow/python/training/proximal_adagrad.py b/tensorflow/python/training/proximal_adagrad.py index da31ab325d..9bd677b8ef 100644 --- a/tensorflow/python/training/proximal_adagrad.py +++ b/tensorflow/python/training/proximal_adagrad.py @@ -23,8 +23,10 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.ProximalAdagradOptimizer") class ProximalAdagradOptimizer(optimizer.Optimizer): # pylint: disable=line-too-long """Optimizer that implements the Proximal Adagrad algorithm. diff --git a/tensorflow/python/training/proximal_gradient_descent.py b/tensorflow/python/training/proximal_gradient_descent.py index 53e9dc2ef2..369b6cbb50 100644 --- a/tensorflow/python/training/proximal_gradient_descent.py +++ b/tensorflow/python/training/proximal_gradient_descent.py @@ -24,8 +24,10 @@ from tensorflow.python.ops import math_ops # pylint: enable=unused-import from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.ProximalGradientDescentOptimizer") class ProximalGradientDescentOptimizer(optimizer.Optimizer): # pylint: disable=line-too-long """Optimizer that implements the proximal gradient descent algorithm. diff --git a/tensorflow/python/training/queue_runner_impl.py b/tensorflow/python/training/queue_runner_impl.py index 4e7c81d7b2..07afba79ab 100644 --- a/tensorflow/python/training/queue_runner_impl.py +++ b/tensorflow/python/training/queue_runner_impl.py @@ -27,8 +27,10 @@ from tensorflow.python.eager import context from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.queue_runner.QueueRunner", "train.QueueRunner") class QueueRunner(object): """Holds a list of enqueue operations for a queue, each to be run in a thread. @@ -384,6 +386,7 @@ class QueueRunner(object): import_scope=import_scope) +@tf_export("train.queue_runner.add_queue_runner", "train.add_queue_runner") def add_queue_runner(qr, collection=ops.GraphKeys.QUEUE_RUNNERS): """Adds a `QueueRunner` to a collection in the graph. @@ -402,6 +405,8 @@ def add_queue_runner(qr, collection=ops.GraphKeys.QUEUE_RUNNERS): ops.add_to_collection(collection, qr) +@tf_export("train.queue_runner.start_queue_runners", + "train.start_queue_runners") def start_queue_runners(sess=None, coord=None, daemon=True, start=True, collection=ops.GraphKeys.QUEUE_RUNNERS): """Starts all queue runners collected in the graph. diff --git a/tensorflow/python/training/rmsprop.py b/tensorflow/python/training/rmsprop.py index 745e612018..89d1099a49 100644 --- a/tensorflow/python/training/rmsprop.py +++ b/tensorflow/python/training/rmsprop.py @@ -46,8 +46,10 @@ from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.RMSPropOptimizer") class RMSPropOptimizer(optimizer.Optimizer): """Optimizer that implements the RMSProp algorithm. diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index abc700b810..3888e9bba4 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -53,6 +53,7 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import training_util from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export # Op names which identify variable reads which should be saved. @@ -889,6 +890,7 @@ def _GetCheckpointFilename(save_dir, latest_filename): return os.path.join(save_dir, latest_filename) +@tf_export("train.generate_checkpoint_state_proto") def generate_checkpoint_state_proto(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None): @@ -933,6 +935,7 @@ def generate_checkpoint_state_proto(save_dir, return coord_checkpoint_proto +@tf_export("train.update_checkpoint_state") def update_checkpoint_state(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None, @@ -1025,6 +1028,7 @@ def _update_checkpoint_state(save_dir, text_format.MessageToString(ckpt)) +@tf_export("train.get_checkpoint_state") def get_checkpoint_state(checkpoint_dir, latest_filename=None): """Returns CheckpointState proto from the "checkpoint" file. @@ -1082,6 +1086,7 @@ def get_checkpoint_state(checkpoint_dir, latest_filename=None): return ckpt +@tf_export("train.Saver") class Saver(object): """Saves and restores variables. @@ -1783,6 +1788,7 @@ def _prefix_to_checkpoint_path(prefix, format_version): return prefix # Just the data file. +@tf_export("train.latest_checkpoint") def latest_checkpoint(checkpoint_dir, latest_filename=None): """Finds the filename of latest saved checkpoint file. @@ -1812,6 +1818,7 @@ def latest_checkpoint(checkpoint_dir, latest_filename=None): return None +@tf_export("train.import_meta_graph") def import_meta_graph(meta_graph_or_file, clear_devices=False, import_scope=None, **kwargs): """Recreates a Graph saved in a `MetaGraphDef` proto. @@ -1913,6 +1920,7 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, return None +@tf_export("train.export_meta_graph") def export_meta_graph(filename=None, meta_info_def=None, graph_def=None, @@ -1989,6 +1997,7 @@ def export_meta_graph(filename=None, return meta_graph_def +@tf_export("train.checkpoint_exists") def checkpoint_exists(checkpoint_prefix): """Checks whether a V1 or V2 checkpoint exists with the specified prefix. @@ -2013,6 +2022,7 @@ def checkpoint_exists(checkpoint_prefix): return False +@tf_export("train.get_checkpoint_mtimes") def get_checkpoint_mtimes(checkpoint_prefixes): """Returns the mtimes (modification timestamps) of the checkpoints. diff --git a/tensorflow/python/training/server_lib.py b/tensorflow/python/training/server_lib.py index 29da67a30a..2f421d1cc0 100644 --- a/tensorflow/python/training/server_lib.py +++ b/tensorflow/python/training/server_lib.py @@ -23,6 +23,7 @@ from tensorflow.core.protobuf import tensorflow_server_pb2 from tensorflow.python import pywrap_tensorflow from tensorflow.python.framework import errors from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export def _make_server_def(server_or_cluster_def, job_name, task_index, protocol, @@ -92,6 +93,7 @@ def _make_server_def(server_or_cluster_def, job_name, task_index, protocol, return server_def +@tf_export("train.Server") class Server(object): """An in-process TensorFlow server, for use in distributed training. @@ -221,6 +223,7 @@ class Server(object): start=start) +@tf_export("train.ClusterSpec") class ClusterSpec(object): """Represents a cluster as a set of "tasks", organized into "jobs". diff --git a/tensorflow/python/training/session_manager.py b/tensorflow/python/training/session_manager.py index b396a1e7d0..360e02fb44 100644 --- a/tensorflow/python/training/session_manager.py +++ b/tensorflow/python/training/session_manager.py @@ -25,6 +25,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver as saver_mod +from tensorflow.python.util.tf_export import tf_export def _maybe_name(obj): @@ -44,6 +45,7 @@ def _maybe_name(obj): return "" % type(obj) +@tf_export("train.SessionManager") class SessionManager(object): """Training helper that restores from checkpoint and creates session. diff --git a/tensorflow/python/training/session_run_hook.py b/tensorflow/python/training/session_run_hook.py index 5b023d8a26..89f4030065 100644 --- a/tensorflow/python/training/session_run_hook.py +++ b/tensorflow/python/training/session_run_hook.py @@ -96,8 +96,10 @@ from __future__ import division from __future__ import print_function import collections +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.SessionRunHook") class SessionRunHook(object): """Hook to extend calls to MonitoredSession.run().""" @@ -189,6 +191,7 @@ class SessionRunHook(object): pass +@tf_export("train.SessionRunArgs") class SessionRunArgs( collections.namedtuple("SessionRunArgs", ["fetches", "feed_dict", "options"])): @@ -213,6 +216,7 @@ class SessionRunArgs( return super(SessionRunArgs, cls).__new__(cls, fetches, feed_dict, options) +@tf_export("train.SessionRunContext") class SessionRunContext(object): """Provides information about the `session.run()` call being made. @@ -264,6 +268,7 @@ class SessionRunContext(object): self._stop_requested = True +@tf_export("train.SessionRunValues") class SessionRunValues( collections.namedtuple("SessionRunValues", ["results", "options", "run_metadata"])): diff --git a/tensorflow/python/training/supervisor.py b/tensorflow/python/training/supervisor.py index e4514aaea2..d2ad34773e 100644 --- a/tensorflow/python/training/supervisor.py +++ b/tensorflow/python/training/supervisor.py @@ -37,8 +37,10 @@ from tensorflow.python.training import saver as saver_mod from tensorflow.python.training import session_manager as session_manager_mod from tensorflow.python.training import training_util from tensorflow.python.util import deprecation +from tensorflow.python.util.tf_export import tf_export +@tf_export("train.Supervisor") class Supervisor(object): """A training helper that checkpoints models and computes summaries. diff --git a/tensorflow/python/training/sync_replicas_optimizer.py b/tensorflow/python/training/sync_replicas_optimizer.py index 47702fdad0..0c6cf910d1 100644 --- a/tensorflow/python/training/sync_replicas_optimizer.py +++ b/tensorflow/python/training/sync_replicas_optimizer.py @@ -31,6 +31,7 @@ from tensorflow.python.training import optimizer from tensorflow.python.training import queue_runner from tensorflow.python.training import session_manager from tensorflow.python.training import session_run_hook +from tensorflow.python.util.tf_export import tf_export # Please note that the gradients from replicas are averaged instead of summed @@ -38,6 +39,7 @@ from tensorflow.python.training import session_run_hook # rate according to the number of replicas. This change is introduced to be # consistent with how gradients are aggregated (averaged) within a batch in a # replica. +@tf_export("train.SyncReplicasOptimizer") class SyncReplicasOptimizer(optimizer.Optimizer): """Class to synchronize, aggregate gradients and pass them to the optimizer. diff --git a/tensorflow/python/training/training_util.py b/tensorflow/python/training/training_util.py index 89a9e12932..499f1feb2d 100644 --- a/tensorflow/python/training/training_util.py +++ b/tensorflow/python/training/training_util.py @@ -29,6 +29,7 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.util.tf_export import tf_export # Picked a long key value to minimize the chance of collision with user defined @@ -40,6 +41,7 @@ GLOBAL_STEP_READ_KEY = 'global_step_read_op_cache' write_graph = graph_io.write_graph +@tf_export('train.global_step') def global_step(sess, global_step_tensor): """Small helper to get the global step. @@ -67,6 +69,7 @@ def global_step(sess, global_step_tensor): return int(sess.run(global_step_tensor)) +@tf_export('train.get_global_step') def get_global_step(graph=None): """Get the global step tensor. @@ -101,6 +104,7 @@ def get_global_step(graph=None): return global_step_tensor +@tf_export('train.create_global_step') def create_global_step(graph=None): """Create global step tensor in graph. @@ -139,6 +143,7 @@ def create_global_step(graph=None): ops.GraphKeys.GLOBAL_STEP]) +@tf_export('train.get_or_create_global_step') def get_or_create_global_step(graph=None): """Returns and create (if necessary) the global step tensor. @@ -156,6 +161,7 @@ def get_or_create_global_step(graph=None): return global_step_tensor +@tf_export('train.assert_global_step') def assert_global_step(global_step_tensor): """Asserts `global_step_tensor` is a scalar int `Variable` or `Tensor`. diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index d110316395..036bdd6d29 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -77,6 +77,7 @@ genrule( "api/nn/rnn_cell/__init__.py", "api/sets/__init__.py", "api/summary/__init__.py", + "api/train/queue_runner/__init__.py", ], cmd = "$(location create_python_api) $(OUTS)", tools = ["create_python_api"], -- GitLab From ad2a242edb6f6563e6993d8de2b05154a3a1ac26 Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Tue, 30 Jan 2018 21:34:02 -0800 Subject: [PATCH 315/423] Adds input_fn-return-dataset for Per-Host input pipeline deployment. PiperOrigin-RevId: 183940666 --- .../contrib/tpu/python/tpu/tpu_estimator.py | 180 ++++++++++++++---- 1 file changed, 146 insertions(+), 34 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index bc55dbcb50..23960bb030 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -41,6 +41,7 @@ from tensorflow.contrib.tpu.python.tpu import util as util_lib from tensorflow.core.framework.summary_pb2 import Summary from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator as estimator_lib from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator import util @@ -70,7 +71,12 @@ _BATCH_SIZE_KEY = 'batch_size' _CROSS_REPLICA_SUM_OP = 'CrossReplicaSum' _RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY] -# TODO(b/65703635): Flip the value and remove all dead code. + +# TODO(b/65703635): Flip the value and remove all dead code. Currently, this is +# only used for per-core based deployments. For per-host based pipelines, if a +# user returns a Dataset instance it will be automatically wrapped in a +# tf.while_loop (This can be disabled by returning features and labels +# explicitly). _WRAP_INPUT_FN_INTO_WHILE_LOOP = False @@ -499,12 +505,19 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): dequeue. """ - def __init__(self, ctx, enqueue_ops, dequeue_ops): + def __init__(self, + ctx, + enqueue_ops, + dequeue_ops, + run_infeed_loop_on_coordinator=True): self._master_job = ctx.master_job self._enqueue_ops = enqueue_ops self._dequeue_ops = dequeue_ops + + 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 @@ -587,15 +600,15 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): logging.info('%s thread starting after sleep', self._name) try: - if _WRAP_INPUT_FN_INTO_WHILE_LOOP: - for _ in queue_ctx.read_iteration_counts(): - session.run(self._enqueue_ops) - else: + if self._run_infeed_loop_on_coordinator: for count, steps in enumerate(queue_ctx.read_iteration_counts()): for i in xrange(steps): logging.debug('Infeed enqueue for iteration (%d, %d)', count, i) session.run(self._enqueue_ops) - logging.debug('Infeed thread finished, shutting down.') + else: + 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) @@ -606,6 +619,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): 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) @@ -633,7 +647,6 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): logging.info('Enqueue next (%d) batch(es) of data to infeed.', iterations) self._infeed_controller.send_next_batch_signal(iterations) - # TODO(xiejw): Refactor the outfeed dequeue into tf.while_loop. logging.info('Dequeue next (%d) batch(es) of data from outfeed.', iterations) self._outfeed_controller.send_next_batch_signal(iterations) @@ -752,11 +765,14 @@ def generate_per_core_enqueue_ops_fn_for_host(ctx, input_fn, per_host_sharded_inputs = [] for core_ordinal in range(num_cores_per_host): with ops.name_scope('ordinal_%d' % (core_ordinal)): - inputs = input_fn() - if isinstance(inputs, tuple): - features, labels = inputs - else: - features, labels = inputs, None + inputs = _Inputs.from_input_fn(input_fn()) + if inputs.is_dataset: + raise TypeError( + '`input_fn` returning `Dataset` is not yet supported in ' + 'per-Core input pipeline deployment yet. Please set ' + 'TPUConfig.per_host_input_for_training to True or return ' + '`features` and `labels` from `input_fn`') + features, labels = inputs.features_and_labels() inputs_structure_recorder.validate_and_record_structure( features, labels) @@ -783,14 +799,23 @@ def generate_per_host_enqueue_ops_fn_for_host( """Generates infeed enqueue ops for per-host input_fn on a single host.""" captured_infeed_queue = _CapturedObject() + hooks = [] + + with ops.device(device): + inputs = _Inputs.from_input_fn(input_fn()) + + is_dataset = inputs.is_dataset + if is_dataset: + hooks.append(inputs.dataset_initializer_hook()) + def enqueue_ops_fn(): with ops.device(device): num_cores_per_host = ctx.num_of_cores_per_host - inputs = input_fn() - if isinstance(inputs, tuple): - features, labels = inputs - else: - features, labels = inputs, None + # Convert user input to features and labels. If the user returns a + # dataset, it is initialized and the features and labels extracted via + # `dataset.iterator.get_next()` + features, labels = inputs.features_and_labels() + inputs_structure_recorder.validate_and_record_structure(features, labels) unsharded_tensor_list = ( inputs_structure_recorder.flatten_features_and_labels( @@ -808,7 +833,7 @@ def generate_per_host_enqueue_ops_fn_for_host( unsharded_tensor_list, placement_function=lambda x: device)) return per_host_enqueue_ops - return enqueue_ops_fn, captured_infeed_queue + return enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset class _InputPipeline(object): @@ -944,7 +969,7 @@ class _InputPipeline(object): # Single tensor case. unflattened_label = flattened_inputs[expected_num_features] - return unflattened_features, unflattened_label + return _Inputs(unflattened_features, unflattened_label) def __init__(self, input_fn, batch_axis, ctx): """Constructor. @@ -972,7 +997,8 @@ class _InputPipeline(object): # While tf.while_loop is called, the body function, which invokes # `enqueue_fn` passed in, is called to construct the graph. So, input_fn # structure is recorded. - enqueue_ops = self._invoke_input_fn_and_record_structure() + enqueue_ops, all_hooks, run_infeed_loop_on_coordinator = ( + self._invoke_input_fn_and_record_structure()) self._validate_input_pipeline() @@ -983,14 +1009,18 @@ class _InputPipeline(object): return self._inputs_structure_recorder.unflatten_features_and_labels( values) - return (enqueue_ops, dequeue_fn) + return (enqueue_ops, dequeue_fn, all_hooks, run_infeed_loop_on_coordinator) def _invoke_input_fn_and_record_structure(self): """Deploys the input pipeline and record input structure.""" enqueue_ops = [] infeed_queues = [] + all_hooks = [] num_hosts = self._ctx.num_hosts tpu_host_placement_fn = self._ctx.tpu_host_placement_function + + run_infeed_loop_on_coordinator = True + if self._sharded_per_core: # Per-Core input pipeline deployment. # Invoke input pipeline for each core and placed on the corresponding @@ -1004,6 +1034,7 @@ class _InputPipeline(object): self._ctx, self._input_fn, self._inputs_structure_recorder)) if _WRAP_INPUT_FN_INTO_WHILE_LOOP: + run_infeed_loop_on_coordinator = False enqueue_ops.append( _wrap_computation_in_while_loop( device=host_device, op_fn=enqueue_ops_fn)) @@ -1017,12 +1048,26 @@ class _InputPipeline(object): host_device = tpu_host_placement_fn(host_id=host_id) with ops.device(host_device): with ops.name_scope('input_pipeline_task%d' % (host_id)): - enqueue_ops_fn, captured_infeed_queue = ( + enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset = ( generate_per_host_enqueue_ops_fn_for_host( self._ctx, self._input_fn, self._inputs_structure_recorder, self._batch_axis, host_device)) - - if _WRAP_INPUT_FN_INTO_WHILE_LOOP: + all_hooks.extend(hooks) + + # NOTE(xiejw): We dispatch here based on the return type of the + # users `input_fn`. + # + # 1. If input_fn returns a Dataset instance, we initialize the + # iterator outside of tf.while_loop, and call the iterator.get_next + # inside tf.while_loop. This should be always safe. + # + # 2. If input_fn returns (features, labels), it is too late to wrap + # them inside tf.while_loop, as resource initialization cannot be + # handled in TF control flow properly. In this case, we will use + # python loop to enqueue the data into TPU system. This may be + # slow compared to the previous case. + 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)) @@ -1033,7 +1078,7 @@ class _InputPipeline(object): # dequeue is dtypes and types. So, any one can be used. Here, grab the # first one. self._infeed_queue = infeed_queues[0] - return enqueue_ops + return enqueue_ops, all_hooks, run_infeed_loop_on_coordinator def _validate_input_pipeline(self): # Perform some sanity checks to log user friendly information. We should @@ -1099,7 +1144,8 @@ class _ModelFnWrapper(object): def train_step(loss): """Training step function for use inside a while loop.""" del loss # unused; required in function signature. - features, labels = dequeue_fn() + inputs = dequeue_fn() + features, labels = inputs.features_and_labels() estimator_spec = self._verify_estimator_spec( self._call_model_fn(features, labels)) @@ -1150,7 +1196,8 @@ class _ModelFnWrapper(object): def eval_step(total_loss): """Evaluation step function for use inside a while loop.""" - features, labels = dequeue_fn() + inputs = dequeue_fn() + features, labels = inputs.features_and_labels() tpu_estimator_spec = self._call_model_fn(features, labels) if not isinstance(tpu_estimator_spec, TPUEstimatorSpec): @@ -1757,7 +1804,7 @@ class TPUEstimator(estimator_lib.Estimator): input_fn = features input_holders = _InputPipeline(input_fn, batch_axis, ctx) - enqueue_ops, dequeue_fn = ( + enqueue_ops, dequeue_fn, input_hooks, run_infeed_loop_on_coordinator = ( input_holders.generate_infeed_enqueue_ops_and_dequeue_fn()) if mode == model_fn_lib.ModeKeys.TRAIN: @@ -1767,7 +1814,12 @@ class TPUEstimator(estimator_lib.Estimator): if host_ops is None: host_ops = [] hooks = [ - TPUInfeedOutfeedSessionHook(ctx, enqueue_ops, host_ops), + TPUInfeedOutfeedSessionHook( + ctx, + enqueue_ops, + host_ops, + run_infeed_loop_on_coordinator=( + run_infeed_loop_on_coordinator)), ExamplesPerSecondHook(ctx.global_batch_size), InstallSignalHandlerHook(), training.LoggingTensorHook( @@ -1776,7 +1828,7 @@ class TPUEstimator(estimator_lib.Estimator): 'step': training.get_global_step() }, every_n_secs=30) - ] + ] + input_hooks summary.scalar(model_fn_lib.LOSS_METRIC_KEY, loss) with ops.control_dependencies([loss]): update_ops = _sync_variables_ops() @@ -1826,9 +1878,12 @@ class TPUEstimator(estimator_lib.Estimator): else: host_ops = host_call_ret['host_call'] hooks = [ - TPUInfeedOutfeedSessionHook(ctx, enqueue_ops, - eval_update_ops + host_ops), - ] + TPUInfeedOutfeedSessionHook( + ctx, + enqueue_ops, + eval_update_ops + host_ops, + run_infeed_loop_on_coordinator=run_infeed_loop_on_coordinator), + ] + input_hooks return model_fn_lib.EstimatorSpec( mode, @@ -1996,3 +2051,60 @@ class _CapturingContext(control_flow_ops.ControlFlowContext): def __exit__(self, _, __, ___): # pylint: disable=invalid-name self._g._set_control_flow_context(self._old) # pylint: disable=protected-access + + +# TODO(xiejw): Extend this to support internal signal. +class _Inputs(object): + """A data structure representing the input_fn returned values. + + This also supports the returned value from input_fn as `Dataset`. + """ + + def __init__(self, features=None, labels=None, dataset=None): + if dataset is not None and (features is not None or labels is not None): + raise RuntimeError('Internal Error: Either (features and labels) or ' + 'dataset should be provided, not both. Please file ' + 'bug') + + self._features = features + self._labels = labels + + self._dataset = dataset + self._iterator = None + + @staticmethod + def from_input_fn(return_values): + """Returns an `_Inputs` instance according to `input_fn` return value.""" + if isinstance(return_values, dataset_ops.Dataset): + dataset = return_values + return _Inputs(dataset=dataset) + + if isinstance(return_values, tuple): + features, labels = return_values + else: + features, labels = return_values, None + return _Inputs(features, labels) + + @property + def is_dataset(self): + """Returns True if the return value from input_fn is Dataset.""" + return self._dataset is not None + + def dataset_initializer_hook(self): + """Returns a `SessionRunHook` to initialize this dataset. + + This must be called before `features_and_labels`. + """ + iterator = self._dataset.make_initializable_iterator() + # pylint: disable=protected-access + hook = estimator_lib._DatasetInitializerHook(iterator) + self._iterator = iterator + return hook + + def features_and_labels(self): + """Gets `features` and `labels`.""" + if self.is_dataset: + return (_Inputs.from_input_fn( + self._iterator.get_next()).features_and_labels()) + + return (self._features, self._labels) -- GitLab From 66cffe9e4520208c0097283752bd2bfdbea94ae2 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Tue, 30 Jan 2018 23:15:28 -0800 Subject: [PATCH 316/423] Go: Support control dependencies. Fixes #16464 PiperOrigin-RevId: 183946928 --- tensorflow/go/graph.go | 9 ++++++++- tensorflow/go/op/scope.go | 27 +++++++++++++++++++++++---- tensorflow/go/op/scope_test.go | 34 ++++++++++++++++++++++++++++++++++ 3 files changed, 65 insertions(+), 5 deletions(-) diff --git a/tensorflow/go/graph.go b/tensorflow/go/graph.go index fc087d9d99..08943a527c 100644 --- a/tensorflow/go/graph.go +++ b/tensorflow/go/graph.go @@ -173,7 +173,11 @@ type OpSpec struct { // operation. Attrs map[string]interface{} - // Other possible fields: Device, ColocateWith, ControlInputs. + // Operations that must be executed before executing the operation + // being added. + ControlDependencies []*Operation + + // Other possible fields: Device, ColocateWith. } // AddOperation adds an operation to g. @@ -204,6 +208,9 @@ func (g *Graph) AddOperation(args OpSpec) (*Operation, error) { } } } + for _, in := range args.ControlDependencies { + C.TF_AddControlInput(cdesc, in.c) + } status := newStatus() for name, value := range args.Attrs { if err := setAttr(cdesc, status, name, value); err != nil { diff --git a/tensorflow/go/op/scope.go b/tensorflow/go/op/scope.go index a9ec79463a..2cf1f30187 100644 --- a/tensorflow/go/op/scope.go +++ b/tensorflow/go/op/scope.go @@ -33,10 +33,11 @@ import ( // A Scope object and all its derivates (e.g., obtained from Scope.SubScope) // are not safe for concurrent use by multiple goroutines. type Scope struct { - graph *tf.Graph - namemap map[string]int - namespace string - err *scopeErr + graph *tf.Graph + namemap map[string]int + namespace string + controlDependencies []*tf.Operation + err *scopeErr } // scopeErr is used to share errors between all derivatives of a root scope. @@ -80,6 +81,7 @@ func (s *Scope) AddOperation(args tf.OpSpec) *tf.Operation { if s.namespace != "" { args.Name = s.namespace + "/" + args.Name } + args.ControlDependencies = append(args.ControlDependencies, s.controlDependencies...) op, err := s.graph.AddOperation(args) if err != nil { s.UpdateErr(args.Type, err) @@ -103,6 +105,23 @@ func (s *Scope) SubScope(namespace string) *Scope { } } +// WithControlDependencies returns a new Scope which will cause all operations +// added to the graph to execute only after all the provided operations have +// executed first (in addition to any other control dependencies in s). +func (s *Scope) WithControlDependencies(ops ...*tf.Operation) *Scope { + return &Scope{ + graph: s.graph, + namemap: s.namemap, + namespace: s.namespace, + // append(ops, s.controlDependencies) and not the other way + // around so that we end up with a copy of the underlying array + // (and other calls to s.WithControlDependencies() do not stomp + // on each other). + controlDependencies: append(ops, s.controlDependencies...), + err: s.err, + } +} + // Err returns the error, if any, encountered during the construction // of the Graph managed by s. // diff --git a/tensorflow/go/op/scope_test.go b/tensorflow/go/op/scope_test.go index 6fb5d32e50..4f533881d0 100644 --- a/tensorflow/go/op/scope_test.go +++ b/tensorflow/go/op/scope_test.go @@ -69,6 +69,40 @@ func TestScopeSubScopeErrors(t *testing.T) { } } +func TestControlDependencies(t *testing.T) { + var ( + s = NewScope() + zero = Const(s.SubScope("zero"), int32(0)) + one = Const(s.SubScope("one"), int32(1)) + variable = VarHandleOp(s, tf.Int32, tf.ScalarShape()) + init = AssignVariableOp(s, variable, zero) + update = AssignAddVariableOp(s, variable, one) + read = ReadVariableOp(s.WithControlDependencies(update), variable, tf.Int32) + ) + graph, err := s.Finalize() + if err != nil { + t.Fatal(err) + } + sess, err := tf.NewSession(graph, nil) + if err != nil { + t.Fatal(err) + } + if _, err = sess.Run(nil, nil, []*tf.Operation{init}); err != nil { + t.Fatal(err) + } + // Without the control dependency, the read operation may not see the + // update. + for i := int32(0); i < 10; i++ { + out, err := sess.Run(nil, []tf.Output{read}, nil) + if err != nil { + t.Fatal(err) + } + if got, want := out[0].Value().(int32), i+1; got != want { + t.Errorf("Got %d, want %d", got, want) + } + } +} + func TestScopeFinalize(t *testing.T) { var ( root = NewScope() -- GitLab From 76b390539b296af43c9d3f4b5cfe0dfbf5b3cfd6 Mon Sep 17 00:00:00 2001 From: Eric Liu Date: Wed, 31 Jan 2018 01:26:29 -0800 Subject: [PATCH 317/423] Remove a failing test for gpu numbers when XLA is enabled. The test assumes that all devices are either CPU or GPU, which is not true when XLA is enabled. PiperOrigin-RevId: 183956872 --- tensorflow/contrib/eager/python/tfe_test.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tensorflow/contrib/eager/python/tfe_test.py b/tensorflow/contrib/eager/python/tfe_test.py index 0dedb2fd7c..b6659c2a17 100644 --- a/tensorflow/contrib/eager/python/tfe_test.py +++ b/tensorflow/contrib/eager/python/tfe_test.py @@ -102,10 +102,6 @@ class TFETest(test_util.TensorFlowTestCase): # Expect at least one device. self.assertTrue(tfe.list_devices()) - def testNumGPUs(self): - devices = tfe.list_devices() - self.assertEqual(len(devices) - 1, tfe.num_gpus()) - def testAddCheckNumericsOpsRaisesError(self): with self.assertRaisesRegexp( RuntimeError, -- GitLab From a55c018ded951ff4530557026fc97e9e6736a336 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Wed, 31 Jan 2018 08:16:59 -0800 Subject: [PATCH 318/423] Fix docs generation for cluster_resolvers Adds "cluster_resolver_pip" as a dependancy to opensource contrib, and applies a standard `remove_undocumented` to clear extra symbols. Docs are build from a bazel bulid, and without this change the cluster resolvers are not directly accessible in "tf.contirb.cluster_resolver" during the docs build, so they do not get documented. PiperOrigin-RevId: 183993115 --- tensorflow/contrib/cluster_resolver/BUILD | 1 + tensorflow/contrib/cluster_resolver/__init__.py | 12 ++++++++++++ 2 files changed, 13 insertions(+) diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD index 15abd2be03..80e18a43a7 100644 --- a/tensorflow/contrib/cluster_resolver/BUILD +++ b/tensorflow/contrib/cluster_resolver/BUILD @@ -34,6 +34,7 @@ py_library( ":cluster_resolver_py", ":gce_cluster_resolver_py", ":tpu_cluster_resolver_py", + "//tensorflow/python:util", ], ) diff --git a/tensorflow/contrib/cluster_resolver/__init__.py b/tensorflow/contrib/cluster_resolver/__init__.py index d17501e87e..b4d8cd4a7c 100644 --- a/tensorflow/contrib/cluster_resolver/__init__.py +++ b/tensorflow/contrib/cluster_resolver/__init__.py @@ -26,3 +26,15 @@ from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import from tensorflow.contrib.cluster_resolver.python.training.gce_cluster_resolver import GceClusterResolver from tensorflow.contrib.cluster_resolver.python.training.tpu_cluster_resolver import TPUClusterResolver # pylint: enable=wildcard-import,unused-import + +from tensorflow.python.util.all_util import remove_undocumented + +_allowed_symbols = [ + 'ClusterResolver', + 'SimpleClusterResolver', + 'UnionClusterResolver', + 'GceClusterResolver', + 'TPUClusterResolver', +] + +remove_undocumented(__name__, _allowed_symbols) -- GitLab From 76989a191815bdd96390626db154676ac42b890d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 09:40:48 -0800 Subject: [PATCH 319/423] Verify contents of tensors PiperOrigin-RevId: 184003263 --- tensorflow/contrib/BUILD | 1 + tensorflow/contrib/lite/tools/BUILD | 4 + tensorflow/contrib/lite/tools/verifier.cc | 170 ++++++++++++++- tensorflow/contrib/lite/tools/verifier.h | 4 +- .../contrib/lite/tools/verifier_test.cc | 200 +++++++++++++----- 5 files changed, 325 insertions(+), 54 deletions(-) diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index efb6449bb0..ac6f01365b 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -24,6 +24,7 @@ py_library( "//tensorflow/contrib/bayesflow:bayesflow_py", "//tensorflow/contrib/boosted_trees:init_py", "//tensorflow/contrib/cloud:cloud_py", + "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", "//tensorflow/contrib/cluster_resolver:cluster_resolver_py", "//tensorflow/contrib/coder:coder_ops_py", "//tensorflow/contrib/compiler:compiler_py", diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index 1bffcfb987..4d3b553b22 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -99,8 +99,11 @@ cc_library( srcs = ["verifier.cc"], hdrs = ["verifier.h"], deps = [ + "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/schema:schema_fbs", + "@com_google_absl//absl/base:core_headers", ], ) @@ -112,6 +115,7 @@ cc_test( ":verifier", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/schema:schema_fbs", "//tensorflow/contrib/lite/testing:util", "@com_google_googletest//:gtest", diff --git a/tensorflow/contrib/lite/tools/verifier.cc b/tensorflow/contrib/lite/tools/verifier.cc index 95a0895379..726e2aaa31 100644 --- a/tensorflow/contrib/lite/tools/verifier.cc +++ b/tensorflow/contrib/lite/tools/verifier.cc @@ -14,13 +14,32 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/tools/verifier.h" +#include #include "tensorflow/contrib/lite/schema/schema_generated.h" +#include "tensorflow/contrib/lite/string_util.h" #include "tensorflow/contrib/lite/version.h" namespace tflite { namespace { +// Reports error message when the reporter is set. +void ReportError(ErrorReporter* error_reporter, const char* format, ...) { + if (error_reporter) { + va_list args; + va_start(args, format); + error_reporter->Report(format, args); + va_end(args); + } +} + +// Returns the int32_t value pointed by ptr. +const uint32_t* GetIntPtr(const char* ptr) { + return reinterpret_cast(ptr); +} + +// Verifies flatbuffer format of the model contents and returns the in-memory +// model. const Model* VerifyFlatbufferAndGetModel(const void* buf, size_t len) { ::flatbuffers::Verifier verifier(static_cast(buf), len); if (VerifyModelBuffer(verifier)) { @@ -30,14 +49,159 @@ const Model* VerifyFlatbufferAndGetModel(const void* buf, size_t len) { } } +const uint32_t kMaxNumString = UINT_MAX / sizeof(int32_t) - 2; + +// Verifies string tensor has legit buffer contents that follow the schema +// defined in lite/string_util.h +bool VerifyStringTensorBuffer(const Buffer& buffer, + ErrorReporter* error_reporter) { + uint32_t buffer_size = buffer.data()->size(); + const char* buffer_ptr = reinterpret_cast(buffer.data()->data()); + + uint32_t num_strings = *GetIntPtr(buffer_ptr); + if (num_strings > kMaxNumString) { + ReportError(error_reporter, + "String tensor has invalid num of string set: %d", num_strings); + return false; + } + uint32_t header_offsets = + static_cast(num_strings + 2) * sizeof(int32_t); + + if (buffer_size < header_offsets) { + ReportError(error_reporter, + "String tensor buffer requires at least %d bytes, but is " + "allocated with %d bytes", + header_offsets, buffer_size); + return false; + } + + uint32_t prev_ptr = header_offsets; + uint32_t offset = sizeof(int32_t); + + if (*GetIntPtr(buffer_ptr + offset) != header_offsets) { + ReportError(error_reporter, + "String tensor buffer initial offset must be: %d", + header_offsets); + return false; + } + offset += sizeof(int32_t); + for (int i = 1; i <= num_strings; i++, offset += sizeof(int32_t)) { + int string_offset = *GetIntPtr(buffer_ptr + offset); + if (string_offset < prev_ptr || string_offset > buffer_size) { + ReportError(error_reporter, "String tensor buffer is invalid: index %d", + i); + return false; + } + } + if (*GetIntPtr(buffer_ptr + offset - sizeof(int32_t)) != buffer_size) { + ReportError(error_reporter, "String tensor buffer last offset must be %d", + buffer_size); + return false; + } + return true; +} + +// Verifies numeric tensor has legit buffer. +bool VerifyNumericTensorBuffer(const Tensor& tensor, const Buffer& buffer, + ErrorReporter* error_reporter) { + uint64_t bytes_required = 1; + for (int dim : *tensor.shape()) { + bytes_required *= dim; + if (bytes_required > UINT_MAX) { + ReportError(error_reporter, "Tensor dimension overflow"); + return false; + } + } + switch (tensor.type()) { + case TensorType_FLOAT32: + bytes_required *= sizeof(float); + break; + case TensorType_INT32: + bytes_required *= sizeof(int32_t); + break; + case TensorType_UINT8: + bytes_required *= sizeof(uint8_t); + break; + case TensorType_INT64: + bytes_required *= sizeof(int64_t); + break; + case TensorType_FLOAT16: + // FALLTHROUGH_INTENDED; + default: + ReportError(error_reporter, "Invalid tensor type: %d", tensor.type()); + return false; + } + if (bytes_required > UINT_MAX) { + ReportError(error_reporter, "Tensor dimension overflow"); + return false; + } + + if (bytes_required != buffer.data()->size()) { + ReportError( + error_reporter, + "Tensor requires %d bytes, but is allocated with %d bytes buffer", + bytes_required, buffer.data()->size()); + return false; + } + return true; + + // TODO(yichengfan): verify quantized tensors. +} + +// Verifies tensors have valid properties and legit buffer if set. +bool VerifyTensors(const Model& model, ErrorReporter* error_reporter) { + if (!model.subgraphs()) { + return true; + } + for (const auto& subgraph : *model.subgraphs()) { + if (!subgraph->tensors()) { + return true; + } + for (const auto& tensor : *subgraph->tensors()) { + if (!tensor->buffer()) { + return true; + } + if (tensor->buffer() >= model.buffers()->size()) { + ReportError(error_reporter, "Invalid tensor buffer index: %d", + tensor->buffer()); + return false; + } + auto* buffer = model.buffers()->Get(tensor->buffer()); + if (!buffer || !buffer->data()) { + ReportError(error_reporter, "Tensor buffer %d not set", + tensor->buffer()); + return false; + } + + if (tensor->type() == TensorType_STRING) { + if (!VerifyStringTensorBuffer(*buffer, error_reporter)) { + return false; + } + } else { + if (!VerifyNumericTensorBuffer(*tensor, *buffer, error_reporter)) { + return false; + } + } + } + } + return true; +} + } // namespace -bool Verify(const void* buf, size_t len) { +bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter) { const Model* model = VerifyFlatbufferAndGetModel(buf, len); if (model == nullptr) { + ReportError(error_reporter, "Invalid flatbuffer format"); return false; } - - return model->version() == TFLITE_SCHEMA_VERSION; + if (model->version() != TFLITE_SCHEMA_VERSION) { + ReportError(error_reporter, "Invalid model version %d", model->version()); + return false; + } + if (!VerifyTensors(*model, error_reporter)) { + return false; + } + return true; } } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier.h b/tensorflow/contrib/lite/tools/verifier.h index 03e1f22b7e..d2bf3c91d5 100644 --- a/tensorflow/contrib/lite/tools/verifier.h +++ b/tensorflow/contrib/lite/tools/verifier.h @@ -18,13 +18,15 @@ limitations under the License. #include +#include "tensorflow/contrib/lite/error_reporter.h" + namespace tflite { // Verifies the integrity of a Tensorflow Lite flatbuffer model file. // Currently, it verifies: // * The file is following a legit flatbuffer schema. // * The model is in supported version. -bool Verify(const void* buf, size_t len); +bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter); } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier_test.cc b/tensorflow/contrib/lite/tools/verifier_test.cc index 0481a55a78..244d4f0396 100644 --- a/tensorflow/contrib/lite/tools/verifier_test.cc +++ b/tensorflow/contrib/lite/tools/verifier_test.cc @@ -28,31 +28,62 @@ using flatbuffers::FlatBufferBuilder; using flatbuffers::Offset; using flatbuffers::Vector; -// Class that abstracts the list of buffers at the end of the TF Lite structure -class DeferredBufferWriter { +// Build single subgraph model. +class TfLiteFlatbufferModelBuilder { public: - DeferredBufferWriter() { - data_.push_back({}); // sentinel empty buffer. + TfLiteFlatbufferModelBuilder() { + buffers_.push_back( + CreateBuffer(builder_, builder_.CreateVector(std::vector{}))); } - Offset>> BuildBuffers(FlatBufferBuilder *builder) { - std::vector> buffer_vector; - for (const auto &vec : data_) { - auto data_buffer = builder->CreateVector(vec.data(), vec.size()); - buffer_vector.push_back(tflite::CreateBuffer(*builder, data_buffer)); + void AddTensor(const std::vector& shape, tflite::TensorType type, + const std::vector& buffer, const char* name) { + int buffer_index = 0; + if (!buffer.empty()) { + buffer_index = buffers_.size(); + buffers_.push_back(CreateBuffer(builder_, builder_.CreateVector(buffer))); } - return builder->CreateVector(buffer_vector); + tensors_.push_back(CreateTensorDirect(builder_, &shape, type, buffer_index, + name, /*quantization=*/0)); } - // Registers a buffer index and takes ownership of the data to write to it. - int Record(std::vector data) { - int buffer_index = data_.size(); - data_.emplace_back(std::move(data)); - return buffer_index; + void AddOperator(const std::vector& inputs, + const std::vector& outputs, + tflite::BuiltinOperator builtin_op, const char* custom_op) { + operator_codes_.push_back( + CreateOperatorCodeDirect(builder_, builtin_op, custom_op)); + operators_.push_back(CreateOperator( + builder_, operator_codes_.size() - 1, builder_.CreateVector(inputs), + builder_.CreateVector(outputs), BuiltinOptions_NONE, + /*builtin_options=*/0, + /*custom_options=*/0, tflite::CustomOptionsFormat_FLEXBUFFERS)); + } + + void FinishModel(const std::vector& inputs, + const std::vector& outputs) { + auto subgraph = std::vector>({CreateSubGraph( + builder_, builder_.CreateVector(tensors_), + builder_.CreateVector(inputs), builder_.CreateVector(outputs), + builder_.CreateVector(operators_), + builder_.CreateString("test_subgraph"))}); + auto result = CreateModel( + builder_, TFLITE_SCHEMA_VERSION, builder_.CreateVector(operator_codes_), + builder_.CreateVector(subgraph), builder_.CreateString("test_model"), + builder_.CreateVector(buffers_)); + tflite::FinishModelBuffer(builder_, result); + } + + bool Verify() { + return tflite::Verify(builder_.GetBufferPointer(), builder_.GetSize(), + DefaultErrorReporter()); } private: - std::vector> data_; + FlatBufferBuilder builder_; + std::vector> operators_; + std::vector> operator_codes_; + std::vector> tensors_; + std::vector> buffers_; }; TEST(VerifyModel, TestEmptyModel) { @@ -62,43 +93,26 @@ TEST(VerifyModel, TestEmptyModel) { /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); + ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize(), + DefaultErrorReporter())); } TEST(VerifyModel, TestSimpleModel) { - FlatBufferBuilder builder; - auto inputs = builder.CreateVector({0}); - auto outputs = builder.CreateVector({1}); - auto operator_codes = builder.CreateVector(std::vector>{ - CreateOperatorCodeDirect(builder, BuiltinOperator_CUSTOM, "test")}); - auto operators = - builder.CreateVector(std::vector>{CreateOperator( - builder, /*opcode_index=*/0, - /*inputs=*/builder.CreateVector({0}), - /*outputs=*/builder.CreateVector({1}), BuiltinOptions_NONE, - /*builtin_options=*/0, - /*custom_options=*/0, ::tflite::CustomOptionsFormat_FLEXBUFFERS)}); - std::vector shape; - auto tensors = builder.CreateVector(std::vector>{ - CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, - "input", /*quantization=*/0), - CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, - "output", /*quantization=*/0)}); - auto subgraph = std::vector>( - {CreateSubGraph(builder, tensors, inputs, outputs, operators, - builder.CreateString("Main"))}); - - auto model = CreateModel(builder, TFLITE_SCHEMA_VERSION, operator_codes, - builder.CreateVector(subgraph), - builder.CreateString("SmartReply"), /*buffers=*/0); - - ::tflite::FinishModelBuffer(builder, model); - ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); + TfLiteFlatbufferModelBuilder builder; + builder.AddOperator({0, 1}, {2}, BuiltinOperator_CUSTOM, "test"); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4, 5, 6}, "input"); + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 19, 0, 0, 0, 'A', 'B', 'C'}, + "data"); + builder.AddTensor({2, 3}, TensorType_INT32, {}, "output"); + builder.FinishModel({0, 1}, {2}); + ASSERT_TRUE(builder.Verify()); } TEST(VerifyModel, TestCorruptedData) { string model = "123"; - ASSERT_FALSE(Verify(model.data(), model.size())); + ASSERT_FALSE(Verify(model.data(), model.size(), /*error_reporter=*/nullptr)); } TEST(VerifyModel, TestUnsupportedVersion) { @@ -106,7 +120,8 @@ TEST(VerifyModel, TestUnsupportedVersion) { auto model = CreateModel(builder, /*version=*/1, /*operator_codes=*/0, /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize())); + ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), + DefaultErrorReporter())); } TEST(VerifyModel, TestRandomModificationIsNotAllowed) { @@ -116,20 +131,105 @@ TEST(VerifyModel, TestRandomModificationIsNotAllowed) { /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - string model_content(reinterpret_cast(builder.GetBufferPointer()), + string model_content(reinterpret_cast(builder.GetBufferPointer()), builder.GetSize()); for (int i = 0; i < model_content.size(); i++) { model_content[i] = (model_content[i] + 137) % 255; - EXPECT_FALSE(Verify(model_content.data(), model_content.size())) + EXPECT_FALSE(Verify(model_content.data(), model_content.size(), + DefaultErrorReporter())) << "Fail at position: " << i; } } +TEST(VerifyModel, TestIntTensorShapeIsGreaterThanBuffer) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, TestIntTensorShapeIsSmallerThanBuffer) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor({2, 1}, TensorType_UINT8, {1, 2, 3, 4}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, TestIntTensorShapeOverflow) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor({1024, 2048, 4096}, TensorType_UINT8, {1, 2, 3, 4}, + "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, TensorBufferIsNotValid) { + FlatBufferBuilder builder; + std::vector shape = {2, 3}; + auto tensors = builder.CreateVector(std::vector>{ + CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/2, + "input", /*quantization=*/0)}); + auto subgraph = std::vector>( + {CreateSubGraph(builder, tensors, /*inputs=*/0, /*outputs=*/0, + /*operators=*/0, builder.CreateString("Main"))}); + + auto buffers = builder.CreateVector(std::vector>{ + CreateBuffer(builder, + builder.CreateVector(std::vector{1, 2, 3, 4, 5, 6})), + }); + + auto model = CreateModel(builder, TFLITE_SCHEMA_VERSION, /*operator_codes=*/0, + builder.CreateVector(subgraph), + builder.CreateString("SmartReply"), buffers); + + ::tflite::FinishModelBuffer(builder, model); + ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), + DefaultErrorReporter())); +} + +TEST(VerifyModel, StringTensorHasInvalidNumString) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {0x00, 0x00, 0x00, 0x20, 16, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B'}, + "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, StringTensorOffsetTooSmall) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 12, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B'}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, StringTensorOffsetOutOfRange) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 22, 0, 0, 0, 'A', 'B'}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, StringTensorIsLargerThanRequired) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B', 'C'}, + "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + // TODO(yichengfan): make up malicious files to test with. } // namespace tflite -int main(int argc, char **argv) { +int main(int argc, char** argv) { ::tflite::LogToStderr(); ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); -- GitLab From 16b9fc676be6f8aacf06977a7f9439a56ffccefa Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 09:53:17 -0800 Subject: [PATCH 320/423] Extending sparsify_gather to remove variables from the tensorflow summaries. PiperOrigin-RevId: 184004859 --- .../meta_graph_transform.py | 7 ++ .../tools/graph_transforms/sparsify_gather.cc | 80 ++++++++++++----- .../graph_transforms/sparsify_gather_test.cc | 86 ++++++++++++++++--- 3 files changed, 138 insertions(+), 35 deletions(-) diff --git a/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py b/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py index 2932ae1c8d..ff88b4fa84 100644 --- a/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py +++ b/tensorflow/contrib/meta_graph_transform/meta_graph_transform.py @@ -171,7 +171,14 @@ def _clean_save_and_restore(graph_def, op, removed_op_names): shape_op_value_tensor.tensor_shape.dim[0].size = len(shapes) op.attr['dtypes'].list.type[:] = dtypes + if not name_op.attr['_output_shapes'].list.shape: + name_op.attr['_output_shapes'].list.shape.add() + name_op.attr['_output_shapes'].list.shape[0].dim.add() name_op.attr['_output_shapes'].list.shape[0].dim[0].size = len(names) + + if not shape_op.attr['_output_shapes'].list.shape: + shape_op.attr['_output_shapes'].list.shape.add() + shape_op.attr['_output_shapes'].list.shape[0].dim.add() shape_op.attr['_output_shapes'].list.shape[0].dim[0].size = len(shapes) diff --git a/tensorflow/tools/graph_transforms/sparsify_gather.cc b/tensorflow/tools/graph_transforms/sparsify_gather.cc index 593c654f9f..9c583d83ca 100644 --- a/tensorflow/tools/graph_transforms/sparsify_gather.cc +++ b/tensorflow/tools/graph_transforms/sparsify_gather.cc @@ -181,6 +181,14 @@ Status ObtainVariableInfo( return Status::OK(); } +Status RemoveInputAtIndex(NodeDef* n, int index) { + for (int i = index; i < n->input_size() - 1; i++) { + n->mutable_input()->SwapElements(i, i + 1); + } + n->mutable_input()->RemoveLast(); + return Status::OK(); +} + Status SparsifyGatherInternal( const GraphDef& input_graph_def, const std::unique_ptr >& @@ -301,13 +309,13 @@ Status SparsifyGatherInternal( TF_RETURN_IF_ERROR(ReadTensorFromCheckpoint( weights_node.name(), ckpt_reader, (*shapes_and_slices)[weights_node.name()], &weight)); - // Add both both weight and identity node names. - removed_node_names.push_back(weights_node.name()); - removed_node_names.push_back(match.inputs[0].node.name()); - for (auto input_node : match.inputs[0].node.input()) { - auto parsed_input = StringReplace(input_node, "^", "", true); - refs[parsed_input]--; - } + } + // Add both both weight and identity node names. + removed_node_names.push_back(weights_node.name()); + removed_node_names.push_back(match.inputs[0].node.name()); + for (auto input_node : match.inputs[0].node.input()) { + auto parsed_input = StringReplace(input_node, "^", "", true); + refs[parsed_input]--; } Tensor indices_tensor; Tensor values_tensor; @@ -468,26 +476,49 @@ Status SparsifyGatherInternal( continue; } int j = 0; + bool deleted_inputs = false; while (j < replaced_graph_def.node(i).input_size()) { if (replaced_graph_def.node(i).input(j) == name || replaced_graph_def.node(i).input(j) == ("^" + name)) { - replaced_graph_def.mutable_node(i)->mutable_input()->SwapElements( - j, replaced_graph_def.node(i).input_size() - 1); - replaced_graph_def.mutable_node(i)->mutable_input()->RemoveLast(); + TF_RETURN_IF_ERROR( + RemoveInputAtIndex(replaced_graph_def.mutable_node(i), j)); + deleted_inputs = true; continue; } j++; } - if (!replaced_graph_def.node(i).input_size()) { - if ((refs.find(replaced_graph_def.node(i).name()) != refs.end()) && - (refs[replaced_graph_def.node(i).name()] == 0)) { + if (deleted_inputs) { + if (replaced_graph_def.node(i).op() == "ConcatV2") { + if (replaced_graph_def.node(i).input_size() > 2) { + SetNodeAttr("N", replaced_graph_def.node(i).input_size() - 1, + replaced_graph_def.mutable_node(i)); + } else if (replaced_graph_def.node(i).input_size() == 2) { + if (refs[replaced_graph_def.node(i).input(1)] != 1) { + return errors::Internal( + "Expect axis tensor of ConcatV2 node to only be referenced " + "once."); + } + refs[replaced_graph_def.node(i).input(1)] -= 1; + removed_node_names.push_back(replaced_graph_def.node(i).input(1)); + replaced_graph_def.mutable_node(i)->mutable_input()->RemoveLast(); + replaced_graph_def.mutable_node(i)->mutable_attr()->erase("N"); + replaced_graph_def.mutable_node(i)->set_op("Identity"); + } else { + return errors::Internal( + "ConcatV2 should have at least two elements"); + } + } + if ((replaced_graph_def.node(i).op() == "Assign" || + replaced_graph_def.node(i).op() == "Reshape" || + replaced_graph_def.node(i).op() == "Equal" || + replaced_graph_def.node(i).op() == "Mean" || + replaced_graph_def.node(i).op() == "ScalarSummary") && + replaced_graph_def.node(i).input_size() == 1) { + removed_node_names.push_back(replaced_graph_def.node(i).name()); + } + if (!replaced_graph_def.node(i).input_size()) { removed_node_names.push_back(replaced_graph_def.node(i).name()); } - } - - if (replaced_graph_def.node(i).op() == "Assign" && - replaced_graph_def.node(i).input_size() == 1) { - removed_node_names.push_back(replaced_graph_def.node(i).name()); } i++; } @@ -528,17 +559,22 @@ Status SparsifyGather(const GraphDef& input_graph_def, }; // clang-format on + GraphDef cleaned_input_graph_def; + RemoveAttributes(input_graph_def, {"_output_shapes"}, + &cleaned_input_graph_def); + GraphDef temp_output; std::unique_ptr ckpt_reader; TF_RETURN_IF_ERROR(InitializeCheckpointReader(context, &ckpt_reader)); std::unique_ptr > shapes_and_slices; - TF_RETURN_IF_ERROR(ObtainVariableInfo(input_graph_def, &shapes_and_slices)); + TF_RETURN_IF_ERROR( + ObtainVariableInfo(cleaned_input_graph_def, &shapes_and_slices)); - TF_RETURN_IF_ERROR(SparsifyGatherInternal(input_graph_def, shapes_and_slices, - context, gather_pattern, - ckpt_reader, &temp_output)); + TF_RETURN_IF_ERROR(SparsifyGatherInternal( + cleaned_input_graph_def, shapes_and_slices, context, gather_pattern, + ckpt_reader, &temp_output)); TF_RETURN_IF_ERROR(SparsifyGatherInternal(temp_output, shapes_and_slices, context, gather_v2_pattern, diff --git a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc index 6627df1331..203ed3e0f9 100644 --- a/tensorflow/tools/graph_transforms/sparsify_gather_test.cc +++ b/tensorflow/tools/graph_transforms/sparsify_gather_test.cc @@ -71,7 +71,7 @@ class SparsifyGatherTest : public ::testing::Test { } void TestSinglePartition(bool gather_v2, bool include_shared_init, - bool test_variable, + bool test_variable, bool test_kept_concat, const string& shared_init_name = "group_deps") { GraphDef graph_def; @@ -139,6 +139,26 @@ class SparsifyGatherTest : public ::testing::Test { } } + NodeDef* concat_axis_node = + CreateNode("linear/concat/axis", "Const", {}, &graph_def); + NodeDef* concat_input_node = + CreateNode("concat/input/node", "Const", {}, &graph_def); + NodeDef* concat_node = nullptr; + if (!test_kept_concat) { + concat_node = CreateNode( + "concat/node", "ConcatV2", + {identity_node, concat_input_node, concat_axis_node}, &graph_def); + SetNodeAttr("N", 2, concat_node); + } else { + NodeDef* concat_input_node_2 = + CreateNode("concat/input/node_2", "Const", {}, &graph_def); + concat_node = CreateNode("concat/node", "ConcatV2", + {identity_node, concat_input_node, + concat_input_node_2, concat_axis_node}, + &graph_def); + SetNodeAttr("N", 3, concat_node); + } + // Run the op. GraphDef result; TransformFuncContext context; @@ -166,6 +186,23 @@ class SparsifyGatherTest : public ::testing::Test { EXPECT_EQ(1, node_lookup.count("ids")); EXPECT_EQ("Const", node_lookup.at("ids")->op()); + EXPECT_EQ(1, node_lookup.count("concat/node")); + + if (!test_kept_concat) { + EXPECT_EQ(0, node_lookup.count("linear/concat/axis")); + EXPECT_EQ("Identity", node_lookup.at("concat/node")->op()); + EXPECT_EQ(1, node_lookup.at("concat/node")->input_size()); + EXPECT_EQ("concat/input/node", node_lookup.at("concat/node")->input(0)); + } else { + EXPECT_EQ(1, node_lookup.count("linear/concat/axis")); + EXPECT_EQ("ConcatV2", node_lookup.at("concat/node")->op()); + EXPECT_EQ(3, node_lookup.at("concat/node")->input_size()); + EXPECT_EQ("concat/input/node", node_lookup.at("concat/node")->input(0)); + EXPECT_EQ("concat/input/node_2", node_lookup.at("concat/node")->input(1)); + EXPECT_EQ("linear/concat/axis", node_lookup.at("concat/node")->input(2)); + EXPECT_EQ(2, node_lookup.at("concat/node")->attr().at("N").i()); + } + EXPECT_EQ(1, node_lookup.count("w/part_1/indices")); EXPECT_EQ("Const", node_lookup.at("w/part_1/indices")->op()); Tensor expected_indices_tensor(DT_INT64, TensorShape({3})); @@ -344,6 +381,13 @@ class SparsifyGatherTest : public ::testing::Test { MakeGather("gather1", gather_v2, identity_node1, input_node, &graph_def); MakeGather("gather2", gather_v2, identity_node2, input_node, &graph_def); + NodeDef* concat_axis_node = + CreateNode("linear/concat/axis", "Const", {}, &graph_def); + NodeDef* concat_node = CreateNode( + "concat/node", "ConcatV2", + {identity_node1, identity_node2, concat_axis_node}, &graph_def); + SetNodeAttr("N", 2, concat_node); + // Shared init node if (include_shared_init) { if (!test_variable) { @@ -515,6 +559,9 @@ class SparsifyGatherTest : public ::testing::Test { node_lookup.at("gather2/LookupTableFind")->input(2)); EXPECT_EQ("gather2/LookupTableFind", node_lookup.at("gather2")->input(0)); + EXPECT_EQ(0, node_lookup.count("linear/concat/axis")); + EXPECT_EQ(0, node_lookup.count("concat/node")); + // Check control deps. EXPECT_EQ(2, node_lookup.at(shared_init_name)->input_size()); EXPECT_NE(std::find(node_lookup.at(shared_init_name)->input().begin(), @@ -550,18 +597,31 @@ class SparsifyGatherTest : public ::testing::Test { }; TEST_F(SparsifyGatherTest, TestSinglePartition) { - TestSinglePartition(false, false, false); - TestSinglePartition(false, true, false); - TestSinglePartition(true, false, false); - TestSinglePartition(true, true, false); - TestSinglePartition(false, false, true); - TestSinglePartition(false, true, true); - TestSinglePartition(true, false, true); - TestSinglePartition(true, true, true); - TestSinglePartition(false, true, false, "shared_inits"); - TestSinglePartition(true, true, false, "shared_inits"); - TestSinglePartition(false, true, true, "shared_inits"); - TestSinglePartition(true, true, true, "shared_inits"); + TestSinglePartition(false, false, false, false); + TestSinglePartition(false, true, false, false); + TestSinglePartition(true, false, false, false); + TestSinglePartition(true, true, false, false); + TestSinglePartition(false, false, true, false); + TestSinglePartition(false, true, true, false); + TestSinglePartition(true, false, true, false); + TestSinglePartition(true, true, true, false); + TestSinglePartition(false, true, false, false, "shared_inits"); + TestSinglePartition(true, true, false, false, "shared_inits"); + TestSinglePartition(false, true, true, false, "shared_inits"); + TestSinglePartition(true, true, true, false, "shared_inits"); + + TestSinglePartition(false, false, false, true); + TestSinglePartition(false, true, false, true); + TestSinglePartition(true, false, false, true); + TestSinglePartition(true, true, false, true); + TestSinglePartition(false, false, true, true); + TestSinglePartition(false, true, true, true); + TestSinglePartition(true, false, true, true); + TestSinglePartition(true, true, true, true); + TestSinglePartition(false, true, false, true, "shared_inits"); + TestSinglePartition(true, true, false, true, "shared_inits"); + TestSinglePartition(false, true, true, true, "shared_inits"); + TestSinglePartition(true, true, true, true, "shared_inits"); } TEST_F(SparsifyGatherTest, TestMultiPartition) { -- GitLab From e818d10f84bad3faad398f4b55831064666af5df Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 10:06:24 -0800 Subject: [PATCH 321/423] Update external protobuf codebase version PiperOrigin-RevId: 184006959 --- .../util/example_proto_fast_parsing_test.cc | 1 + tensorflow/workspace.bzl | 24 +++++++++---------- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/tensorflow/core/util/example_proto_fast_parsing_test.cc b/tensorflow/core/util/example_proto_fast_parsing_test.cc index 9b6a8e1251..13e41c17f7 100644 --- a/tensorflow/core/util/example_proto_fast_parsing_test.cc +++ b/tensorflow/core/util/example_proto_fast_parsing_test.cc @@ -57,6 +57,7 @@ void TestCorrectness(const string& serialized) { Example example; Example fast_example; EXPECT_TRUE(example.ParseFromString(serialized)); + example.DiscardUnknownFields(); EXPECT_TRUE(TestFastParse(serialized, &fast_example)); EXPECT_EQ(example.DebugString(), fast_example.DebugString()); if (example.DebugString() != fast_example.DebugString()) { diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 0a669eeccd..d082b6747a 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -352,11 +352,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "protobuf_archive", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/b04e5cba356212e4e8c66c61bbe0c3a20537c5b9.tar.gz", - "https://github.com/google/protobuf/archive/b04e5cba356212e4e8c66c61bbe0c3a20537c5b9.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", ], - sha256 = "e178a25c52efcb6b05988bdbeace4c0d3f2d2fe5b46696d1d9898875c3803d6a", - strip_prefix = "protobuf-b04e5cba356212e4e8c66c61bbe0c3a20537c5b9", + sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", + strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", ) # We need to import the protobuf library under the names com_google_protobuf @@ -365,21 +365,21 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "com_google_protobuf", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/b04e5cba356212e4e8c66c61bbe0c3a20537c5b9.tar.gz", - "https://github.com/google/protobuf/archive/b04e5cba356212e4e8c66c61bbe0c3a20537c5b9.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", ], - sha256 = "e178a25c52efcb6b05988bdbeace4c0d3f2d2fe5b46696d1d9898875c3803d6a", - strip_prefix = "protobuf-b04e5cba356212e4e8c66c61bbe0c3a20537c5b9", + sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", + strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", ) tf_http_archive( name = "com_google_protobuf_cc", urls = [ - "https://mirror.bazel.build/github.com/google/protobuf/archive/b04e5cba356212e4e8c66c61bbe0c3a20537c5b9.tar.gz", - "https://github.com/google/protobuf/archive/b04e5cba356212e4e8c66c61bbe0c3a20537c5b9.tar.gz", + "https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", + "https://github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz", ], - sha256 = "e178a25c52efcb6b05988bdbeace4c0d3f2d2fe5b46696d1d9898875c3803d6a", - strip_prefix = "protobuf-b04e5cba356212e4e8c66c61bbe0c3a20537c5b9", + sha256 = "846d907acf472ae233ec0882ef3a2d24edbbe834b80c305e867ac65a1f2c59e3", + strip_prefix = "protobuf-396336eb961b75f03b25824fe86cf6490fb75e3a", ) tf_http_archive( -- GitLab From 950a4c41f73221e72229172cda4795ea3194c0b2 Mon Sep 17 00:00:00 2001 From: Blake Hechtman Date: Wed, 31 Jan 2018 10:19:17 -0800 Subject: [PATCH 322/423] [XLA] Disable transpose folding into reduce for reduces of rank 2 or higher. PiperOrigin-RevId: 184009219 --- .../xla/service/algebraic_simplifier.cc | 7 +++++-- tensorflow/compiler/xla/tests/reduce_test.cc | 20 +++++++++++++++++++ 2 files changed, 25 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index ba82e822b2..fb857559f9 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -1618,9 +1618,12 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { reduce, HloInstruction::CreateBroadcast(reduce->shape(), init_value, {})); } + // A Transpose feeding a reduce can simply permute the reduction dimensions - // field. - if (arg->opcode() == HloOpcode::kTranspose) { + // field if the output of the reduce is a vector or scalar. Higher ranked + // result may require a transpose of the output. + if (ShapeUtil::Rank(reduce->shape()) <= 1 && + arg->opcode() == HloOpcode::kTranspose) { auto transpose_dimensions = arg->dimensions(); std::vector new_reduce_dimensions; for (auto dim : dimensions) { diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc index a766fa2db0..50d7b5074d 100644 --- a/tensorflow/compiler/xla/tests/reduce_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_test.cc @@ -494,6 +494,26 @@ XLA_TEST_F(ReduceTest, TransposeAndReduceElementwiseR2_111x50_To_R1) { ErrorSpec(0.01, 1e-4)); } +// Test that algebraic simplifier does not incorrectly fold a transpose into a +// reduction operation. +XLA_TEST_F(ReduceTest, TransposeAndReduceR3_12x111x50_To_R2) { + ComputationBuilder builder(client_, TestName()); + Computation add_f32 = CreateScalarAddComputation(F32, &builder); + const Shape input_shape = ShapeUtil::MakeShape(F32, {12, 111, 50}); + ComputationDataHandle input = builder.Parameter(0, input_shape, "input"); + ComputationDataHandle zero = builder.ConstantR0(0.0); + ComputationDataHandle transpose = + builder.Transpose(input, /*permutation=*/{1, 0, 2}); + ComputationDataHandle reduce = + builder.Reduce(transpose, zero, add_f32, /*dimensions_to_reduce=*/{0}); + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr input_data, + MakeFakeLiteral(input_shape)); + + ComputeAndCompare(&builder, reduce, {std::move(*input_data)}, + ErrorSpec(0.01, 1e-4)); +} + XLA_TEST_F(ReduceTest, Reshape_111x2x25Reduce_111x50_To_R1) { const int64 rows = 111, cols = 50; -- GitLab From 46093eabe85895ed62a447d08b4988edf8fccb1a Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Wed, 31 Jan 2018 10:29:29 -0800 Subject: [PATCH 323/423] Whitelisting stateful op for dataset checkpointing temporarily. PiperOrigin-RevId: 184010966 --- tensorflow/core/framework/dataset.h | 2 ++ tensorflow/core/ops/lookup_ops.cc | 3 +++ 2 files changed, 5 insertions(+) diff --git a/tensorflow/core/framework/dataset.h b/tensorflow/core/framework/dataset.h index 2c2c7e7c58..f866183f61 100644 --- a/tensorflow/core/framework/dataset.h +++ b/tensorflow/core/framework/dataset.h @@ -16,6 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_FRAMEWORK_DATASET_H_ #define TENSORFLOW_FRAMEWORK_DATASET_H_ +#include "tensorflow/core/lib/core/status.h" + namespace tensorflow { namespace dataset { // Registry for stateful ops that need to be used in dataset functions. diff --git a/tensorflow/core/ops/lookup_ops.cc b/tensorflow/core/ops/lookup_ops.cc index a67267418d..50ea8ad01a 100644 --- a/tensorflow/core/ops/lookup_ops.cc +++ b/tensorflow/core/ops/lookup_ops.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_def_builder.h" #include "tensorflow/core/framework/shape_inference.h" @@ -102,6 +103,8 @@ REGISTER_OP("LookupTableFindV2") c->set_output(0, c->UnknownShape()); return Status::OK(); }); +WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS("LookupTableFindV2"); +// TODO(b/72710477): Update this. REGISTER_OP("LookupTableInsert") .Input("table_handle: Ref(string)") -- GitLab From 35cc45f0a8832a00f69f33858ce08c4242433ce7 Mon Sep 17 00:00:00 2001 From: Eric Liu Date: Wed, 31 Jan 2018 10:38:57 -0800 Subject: [PATCH 324/423] Add std:: to min/max in cuda code to unbreak cuda-clang build. PiperOrigin-RevId: 184012479 --- tensorflow/core/kernels/conv_ops_gpu_3.cu.cc | 28 +++++++++++--------- 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc index e58f5f61f3..a376534bad 100644 --- a/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc +++ b/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc @@ -648,8 +648,9 @@ struct BatchNarrowMatrixTransposeDispatcher { static_assert( (TileLongSide & (TileLongSide - 1)) == 0, "The length of the longer side of the tile is always a power of 2."); - bool request_satisfied = max(tile_size_i, tile_size_j) <= TileLongSide && - min(tile_size_i, tile_size_j) <= TileShortSide; + bool request_satisfied = + std::max(tile_size_i, tile_size_j) <= TileLongSide && + std::min(tile_size_i, tile_size_j) <= TileShortSide; if (request_satisfied) { LaunchBatchNarrowMatrixTransposeKernel( @@ -662,7 +663,7 @@ struct BatchNarrowMatrixTransposeDispatcher { // determine whether it is the long side or the short side that falls short // of the request and increase that parameter accordingly. const bool long_side_request_not_satisfied = - max(tile_size_i, tile_size_j) > TileLongSide; + std::max(tile_size_i, tile_size_j) > TileLongSide; if (long_side_request_not_satisfied) { BatchNarrowMatrixTransposeDispatcher< @@ -690,8 +691,9 @@ struct BatchNarrowMatrixTransposeDispatcher< static_assert( (TileLongSide & (TileLongSide - 1)) == 0, "The length of the longer side of the tile is always a power of 2."); - bool request_satisfied = max(tile_size_i, tile_size_j) <= TileLongSide && - min(tile_size_i, tile_size_j) <= TileShortSide; + bool request_satisfied = + std::max(tile_size_i, tile_size_j) <= TileLongSide && + std::min(tile_size_i, tile_size_j) <= TileShortSide; if (request_satisfied) { LaunchBatchNarrowMatrixTransposeKernel( @@ -816,7 +818,7 @@ void SwapDimension1And2InTensor3WithNarrowMatrices( int tile_long_side_len = 0; int tile_short_side_len = 0; float lowest_cost = std::numeric_limits::max(); - int data_long_side = max(input_dims[1], input_dims[2]); + int data_long_side = std::max(input_dims[1], input_dims[2]); for (auto tile_size_pair : tile_spec) { int proposed_tile_long_side_len = tile_size_pair.first; @@ -861,12 +863,14 @@ void SwapDimension1And2InTensor3WithNarrowMatrices( // Truncate the shorter size requested according to the manual limit set in // tile_spec to make sure that we do not launch configurations violating // hardware limits. - requested_tile_size_i = requested_tile_size_i == tile_long_side_len - ? tile_long_side_len - : min(requested_tile_size_i, tile_short_side_len); - requested_tile_size_j = requested_tile_size_j == tile_long_side_len - ? tile_long_side_len - : min(requested_tile_size_j, tile_short_side_len); + requested_tile_size_i = + requested_tile_size_i == tile_long_side_len + ? tile_long_side_len + : std::min(requested_tile_size_i, tile_short_side_len); + requested_tile_size_j = + requested_tile_size_j == tile_long_side_len + ? tile_long_side_len + : std::min(requested_tile_size_j, tile_short_side_len); Dimension<3> input_dims_in_tiles = { input_dims[0], -- GitLab From acb2d6147415f8556ac74cbf45118baf00ca64df Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 10:59:49 -0800 Subject: [PATCH 325/423] Typo Correction. PiperOrigin-RevId: 184016082 --- tensorflow/contrib/factorization/python/ops/kmeans.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py index 4d0f9b2424..c861cfff54 100644 --- a/tensorflow/contrib/factorization/python/ops/kmeans.py +++ b/tensorflow/contrib/factorization/python/ops/kmeans.py @@ -143,7 +143,7 @@ class _ModelFn(object): def model_fn(self, features, mode, config): """Model function for the estimator. - Note that this does not take a `1abels` arg. This works, but `input_fn` must + Note that this does not take a `labels` arg. This works, but `input_fn` must return either `features` or, equivalently, `(features, None)`. Args: -- GitLab From adc9ee7150c45830eb0857f6b9e935b9672b7bb1 Mon Sep 17 00:00:00 2001 From: Anna R Date: Wed, 31 Jan 2018 11:25:38 -0800 Subject: [PATCH 326/423] Adding tf_export decorators/calls to TensorFlow functions and constants. PiperOrigin-RevId: 184020524 --- tensorflow/python/client/session.py | 3 +++ tensorflow/python/data/ops/dataset_ops.py | 2 ++ tensorflow/python/data/ops/iterator_ops.py | 2 ++ tensorflow/python/data/ops/readers.py | 4 ++++ tensorflow/python/estimator/export/export.py | 4 ++++ .../python/estimator/export/export_output.py | 5 +++++ .../python/estimator/inputs/numpy_io.py | 2 ++ .../python/estimator/inputs/pandas_io.py | 2 ++ .../python/feature_column/feature_column.py | 14 ++++++++++++ tensorflow/python/lib/io/file_io.py | 13 +++++++++++ tensorflow/python/lib/io/tf_record.py | 5 +++++ tensorflow/python/ops/losses/losses_impl.py | 13 +++++++++++ tensorflow/python/ops/losses/util.py | 6 +++++ tensorflow/python/platform/app.py | 2 ++ tensorflow/python/platform/resource_loader.py | 6 +++++ tensorflow/python/platform/tf_logging.py | 22 +++++++++++++++++++ tensorflow/python/profiler/model_analyzer.py | 4 ++++ tensorflow/python/profiler/option_builder.py | 2 ++ tensorflow/python/profiler/tfprof_logger.py | 2 ++ tensorflow/python/summary/writer/writer.py | 2 ++ .../python/summary/writer/writer_cache.py | 2 ++ tensorflow/python/util/compat.py | 9 ++++++++ tensorflow/tools/api/generator/BUILD | 9 ++++++++ 23 files changed, 135 insertions(+) diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index e6f94396b8..6befeb846d 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -35,6 +35,7 @@ from tensorflow.python.ops import session_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat from tensorflow.python.util import nest +from tensorflow.python.util.tf_export import tf_export class SessionInterface(object): @@ -1441,6 +1442,7 @@ class BaseSession(SessionInterface): return handles +@tf_export('Session') class Session(BaseSession): """A class for running TensorFlow operations. @@ -1581,6 +1583,7 @@ class Session(BaseSession): tf_session.TF_Reset(target, containers, config) +@tf_export('InteractiveSession') class InteractiveSession(BaseSession): """A TensorFlow `Session` for use in interactive contexts, such as a shell. diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 0594c6d6a7..f8798c1d6f 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -41,8 +41,10 @@ from tensorflow.python.ops import gen_io_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import script_ops from tensorflow.python.util import deprecation +from tensorflow.python.util.tf_export import tf_export +@tf_export("data.Dataset") class Dataset(object): """Represents a potentially large set of elements. diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index 53a3244ce1..e573fe0192 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -25,6 +25,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.util.tf_export import tf_export # NOTE(mrry): It is legitimate to call `Iterator.get_next()` multiple @@ -47,6 +48,7 @@ GET_NEXT_CALL_WARNING_MESSAGE = ( "`next_element` inside the loop.") +@tf_export("data.Iterator") class Iterator(object): """Represents the state of iterating through a `Dataset`.""" diff --git a/tensorflow/python/data/ops/readers.py b/tensorflow/python/data/ops/readers.py index 830dc5cec4..fa7601741b 100644 --- a/tensorflow/python/data/ops/readers.py +++ b/tensorflow/python/data/ops/readers.py @@ -23,12 +23,14 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.util.tf_export import tf_export # TODO(b/64974358): Increase default buffer size to 256 MB. _DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024 # 256 KB +@tf_export("data.TextLineDataset") class TextLineDataset(Dataset): """A `Dataset` comprising lines from one or more text files.""" @@ -71,6 +73,7 @@ class TextLineDataset(Dataset): return dtypes.string +@tf_export("data.TFRecordDataset") class TFRecordDataset(Dataset): """A `Dataset` comprising records from one or more TFRecord files.""" @@ -115,6 +118,7 @@ class TFRecordDataset(Dataset): return dtypes.string +@tf_export("data.FixedLengthRecordDataset") class FixedLengthRecordDataset(Dataset): """A `Dataset` of fixed-length records from one or more binary files.""" diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py index 51075731dd..83251c79fc 100644 --- a/tensorflow/python/estimator/export/export.py +++ b/tensorflow/python/estimator/export/export.py @@ -36,12 +36,14 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import signature_def_utils from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export _SINGLE_FEATURE_DEFAULT_NAME = 'feature' _SINGLE_RECEIVER_DEFAULT_NAME = 'input' +@tf_export('estimator.export.ServingInputReceiver') class ServingInputReceiver(collections.namedtuple( 'ServingInputReceiver', ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])): @@ -118,6 +120,7 @@ class ServingInputReceiver(collections.namedtuple( receiver_tensors_alternatives=receiver_tensors_alternatives) +@tf_export('estimator.export.build_parsing_serving_input_receiver_fn') def build_parsing_serving_input_receiver_fn(feature_spec, default_batch_size=None): """Build a serving_input_receiver_fn expecting fed tf.Examples. @@ -146,6 +149,7 @@ def build_parsing_serving_input_receiver_fn(feature_spec, return serving_input_receiver_fn +@tf_export('estimator.export.build_raw_serving_input_receiver_fn') def build_raw_serving_input_receiver_fn(features, default_batch_size=None): """Build a serving_input_receiver_fn expecting feature Tensors. diff --git a/tensorflow/python/estimator/export/export_output.py b/tensorflow/python/estimator/export/export_output.py index 863af6d41d..87b964be37 100644 --- a/tensorflow/python/estimator/export/export_output.py +++ b/tensorflow/python/estimator/export/export_output.py @@ -26,8 +26,10 @@ import six from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.saved_model import signature_def_utils +from tensorflow.python.util.tf_export import tf_export +@tf_export('estimator.export.ExportOutput') class ExportOutput(object): """Represents an output of a model that can be served. @@ -50,6 +52,7 @@ class ExportOutput(object): pass +@tf_export('estimator.export.ClassificationOutput') class ClassificationOutput(ExportOutput): """Represents the output of a classification head. @@ -118,6 +121,7 @@ class ClassificationOutput(ExportOutput): examples, self.classes, self.scores) +@tf_export('estimator.export.RegressionOutput') class RegressionOutput(ExportOutput): """Represents the output of a regression head.""" @@ -153,6 +157,7 @@ class RegressionOutput(ExportOutput): _SINGLE_OUTPUT_DEFAULT_NAME = 'output' +@tf_export('estimator.export.PredictOutput') class PredictOutput(ExportOutput): """Represents the output of a generic prediction head. diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index c4c2e30e87..a6f4712910 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -24,6 +24,7 @@ import numpy as np from six import string_types from tensorflow.python.estimator.inputs.queues import feeding_functions +from tensorflow.python.util.tf_export import tf_export # Key name to pack the target into dict of `features`. See # `_get_unique_target_key` for details. @@ -86,6 +87,7 @@ def _validate_and_convert_features(x): return ordered_dict_data +@tf_export('estimator.inputs.numpy_input_fn') def numpy_input_fn(x, y=None, batch_size=128, diff --git a/tensorflow/python/estimator/inputs/pandas_io.py b/tensorflow/python/estimator/inputs/pandas_io.py index 90d6145377..bd06843021 100644 --- a/tensorflow/python/estimator/inputs/pandas_io.py +++ b/tensorflow/python/estimator/inputs/pandas_io.py @@ -21,6 +21,7 @@ from __future__ import print_function import numpy as np from tensorflow.python.estimator.inputs.queues import feeding_functions +from tensorflow.python.util.tf_export import tf_export try: # pylint: disable=g-import-not-at-top @@ -34,6 +35,7 @@ except ImportError: HAS_PANDAS = False +@tf_export('estimator.inputs.pandas_input_fn') def pandas_input_fn(x, y=None, batch_size=128, diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index 7feb209cc4..5947d8f6e2 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -157,6 +157,7 @@ from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import checkpoint_utils from tensorflow.python.util import nest +from tensorflow.python.util.tf_export import tf_export def _internal_input_layer(features, @@ -209,6 +210,7 @@ def _internal_input_layer(features, return array_ops.concat(output_tensors, 1) +@tf_export('feature_column.input_layer') def input_layer(features, feature_columns, weight_collections=None, @@ -329,6 +331,7 @@ class InputLayer(object): return self._input_layer_template.weights +@tf_export('feature_column.linear_model') def linear_model(features, feature_columns, units=1, @@ -498,6 +501,7 @@ def _transform_features(features, feature_columns): return outputs +@tf_export('feature_column.make_parse_example_spec') def make_parse_example_spec(feature_columns): """Creates parsing spec dictionary from input feature_columns. @@ -557,6 +561,7 @@ def make_parse_example_spec(feature_columns): return result +@tf_export('feature_column.embedding_column') def embedding_column( categorical_column, dimension, combiner='mean', initializer=None, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, @@ -807,6 +812,7 @@ def shared_embedding_columns( return result +@tf_export('feature_column.numeric_column') def numeric_column(key, shape=(1,), default_value=None, @@ -881,6 +887,7 @@ def numeric_column(key, normalizer_fn=normalizer_fn) +@tf_export('feature_column.bucketized_column') def bucketized_column(source_column, boundaries): """Represents discretized dense input. @@ -970,6 +977,7 @@ def _assert_string_or_int(dtype, prefix): '{} dtype must be string or integer. dtype: {}.'.format(prefix, dtype)) +@tf_export('feature_column.categorical_column_with_hash_bucket') def categorical_column_with_hash_bucket(key, hash_bucket_size, dtype=dtypes.string): @@ -1026,6 +1034,7 @@ def categorical_column_with_hash_bucket(key, return _HashedCategoricalColumn(key, hash_bucket_size, dtype) +@tf_export('feature_column.categorical_column_with_vocabulary_file') def categorical_column_with_vocabulary_file(key, vocabulary_file, vocabulary_size=None, @@ -1145,6 +1154,7 @@ def categorical_column_with_vocabulary_file(key, dtype=dtype) +@tf_export('feature_column.categorical_column_with_vocabulary_list') def categorical_column_with_vocabulary_list( key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0): """A `_CategoricalColumn` with in-memory vocabulary. @@ -1255,6 +1265,7 @@ def categorical_column_with_vocabulary_list( default_value=default_value, num_oov_buckets=num_oov_buckets) +@tf_export('feature_column.categorical_column_with_identity') def categorical_column_with_identity(key, num_buckets, default_value=None): """A `_CategoricalColumn` that returns identity values. @@ -1322,6 +1333,7 @@ def categorical_column_with_identity(key, num_buckets, default_value=None): key=key, num_buckets=num_buckets, default_value=default_value) +@tf_export('feature_column.indicator_column') def indicator_column(categorical_column): """Represents multi-hot representation of given categorical column. @@ -1350,6 +1362,7 @@ def indicator_column(categorical_column): return _IndicatorColumn(categorical_column) +@tf_export('feature_column.weighted_categorical_column') def weighted_categorical_column( categorical_column, weight_feature_key, dtype=dtypes.float32): """Applies weight values to a `_CategoricalColumn`. @@ -1424,6 +1437,7 @@ def weighted_categorical_column( dtype=dtype) +@tf_export('feature_column.crossed_column') def crossed_column(keys, hash_bucket_size, hash_key=None): """Returns a column for performing crosses of categorical features. diff --git a/tensorflow/python/lib/io/file_io.py b/tensorflow/python/lib/io/file_io.py index 4e3071d851..59f5075f17 100644 --- a/tensorflow/python/lib/io/file_io.py +++ b/tensorflow/python/lib/io/file_io.py @@ -31,6 +31,7 @@ from tensorflow.python.framework import c_api_util from tensorflow.python.framework import errors from tensorflow.python.util import compat from tensorflow.python.util import deprecation +from tensorflow.python.util.tf_export import tf_export class FileIO(object): @@ -235,6 +236,7 @@ class FileIO(object): self._writable_file = None +@tf_export("gfile.Exists") def file_exists(filename): """Determines whether a path exists or not. @@ -256,6 +258,7 @@ def file_exists(filename): return True +@tf_export("gfile.Remove") def delete_file(filename): """Deletes the file located at 'filename'. @@ -306,6 +309,7 @@ def write_string_to_file(filename, file_content): f.write(file_content) +@tf_export("gfile.Glob") def get_matching_files(filename): """Returns a list of files that match the given pattern(s). @@ -336,6 +340,7 @@ def get_matching_files(filename): ] +@tf_export("gfile.MkDir") def create_dir(dirname): """Creates a directory with the name 'dirname'. @@ -353,6 +358,7 @@ def create_dir(dirname): pywrap_tensorflow.CreateDir(compat.as_bytes(dirname), status) +@tf_export("gfile.MakeDirs") def recursive_create_dir(dirname): """Creates a directory and all parent/intermediate directories. @@ -368,6 +374,7 @@ def recursive_create_dir(dirname): pywrap_tensorflow.RecursivelyCreateDir(compat.as_bytes(dirname), status) +@tf_export("gfile.Copy") def copy(oldpath, newpath, overwrite=False): """Copies data from oldpath to newpath. @@ -385,6 +392,7 @@ def copy(oldpath, newpath, overwrite=False): compat.as_bytes(oldpath), compat.as_bytes(newpath), overwrite, status) +@tf_export("gfile.Rename") def rename(oldname, newname, overwrite=False): """Rename or move a file / directory. @@ -426,6 +434,7 @@ def atomic_write_string_to_file(filename, contents, overwrite=True): raise +@tf_export("gfile.DeleteRecursively") def delete_recursively(dirname): """Deletes everything under dirname recursively. @@ -439,6 +448,7 @@ def delete_recursively(dirname): pywrap_tensorflow.DeleteRecursively(compat.as_bytes(dirname), status) +@tf_export("gfile.IsDirectory") def is_directory(dirname): """Returns whether the path is a directory or not. @@ -452,6 +462,7 @@ def is_directory(dirname): return pywrap_tensorflow.IsDirectory(compat.as_bytes(dirname), status) +@tf_export("gfile.ListDirectory") def list_directory(dirname): """Returns a list of entries contained within a directory. @@ -479,6 +490,7 @@ def list_directory(dirname): ] +@tf_export("gfile.Walk") def walk(top, in_order=True): """Recursive directory tree generator for directories. @@ -522,6 +534,7 @@ def walk(top, in_order=True): yield here +@tf_export("gfile.Stat") def stat(filename): """Returns file statistics for a given path. diff --git a/tensorflow/python/lib/io/tf_record.py b/tensorflow/python/lib/io/tf_record.py index df19010068..48ea107a14 100644 --- a/tensorflow/python/lib/io/tf_record.py +++ b/tensorflow/python/lib/io/tf_record.py @@ -22,8 +22,10 @@ from __future__ import print_function from tensorflow.python import pywrap_tensorflow from tensorflow.python.framework import errors from tensorflow.python.util import compat +from tensorflow.python.util.tf_export import tf_export +@tf_export("python_io.TFRecordCompressionType") class TFRecordCompressionType(object): """The type of compression for the record.""" NONE = 0 @@ -33,6 +35,7 @@ class TFRecordCompressionType(object): # NOTE(vrv): This will eventually be converted into a proto. to match # the interface used by the C++ RecordWriter. +@tf_export("python_io.TFRecordOptions") class TFRecordOptions(object): """Options used for manipulating TFRecord files.""" compression_type_map = { @@ -51,6 +54,7 @@ class TFRecordOptions(object): return cls.compression_type_map[options.compression_type] +@tf_export("python_io.tf_record_iterator") def tf_record_iterator(path, options=None): """An iterator that read the records from a TFRecords file. @@ -81,6 +85,7 @@ def tf_record_iterator(path, options=None): reader.Close() +@tf_export("python_io.TFRecordWriter") class TFRecordWriter(object): """A class to write records to a TFRecords file. diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 72508eb435..73563486e1 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -28,8 +28,10 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.ops import weights_broadcast_ops from tensorflow.python.ops.losses import util from tensorflow.python.util.deprecation import deprecated_args +from tensorflow.python.util.tf_export import tf_export +@tf_export("losses.Reduction") class Reduction(object): """Types of loss reduction. @@ -152,6 +154,7 @@ def _num_elements(losses): return array_ops.size(losses, name=scope, out_type=losses.dtype) +@tf_export("losses.compute_weighted_loss") def compute_weighted_loss( losses, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS): @@ -211,6 +214,7 @@ def compute_weighted_loss( return loss +@tf_export("losses.absolute_difference") def absolute_difference( labels, predictions, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES, @@ -258,6 +262,7 @@ def absolute_difference( losses, weights, scope, loss_collection, reduction=reduction) +@tf_export("losses.cosine_distance") @deprecated_args(None, "dim is deprecated, use axis instead", "dim") def cosine_distance( labels, predictions, axis=None, weights=1.0, scope=None, @@ -311,6 +316,7 @@ def cosine_distance( losses, weights, scope, loss_collection, reduction=reduction) +@tf_export("losses.hinge_loss") def hinge_loss(labels, logits, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS): @@ -352,6 +358,7 @@ def hinge_loss(labels, logits, weights=1.0, scope=None, losses, weights, scope, loss_collection, reduction=reduction) +@tf_export("losses.huber_loss") def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS): @@ -420,6 +427,7 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None, losses, weights, scope, loss_collection, reduction=reduction) +@tf_export("losses.log_loss") def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None, loss_collection=ops.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS): @@ -471,6 +479,7 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None, # TODO(b/37208492): Add reduction arg. +@tf_export("losses.mean_pairwise_squared_error") def mean_pairwise_squared_error( labels, predictions, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES): @@ -557,6 +566,7 @@ def mean_pairwise_squared_error( return mean_loss +@tf_export("losses.mean_squared_error") def mean_squared_error( labels, predictions, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES, @@ -604,6 +614,7 @@ def mean_squared_error( losses, weights, scope, loss_collection, reduction=reduction) +@tf_export("losses.sigmoid_cross_entropy") def sigmoid_cross_entropy( multi_class_labels, logits, weights=1.0, label_smoothing=0, scope=None, loss_collection=ops.GraphKeys.LOSSES, @@ -662,6 +673,7 @@ def sigmoid_cross_entropy( losses, weights, scope, loss_collection, reduction=reduction) +@tf_export("losses.softmax_cross_entropy") def softmax_cross_entropy( onehot_labels, logits, weights=1.0, label_smoothing=0, scope=None, loss_collection=ops.GraphKeys.LOSSES, @@ -771,6 +783,7 @@ def _remove_squeezable_dimensions( return labels, predictions, weights +@tf_export("losses.sparse_softmax_cross_entropy") def sparse_softmax_cross_entropy( labels, logits, weights=1.0, scope=None, loss_collection=ops.GraphKeys.LOSSES, diff --git a/tensorflow/python/ops/losses/util.py b/tensorflow/python/ops/losses/util.py index 3718c481c2..b835d96386 100644 --- a/tensorflow/python/ops/losses/util.py +++ b/tensorflow/python/ops/losses/util.py @@ -30,8 +30,10 @@ from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops +from tensorflow.python.util.tf_export import tf_export +@tf_export("losses.add_loss") def add_loss(loss, loss_collection=ops.GraphKeys.LOSSES): """Adds a externally defined loss to the collection of losses. @@ -43,6 +45,7 @@ def add_loss(loss, loss_collection=ops.GraphKeys.LOSSES): ops.add_to_collection(loss_collection, loss) +@tf_export("losses.get_losses") def get_losses(scope=None, loss_collection=ops.GraphKeys.LOSSES): """Gets the list of losses from the loss_collection. @@ -56,6 +59,7 @@ def get_losses(scope=None, loss_collection=ops.GraphKeys.LOSSES): return ops.get_collection(loss_collection, scope) +@tf_export("losses.get_regularization_losses") def get_regularization_losses(scope=None): """Gets the list of regularization losses. @@ -68,6 +72,7 @@ def get_regularization_losses(scope=None): return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope) +@tf_export("losses.get_regularization_loss") def get_regularization_loss(scope=None, name="total_regularization_loss"): """Gets the total regularization loss. @@ -85,6 +90,7 @@ def get_regularization_loss(scope=None, name="total_regularization_loss"): return constant_op.constant(0.0) +@tf_export("losses.get_total_loss") def get_total_loss(add_regularization_losses=True, name="total_loss"): """Returns a tensor whose value represents the total loss. diff --git a/tensorflow/python/platform/app.py b/tensorflow/python/platform/app.py index 9b92d9a180..cce64c0cca 100644 --- a/tensorflow/python/platform/app.py +++ b/tensorflow/python/platform/app.py @@ -23,6 +23,7 @@ import sys as _sys from tensorflow.python.platform import flags from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export def _usage(shorthelp): @@ -108,6 +109,7 @@ def _define_help_flags(): _define_help_flags_called = True +@tf_export('app.run') def run(main=None, argv=None): """Runs the program with an optional 'main' function and 'argv' list.""" diff --git a/tensorflow/python/platform/resource_loader.py b/tensorflow/python/platform/resource_loader.py index 2455acb4c0..8f7b12e2b2 100644 --- a/tensorflow/python/platform/resource_loader.py +++ b/tensorflow/python/platform/resource_loader.py @@ -29,8 +29,10 @@ import sys as _sys from tensorflow.python.util import tf_inspect as _inspect from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export +@tf_export('resource_loader.load_resource') def load_resource(path): """Load the resource at given path, where path is relative to tensorflow/. @@ -52,6 +54,7 @@ def load_resource(path): # pylint: disable=protected-access +@tf_export('resource_loader.get_data_files_path') def get_data_files_path(): """Get a direct path to the data files colocated with the script. @@ -62,6 +65,7 @@ def get_data_files_path(): return _os.path.dirname(_inspect.getfile(_sys._getframe(1))) +@tf_export('resource_loader.get_root_dir_with_all_resources') def get_root_dir_with_all_resources(): """Get a root directory containing all the data attributes in the build rule. @@ -101,6 +105,7 @@ def get_root_dir_with_all_resources(): return data_files_dir or script_dir +@tf_export('resource_loader.get_path_to_datafile') def get_path_to_datafile(path): """Get the path to the specified file in the data dependencies. @@ -120,6 +125,7 @@ def get_path_to_datafile(path): return _os.path.join(data_files_path, path) +@tf_export('resource_loader.readahead_file_path') def readahead_file_path(path, readahead='128M'): # pylint: disable=unused-argument """Readahead files not implemented; simply returns given path.""" return path diff --git a/tensorflow/python/platform/tf_logging.py b/tensorflow/python/platform/tf_logging.py index 85ed4f071c..22aabfd712 100644 --- a/tensorflow/python/platform/tf_logging.py +++ b/tensorflow/python/platform/tf_logging.py @@ -35,6 +35,7 @@ import threading import six from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export # Don't use this directly. Use _get_logger() instead. @@ -90,30 +91,37 @@ def _get_logger(): _logger_lock.release() +@tf_export('logging.log') def log(level, msg, *args, **kwargs): _get_logger().log(level, msg, *args, **kwargs) +@tf_export('logging.debug') def debug(msg, *args, **kwargs): _get_logger().debug(msg, *args, **kwargs) +@tf_export('logging.error') def error(msg, *args, **kwargs): _get_logger().error(msg, *args, **kwargs) +@tf_export('logging.fatal') def fatal(msg, *args, **kwargs): _get_logger().fatal(msg, *args, **kwargs) +@tf_export('logging.info') def info(msg, *args, **kwargs): _get_logger().info(msg, *args, **kwargs) +@tf_export('logging.warn') def warn(msg, *args, **kwargs): _get_logger().warn(msg, *args, **kwargs) +@tf_export('logging.warning') def warning(msg, *args, **kwargs): _get_logger().warning(msg, *args, **kwargs) @@ -136,15 +144,18 @@ _log_prefix = None # later set to google2_log_prefix _log_counter_per_token = {} +@tf_export('logging.TaskLevelStatusMessage') def TaskLevelStatusMessage(msg): error(msg) +@tf_export('logging.flush') def flush(): raise NotImplementedError() # Code below is taken from pyglib/logging +@tf_export('logging.vlog') def vlog(level, msg, *args, **kwargs): _get_logger().log(level, msg, *args, **kwargs) @@ -164,6 +175,7 @@ def _GetNextLogCountPerToken(token): return _log_counter_per_token[token] +@tf_export('logging.log_every_n') def log_every_n(level, msg, n, *args): """Log 'msg % args' at level 'level' once per 'n' times. @@ -180,6 +192,7 @@ def log_every_n(level, msg, n, *args): log_if(level, msg, not (count % n), *args) +@tf_export('logging.log_first_n') def log_first_n(level, msg, n, *args): # pylint: disable=g-bad-name """Log 'msg % args' at level 'level' only first 'n' times. @@ -195,6 +208,7 @@ def log_first_n(level, msg, n, *args): # pylint: disable=g-bad-name log_if(level, msg, count < n, *args) +@tf_export('logging.log_if') def log_if(level, msg, condition, *args): """Log 'msg % args' at level 'level' only if condition is fulfilled.""" if condition: @@ -251,11 +265,13 @@ def google2_log_prefix(level, timestamp=None, file_and_line=None): return s +@tf_export('logging.get_verbosity') def get_verbosity(): """Return how much logging output will be produced.""" return _get_logger().getEffectiveLevel() +@tf_export('logging.set_verbosity') def set_verbosity(v): """Sets the threshold for what messages will be logged.""" _get_logger().setLevel(v) @@ -296,4 +312,10 @@ _allowed_symbols = [ 'warning', ] +tf_export('logging.DEBUG').export_constant(__name__, 'DEBUG') +tf_export('logging.ERROR').export_constant(__name__, 'ERROR') +tf_export('logging.FATAL').export_constant(__name__, 'FATAL') +tf_export('logging.INFO').export_constant(__name__, 'INFO') +tf_export('logging.WARN').export_constant(__name__, 'WARN') + remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/python/profiler/model_analyzer.py b/tensorflow/python/profiler/model_analyzer.py index 8f78054560..0e20ca35bb 100644 --- a/tensorflow/python/profiler/model_analyzer.py +++ b/tensorflow/python/profiler/model_analyzer.py @@ -33,6 +33,7 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.profiler import option_builder from tensorflow.python.profiler import tfprof_logger +from tensorflow.python.util.tf_export import tf_export _DEFAULT_PROFILE_OPTIONS = 0 _DEFAULT_ADVISE_OPTIONS = 0 @@ -121,6 +122,7 @@ def _build_advisor_options(options): return opts +@tf_export('profiler.Profiler') class Profiler(object): """TensorFlow multi-step profiler. @@ -304,6 +306,7 @@ class Profiler(object): print_mdl.WriteProfile(filename) +@tf_export('profiler.profile') def profile(graph=None, run_meta=None, op_log=None, @@ -378,6 +381,7 @@ def profile(graph=None, return tfprof_node +@tf_export('profiler.advise') def advise(graph=None, run_meta=None, options=_DEFAULT_ADVISE_OPTIONS): """Auto profile and advise. diff --git a/tensorflow/python/profiler/option_builder.py b/tensorflow/python/profiler/option_builder.py index 13942ad6a2..957ebe6ddd 100644 --- a/tensorflow/python/profiler/option_builder.py +++ b/tensorflow/python/profiler/option_builder.py @@ -20,8 +20,10 @@ from __future__ import print_function import copy from tensorflow.python.profiler import tfprof_logger +from tensorflow.python.util.tf_export import tf_export +@tf_export('profiler.ProfileOptionBuilder') class ProfileOptionBuilder(object): # pylint: disable=line-too-long """Option Builder for Profiling API. diff --git a/tensorflow/python/profiler/tfprof_logger.py b/tensorflow/python/profiler/tfprof_logger.py index ffda7ddad7..8d12106496 100644 --- a/tensorflow/python/profiler/tfprof_logger.py +++ b/tensorflow/python/profiler/tfprof_logger.py @@ -30,6 +30,7 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.platform import gfile from tensorflow.python.profiler.internal import flops_registry # pylint: disable=unused-import +from tensorflow.python.util.tf_export import tf_export TRAINABLE_VARIABLES = '_trainable_variables' REGISTERED_FLOP_STATS = 'flops' @@ -187,6 +188,7 @@ def merge_default_with_oplog(graph, op_log=None, run_meta=None, return tmp_op_log +@tf_export('profiler.write_op_log') def write_op_log(graph, log_dir, op_log=None, run_meta=None, add_trace=True): """Log provided 'op_log', and add additional model information below. diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py index 12f120116f..1f3f228704 100644 --- a/tensorflow/python/summary/writer/writer.py +++ b/tensorflow/python/summary/writer/writer.py @@ -32,6 +32,7 @@ from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import plugin_asset from tensorflow.python.summary.writer.event_file_writer import EventFileWriter +from tensorflow.python.util.tf_export import tf_export _PLUGINS_DIR = "plugins" @@ -276,6 +277,7 @@ class SummaryToEventTransformer(object): self.event_writer.add_event(event) +@tf_export("summary.FileWriter") class FileWriter(SummaryToEventTransformer): """Writes `Summary` protocol buffers to event files. diff --git a/tensorflow/python/summary/writer/writer_cache.py b/tensorflow/python/summary/writer/writer_cache.py index bad289303c..645fa28a37 100644 --- a/tensorflow/python/summary/writer/writer_cache.py +++ b/tensorflow/python/summary/writer/writer_cache.py @@ -22,8 +22,10 @@ import threading from tensorflow.python.framework import ops from tensorflow.python.summary.writer.writer import FileWriter +from tensorflow.python.util.tf_export import tf_export +@tf_export('summary.FileWriterCache') class FileWriterCache(object): """Cache for file writers. diff --git a/tensorflow/python/util/compat.py b/tensorflow/python/util/compat.py index 270d96a3c7..7e5f192b8f 100644 --- a/tensorflow/python/util/compat.py +++ b/tensorflow/python/util/compat.py @@ -41,8 +41,10 @@ import numpy as _np import six as _six from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.util.tf_export import tf_export +@tf_export('compat.as_bytes', 'compat.as_str') def as_bytes(bytes_or_text, encoding='utf-8'): """Converts either bytes or unicode to `bytes`, using utf-8 encoding for text. @@ -65,6 +67,7 @@ def as_bytes(bytes_or_text, encoding='utf-8'): (bytes_or_text,)) +@tf_export('compat.as_text') def as_text(bytes_or_text, encoding='utf-8'): """Returns the given argument as a unicode string. @@ -93,6 +96,7 @@ else: as_str = as_text +@tf_export('compat.as_str_any') def as_str_any(value): """Converts to `str` as `str(value)`, but use `as_str` for `bytes`. @@ -125,11 +129,16 @@ def path_to_str(path): # Numpy 1.8 scalars don't inherit from numbers.Integral in Python 3, so we # need to check them specifically. The same goes from Real and Complex. integral_types = (_numbers.Integral, _np.integer) +tf_export('compat.integral_types').export_constant(__name__, 'integral_types') real_types = (_numbers.Real, _np.integer, _np.floating) +tf_export('compat.real_types').export_constant(__name__, 'real_types') complex_types = (_numbers.Complex, _np.number) +tf_export('compat.complex_types').export_constant(__name__, 'complex_types') # Either bytes or text. bytes_or_text_types = (bytes, _six.text_type) +tf_export('compat.bytes_or_text_types').export_constant(__name__, + 'bytes_or_text_types') _allowed_symbols = [ 'as_str', diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index 036bdd6d29..66bbd572a6 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -78,6 +78,15 @@ genrule( "api/sets/__init__.py", "api/summary/__init__.py", "api/train/queue_runner/__init__.py", + "api/compat/__init__.py", + "api/data/__init__.py", + "api/estimator/__init__.py", + "api/estimator/export/__init__.py", + "api/estimator/inputs/__init__.py", + "api/feature_column/__init__.py", + "api/losses/__init__.py", + "api/profiler/__init__.py", + "api/python_io/__init__.py", ], cmd = "$(location create_python_api) $(OUTS)", tools = ["create_python_api"], -- GitLab From d418a14176f2e5cf6c8d16a155c005947c41974b Mon Sep 17 00:00:00 2001 From: Jonathan Hseu Date: Wed, 31 Jan 2018 11:30:36 -0800 Subject: [PATCH 327/423] TPUEstimator: support host_call when use_tpu=False. PiperOrigin-RevId: 184021299 --- .../contrib/tpu/python/tpu/tpu_estimator.py | 60 +++++++++++++++---- 1 file changed, 49 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 23960bb030..c7008533f3 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -410,14 +410,13 @@ class TPUEstimatorSpec( function should not capture any Tensors in `model_fn`. `host_call` is a tuple of a `function` and a list or dictionary of `tensors` - to pass to that function. `host_call` currently works for train() and - evaluate(). The function's graph is executed on the CPU on every step, so - there is communication overhead when sending tensors from TPU to CPU. To - reduce the overhead, try reducing the size of the tensors. The `tensors` are - concatenated along their major (batch) dimension, and so must be >= rank 1. - The `host_call` is useful for writing summaries with - @{tf.contrib.summary.create_file_writer}. Note that `host_call` does not - currently work if `use_tpu` is set to False. + to pass to that function and returns a list of Tensors. `host_call` currently + works for train() and evaluate(). The Tensors returned by the function is + executed on the CPU on every step, so there is communication overhead when + sending tensors from TPU to CPU. To reduce the overhead, try reducing the + size of the tensors. The `tensors` are concatenated along their major (batch) + dimension, and so must be >= rank 1. The `host_call` is useful for writing + summaries with @{tf.contrib.summary.create_file_writer}. """ def __new__(cls, @@ -449,10 +448,18 @@ class TPUEstimatorSpec( def as_estimator_spec(self): """Creates an equivalent `EstimatorSpec` used by CPU train/eval.""" + host_calls = {} + if self.eval_metrics is not None: + host_calls['eval_metrics'] = self.eval_metrics + if self.host_call is not None: + host_calls['host_call'] = self.host_call + host_call_ret = _OutfeedHostCall.create_cpu_hostcall(host_calls) eval_metric_ops = None if self.eval_metrics is not None: - eval_metric_ops = _OutfeedHostCall.create_cpu_hostcall( - {'eval_metrics': self.eval_metrics})['eval_metrics'] + eval_metric_ops = host_call_ret['eval_metrics'] + hooks = None + if self.host_call is not None: + hooks = [_OutfeedHostCallHook(host_call_ret['host_call'])] scaffold = self.scaffold_fn() if self.scaffold_fn else None return model_fn_lib.EstimatorSpec( mode=self.mode, @@ -461,7 +468,10 @@ class TPUEstimatorSpec( train_op=self.train_op, eval_metric_ops=eval_metric_ops, export_outputs=self.export_outputs, - scaffold=scaffold) + scaffold=scaffold, + training_hooks=hooks, + evaluation_hooks=hooks, + prediction_hooks=hooks) class _OpQueueContext(object): @@ -1450,6 +1460,34 @@ class _OutfeedHostCall(object): return ret +class _OutfeedHostCallHook(session_run_hook.SessionRunHook): + """Hook to run host calls when use_tpu=False.""" + + def __init__(self, tensors): + self._tensors = tensors + + def begin(self): + # We duplicate this code from the TPUInfeedOutfeedSessionHook rather than + # create a separate hook to guarantee execution order, because summaries + # need to be initialized before the outfeed thread starts. + # TODO(jhseu): Make a wrapper hook instead? + self._init_ops = contrib_summary.summary_writer_initializer_op() + # Get all the writer resources from the initializer, so we know what to + # flush. + self._finalize_ops = [] + for op in self._init_ops: + self._finalize_ops.append(contrib_summary.flush(writer=op.inputs[0])) + + def after_create_session(self, session, coord): + session.run(self._init_ops) + + def before_run(self, run_context): + return basic_session_run_hooks.SessionRunArgs(self._tensors) + + def end(self, session): + session.run(self._finalize_ops) + + class ExamplesPerSecondHook(basic_session_run_hooks.StepCounterHook): """Count examples during runtime.""" -- GitLab From b79c3b2d1e6825b59b72818ce467dc18f19b57ad Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Wed, 31 Jan 2018 11:32:35 -0800 Subject: [PATCH 328/423] Go: Fix Scope.WithControlDependencies array-copying behavior. The test fails with the old code, and passes with the new code. PiperOrigin-RevId: 184021596 --- tensorflow/go/op/scope.go | 21 +++++++++++++-------- tensorflow/go/op/scope_test.go | 11 ++++++++++- 2 files changed, 23 insertions(+), 9 deletions(-) diff --git a/tensorflow/go/op/scope.go b/tensorflow/go/op/scope.go index 2cf1f30187..13de4294dc 100644 --- a/tensorflow/go/op/scope.go +++ b/tensorflow/go/op/scope.go @@ -109,15 +109,20 @@ func (s *Scope) SubScope(namespace string) *Scope { // added to the graph to execute only after all the provided operations have // executed first (in addition to any other control dependencies in s). func (s *Scope) WithControlDependencies(ops ...*tf.Operation) *Scope { + // Force a copy of the control dependencies into a new underlying array on + // every call. We cannot alias the same underlying array as `ops`, otherwise + // the user could modify that array after calling s.WithControlDependencies, + // which would be confusing. We cannot alias the same underlying array as the + // original `s.controlDependencies`, since Scopes form a logical tree, and + // other calls to s.WithControlDependencies could stomp on each other. + deps := make([]*tf.Operation, 0, len(s.controlDependencies)+len(ops)) + deps = append(deps, s.controlDependencies...) + deps = append(deps, ops...) return &Scope{ - graph: s.graph, - namemap: s.namemap, - namespace: s.namespace, - // append(ops, s.controlDependencies) and not the other way - // around so that we end up with a copy of the underlying array - // (and other calls to s.WithControlDependencies() do not stomp - // on each other). - controlDependencies: append(ops, s.controlDependencies...), + graph: s.graph, + namemap: s.namemap, + namespace: s.namespace, + controlDependencies: deps, err: s.err, } } diff --git a/tensorflow/go/op/scope_test.go b/tensorflow/go/op/scope_test.go index 4f533881d0..b58a61de98 100644 --- a/tensorflow/go/op/scope_test.go +++ b/tensorflow/go/op/scope_test.go @@ -77,8 +77,17 @@ func TestControlDependencies(t *testing.T) { variable = VarHandleOp(s, tf.Int32, tf.ScalarShape()) init = AssignVariableOp(s, variable, zero) update = AssignAddVariableOp(s, variable, one) - read = ReadVariableOp(s.WithControlDependencies(update), variable, tf.Int32) + readDeps = []*tf.Operation{update} ) + // We intend for `read` to have a control dependency on `update`. + s = s.WithControlDependencies(readDeps...) + // Ensure that Scope.WithControlDependencies makes a copy of the underlying + // array, rather than just holding a slice reference to the same user-supplied + // underlying array. If the copy is correctly performed, overwriting + // readDeps[0] should have no effect on control dependencies for `read`. + readDeps[0] = init + read := ReadVariableOp(s, variable, tf.Int32) + graph, err := s.Finalize() if err != nil { t.Fatal(err) -- GitLab From 099c91b506214cb64840149df50edff19235b2bb Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Wed, 31 Jan 2018 11:34:56 -0800 Subject: [PATCH 329/423] De-bazel filename_test. Part of the effort to remove all_opensource_files. PiperOrigin-RevId: 184021942 --- tensorflow/tools/ci_build/ci_sanity.sh | 9 +++-- tensorflow/tools/test/file_name_test.py | 48 +++++++++++++++++++++++++ 2 files changed, 55 insertions(+), 2 deletions(-) create mode 100644 tensorflow/tools/test/file_name_test.py diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 106ea19d46..a58db51cb8 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -517,9 +517,14 @@ do_check_futures_test() { python check_futures_test.py } +do_check_file_name_test() { + cd "$ROOT_DIR/tensorflow/tools/test" + python file_name_test.py +} + # Supply all sanity step commands and descriptions -SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check" "do_cmake_python_sanity") -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") +SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check" "do_cmake_python_sanity" "do_check_file_name_test") +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" diff --git a/tensorflow/tools/test/file_name_test.py b/tensorflow/tools/test/file_name_test.py new file mode 100644 index 0000000000..16fb8a822d --- /dev/null +++ b/tensorflow/tools/test/file_name_test.py @@ -0,0 +1,48 @@ +#!/usr/bin/python +# 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. +# ============================================================================== +# +# Test that checks if we have any issues with case insensitive filesystems. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')) +ERROR_MESSAGE = """ +Files with same name but different case detected in directory: {} +""" + + +def main(): + # Make sure BASE_DIR ends with tensorflow. If it doesn't, we probably + # computed the wrong directory. + if os.path.split(BASE_DIR)[-1] != 'tensorflow': + raise AssertionError( + "BASE_DIR = '%s' doesn't end with tensorflow" % BASE_DIR) + + for dirpath, dirnames, filenames in os.walk(BASE_DIR, followlinks=True): + lowercase_directories = [x.lower() for x in dirnames] + lowercase_files = [x.lower() for x in filenames] + + lowercase_dir_contents = lowercase_directories + lowercase_files + if len(lowercase_dir_contents) != len(set(lowercase_dir_contents)): + raise AssertionError(ERROR_MESSAGE.format(dirpath)) + + +if __name__ == '__main__': + main() -- GitLab From f5b3824be1082013f86156c32e55f8e63376bec9 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Wed, 31 Jan 2018 12:22:10 -0800 Subject: [PATCH 330/423] Increfing tapes when keeping them around calls to python API functions. These calls can trigger GIL releases or python GC which can trigger a change to the set of live tapes and segmentation faults. PiperOrigin-RevId: 184029146 --- tensorflow/python/eager/pywrap_tfe_src.cc | 41 +++++++++++++++++------ 1 file changed, 31 insertions(+), 10 deletions(-) diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 836998cfdc..d927f3abed 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -528,6 +528,34 @@ tensorflow::gtl::CompactPointerSet* GetTapeSet() { return tape_set; } +// A safe copy of the current tapeset. Does not get affected by other python +// threads changing the set of active tapes. +class SafeTapeSet { + public: + SafeTapeSet() : tape_set_(*GetTapeSet()) { + for (auto* tape : tape_set_) { + Py_INCREF(tape); + } + } + + ~SafeTapeSet() { + for (auto* tape : tape_set_) { + Py_DECREF(tape); + } + } + + tensorflow::gtl::CompactPointerSet::const_iterator begin() { + return tape_set_.begin(); + } + + tensorflow::gtl::CompactPointerSet::const_iterator end() { + return tape_set_.end(); + } + + private: + tensorflow::gtl::CompactPointerSet tape_set_; +}; + // xcode 7 doesn't define thread_local, so for compatibility we implement our // own. TODO(apassos) remove once we can deprecate xcode 7. #ifndef __APPLE__ @@ -718,10 +746,7 @@ void TFE_Py_TapeSetWatchVariable(PyObject* variable) { if (*ThreadTapeIsStopped()) { return; } - // Note: making a copy because watching a variable can trigger a change to the - // set of tapes by allowing python's garbage collector to run. - auto tape_set = *GetTapeSet(); - for (TFE_Py_Tape* tape : tape_set) { + for (TFE_Py_Tape* tape : SafeTapeSet()) { tape->tape->WatchVariable(variable); } } @@ -777,8 +802,7 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors, return; } - auto set = *GetTapeSet(); - for (TFE_Py_Tape* tape : set) { + for (TFE_Py_Tape* tape : SafeTapeSet()) { Py_INCREF(backward_function); tape->tape->RecordOperation( op_type_str, output_info, input_ids, backward_function, @@ -787,10 +811,7 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors, } void TFE_Py_TapeSetDeleteTrace(tensorflow::int64 tensor_id) { - // Note: making a copy because deleting the trace can trigger a change to the - // set of tapes by allowing python's garbage collector to run. - auto tape_set = *GetTapeSet(); - for (TFE_Py_Tape* tape : tape_set) { + for (TFE_Py_Tape* tape : SafeTapeSet()) { tape->tape->DeleteTrace(tensor_id); } } -- GitLab From e9650510fe2a9dfde21158b052a751006a74e339 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 31 Jan 2018 12:27:38 -0800 Subject: [PATCH 331/423] [TF:XLA] Bump open source llvm revision to r323874 PiperOrigin-RevId: 184029790 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index d082b6747a..34ec291256 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -472,11 +472,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/36a30fc7c9ee6fdfe5157190ad15c1801b1ab2de.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/36a30fc7c9ee6fdfe5157190ad15c1801b1ab2de.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/f135378ec6365e852f7d5a3cfcdce342f08cb5f3.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/f135378ec6365e852f7d5a3cfcdce342f08cb5f3.tar.gz", ], - sha256 = "3b74ecd8f59c712b4daf715a4da15c43ebdd40edcd4c30737bffef62f6a2bc9d", - strip_prefix = "llvm-36a30fc7c9ee6fdfe5157190ad15c1801b1ab2de", + sha256 = "296ab832167e6c46eb65ef1f9a2b5fc31c77fcd2248799b306aa2d5d2e4edbfe", + strip_prefix = "llvm-f135378ec6365e852f7d5a3cfcdce342f08cb5f3", build_file = str(Label("//third_party/llvm:llvm.BUILD")), ) -- GitLab From 1699bc041e624cdaf249a80136b467743357fbfc Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Wed, 31 Jan 2018 12:28:13 -0800 Subject: [PATCH 332/423] [TF:XLA] Implement Acos, Asin, Atan in terms of Atan2 using half-angle formulae. This may not be the most efficient implementation but it is better than no implementation. PiperOrigin-RevId: 184029858 --- tensorflow/compiler/tests/unary_ops_test.py | 15 ++++++++++++ .../compiler/tf2xla/kernels/unary_ops.cc | 23 +++++++++++++++++++ 2 files changed, 38 insertions(+) diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py index 8e4b8a3833..3d3e112f48 100644 --- a/tensorflow/compiler/tests/unary_ops_test.py +++ b/tensorflow/compiler/tests/unary_ops_test.py @@ -154,6 +154,21 @@ class UnaryOpsTest(XLATestCase): def testFloatOps(self): for dtype in self.float_types: + x = np.arange(-0.90, 0.90, 0.25) + self._assertOpOutputMatchesExpected( + math_ops.acos, + x.astype(dtype), + expected=np.arccos(x).astype(dtype)) + self._assertOpOutputMatchesExpected( + math_ops.asin, + x.astype(dtype), + expected=np.arcsin(x).astype(dtype)) + x = np.arange(-3, 3).reshape(1, 3, 2) + self._assertOpOutputMatchesExpected( + math_ops.atan, + x.astype(dtype), + expected=np.arctan(x).astype(dtype)) + self._assertOpOutputMatchesExpected( math_ops.acosh, np.array([1, 2, 3, 4], dtype=dtype), diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index a266e9013c..0c5ad9e525 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -50,18 +50,41 @@ XLAJIT_MAKE_UNARY(Conj, b->Conj(x)); // Return x if x>0, otherwise -x. XLAJIT_MAKE_UNARY(Abs, b->Abs(x)); +// acos(x) = 2 * atan(sqrt(1 - x^2) / (1 + x)) +XLAJIT_MAKE_UNARY( + Acos, + b->Mul(XlaHelpers::FloatLiteral(b, input_type(0), 2.0), + b->Atan2(b->Pow(b->Sub(XlaHelpers::One(b, input_type(0)), + b->Mul(x, x)), + XlaHelpers::FloatLiteral(b, input_type(0), 0.5)), + b->Add(XlaHelpers::One(b, input_type(0)), x)))); + // acosh(x) = log(x + sqrt(x^2 - 1)) XLAJIT_MAKE_UNARY( Acosh, b->Log(b->Add(x, b->Pow(b->Sub(b->Mul(x, x), XlaHelpers::One(b, input_type(0))), XlaHelpers::FloatLiteral(b, input_type(0), 0.5))))); + +// asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2))) +XLAJIT_MAKE_UNARY( + Asin, + b->Mul(XlaHelpers::FloatLiteral(b, input_type(0), 2.0), + b->Atan2(x, b->Add(XlaHelpers::One(b, input_type(0)), + b->Pow(b->Sub(XlaHelpers::One(b, input_type(0)), + b->Mul(x, x)), + XlaHelpers::FloatLiteral(b, input_type(0), + 0.5)))))); + // asinh(x) = log(x + sqrt(x^2 + 1)) XLAJIT_MAKE_UNARY( Asinh, b->Log(b->Add(x, b->Pow(b->Add(b->Mul(x, x), XlaHelpers::One(b, input_type(0))), XlaHelpers::FloatLiteral(b, input_type(0), 0.5))))); + +XLAJIT_MAKE_UNARY(Atan, b->Atan2(x, XlaHelpers::One(b, input_type(0)))); + // atanh(x) = 0.5 * log((1 + x) / (1 - x)) XLAJIT_MAKE_UNARY( Atanh, b->Mul(b->Log(b->Div(b->Add(XlaHelpers::One(b, input_type(0)), x), -- GitLab From 2639cda72ba92a4a76c3cd2081268448461f8227 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 12:32:38 -0800 Subject: [PATCH 333/423] Remove contacts of ex-Googler PiperOrigin-RevId: 184030353 --- tensorflow/core/profiler/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/profiler/README.md b/tensorflow/core/profiler/README.md index 9e628b1065..460f935e4a 100644 --- a/tensorflow/core/profiler/README.md +++ b/tensorflow/core/profiler/README.md @@ -256,7 +256,7 @@ bug fix. `OpLogProto` is a good plus if it is used. #### Teams -* Xin Pan (xpan@google.com, github: panyx0718) +* Xin Pan * Chris Antaki * Yao Zhang * Jon Shlens -- GitLab From 9f75f8e6d55fb9ad605bce80b656e3e19781ee43 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Wed, 31 Jan 2018 12:58:05 -0800 Subject: [PATCH 334/423] [TF:XLA] Implement ExtractImagePatches. PiperOrigin-RevId: 184033616 --- tensorflow/compiler/tests/BUILD | 12 ++ .../tests/extract_image_patches_op_test.py | 134 ++++++++++++++ .../tf2xla/g3doc/cpu_supported_ops.md | 4 + .../tf2xla/g3doc/gpu_supported_ops.md | 4 + tensorflow/compiler/tf2xla/kernels/BUILD | 1 + .../kernels/extract_image_patches_op.cc | 169 ++++++++++++++++++ .../extract_image_patches_op_test.py | 18 +- 7 files changed, 340 insertions(+), 2 deletions(-) create mode 100644 tensorflow/compiler/tests/extract_image_patches_op_test.py create mode 100644 tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 9e64f3e9a3..7277ba42ce 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -255,6 +255,18 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "extract_image_patches_op_test", + size = "small", + srcs = ["extract_image_patches_op_test.py"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform_test", + ], +) + tf_xla_py_test( name = "fft_test", size = "medium", diff --git a/tensorflow/compiler/tests/extract_image_patches_op_test.py b/tensorflow/compiler/tests/extract_image_patches_op_test.py new file mode 100644 index 0000000000..0361702e7a --- /dev/null +++ b/tensorflow/compiler/tests/extract_image_patches_op_test.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. +# ============================================================================== +"""Functional tests for ExtractImagePatches op.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class ExtractImagePatches(XLATestCase): + """Functional tests for ExtractImagePatches op.""" + + def _VerifyValues(self, image, ksizes, strides, rates, padding, patches): + """Tests input-output pairs for the ExtractImagePatches op. + + Args: + image: Input tensor with shape: [batch, in_rows, in_cols, depth]. + ksizes: Patch size specified as: [ksize_rows, ksize_cols]. + strides: Output strides, specified as [stride_rows, stride_cols]. + rates: Atrous rates, specified as [rate_rows, rate_cols]. + padding: Padding type. + patches: Expected output. + """ + ksizes = [1] + ksizes + [1] + strides = [1] + strides + [1] + rates = [1] + rates + [1] + + with self.test_session(): + image_placeholder = array_ops.placeholder(dtypes.float32) + with self.test_scope(): + out_tensor = array_ops.extract_image_patches( + image_placeholder, + ksizes=ksizes, + strides=strides, + rates=rates, + padding=padding, + name="im2col") + feed_dict = {image_placeholder: image} + self.assertAllClose(patches, out_tensor.eval(feed_dict=feed_dict)) + + def testKsize1x1Stride1x1Rate1x1(self): + """Verifies that for 1x1 kernel the output equals the input.""" + # [2, 3, 4, 5] + image = np.reshape(range(120), [2, 3, 4, 5]) + # [2, 3, 4, 5] + patches = np.reshape(range(120), [2, 3, 4, 5]) + for padding in ["VALID", "SAME"]: + self._VerifyValues( + image, + ksizes=[1, 1], + strides=[1, 1], + rates=[1, 1], + padding=padding, + patches=patches) + + def testKsize1x1Stride2x3Rate1x1(self): + """Test for 1x1 kernel and strides.""" + # [2, 4, 5, 3] + image = np.reshape(range(120), [2, 4, 5, 3]) + # [2, 2, 2, 3] + patches = image[:, ::2, ::3, :] + for padding in ["VALID", "SAME"]: + self._VerifyValues( + image, + ksizes=[1, 1], + strides=[2, 3], + rates=[1, 1], + padding=padding, + patches=patches) + + def testKsize2x2Stride1x1Rate1x1Valid(self): + """Test for 2x2 kernel with VALID padding.""" + # [1, 2, 2, 1] + image = [[[[1], [2]], [[3], [4]]]] + # [1, 1, 1, 4] + patches = [[[[1, 2, 3, 4]]]] + self._VerifyValues( + image, + ksizes=[2, 2], + strides=[1, 1], + rates=[1, 1], + padding="VALID", + patches=patches) + + def testKsize2x2Stride1x1Rate1x1Same(self): + """Test for 2x2 kernel with SAME padding.""" + # [1, 2, 2, 1] + image = [[[[1], [2]], [[3], [4]]]] + # [1, 2, 2, 4] + patches = [[[[1, 2, 3, 4], [2, 0, 4, 0]], [[3, 4, 0, 0], [4, 0, 0, 0]]]] + self._VerifyValues( + image, + ksizes=[2, 2], + strides=[1, 1], + rates=[1, 1], + padding="SAME", + patches=patches) + + def testKsize2x2Stride1x1Rate2x2Valid(self): + """Test for 2x2 kernel with 2x2 dilation.""" + # [1, 2, 2, 1] + image = np.arange(16).reshape(1, 4, 4, 1).astype(np.float32) + # [1, 2, 2, 4] + patches = [[[[0, 2, 8, 10], [1, 3, 9, 11]], + [[4, 6, 12, 14], [5, 7, 13, 15]]]] + self._VerifyValues( + image, + ksizes=[2, 2], + strides=[1, 1], + rates=[2, 2], + padding="VALID", + patches=patches) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md index 44f7db5ffd..91351421bc 100644 --- a/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/cpu_supported_ops.md @@ -71,6 +71,7 @@ Operator | Type Constraint `Exp` | `T={complex64,double,float}` `ExpandDims` | `Tdim={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Expm1` | `T={complex64,double,float}` +`ExtractImagePatches` | `T={double,float,int32,int64,uint32,uint64}` `FFT` | `FFT2D` | `FFT3D` | @@ -124,6 +125,8 @@ Operator | Type Constraint `MaxPool3D` | `T={float}` `MaxPool3DGrad` | `TInput={float}`
`T={float}` `MaxPoolGrad` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolGradV2` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolV2` | `T={double,float,int32,int64}` `Maximum` | `T={double,float,int32,int64}` `Mean` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `Min` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` @@ -176,6 +179,7 @@ Operator | Type Constraint `ResourceGather` | `Tindices={int32,int64}`
`dtype={complex64,double,float,int32,int64,uint32,uint64}` `ResourceStridedSliceAssign` | `Index={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Reverse` | `T={bool,complex64,double,float,int32,int64}` +`ReverseSequence` | `Tlen={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `ReverseV2` | `T={bool,complex64,double,float,int32,int64}`
`Tidx={int32,int64}` `RightShift` | `T={int32,int64,uint32,uint64}` `Rint` | `T={double,float}` diff --git a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md index eb1f891125..b9bdb829d7 100644 --- a/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md +++ b/tensorflow/compiler/tf2xla/g3doc/gpu_supported_ops.md @@ -71,6 +71,7 @@ Operator | Type Constraint `Exp` | `T={complex64,double,float}` `ExpandDims` | `Tdim={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Expm1` | `T={complex64,double,float}` +`ExtractImagePatches` | `T={double,float,int32,int64,uint32,uint64}` `FFT` | `FFT2D` | `FFT3D` | @@ -124,6 +125,8 @@ Operator | Type Constraint `MaxPool3D` | `T={float}` `MaxPool3DGrad` | `TInput={float}`
`T={float}` `MaxPoolGrad` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolGradV2` | `T={double,float,int32,int64,uint32,uint64}` +`MaxPoolV2` | `T={double,float,int32,int64}` `Maximum` | `T={double,float,int32,int64}` `Mean` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` `Min` | `Tidx={int32,int64}`
`T={complex64,double,float,int32,int64,uint32,uint64}` @@ -173,6 +176,7 @@ Operator | Type Constraint `ResourceGather` | `Tindices={int32,int64}`
`dtype={complex64,double,float,int32,int64,uint32,uint64}` `ResourceStridedSliceAssign` | `Index={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `Reverse` | `T={bool,complex64,double,float,int32,int64}` +`ReverseSequence` | `Tlen={int32,int64}`
`T={bool,complex64,double,float,int32,int64,uint32,uint64}` `ReverseV2` | `T={bool,complex64,double,float,int32,int64}`
`Tidx={int32,int64}` `RightShift` | `T={int32,int64,uint32,uint64}` `Rint` | `T={double,float}` diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 84fa43f4fb..67be1a4ba6 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -31,6 +31,7 @@ tf_kernel_library( "diag_op.cc", "dynamic_stitch_op.cc", "elu_op.cc", + "extract_image_patches_op.cc", "fft_ops.cc", "fill_op.cc", "function_ops.cc", diff --git a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc new file mode 100644 index 0000000000..b2970eae20 --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc @@ -0,0 +1,169 @@ +/* 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/tf2xla/type_util.h" +#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/core/util/tensor_format.h" + +namespace tensorflow { + +namespace { + +class ExtractImagePatchesOp : public XlaOpKernel { + public: + explicit ExtractImagePatchesOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("ksizes", &ksizes_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("strides", &strides_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("rates", &dilations_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("padding", &padding_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + const TensorFormat data_format = FORMAT_NHWC; + const int num_dims = ksizes_.size(); + + OP_REQUIRES( + ctx, num_dims >= 3, + errors::InvalidArgument("Kernel size must have at least 3 dimensions")); + const int num_spatial_dims = num_dims - 2; + + OP_REQUIRES(ctx, strides_.size() == num_dims, + errors::InvalidArgument("Sliding window strides field must " + "specify ", + num_dims, " dimensions")); + OP_REQUIRES(ctx, dilations_.size() == num_dims, + errors::InvalidArgument("Dilations field must " + "specify ", + num_dims, " dimensions")); + + int batch_dim = GetTensorBatchDimIndex(num_dims, data_format); + int feature_dim = GetTensorFeatureDimIndex(num_dims, data_format); + OP_REQUIRES( + ctx, ksizes_[batch_dim] == 1 && ksizes_[feature_dim] == 1, + errors::Unimplemented("Current implementation does not yet support " + "kernel sizes > 1 in the batch and depth " + "dimensions.")); + OP_REQUIRES( + ctx, strides_[batch_dim] == 1 && strides_[feature_dim] == 1, + errors::Unimplemented("Current implementation does not yet support " + "strides in the batch and depth dimensions.")); + OP_REQUIRES( + ctx, dilations_[batch_dim] == 1 && dilations_[feature_dim] == 1, + errors::Unimplemented("Current implementation does not support " + "dilations in the batch and depth dimensions.")); + + for (int i = 0; i < num_spatial_dims; ++i) { + int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); + OP_REQUIRES( + ctx, ksizes_[input_dim] >= 0, + errors::Unimplemented("Kernel size values must be non-negative; ", i, + "th spatial dimension had dilation ", + dilations_[input_dim])); + OP_REQUIRES(ctx, strides_[input_dim] >= 1, + errors::Unimplemented("Stride values must be positive; ", i, + "th spatial dimension had dilation ", + dilations_[input_dim])); + OP_REQUIRES(ctx, dilations_[input_dim] >= 1, + errors::Unimplemented("Dilation values must be positive; ", i, + "th spatial dimension had dilation ", + dilations_[input_dim])); + } + + xla::PrimitiveType type; + OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(ctx->input_type(0), &type)); + + const TensorShape input_shape = ctx->InputShape(0); + OP_REQUIRES( + ctx, input_shape.dims() == num_dims, + errors::InvalidArgument("input must be ", num_dims, "-dimensional", + input_shape.DebugString())); + const int64 depth = input_shape.dim_size(feature_dim); + + xla::ComputationBuilder* builder = ctx->builder(); + + // The following code is equivalent to: + // eye = np.eye(kH * kW * D).reshape([kH, kW, D, kH * kW * kD]) + int64 kernel_size = 1; + std::vector lhs_shape(num_dims, 1); + for (int i = 0; i < num_spatial_dims; ++i) { + int input_dim = GetTensorSpatialDimIndex(num_dims, data_format, i); + lhs_shape[i] = ksizes_[input_dim]; + kernel_size *= ksizes_[input_dim]; + } + lhs_shape[num_spatial_dims] = depth; + lhs_shape[num_spatial_dims + 1] = 1; + + // Builds an identity matrix as a broadcast equality of iotas. + // iota = np.arange(np.prod(ksize), depth) + // filter = np.equal(np.reshape(iota, [-1, 1]), iota).astype(np.float32) + xla::ComputationDataHandle iota; + TF_CHECK_OK(XlaHelpers::Iota(builder, DataType::DT_INT32, + kernel_size * depth, &iota)); + + auto lhs = builder->Reshape(iota, lhs_shape); + auto filter = builder->ConvertElementType( + builder->Eq(lhs, iota, {num_spatial_dims + 1}), type); + + xla::ConvolutionDimensionNumbers dims; + std::vector window_strides(num_spatial_dims); + std::vector lhs_dilation(num_spatial_dims, 1); + std::vector rhs_dilation(num_spatial_dims); + std::vector> padding(num_spatial_dims); + + dims.set_input_batch_dimension(batch_dim); + dims.set_output_batch_dimension(batch_dim); + dims.set_input_feature_dimension(feature_dim); + dims.set_output_feature_dimension(feature_dim); + dims.set_kernel_input_feature_dimension(num_spatial_dims); + dims.set_kernel_output_feature_dimension(num_spatial_dims + 1); + + for (int i = 0; i < num_spatial_dims; ++i) { + const int64 dim = GetTensorSpatialDimIndex(num_dims, data_format, i); + dims.add_input_spatial_dimensions(dim); + dims.add_kernel_spatial_dimensions(i); + dims.add_output_spatial_dimensions(dim); + window_strides[i] = strides_.at(dim); + rhs_dilation[i] = dilations_.at(dim); + + int64 unused_output_size; + OP_REQUIRES_OK( + ctx, GetWindowedOutputSizeVerboseV2( + input_shape.dim_size(dim), ksizes_[dim], rhs_dilation[i], + window_strides[i], padding_, &unused_output_size, + &padding[i].first, &padding[i].second)); + } + + xla::ComputationDataHandle conv = + builder->ConvGeneralDilated(ctx->Input(0), filter, window_strides, + padding, lhs_dilation, rhs_dilation, dims); + ctx->SetOutput(0, conv); + } + + protected: + std::vector ksizes_; + std::vector dilations_; + std::vector strides_; + Padding padding_; + + private: + TF_DISALLOW_COPY_AND_ASSIGN(ExtractImagePatchesOp); +}; + +REGISTER_XLA_OP(Name("ExtractImagePatches"), ExtractImagePatchesOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/python/kernel_tests/extract_image_patches_op_test.py b/tensorflow/python/kernel_tests/extract_image_patches_op_test.py index 5c7624f1f6..6ea9f1badc 100644 --- a/tensorflow/python/kernel_tests/extract_image_patches_op_test.py +++ b/tensorflow/python/kernel_tests/extract_image_patches_op_test.py @@ -84,7 +84,7 @@ class ExtractImagePatches(test.TestCase): patches=patches) def testKsize2x2Stride1x1Rate1x1Valid(self): - """Test for 1x1 kernel .""" + """Test for 2x2 kernel with VALID padding.""" # [1, 2, 2, 1] image = [[[[1], [2]], [[3], [4]]]] # [1, 1, 1, 4] @@ -98,7 +98,7 @@ class ExtractImagePatches(test.TestCase): patches=patches) def testKsize2x2Stride1x1Rate1x1Same(self): - """Test for 1x1 kernel .""" + """Test for 2x2 kernel with SAME padding.""" # [1, 2, 2, 1] image = [[[[1], [2]], [[3], [4]]]] # [1, 2, 2, 4] @@ -111,6 +111,20 @@ class ExtractImagePatches(test.TestCase): padding="SAME", patches=patches) + def testKsize2x2Stride1x1Rate2x2Valid(self): + """Test for 2x2 kernel with 2x2 dilation.""" + # [1, 2, 2, 1] + image = np.arange(16).reshape(1, 4, 4, 1).astype(np.float32) + # [1, 2, 2, 4] + patches = [[[[0, 2, 8, 10], [1, 3, 9, 11]], + [[4, 6, 12, 14], [5, 7, 13, 15]]]] + self._VerifyValues( + image, + ksizes=[2, 2], + strides=[1, 1], + rates=[2, 2], + padding="VALID", + patches=patches) if __name__ == "__main__": test.main() -- GitLab From 0bd78003c36dd194083ec22501c2b0b6db208f4c Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 31 Jan 2018 13:22:49 -0800 Subject: [PATCH 335/423] [XLA:CPU] Generate correct IR for integer clamp PiperOrigin-RevId: 184037078 --- .../xla/service/elemental_ir_emitter.cc | 45 ++++++++++++++----- .../xla/service/elemental_ir_emitter.h | 6 +++ .../xla/tests/array_elementwise_ops_test.cc | 20 +++++++++ 3 files changed, 60 insertions(+), 11 deletions(-) diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 28cd425309..4468adbadb 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -1043,17 +1043,9 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( is_signed ? llvm::CmpInst::ICMP_SGE : llvm::CmpInst::ICMP_UGE, lhs_value, rhs_value, ir_builder_); case HloOpcode::kMinimum: - 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 EmitIntegralMin(lhs_value, rhs_value, is_signed); case HloOpcode::kMaximum: - 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 EmitIntegralMax(lhs_value, rhs_value, is_signed); case HloOpcode::kAnd: return ir_builder_->CreateAnd(lhs_value, rhs_value); case HloOpcode::kOr: @@ -1070,6 +1062,26 @@ 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); +} + +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); +} + llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( const llvm_ir::IrArray::Index& target_index, const HloInstruction& hlo, int64 operand_no) const { @@ -1366,7 +1378,18 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( TF_ASSIGN_OR_RETURN(llvm::Value * max_value, operand_to_generator.at(hlo->operand(2))( ElementwiseSourceIndex(index, *hlo, 2))); - return EmitFloatMin(max_value, EmitFloatMax(min_value, arg_value)); + PrimitiveType prim_type = hlo->shape().element_type(); + if (primitive_util::IsFloatingPointType(prim_type)) { + return EmitFloatMin(max_value, EmitFloatMax(min_value, arg_value)); + } else if (primitive_util::IsIntegralType(prim_type)) { + bool is_signed = primitive_util::IsSignedIntegralType(prim_type); + return EmitIntegralMin( + max_value, EmitIntegralMax(min_value, arg_value, is_signed), + is_signed); + } else { + return Unimplemented("Clamp unimplemented for %s", + PrimitiveType_Name(prim_type).c_str()); + } }; case HloOpcode::kReducePrecision: return [this, hlo, &operand_to_generator]( diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h index 1a48eb5fcb..c516a826d9 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h @@ -86,6 +86,12 @@ class ElementalIrEmitter { virtual llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value) const; + llvm::Value* EmitIntegralMax(llvm::Value* lhs_value, llvm::Value* rhs_value, + bool is_signed) const; + + llvm::Value* EmitIntegralMin(llvm::Value* lhs_value, llvm::Value* rhs_value, + bool is_signed) const; + virtual StatusOr EmitErfInv(PrimitiveType prim_type, llvm::Value* value) const; diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index 56fc21d019..52e14a1f7b 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -1893,6 +1893,26 @@ XLA_TEST_F(ArrayElementwiseOpTest, ClampF32ScalarVector) { error_spec_); } +XLA_TEST_F(ArrayElementwiseOpTest, ClampS32Vector) { + ComputationBuilder builder(client_, TestName()); + auto min_vector = builder.ConstantR1({1, -6, 1, 2, 0, -5}); + auto arg_vector = builder.ConstantR1({2, 10, -5, 1, 4, 10}); + auto max_vector = builder.ConstantR1({3, 0, 25, 5, 123, -1}); + auto clamp = builder.Clamp(min_vector, arg_vector, max_vector); + + ComputeAndCompareR1(&builder, {2, 0, 1, 2, 4, -1}, {}); +} + +XLA_TEST_F(ArrayElementwiseOpTest, ClampU32Vector) { + ComputationBuilder builder(client_, TestName()); + auto min_vector = builder.ConstantR1({1, 2, 1, 2, 0, ~0u - 4}); + auto arg_vector = builder.ConstantR1({2, 10, 5, 1, 4, 10}); + auto max_vector = builder.ConstantR1({3, 5, 25, 5, 123, ~0u}); + auto clamp = builder.Clamp(min_vector, arg_vector, max_vector); + + ComputeAndCompareR1(&builder, {2, 5, 5, 2, 4, ~0u - 4}, {}); +} + XLA_TEST_F(ArrayElementwiseOpTest, AddTwoParametersF32s) { ComputationBuilder builder(client_, TestName()); -- GitLab From 6ac8f0947980fc9f88a19f4f1e211db8cd3e2d79 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 13:35:23 -0800 Subject: [PATCH 336/423] Automated g4 rollback of changelist 184003263 PiperOrigin-RevId: 184038801 --- tensorflow/contrib/lite/tools/BUILD | 4 - tensorflow/contrib/lite/tools/verifier.cc | 170 +-------------- tensorflow/contrib/lite/tools/verifier.h | 4 +- .../contrib/lite/tools/verifier_test.cc | 200 +++++------------- 4 files changed, 54 insertions(+), 324 deletions(-) diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index 4d3b553b22..1bffcfb987 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -99,11 +99,8 @@ cc_library( srcs = ["verifier.cc"], hdrs = ["verifier.h"], deps = [ - "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", - "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/schema:schema_fbs", - "@com_google_absl//absl/base:core_headers", ], ) @@ -115,7 +112,6 @@ cc_test( ":verifier", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", - "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/schema:schema_fbs", "//tensorflow/contrib/lite/testing:util", "@com_google_googletest//:gtest", diff --git a/tensorflow/contrib/lite/tools/verifier.cc b/tensorflow/contrib/lite/tools/verifier.cc index 726e2aaa31..95a0895379 100644 --- a/tensorflow/contrib/lite/tools/verifier.cc +++ b/tensorflow/contrib/lite/tools/verifier.cc @@ -14,32 +14,13 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/tools/verifier.h" -#include #include "tensorflow/contrib/lite/schema/schema_generated.h" -#include "tensorflow/contrib/lite/string_util.h" #include "tensorflow/contrib/lite/version.h" namespace tflite { namespace { -// Reports error message when the reporter is set. -void ReportError(ErrorReporter* error_reporter, const char* format, ...) { - if (error_reporter) { - va_list args; - va_start(args, format); - error_reporter->Report(format, args); - va_end(args); - } -} - -// Returns the int32_t value pointed by ptr. -const uint32_t* GetIntPtr(const char* ptr) { - return reinterpret_cast(ptr); -} - -// Verifies flatbuffer format of the model contents and returns the in-memory -// model. const Model* VerifyFlatbufferAndGetModel(const void* buf, size_t len) { ::flatbuffers::Verifier verifier(static_cast(buf), len); if (VerifyModelBuffer(verifier)) { @@ -49,159 +30,14 @@ const Model* VerifyFlatbufferAndGetModel(const void* buf, size_t len) { } } -const uint32_t kMaxNumString = UINT_MAX / sizeof(int32_t) - 2; - -// Verifies string tensor has legit buffer contents that follow the schema -// defined in lite/string_util.h -bool VerifyStringTensorBuffer(const Buffer& buffer, - ErrorReporter* error_reporter) { - uint32_t buffer_size = buffer.data()->size(); - const char* buffer_ptr = reinterpret_cast(buffer.data()->data()); - - uint32_t num_strings = *GetIntPtr(buffer_ptr); - if (num_strings > kMaxNumString) { - ReportError(error_reporter, - "String tensor has invalid num of string set: %d", num_strings); - return false; - } - uint32_t header_offsets = - static_cast(num_strings + 2) * sizeof(int32_t); - - if (buffer_size < header_offsets) { - ReportError(error_reporter, - "String tensor buffer requires at least %d bytes, but is " - "allocated with %d bytes", - header_offsets, buffer_size); - return false; - } - - uint32_t prev_ptr = header_offsets; - uint32_t offset = sizeof(int32_t); - - if (*GetIntPtr(buffer_ptr + offset) != header_offsets) { - ReportError(error_reporter, - "String tensor buffer initial offset must be: %d", - header_offsets); - return false; - } - offset += sizeof(int32_t); - for (int i = 1; i <= num_strings; i++, offset += sizeof(int32_t)) { - int string_offset = *GetIntPtr(buffer_ptr + offset); - if (string_offset < prev_ptr || string_offset > buffer_size) { - ReportError(error_reporter, "String tensor buffer is invalid: index %d", - i); - return false; - } - } - if (*GetIntPtr(buffer_ptr + offset - sizeof(int32_t)) != buffer_size) { - ReportError(error_reporter, "String tensor buffer last offset must be %d", - buffer_size); - return false; - } - return true; -} - -// Verifies numeric tensor has legit buffer. -bool VerifyNumericTensorBuffer(const Tensor& tensor, const Buffer& buffer, - ErrorReporter* error_reporter) { - uint64_t bytes_required = 1; - for (int dim : *tensor.shape()) { - bytes_required *= dim; - if (bytes_required > UINT_MAX) { - ReportError(error_reporter, "Tensor dimension overflow"); - return false; - } - } - switch (tensor.type()) { - case TensorType_FLOAT32: - bytes_required *= sizeof(float); - break; - case TensorType_INT32: - bytes_required *= sizeof(int32_t); - break; - case TensorType_UINT8: - bytes_required *= sizeof(uint8_t); - break; - case TensorType_INT64: - bytes_required *= sizeof(int64_t); - break; - case TensorType_FLOAT16: - // FALLTHROUGH_INTENDED; - default: - ReportError(error_reporter, "Invalid tensor type: %d", tensor.type()); - return false; - } - if (bytes_required > UINT_MAX) { - ReportError(error_reporter, "Tensor dimension overflow"); - return false; - } - - if (bytes_required != buffer.data()->size()) { - ReportError( - error_reporter, - "Tensor requires %d bytes, but is allocated with %d bytes buffer", - bytes_required, buffer.data()->size()); - return false; - } - return true; - - // TODO(yichengfan): verify quantized tensors. -} - -// Verifies tensors have valid properties and legit buffer if set. -bool VerifyTensors(const Model& model, ErrorReporter* error_reporter) { - if (!model.subgraphs()) { - return true; - } - for (const auto& subgraph : *model.subgraphs()) { - if (!subgraph->tensors()) { - return true; - } - for (const auto& tensor : *subgraph->tensors()) { - if (!tensor->buffer()) { - return true; - } - if (tensor->buffer() >= model.buffers()->size()) { - ReportError(error_reporter, "Invalid tensor buffer index: %d", - tensor->buffer()); - return false; - } - auto* buffer = model.buffers()->Get(tensor->buffer()); - if (!buffer || !buffer->data()) { - ReportError(error_reporter, "Tensor buffer %d not set", - tensor->buffer()); - return false; - } - - if (tensor->type() == TensorType_STRING) { - if (!VerifyStringTensorBuffer(*buffer, error_reporter)) { - return false; - } - } else { - if (!VerifyNumericTensorBuffer(*tensor, *buffer, error_reporter)) { - return false; - } - } - } - } - return true; -} - } // namespace -bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter) { +bool Verify(const void* buf, size_t len) { const Model* model = VerifyFlatbufferAndGetModel(buf, len); if (model == nullptr) { - ReportError(error_reporter, "Invalid flatbuffer format"); return false; } - if (model->version() != TFLITE_SCHEMA_VERSION) { - ReportError(error_reporter, "Invalid model version %d", model->version()); - return false; - } - if (!VerifyTensors(*model, error_reporter)) { - return false; - } - return true; + + return model->version() == TFLITE_SCHEMA_VERSION; } } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier.h b/tensorflow/contrib/lite/tools/verifier.h index d2bf3c91d5..03e1f22b7e 100644 --- a/tensorflow/contrib/lite/tools/verifier.h +++ b/tensorflow/contrib/lite/tools/verifier.h @@ -18,15 +18,13 @@ limitations under the License. #include -#include "tensorflow/contrib/lite/error_reporter.h" - namespace tflite { // Verifies the integrity of a Tensorflow Lite flatbuffer model file. // Currently, it verifies: // * The file is following a legit flatbuffer schema. // * The model is in supported version. -bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter); +bool Verify(const void* buf, size_t len); } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier_test.cc b/tensorflow/contrib/lite/tools/verifier_test.cc index 244d4f0396..0481a55a78 100644 --- a/tensorflow/contrib/lite/tools/verifier_test.cc +++ b/tensorflow/contrib/lite/tools/verifier_test.cc @@ -28,62 +28,31 @@ using flatbuffers::FlatBufferBuilder; using flatbuffers::Offset; using flatbuffers::Vector; -// Build single subgraph model. -class TfLiteFlatbufferModelBuilder { +// Class that abstracts the list of buffers at the end of the TF Lite structure +class DeferredBufferWriter { public: - TfLiteFlatbufferModelBuilder() { - buffers_.push_back( - CreateBuffer(builder_, builder_.CreateVector(std::vector{}))); + DeferredBufferWriter() { + data_.push_back({}); // sentinel empty buffer. } - void AddTensor(const std::vector& shape, tflite::TensorType type, - const std::vector& buffer, const char* name) { - int buffer_index = 0; - if (!buffer.empty()) { - buffer_index = buffers_.size(); - buffers_.push_back(CreateBuffer(builder_, builder_.CreateVector(buffer))); + Offset>> BuildBuffers(FlatBufferBuilder *builder) { + std::vector> buffer_vector; + for (const auto &vec : data_) { + auto data_buffer = builder->CreateVector(vec.data(), vec.size()); + buffer_vector.push_back(tflite::CreateBuffer(*builder, data_buffer)); } - tensors_.push_back(CreateTensorDirect(builder_, &shape, type, buffer_index, - name, /*quantization=*/0)); + return builder->CreateVector(buffer_vector); } - void AddOperator(const std::vector& inputs, - const std::vector& outputs, - tflite::BuiltinOperator builtin_op, const char* custom_op) { - operator_codes_.push_back( - CreateOperatorCodeDirect(builder_, builtin_op, custom_op)); - operators_.push_back(CreateOperator( - builder_, operator_codes_.size() - 1, builder_.CreateVector(inputs), - builder_.CreateVector(outputs), BuiltinOptions_NONE, - /*builtin_options=*/0, - /*custom_options=*/0, tflite::CustomOptionsFormat_FLEXBUFFERS)); - } - - void FinishModel(const std::vector& inputs, - const std::vector& outputs) { - auto subgraph = std::vector>({CreateSubGraph( - builder_, builder_.CreateVector(tensors_), - builder_.CreateVector(inputs), builder_.CreateVector(outputs), - builder_.CreateVector(operators_), - builder_.CreateString("test_subgraph"))}); - auto result = CreateModel( - builder_, TFLITE_SCHEMA_VERSION, builder_.CreateVector(operator_codes_), - builder_.CreateVector(subgraph), builder_.CreateString("test_model"), - builder_.CreateVector(buffers_)); - tflite::FinishModelBuffer(builder_, result); - } - - bool Verify() { - return tflite::Verify(builder_.GetBufferPointer(), builder_.GetSize(), - DefaultErrorReporter()); + // Registers a buffer index and takes ownership of the data to write to it. + int Record(std::vector data) { + int buffer_index = data_.size(); + data_.emplace_back(std::move(data)); + return buffer_index; } private: - FlatBufferBuilder builder_; - std::vector> operators_; - std::vector> operator_codes_; - std::vector> tensors_; - std::vector> buffers_; + std::vector> data_; }; TEST(VerifyModel, TestEmptyModel) { @@ -93,26 +62,43 @@ TEST(VerifyModel, TestEmptyModel) { /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize(), - DefaultErrorReporter())); + ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); } TEST(VerifyModel, TestSimpleModel) { - TfLiteFlatbufferModelBuilder builder; - builder.AddOperator({0, 1}, {2}, BuiltinOperator_CUSTOM, "test"); - builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4, 5, 6}, "input"); - builder.AddTensor( - {2}, TensorType_STRING, - {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 19, 0, 0, 0, 'A', 'B', 'C'}, - "data"); - builder.AddTensor({2, 3}, TensorType_INT32, {}, "output"); - builder.FinishModel({0, 1}, {2}); - ASSERT_TRUE(builder.Verify()); + FlatBufferBuilder builder; + auto inputs = builder.CreateVector({0}); + auto outputs = builder.CreateVector({1}); + auto operator_codes = builder.CreateVector(std::vector>{ + CreateOperatorCodeDirect(builder, BuiltinOperator_CUSTOM, "test")}); + auto operators = + builder.CreateVector(std::vector>{CreateOperator( + builder, /*opcode_index=*/0, + /*inputs=*/builder.CreateVector({0}), + /*outputs=*/builder.CreateVector({1}), BuiltinOptions_NONE, + /*builtin_options=*/0, + /*custom_options=*/0, ::tflite::CustomOptionsFormat_FLEXBUFFERS)}); + std::vector shape; + auto tensors = builder.CreateVector(std::vector>{ + CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, + "input", /*quantization=*/0), + CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, + "output", /*quantization=*/0)}); + auto subgraph = std::vector>( + {CreateSubGraph(builder, tensors, inputs, outputs, operators, + builder.CreateString("Main"))}); + + auto model = CreateModel(builder, TFLITE_SCHEMA_VERSION, operator_codes, + builder.CreateVector(subgraph), + builder.CreateString("SmartReply"), /*buffers=*/0); + + ::tflite::FinishModelBuffer(builder, model); + ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); } TEST(VerifyModel, TestCorruptedData) { string model = "123"; - ASSERT_FALSE(Verify(model.data(), model.size(), /*error_reporter=*/nullptr)); + ASSERT_FALSE(Verify(model.data(), model.size())); } TEST(VerifyModel, TestUnsupportedVersion) { @@ -120,8 +106,7 @@ TEST(VerifyModel, TestUnsupportedVersion) { auto model = CreateModel(builder, /*version=*/1, /*operator_codes=*/0, /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), - DefaultErrorReporter())); + ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize())); } TEST(VerifyModel, TestRandomModificationIsNotAllowed) { @@ -131,105 +116,20 @@ TEST(VerifyModel, TestRandomModificationIsNotAllowed) { /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - string model_content(reinterpret_cast(builder.GetBufferPointer()), + string model_content(reinterpret_cast(builder.GetBufferPointer()), builder.GetSize()); for (int i = 0; i < model_content.size(); i++) { model_content[i] = (model_content[i] + 137) % 255; - EXPECT_FALSE(Verify(model_content.data(), model_content.size(), - DefaultErrorReporter())) + EXPECT_FALSE(Verify(model_content.data(), model_content.size())) << "Fail at position: " << i; } } -TEST(VerifyModel, TestIntTensorShapeIsGreaterThanBuffer) { - TfLiteFlatbufferModelBuilder builder; - builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input"); - builder.FinishModel({}, {}); - ASSERT_FALSE(builder.Verify()); -} - -TEST(VerifyModel, TestIntTensorShapeIsSmallerThanBuffer) { - TfLiteFlatbufferModelBuilder builder; - builder.AddTensor({2, 1}, TensorType_UINT8, {1, 2, 3, 4}, "input"); - builder.FinishModel({}, {}); - ASSERT_FALSE(builder.Verify()); -} - -TEST(VerifyModel, TestIntTensorShapeOverflow) { - TfLiteFlatbufferModelBuilder builder; - builder.AddTensor({1024, 2048, 4096}, TensorType_UINT8, {1, 2, 3, 4}, - "input"); - builder.FinishModel({}, {}); - ASSERT_FALSE(builder.Verify()); -} - -TEST(VerifyModel, TensorBufferIsNotValid) { - FlatBufferBuilder builder; - std::vector shape = {2, 3}; - auto tensors = builder.CreateVector(std::vector>{ - CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/2, - "input", /*quantization=*/0)}); - auto subgraph = std::vector>( - {CreateSubGraph(builder, tensors, /*inputs=*/0, /*outputs=*/0, - /*operators=*/0, builder.CreateString("Main"))}); - - auto buffers = builder.CreateVector(std::vector>{ - CreateBuffer(builder, - builder.CreateVector(std::vector{1, 2, 3, 4, 5, 6})), - }); - - auto model = CreateModel(builder, TFLITE_SCHEMA_VERSION, /*operator_codes=*/0, - builder.CreateVector(subgraph), - builder.CreateString("SmartReply"), buffers); - - ::tflite::FinishModelBuffer(builder, model); - ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), - DefaultErrorReporter())); -} - -TEST(VerifyModel, StringTensorHasInvalidNumString) { - TfLiteFlatbufferModelBuilder builder; - builder.AddTensor( - {2}, TensorType_STRING, - {0x00, 0x00, 0x00, 0x20, 16, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B'}, - "input"); - builder.FinishModel({}, {}); - ASSERT_FALSE(builder.Verify()); -} - -TEST(VerifyModel, StringTensorOffsetTooSmall) { - TfLiteFlatbufferModelBuilder builder; - builder.AddTensor( - {2}, TensorType_STRING, - {2, 0, 0, 0, 12, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B'}, "input"); - builder.FinishModel({}, {}); - ASSERT_FALSE(builder.Verify()); -} - -TEST(VerifyModel, StringTensorOffsetOutOfRange) { - TfLiteFlatbufferModelBuilder builder; - builder.AddTensor( - {2}, TensorType_STRING, - {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 22, 0, 0, 0, 'A', 'B'}, "input"); - builder.FinishModel({}, {}); - ASSERT_FALSE(builder.Verify()); -} - -TEST(VerifyModel, StringTensorIsLargerThanRequired) { - TfLiteFlatbufferModelBuilder builder; - builder.AddTensor( - {2}, TensorType_STRING, - {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B', 'C'}, - "input"); - builder.FinishModel({}, {}); - ASSERT_FALSE(builder.Verify()); -} - // TODO(yichengfan): make up malicious files to test with. } // namespace tflite -int main(int argc, char** argv) { +int main(int argc, char **argv) { ::tflite::LogToStderr(); ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); -- GitLab From faaa74db7134a2195a6334ab86947005e173db1b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 13:42:18 -0800 Subject: [PATCH 337/423] Tolerate Const nodes with no data or with smaller data than is required by their shape, by zero-extending the Const data to the required size. We wanted to generate an error on that, but too many existing graphs already rely on current lax behavior. PiperOrigin-RevId: 184039876 --- tensorflow/contrib/lite/toco/import_tensorflow.cc | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index ca378af4c5..9862dbe99d 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -173,7 +173,8 @@ void ImportFloatArray(const TensorProto& input_tensor, Array* output_array) { } auto& output_float_data = output_array->GetMutableBuffer().data; - output_float_data.resize(input_flat_size); + output_float_data.resize(RequiredBufferSizeForShape(output_array->shape()), + 0.f); if (input_tensor.float_val_size() == 1) { for (int i = 0; i < input_flat_size; i++) { output_float_data[i] = input_tensor.float_val(0); @@ -203,7 +204,7 @@ void ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) { } auto& output_int_data = output_array->GetMutableBuffer().data; - output_int_data.resize(input_flat_size); + output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); if (input_tensor.int_val_size()) { for (int i = 0; i < input_tensor.int_val_size(); i++) { output_int_data[i] = input_tensor.int_val(i); @@ -229,7 +230,7 @@ void ImportInt32Array(const TensorProto& input_tensor, Array* output_array) { } auto& output_int_data = output_array->GetMutableBuffer().data; - output_int_data.resize(input_flat_size); + output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); if (input_tensor.int_val_size()) { for (int i = 0; i < input_tensor.int_val_size(); i++) { output_int_data[i] = input_tensor.int_val(i); @@ -255,7 +256,7 @@ void ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { } auto& output_int_data = output_array->GetMutableBuffer().data; - output_int_data.resize(input_flat_size); + output_int_data.resize(RequiredBufferSizeForShape(output_array->shape()), 0); if (input_tensor.int64_val_size()) { for (int i = 0; i < input_tensor.int64_val_size(); i++) { output_int_data[i] = input_tensor.int64_val(i); @@ -281,7 +282,7 @@ void ImportStringArray(const TensorProto& input_tensor, Array* output_array) { } auto& output_string_data = output_array->GetMutableBuffer().data; - output_string_data.resize(input_flat_size); + output_string_data.resize(RequiredBufferSizeForShape(output_array->shape())); if (input_flat_size != input_tensor.string_val_size()) { LOG(FATAL) << "Input_content string_val doesn't have the right " "dimensions for this string tensor."; -- GitLab From b47b2a7f2e500e261b7e77e30174a3ea121261c8 Mon Sep 17 00:00:00 2001 From: Anna R Date: Wed, 31 Jan 2018 14:30:22 -0800 Subject: [PATCH 338/423] Internal change. PiperOrigin-RevId: 184047860 --- tensorflow/tools/test/run_and_gather_logs_lib.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/test/run_and_gather_logs_lib.py b/tensorflow/tools/test/run_and_gather_logs_lib.py index a953ed1b53..3b4921bb98 100644 --- a/tensorflow/tools/test/run_and_gather_logs_lib.py +++ b/tensorflow/tools/test/run_and_gather_logs_lib.py @@ -136,7 +136,7 @@ def run_and_gather_logs(name, test_name, test_args, gpu_config = gpu_info_lib.gather_gpu_devices() if gpu_config: gpu_name = gpu_config[0].model - gpu_short_name_match = re.search(r"Tesla (K40|K80|P100)", gpu_name) + gpu_short_name_match = re.search(r"Tesla (K40|K80|P100|V100)", gpu_name) if gpu_short_name_match: gpu_short_name = gpu_short_name_match.group(0) test_adjusted_name = name + "|" + gpu_short_name.replace(" ", "_") -- GitLab From 5260cecbb46360b522d284573caf1ae17dcb677a Mon Sep 17 00:00:00 2001 From: Nick Desaulniers Date: Wed, 31 Jan 2018 14:32:21 -0800 Subject: [PATCH 339/423] [XLA] Initialize linear indices used by NearComparator. In unoptimized builds, we'd see miscompares with insanely large indices when comparing literals containing NaN. The linear indices may never be updated/initialized if the value NaN was compared, since: (NaN > x) == false (NaN < x) == false Adds a few tests that were used when debugging, but not a perfect one for this case. It's currently not possible to test that LiteralTestUtil::Near() fails when given bad input since it uses the EXPECT_* family of macros, which would cause the intentional miscompares to fail the test no matter what. PiperOrigin-RevId: 184048275 --- .../compiler/xla/tests/literal_test_util.cc | 8 +++++-- .../xla/tests/literal_test_util_test.cc | 24 +++++++++++++++++++ 2 files changed, 30 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc index 39c07297d6..474d2547ae 100644 --- a/tensorflow/compiler/xla/tests/literal_test_util.cc +++ b/tensorflow/compiler/xla/tests/literal_test_util.cc @@ -376,6 +376,10 @@ class NearComparator { abs_expected_miscompare_sum_ = 0.0; max_rel_err_ = 0.0; max_abs_err_ = 0.0; + first_linear_index_ = -1; + last_linear_index_ = -1; + max_rel_linear_index_ = -1; + max_abs_linear_index_ = -1; miscompares_ = Literal(ShapeUtil::ChangeElementType(actual.shape(), PRED)); miscompares_.PopulateWithValue(false); multi_index_.resize(expected.shape().dimensions_size(), 0); @@ -482,11 +486,11 @@ class NearComparator { const float rel_err = abs_diff / std::abs(expected); abs_diff_sum_ += abs_diff; abs_expected_sum_ += std::abs(expected); - if (rel_err > max_rel_err_) { + if (rel_err > max_rel_err_ || std::isnan(rel_err)) { max_rel_err_ = rel_err; max_rel_linear_index_ = linear_index; } - if (abs_diff > max_abs_err_) { + if (abs_diff > max_abs_err_ || std::isnan(abs_diff)) { max_abs_err_ = abs_diff; max_abs_linear_index_ = linear_index; } diff --git a/tensorflow/compiler/xla/tests/literal_test_util_test.cc b/tensorflow/compiler/xla/tests/literal_test_util_test.cc index e477784557..3a421f8458 100644 --- a/tensorflow/compiler/xla/tests/literal_test_util_test.cc +++ b/tensorflow/compiler/xla/tests/literal_test_util_test.cc @@ -97,5 +97,29 @@ TEST(LiteralTestUtilTest, ExpectNearFailurePlacesResultsInTemporaryDirectory) { } } +TEST(LiteralTestUtilTest, NearComparatorR1) { + auto a = + Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); + auto b = + Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); + EXPECT_TRUE(LiteralTestUtil::Near(*a, *b, ErrorSpec{0.0001})); +} + +TEST(LiteralTestUtilTest, NearComparatorR1Nan) { + auto a = + Literal::CreateR1({0.0, 0.1, 0.2, 0.3, NAN, 0.5, 0.6, 0.7, 0.8}); + auto b = + Literal::CreateR1({0.0, 0.1, 0.2, 0.3, NAN, 0.5, 0.6, 0.7, 0.8}); + EXPECT_TRUE(LiteralTestUtil::Near(*a, *b, ErrorSpec{0.0001})); +} + +TEST(LiteralTestUtil, NearComparatorDifferentLengths) { + auto a = + Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); + auto b = Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7}); + EXPECT_FALSE(LiteralTestUtil::Near(*a, *b, ErrorSpec{0.0001})); + EXPECT_FALSE(LiteralTestUtil::Near(*b, *a, ErrorSpec{0.0001})); +} + } // namespace } // namespace xla -- GitLab From 65002aa25f8f77987e7183e972966b62208e384a Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Wed, 31 Jan 2018 14:35:37 -0800 Subject: [PATCH 340/423] Fixes minor typos. PiperOrigin-RevId: 184048812 --- .../python/kernel_tests/dataset_serialization_test_base.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py index 4574a625a3..3f64475e47 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py +++ b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py @@ -41,8 +41,9 @@ class DatasetSerializationTestBase(test.TestCase): def tearDown(self): self._delete_ckpt() - # TODO(b/72657739): Remove sparse_tensor argument to test the (deprecated) - # `from_sparse_tensor_slices()` API once the API and tests are deleted. + # TODO(b/72657739): Remove sparse_tensor argument, which is to test the + # (deprecated) saveable `SparseTensorSliceDataset`, once the API + # `from_sparse_tensor_slices()`and related tests are deleted. def run_core_tests(self, ds_fn1, ds_fn2, num_outputs, sparse_tensors=False): """Runs the core tests. @@ -589,7 +590,7 @@ class DatasetSerializationTestBase(test.TestCase): # `_get_iterator_ops_from_collection`. # TODO(shivaniagrwal): `output_classes` is a nested structure of classes, - # this base class is specific to current test cases. Update when test are + # this base class is specific to current test cases. Update when tests are # added with `output_classes` as a nested structure with at least one of the # component being `tf.SparseTensor`. if (sparse_tensors or -- GitLab From 549581f635a439ddcc6fdabf05f972c8ced1738d Mon Sep 17 00:00:00 2001 From: Shanqing Cai Date: Wed, 31 Jan 2018 14:55:13 -0800 Subject: [PATCH 341/423] Add grpcio as a pip dependency of tensorflow PiperOrigin-RevId: 184052073 --- 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 62df6453fb..1b1d60c4f3 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -35,6 +35,7 @@ REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', 'astor >= 0.6.0', 'gast >= 0.2.0', + 'grpcio >= 1.8.6', 'numpy >= 1.12.1', 'six >= 1.10.0', 'protobuf >= 3.4.0', -- GitLab From b976a142ac5ef8b3088f0adf8c1a5aa1503a01d7 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Wed, 31 Jan 2018 14:57:24 -0800 Subject: [PATCH 342/423] Enable AVX in all TF windows builds. PiperOrigin-RevId: 184052414 --- tensorflow/tools/ci_build/windows/cpu/cmake/run_build.bat | 2 +- tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/cmake/run_build.bat b/tensorflow/tools/ci_build/windows/cpu/cmake/run_build.bat index 957729bb37..c1bc718507 100644 --- a/tensorflow/tools/ci_build/windows/cpu/cmake/run_build.bat +++ b/tensorflow/tools/ci_build/windows/cpu/cmake/run_build.bat @@ -36,7 +36,7 @@ SET CMAKE_DIR=%REPO_ROOT%\tensorflow\contrib\cmake SET MSBUILD_EXE="C:\Program Files (x86)\MSBuild\14.0\Bin\msbuild.exe" :: Run cmake to create Visual Studio Project files. -%CMAKE_EXE% %CMAKE_DIR% -A x64 -DSWIG_EXECUTABLE=%SWIG_EXE% -DPYTHON_EXECUTABLE=%PY_EXE% -DCMAKE_BUILD_TYPE=Release -DPYTHON_LIBRARIES=%PY_LIB% -Dtensorflow_BUILD_PYTHON_TESTS=%BUILD_PYTHON_TESTS% -Dtensorflow_BUILD_CC_TESTS=%BUILD_CC_TESTS% -Dtensorflow_TF_NIGHTLY=%TF_NIGHTLY% -Dtensorflow_DISABLE_EIGEN_FORCEINLINE=%DISABLE_FORCEINLINE% +%CMAKE_EXE% %CMAKE_DIR% -A x64 -DSWIG_EXECUTABLE=%SWIG_EXE% -DPYTHON_EXECUTABLE=%PY_EXE% -DCMAKE_BUILD_TYPE=Release -DPYTHON_LIBRARIES=%PY_LIB% -Dtensorflow_BUILD_PYTHON_TESTS=%BUILD_PYTHON_TESTS% -Dtensorflow_BUILD_CC_TESTS=%BUILD_CC_TESTS% -Dtensorflow_TF_NIGHTLY=%TF_NIGHTLY% -Dtensorflow_DISABLE_EIGEN_FORCEINLINE=%DISABLE_FORCEINLINE% -Dtensorflow_WIN_CPU_SIMD_OPTIONS=/arch:AVX :: Run msbuild in the resulting VS project files to build a pip package. %MSBUILD_EXE% /p:Configuration=Release /maxcpucount:32 tf_python_build_pip_package.vcxproj diff --git a/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat b/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat index 5a362de399..b87e4a9bec 100644 --- a/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat +++ b/tensorflow/tools/ci_build/windows/gpu/cmake/run_build.bat @@ -37,7 +37,7 @@ SET CMAKE_DIR=%REPO_ROOT%\tensorflow\contrib\cmake SET MSBUILD_EXE="C:\Program Files (x86)\MSBuild\14.0\Bin\msbuild.exe" :: Run cmake to create Visual Studio Project files. -%CMAKE_EXE% %CMAKE_DIR% -A x64 -DSWIG_EXECUTABLE=%SWIG_EXE% -DPYTHON_EXECUTABLE=%PY_EXE% -DCMAKE_BUILD_TYPE=Release -DPYTHON_LIBRARIES=%PY_LIB% -Dtensorflow_BUILD_PYTHON_TESTS=%BUILD_PYTHON_TESTS% -Dtensorflow_BUILD_CC_TESTS=%BUILD_CC_TESTS% -Dtensorflow_ENABLE_GPU=ON -DCUDNN_HOME=%CUDNN_HOME% -Dtensorflow_TF_NIGHTLY=%TF_NIGHTLY% -Dtensorflow_DISABLE_EIGEN_FORCEINLINE=%DISABLE_FORCEINLINE% +%CMAKE_EXE% %CMAKE_DIR% -A x64 -DSWIG_EXECUTABLE=%SWIG_EXE% -DPYTHON_EXECUTABLE=%PY_EXE% -DCMAKE_BUILD_TYPE=Release -DPYTHON_LIBRARIES=%PY_LIB% -Dtensorflow_BUILD_PYTHON_TESTS=%BUILD_PYTHON_TESTS% -Dtensorflow_BUILD_CC_TESTS=%BUILD_CC_TESTS% -Dtensorflow_ENABLE_GPU=ON -DCUDNN_HOME=%CUDNN_HOME% -Dtensorflow_TF_NIGHTLY=%TF_NIGHTLY% -Dtensorflow_DISABLE_EIGEN_FORCEINLINE=%DISABLE_FORCEINLINE% -Dtensorflow_WIN_CPU_SIMD_OPTIONS=/arch:AVX :: Run msbuild in the resulting VS project files to build a pip package. %MSBUILD_EXE% /p:Configuration=Release /maxcpucount:32 tf_python_build_pip_package.vcxproj -- GitLab From 07bec47ba5db4c2f2e33ecb49f23253a371bfbbe Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 15:32:39 -0800 Subject: [PATCH 343/423] Update external protobuf codebase version for Windows cmake build PiperOrigin-RevId: 184057827 --- tensorflow/contrib/cmake/external/protobuf.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/cmake/external/protobuf.cmake b/tensorflow/contrib/cmake/external/protobuf.cmake index aedb793d2a..fd05fa6d47 100644 --- a/tensorflow/contrib/cmake/external/protobuf.cmake +++ b/tensorflow/contrib/cmake/external/protobuf.cmake @@ -16,7 +16,7 @@ include (ExternalProject) set(PROTOBUF_INCLUDE_DIRS ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/src) set(PROTOBUF_URL https://github.com/google/protobuf.git) -set(PROTOBUF_TAG b04e5cba356212e4e8c66c61bbe0c3a20537c5b9) +set(PROTOBUF_TAG 396336eb961b75f03b25824fe86cf6490fb75e3a) if(WIN32) set(protobuf_STATIC_LIBRARIES -- GitLab From 970efe54114f5064b35ad94e938c83977e771bc2 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Wed, 31 Jan 2018 18:58:24 -0800 Subject: [PATCH 344/423] Update ISSUE_TEMPLATE.md (#16635) Fixes 16350 --- ISSUE_TEMPLATE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md index 1a401997c6..2f3df7cda9 100644 --- a/ISSUE_TEMPLATE.md +++ b/ISSUE_TEMPLATE.md @@ -4,7 +4,7 @@ https://stackoverflow.com/questions/tagged/tensorflow If you open a GitHub issue, here is our policy: -1. It must be a bug or a feature request. +1. It must be a bug, a feature request, or a significant problem with documentation (for small docs fixes please send a PR instead). 2. The form below must be filled out. 3. It shouldn't be a TensorBoard issue. Those go [here](https://github.com/tensorflow/tensorboard/issues). -- GitLab From d04292cbe15c3b8e61edc4587ca1663572ca05c2 Mon Sep 17 00:00:00 2001 From: Cole Gerdemann Date: Wed, 31 Jan 2018 20:58:56 -0600 Subject: [PATCH 345/423] Fixed typo (#16610) --- .../src/org/tensorflow/demo/tracking/MultiBoxTracker.java | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java b/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java index 2fe2ba539e..af6af2bc8f 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/tracking/MultiBoxTracker.java @@ -199,7 +199,7 @@ public class MultiBoxTracker { final int w, final int h, final int rowStride, - final int sensorOrienation, + final int sensorOrientation, final byte[] frame, final long timestamp) { if (objectTracker == null && !initialized) { @@ -209,7 +209,7 @@ public class MultiBoxTracker { objectTracker = ObjectTracker.getInstance(w, h, rowStride, true); frameWidth = w; frameHeight = h; - this.sensorOrientation = sensorOrienation; + this.sensorOrientation = sensorOrientation; initialized = true; if (objectTracker == null) { -- GitLab From 72074b7d74df8283a8f86ad8e102d8844eae13ad Mon Sep 17 00:00:00 2001 From: simsicon Date: Thu, 1 Feb 2018 10:59:10 +0800 Subject: [PATCH 346/423] Remove duplicated identical lines (#16573) --- tensorflow/examples/tutorials/word2vec/word2vec_basic.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index d055d15745..f6906b0f79 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -270,12 +270,6 @@ with tf.Session(graph=graph) as session: run_metadata=run_metadata) average_loss += loss_val - # Add returned summaries to writer in each step. - writer.add_summary(summary, step) - # Add metadata to visualize the graph for the last run. - if step == (num_steps - 1): - writer.add_run_metadata(run_metadata, 'step%d' % step) - # Add returned summaries to writer in each step. writer.add_summary(summary, step) # Add metadata to visualize the graph for the last run. -- GitLab From 3d958a5a4bb0009854e8dfa48987c04c0897cd96 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dandelion=20Man=C3=A9?= Date: Wed, 31 Jan 2018 18:59:31 -0800 Subject: [PATCH 347/423] Replace 'Dan' with 'Dandelion' in the citations (#16630) --- tensorflow/docs_src/about/bib.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/about/bib.md b/tensorflow/docs_src/about/bib.md index c9f0c532c6..5593a3d95c 100644 --- a/tensorflow/docs_src/about/bib.md +++ b/tensorflow/docs_src/about/bib.md @@ -60,7 +60,7 @@ author={ Lukasz~Kaiser and Manjunath~Kudlur and Josh~Levenberg and - Dan~Man\'{e} and + Dandelion~Man\'{e} and Rajat~Monga and Sherry~Moore and Derek~Murray and -- GitLab From ec92f62e039055fa1fbc844ecde57017e8dd3311 Mon Sep 17 00:00:00 2001 From: Colin Raffel Date: Wed, 31 Jan 2018 19:00:33 -0800 Subject: [PATCH 348/423] Remove query_layer in LuongMonotonicAttention (#16602) In the constructor for LuongMonotonicAttention, a query layer was being created but it was ultimately never used. Fixes #16287. --- tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py index 95dea312f3..d6b5eceb47 100644 --- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py +++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py @@ -924,8 +924,7 @@ class LuongMonotonicAttention(_BaseMonotonicAttentionMechanism): _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode, seed=sigmoid_noise_seed) super(LuongMonotonicAttention, self).__init__( - query_layer=layers_core.Dense( - num_units, name="query_layer", use_bias=False, dtype=dtype), + query_layer=None, memory_layer=layers_core.Dense( num_units, name="memory_layer", use_bias=False, dtype=dtype), memory=memory, -- GitLab From 80bfeb893245ed69079b04fdb7fcb57edd8cb766 Mon Sep 17 00:00:00 2001 From: hsm207 Date: Wed, 31 Jan 2018 22:00:51 -0500 Subject: [PATCH 349/423] Fix typos. (#16570) --- tensorflow/docs_src/programmers_guide/graphs.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/programmers_guide/graphs.md index 2b4896c381..9049a5a9f3 100644 --- a/tensorflow/docs_src/programmers_guide/graphs.md +++ b/tensorflow/docs_src/programmers_guide/graphs.md @@ -125,14 +125,14 @@ an operation: @{tf.Tensor} accepts an optional `name` argument. For example, `tf.constant(42.0, name="answer")` creates a new @{tf.Operation} named `"answer"` and returns a @{tf.Tensor} named `"answer:0"`. If the default graph - already contained an operation named `"answer"`, the TensorFlow would append + already contains an operation named `"answer"`, then TensorFlow would append `"_1"`, `"_2"`, and so on to the name, in order to make it unique. * The @{tf.name_scope} function makes it possible to add a **name scope** prefix to all operations created in a particular context. The current name scope prefix is a `"/"`-delimited list of the names of all active @{tf.name_scope} context managers. If a name scope has already been used in the current - context, TensorFlow appens `"_1"`, `"_2"`, and so on. For example: + context, TensorFlow appends `"_1"`, `"_2"`, and so on. For example: ```python c_0 = tf.constant(0, name="c") # => operation named "c" -- GitLab From 94c6c14ea0088ea070cf304d0b1ab638f20bf785 Mon Sep 17 00:00:00 2001 From: ImSheridan Date: Thu, 1 Feb 2018 11:03:18 +0800 Subject: [PATCH 350/423] Fix the FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated (#16591) --- tensorflow/contrib/learn/python/learn/estimators/dnn_test.py | 2 +- tensorflow/python/debug/cli/tensor_format.py | 2 +- tensorflow/python/debug/lib/debug_data.py | 2 +- tensorflow/python/eager/execution_callbacks.py | 2 +- tensorflow/python/kernel_tests/topk_op_test.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py index 12f9bba531..2bd57597c2 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_test.py @@ -1224,7 +1224,7 @@ class DNNRegressorTest(test.TestCase): self, predictions, expected_shape): predictions_nparray = np.array(predictions) self.assertAllEqual(expected_shape, predictions_nparray.shape) - self.assertTrue(np.issubdtype(predictions_nparray.dtype, np.float)) + self.assertTrue(np.issubdtype(predictions_nparray.dtype, np.floating)) def testPredict_AsIterableFalse(self): """Tests predict method with as_iterable=False.""" diff --git a/tensorflow/python/debug/cli/tensor_format.py b/tensorflow/python/debug/cli/tensor_format.py index d4aea76d65..e0759a8bc1 100644 --- a/tensorflow/python/debug/cli/tensor_format.py +++ b/tensorflow/python/debug/cli/tensor_format.py @@ -535,7 +535,7 @@ def numeric_summary(tensor): if not isinstance(tensor, np.ndarray) or not np.size(tensor): return debugger_cli_common.RichTextLines([ "No numeric summary available due to empty tensor."]) - elif (np.issubdtype(tensor.dtype, np.float) or + elif (np.issubdtype(tensor.dtype, np.floating) or np.issubdtype(tensor.dtype, np.complex) or np.issubdtype(tensor.dtype, np.integer)): counts = [ diff --git a/tensorflow/python/debug/lib/debug_data.py b/tensorflow/python/debug/lib/debug_data.py index c4b13a1045..8d355aa27f 100644 --- a/tensorflow/python/debug/lib/debug_data.py +++ b/tensorflow/python/debug/lib/debug_data.py @@ -222,7 +222,7 @@ def has_inf_or_nan(datum, tensor): # Also return False for data types that cannot be represented as numpy # arrays. return False - elif (np.issubdtype(tensor.dtype, np.float) or + elif (np.issubdtype(tensor.dtype, np.floating) or np.issubdtype(tensor.dtype, np.complex) or np.issubdtype(tensor.dtype, np.integer)): return np.any(np.isnan(tensor)) or np.any(np.isinf(tensor)) diff --git a/tensorflow/python/eager/execution_callbacks.py b/tensorflow/python/eager/execution_callbacks.py index 2f1654dda4..988442c971 100644 --- a/tensorflow/python/eager/execution_callbacks.py +++ b/tensorflow/python/eager/execution_callbacks.py @@ -153,7 +153,7 @@ def inf_nan_callback(op_type, continue numpy_dtype = output.dtype.as_numpy_dtype - if (np.issubdtype(numpy_dtype, np.float) or + if (np.issubdtype(numpy_dtype, np.floating) or np.issubdtype(numpy_dtype, np.complex) or np.issubdtype(numpy_dtype, np.integer)): try: diff --git a/tensorflow/python/kernel_tests/topk_op_test.py b/tensorflow/python/kernel_tests/topk_op_test.py index efb5b9f364..6ab931fdb9 100644 --- a/tensorflow/python/kernel_tests/topk_op_test.py +++ b/tensorflow/python/kernel_tests/topk_op_test.py @@ -58,7 +58,7 @@ class TopKTest(test.TestCase): # Do some special casing of equality of indices: if indices # are not the same, but values are floating type, ensure that # the values are within epsilon of each other. - if not np.issubdtype(np_expected_values.dtype, np.float): + if not np.issubdtype(np_expected_values.dtype, np.floating): # Values are not floating point type; check indices exactly self.assertAllEqual(np_expected_indices, indices) else: -- GitLab From 1fc1fb4a0c30d498cd2bb1d8deccce27a63f74b2 Mon Sep 17 00:00:00 2001 From: Tatiana Shpeisman Date: Wed, 31 Jan 2018 15:41:03 -0800 Subject: [PATCH 351/423] Change recommended option for building TensorFlow with MKL from -c opt to --config=opt. -c opt triggers optimized C++ compilation. --config=opt also uses additional optimization flags as set by running ./configure. PiperOrigin-RevId: 184059060 --- 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 10e7ad7ada..cd47fc2803 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -498,7 +498,7 @@ For TensorFlow source versions after 1.3.0: ```bash ./configure # Pick the desired options -bazel build --config=mkl -c opt //tensorflow/tools/pip_package:build_pip_package +bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package ``` -- GitLab From 371923a0ac7b76dc5cbdd8d100679a20e594b827 Mon Sep 17 00:00:00 2001 From: Tatiana Shpeisman Date: Wed, 31 Jan 2018 15:47:42 -0800 Subject: [PATCH 352/423] MKL is no longer enabled via ./configure. Fixed documentation to reflect this. PiperOrigin-RevId: 184060046 --- tensorflow/docs_src/install/install_sources.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index f494cc7a7c..485863bf2e 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -272,8 +272,6 @@ Found possible Python library paths: 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 -Do you wish to build TensorFlow with MKL support? [y/N] -No MKL 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]: Do you wish to use jemalloc as the malloc implementation? [Y/n] jemalloc enabled -- GitLab From 90ac0229a95f3f0e4f29b359e8e12cf8054064ef Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Wed, 31 Jan 2018 16:17:42 -0800 Subject: [PATCH 353/423] eager: Fix dropout in MNIST example. The dropout layer takes a training argument in __call__, which defaults to false. So without this change, dropout was not being applied. PiperOrigin-RevId: 184064465 --- tensorflow/contrib/eager/python/examples/mnist/mnist.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist.py b/tensorflow/contrib/eager/python/examples/mnist/mnist.py index 2a7be95811..ed7dbc8904 100644 --- a/tensorflow/contrib/eager/python/examples/mnist/mnist.py +++ b/tensorflow/contrib/eager/python/examples/mnist/mnist.py @@ -95,8 +95,7 @@ class MNISTModel(tfe.Network): x = self.max_pool2d(x) x = tf.layers.flatten(x) x = self.fc1(x) - if training: - x = self.dropout(x) + x = self.dropout(x, training=training) x = self.fc2(x) return x -- GitLab From ddb814cfdf1047187b7e09f53618a6d932b4b64f Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Wed, 31 Jan 2018 19:15:44 -0800 Subject: [PATCH 354/423] Add note about ptxas bug affecting XLA:GPU to relnotes. (#16636) --- RELEASE.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/RELEASE.md b/RELEASE.md index e63971ef93..d3b4037061 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -5,6 +5,27 @@ * Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs. +## Known Bugs +* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or + `CUDA_ILLEGAL_ADDRESS` failures. + + Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9 + and CUDA 9.1 sometimes does not properly compute the carry bit when + decomposing 64-bit address calculations with large offsets (e.g. `load [x + + large_constant]`) into 32-bit arithmetic in SASS. + + As a result, these versions of `ptxas` miscompile most XLA programs which use + more than 4GB of temp memory. This results in garbage results and/or + `CUDA_ERROR_ILLEGAL_ADDRESS` failures. + + A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a + fix for CUDA 9.0.x. Until the fix is available, the only workaround is to + [downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x + or disable XLA:GPU. + + TensorFlow will print a warning if you use XLA:GPU with a known-bad version of + CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122. + ## Major Features And Improvements * [Eager execution](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager) preview version is now available. -- GitLab From 70e1d48ef5d9d9025c1fdb5777acfc1d864a9f30 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Wed, 31 Jan 2018 16:40:10 -0800 Subject: [PATCH 355/423] Add type check when assigning to a resource variable slice. PiperOrigin-RevId: 184067663 --- tensorflow/core/kernels/strided_slice_op.cc | 5 ++++ .../python/kernel_tests/array_ops_test.py | 26 +++++++++++++++++++ 2 files changed, 31 insertions(+) diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc index 8f7f91c9df..7745effe2a 100644 --- a/tensorflow/core/kernels/strided_slice_op.cc +++ b/tensorflow/core/kernels/strided_slice_op.cc @@ -294,6 +294,11 @@ class StridedSliceAssignOp : public OpKernel { OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0), &v)); old_lhs = *v->tensor(); + OP_REQUIRES(context, old_lhs.dtype() == DataTypeToEnum::value, + errors::InvalidArgument( + "l-value dtype ", DataTypeString(old_lhs.dtype()), + " does not match r-value dtype ", + DataTypeString(DataTypeToEnum::value))); } else { context->forward_ref_input_to_ref_output(0, 0); old_lhs = context->mutable_input(0, true); diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index a96b88d96f..82dbe90002 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -952,6 +952,32 @@ class SliceAssignTest(test_util.TensorFlowTestCase): v = variables.Variable([1, 2]) sess.run(v[:].assign([1, 2])) + def testTypeError(self): + init_val = constant_op.constant([1, 2], dtype=dtypes.int32) + too_small_val = constant_op.constant([3, 4], dtype=dtypes.int8) + too_large_val = constant_op.constant([3, 4], dtype=dtypes.int64) + v = variables.Variable(init_val) + with self.assertRaises(TypeError): + v[:].assign(too_small_val) + with self.assertRaises(TypeError): + v[:].assign(too_large_val) + + def testTypeErrorResource(self): + init_val = constant_op.constant([1, 2], dtype=dtypes.int32) + too_small_val = constant_op.constant([3, 4], dtype=dtypes.int8) + too_large_val = constant_op.constant([3, 4], dtype=dtypes.int64) + v = resource_variable_ops.ResourceVariable(init_val) + with self.test_session() as sess: + sess.run(v.initializer) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + "l-value dtype int32 does not match r-value dtype int64"): + sess.run(v[:].assign(too_large_val)) + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + "l-value dtype int32 does not match r-value dtype int8"): + sess.run(v[:].assign(too_small_val)) + class ShapeSizeRankTest(test_util.TensorFlowTestCase): -- GitLab From 516444248d7b565e7a17504955f0f37dc447b36c Mon Sep 17 00:00:00 2001 From: Roy Frostig Date: Wed, 31 Jan 2018 16:54:20 -0800 Subject: [PATCH 356/423] [XLA] Support array layout specification in local Python XLA client. PiperOrigin-RevId: 184069454 --- .../xla/python/local_computation_builder.cc | 50 +++++-- .../xla/python/local_computation_builder.h | 13 +- .../xla/python/local_computation_builder.i | 66 +++++++-- .../compiler/xla/python/numpy_bridge.cc | 138 ++++++++++-------- tensorflow/compiler/xla/python/numpy_bridge.h | 10 +- tensorflow/compiler/xla/python/xla_client.py | 117 ++++++++++----- 6 files changed, 264 insertions(+), 130 deletions(-) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 67a73bc33d..9e3cf79383 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -98,15 +98,25 @@ const std::unique_ptr& LocalShapedBuffer::shaped_buffer() return shaped_buffer_; } +static StatusOr> ToBuffer( + LocalClient* client, int device_ordinal, const Literal& arg) { + return client->LiteralToShapedBuffer(arg, device_ordinal, + client->backend().memory_allocator()); +} + /* static */ -LocalShapedBuffer* LocalShapedBuffer::FromLiteral(const Literal& argument) { +LocalShapedBuffer* LocalShapedBuffer::FromLiteral( + const Literal& argument, + const tensorflow::gtl::optional& shape_with_layout) { LocalClient* client = GetOrCreateLocalClient(); - std::unique_ptr buf = - client - ->LiteralToShapedBuffer(argument, - /*device_ordinal=*/0, - client->backend().memory_allocator()) - .ConsumeValueOrDie(); + std::unique_ptr buf; + if (shape_with_layout) { + std::unique_ptr relaid = + argument.Relayout(shape_with_layout.value()); + buf = ToBuffer(client, /*device_ordinal=*/0, *relaid).ConsumeValueOrDie(); + } else { + buf = ToBuffer(client, /*device_ordinal=*/0, argument).ConsumeValueOrDie(); + } return new LocalShapedBuffer(std::move(buf)); } @@ -120,7 +130,8 @@ CompiledLocalComputation::CompiledLocalComputation( : executable_(std::move(executable)) {} StatusOr> CompiledLocalComputation::Execute( - const std::vector& arguments) { + const std::vector& arguments, + const std::vector>& shapes_with_layout) { LocalClient* client = GetOrCreateLocalClient(); VLOG(1) << "Execution requested with " << GetReplicaCount() << " replicas."; @@ -133,7 +144,8 @@ StatusOr> CompiledLocalComputation::Execute( GetReplicaCount()); for (int replica = 0; replica < GetReplicaCount(); ++replica) { - pool.Schedule([this, client, replica, &arguments, &results] { + pool.Schedule([this, client, replica, &arguments, &shapes_with_layout, + &results] { StatusOr device_ordinal_status = client->ReplicaNumberToDeviceOrdinal(replica); if (!device_ordinal_status.ok()) { @@ -144,18 +156,28 @@ StatusOr> CompiledLocalComputation::Execute( VLOG(3) << "Replica " << replica << " mapped to device ordinal for execution: " << device_ordinal; + // Transfer arguments in std::vector> scoped_buffers; scoped_buffers.reserve(arguments.size()); - for (const Literal& argument : arguments) { - StatusOr> pushed = - client->LiteralToShapedBuffer( - argument, device_ordinal, - client->backend().memory_allocator()); + for (int i = 0; i < arguments.size(); ++i) { + const Literal& argument = arguments[i]; + const tensorflow::gtl::optional& shape_with_layout = + shapes_with_layout[i]; + + StatusOr> pushed; + if (shape_with_layout) { + std::unique_ptr relaid = + argument.Relayout(shape_with_layout.value()); + pushed = ToBuffer(client, device_ordinal, *relaid); + } else { + pushed = ToBuffer(client, device_ordinal, argument); + } if (!pushed.ok()) { results[replica] = pushed.status(); return; } + scoped_buffers.push_back(std::move(pushed).ValueOrDie()); } diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index d5c4c58040..ae5bbc03cb 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -59,7 +59,9 @@ StatusOr > TransferFromOutfeedLocalReplica( // client. class LocalShapedBuffer { public: - static LocalShapedBuffer* FromLiteral(const Literal& argument); + static LocalShapedBuffer* FromLiteral( + const Literal& argument, + const tensorflow::gtl::optional& shape_with_layout); LocalShapedBuffer(std::unique_ptr shaped_buffer); const std::unique_ptr& shaped_buffer() const; std::unique_ptr ToLiteral() const; @@ -77,8 +79,15 @@ class LocalShapedBuffer { class CompiledLocalComputation { public: CompiledLocalComputation(std::unique_ptr executable); + + // Execute the computation with the given argument literals, and + // with optionally-specified argument layouts. The literals will be + // re-laid out according to the corresponding elements of + // shapes_with_layout. StatusOr > Execute( - const std::vector& arguments); + const std::vector& arguments, + const std::vector >& shapes_with_layout); + LocalShapedBuffer* ExecuteWithShapedBuffers( tensorflow::gtl::ArraySlice argument_handles); diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index 89f8385501..fdd05692fc 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -27,8 +27,9 @@ limitations under the License. // ArraySlice <- sequence of int // Literal <-> (nested tuple of) numpy ndarray // std::vector <- sequence of (nested tuple of) ndarray -// Shape <-> pair holding (dtype, dimensions) -// std::vector <- sequence of shape information pairs +// Shape -> pair holding (dtype, dimensions) +// <- object duck-typed as xla_client.Shape +// std::vector <- sequence of xla_client.Shape objects // PrimitiveType <- int // ArraySlice> <- sequence of int pairs // PaddingConfig proto <- corresponding Python proto @@ -55,7 +56,7 @@ limitations under the License. // translates to a tuple-shaped XLA Literal, whose component subshapes // are a 2x3 F32-shaped literal followed by two tuple-shaped literals. // -// The Python objects corresponding to C++ Shapes have the type: +// Shapes output by C++ become Python objects with the type: // // T = (dtype, S) // S = DIMENSIONS | TUPLE_SHAPES @@ -353,15 +354,31 @@ tensorflow::ImportNumpy(); // Shape %typemap(in) const Shape& (Shape temp) { - Status shape_status = numpy::CheckPyShapeInfo($input); - if (!shape_status.ok()) { - PyErr_SetString(PyExc_RuntimeError, shape_status.ToString().c_str()); + StatusOr statusor = numpy::XlaShapeFromPyShape($input); + if (!statusor.ok()) { + PyErr_SetString(PyExc_RuntimeError, statusor.status().ToString().c_str()); return NULL; } - temp = numpy::XlaShapeFromPyShapeInfo($input); + temp = std::move(statusor).ValueOrDie(); $1 = &temp; } +%typemap(in) const tensorflow::gtl::optional& ( + tensorflow::gtl::optional temp) { + if ($input == Py_None) { + temp = tensorflow::gtl::nullopt; + $1 = &temp; + } else { + StatusOr statusor = numpy::XlaShapeFromPyShape($input); + if (!statusor.ok()) { + PyErr_SetString(PyExc_RuntimeError, statusor.status().ToString().c_str()); + return NULL; + } + temp = std::move(statusor).ValueOrDie(); + $1 = &temp; + } +} + %typemap(out) std::unique_ptr { $result = numpy::PyShapeInfoFromXlaShape(*$1); } @@ -374,14 +391,37 @@ tensorflow::ImportNumpy(); const int size = PySequence_Size($input); for (int i = 0; i < size; ++i) { PyObject* o = PySequence_GetItem($input, i); - Status shape_status = numpy::CheckPyShapeInfo(o); - if (!shape_status.ok()) { - PyErr_SetString(PyExc_RuntimeError, shape_status.ToString().c_str()); - Py_DECREF(o); + StatusOr statusor = numpy::XlaShapeFromPyShape(o); + Py_DECREF(o); + if (!statusor.ok()) { + PyErr_SetString(PyExc_RuntimeError, statusor.status().ToString().c_str()); return NULL; } - temps.push_back(numpy::XlaShapeFromPyShapeInfo(o)); - Py_DECREF(o); + temps.push_back(statusor.ConsumeValueOrDie()); + } + $1 = &temps; +} + +%typemap(in) const std::vector >& ( + std::vector > temps) { + if (!PySequence_Check($input)) { + PyErr_SetString(PyExc_TypeError, "Argument is not a sequence"); + return NULL; + } + const int size = PySequence_Size($input); + for (int i = 0; i < size; ++i) { + PyObject* o = PySequence_GetItem($input, i); + if (o == Py_None) { + temps.push_back(tensorflow::gtl::nullopt); + } else { + StatusOr statusor = numpy::XlaShapeFromPyShape(o); + Py_DECREF(o); + if (!statusor.ok()) { + PyErr_SetString(PyExc_RuntimeError, statusor.status().ToString().c_str()); + return NULL; + } + temps.push_back(statusor.ConsumeValueOrDie()); + } } $1 = &temps; } diff --git a/tensorflow/compiler/xla/python/numpy_bridge.cc b/tensorflow/compiler/xla/python/numpy_bridge.cc index 5c722623e3..3d87480728 100644 --- a/tensorflow/compiler/xla/python/numpy_bridge.cc +++ b/tensorflow/compiler/xla/python/numpy_bridge.cc @@ -176,85 +176,107 @@ static string PyObjectCppRepr(PyObject* o) { return ExtractStringAndDecref(r); } -Status CheckPyShapeInfo(PyObject* o) { +StatusOr XlaShapeFromPyShape(PyObject* o) { auto error = [o](const string& prefix) { return InvalidArgument("%s; got %s", prefix.c_str(), PyObjectCppRepr(o).c_str()); }; - // The object is a tuple (a pair) - if (!PyTuple_Check(o)) { - return error("Shape record must be a tuple"); - } - if (PyTuple_Size(o) != 2) { - return error("Shape record tuple must be of length 2"); - } - // It has a first element, which is a numpy dtype object - PyObject* first = PyTuple_GetItem(o, 0); - if (first == nullptr) { - return error("Tuple has no item 0 (shape dtype)"); - } - if (first->ob_type != &PyArrayDescr_Type) { - return error( - "Shape record does not have a numpy dtype as its first element"); - } - const int np_type = NumpyTypenum(first); - if (!NumpyTypeIsValid(np_type)) { - return error("Shape record has an invalid integer dtype"); - } + auto get_attr = [o, &error](const string& field) -> StatusOr { + PyObject* result = + PyObject_GetAttrString(o, const_cast(field.c_str())); + if (result == nullptr) { + return error(tensorflow::strings::StrCat( + "Failed to get attribute of Shape object:", field)); + } + return result; + }; - // It has a second element, which is a tuple, either of shape - // records or of Python ints - PyObject* second = PyTuple_GetItem(o, 1); - if (!second) { - return error("Tuple has no item 0 (shape dimensions)"); - } - if (!PyTuple_Check(second)) { - return error("Shape record does not have a tuple as its second element"); - } - const int length = PyTuple_Size(second); - const PrimitiveType element_type = NumpyTypeToPrimitiveType(np_type); - for (int i = 0; i < length; i++) { - PyObject* dimension = PyTuple_GetItem(second, i); - if (element_type == TUPLE) { - VLOG(3) << "element_type is tuple, checking member: " << i; - Status result = CheckPyShapeInfo(dimension); - if (!result.ok()) { - return AddStatus( - result, tensorflow::strings::StrCat("Validating tuple member ", i, - " of ", PyObjectCppRepr(o))); - } - } else if (!CheckPyIntOrLong(dimension)) { - return error("Non-tuple shape record has a non-integer dimension"); + auto call_method = [o, &error](const string& method) -> StatusOr { + PyObject* result = + PyObject_CallMethod(o, const_cast(method.c_str()), nullptr); + if (result == nullptr) { + return error(tensorflow::strings::StrCat( + "Failed to call method of shape object:", method)); } - } + return result; + }; - return Status::OK(); -} + PyObject* np_type; + TF_ASSIGN_OR_RETURN(np_type, get_attr("np_dtype")); + if (np_type->ob_type != &PyArrayDescr_Type) { + return error("Shape attribute np_dtype is not an integer numpy dtype"); + } + if (!NumpyTypeIsValid(NumpyTypenum(np_type))) { + return error("Shape attribute np_dtype is not a valid integer numpy dtype"); + } + const PrimitiveType element_type = + NumpyTypeToPrimitiveType(NumpyTypenum(np_type)); + Py_DECREF(np_type); -// Precondition: CheckPyShapeInfo(o) -Shape XlaShapeFromPyShapeInfo(PyObject* o) { - const int np_type = NumpyTypenum(PyTuple_GetItem(o, 0)); - const PrimitiveType element_type = NumpyTypeToPrimitiveType(np_type); - PyObject* py_dimensions = PyTuple_GetItem(o, 1); - const int length = PyTuple_Size(py_dimensions); if (element_type == TUPLE) { + PyObject* py_subshapes; + TF_ASSIGN_OR_RETURN(py_subshapes, call_method("tuple_shapes")); + if (!PyTuple_Check(py_subshapes)) { + return error( + "Return value of Shape method tuple_shapes() is not a tuple"); + } + const int length = PyTuple_Size(py_subshapes); std::vector subshapes; subshapes.reserve(length); for (int i = 0; i < length; i++) { - subshapes.push_back( - XlaShapeFromPyShapeInfo(PyTuple_GetItem(py_dimensions, i))); + TF_ASSIGN_OR_RETURN( + const Shape& subshape, + XlaShapeFromPyShape(PyTuple_GetItem(py_subshapes, i))); + subshapes.push_back(subshape); } + Py_DECREF(py_subshapes); return ShapeUtil::MakeTupleShape(subshapes); } else { + PyObject* py_dimensions; + PyObject* py_minor_to_major; + TF_ASSIGN_OR_RETURN(py_dimensions, call_method("dimensions")); + TF_ASSIGN_OR_RETURN(py_minor_to_major, call_method("minor_to_major")); + if (!PyTuple_Check(py_dimensions)) { + return error("Return value of Shape method dimensions() is not a tuple"); + } + if (py_minor_to_major != Py_None && !PyTuple_Check(py_minor_to_major)) { + return error( + "Return value of Shape method minor_to_major() is neither a tuple " + "nor None"); + } + const int length = PyTuple_Size(py_dimensions); + if (py_minor_to_major != Py_None && + length != PyTuple_Size(py_minor_to_major)) { + return error( + "Shape methods dimensions() and minor_to_major() return " + "different-length tuples"); + } std::vector dimensions(length); + std::vector minor_to_major(length); for (int i = 0; i < length; i++) { dimensions[i] = PyIntOrPyLongToLong(PyTuple_GetItem(py_dimensions, i)); - if (dimensions[i] == -1) { - CHECK(!PyErr_Occurred()); + if (dimensions[i] == -1 && PyErr_Occurred()) { + return error("Dimension is not an int"); } + + if (py_minor_to_major != Py_None) { + minor_to_major[i] = + PyIntOrPyLongToLong(PyTuple_GetItem(py_minor_to_major, i)); + if (minor_to_major[i] == -1 && PyErr_Occurred()) { + return error("Minor-to-major value is not an int"); + } + } + } + bool with_layout = py_minor_to_major != Py_None; + Py_DECREF(py_dimensions); + Py_DECREF(py_minor_to_major); + if (with_layout) { + return ShapeUtil::MakeShapeWithLayout(element_type, dimensions, + minor_to_major); + } else { + return ShapeUtil::MakeShape(element_type, dimensions); } - return ShapeUtil::MakeShape(element_type, dimensions); } } diff --git a/tensorflow/compiler/xla/python/numpy_bridge.h b/tensorflow/compiler/xla/python/numpy_bridge.h index 6ff1c34cfc..adfcc3b858 100644 --- a/tensorflow/compiler/xla/python/numpy_bridge.h +++ b/tensorflow/compiler/xla/python/numpy_bridge.h @@ -56,15 +56,11 @@ bool NumpyTypeIsValid(int np_type); // The return value is a new reference. PyObject* PyShapeInfoFromXlaShape(const Shape& shape); -// Returns the outcome of a best-effort check that the Python object -// is a pair of the form (numpy dtype, dimensions), as produced by -// PyShapeInfoFromXlaShape. -Status CheckPyShapeInfo(PyObject* o); - -// Performs the inverse conversion to that of PyShapeInfoFromXlaShape. +// Converts a Python object with a method interface mathing that of +// xla_client.Shape into an XLA Shape object. // // The return value is a new reference. -Shape XlaShapeFromPyShapeInfo(PyObject* o); +StatusOr XlaShapeFromPyShape(PyObject* o); // Converts a PyObject that represents operation metadata into protocol buffer // form. diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index 7ee5febc09..bb42e8d703 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -156,9 +156,14 @@ class LocalBuffer(object): self._delete = c_api.DeleteLocalShapedBuffer @staticmethod - def from_py(npval): + def from_py(npval, layout_fn=None): npval = require_numpy_array_layout(npval) - return LocalBuffer(c_api.LocalShapedBuffer.FromLiteral(npval)) + if layout_fn: + shape = Shape.from_numpy(npval) + shape = shape.map_leaves(layout_fn) + else: + shape = None + return LocalBuffer(c_api.LocalShapedBuffer.FromLiteral(npval, shape)) def to_py(self): return self.c_local_shaped_buffer.ToLiteral() @@ -183,13 +188,17 @@ class Shape(object): represents an XLA tuple. """ - def __init__(self, np_dtype, dimensions): + def __init__(self, np_dtype, dimensions, minor_to_major=None): + assert isinstance(dimensions, tuple) self.np_dtype = np_dtype self._dimensions = dimensions + self._minor_to_major = minor_to_major + self._check_minor_to_major() def __repr__(self): - return 'xla_client.Shape(np_dtype={!r}, dimensions={!r})'.format( - self.np_dtype, self._dimensions) + return ('xla_client.Shape(np_dtype={!r}, dimensions={!r}, ' + 'minor_to_major={!r})').format(self.np_dtype, self._dimensions, + self._minor_to_major) def element_type(self): return DTYPE_TO_XLA_ELEMENT_TYPE[str(self.np_dtype)] @@ -202,11 +211,49 @@ class Shape(object): raise ValueError('Tuple shape has no dimensions') return self._dimensions + def minor_to_major(self): + return self._minor_to_major + def tuple_shapes(self): if not self.is_tuple(): raise ValueError('Shape is not a tuple shape') return self._dimensions + def rank(self): + return len(self.dimensions()) + + def map_leaves(self, f): + """Map f over each leaf-level array subshape. + + Args: + f: The function to apply. Whenever f returns None, the identity is + applied instead. + + Returns: + A new Shape with the mapped leaves. + """ + if self.is_tuple(): + children = tuple(child.map_leaves(f) for child in self.tuple_shapes()) + return Shape(np.dtype('O'), children) + else: + mapped = f(self) + return self if mapped is None else mapped + + def _check_minor_to_major(self): + mtm = self._minor_to_major + if self.is_tuple(): + assert mtm is None, self + if mtm is not None: + assert self.rank() == len(mtm), self + assert sorted(mtm) == range(len(mtm)), self + + def update_minor_to_major(self, minor_to_major): + if not isinstance(minor_to_major, tuple): + raise TypeError('minor_to_major must be a tuple') + updated = Shape(self.np_dtype, tuple(self.dimensions()), minor_to_major) + updated._check_minor_to_major() # pylint: disable=protected-access + return updated + @staticmethod def from_numpy(npval): @@ -223,23 +270,10 @@ def _wrap_shape(shape_info): dtype, dims = shape_info element_type = DTYPE_TO_XLA_ELEMENT_TYPE[str(dtype)] if element_type == xla_data_pb2.TUPLE: - dims = [_wrap_shape(subshape_info) for subshape_info in dims] + dims = tuple(_wrap_shape(subshape_info) for subshape_info in dims) return Shape(dtype, dims) -def _unwrap_shape(shape): - if shape.is_tuple(): - components = tuple( - _unwrap_shape(subshape) for subshape in shape.tuple_shapes()) - else: - components = shape.dimensions() - return (shape.np_dtype, components) - - -def _unwrap_shapes(shapes): - return [_unwrap_shape(shape) for shape in shapes] - - def _wrap_data_handle(handle): cdh = xla_data_pb2.ComputationDataHandle() cdh.handle = handle @@ -303,8 +337,7 @@ def transfer_from_outfeed(shape, replica_number=None): Returns: The literal value that is produced from the outfeed queue. """ - return c_api.TransferFromOutfeedLocalReplica( - _unwrap_shape(shape), replica_number or 0) + return c_api.TransferFromOutfeedLocalReplica(shape, replica_number or 0) class LocalComputation(object): @@ -325,24 +358,39 @@ class LocalComputation(object): else: self._delete = c_api.DeleteLocalComputation - def Compile(self, argument_shapes=(), compile_options=None): + def Compile(self, argument_shapes=(), compile_options=None, layout_fn=None): if self.is_compiled: raise ValueError('Attempt to compile a compiled local XLA computation.') + if layout_fn: + argument_shapes = [ + shape.map_leaves(layout_fn) for shape in argument_shapes + ] return LocalComputation( - self.c_local_computation.Compile( - _unwrap_shapes(argument_shapes), compile_options), + self.c_local_computation.Compile(argument_shapes, compile_options), is_compiled=True) - def CompileWithExampleArguments(self, arguments=(), compile_options=None): + def CompileWithExampleArguments(self, + arguments=(), + compile_options=None, + layout_fn=None): return self.Compile( argument_shapes=[Shape.from_numpy(arg) for arg in arguments], - compile_options=compile_options) + compile_options=compile_options, + layout_fn=layout_fn) - def Execute(self, arguments=()): + def Execute(self, arguments=(), layout_fn=None): + """Execute with Python values as arguments and return value.""" if not self.is_compiled: raise ValueError('Cannot execute an uncompiled local XLA computation.') + argument_shapes = [Shape.from_numpy(arg) for arg in arguments] + if layout_fn: + argument_shapes = [ + shape.map_leaves(layout_fn) for shape in argument_shapes + ] + else: + argument_shapes = [None for shape in argument_shapes] arguments = tuple(map(require_numpy_array_layout, arguments)) - return self.c_local_computation.Execute(arguments) + return self.c_local_computation.Execute(arguments, argument_shapes) def ExecuteWithLocalBuffers(self, arguments=()): """Execute with LocalBuffer arguments and return value.""" @@ -398,7 +446,7 @@ class ComputationBuilder(object): Returns: A ComputationDataHandle message. """ - return _wrap_data_handle(self._client.Infeed(_unwrap_shape(shape))) + return _wrap_data_handle(self._client.Infeed(shape)) def Outfeed(self, operand): """Enqueues an outfeed op onto the computation. @@ -407,7 +455,7 @@ class ComputationBuilder(object): outfeed queue for subsequent dequeue via the client API. """ self._client.Outfeed( - _unwrap_data_handle(operand), _unwrap_shape(self.GetShape(operand)), + _unwrap_data_handle(operand), self.GetShape(operand), ''.encode('utf-8')) def Constant(self, value): @@ -498,8 +546,7 @@ class ComputationBuilder(object): parameter_num = next(self._parameter_numbering) return _wrap_data_handle( - self._client.Parameter( - parameter_num, _unwrap_shape(shape), name.encode('utf8'))) + self._client.Parameter(parameter_num, shape, name.encode('utf8'))) def ParameterFromNumpy(self, value, name=None, parameter_num=None): """Enqueues a Parameter op onto the computation. @@ -846,8 +893,7 @@ class ComputationBuilder(object): shape = Shape(self.GetShape(mu).np_dtype, dims) return _wrap_data_handle( self._client.RngNormal( - _unwrap_data_handle(mu), _unwrap_data_handle(sigma), - _unwrap_shape(shape))) + _unwrap_data_handle(mu), _unwrap_data_handle(sigma), shape)) def RngUniform(self, a, b, dims): """Enqueues an RngUniform operation onto the computation. @@ -867,8 +913,7 @@ class ComputationBuilder(object): shape = Shape(self.GetShape(a).np_dtype, dims) return _wrap_data_handle( self._client.RngUniform( - _unwrap_data_handle(a), _unwrap_data_handle(b), - _unwrap_shape(shape))) + _unwrap_data_handle(a), _unwrap_data_handle(b), shape)) def While(self, cond, body, init): """Enqueues a While operation onto the computation. -- GitLab From 5477012060749cd1164dc85ff75fc31ae1351482 Mon Sep 17 00:00:00 2001 From: Anjum Sayed Date: Thu, 1 Feb 2018 06:22:54 +0300 Subject: [PATCH 357/423] Change RELEASE.md to specify CUDA 9.0 (#16486) PR for https://github.com/tensorflow/tensorflow/issues/16348 (tinyest PR ever?) --- RELEASE.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index d3b4037061..af6440acef 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,7 +1,9 @@ # Release 1.5.0 ## Breaking Changes -* Prebuilt binaries are now built against CUDA 9 and cuDNN 7. +* Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7. +* Our Linux binaries are built using ubuntu 16 containers, potentially + introducing glibc incompatibility issues with ubuntu 14. * Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs. @@ -31,7 +33,7 @@ preview version is now available. * [TensorFlow Lite](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/lite) dev preview is now available. -* CUDA 9 and cuDNN 7 support. +* CUDA 9.0 and cuDNN 7 support. * Accelerated Linear Algebra (XLA): * Add `complex64` support to XLA compiler. * `bfloat` support is now added to XLA infrastructure. -- GitLab From 17c0e1118b4f063d9bbfb3faf24befc69bc5f6c4 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Wed, 31 Jan 2018 19:28:01 -0800 Subject: [PATCH 358/423] Update docs for tf.matching_files to mention non-deterministic (#16633) This fix tries to close the issue of 15374 by updating the docs of `tf.matching_files` (as well as `Dataset.list_files` and `train.match_filenames_once`), to mention that the file names returned could be non-deterministic. This fix fixes 15374. Signed-off-by: Yong Tang --- tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt | 1 + tensorflow/python/data/ops/dataset_ops.py | 2 ++ tensorflow/python/training/input.py | 2 ++ 3 files changed, 5 insertions(+) diff --git a/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt b/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt index 8da76684e5..97fd39f647 100644 --- a/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_MatchingFiles.pbtxt @@ -16,5 +16,6 @@ END description: < Date: Wed, 31 Jan 2018 22:28:25 -0500 Subject: [PATCH 359/423] By default, only download inception if it doesn't exist already (#16577) * By default, only download inception if it doesn't exist already * fix filename --- .../eval/python/classifier_metrics_impl.py | 28 +++++++++++++------ 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py index 986a5ff6dc..7bede4e240 100644 --- a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py +++ b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py @@ -28,6 +28,7 @@ from __future__ import division from __future__ import print_function import functools +import os import sys import tarfile @@ -189,20 +190,31 @@ def get_graph_def_from_resource(filename): return graph_pb2.GraphDef.FromString(resource_loader.load_resource(filename)) -def get_graph_def_from_url_tarball(url, filename): - """Get a GraphDef proto from a tarball on the web.""" - def _progress(count, block_size, total_size): - sys.stdout.write('\r>> Downloading %s %.1f%%' % ( - url, float(count * block_size) / float(total_size) * 100.0)) - sys.stdout.flush() - tar_filename, _ = urllib.request.urlretrieve(url, reporthook=_progress) +def get_graph_def_from_url_tarball(url, filename, tar_filename=None): + """Get a GraphDef proto from a tarball on the web. + + Args: + url: Web address of tarball + filename: Filename of graph definition within tarball + tar_filename: Temporary download filename (None = always download) + + Returns: + A GraphDef loaded from a file in the downloaded tarball. + """ + if not (tar_filename and os.path.exists(tar_filename)): + def _progress(count, block_size, total_size): + sys.stdout.write('\r>> Downloading %s %.1f%%' % ( + url, float(count * block_size) / float(total_size) * 100.0)) + sys.stdout.flush() + tar_filename, _ = urllib.request.urlretrieve(url, filename=tar_filename, reporthook=_progress) with tarfile.open(tar_filename, 'r:gz') as tar: proto_str = tar.extractfile(filename).read() return graph_pb2.GraphDef.FromString(proto_str) def _default_graph_def_fn(): - return get_graph_def_from_url_tarball(INCEPTION_URL, INCEPTION_FROZEN_GRAPH) + return get_graph_def_from_url_tarball(INCEPTION_URL, INCEPTION_FROZEN_GRAPH, + os.path.basename(INCEPTION_URL)) def run_inception(images, -- GitLab From df62a679e960df935928c0a602851dfb1e867480 Mon Sep 17 00:00:00 2001 From: Andrew Harp Date: Wed, 31 Jan 2018 23:42:56 -0500 Subject: [PATCH 360/423] Re-add missing argument specifier (#16632) The ":" was erroneously removed in 76f70f5d62f35b5cc95121e6dfffa63a8214b626 --- tensorflow/contrib/makefile/build_all_android.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh index 281c4653c6..f67c516186 100755 --- a/tensorflow/contrib/makefile/build_all_android.sh +++ b/tensorflow/contrib/makefile/build_all_android.sh @@ -37,7 +37,7 @@ fi ARCH=armeabi-v7a -while getopts "Es:t:Tx:a" opt_name; do +while getopts "Es:t:Tx:a:" opt_name; do case "$opt_name" in E) ENABLE_EXPERIMENTAL_HEXNN_OPS="true";; s) SUB_MAKEFILES="${OPTARG}";; -- GitLab From 9e7ce91845500e5111e0400766983e69701a1733 Mon Sep 17 00:00:00 2001 From: Andrew Harp Date: Wed, 31 Jan 2018 23:43:18 -0500 Subject: [PATCH 361/423] Support multiple build types in Android build.gradle with the makefile build (#16640) * updating CUDA srcs for Makefile build to fix unsatisfied link error * more makefile refactoring * Fixing Tegra build logic for Android * reverting ndk dir * set ccache back to default --- tensorflow/contrib/makefile/Makefile | 12 ++++++------ .../makefile/sub_makefiles/android/Makefile.in | 2 +- tensorflow/examples/android/build.gradle | 9 ++++++--- 3 files changed, 13 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile index c573cf15da..81327407d4 100644 --- a/tensorflow/contrib/makefile/Makefile +++ b/tensorflow/contrib/makefile/Makefile @@ -407,7 +407,7 @@ $(MARCH_OPTION) \ -I$(JETPACK)/cuda/extras/CUPTI/include - LIBS += \ + CUDA_LIBS := \ -ltfcuda \ -lcudart_static \ -lcudnn \ @@ -420,10 +420,10 @@ $(MARCH_OPTION) \ -lculibos \ -lcurand_static - OBJDIR := $(OBJDIR)Tegra/ - LIBDIR := $(LIBDIR)Tegra/ - BINDIR := $(BINDIR)Tegra/ - DEPDIR := $(DEPDIR)Tegra/ + OBJDIR := $(OBJDIR)android_arm64-v8a/ + LIBDIR := $(LIBDIR)android_arm64-v8a/ + BINDIR := $(BINDIR)android_arm64-v8a/ + DEPDIR := $(DEPDIR)android_arm64-v8a/ TEGRA_LIBS := \ -L$(JETPACK)/cuda/targets/aarch64-linux-androideabi/lib \ @@ -729,7 +729,7 @@ $(BENCHMARK_NAME): $(BENCHMARK_OBJS) $(LIB_PATH) $(CUDA_LIB_DEPS) @mkdir -p $(dir $@) $(CXX) $(CXXFLAGS) $(INCLUDES) \ -o $(BENCHMARK_NAME) $(BENCHMARK_OBJS) \ - $(LIBFLAGS) $(TEGRA_LIBS) $(LIB_PATH) $(LDFLAGS) $(LIBS) + $(LIBFLAGS) $(TEGRA_LIBS) $(LIB_PATH) $(LDFLAGS) $(LIBS) $(CUDA_LIBS) # NVCC compilation rules for Tegra ifeq ($(BUILD_FOR_TEGRA),1) diff --git a/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in b/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in index d9277ed60c..3081084ee7 100644 --- a/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in +++ b/tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in @@ -54,7 +54,7 @@ $(INFERENCE_SO_PATH): $(LIB_OBJS) $(INFERENCE_OBJS) $(CUDA_LIB_DEPS) -o $@ $(INFERENCE_OBJS) $(LIB_OBJS) $(TEGRA_LIBS) \ $(LIBFLAGS) $(LDFLAGS) \ -shared -Wl,-soname,$(INFERENCE_SO_NAME) \ - $(LIBS) + $(LIBS) $(CUDA_LIBS) $(INFERENCE_SO_NAME): $(INFERENCE_SO_PATH) diff --git a/tensorflow/examples/android/build.gradle b/tensorflow/examples/android/build.gradle index f7bdf8b816..0767726aa9 100644 --- a/tensorflow/examples/android/build.gradle +++ b/tensorflow/examples/android/build.gradle @@ -56,10 +56,12 @@ def nativeOutDir = 'libs/' + cpuType def nativeBuildRule = 'buildNativeBazel' def demoLibPath = '../../../bazel-bin/tensorflow/examples/android/libtensorflow_demo.so' def inferenceLibPath = '../../../bazel-bin/tensorflow/contrib/android/libtensorflow_inference.so' + +// Override for Makefile builds. if (nativeBuildSystem == 'makefile') { nativeBuildRule = 'buildNativeMake' - demoLibPath = '../../../tensorflow/contrib/makefile/gen/lib/libtensorflow_demo.so' - inferenceLibPath = '../../../tensorflow/contrib/makefile/gen/lib/libtensorflow_inference.so' + demoLibPath = '../../../tensorflow/contrib/makefile/gen/lib/android_' + cpuType + '/libtensorflow_demo.so' + inferenceLibPath = '../../../tensorflow/contrib/makefile/gen/lib/android_' + cpuType + '/libtensorflow_inference.so' } // If building with Bazel, this is the location of the bazel binary. @@ -154,7 +156,8 @@ task buildNativeMake(type: Exec) { '-s', \ 'tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in', \ '-t', \ - 'libtensorflow_inference.so libtensorflow_demo.so' \ + 'libtensorflow_inference.so libtensorflow_demo.so all' \ + , '-a', cpuType \ //, '-T' // Uncomment to skip protobuf and speed up subsequent builds. } -- GitLab From b06326cadc7b45078291e8fb3da0b7e941978540 Mon Sep 17 00:00:00 2001 From: Mahmoud Abuzaina Date: Thu, 1 Feb 2018 00:37:47 -0800 Subject: [PATCH 362/423] Pooling and AddN fixes (#16607) --- tensorflow/core/kernels/mkl_aggregate_ops.cc | 7 +++++-- tensorflow/core/kernels/mkl_avgpooling_op.cc | 22 ++++++++++++++++++++ 2 files changed, 27 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_aggregate_ops.cc b/tensorflow/core/kernels/mkl_aggregate_ops.cc index 49c34fed02..ef724f0a29 100644 --- a/tensorflow/core/kernels/mkl_aggregate_ops.cc +++ b/tensorflow/core/kernels/mkl_aggregate_ops.cc @@ -317,8 +317,11 @@ class MklAddNOp : public OpKernel { : src2_tensor.dims(); // if the shapes of two tensors are not same raise op error TensorShape src1_shape, src2_shape; - src1_shape = src1_tensor.shape(); - src2_shape = src2_tensor.shape(); + src1_shape = input1_in_mkl_format ? src1_mkl_shape.GetTfShape() + : src1_tensor.shape(); + src2_shape = input2_in_mkl_format ? src2_mkl_shape.GetTfShape() + : src2_tensor.shape(); + if (!src1_shape.IsSameSize(src2_shape)) { ctx->SetStatus(errors::InvalidArgument( "Inputs to operation ", this->name(), " of type ", diff --git a/tensorflow/core/kernels/mkl_avgpooling_op.cc b/tensorflow/core/kernels/mkl_avgpooling_op.cc index ebaa0f4e2a..cff1bd18a7 100644 --- a/tensorflow/core/kernels/mkl_avgpooling_op.cc +++ b/tensorflow/core/kernels/mkl_avgpooling_op.cc @@ -468,6 +468,28 @@ class MklAvgPoolingOp : public MklPoolingForwardOpBase { memory::dims output_dims_mkl_order; this->GetOutputDims(pool_params, &output_dims_mkl_order); + // If input is an empty tensor, allocate an empty output tensor and return + if (input_tensor.NumElements() == 0) { + MklDnnShape output_mkl_shape; + output_mkl_shape.SetMklTensor(false); + TensorShape output_tf_shape; + if (pool_params.data_format == TensorFormat::FORMAT_NCHW) { + output_tf_shape = MklDnnDimsToTFShape(output_dims_mkl_order); + } else { + memory::dims output_dims_NHWC_order; + output_dims_NHWC_order = {pool_params.tensor_in_batch, + static_cast(pool_params.out_height), + static_cast(pool_params.out_width), + pool_params.out_depth}; + output_tf_shape = MklDnnDimsToTFShape(output_dims_NHWC_order); + } + const int kOutputIndex = 0; + AllocateOutputSetMklShape(context, kOutputIndex, &output_tensor, + output_tf_shape, output_mkl_shape); + CHECK_NOTNULL(output_tensor); + return; + } + // If input is in Mkl layout, then just get the memory format from it // directly, instead of using input data_format to AvgPool. if (dnn_shape_input.IsMklTensor()) { -- GitLab From 3c2e6f883e87df550521b11edbd837a2479780fc Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Wed, 31 Jan 2018 17:46:43 -0800 Subject: [PATCH 363/423] Add a doc explaining how to convert an `Estimator` to run on a Cloud TPU. PiperOrigin-RevId: 184075365 --- .../api_guides/python/TPUEstimator.md | 396 ++++++++++++++++++ 1 file changed, 396 insertions(+) create mode 100644 tensorflow/docs_src/api_guides/python/TPUEstimator.md diff --git a/tensorflow/docs_src/api_guides/python/TPUEstimator.md b/tensorflow/docs_src/api_guides/python/TPUEstimator.md new file mode 100644 index 0000000000..d74d7f3181 --- /dev/null +++ b/tensorflow/docs_src/api_guides/python/TPUEstimator.md @@ -0,0 +1,396 @@ +# Using TPUs + +This document walks through the principal TensorFlow APIs necessary to make +effective use of a [Cloud TPU](https://cloud.google.com/tpu/), and highlights +the differences between regular TensorFlow usage, and usage on a TPU. + +This doc is aimed at users who: + +* Are familiar with TensorFlow's `Estimator` and `Dataset` APIs +* Have maybe [tried out a Cloud TPU](https://cloud.google.com/tpu/docs/quickstart) + using an existing model. +* Have, perhaps, skimmed the code of an example TPU model + [[1]](https://github.com/tensorflow/models/blob/master/official/mnist/mnist_tpu.py) + [[2]](https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models). +* Are interested in porting an existing `Estimator` model to + run on Cloud TPUs + +## TPUEstimator + +@{tf.estimator.Estimator$Estimators} are TensorFlow's model-level abstraction. +Standard `Estimators` can drive models on CPU and GPUs. You must use +@{tf.contrib.tpu.TPUEstimator} to drive a model on TPUs. + +Refer to TensorFlow's Getting Started section for an introduction to the basics +of using a @{$get_started/premade_estimators$pre-made `Estimator`}, and +@{$get_started/custom_estimators$custom `Estimator`s}. + +The `TPUEstimator` class differs somewhat from the `Estimator` class. + +The simplest way to maintain a model that can be run both on CPU/GPU or on a +Cloud TPU is to define the model's inference phase (from inputs to predictions) +outside of the `model_fn`. Then maintain separate implementations of the +`Estimator` setup and `model_fn`, both wrapping this inference step. For an +example of this pattern compare the `mnist.py` and `mnist_tpu.py` implementation in +[tensorflow/models](https://github.com/tensorflow/models/tree/master/official/mnist). + +### Running a `TPUEstimator` locally + +To create a standard `Estimator` you call the constructor, and pass it a +`model_fn`, for example: + +``` +my_estimator = tf.estimator.Estimator( + model_fn=my_model_fn) +``` + +The changes required to use a @{tf.contrib.tpu.TPUEstimator} on your local +machine are relatively minor. The constructor requires two additional arguments. +You should set the `use_tpu` argument to `False`, and pass a +@{tf.contrib.tpu.RunConfig} as the `config` argument, as shown below: + +``` python +my_tpu_estimator = tf.contrib.tpu.TPUEstimator( + model_fn=my_model_fn, + config=tf.contrib.tpu.RunConfig() + use_tpu=False) +``` + +Just this simple change will allow you to run a `TPUEstimator` locally. +The majority of example TPU models can be run in this local mode, +by setting the command line flags as follows: + + +``` +$> python mnist_tpu.py --use_tpu=false --master='' +``` + +Note: This `use_tpu=False` argument is useful for trying out the `TPUEstimator` +API. It is not meant to be a complete TPU compatibility test. Successfully +running a model locally in a `TPUEstimator` does not guarantee that it will +work on a TPU. + + +### Building a `tpu.RunConfig` + +While the default `RunConfig` is sufficient for local training, these settings +cannot be ignored in real usage. + +A more typical setup for a `RunConfig`, that can be switched to use a Cloud +TPU, might be as follows: + +``` python +import tempfile +import subprocess + +class FLAGS(object): + use_tpu=False + tpu_name=None + # Use a local temporary path for the `model_dir` + model_dir = tempfile.mkdtemp() + # Number of training steps to run on the Cloud TPU before returning control. + iterations = 50 + # A single Cloud TPU has 8 shards. + num_shards = 8 + +if FLAGS.use_tpu: + my_project_name = subprocess.check_output([ + 'gcloud','config','get-value','project']) + my_zone = subprocess.check_output([ + 'gcloud','config','get-value','compute/zone']) + cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( + tpu_names=[FLAGS.tpu_name], + zone=my_zone, + project=my_project) + master = tpu_cluster_resolver.get_master() +else: + master = '' + +my_tpu_run_config = tf.contrib.tpu.RunConfig( + master=master, + evaluation_master=master, + model_dir=FLAGS.model_dir, + session_config=tf.ConfigProto( + allow_soft_placement=True, log_device_placement=True), + tpu_config=tf.contrib.tpu.TPUConfig(FLAGS.iterations, + FLAGS.num_shards), +) +``` + +Then you must pass the @{tf.contrib.tpu.RunConfig} to the constructor: + +``` python +my_tpu_estimator = tf.contrib.tpu.TPUEstimator( + model_fn=my_model_fn, + config = my_tpu_run_config, + use_tpu=FLAGS.use_tpu) +``` + +Typically the `FLAGS` would be set by command line arguments. To switch from +training locally to training on a cloud TPU you would need to: + + 1) Set `FLAGS.use_tpu` to `True` + 1) Set `FLAGS.tpu_name` so the + `tf.contrib.cluster_resolver.TPUClusterResolver` can find it + 1) Set `FLAGS.model_dir` to a Google Cloud Storage bucket url (`gs://`). + + +## Optimizer + +When training on a cloud TPU you **must** wrap the optimizer in a +@{tf.contrib.tpu.CrossShardOptimizer}, which uses an `allreduce` to aggregate +gradients and broadcast the result to each shard (each TPU core). + +The `CrossShardOptimizer` is not compatible with local training. So, to have +the same code run both locally and on a Cloud TPU, add lines like the following: + +``` python +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +if FLAGS.use_tpu: + optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) +``` + +If you prefer to avoid a global `FLAGS` variable in your model code, one +approach is to set the optimizer as one of the `Estimator`'s params, +as follows: + +``` python +my_tpu_estimator = tf.contrib.tpu.TPUEstimator( + model_fn=my_model_fn, + config = my_tpu_run_config, + use_tpu=FLAGS.use_tpu, + params={'optimizer':optimizer}) +``` + +## Model Function + +This section details the changes you must make to the model function +(`model_fn()`) to make it `TPUEstimator` compatible. + +### Static shapes + +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$Tensor Shapes} for more details. + +To run on Cloud TPUs TensorFlow models are compiled using @{$xla$XLA}. +XLA uses a similar system for determining shapes at compile time. XLA requires +that all tensor dimensions be statically defined at compile time. All shapes +must evaluate to a constant, and not depend on external data, or stateful +operations like variables or a random number generator. + + +### Summaries + +Remove any use of `tf.summary` from your model. + +@{$summaries_and_tensorboard$TensorBoard summaries} are a great way see inside +your model. A minimal set of basic summaries are automatically recorded by the +`TPUEstimator`, to `event` files in the `model_dir`. Custom summaries, however, +are currently unsupported when training on a Cloud TPU. So while the +`TPUEstimator` will still run locally with summaries, it will fail if used on a +TPU. + +### Metrics + +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. + +A standard @{tf.metrics} returns two tensors. The first returns the running +average of the metric value, while the second updates the running average and +returns the value for this batch: + +``` +running_average, current_batch = tf.metrics.accuracy(labels, predictions) +``` + +In a standard `Estimator` you create a dictionary of these pairs, and return it +as part of the `EstimatorSpec`. + +```python +my_metrics = {'accuracy': tf.metrics.accuracy(labels, predictions)} + +return tf.estimator.EstimatorSpec( + ... + eval_metric_ops=my_metrics +) +``` + +In a `TPUEstimator` you instead pass a function (which returns a metrics +dictionary) and a list of argument tensors, as shown below: + +```python +def my_metric_fn(labels, predictions): + return {'accuracy': tf.metrics.accuracy(labels, predictions)} + +return tf.contrib.tpu.TPUEstimatorSpec( + ... + eval_metrics=(my_metric_fn, [labels, predictions]) +) +``` + +### Use `TPUEstimatorSpec` + +`TPUEstimatorSpec` do not support hooks, and require function wrappers for +some fields. + +An `Estimator`'s `model_fn` must return an `EstimatorSpec`. An `EstimatorSpec` +is a simple structure of named fields containing all the `tf.Tensors` of the +model that the `Estimator` may need to interact with. + +`TPUEstimators` use a @{tf.contrib.tpu.TPUEstimatorSpec}. There are a few +differences between it and a standard @{tf.estimator.EstimatorSpec}: + + +* The `eval_metric_ops` must be wrapped into a `metrics_fn`, this field is + renamed `eval_metrics` ([see above](#metrics)). +* The @{tf.train.SessionRunHook$hooks} are unsupported, so these fields are + omitted. +* The @{tf.train.Scaffold$`scaffold`}, if used, must also be wrapped in a + function. This field is renamed to `scaffold_fn`. + +`Scaffold` and `Hooks` are for advanced usage, and can typically be omitted. + +## Input functions + +Input functions work mainly unchanged as they run on the host computer, not the +Cloud TPU itself. This section explains the two necessary adjustments. + +### Params argument + + + +The `input_fn` for a standard `Estimator` _can_ include a +`params` argument; the `input_fn` for a `TPUEstimator` *must* include a +`params` argument. This is necessary to allow the estimator to set the batch +size for each replica of the input stream. So the minimum signature for an +`input_fn` for a `TPUEstimator` is: + +``` +def my_input_fn(params): + pass +``` + +Where `params['batch-size']` will contain the batch size. + +### Static shapes and batch size + +The input pipeline generated by your `input_fn` is run on CPU. So it is mostly +free strict static shape requirements imposed by the XLA/TPU environment. The +one requirement is that the batches of data fed from your input pipeline to +the TPU have a static shape, as determined by the standard TensorFlow shape +inference algorithm. Intermediate tensors are free to have a dynamic shapes. +If shape inference has failed, but the shape is known it is possible to +impose the correct shape using `tf.set_shape()`. + +In the example below the shape +inference algorithm fails, but it is corrected using `set_shape`: + +``` +>>> x = tf.zeros(tf.constant([1,2,3])+1) +>>> x.shape + +TensorShape([Dimension(None), Dimension(None), Dimension(None)]) + +>>> x.set_shape([2,3,4]) +``` + +In many cases the batch size is the only unknown dimension. + +A typical input pipeline, using `tf.data`, will usually produce batches of a +fixed size. The last batch of a finite `Dataset`, however, is typically smaller, +containing just the remaining elements. Since a `Dataset` does not know its own +length or finiteness, the standard @{tf.data.Dataset.batch$`batch`} method +cannot determine if all batches will have a fixed size batch on its own: + +``` +>>> params = {'batch_size':32} +>>> ds = tf.data.Dataset.from_tensors([0, 1, 2]) +>>> ds = ds.repeat().batch(params['batch-size']) +>>> ds + + +``` + +The most straightforward fix is to +@{tf.data.Dataset.apply$apply} @{tf.contrib.data.batch_and_drop_remainder} +as follows: + +``` +>>> params = {'batch_size':32} +>>> ds = tf.data.Dataset.from_tensors([0, 1, 2]) +>>> ds = ds.repeat().apply( +... tf.contrib.data.batch_and_drop_remainder(params['batch-size'])) +>>> ds + + <_RestructuredDataset shapes: (32, 3), types: tf.int32> +``` + +The one downside to this approach is that, as the name implies, this batching +method throws out any fractional batch at the end of the dataset. This is fine +for an infinitely repeating dataset being used for training, but could be a +problem if you want to train for an exact number of epochs. + +To do an exact 1-epoch of _evaluation_ you can work around this by manually +padding the length of the batches, and setting the padding entries to have zero +weight when creating your `tf.metrics`. + +## Datasets + +Efficient use of the `tf.data.Dataset` API is critical when using a Cloud +TPU, as it is impossible to use the Cloud TPU's unless you can feed it data +quickly enough. See @{$datasets_performance} for details on dataset performance. + +For all but the simplest experimentation (using +@{tf.data.Dataset.from_tensor_slices} or other in-graph data) you will need to +store all data files read by the `TPUEstimator`'s `Dataset` in Google Cloud +Storage Buckets. + + + +For most use-cases, we recommend converting your data into `TFRecord` +format and using a @{tf.data.TFRecordDataset} to read it. This, however, is not +a hard requirement and you can use other dataset readers +(`FixedLengthRecordDataset` or `TextLineDataset`) if you prefer. + +Small datasets can be loaded entirely into memory using +@{tf.data.Dataset.cache}. + +Regardless of the data format used, it is strongly recommended that you +@{$performance_guide#use_large_files$use large files}, on the order of +100MB. This is especially important in this networked setting as the overhead +of opening a file is significantly higher. + +It is also important, regardless of the type of reader used, to enable buffering +using the `buffer_size` argument to the constructor. This argument is specified +in bytes. A minimum of a few MB (`buffer_size=8*1024*1024`) is recommended so +that data is available when needed. + +The TPU-demos repo includes +[a script](https://github.com/tensorflow/tpu-demos/blob/master/cloud_tpu/datasets/imagenet_to_gcs.py) +for downloading the imagenet dataset and converting it to an appropriate format. +This together with the imagenet +[models](https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models) +included in the repo demonstrate all of these best-practices. + + +## What Next + +For details on how to actually set up and run a Cloud TPU see: + + * [Google Cloud TPU Documentation](https://cloud.google.com/tpu/docs/) + +This document is by no means exhaustive. The best source of more detail on how +to make a Cloud TPU compatible model are the example models published in: + + * The [TPU Demos Repository.](https://github.com/tensorflow/tpu-demos/) + +For more information about tuning TensorFlow code for performance see: + + * The @{$performance$Performance Section.} + -- GitLab From 5409f55e104f2c9be60a850f128b256460daccc0 Mon Sep 17 00:00:00 2001 From: Akshay Agrawal Date: Wed, 31 Jan 2018 18:03:25 -0800 Subject: [PATCH 364/423] TFE: Support `IndexedSlices` as inputs and outputs for `tfe.defun`. In particular, this change fixes a bug that arose when attempting to differentiate a defun-d function whose gradient yielded `IndexedSlices`. PiperOrigin-RevId: 184077261 --- tensorflow/python/eager/function.py | 95 +++++++++++++++++++----- tensorflow/python/eager/function_test.py | 72 ++++++++++++++++++ 2 files changed, 150 insertions(+), 17 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 81b1f6f12a..f5d0759bdc 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -292,6 +292,22 @@ def _map_sequence_obj_to_idx(sequence): return {id(x): i for i, x in enumerate(sequence)} +def _flatten(sequence): + """A wrapper around `nest.flatten` that also unpacks `IndexedSlices`.""" + # TODO(akshayka): Support `SparseTensor` in a similar fashion. + flat_sequence = nest.flatten(sequence) + outputs = [] + for item in flat_sequence: + if isinstance(item, ops.IndexedSlices): + if item.dense_shape is not None: + outputs.extend([item.values, item.indices, item.dense_shape]) + else: + outputs.extend([item.values, item.indices]) + else: + outputs.append(item) + return outputs + + class GraphModeFunction(object): """Callable object representing a graph-mode function. @@ -333,14 +349,14 @@ class GraphModeFunction(object): self._input_placeholders = input_placeholders self._extra_inputs = list(extra_inputs) self._graph = graph - self._has_backprop = False + self._backward_function = None self._func_name = name self._function_def = defined_function self._num_outputs = len(defined_function.signature.output_arg) self._ops = operations self._func_outputs = func_outputs self._returns = [func_outputs] if isinstance( - func_outputs, (ops.Tensor, type(None))) else list(func_outputs) + func_outputs, (ops.Tensor, type(None))) else _flatten(func_outputs) self._output_shapes = output_shapes self._variables = variables if variables is not None else [] @@ -348,9 +364,8 @@ class GraphModeFunction(object): def variables(self): return self._variables - def _compute_backprop(self): - """Computes the backprop function object for this function.""" - self._has_backprop = True + def _construct_backprop_function(self): + """Constructs the backprop function object for this function.""" with self._graph.as_default(), context.graph_mode(): c = _CapturingContext() with c: @@ -361,13 +376,16 @@ class GraphModeFunction(object): filtered_outputs, self._input_placeholders, grad_ys=self._out_grad_placeholders) - shapes = tuple(x.shape for x in in_gradients if x is not None) + + 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)) forward_name = _forward_name(self._func_name) self._forward_fdef = _EagerDefinedFunction( forward_name, self._graph, self._ops, self._input_placeholders, filtered_outputs + captures) - backward_outputs = tuple(x for x in in_gradients if x is not None) all_inputs = self._out_grad_placeholders + captures # Excluding input ops from the body as we do not intend to execute these # operations when the function is executed. @@ -381,7 +399,7 @@ class GraphModeFunction(object): bname = _backward_name(self._func_name) self._backward_function = GraphModeFunction( bname, all_inputs, [], self._graph, function_def_ops, - backward_outputs, in_gradients, shapes) + backward_outputs, in_gradients, output_shapes) def _backprop_call(self, args): """Calls the wrapped function and records the result on a tape.""" @@ -426,9 +444,24 @@ class GraphModeFunction(object): @property def output_shapes(self): + """The function's output shapes.""" # TODO(ebrevdo): Should we only keep the output shapes associated # with len(self._returns) outputs? - return nest.pack_sequence_as(self._func_outputs, self._output_shapes) + outputs_list = nest.flatten(self._func_outputs) + j = 0 + for i, o in enumerate(outputs_list): + if o is not None: + if isinstance(o, ops.IndexedSlices): + # Extract the shape of the `IndexedSlices` object's `values` field. + outputs_list[i] = self._output_shapes[j] # the `values` shape + if o.dense_shape is not None: + j += 3 # skip over shapes for `values`, `indices`, `dense_shape` + else: + j += 2 # skip over shapes for `values`, `indices` + else: + outputs_list[i] = self._output_shapes[j] + j += 1 + return nest.pack_sequence_as(self._func_outputs, outputs_list) @property def output_dtypes(self): @@ -457,12 +490,11 @@ class GraphModeFunction(object): if v._trainable: # pylint: disable=protected-access tape.watch_variable(v) - tensor_inputs = [x for x in nest.flatten(args) - if isinstance(x, ops.Tensor)] + tensor_inputs = [x for x in nest.flatten(args) if isinstance(x, ops.Tensor)] if tape.should_record(tensor_inputs) or tape.should_record( self._extra_inputs): - if not self._has_backprop: - self._compute_backprop() + if self._backward_function is None: + self._construct_backprop_function() return self._backprop_call(tensor_inputs) ctx = context.context() @@ -503,13 +535,30 @@ class GraphModeFunction(object): """ if self._func_outputs is None: return None + # Use `nest.flatten` instead of `_flatten` in order to preserve any + # IndexedSlices in `self._func_outputs`. outputs_list = nest.flatten(self._func_outputs) j = 0 for i, o in enumerate(outputs_list): if o is not None: - outputs_list[i] = result[j] - j += 1 - return nest.pack_sequence_as(self._func_outputs, outputs_list) + if isinstance(o, ops.IndexedSlices): + # Repack Tensors for IndexedSlices. + if o.dense_shape is not None: + outputs_list[i] = ops.IndexedSlices( + values=result[j], + indices=result[j + 1], + dense_shape=result[j + 2]) + j += 3 + else: + outputs_list[i] = ops.IndexedSlices( + values=result[j], + indices=result[j + 1]) + j += 2 + else: + outputs_list[i] = result[j] + j += 1 + ret = nest.pack_sequence_as(self._func_outputs, outputs_list) + return ret def _get_defun_inputs(args): @@ -555,7 +604,7 @@ def _defun_internal(name, func, args, kwds): # Returning a closed-over tensor as an output does not trigger a # call to convert_to_tensor, so we manually capture all such tensors. - outputs_list = nest.flatten(func_outputs) + outputs_list = _flatten(func_outputs) func_def_outputs = [ _convert_to_graph_tensor(x) for x in outputs_list if x is not None ] @@ -600,6 +649,18 @@ def _cache_key(x): """Cache key for tfe functions.""" if isinstance(x, ops.Tensor): return _TensorDtype(x.dtype, x._shape_tuple()) # pylint: disable=protected-access + if isinstance(x, ops.IndexedSlices): + if x.dense_shape is not None: + return tuple([ + _TensorDtype(x.values.dtype, x.values._shape_tuple()), # pylint: disable=protected-access + _TensorDtype(x.indices.dtype, x.indices._shape_tuple()), # pylint: disable=protected-access + _TensorDtype(x.dense_shape.dtype, x.dense_shape._shape_tuple()) # pylint: disable=protected-access + ]) + else: + return tuple([ + _TensorDtype(x.values.dtype, x.values._shape_tuple()), # pylint: disable=protected-access + _TensorDtype(x.indices.dtype, x.indices._shape_tuple()) # pylint: disable=protected-access + ]) if isinstance(x, np.ndarray): return ("array", x.shape, tuple(x.reshape(-1))) if isinstance(x, (list, tuple)): diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index 2cb2cfb76c..3e8e67ac7e 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -374,6 +374,78 @@ class FunctionTest(test.TestCase): self.assertAllEqual(f(constant_op.constant(1.0)), 2.0) + def testGradientOfGatherWithDefun(self): + + v = resource_variable_ops.ResourceVariable([0.0, 1.0, 2.0]) + + def sum_gather(): + return math_ops.reduce_sum(array_ops.gather(v, [1, 2])) + + grad_fn = backprop.implicit_grad(sum_gather) + gradient = grad_fn() + defun_grad_fn = backprop.implicit_grad(function.defun(sum_gather)) + defun_gradient = defun_grad_fn() + self.assertEqual(len(gradient), len(defun_gradient)) + + gradient = gradient[0][0] + defun_gradient = defun_gradient[0][0] + self.assertAllEqual(gradient.values, defun_gradient.values) + self.assertAllEqual(gradient.indices, defun_gradient.indices) + self.assertAllEqual(gradient.dense_shape, defun_gradient.dense_shape) + + def testReturningIndexedSlicesWithDefun(self): + + def validate(indexed_slice): + def f(): + return indexed_slice + + output = function.defun(f)() + self.assertTrue(isinstance(output, ops.IndexedSlices)) + self.assertAllEqual(indexed_slice.values, output.values) + self.assertAllEqual(indexed_slice.indices, output.indices) + self.assertAllEqual(indexed_slice.dense_shape, output.dense_shape) + + self.assertEqual( + function.make_defun_op(f).output_shapes, indexed_slice.values.shape) + + arg = ops.IndexedSlices( + values=constant_op.constant([1, 2]), + indices=constant_op.constant([0, 1]), + dense_shape=constant_op.constant([2])) + validate(arg) + + arg = ops.IndexedSlices( + values=constant_op.constant([1, 2]), + indices=constant_op.constant([0, 1]), + dense_shape=None) + validate(arg) + + def testIndexedSliceAsArgumentWithDefun(self): + + @function.defun + def f(indexed_slice): + return indexed_slice + + def validate(arg): + output = f(arg) + self.assertTrue(isinstance(output, ops.IndexedSlices)) + self.assertAllEqual(arg.values, output.values) + self.assertAllEqual(arg.indices, output.indices) + self.assertAllEqual(arg.dense_shape, output.dense_shape) + + indexed_slice = ops.IndexedSlices( + values=constant_op.constant([1]), + indices=constant_op.constant([0]), + dense_shape=constant_op.constant([1])) + validate(indexed_slice) + + # Test that `f` works even when `dense_shape` is None. + indexed_slice = ops.IndexedSlices( + values=constant_op.constant([1]), + indices=constant_op.constant([0]), + dense_shape=None) + validate(indexed_slice) + def testFunctionOnDevice(self): if not context.context().num_gpus(): self.skipTest('No GPUs found') -- GitLab From e8e33b0050e7e1ff686312bcbdafa270c2e29462 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Wed, 31 Jan 2018 18:08:33 -0800 Subject: [PATCH 365/423] Test all TFLite kernel implementations (reference/optimized/...) PiperOrigin-RevId: 184077940 --- tensorflow/contrib/lite/kernels/conv.cc | 122 +++++++++++-------- tensorflow/contrib/lite/kernels/conv_test.cc | 69 ++++++++--- tensorflow/contrib/lite/kernels/test_util.cc | 11 +- tensorflow/contrib/lite/kernels/test_util.h | 52 ++++++++ 4 files changed, 182 insertions(+), 72 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc index 37f499a4d0..a5095e1e64 100644 --- a/tensorflow/contrib/lite/kernels/conv.cc +++ b/tensorflow/contrib/lite/kernels/conv.cc @@ -42,7 +42,7 @@ namespace conv { enum KernelType { kReference, kGenericOptimized, // Neon-free - kNeonOptimized, + kMultithreadOptimized, }; struct OpData { @@ -290,26 +290,33 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node, auto filter_offset = -filter->params.zero_point; auto output_offset = output->params.zero_point; - if (kernel_type == kReference) { - reference_ops::Conv( - GetTensorData(input), GetTensorDims(input), input_offset, - GetTensorData(filter), GetTensorDims(filter), filter_offset, - GetTensorData(bias), GetTensorDims(bias), params->stride_width, - params->stride_height, data->padding.width, data->padding.height, - output_offset, data->output_multiplier, data->output_shift, - data->output_activation_min, data->output_activation_max, - GetTensorData(output), GetTensorDims(output), - GetTensorData(im2col), GetTensorDims(im2col), gemm_context); - } else { - optimized_ops::Conv( - GetTensorData(input), GetTensorDims(input), input_offset, - GetTensorData(filter), GetTensorDims(filter), filter_offset, - GetTensorData(bias), GetTensorDims(bias), params->stride_width, - params->stride_height, data->padding.width, data->padding.height, - output_offset, data->output_multiplier, data->output_shift, - data->output_activation_min, data->output_activation_max, - GetTensorData(output), GetTensorDims(output), - GetTensorData(im2col), GetTensorDims(im2col), gemm_context); + switch (kernel_type) { + case kReference: + reference_ops::Conv( + GetTensorData(input), GetTensorDims(input), input_offset, + GetTensorData(filter), GetTensorDims(filter), filter_offset, + GetTensorData(bias), GetTensorDims(bias), + params->stride_width, params->stride_height, data->padding.width, + data->padding.height, output_offset, data->output_multiplier, + data->output_shift, data->output_activation_min, + data->output_activation_max, GetTensorData(output), + GetTensorDims(output), GetTensorData(im2col), + GetTensorDims(im2col), gemm_context); + break; + case kGenericOptimized: + case kMultithreadOptimized: + // There is only one optimized implementation for Quantized Conv. + optimized_ops::Conv( + GetTensorData(input), GetTensorDims(input), input_offset, + GetTensorData(filter), GetTensorDims(filter), filter_offset, + GetTensorData(bias), GetTensorDims(bias), + params->stride_width, params->stride_height, data->padding.width, + data->padding.height, output_offset, data->output_multiplier, + data->output_shift, data->output_activation_min, + data->output_activation_max, GetTensorData(output), + GetTensorDims(output), GetTensorData(im2col), + GetTensorDims(im2col), gemm_context); + break; } } @@ -322,31 +329,46 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, CalculateActivationRangeFloat(params->activation, &output_activation_min, &output_activation_max); - if (kernel_type == kReference) { - reference_ops::Conv(GetTensorData(input), GetTensorDims(input), - GetTensorData(filter), GetTensorDims(filter), - GetTensorData(bias), GetTensorDims(bias), - params->stride_width, params->stride_height, - data->padding.width, data->padding.height, - output_activation_min, output_activation_max, - GetTensorData(output), GetTensorDims(output), - GetTensorData(im2col), GetTensorDims(im2col)); - } else { - const float* filter_data; - if (data->need_hwcn_weights) { - filter_data = GetTensorData(hwcn_weights); - } else { - filter_data = GetTensorData(filter); + switch (kernel_type) { + case kReference: { + reference_ops::Conv(GetTensorData(input), GetTensorDims(input), + GetTensorData(filter), GetTensorDims(filter), + GetTensorData(bias), GetTensorDims(bias), + params->stride_width, params->stride_height, + data->padding.width, data->padding.height, + output_activation_min, output_activation_max, + GetTensorData(output), GetTensorDims(output), + GetTensorData(im2col), GetTensorDims(im2col)); + break; + } + case kGenericOptimized: { + optimized_ops::Conv(GetTensorData(input), GetTensorDims(input), + GetTensorData(filter), GetTensorDims(filter), + GetTensorData(bias), GetTensorDims(bias), + params->stride_width, params->stride_height, + data->padding.width, data->padding.height, + output_activation_min, output_activation_max, + GetTensorData(output), GetTensorDims(output), + GetTensorData(im2col), GetTensorDims(im2col)); + break; + } + case kMultithreadOptimized: { + const float* filter_data; + if (data->need_hwcn_weights) { + filter_data = GetTensorData(hwcn_weights); + } else { + filter_data = GetTensorData(filter); + } + multithreaded_ops::Conv( + GetTensorData(input), GetTensorDims(input), filter_data, + GetTensorDims(filter), GetTensorData(bias), + GetTensorDims(bias), params->stride_width, params->stride_height, + data->padding.width, data->padding.height, params->padding, + output_activation_min, output_activation_max, + GetTensorData(output), GetTensorDims(output), + GetTensorData(im2col), GetTensorDims(im2col)); + break; } - - multithreaded_ops::Conv( - GetTensorData(input), GetTensorDims(input), filter_data, - GetTensorDims(filter), GetTensorData(bias), GetTensorDims(bias), - params->stride_width, params->stride_height, data->padding.width, - data->padding.height, params->padding, output_activation_min, - output_activation_max, GetTensorData(output), - GetTensorDims(output), GetTensorData(im2col), - GetTensorDims(im2col)); } } @@ -407,18 +429,14 @@ TfLiteRegistration* Register_CONVOLUTION_GENERIC_OPT() { return &r; } -TfLiteRegistration* Register_CONVOLUTION_NEON_OPT() { +TfLiteRegistration* Register_CONVOLUTION_MULTITHREADED_OPT() { static TfLiteRegistration r = {conv::Init, conv::Free, conv::Prepare, - conv::Eval}; + conv::Eval}; return &r; } TfLiteRegistration* Register_CONV_2D() { -#ifdef USE_NEON - return Register_CONVOLUTION_NEON_OPT(); -#else - return Register_CONVOLUTION_GENERIC_OPT(); -#endif + return Register_CONVOLUTION_MULTITHREADED_OPT(); } } // namespace builtin diff --git a/tensorflow/contrib/lite/kernels/conv_test.cc b/tensorflow/contrib/lite/kernels/conv_test.cc index 1d0a81c313..461efffe39 100644 --- a/tensorflow/contrib/lite/kernels/conv_test.cc +++ b/tensorflow/contrib/lite/kernels/conv_test.cc @@ -21,6 +21,17 @@ limitations under the License. #include "tensorflow/contrib/lite/model.h" namespace tflite { + +namespace ops { +namespace builtin { + +TfLiteRegistration* Register_CONVOLUTION_REF(); +TfLiteRegistration* Register_CONVOLUTION_GENERIC_OPT(); +TfLiteRegistration* Register_CONVOLUTION_MULTITHREADED_OPT(); + +} // namespace builtin +} // namespace ops + namespace { using ::testing::ElementsAreArray; @@ -30,9 +41,9 @@ class BaseConvolutionOpModel : public SingleOpModel { // TODO(ahentz): Also test different activation types, bias, padding types, // stride values. BaseConvolutionOpModel( - const TensorData& input, const TensorData& filter, - const TensorData& output, int stride_width = 2, int stride_height = 2, - enum Padding padding = Padding_VALID, + TfLiteRegistration* registration, const TensorData& input, + const TensorData& filter, const TensorData& output, int stride_width = 2, + int stride_height = 2, enum Padding padding = Padding_VALID, enum ActivationFunctionType activation = ActivationFunctionType_NONE) { input_ = AddInput(input); filter_ = AddInput(filter); @@ -62,6 +73,8 @@ class BaseConvolutionOpModel : public SingleOpModel { stride_height, activation) .Union()); + resolver_ = absl::make_unique(BuiltinOperator_CONV_2D, + registration); BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)}); } @@ -83,12 +96,25 @@ class ConvolutionOpModel : public BaseConvolutionOpModel { void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } - std::vector GetOutput() { return ExtractVector(output_); } }; -TEST(ConvolutionOpTest, SimpleTestFloat32) { - ConvolutionOpModel m({TensorType_FLOAT32, {2, 2, 4, 1}}, +const auto kKernelMap = new std::map({ + {"Reference", ops::builtin::Register_CONVOLUTION_REF()}, + {"GenericOptimized", ops::builtin::Register_CONVOLUTION_GENERIC_OPT()}, + {"MultithreadedOptimized", + ops::builtin::Register_CONVOLUTION_MULTITHREADED_OPT()}, +}); + +class ConvolutionOpTest : public SingleOpTest { + protected: + const std::map& GetKernelMap() override { + return *kKernelMap; + } +}; + +TEST_P(ConvolutionOpTest, SimpleTestFloat32) { + ConvolutionOpModel m(GetRegistration(), {TensorType_FLOAT32, {2, 2, 4, 1}}, {TensorType_FLOAT32, {3, 2, 2, 1}}, {TensorType_FLOAT32, {}}); @@ -117,8 +143,8 @@ TEST(ConvolutionOpTest, SimpleTestFloat32) { })); } -TEST(ConvolutionOpTest, SimpleTestFloat32WithAnisotropicStrides) { - ConvolutionOpModel m({TensorType_FLOAT32, {1, 3, 6, 1}}, +TEST_P(ConvolutionOpTest, SimpleTestFloat32WithAnisotropicStrides) { + ConvolutionOpModel m(GetRegistration(), {TensorType_FLOAT32, {1, 3, 6, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {}}, /*stride_width=*/3, /*stride_height=*/1); @@ -139,7 +165,7 @@ TEST(ConvolutionOpTest, SimpleTestFloat32WithAnisotropicStrides) { })); } -TEST(ConvolutionOpTest, HandCalculatedFloat32) { +TEST_P(ConvolutionOpTest, HandCalculatedFloat32) { const int depth = 1; const int image_width = 4; const int image_height = 3; @@ -150,6 +176,7 @@ TEST(ConvolutionOpTest, HandCalculatedFloat32) { const int stride_height = 1; const Padding padding = Padding_SAME; ConvolutionOpModel m( + GetRegistration(), {TensorType_FLOAT32, {image_batch_count, image_height, image_width, depth}}, {TensorType_FLOAT32, {depth, filter_size, filter_size, filter_count}}, @@ -192,7 +219,7 @@ TEST(ConvolutionOpTest, HandCalculatedFloat32) { 178, 187, 234, 261, 121})); } -TEST(ConvolutionOpTest, HandCalculatedWithBiasFloat32) { +TEST_P(ConvolutionOpTest, HandCalculatedWithBiasFloat32) { const int depth = 1; const int image_width = 4; const int image_height = 3; @@ -203,6 +230,7 @@ TEST(ConvolutionOpTest, HandCalculatedWithBiasFloat32) { const int stride_height = 1; const Padding padding = Padding_SAME; ConvolutionOpModel m( + GetRegistration(), {TensorType_FLOAT32, {image_batch_count, image_height, image_width, depth}}, {TensorType_FLOAT32, {depth, filter_size, filter_size, filter_count}}, @@ -245,7 +273,7 @@ TEST(ConvolutionOpTest, HandCalculatedWithBiasFloat32) { 367, 188, 197, 244, 271, 131})); } -TEST(ConvolutionOpTest, HandCalculatedWithReluFloat32) { +TEST_P(ConvolutionOpTest, HandCalculatedWithReluFloat32) { const int depth = 1; const int image_width = 4; const int image_height = 3; @@ -256,6 +284,7 @@ TEST(ConvolutionOpTest, HandCalculatedWithReluFloat32) { const int stride_height = 1; const Padding padding = Padding_SAME; ConvolutionOpModel m( + GetRegistration(), {TensorType_FLOAT32, {image_batch_count, image_height, image_width, depth}}, {TensorType_FLOAT32, {depth, filter_size, filter_size, filter_count}}, @@ -300,7 +329,7 @@ TEST(ConvolutionOpTest, HandCalculatedWithReluFloat32) { ElementsAreArray({0, 0, 0, 0, 35, 112, 157, 0, 0, 34, 61, 0})); } -TEST(ConvolutionOpTest, HandCalculatedValidFloat32) { +TEST_P(ConvolutionOpTest, HandCalculatedValidFloat32) { const int depth = 1; const int image_width = 4; const int image_height = 3; @@ -311,6 +340,7 @@ TEST(ConvolutionOpTest, HandCalculatedValidFloat32) { const int stride_height = 1; const Padding padding = Padding_VALID; ConvolutionOpModel m( + GetRegistration(), {TensorType_FLOAT32, {image_batch_count, image_height, image_width, depth}}, {TensorType_FLOAT32, {depth, filter_size, filter_size, filter_count}}, @@ -366,8 +396,9 @@ class QuantizedConvolutionOpModel : public BaseConvolutionOpModel { // In this tests we set the input and output scales so that the results // match exactly the 'non-quantized' version. -TEST(ConvolutionOpTest, SimpleTestQuantized) { - QuantizedConvolutionOpModel m({TensorType_UINT8, {2, 2, 4, 1}, -63.5, 64}, +TEST_P(ConvolutionOpTest, SimpleTestQuantized) { + QuantizedConvolutionOpModel m(GetRegistration(), + {TensorType_UINT8, {2, 2, 4, 1}, -63.5, 64}, {TensorType_UINT8, {3, 2, 2, 1}, -63.5, 64}, {TensorType_UINT8, {}, -127, 128}); m.SetInput({ @@ -405,8 +436,9 @@ TEST(ConvolutionOpTest, SimpleTestQuantized) { })); } -TEST(ConvolutionOpTest, SimpleTestQuantizedWithAnisotropicStrides) { - QuantizedConvolutionOpModel m({TensorType_UINT8, {1, 3, 6, 1}, -63.5, 64}, +TEST_P(ConvolutionOpTest, SimpleTestQuantizedWithAnisotropicStrides) { + QuantizedConvolutionOpModel m(GetRegistration(), + {TensorType_UINT8, {1, 3, 6, 1}, -63.5, 64}, {TensorType_UINT8, {1, 2, 2, 1}, -63.5, 64}, {TensorType_UINT8, {}, -127, 128}, /*stride_width=*/3, /*stride_height=*/1); @@ -430,6 +462,11 @@ TEST(ConvolutionOpTest, SimpleTestQuantizedWithAnisotropicStrides) { 167, 93, // })); } + +INSTANTIATE_TEST_CASE_P( + ConvolutionOpTest, ConvolutionOpTest, + ::testing::ValuesIn(SingleOpTest::GetKernelTags(*kKernelMap))); + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/test_util.cc b/tensorflow/contrib/lite/kernels/test_util.cc index 3a58e7ec32..6f56aa6bf3 100644 --- a/tensorflow/contrib/lite/kernels/test_util.cc +++ b/tensorflow/contrib/lite/kernels/test_util.cc @@ -172,11 +172,14 @@ void SingleOpModel::BuildInterpreter( auto* model = GetModel(builder_.GetBufferPointer()); - ops::builtin::BuiltinOpResolver builtins; - for (const auto& reg : custom_registrations_) { - builtins.AddCustom(reg.first.data(), reg.second()); + if (!resolver_) { + auto resolver = new ops::builtin::BuiltinOpResolver(); + for (const auto& reg : custom_registrations_) { + resolver->AddCustom(reg.first.data(), reg.second()); + } + resolver_ = std::unique_ptr(resolver); } - InterpreterBuilder(model, builtins)(&interpreter_); + InterpreterBuilder(model, *resolver_)(&interpreter_); CHECK(interpreter_ != nullptr); diff --git a/tensorflow/contrib/lite/kernels/test_util.h b/tensorflow/contrib/lite/kernels/test_util.h index cc445299ff..7d476ba1ea 100644 --- a/tensorflow/contrib/lite/kernels/test_util.h +++ b/tensorflow/contrib/lite/kernels/test_util.h @@ -85,6 +85,23 @@ struct TensorData { int32_t zero_point; }; +class SingleOpResolver : public OpResolver { + public: + SingleOpResolver(const BuiltinOperator op, TfLiteRegistration* registration) + : op_(op), registration_(registration) {} + TfLiteRegistration* FindOp(BuiltinOperator op) const override { + if (op == op_) { + return registration_; + } + return nullptr; + } + TfLiteRegistration* FindOp(const char* op) const override { return nullptr; } + + private: + const BuiltinOperator op_; + TfLiteRegistration* registration_; +}; + class SingleOpModel { public: SingleOpModel() {} @@ -178,11 +195,16 @@ class SingleOpModel { return result; } + void SetResolver(std::unique_ptr resolver) { + resolver_ = std::move(resolver); + } + protected: int32_t GetTensorSize(int index) const; flatbuffers::FlatBufferBuilder builder_; std::unique_ptr interpreter_; + std::unique_ptr resolver_; private: int AddTensor(TensorData t, std::initializer_list data); @@ -197,6 +219,36 @@ class SingleOpModel { std::map> custom_registrations_; }; +// Base class for single op unit tests. +// The tests are parameterized to test multiple kernels for a single op. +// The parameters are strings like "optimized" and "reference" to have better +// readability in test reports. +// +// To use this class: +// * Define a constant map from strings to TfLiteRegistration. +// * Implement a test class that inherits SingleOpTest. +// * Instantiate the test cases with SingleOpTest::GetKernelTags helper +// function. +// * Call GetRegistration to get the TfLiteRegistration to be used before +// building the interpreter. +class SingleOpTest : public ::testing::TestWithParam { + public: + static std::vector GetKernelTags( + const std::map& kernel_map) { + std::vector tags; + for (auto it : kernel_map) { + tags.push_back(it.first); + } + return tags; + } + + protected: + virtual const std::map& GetKernelMap() = 0; + TfLiteRegistration* GetRegistration() { + return GetKernelMap().at(GetParam()); + } +}; + // Strings have a special implementation that is in test_util.cc template <> std::vector SingleOpModel::ExtractVector(int index); -- GitLab From 02937cb516facfc63935f82acdac26cb76a8bbf0 Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Wed, 31 Jan 2018 18:18:04 -0800 Subject: [PATCH 366/423] Add a new Dataset: PrependFromQueueAndPaddedBatchDataset. PiperOrigin-RevId: 184078894 --- .../dataset_serialization_test_base.py | 21 + tensorflow/contrib/training/BUILD | 23 + .../python/training/tensor_queue_dataset.py | 200 ++++++ .../training/tensor_queue_dataset_test.py | 355 ++++++++++ .../api_def_EnqueueInQueueDataset.pbtxt | 3 + ...rependFromQueueAndPaddedBatchDataset.pbtxt | 3 + tensorflow/core/kernels/batch_util.cc | 113 +++ tensorflow/core/kernels/batch_util.h | 10 + tensorflow/core/kernels/data/BUILD | 15 + .../kernels/data/padded_batch_dataset_op.cc | 116 +--- .../kernels/data/tensor_queue_dataset_op.cc | 646 ++++++++++++++++++ tensorflow/core/kernels/gather_op.cc | 3 + tensorflow/core/kernels/strided_slice_op.cc | 1 + .../core/kernels/strided_slice_op_impl.h | 3 + tensorflow/core/ops/dataset_ops.cc | 25 + tensorflow/python/data/ops/dataset_ops.py | 25 +- 16 files changed, 1449 insertions(+), 113 deletions(-) create mode 100644 tensorflow/contrib/training/python/training/tensor_queue_dataset.py create mode 100644 tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py create mode 100644 tensorflow/core/api_def/base_api/api_def_EnqueueInQueueDataset.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt create mode 100644 tensorflow/core/kernels/data/tensor_queue_dataset_op.cc diff --git a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py index 3f64475e47..dbc35097dd 100644 --- a/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py +++ b/tensorflow/contrib/data/python/kernel_tests/dataset_serialization_test_base.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.contrib.data.python.ops import iterator_ops as contrib_iterator_ops from tensorflow.python.data.ops import iterator_ops +from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor @@ -35,6 +36,20 @@ from tensorflow.python.training import saver as saver_lib from tensorflow.python.util import nest +def remove_variants(get_next_op): + # TODO(b/72408568): Remove this once session.run can get + # variant tensors. + """Remove variants from a nest structure, so sess.run will execute.""" + + def _remove_variant(x): + if isinstance(x, ops.Tensor) and x.dtype == dtypes.variant: + return () + else: + return x + + return nest.map_structure(_remove_variant, get_next_op) + + class DatasetSerializationTestBase(test.TestCase): """Base class for testing serializable datasets.""" @@ -235,6 +250,7 @@ class DatasetSerializationTestBase(test.TestCase): saver = self._import_meta_graph() init_op, get_next_op = self._get_iterator_ops_from_collection( ds_fn, sparse_tensors=sparse_tensors) + get_next_op = remove_variants(get_next_op) with self.test_session(graph=g) as sess: self._restore(saver, sess) self._initialize(init_op, sess) @@ -297,6 +313,7 @@ class DatasetSerializationTestBase(test.TestCase): with ops.Graph().as_default() as g: _, get_next_op, saver = self._build_graph( ds_fn2, sparse_tensors=sparse_tensors) + get_next_op = remove_variants(get_next_op) with self.test_session(graph=g) as sess: self._restore(saver, sess) for _ in range(num_outputs - break_point): @@ -357,6 +374,7 @@ class DatasetSerializationTestBase(test.TestCase): with ops.Graph().as_default() as g: get_next_op, saver = self._build_empty_graph( ds_fn, sparse_tensors=sparse_tensors) + get_next_op = remove_variants(get_next_op) with self.test_session(graph=g) as sess: self._restore(saver, sess) for _ in range(num_outputs - break_point): @@ -390,6 +408,7 @@ class DatasetSerializationTestBase(test.TestCase): with ops.Graph().as_default() as g: init_op, get_next_op, saver = self._build_graph( ds_fn, sparse_tensors=sparse_tensors) + get_next_op = remove_variants(get_next_op) with self.test_session(graph=g) as sess: self._initialize(init_op, sess) for _ in range(break_point): @@ -485,11 +504,13 @@ class DatasetSerializationTestBase(test.TestCase): else: init_op, get_next_op, saver = self._build_graph( ds_fn, sparse_tensors=sparse_tensors) + get_next_op = remove_variants(get_next_op) return init_op, get_next_op, saver for i in range(len(break_points) + 1): with ops.Graph().as_default() as g: init_op, get_next_op, saver = get_ops() + get_next_op = remove_variants(get_next_op) with self.test_session(graph=g) as sess: if ckpt_saved: if init_before_restore: diff --git a/tensorflow/contrib/training/BUILD b/tensorflow/contrib/training/BUILD index cccaa2b833..6db373d2d5 100644 --- a/tensorflow/contrib/training/BUILD +++ b/tensorflow/contrib/training/BUILD @@ -26,6 +26,7 @@ py_library( "python/training/resample.py", "python/training/sampling_ops.py", "python/training/sequence_queueing_state_saver.py", + "python/training/tensor_queue_dataset.py", "python/training/training.py", "python/training/tuner.py", ], @@ -285,6 +286,28 @@ py_test( ], ) +py_test( + name = "tensor_queue_dataset_test", + size = "large", + srcs = ["python/training/tensor_queue_dataset_test.py"], + srcs_version = "PY2AND3", + tags = ["notsan"], + deps = [ + ":training_py", + "//tensorflow/contrib/data/python/kernel_tests:dataset_serialization_test", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:gradients", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform", + "//tensorflow/python:random_seed", + "//tensorflow/python:training", + "//tensorflow/python:variables", + "//tensorflow/python/data", + "//third_party/py/numpy", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/training/python/training/tensor_queue_dataset.py b/tensorflow/contrib/training/python/training/tensor_queue_dataset.py new file mode 100644 index 0000000000..409aba817c --- /dev/null +++ b/tensorflow/contrib/training/python/training/tensor_queue_dataset.py @@ -0,0 +1,200 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python wrappers for Datasets and Iterators.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.data.util import nest +from tensorflow.python.data.util import sparse +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import tensor_util +from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.util import nest as tf_nest + + +class _PrependFromQueueAndPaddedBatchDataset(dataset_ops.Dataset): + """A `Dataset` that prepends a queue to another `Dataset`. + + A vector of handles to the queue is returned as the first component of + the associated iterator. This vector can be passed to + `enqueue_in_queue_dataset` to add new elements to the queue. + """ + + def __init__(self, input_dataset, batch_size, padded_shapes, padding_values): + """Initialize `PrependFromQueueAndPaddedBatchDataset`.""" + super(_PrependFromQueueAndPaddedBatchDataset, self).__init__() + if sparse.any_sparse(input_dataset.output_classes): + raise TypeError( + "Batching of padded sparse tensors is not currently supported") + self._input_dataset = input_dataset + self._batch_size = ops.convert_to_tensor( + batch_size, dtype=dtypes.int64, name="batch_size") + # pylint: disable=protected-access + if padded_shapes is None: + self._padded_shapes = nest.map_structure( + dataset_ops._partial_shape_to_tensor, input_dataset.output_shapes) + else: + self._padded_shapes = nest.map_structure_up_to( + input_dataset.output_shapes, dataset_ops._partial_shape_to_tensor, + padded_shapes) + padding_values = ( + padding_values if padding_values is not None else + dataset_ops._default_padding(input_dataset)) + self._padding_values = nest.map_structure_up_to( + input_dataset.output_shapes, dataset_ops._padding_value_to_tensor, + padding_values, input_dataset.output_types) + # pylint: enable=protected-access + + def _as_variant_tensor(self): + # pylint: disable=protected-access + return gen_dataset_ops.prepend_from_queue_and_padded_batch_dataset( + self._input_dataset._as_variant_tensor(), + batch_size=self._batch_size, + padded_shapes=[ + ops.convert_to_tensor(s, dtype=dtypes.int64) + for s in nest.flatten(self._padded_shapes) + ], + padding_values=nest.flatten(self._padding_values), + output_shapes=nest.flatten( + sparse.as_dense_shapes(self.output_shapes, self.output_classes))) + # pylint: enable=protected-access + + @property + def output_classes(self): + return (ops.Tensor, self._input_dataset.output_classes) + + def _as_batch_shape(self, shape_like): + return tensor_shape.vector(None).concatenate( + tensor_util.constant_value_as_shape(shape_like)) + + @property + def output_shapes(self): + # First output is a variant representing the Queue + return (tensor_shape.vector(None), + nest.map_structure(self._as_batch_shape, self._padded_shapes)) + + @property + def output_types(self): + # First output is a variant representing the Queue + return (dtypes.variant, self._input_dataset.output_types) + + +def prepend_from_queue_and_padded_batch_dataset(batch_size, + padding_values=None, + padded_shapes=None): + """A transformation that prepends a queue to a `Dataset` and batches results. + + A vector of handles to the queue is returned as the first component of the + associated iterator. This vector can be passed to `enqueue_in_queue_dataset` + to add new elements to the queue. + + Below is an example of how this dataset might be used to split incoming + variable-length sequences into "head" and "rest" parts, where "rest" parts + are re-enqueued back into the dataset. A more realistic example would + perform some calculation on the "head" and modify some components of "rest" + with the result (before re-enqueueing). + + ```python + dataset = tf.data.Dataset.from_tensor_slices([2*x for x in range(10)]) + # Make a dataset of variable-length vectors and their lengths. + dataset = dataset.map(lambda count: (count, tf.ones((count,)))) + # Emit a queue we can prepend to, and counts/values as padded batch. + dataset = dataset.apply( + tf.contrib.training.prepend_from_queue_and_padded_batch_dataset( + batch_size=10)) + dataset = dataset.prefetch(1) + + iterator = dataset.make_one_shot_iterator() + queue, (count, padded_value) = iterator.get_next() + + # Split the padded_value into two pieces: head and rest + rest_indices = tf.squeeze(tf.where(count > 3), axis=1) + bound = tf.minimum(3, tf.reduce_max(count)) + value_head = padded_value[:, :bound] + count_rest = tf.gather(count - 3, rest_indices) + value_rest = tf.gather(padded_value[:, bound:], rest_indices) + queue_rest = tf.gather(queue, rest_indices) + enqueue_rest_op = tf.contrib.training.enqueue_in_queue_dataset( + queue_rest, (count_rest, value_rest)) + with tf.control_dependencies([enqueue_rest_op]): + calculation = fn(value_head) + + while True: # Will raise OutOfRange when finished with all pieces. + session.run(calculation) + ``` + + Args: + batch_size: `int64` scalar tensor. The batch size to use when performing + padded batching. + padding_values: (optional) Nested tuple of scalar tensors. If provided, + the structure and dtypes of padding_values should match that of + incoming dataset's `output_types`. + padded_shapes: (optional) Nested tuple of `int64` vector tensors. + If provided, the structure must match that of the incoming dataset's + `output_types`. If not provided, the incoming dataset's `output_shapes` + is used. Any unknown (`None` or `-1`) dimensions in the shapes are + treated as being unique per-batch: for each batch time, an unknown + dimension is replaced with the maximum given value of this dimension + across all tensors for the given component in the batch. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + + def _apply_fn(dataset): + return _PrependFromQueueAndPaddedBatchDataset( + dataset, + batch_size=batch_size, + padding_values=padding_values, + padded_shapes=padded_shapes) + + return _apply_fn + + +def enqueue_in_queue_dataset(queue, components): + """Enqueue components into queue from `PrependFromQueueAndPaddedBatchDataset`. + + The components' dtypes and shapes must be compatible with the `output_shapes` + attribute of the `dataset` created by + `prepend_from_queue_and_padded_batch_dataset`. This operation supports both + non-batched and batched modes. + + For more details, see the example in the docstring for + `prepend_from_queue_and_padded_batch_dataset`. + + Args: + queue: `variant` scalar or vector tensor. + The tensor emitted by the first component of the iterator associated with + `prepend_from_queue_and_padded_batch_dataset`. If this is a scalar, + then the `components` input tensors should not have a prepended batch + dimension. + components: Nested tuple of tensors, each with a leading batch dimension + if `queue` is a vector. The structure, dtypes, and shapes + (excluding batch dimension) must match the nested tuples + `dataset.output_types[1]` and `dataset.output_shapes[1]` (the non-queue + output types and shapes) of the `dataset` emitted by + the original `prepend_from_queue_and_padded_batch_dataset` call. + + Returns: + An `Operation` that enqueues `components` into the dataset(s) associated + with entries of `queue`. + """ + return gen_dataset_ops.enqueue_in_queue_dataset( + queue=queue, components=tf_nest.flatten(components)) diff --git a/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py b/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py new file mode 100644 index 0000000000..0338f409a2 --- /dev/null +++ b/tensorflow/contrib/training/python/training/tensor_queue_dataset_test.py @@ -0,0 +1,355 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for TensorQueueDataset.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base +from tensorflow.contrib.training.python.training import tensor_queue_dataset as tqd +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +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 string_ops +from tensorflow.python.platform import test + + +class PrependFromQueueAndPaddedBatchDatasetTest(test.TestCase): + + def testNoEnqueue(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 1, 2]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=1)) + self.assertEqual((dtypes.variant, dtypes.int32), dataset.output_types) + self.assertAllEqual(([None],) * 2, + [x.as_list() for x in dataset.output_shapes]) + iterator = dataset.make_one_shot_iterator() + _, value = iterator.get_next() + self.assertEqual([0], self.evaluate(value)) + self.assertEqual([1], self.evaluate(value)) + self.assertEqual([2], self.evaluate(value)) + with self.assertRaisesOpError("End of sequence"): + self.evaluate(value) + + def testBatchedNoEnqueue(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 1, 2]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=2)) + iterator = dataset.make_one_shot_iterator() + _, value = iterator.get_next() + self.assertAllEqual([0, 1], self.evaluate(value)) + self.assertAllEqual([2], self.evaluate(value)) + with self.assertRaisesOpError("End of sequence"): + self.evaluate(value) + + def testBatchedWithBiggerPaddingNoEnqueue(self): + dataset = dataset_ops.Dataset.from_tensor_slices([[0], [1], [2]]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset( + batch_size=2, padded_shapes=[3])) + iterator = dataset.make_one_shot_iterator() + _, value = iterator.get_next() + self.assertAllEqual([[0, 0, 0], [1, 0, 0]], self.evaluate(value)) + self.assertAllEqual([[2, 0, 0]], self.evaluate(value)) + with self.assertRaisesOpError("End of sequence"): + self.evaluate(value) + + def testBatchedWithBiggerPaddingOneEnqueue(self): + dataset = dataset_ops.Dataset.from_tensor_slices([[0], [1], [2]]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset( + batch_size=1, padded_shapes=[3])) + iterator = dataset.make_one_shot_iterator() + queue_handle, value = iterator.get_next() + enqueue_negative = tqd.enqueue_in_queue_dataset(queue_handle, -value) + with self.test_session() as sess: + self.assertAllEqual([[0, 0, 0]], sess.run(value)) + value_1, _ = sess.run([value, enqueue_negative]) + self.assertAllEqual([[1, 0, 0]], value_1) + value_2, _ = sess.run([value, enqueue_negative]) + self.assertAllEqual([[-1, 0, 0]], value_2) + value_3 = sess.run(value) + self.assertAllEqual([[1, 0, 0]], value_3) + value_4, _ = sess.run([value, enqueue_negative]) + self.assertAllEqual([[2, 0, 0]], value_4) + value_5 = sess.run(value) + self.assertAllEqual([[-2, 0, 0]], value_5) + with self.assertRaisesOpError("End of sequence"): + sess.run(value) + + def testOneEnqueue(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 1, 2]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=1)) + iterator = dataset.make_one_shot_iterator() + queue_handle, value = iterator.get_next() + enqueue_negative = tqd.enqueue_in_queue_dataset(queue_handle, -value) + with self.test_session() as sess: + self.assertEqual([0], sess.run(value)) + value_1, _ = sess.run([value, enqueue_negative]) + self.assertEqual([1], value_1) + value_2, _ = sess.run([value, enqueue_negative]) + self.assertEqual([-1], value_2) + value_3 = sess.run(value) + self.assertEqual([1], value_3) + value_4, _ = sess.run([value, enqueue_negative]) + self.assertEqual([2], value_4) + value_5 = sess.run(value) + self.assertEqual([-2], value_5) + with self.assertRaisesOpError("End of sequence"): + sess.run(value) + + def testBatchedOneEnqueue(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 1, 2]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=2)) + iterator = dataset.make_one_shot_iterator() + queue_handle, value = iterator.get_next() + enqueue_negative = tqd.enqueue_in_queue_dataset(queue_handle, -value) + enqueue_zeroth = tqd.enqueue_in_queue_dataset([queue_handle[0]], + array_ops.expand_dims( + value[0], axis=0)) + with self.test_session() as sess: + value_0, _ = sess.run([value, enqueue_negative]) + self.assertAllEqual([0, 1], value_0) + value_1, _ = sess.run([value, enqueue_zeroth]) + self.assertAllEqual([0, -1], value_1) + value_2, _ = sess.run([value, enqueue_negative]) + self.assertAllEqual([0, 2], value_2) + self.assertAllEqual([0, -2], sess.run(value)) + with self.assertRaisesOpError("End of sequence"): + sess.run(value) + + def testManyEnqueue(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 1]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=1)) + iterator = dataset.make_one_shot_iterator() + queue_handle, value = iterator.get_next() + enqueue_many_more = [ + tqd.enqueue_in_queue_dataset(queue_handle, value + 100 + i) + for i in range(1000) + ] + with self.test_session() as sess: + value_0, _ = sess.run((value, enqueue_many_more)) + self.assertEqual([0], value_0) + rest = [] + for _ in range(1000): + rest.append(sess.run(value)) + self.assertEquals([[100 + i] for i in range(1000)], sorted(rest)) + # Going back to the original input. + value_1, _ = sess.run((value, enqueue_many_more)) + self.assertEqual(1, value_1) + rest = [] + for _ in range(1000): + rest.append(sess.run(value)) + self.assertEquals([[100 + i + 1] for i in range(1000)], sorted(rest)) + with self.assertRaisesOpError("End of sequence"): + sess.run(value) + + def testEnqueueWithPrefetch(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=1)) + # Prefetching will request additional values before they are + # available to the queue. + dataset = dataset.prefetch(buffer_size=3) + iterator = dataset.make_one_shot_iterator() + queue_handle, value = iterator.get_next() + enqueue = tqd.enqueue_in_queue_dataset(queue_handle, value + 1) + with self.test_session() as sess: + i = 0 + while i < 4: + received, _ = sess.run((value, enqueue)) + if received.size > 0: + self.assertAllEqual([i], received) + i += 1 + received_last = False + while True: + try: + received = sess.run(value) + if received.size > 0: + self.assertAllEqual([4], received) + received_last = True + except errors.OutOfRangeError: + break + self.assertTrue(received_last) + + def testDatasetWithPaddedShapeSmallerThanInputFails(self): + dataset = dataset_ops.Dataset.from_tensor_slices([[0, 0, 0]]).repeat(None) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset( + batch_size=1, padded_shapes=[2])) + iterator = dataset.make_one_shot_iterator() + _, value = iterator.get_next() + with self.test_session() as sess: + with self.assertRaisesOpError( + r"Incompatible input shapes at component 0 between " + r"input dataset this dataset: \[3\] vs. \[2\]"): + sess.run(value) + + def testEnqueueWithIncompatibleInputsFailsWithInformativeError(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0]).repeat(None) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=1)) + iterator = dataset.make_one_shot_iterator() + queue_handle, value = iterator.get_next() + + enqueue_bad_structure = tqd.enqueue_in_queue_dataset( + queue_handle, (value, value)) + enqueue_bad_dtype = tqd.enqueue_in_queue_dataset(queue_handle, + np.array( + [1.0], + dtype=np.float32)) + enqueue_bad_shape_no_batch_dim = tqd.enqueue_in_queue_dataset( + queue_handle, ([1],)) + enqueue_bad_shape = tqd.enqueue_in_queue_dataset(queue_handle, + np.array( + [[1]], dtype=np.int32)) + + with self.test_session() as sess: + with self.assertRaisesOpError( + "mismatched number of tensors. Queue expects 1 tensors but " + "tried to insert 2"): + sess.run(enqueue_bad_structure) + with self.assertRaisesOpError(r"Expected component 0 to have batched " + r"shape \[1,...\], but saw shape: \[\]"): + sess.run(enqueue_bad_shape_no_batch_dim) + with self.assertRaisesOpError( + r"mismatched shapes at component 0. Attempted to insert tensor " + r"with shape \[1\] but queue expected shape: \[\]"): + sess.run(enqueue_bad_shape) + with self.assertRaisesOpError( + r"mismatched dtypes at component 0. Attempted to insert tensor " + r"of type float but queue expected type: int32"): + sess.run(enqueue_bad_dtype) + + def testEnqueueWithPaddedBatchFailsWithInformativeError(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 1, 2]) + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=1)) + with self.assertRaisesRegexp( + TypeError, r"Unable to create padding for field of type 'variant'"): + dataset.padded_batch(batch_size=10, padded_shapes=[1]) + + def testOneEnqueueWithPadding(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 2, 4, 6]) + # Make a dataset of variable-length vectors and their lengths. + dataset = dataset.map( + lambda c: (c, c * array_ops.ones((c,), dtype=c.dtype))) + # Emit a queue we can prepend to, and counts/values as padded + # batch. + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=3)) + + iterator = dataset.make_one_shot_iterator() + queue, (count, padded_value) = iterator.get_next() + + # Split the padded_value into two pieces: head and rest + rest_indices = array_ops.squeeze(array_ops.where(count > 2), axis=1) + bound = math_ops.minimum(2, math_ops.reduce_max(count)) + value_head = padded_value[:, :bound] + count_rest = array_ops.gather(count - 2, rest_indices) + value_rest = array_ops.gather(padded_value, rest_indices)[:, bound:] + queue_rest = array_ops.gather(queue, rest_indices) + enqueue_rest_op = tqd.enqueue_in_queue_dataset(queue_rest, + (count_rest, value_rest)) + with ops.control_dependencies([enqueue_rest_op]): + calc = array_ops.identity(value_head) + + with self.test_session() as sess: + self.assertAllEqual([[0, 0], [2, 2], [4, 4]], sess.run(calc)) + self.assertAllEqual([[4, 4], [6, 6]], sess.run(calc)) + self.assertAllEqual([[6, 6]], sess.run(calc)) + self.assertAllEqual([[6, 6]], sess.run(calc)) + # Get some final batches due to prefetching. + for _ in range(3): + try: + self.assertAllEqual( + np.empty(shape=(0, 0), dtype=np.int32), sess.run(calc)) + except errors.OutOfRangeError as e: + self.assertTrue(str(e).startswith("End of sequence")) + + def testNonstandardPadding(self): + dataset = dataset_ops.Dataset.from_tensor_slices([0, 2, 4, 6]) + # Make a dataset of variable-length vectors and their lengths. + dataset = dataset.map( + lambda c: (c, c * array_ops.ones((c,), dtype=c.dtype))) + # Emit a queue we can prepend to, and counts/values as padded + # batch. + dataset = dataset.apply( + tqd.prepend_from_queue_and_padded_batch_dataset( + batch_size=3, padding_values=( + 0, + -1, + ))) + + iterator = dataset.make_one_shot_iterator() + _, (unused_count, padded_value) = iterator.get_next() + + with self.test_session() as sess: + self.assertAllEqual([[-1, -1, -1, -1], [2, 2, -1, -1], [4, 4, 4, 4]], + sess.run(padded_value)) + self.assertAllEqual([[6] * 6], sess.run(padded_value)) + with self.assertRaisesOpError("End of sequence"): + sess.run(padded_value) + + +# TODO(ebrevdo): Figure out how to use run_core_tests to test state +# saving of an iterator that's had some tensors enqueued into its queue. +class PrependFromQueueAndPaddedBatchDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def testPrependFromQueueAndPaddedBatch(self): + + def build_dataset(seq_lens): + return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( + lambda x: array_ops.fill([x], x)).apply( + tqd.prepend_from_queue_and_padded_batch_dataset(batch_size=4)) + + seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) + seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) + self.run_core_tests(lambda: build_dataset(seq_lens1), + lambda: build_dataset(seq_lens2), 8) + + def testPrependFromQueueAndPaddedBatchNonDefaultPadding(self): + + def build_dataset(seq_lens): + + def fill_tuple(x): + filled = array_ops.fill([x], x) + return (filled, string_ops.as_string(filled)) + + padded_shape = [-1] + return dataset_ops.Dataset.from_tensor_slices(seq_lens).map( + fill_tuple).apply( + tqd.prepend_from_queue_and_padded_batch_dataset( + batch_size=4, + padded_shapes=(padded_shape, padded_shape), + padding_values=(-1, ""))) + + seq_lens1 = np.random.randint(1, 20, size=(32,)).astype(np.int32) + seq_lens2 = np.random.randint(21, 40, size=(32,)).astype(np.int32) + self.run_core_tests(lambda: build_dataset(seq_lens1), + lambda: build_dataset(seq_lens2), 8) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/core/api_def/base_api/api_def_EnqueueInQueueDataset.pbtxt b/tensorflow/core/api_def/base_api/api_def_EnqueueInQueueDataset.pbtxt new file mode 100644 index 0000000000..9722f5ede3 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_EnqueueInQueueDataset.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "EnqueueInQueueDataset" +} diff --git a/tensorflow/core/api_def/base_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt b/tensorflow/core/api_def/base_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt new file mode 100644 index 0000000000..d4549340fa --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PrependFromQueueAndPaddedBatchDataset" +} diff --git a/tensorflow/core/kernels/batch_util.cc b/tensorflow/core/kernels/batch_util.cc index 7f2df95e2d..87d455faa7 100644 --- a/tensorflow/core/kernels/batch_util.cc +++ b/tensorflow/core/kernels/batch_util.cc @@ -19,6 +19,8 @@ limitations under the License. #include "tensorflow/core/framework/types.h" #include "tensorflow/core/lib/core/errors.h" +#define TF_CALL_DATASET_TYPES(m) TF_CALL_ALL_TYPES(m) TF_CALL_QUANTIZED_TYPES(m) + namespace tensorflow { namespace batch_util { @@ -61,6 +63,21 @@ Status HandleElementToSlice(Tensor element, Tensor* parent, int64 index, return Status::OK(); } +template <> +Status HandleElementToSlice(Tensor element, Tensor* parent, + int64 index, bool can_move) { + auto parent_as_matrix = parent->flat_outer_dims(); + auto element_flat = element.flat(); + if (can_move) { + for (int64 i = 0; i < element.NumElements(); ++i) { + parent_as_matrix(index, i) = std::move(element_flat(i)); + } + } else { + parent_as_matrix.chip(index, 0) = element_flat; + } + return Status::OK(); +} + // TODO(jsimsa): Add HandleElementToSlice specialization that moves // the data when possible. @@ -115,5 +132,101 @@ Status CopySliceToElement(const Tensor& parent, Tensor* element, int64 index) { } } +// The following five functions are copied from padding_fifo_queue.cc. +// TODO(mrry): Reconcile these functions with the similar methods in the +// queue implementation. +Status ValidateElementToLargerSlice(const Tensor& element, Tensor* parent) { + DCHECK_NE(parent->dim_size(0), 0); + if (element.NumElements() > (parent->NumElements() / parent->dim_size(0))) { + TensorShape chip_shape = parent->shape(); + chip_shape.RemoveDim(0); + return errors::Internal( + "HandleElementToLargerSlice Cannot copy slice: number of entries in " + "element is greater than number of elements in parent slice. ", + "Shapes are: [element]: ", element.shape().DebugString(), + ", [parent slice]: ", chip_shape.DebugString()); + } + return Status::OK(); +} + +template +Status HandleElementToLargerSlice(const Tensor& element, Tensor* parent, + int index) { + TF_RETURN_IF_ERROR(ValidateElementToLargerSlice(element, parent)); + if (element.NumElements() == 0) { + return Status::OK(); + } + auto element_t = element.tensor(); + auto parent_t = parent->tensor(); + Eigen::DSizes slice_indices; + slice_indices[0] = index; + Eigen::DSizes slice_size; + slice_size[0] = 1; + for (size_t i = 1; i < slice_size.size(); ++i) { + slice_size[i] = element_t.dimension(i - 1); + } + parent_t.slice(slice_indices, slice_size) = element_t.reshape(slice_size); + return Status::OK(); +} + +template +Status HandleElementToLargerSliceWithRank(const Tensor& element, Tensor* parent, + int index) { +#define HANDLE_TYPE(T) \ + case DataTypeToEnum::value: { \ + return HandleElementToLargerSlice(element, parent, index); \ + } + + switch (element.dtype()) { + TF_CALL_DATASET_TYPES(HANDLE_TYPE); +#undef HANDLE_TYPE + default: + return errors::Unimplemented( + "HandleElementToLargerSliceWithRank Unhandled data type: ", + element.dtype()); + } +} + +Status CopyElementToLargerSlice(const Tensor& element, Tensor* parent, + int index) { + if (parent->dims() != element.dims() + 1) { + return errors::Internal( + "Mismatched ranks. Element's rank is: ", element.dims(), + " but element is meant to be a slice in output Tensor having rank: ", + parent->dims(), " (should be: ", element.dims() + 1, ")"); + } + +#define HANDLE_DIMS(NDIMS) \ + case NDIMS: { \ + TF_RETURN_IF_ERROR( \ + HandleElementToLargerSliceWithRank(element, parent, index)); \ + return Status::OK(); \ + } + + switch (element.dims()) { + HANDLE_DIMS(0); + HANDLE_DIMS(1); + HANDLE_DIMS(2); + HANDLE_DIMS(3); + HANDLE_DIMS(4); +#undef HANDLE_DIMS + default: + return errors::Unimplemented("CopyElementToLargerSlice Unhandled rank: ", + element.dims()); + } +} + +Status SetElementZero(Tensor* element, const Tensor& padding) { +#define HANDLE_TYPE(T) \ + if (element->dtype() == DataTypeToEnum::value) { \ + element->flat().setConstant(padding.scalar()()); \ + return Status::OK(); \ + } + TF_CALL_DATASET_TYPES(HANDLE_TYPE); +#undef HANDLE_TYPE + return errors::Unimplemented("SetElementZero Unhandled data type: ", + element->dtype()); +} + } // namespace batch_util } // namespace tensorflow diff --git a/tensorflow/core/kernels/batch_util.h b/tensorflow/core/kernels/batch_util.h index 0d634ae7b0..a47bf1935d 100644 --- a/tensorflow/core/kernels/batch_util.h +++ b/tensorflow/core/kernels/batch_util.h @@ -32,6 +32,16 @@ Status CopyElementToSlice(Tensor element, Tensor* parent, int64 index); // Copies the index^th slice of parent (in the 0th dimension) into element. Status CopySliceToElement(const Tensor& parent, Tensor* element, int64 index); +// Zero-initializes the tensor `element` using the scalar stored in `padding`. +// Both `element` and `padding` must have matching `dtype`. +Status SetElementZero(Tensor* element, const Tensor& padding); + +// Copies `element` into a (0th dimension) slice of `parent`, assuming +// the shape of `element` is strictly not larger along any axis than a +// slice. +Status CopyElementToLargerSlice(const Tensor& element, Tensor* parent, + int index); + } // namespace batch_util } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/BUILD b/tensorflow/core/kernels/data/BUILD index cdb4023861..c4e21257ff 100644 --- a/tensorflow/core/kernels/data/BUILD +++ b/tensorflow/core/kernels/data/BUILD @@ -121,6 +121,7 @@ tf_kernel_library( "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", + "//tensorflow/core/kernels:batch_util", ], ) @@ -401,6 +402,19 @@ tf_kernel_library( ], ) +tf_kernel_library( + name = "tensor_queue_dataset_op", + srcs = ["tensor_queue_dataset_op.cc"], + deps = [ + ":dataset", + "//tensorflow/core:dataset_ops_op_lib", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//tensorflow/core:lib_internal", + "//tensorflow/core/kernels:batch_util", + ], +) + tf_kernel_library( name = "tensor_slice_dataset_op", srcs = ["tensor_slice_dataset_op.cc"], @@ -539,6 +553,7 @@ tf_kernel_library( ":stats_dataset_ops", ":take_dataset_op", ":tensor_dataset_op", + ":tensor_queue_dataset_op", ":tensor_slice_dataset_op", ":unique_dataset_op", ":zip_dataset_op", diff --git a/tensorflow/core/kernels/data/padded_batch_dataset_op.cc b/tensorflow/core/kernels/data/padded_batch_dataset_op.cc index 4fe4e8e294..cfb4efda9a 100644 --- a/tensorflow/core/kernels/data/padded_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/padded_batch_dataset_op.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_util.h" +#include "tensorflow/core/kernels/batch_util.h" #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { @@ -24,102 +25,6 @@ namespace { // See documentation in ../ops/dataset_ops.cc for a high-level // description of the following op. -// The following five functions are copied from padding_fifo_queue.cc. -// TODO(mrry): Reconcile these functions with the similar methods in the -// queue implementation. -Status ValidateElementToLargerSlice(const Tensor& element, Tensor* parent) { - DCHECK_NE(parent->dim_size(0), 0); - if (element.NumElements() > (parent->NumElements() / parent->dim_size(0))) { - TensorShape chip_shape = parent->shape(); - chip_shape.RemoveDim(0); - return errors::Internal( - "HandleElementToLargerSlice Cannot copy slice: number of entries in " - "element is greater than number of elements in parent slice. ", - "Shapes are: [element]: ", element.shape().DebugString(), - ", [parent slice]: ", chip_shape.DebugString()); - } - return Status::OK(); -} - -template -Status HandleElementToLargerSlice(const Tensor& element, Tensor* parent, - int index) { - TF_RETURN_IF_ERROR(ValidateElementToLargerSlice(element, parent)); - if (element.NumElements() == 0) { - return Status::OK(); - } - auto element_t = element.tensor(); - auto parent_t = parent->tensor(); - Eigen::DSizes slice_indices; - slice_indices[0] = index; - Eigen::DSizes slice_size; - slice_size[0] = 1; - for (size_t i = 1; i < slice_size.size(); ++i) { - slice_size[i] = element_t.dimension(i - 1); - } - parent_t.slice(slice_indices, slice_size) = element_t.reshape(slice_size); - return Status::OK(); -} - -template -Status HandleElementToLargerSliceWithRank(const Tensor& element, Tensor* parent, - int index) { -#define HANDLE_TYPE(T) \ - case DataTypeToEnum::value: { \ - return HandleElementToLargerSlice(element, parent, index); \ - } - - switch (element.dtype()) { - TF_CALL_DATASET_TYPES(HANDLE_TYPE); -#undef HANDLE_TYPE - default: - return errors::Unimplemented( - "HandleElementToLargerSliceWithRank Unhandled data type: ", - element.dtype()); - } -} - -Status CopyElementToLargerSlice(const Tensor& element, Tensor* parent, - int index) { - if (parent->dims() != element.dims() + 1) { - return errors::Internal( - "Mismatched ranks. Element's rank is: ", element.dims(), - " but element is meant to be a slice in output Tensor having rank: ", - parent->dims(), " (should be: ", element.dims() + 1, ")"); - } - -#define HANDLE_DIMS(NDIMS) \ - case NDIMS: { \ - TF_RETURN_IF_ERROR( \ - HandleElementToLargerSliceWithRank(element, parent, index)); \ - return Status::OK(); \ - } - - switch (element.dims()) { - HANDLE_DIMS(0); - HANDLE_DIMS(1); - HANDLE_DIMS(2); - HANDLE_DIMS(3); - HANDLE_DIMS(4); -#undef HANDLE_DIMS - default: - return errors::Unimplemented("CopyElementToLargerSlice Unhandled rank: ", - element.dims()); - } -} - -Status SetElementZero(Tensor* element, const Tensor& padding) { -#define HANDLE_TYPE(T) \ - if (element->dtype() == DataTypeToEnum::value) { \ - element->flat().setConstant(padding.scalar()()); \ - return Status::OK(); \ - } - TF_CALL_DATASET_TYPES(HANDLE_TYPE); -#undef HANDLE_TYPE - return errors::Unimplemented("SetElementZero Unhandled data type: ", - element->dtype()); -} - class PaddedBatchDatasetOp : public UnaryDatasetOpKernel { public: explicit PaddedBatchDatasetOp(OpKernelConstruction* ctx) @@ -379,17 +284,24 @@ class PaddedBatchDatasetOp : public UnaryDatasetOpKernel { Tensor batch_component(ctx->allocator({}), output_dtypes()[component_index], batch_component_shape); - TF_RETURN_IF_ERROR(SetElementZero( + TF_RETURN_IF_ERROR(batch_util::SetElementZero( &batch_component, dataset()->padding_values_[component_index])); // Build the output tuple component by copying one slice // from each input element in the batch. + TensorShape component_shape({}); + for (int i = 1; i < batch_component_shape.dims(); ++i) { + component_shape.AddDim(batch_component_shape.dim_size(i)); + } for (int64 i = 0; i < num_batch_elements; ++i) { - TF_RETURN_IF_ERROR(ValidateElementToLargerSlice( - batch_elements[i][component_index], &batch_component)); - - TF_RETURN_IF_ERROR(CopyElementToLargerSlice( - batch_elements[i][component_index], &batch_component, i)); + // Take the fast path if possible. + if (batch_elements[i][component_index].shape() == component_shape) { + TF_RETURN_IF_ERROR(batch_util::CopyElementToSlice( + batch_elements[i][component_index], &batch_component, i)); + } else { + TF_RETURN_IF_ERROR(batch_util::CopyElementToLargerSlice( + batch_elements[i][component_index], &batch_component, i)); + } } out_tensors->push_back(std::move(batch_component)); } diff --git a/tensorflow/core/kernels/data/tensor_queue_dataset_op.cc b/tensorflow/core/kernels/data/tensor_queue_dataset_op.cc new file mode 100644 index 0000000000..ff412a4671 --- /dev/null +++ b/tensorflow/core/kernels/data/tensor_queue_dataset_op.cc @@ -0,0 +1,646 @@ +/* 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 + +#include "tensorflow/core/framework/partial_tensor_shape.h" +#include "tensorflow/core/framework/resource_mgr.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/variant.h" +#include "tensorflow/core/framework/variant_encode_decode.h" +#include "tensorflow/core/kernels/batch_util.h" +#include "tensorflow/core/kernels/data/dataset.h" + +namespace tensorflow { + +namespace { + +bool IsGreaterEqualToOrCompatibleWith(const PartialTensorShape& a, + const PartialTensorShape& b) { + // Returns true if dims[a] >= dims[b], or are compatible. + if (a.unknown_rank()) return true; + if (a.dims() != b.dims()) return false; + for (int d = 0; d < a.dims(); ++d) { + if (a.dim_size(d) == -1 || b.dim_size(d) == -1) continue; + if (a.dim_size(d) < b.dim_size(d)) return false; + } + return true; +} + +DataTypeVector PrependQueueType(const DataTypeVector& dtypes) { + DataTypeVector out; + out.reserve(dtypes.size() + 1); + out.push_back(DT_VARIANT); // The queue component. + for (const DataType& d : dtypes) out.push_back(d); + return out; +} + +std::vector PrependQueueShapeWithBatch( + const std::vector& shapes) { + std::vector out; + out.reserve(shapes.size() + 1); + out.emplace_back(PartialTensorShape({-1})); // The queue component. + for (PartialTensorShape s : shapes) { + s.InsertDim(0, -1); // Unknown batch size. + out.push_back(std::move(s)); + } + return out; +} + +class EnqueueInQueueDatasetOp; + +class PrependFromQueueAndPaddedBatchDataset : public GraphDatasetBase { + public: + PrependFromQueueAndPaddedBatchDataset( + OpKernelContext* ctx, const int64 batch_size, const DatasetBase* input, + const DataTypeVector& dtypes, + const std::vector& shapes, + std::vector padding_values) + : GraphDatasetBase(ctx), + batch_size_(batch_size), + input_(input), + dtypes_(dtypes), + shapes_(shapes), + padding_values_(std::move(padding_values)), + dtypes_with_queue_(PrependQueueType(dtypes)), + batched_shapes_with_queue_(PrependQueueShapeWithBatch(shapes)) { + input_->Ref(); + } + + ~PrependFromQueueAndPaddedBatchDataset() override { input_->Unref(); } + + std::unique_ptr MakeIterator( + const string& prefix) const override { + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::PrependFromQueueAndPaddedBatch")})); + } + + const DataTypeVector& output_dtypes() const override { + return dtypes_with_queue_; + } + const std::vector& output_shapes() const override { + return batched_shapes_with_queue_; + } + + string DebugString() override { + return "PrependFromQueueAndPaddedBatchDatasetOp::Dataset"; + } + + protected: + Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Node** output) const override { + Node* input_graph = nullptr; + TF_RETURN_IF_ERROR(b->AddParentDataset(ctx, input_, &input_graph)); + Node* batch_size = nullptr; + TF_RETURN_IF_ERROR(b->AddScalar(batch_size_, &batch_size)); + + std::vector padded_shapes; + padded_shapes.reserve(shapes_.size()); + for (int i = 0; i < shapes_.size(); i++) { + Node* node; + Tensor t(DT_INT64, TensorShape({shapes_[i].dims()})); + for (int j = 0; j < shapes_[i].dims(); j++) { + t.vec()(j) = shapes_[i].dim_size(j); + } + TF_RETURN_IF_ERROR(b->AddTensor(t, &node)); + padded_shapes.emplace_back(node); + } + + std::vector padding_values; + padding_values.reserve(padding_values_.size()); + for (const Tensor& t : padding_values_) { + Node* node; + TF_RETURN_IF_ERROR(b->AddTensor(t, &node)); + padding_values.emplace_back(node); + } + + AttrValue output_types; + b->BuildAttrValue(dtypes_, &output_types); + + AttrValue output_shapes; + b->BuildAttrValue(batched_shapes_with_queue_, &output_shapes); + + AttrValue N; + b->BuildAttrValue(shapes_.size(), &N); + + TF_RETURN_IF_ERROR(b->AddDataset(this, {{0, input_graph}, {1, batch_size}}, + {{2, padded_shapes}, {3, padding_values}}, + {{"Toutput_types", output_types}, + {"output_shapes", output_shapes}, + {"N", N}}, + output)); + + return Status::OK(); + } + + private: + friend class EnqueueInQueueDatasetOp; + + class Iterator + : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params), + queue_(new TensorQueue(/*input_impl*/ + params.dataset->input_->MakeIterator( + params.prefix), + params.dataset->dtypes_, + params.dataset->shapes_)) {} + + ~Iterator() override { queue_->Unref(); } + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + std::vector> batch; + TF_RETURN_IF_ERROR(queue_->GetNext(ctx, dataset()->batch_size_, &batch, + end_of_sequence)); + const auto& dtypes = dataset()->dtypes_; + const auto& shapes = dataset()->shapes_; + const auto& input_shapes = dataset()->input_->output_shapes(); + const auto& padding_values = dataset()->padding_values_; + const int64 batch_size = batch.size(); + out_tensors->reserve(dtypes.size()); + + std::vector max_shapes; // Of non-queue components. + for (int i = 0; i < dtypes.size(); ++i) { + const PartialTensorShape& shape = shapes[i]; + TensorShape out_shape({batch_size}); + for (int r = 0; r < shape.dims(); ++r) { + if (shape.dim_size(r) >= 0) { + // padded_shape[r] is known. + out_shape.AddDim(shape.dim_size(r)); + } else { + // padded_shape[r] is unknown, find the maximum across + // the batch. + int64 dim = 0; + for (int b = 0; b < batch.size(); ++b) { + dim = std::max(dim, batch[b][i].dim_size(r)); + } + out_shape.AddDim(dim); + } + } + max_shapes.push_back(std::move(out_shape)); + } + + Tensor queues_t(cpu_allocator(), DT_VARIANT, TensorShape({batch_size})); + if (!batch.empty()) { + auto queues = queues_t.flat(); + Variant& queue_inserter = queues(0); + queue_inserter = TensorQueueInserter(); + queue_inserter.get()->set_queue(queue_); + for (int b = 1; b < batch.size(); ++b) { + // Copy the TensorQueueInserter. Each copy increments the + // Ref on the queue_. + queues(b) = queues(0); + } + } + out_tensors->push_back(std::move(queues_t)); + + for (int i = 0; i < max_shapes.size(); ++i) { + Tensor component(cpu_allocator(), dtypes[i], max_shapes[i]); + // Try hard to take the fast path. + if (shapes[i].IsFullyDefined() && + shapes[i].IsIdenticalTo(input_shapes[i])) { + // Take the fast path if we know all the shapes statically. + for (int64 b = 0; b < batch.size(); ++b) { + TF_RETURN_IF_ERROR( + batch_util::CopyElementToSlice(batch[b][i], &component, b)); + } + } else { + TF_RETURN_IF_ERROR( + batch_util::SetElementZero(&component, padding_values[i])); + for (int64 b = 0; b < batch.size(); ++b) { + if (batch[b][i].shape() == max_shapes[i]) { + TF_RETURN_IF_ERROR( + batch_util::CopyElementToSlice(batch[b][i], &component, b)); + } else { + TF_RETURN_IF_ERROR(batch_util::CopyElementToLargerSlice( + batch[b][i], &component, b)); + } + } + } + out_tensors->push_back(std::move(component)); + } + + // end_of_sequence was set before we populated out_tensors, so + // it's ok to return now. + return Status::OK(); + } + + protected: + // Work around bug in MSVC that disallows access to protected + // members of Iterator from within TensorQueue. + class TensorQueue; + friend class TensorQueue; + + class TensorQueue : public core::RefCounted { + public: + TensorQueue(std::unique_ptr input_impl, + const DataTypeVector& dtypes, + const std::vector& shapes) + : dtypes_(dtypes), + shapes_(shapes), + input_impl_(std::move(input_impl)) {} + + void MaybeWaitForNotificationLocked(mutex_lock* lock) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { + // This essentially just releases the lock and immediately relocks. + cv_.wait_for(*lock, std::chrono::milliseconds(0)); + } + + void NotifyLocked() EXCLUSIVE_LOCKS_REQUIRED(mu_) { cv_.notify_all(); } + + Status GetNext(IteratorContext* ctx, const int64 batch_size, + std::vector>* batch, + bool* end_of_sequence) { + mutex_lock lock(mu_); + + *end_of_sequence = false; + + for (int64 b = 0; b < batch_size;) { + if (!entries_.empty()) { + batch->push_back(std::move(entries_.front())); + entries_.pop_front(); + ++b; + continue; + } else { + if (input_impl_) { + // There's still input coming in. + std::vector tensors; + bool input_end; + TF_RETURN_IF_ERROR( + input_impl_->GetNext(ctx, &tensors, &input_end)); + if (!input_end) { + batch->push_back(std::move(tensors)); + ++b; + continue; + } else { + input_impl_.reset(); + } + } + if (!input_impl_) { + // There's no more input coming in. + if (RefCountIsOne()) { + // No TensorQueueInserters in the wild. + if (batch->empty()) { + *end_of_sequence = true; + } + break; + } else { + MaybeWaitForNotificationLocked(&lock); + // If there's data available, try to add entries again. + // Otherwise return a smaller batch and hope the next + // iterator request has a non-empty or unused queue_. + if (entries_.empty()) { + break; + } + } + } + } + } // for (int64 b = ... batch_size) + return Status::OK(); + } + + Status Insert(const std::vector& tensors) { + if (tensors.size() != dtypes_.size()) { + return errors::InvalidArgument( + "TensorQueue::Insert: mismatched number of tensors. Queue " + "expects ", + dtypes_.size(), " tensors but tried to insert ", tensors.size()); + } + for (int i = 0; i < tensors.size(); ++i) { + if (tensors[i].dtype() != dtypes_[i]) { + return errors::InvalidArgument( + "TensorQueue::Insert: mismatched dtypes at component ", i, + ". Attempted " + "to insert tensor of type ", + DataTypeString(tensors[i].dtype()), + " but queue expected type: ", DataTypeString(dtypes_[i])); + } + if (!shapes_[i].IsCompatibleWith(tensors[i].shape())) { + return errors::InvalidArgument( + "TensorQueue::Insert: mismatched shapes at component ", i, + ". Attempted " + "to insert tensor with shape ", + tensors[i].shape().DebugString(), + " but queue expected shape: ", shapes_[i].DebugString()); + } + } + mutex_lock lock(mu_); + entries_.push_back(tensors); + NotifyLocked(); + return Status::OK(); + } + + Status Save(Iterator* iter, IteratorStateWriter* writer) { + mutex_lock lock(mu_); + if (input_impl_) { + TF_RETURN_IF_ERROR(iter->SaveParent(writer, input_impl_)); + } else { + TF_RETURN_IF_ERROR( + writer->WriteScalar(iter->full_name("input_exhausted"), "")); + } + TF_RETURN_IF_ERROR(writer->WriteScalar(iter->full_name("entries_size"), + entries_.size())); + for (int64 b = 0; b < entries_.size(); ++b) { + for (int i = 0; i < dtypes_.size(); ++i) { + TF_RETURN_IF_ERROR( + writer->WriteTensor(strings::StrCat(iter->full_name("entries"), + "[", b, "][", i, "]"), + entries_[b][i])); + } + } + return Status::OK(); + } + + Status Restore(Iterator* iter, IteratorContext* ctx, + IteratorStateReader* reader) { + mutex_lock l(mu_); + if (reader->Contains(iter->full_name("input_exhausted"))) { + input_impl_.reset(); + } else { + input_impl_ = iter->dataset_input()->MakeIterator(iter->prefix()); + TF_RETURN_IF_ERROR(iter->RestoreParent(ctx, reader, input_impl_)); + } + entries_.clear(); + int64 entries_size = -1; + TF_RETURN_IF_ERROR( + reader->ReadScalar(iter->full_name("entries_size"), &entries_size)); + if (entries_size < 0) { + return errors::DataLoss( + "Expected entries_size key '", iter->full_name("entries_size"), + "' to have nonnegative value, but saw: ", entries_size); + } + for (int64 b = 0; b < entries_size; ++b) { + std::vector entry; + for (int i = 0; i < dtypes_.size(); ++i) { + Tensor value; + TF_RETURN_IF_ERROR( + reader->ReadTensor(strings::StrCat(iter->full_name("entries"), + "[", b, "][", i, "]"), + &value)); + entry.push_back(std::move(value)); + } + entries_.push_back(std::move(entry)); + } + return Status::OK(); + } + + mutex* mu() { return &mu_; } + + private: + DataTypeVector dtypes_; + std::vector shapes_; + + mutex mu_; + std::unique_ptr input_impl_ GUARDED_BY(mu_); + std::deque> entries_ GUARDED_BY(mu_); + condition_variable cv_ GUARDED_BY(mu_); + }; + + const DatasetBase* dataset_input() const { return dataset()->input_; } + + Status SaveInternal(IteratorStateWriter* writer) override { + return queue_->Save(this, writer); + } + + Status RestoreInternal(IteratorContext* ctx, + IteratorStateReader* reader) override { + return queue_->Restore(this, ctx, reader); + } + + public: + class TensorQueueInserter { + public: + TensorQueueInserter() : queue_(nullptr) {} + + void set_queue(TensorQueue* queue) { + queue_ = queue; + queue_->Ref(); + } + + TensorQueueInserter(const TensorQueueInserter& rhs) { + queue_ = rhs.queue_; + queue_->Ref(); + }; + + TensorQueueInserter(TensorQueueInserter&& rhs) { + queue_ = rhs.queue_; + rhs.queue_ = nullptr; + } + + TensorQueueInserter& operator=(const TensorQueueInserter& rhs) = delete; + + string TypeName() const { return "tensorflow::TensorQueueInserter"; } + string DebugString() const { return TypeName(); } + + void Encode(VariantTensorData*) const {} + bool Decode(const VariantTensorData&) { return false; } + + ~TensorQueueInserter() { + if (queue_) { + mutex_lock lock(*queue_->mu()); + queue_->Unref(); + queue_->NotifyLocked(); + queue_ = nullptr; + } + } + + Status Insert(const std::vector& tensors) const { + CHECK(queue_); + return queue_->Insert(tensors); + } + + private: + mutable TensorQueue* queue_; + }; + + private: + TensorQueue* const queue_; + }; + + private: + const int64 batch_size_; + const DatasetBase* input_; + const DataTypeVector dtypes_; + const std::vector shapes_; + const std::vector padding_values_; + const DataTypeVector dtypes_with_queue_; + const std::vector batched_shapes_with_queue_; +}; + +class PrependFromQueueAndPaddedBatchDatasetOp : public UnaryDatasetOpKernel { + public: + explicit PrependFromQueueAndPaddedBatchDatasetOp(OpKernelConstruction* ctx) + : UnaryDatasetOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("Toutput_types", &output_types_)); + } + + void MakeDataset(OpKernelContext* ctx, DatasetBase* input, + DatasetBase** output) override { + int64 batch_size = 0; + OP_REQUIRES_OK(ctx, + ParseScalarArgument(ctx, "batch_size", &batch_size)); + OP_REQUIRES( + ctx, batch_size > 0, + errors::InvalidArgument("Batch size must be greater than zero.")); + + OpInputList padded_shape_tensors; + OP_REQUIRES_OK(ctx, + ctx->input_list("padded_shapes", &padded_shape_tensors)); + std::vector padded_shapes; + padded_shapes.reserve(padded_shape_tensors.size()); + OP_REQUIRES(ctx, + padded_shape_tensors.size() == input->output_shapes().size(), + errors::InvalidArgument("Number of padded shapes (", + padded_shape_tensors.size(), + ") must match the number of components " + "in the input dataset's elements (", + input->output_shapes().size(), ")")); + for (const Tensor& padded_shape_t : padded_shape_tensors) { + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(padded_shape_t.shape()), + errors::InvalidArgument("All padded shapes must be vectors")); + PartialTensorShape padded_shape; + OP_REQUIRES_OK(ctx, PartialTensorShape::MakePartialShape( + padded_shape_t.vec().data(), + padded_shape_t.NumElements(), &padded_shape)); + padded_shapes.push_back(std::move(padded_shape)); + } + + OP_REQUIRES( + ctx, input->output_dtypes() == output_types_, + errors::InvalidArgument("Input dataset and this dataset " + "have different output_types: ", + DataTypeVectorString(input->output_dtypes()), + " and ", DataTypeVectorString(output_types_))); + + for (int i = 0; i < input->output_shapes().size(); ++i) { + // Exclude the queue from the tensor_shapes calculation. + const PartialTensorShape& tensor_shape = padded_shapes[i]; + OP_REQUIRES( + ctx, + IsGreaterEqualToOrCompatibleWith(tensor_shape, + input->output_shapes()[i]), + errors::InvalidArgument("Incompatible input shapes at component ", i, + " between input dataset this dataset: ", + input->output_shapes()[i].DebugString(), + " vs. ", tensor_shape.DebugString())); + } + + OpInputList padding_values_list; + OP_REQUIRES_OK(ctx, + ctx->input_list("padding_values", &padding_values_list)); + std::vector padding_values; + OP_REQUIRES(ctx, + padding_values_list.size() == input->output_shapes().size(), + errors::InvalidArgument( + "Number of padding values (", padding_values_list.size(), + ") must match the number of components in the input " + "dataset's elements (", + input->output_shapes().size(), ")")); + for (int i = 0; i < padding_values_list.size(); ++i) { + const Tensor& padding_value_t = padding_values_list[i]; + OP_REQUIRES( + ctx, TensorShapeUtils::IsScalar(padding_value_t.shape()), + errors::InvalidArgument( + "All padding values must be scalars; but at component ", i, + " saw shape: ", padding_value_t.shape().DebugString())); + OP_REQUIRES(ctx, padding_value_t.dtype() == input->output_dtypes()[i], + errors::InvalidArgument( + "Mismatched type between padding value ", i, + " and input dataset's component ", i, ": ", + DataTypeString(padding_value_t.dtype()), " vs. ", + DataTypeString(input->output_dtypes()[i]))); + padding_values.push_back(padding_value_t); + } + + *output = new PrependFromQueueAndPaddedBatchDataset( + ctx, batch_size, input, output_types_, padded_shapes, + std::move(padding_values)); + } + + private: + DataTypeVector output_types_; +}; + +REGISTER_KERNEL_BUILDER( + Name("PrependFromQueueAndPaddedBatchDataset").Device(DEVICE_CPU), + PrependFromQueueAndPaddedBatchDatasetOp); + +class EnqueueInQueueDatasetOp : public OpKernel { + public: + explicit EnqueueInQueueDatasetOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} + void Compute(OpKernelContext* ctx) override { + using TensorQueueInserter = + PrependFromQueueAndPaddedBatchDataset::Iterator::TensorQueueInserter; + + // TODO(ebrevdo): accept list of sequence lengths to do proper + // sub-slicing of tensors for placement into the queue? + const Tensor& tensor_queue_t = ctx->input(0); + OP_REQUIRES(ctx, TensorShapeUtils::IsVector(tensor_queue_t.shape()), + errors::InvalidArgument("queue must be a vector, saw shape: ", + tensor_queue_t.shape().DebugString())); + std::vector inserters; + const int64 batch_size = tensor_queue_t.NumElements(); + inserters.reserve(batch_size); + const Variant* variants = tensor_queue_t.flat().data(); + for (int i = 0; i < batch_size; ++i) { + const auto* inserter = variants[i].get(); + OP_REQUIRES(ctx, inserter != nullptr, + errors::InvalidArgument( + "Could not access TensorQueueInserter from queue[", i, + "]. Received variant: ", variants[i].DebugString())); + inserters.push_back(inserter); + } + + OpInputList components; + OP_REQUIRES_OK(ctx, ctx->input_list("components", &components)); + for (int i = 0; i < components.size(); ++i) { + OP_REQUIRES( + ctx, + components[i].dims() > 0 && components[i].dim_size(0) == batch_size, + errors::InvalidArgument( + "Expected component ", i, " to have batched shape [", batch_size, + ",...], but saw shape: ", components[i].shape().DebugString())); + } + std::vector element_shapes; + for (int i = 0; i < components.size(); ++i) { + TensorShape element_shape = components[i].shape(); + element_shape.RemoveDim(0); + element_shapes.push_back(std::move(element_shape)); + } + for (int64 b = 0; b < batch_size; ++b) { + std::vector tensors; + tensors.reserve(components.size()); + for (int i = 0; i < components.size(); ++i) { + Tensor t(components[i].dtype(), element_shapes[i]); + OP_REQUIRES_OK(ctx, + batch_util::CopySliceToElement(components[i], &t, b)); + tensors.push_back(std::move(t)); + } + // TODO(ebrevdo): Acquire the lock once for all inserters with + // the same underlying queue? Add InsertLocked? + OP_REQUIRES_OK(ctx, inserters[b]->Insert(tensors)); + } + } +}; + +REGISTER_KERNEL_BUILDER(Name("EnqueueInQueueDataset").Device(DEVICE_CPU), + EnqueueInQueueDatasetOp); + +} // namespace + +} // namespace tensorflow diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc index d6cbcf1d93..0a38d3d4af 100644 --- a/tensorflow/core/kernels/gather_op.cc +++ b/tensorflow/core/kernels/gather_op.cc @@ -18,6 +18,8 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/variant.h" +#include "tensorflow/core/framework/variant_encode_decode.h" #include "tensorflow/core/kernels/bounds_check.h" #include "tensorflow/core/kernels/gather_functor.h" #include "tensorflow/core/platform/mem.h" @@ -141,6 +143,7 @@ TF_CALL_ALL_TYPES(REGISTER_GATHER_CPU); TF_CALL_QUANTIZED_TYPES(REGISTER_GATHER_CPU); TF_CALL_quint16(REGISTER_GATHER_CPU); TF_CALL_qint16(REGISTER_GATHER_CPU); +TF_CALL_variant(REGISTER_GATHER_CPU); #undef REGISTER_GATHER_CPU diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc index 7745effe2a..e0b85c6d06 100644 --- a/tensorflow/core/kernels/strided_slice_op.cc +++ b/tensorflow/core/kernels/strided_slice_op.cc @@ -391,6 +391,7 @@ class StridedSliceAssignOp : public OpKernel { StridedSliceAssignOp) TF_CALL_ALL_TYPES(REGISTER_STRIDED_SLICE); +TF_CALL_variant(REGISTER_STRIDED_SLICE); #undef REGISTER_STRIDED_SLICE diff --git a/tensorflow/core/kernels/strided_slice_op_impl.h b/tensorflow/core/kernels/strided_slice_op_impl.h index ac1259a9ac..c3187e49ce 100644 --- a/tensorflow/core/kernels/strided_slice_op_impl.h +++ b/tensorflow/core/kernels/strided_slice_op_impl.h @@ -26,6 +26,8 @@ limitations under the License. #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/register_types_traits.h" #include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/variant.h" +#include "tensorflow/core/framework/variant_encode_decode.h" #include "tensorflow/core/kernels/bounds_check.h" #include "tensorflow/core/kernels/dense_update_functor.h" #include "tensorflow/core/kernels/ops_util.h" @@ -288,6 +290,7 @@ DECLARE_FOR_N_GPU(int64); #endif // END GOOGLE_CUDA TF_CALL_ALL_TYPES(DECLARE_FOR_N_CPU); +TF_CALL_variant(DECLARE_FOR_N_CPU); #ifdef TENSORFLOW_USE_SYCL #define PREVENT_FOR_N_SYCL(T) \ diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index 2cae814eab..3c8e9a8a5f 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -491,4 +491,29 @@ REGISTER_OP("StatsAggregatorSummary") .Output("summary: string") .SetShapeFn(shape_inference::ScalarShape); +REGISTER_OP("PrependFromQueueAndPaddedBatchDataset") + .Input("input_dataset: variant") + .Input("batch_size: int64") + .Input("padded_shapes: N * int64") + .Input("padding_values: Toutput_types") + .Output("handle: variant") + .Attr("Toutput_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .Attr("N: int >= 1") + // TODO(ebrevdo): Validate that `padded_shapes` are all vectors, the lengths + // of `Toutput_types` and `output_shapes` are `N`, that the + // length of `output_types` is `N`, the `output_shapes` are + // (as far as possible to tell statically) compatible with `padded_shapes`, + // and that `padding_values` are all scalars. + .SetShapeFn(shape_inference::ScalarShape); + +REGISTER_OP("EnqueueInQueueDataset") + .Input("queue: variant") + .Input("components: Tcomponents") + .Attr("Tcomponents: list(type) >= 1") + .SetIsStateful() // To avoid CSE on multiple calls to Enqueue. + // TODO(ebrevdo): SetShapeFn to test input dtypes and shapes by + // reading from queue handle (is that even possible?). + .SetShapeFn(shape_inference::NoOutputs); + } // namespace tensorflow diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index f8798c1d6f..5d318531d5 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -1456,6 +1456,19 @@ def _padding_value_to_tensor(value, output_type): return value +def _default_padding(input_dataset): + + def make_zero(t): + if t.base_dtype == dtypes.string: + return "" + elif t.base_dtype == dtypes.variant: + raise TypeError("Unable to create padding for field of type 'variant'") + else: + return np.zeros_like(t.as_numpy_dtype()) + + return nest.map_structure(make_zero, input_dataset.output_types) + + class PaddedBatchDataset(Dataset): """A `Dataset` that batches and pads contiguous elements from its input.""" @@ -1471,23 +1484,13 @@ class PaddedBatchDataset(Dataset): batch_size, dtype=dtypes.int64, name="batch_size") padding_values = ( padding_values - if padding_values is not None else self._default_padding(input_dataset)) + if padding_values is not None else _default_padding(input_dataset)) self._padded_shapes = nest.map_structure_up_to( input_dataset.output_shapes, _partial_shape_to_tensor, padded_shapes) self._padding_values = nest.map_structure_up_to( input_dataset.output_shapes, _padding_value_to_tensor, padding_values, input_dataset.output_types) - def _default_padding(self, input_dataset): - - def make_zero(t): - if t.base_dtype == dtypes.string: - return "" - else: - return np.zeros_like(t.as_numpy_dtype()) - - return nest.map_structure(make_zero, input_dataset.output_types) - def _as_variant_tensor(self): return gen_dataset_ops.padded_batch_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access -- GitLab From f5d40430cb48aa73632f5c45dc4229f530b7404b Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Thu, 1 Feb 2018 11:10:54 -0700 Subject: [PATCH 367/423] Fix for mkl_input conversion for MKL DNN. Fix also enables elemenwise operations. (#16557) --- tensorflow/core/graph/mkl_layout_pass.cc | 14 ++--- .../core/kernels/mkl_input_conversion_op.cc | 56 ++++++++++++++++--- 2 files changed, 56 insertions(+), 14 deletions(-) diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index 18399bb0ac..0e8a1cb26c 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -2452,9 +2452,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // NOTE: names are alphabetically sorted. rinfo_.push_back({csinfo_.addn, mkl_op_registry::GetMklOpName(csinfo_.addn), CopyAttrsAddN, AddNRewrite}); - /* rinfo_.push_back({csinfo_.add, + rinfo_.push_back({csinfo_.add, mkl_op_registry::GetMklOpName(csinfo_.add), - CopyAttrsDataType, AlwaysRewrite}); */ + CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.avg_pool, mkl_op_registry::GetMklOpName(csinfo_.avg_pool), CopyAttrsPooling, AlwaysRewrite}); @@ -2502,15 +2502,15 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.max_pool_grad, mkl_op_registry::GetMklOpName(csinfo_.max_pool_grad), CopyAttrsPooling, AlwaysRewrite}); - /* + rinfo_.push_back({csinfo_.maximum, mkl_op_registry::GetMklOpName(csinfo_.maximum), CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.mul, mkl_op_registry::GetMklOpName(csinfo_.mul), CopyAttrsDataType, AlwaysRewrite}); - */ - rinfo_.push_back({csinfo_.relu, mkl_op_registry::GetMklOpName(csinfo_.relu), + rinfo_.push_back({csinfo_.relu, + mkl_op_registry::GetMklOpName(csinfo_.relu), CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.relu_grad, mkl_op_registry::GetMklOpName(csinfo_.relu_grad), @@ -2529,14 +2529,14 @@ class MklLayoutRewritePass : public GraphOptimizationPass { rinfo_.push_back({csinfo_.softmax, mkl_op_registry::GetMklOpName(csinfo_.softmax), CopyAttrsDataType, AlwaysRewrite}); - /* + rinfo_.push_back({csinfo_.squared_difference, mkl_op_registry::GetMklOpName(csinfo_.squared_difference), CopyAttrsDataType, AlwaysRewrite}); rinfo_.push_back({csinfo_.sub, mkl_op_registry::GetMklOpName(csinfo_.sub), CopyAttrsDataType, AlwaysRewrite}); - */ + // Add info about which ops to add workspace edge to and the slots. wsinfo_.push_back({csinfo_.lrn, csinfo_.lrn_grad, 0, 2, 1, 3}); diff --git a/tensorflow/core/kernels/mkl_input_conversion_op.cc b/tensorflow/core/kernels/mkl_input_conversion_op.cc index 4337e4b49e..acb0db57b3 100644 --- a/tensorflow/core/kernels/mkl_input_conversion_op.cc +++ b/tensorflow/core/kernels/mkl_input_conversion_op.cc @@ -293,14 +293,56 @@ class MklInputConversionOp : public OpKernel { // - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - // If both inputs are in MKL format if (input_shape_0.IsMklTensor() && input_shape_1.IsMklTensor()) { - // If both have the same shape, pass them through if (tf_shapes_are_same) { - VLOG(1) << "MklInputConversionOp: No conversion needed, " - << "copying MKL inputs with identical shapes to output"; - - ForwardMklTensorInToOut(context, 0, 0); - ForwardMklTensorInToOut(context, 1, 1); - return; + auto input0_md = input_shape_0.GetMklLayout(); + auto input1_md = input_shape_1.GetMklLayout(); + + // If both have the same shape and same format, pass them through + if ( input0_md.data.format == input1_md.data.format) { + VLOG(1) << "MklInputConversionOp: No conversion needed, " + << "copying MKL inputs with identical shapes to output"; + + ForwardMklTensorInToOut(context, 0, 0); + ForwardMklTensorInToOut(context, 1, 1); + return; + } else { + VLOG(1) << "MklInputConversionOp: Shape is same, but format is different, " + << "need to convert to same format"; + + // Convert input0, and keep input1 unchanged + // Create MklDnnShape for output mkl tensor based on input0 + Tensor* tensor_out; + MklDnnShape mkl_output_mkl_shape; + mkl_output_mkl_shape.SetMklTensor(true); + mkl_output_mkl_shape.SetElemType(MklDnnType()); + mkl_output_mkl_shape.SetTfLayout(input_shape_0.GetDimension(), + input_shape_0.GetSizesAsMklDnnDims(), + input_shape_0.GetTfDataFormat()); + + // Get MKL layout from input1 as destination layout + mkl_output_mkl_shape.SetMklLayout(&input1_md); + + // Create output Mkl tensor for index 0 + AllocateOutputSetMklShape(context, 0, &tensor_out, + input_tensor_0.shape(), mkl_output_mkl_shape); + + // Create MklDnnData object for input0 tesnsor + auto cpu_engine = engine(engine::cpu, 0); + MklDnnData input(&cpu_engine); + input.SetUsrMem(input0_md, &input_tensor_0); + + // Create reorder from input0's layout to input1's layout + std::vector net; + CHECK_EQ(input.CheckReorderToOpMem(memory::primitive_desc( + input1_md, cpu_engine), + tensor_out, &net), + true); + stream(stream::kind::eager).submit(net).wait(); + + // Input1 will be passed through + ForwardMklTensorInToOut(context, 1, 1); + return; + } } // Sanity check -- GitLab From 0036ba86db170fefe3ad953d19b2bc7291da0bad Mon Sep 17 00:00:00 2001 From: Loo Rong Jie Date: Fri, 2 Feb 2018 02:11:25 +0800 Subject: [PATCH 368/423] Clang on Windows will define __BYTE_ORDER__ etc. for us (#16492) --- tensorflow/core/platform/windows/cpu_info.h | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/core/platform/windows/cpu_info.h b/tensorflow/core/platform/windows/cpu_info.h index d6e78dbc8f..f20939d3c0 100644 --- a/tensorflow/core/platform/windows/cpu_info.h +++ b/tensorflow/core/platform/windows/cpu_info.h @@ -22,8 +22,10 @@ limitations under the License. // Byte order defines provided by gcc. MSVC doesn't define those so // we define them here. // We assume that all windows platform out there are little endian. +#if defined(_MSC_VER) && !defined(__clang__) #define __ORDER_LITTLE_ENDIAN__ 0x4d2 #define __ORDER_BIG_ENDIAN__ 0x10e1 #define __BYTE_ORDER__ __ORDER_LITTLE_ENDIAN__ +#endif #endif // TENSORFLOW_PLATFORM_WINDOWS_CPU_INFO_H_ -- GitLab From 2ce77a160fe8fb99a951fbe51570507dc6ad05a6 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 10:12:34 -0800 Subject: [PATCH 369/423] Fix do_cmake_python_sanity error. (#16650) * Fix do_cmake_python_sanity error. * Update python_modules.txt --- tensorflow/contrib/cmake/python_modules.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 9ce8b3cc9c..914c4124c3 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -216,6 +216,8 @@ tensorflow/contrib/input_pipeline/python/ops tensorflow/contrib/integrate tensorflow/contrib/integrate/python tensorflow/contrib/integrate/python/ops +tensorflow/contrib/kafka/python +tensorflow/contrib/kafka/python/ops tensorflow/contrib/keras tensorflow/contrib/keras/api tensorflow/contrib/keras/api/keras -- GitLab From 5be3f234fc83a4e5533b1f2eeb4cf5fd325b3329 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 1 Feb 2018 10:20:31 -0800 Subject: [PATCH 370/423] Address sanity build issues. (#16647) * Address sanity build issues. * More fixes. * Final pylint fixes. --- .../eval/python/classifier_metrics_impl.py | 2 +- .../kafka/python/ops/kafka_dataset_ops.py | 3 +- .../contrib/layers/python/layers/layers.py | 16 ++-- .../layers/python/layers/layers_test.py | 3 +- .../python/learn/datasets/synthetic_test.py | 2 +- tensorflow/contrib/ndlstm/python/lstm1d.py | 2 +- tensorflow/contrib/py2tf/impl/api.py | 4 +- .../python/kernel_tests/core_rnn_cell_test.py | 2 +- .../rnn/python/kernel_tests/rnn_cell_test.py | 91 ------------------- .../contrib/session_bundle/bundle_shim.py | 12 ++- .../pip_package/cloud_tpu_profiler/main.py | 22 +++-- .../core/platform/default/build_config.bzl | 6 -- tensorflow/python/data/ops/dataset_ops.py | 5 +- tensorflow/python/data/util/nest.py | 4 +- .../python/kernel_tests/tensordot_op_test.py | 2 +- tensorflow/python/ops/image_ops_impl.py | 12 ++- tensorflow/tools/ci_build/ci_sanity.sh | 4 +- 17 files changed, 55 insertions(+), 137 deletions(-) diff --git a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py index 7bede4e240..d9b07e62f8 100644 --- a/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py +++ b/tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py @@ -206,7 +206,7 @@ def get_graph_def_from_url_tarball(url, filename, tar_filename=None): sys.stdout.write('\r>> Downloading %s %.1f%%' % ( url, float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() - tar_filename, _ = urllib.request.urlretrieve(url, filename=tar_filename, reporthook=_progress) + tar_filename, _ = urllib.request.urlretrieve(url, tar_filename, _progress) with tarfile.open(tar_filename, 'r:gz') as tar: proto_str = tar.extractfile(filename).read() return graph_pb2.GraphDef.FromString(proto_str) diff --git a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py index 6590d86ebb..e561f595a4 100644 --- a/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py +++ b/tensorflow/contrib/kafka/python/ops/kafka_dataset_ops.py @@ -30,7 +30,8 @@ class KafkaDataset(Dataset): """A Kafka Dataset that consumes the message. """ - def __init__(self, topics, servers="localhost", group="", eof=False, timeout=1000): + def __init__( + self, topics, servers="localhost", group="", eof=False, timeout=1000): """Create a KafkaReader. Args: diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index c8e3307ee8..fb7b2e315e 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -60,12 +60,12 @@ __all__ = [ 'conv2d_in_plane', 'conv2d_transpose', 'conv3d_transpose', 'convolution', 'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose', 'convolution3d', 'convolution3d_transpose', 'dense_to_sparse', - 'dropout', 'elu', 'flatten', - 'fully_connected', 'GDN', 'gdn', 'layer_norm', 'linear', 'pool', - 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6', 'repeat', - 'scale_gradient', 'separable_conv2d', 'separable_convolution2d', 'softmax', - 'spatial_softmax', 'stack', 'unit_norm', 'legacy_fully_connected', - 'legacy_linear', 'legacy_relu', 'maxout' + 'dropout', 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'layer_norm', + 'linear', 'pool', 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', + 'relu6', 'repeat', 'scale_gradient', 'separable_conv2d', + 'separable_convolution2d', 'softmax', 'spatial_softmax', 'stack', + 'unit_norm', 'legacy_fully_connected', 'legacy_linear', 'legacy_relu', + 'maxout' ] DATA_FORMAT_NCHW = 'NCHW' @@ -1418,7 +1418,9 @@ def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None): with variable_scope.variable_scope( scope, 'dense_to_sparse', [tensor]) as sc: tensor = ops.convert_to_tensor(tensor) - indices = array_ops.where(math_ops.not_equal(tensor, constant_op.constant(eos_token, tensor.dtype))) + indices = array_ops.where( + math_ops.not_equal( + tensor, constant_op.constant(eos_token, tensor.dtype))) values = array_ops.gather_nd(tensor, indices) shape = array_ops.shape(tensor, out_type=dtypes.int64) outputs = sparse_tensor.SparseTensor(indices, values, shape) diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index f05f632246..8945690db8 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -1308,7 +1308,8 @@ class DenseToSparseTest(test.TestCase): expected_constant = np.reshape(np.arange(24, dtype=np.int64), (3, 4, 2)) tensor = constant_op.constant(expected_constant) sparse = _layers.dense_to_sparse(tensor) - dense = sparse_ops.sparse_to_dense(sparse.indices, sparse.dense_shape, sparse.values) + dense = sparse_ops.sparse_to_dense( + sparse.indices, sparse.dense_shape, sparse.values) with self.test_session() as sess: constant = sess.run(dense) self.assertAllEqual(expected_constant, constant) diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py index 19791d7759..5809995c8c 100644 --- a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py +++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py @@ -136,7 +136,7 @@ class SyntheticTest(test.TestCase): self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data) - def test_spirals(self): + def test_spirals_synthetic(self): synthetic.spirals(3) diff --git a/tensorflow/contrib/ndlstm/python/lstm1d.py b/tensorflow/contrib/ndlstm/python/lstm1d.py index b24e332e4a..2e2e9086c0 100644 --- a/tensorflow/contrib/ndlstm/python/lstm1d.py +++ b/tensorflow/contrib/ndlstm/python/lstm1d.py @@ -88,7 +88,7 @@ def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False): if reverse: inputs = array_ops.reverse_v2(inputs, [0]) outputs, _ = rnn.dynamic_rnn( - lstm_cell, inputs, time_major=True, dtype=inputs.dtype) + lstm_cell, inputs, time_major=True, dtype=inputs.dtype) if reverse: outputs = array_ops.reverse_v2(outputs, [0]) return outputs diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py index 4b8cf0527a..85d40f3158 100644 --- a/tensorflow/contrib/py2tf/impl/api.py +++ b/tensorflow/contrib/py2tf/impl/api.py @@ -86,8 +86,8 @@ def convert_inline(f, *args, **kwargs): def convert(recursive=False, arg_types=None): """Decorator that compiles a function to graph mode. - The decorator is dynamic - invoking compilation whenever the decorated function - is called. This means the parameter values are known at compilation. + The decorator is dynamic - invoking compilation whenever the decorated + function is called. This means the parameter values are known at compilation. Args: recursive: Whether to recusrively convert any functions that the decorator diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index 59d7b2466c..9b84635e85 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -157,7 +157,7 @@ class RNNCellTest(test.TestCase): m.name: np.array([[0.1, 0.1]]) }) # Smoke test - self.assertAllClose(res[0], [[0.509682, 0.509682]]) + self.assertAllClose(res[0], [[0.509682, 0.509682]]) def testSRUCellWithDiffSize(self): with self.test_session() as sess: diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py index 8a3894ef9d..7b883ebc5d 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py @@ -1545,97 +1545,6 @@ class BenchmarkLSTMCellXLA(test.Benchmark): ])) -class WeightNormLSTMCellTest(test.TestCase): - """Compared cell output with pre-calculated values.""" - - def _cell_output(self, cell): - """Calculate cell output""" - - with self.test_session() as sess: - init = init_ops.constant_initializer(0.5) - with variable_scope.variable_scope("root", initializer=init): - x = array_ops.zeros([1, 2]) - c0 = array_ops.zeros([1, 2]) - h0 = array_ops.zeros([1, 2]) - - state0 = rnn_cell.LSTMStateTuple(c0, h0) - - xout, sout = cell()(x, state0) - - sess.run([variables.global_variables_initializer()]) - res = sess.run( - [xout, sout], { - x.name: np.array([[1., 1.]]), - c0.name: 0.1 * np.asarray([[0, 1]]), - h0.name: 0.1 * np.asarray([[2, 3]]), - }) - - actual_state_c = res[1].c - actual_state_h = res[1].h - - return actual_state_c, actual_state_h - - def testBasicCell(self): - """Tests cell w/o peepholes and w/o normalisation""" - - def cell(): - return contrib_rnn_cell.WeightNormLSTMCell( - 2, norm=False, use_peepholes=False) - - actual_c, actual_h = self._cell_output(cell) - - expected_c = np.array([[0.65937078, 0.74983585]]) - expected_h = np.array([[0.44923624, 0.49362513]]) - - self.assertAllClose(expected_c, actual_c, 1e-5) - self.assertAllClose(expected_h, actual_h, 1e-5) - - def testNonbasicCell(self): - """Tests cell with peepholes and w/o normalisation""" - - def cell(): - return contrib_rnn_cell.WeightNormLSTMCell( - 2, norm=False, use_peepholes=True) - - actual_c, actual_h = self._cell_output(cell) - - expected_c = np.array([[0.65937084, 0.7574988]]) - expected_h = np.array([[0.4792085, 0.53470564]]) - - self.assertAllClose(expected_c, actual_c, 1e-5) - self.assertAllClose(expected_h, actual_h, 1e-5) - - def testBasicCellWithNorm(self): - """Tests cell w/o peepholes and with normalisation""" - - def cell(): - return contrib_rnn_cell.WeightNormLSTMCell( - 2, norm=True, use_peepholes=False) - - actual_c, actual_h = self._cell_output(cell) - - expected_c = np.array([[0.50125383, 0.58805949]]) - expected_h = np.array([[0.32770363, 0.37397948]]) - - self.assertAllClose(expected_c, actual_c, 1e-5) - self.assertAllClose(expected_h, actual_h, 1e-5) - - def testNonBasicCellWithNorm(self): - """Tests cell with peepholes and with normalisation""" - - def cell(): - return contrib_rnn_cell.WeightNormLSTMCell( - 2, norm=True, use_peepholes=True) - - actual_c, actual_h = self._cell_output(cell) - - expected_c = np.array([[0.50125383, 0.59587258]]) - expected_h = np.array([[0.35041603, 0.40873795]]) - - self.assertAllClose(expected_c, actual_c, 1e-5) - self.assertAllClose(expected_h, actual_h, 1e-5) - - class WeightNormLSTMCellTest(test.TestCase): """Compared cell output with pre-calculated values.""" diff --git a/tensorflow/contrib/session_bundle/bundle_shim.py b/tensorflow/contrib/session_bundle/bundle_shim.py index 3149875e41..69db594f8a 100644 --- a/tensorflow/contrib/session_bundle/bundle_shim.py +++ b/tensorflow/contrib/session_bundle/bundle_shim.py @@ -82,7 +82,8 @@ def _convert_default_signature_to_signature_def(signatures): """ default_signature = signatures.default_signature signature_def = meta_graph_pb2.SignatureDef() - if default_signature.WhichOneof("type") == legacy_constants.REGRESSION_SIGNATURE: + if (default_signature.WhichOneof("type") == + legacy_constants.REGRESSION_SIGNATURE): regression_signature = default_signature.regression_signature signature_def.method_name = signature_constants.REGRESS_METHOD_NAME _add_input_to_signature_def(regression_signature.input.tensor_name, @@ -91,7 +92,8 @@ def _convert_default_signature_to_signature_def(signatures): _add_output_to_signature_def(regression_signature.output.tensor_name, signature_constants.REGRESS_OUTPUTS, signature_def) - elif default_signature.WhichOneof("type") == legacy_constants.CLASSIFICATION_SIGNATURE: + elif (default_signature.WhichOneof("type") == + legacy_constants.CLASSIFICATION_SIGNATURE): classification_signature = default_signature.classification_signature signature_def.method_name = signature_constants.CLASSIFY_METHOD_NAME _add_input_to_signature_def(classification_signature.input.tensor_name, @@ -132,8 +134,10 @@ def _convert_named_signatures_to_signature_def(signatures): signature_constants.PREDICT_OUTPUTS] # TODO(pdudnik): what if there are other signatures? Mimic cr/140900781 once # it is submitted. - if (input_signature.WhichOneof("type") != legacy_constants.GENERIC_SIGNATURE or - output_signature.WhichOneof("type") != legacy_constants.GENERIC_SIGNATURE): + if (input_signature.WhichOneof("type") != + legacy_constants.GENERIC_SIGNATURE or + output_signature.WhichOneof("type") != + legacy_constants.GENERIC_SIGNATURE): raise RuntimeError("Named input and output signatures can only be " "up-converted if they are generic signature. " "Input signature type is %s, output signature type is " diff --git a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py index 885466e5d1..78d237e6a2 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/cloud_tpu_profiler/main.py @@ -25,17 +25,19 @@ import sys import tensorflow as tf -flags.DEFINE_string('service_addr', None, - 'Address of TPU profiler service e.g. localhost:8466') -flags.DEFINE_string('logdir', None, - "Path of TensorBoard log directory e.g. /tmp/tb_log, " - "gs://tb_bucket") +flags.DEFINE_string( + 'service_addr', None, 'Address of TPU profiler service e.g. ' + 'localhost:8466') +flags.DEFINE_string( + 'logdir', None, 'Path of TensorBoard log directory e.g. /tmp/tb_log, ' + 'gs://tb_bucket') flags.DEFINE_integer('duration_ms', 2000, 'Duration of tracing in ms.') -flags.DEFINE_integer('num_tracing_attempts', 3, - "Automatically retry N times when no trace event is " - "collected.") -flags.DEFINE_boolean('include_dataset_ops', True, - "Set to false to profile longer TPU device traces.") +flags.DEFINE_integer( + 'num_tracing_attempts', 3, 'Automatically retry N times when no trace ' + 'event is collected.') +flags.DEFINE_boolean( + 'include_dataset_ops', True, 'Set to false to profile longer TPU ' + 'device traces.') FLAGS = flags.FLAGS EXECUTABLE = 'data/capture_tpu_profile' diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index 119ffa3d9e..2102c5cca3 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -489,12 +489,6 @@ def tf_additional_core_deps(): "//tensorflow/core/platform/s3:s3_file_system", ], "//conditions:default": [], - }) + select({ - "//tensorflow:with_kafka_support": [ - "//tensorflow/contrib/kafka:kafka_kernels", - "//tensorflow/contrib/kafka:kafka_ops_op_lib", - ], - "//conditions:default": [], }) # TODO(jart, jhseu): Delete when GCP is default on. diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index b7afb8af46..7e0feb0669 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -903,10 +903,11 @@ class Dataset(object): Args: transformation_func: A function that takes one `Dataset` argument and - returns a `Dataset`. + returns a `Dataset`. Returns: - Dataset: The `Dataset` returned by applying `transformation_func` to this dataset. + Dataset: The `Dataset` returned by applying `transformation_func` to this + dataset. """ dataset = transformation_func(self) if not isinstance(dataset, Dataset): diff --git a/tensorflow/python/data/util/nest.py b/tensorflow/python/data/util/nest.py index df5498be5f..6d2f730016 100644 --- a/tensorflow/python/data/util/nest.py +++ b/tensorflow/python/data/util/nest.py @@ -479,8 +479,8 @@ def map_structure_up_to(shallow_tree, func, *inputs): The `inputs`, can be thought of as having the same structure as `shallow_tree`, but with leaf nodes that are themselves tree structures. - This function, therefore, will return something with the same base structure as - `shallow_tree`. + This function, therefore, will return something with the same base structure + as `shallow_tree`. Examples: diff --git a/tensorflow/python/kernel_tests/tensordot_op_test.py b/tensorflow/python/kernel_tests/tensordot_op_test.py index 084f98581e..8ad29afd0a 100644 --- a/tensorflow/python/kernel_tests/tensordot_op_test.py +++ b/tensorflow/python/kernel_tests/tensordot_op_test.py @@ -105,7 +105,7 @@ class TensordotTest(test_lib.TestCase): self.assertAllEqual(tf_ans, np_ans) def test_partial_shape_inference(self): - for axes in ([1],[0]), 1: + for axes in ([1], [0]), 1: a = array_ops.placeholder(dtypes.float32) b = array_ops.placeholder(dtypes.float32) output = math_ops.tensordot(a, b, axes) diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index cab1025df1..22636fdbb3 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -1691,7 +1691,8 @@ def rgb_to_yiq(images): images: tensor with the same shape as `images`. """ images = ops.convert_to_tensor(images, name='images') - kernel = ops.convert_to_tensor(_rgb_to_yiq_kernel, dtype=images.dtype, name='kernel') + kernel = ops.convert_to_tensor( + _rgb_to_yiq_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) @@ -1717,7 +1718,8 @@ def yiq_to_rgb(images): images: tensor with the same shape as `images`. """ images = ops.convert_to_tensor(images, name='images') - kernel = ops.convert_to_tensor(_yiq_to_rgb_kernel, dtype=images.dtype, name='kernel') + kernel = ops.convert_to_tensor( + _yiq_to_rgb_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) @@ -1742,7 +1744,8 @@ def rgb_to_yuv(images): images: tensor with the same shape as `images`. """ images = ops.convert_to_tensor(images, name='images') - kernel = ops.convert_to_tensor(_rgb_to_yuv_kernel, dtype=images.dtype, name='kernel') + kernel = ops.convert_to_tensor( + _rgb_to_yuv_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) @@ -1768,7 +1771,8 @@ def yuv_to_rgb(images): images: tensor with the same shape as `images`. """ images = ops.convert_to_tensor(images, name='images') - kernel = ops.convert_to_tensor(_yuv_to_rgb_kernel, dtype=images.dtype, name='kernel') + kernel = ops.convert_to_tensor( + _yuv_to_rgb_kernel, dtype=images.dtype, name='kernel') ndims = images.get_shape().ndims return math_ops.tensordot(images, kernel, axes=[[ndims-1], [0]]) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index a58db51cb8..6e4b821463 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -321,7 +321,7 @@ do_external_licenses_check(){ EXTRA_LICENSES_FILE="$(mktemp)_extra_licenses.log" echo "Getting external dependencies for ${BUILD_TARGET}" - bazel query "attr('licenses', 'notice', deps(${BUILD_TARGET}))" --no_implicit_deps --no_host_deps --keep_going \ + bazel query "attr('licenses', 'notice', deps(${BUILD_TARGET}))" --keep_going \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort \ @@ -330,7 +330,7 @@ do_external_licenses_check(){ echo echo "Getting list of external licenses mentioned in ${LICENSES_TARGET}." - bazel query "deps(${LICENSES_TARGET})" --no_implicit_deps --no_host_deps --keep_going \ + bazel query "deps(${LICENSES_TARGET})" --keep_going \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort \ -- GitLab From f8e328da8861801e1c6c3279042fee8a7c1e933a Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Thu, 1 Feb 2018 10:28:34 -0800 Subject: [PATCH 371/423] Updating the version to 1.6.0-rc0. --- .../eager/python/examples/mnist/mnist.py | 2 +- tensorflow/core/public/version.h | 4 ++-- tensorflow/docs_src/install/install_c.md | 2 +- tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 22 +++++++++---------- tensorflow/docs_src/install/install_linux.md | 22 +++++++++---------- tensorflow/docs_src/install/install_mac.md | 10 ++++----- .../docs_src/install/install_sources.md | 10 ++++++--- tensorflow/tools/docker/Dockerfile.devel | 2 +- .../tools/docker/Dockerfile.devel-cpu-mkl | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 2 +- tensorflow/tools/pip_package/setup.py | 2 +- 12 files changed, 43 insertions(+), 39 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist.py b/tensorflow/contrib/eager/python/examples/mnist/mnist.py index 2a7be95811..cfd5b1bca7 100644 --- a/tensorflow/contrib/eager/python/examples/mnist/mnist.py +++ b/tensorflow/contrib/eager/python/examples/mnist/mnist.py @@ -39,7 +39,7 @@ class MNISTModel(tfe.Network): """MNIST Network. Network structure is equivalent to: - https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py + https://github.com/tensorflow/tensorflow/blob/r1.6/tensorflow/examples/tutorials/mnist/mnist_deep.py and https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index b02f899b87..50bfa91267 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -19,12 +19,12 @@ limitations under the License. // TensorFlow uses semantic versioning, see http://semver.org/. #define TF_MAJOR_VERSION 1 -#define TF_MINOR_VERSION 5 +#define TF_MINOR_VERSION 6 #define TF_PATCH_VERSION 0 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "" +#define TF_VERSION_SUFFIX "-rc0" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 14add7c77e..a783205b4a 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index d2af9d9843..5249e04615 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index e5388c4b1e..0c6c773e62 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.5.0 + 1.6.0-rc0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.5.0 + 1.6.0-rc0 @@ -123,12 +123,12 @@ instead: org.tensorflow libtensorflow - 1.5.0 + 1.6.0-rc0 org.tensorflow libtensorflow_jni_gpu - 1.5.0 + 1.6.0-rc0 ``` @@ -147,7 +147,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.5.0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc0.zip). 3. Extract this .zip file. @@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.5.0.jar HelloTF.java
+
javac -cp libtensorflow-1.6.0-rc0.jar HelloTF.java
### Running @@ -239,11 +239,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.5.0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.6.0-rc0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.5.0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.6.0-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 cd8c14599f..105b225177 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -188,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv: Virtualenv environment: 
(tensorflow)$ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl If you encounter installation problems, see [Common Installation Problems](#common_installation_problems). @@ -293,7 +293,7 @@ take the following steps: 
      $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
If this step fails, see @@ -480,7 +480,7 @@ 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-1.5.0-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl @@ -648,14 +648,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -667,14 +667,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -686,14 +686,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
@@ -705,14 +705,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index f49d3a2f08..a6ea548cfb 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -115,7 +115,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -238,7 +238,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -347,7 +347,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl @@ -520,7 +520,7 @@ This section documents the relevant values for Mac OS installations. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl
@@ -528,5 +528,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index c1e4545d20..d68c7b2f03 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -361,10 +361,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.5.0 on Linux: +for TensorFlow 1.6.0rc0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc0-py2-none-any.whl
## Validate your installation @@ -462,7 +462,8 @@ Stack Overflow and specify the `tensorflow` tag. **Linux** - + + @@ -480,6 +481,7 @@ Stack Overflow and specify the `tensorflow` tag. **Mac**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.6.0rc0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.0N/AN/A
tensorflow_gpu-1.6.0rc0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.9.079
tensorflow-1.5.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.0N/AN/A
tensorflow_gpu-1.5.0GPU2.7, 3.3-3.6GCC 4.8Bazel 0.8.079
tensorflow-1.4.0CPU2.7, 3.3-3.6GCC 4.8Bazel 0.5.4N/AN/A
+ @@ -493,6 +495,8 @@ Stack Overflow and specify the `tensorflow` tag. **Windows**
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.6.0rc0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
tensorflow-1.5.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.8.1N/AN/A
tensorflow-1.4.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.5.4N/AN/A
tensorflow-1.3.0CPU2.7, 3.3-3.6Clang from xcodeBazel 0.4.5N/AN/A
+ + diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 5dc4a053fd..26b136b091 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -70,7 +70,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.5 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1\.6 --depth=1 https://github.com/tensorflow/tensorflow.git . # TODO(craigcitro): Don't install the pip package, since it makes it # more difficult to experiment with local changes. Instead, just add diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl index 96b260ad3a..3f37390801 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel LABEL maintainer="Clayne Robison" # These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1.5 +ARG TF_BRANCH=r1\.6 ARG WHL_DIR=/whl RUN apt-get update && apt-get install -y --no-install-recommends \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 07ffd3839a..4426a6e2c9 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -79,7 +79,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.5 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1\.6 --depth=1 https://github.com/tensorflow/tensorflow.git . # Configure the build for our CUDA configuration. ENV CI_BUILD_PYTHON python diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 6c9b5e46ee..7e793335f2 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -29,7 +29,7 @@ from setuptools.dist import Distribution # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.5.0' +_VERSION = '1.6.0-rc0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From 291497cd60de3b9bd629545898f29afa62d00874 Mon Sep 17 00:00:00 2001 From: Jin-Hwan CHO Date: Fri, 2 Feb 2018 03:32:36 +0900 Subject: [PATCH 372/423] Fix an imperfect implementation of tf.losses.mean_pairwise_squared_error (#16433) * Imperfect implementation of tf.losses.mean_pairwise_squared_error (#15968) https://github.com/tensorflow/tensorflow/issues/15968 * To pass the test, tensorflow/python/kernel_tests/losses_test.py needs to be fixed. --- tensorflow/python/kernel_tests/losses_test.py | 12 ++++++------ tensorflow/python/ops/losses/losses_impl.py | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py index 81af3a0887..59fe3df3e5 100644 --- a/tensorflow/python/kernel_tests/losses_test.py +++ b/tensorflow/python/kernel_tests/losses_test.py @@ -953,14 +953,14 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): # Compute the expected loss 'manually'. total = np.zeros((batch_size,)) for b in range(batch_size): - for i in range(dims): - for j in range(dims): + for i in range(dims-1): + for j in range(i+1, dims): x = self._predictions[b, i].item() - self._predictions[b, j].item() y = self._labels[b, i].item() - self._labels[b, j].item() diff = (x - y) total[b] += (diff * diff) - self._expected_losses = np.divide(total, 9.0) + self._expected_losses = np.divide(total, 3.0) def testValueErrorThrownWhenWeightIsNone(self): with self.test_session(): @@ -1060,7 +1060,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): [[8, 1, 3], [7, 8, 9], [10, 11, 12]], ]) self._test_valid_weights( - labels, predictions, expected_loss=122.22222) + labels, predictions, expected_loss=137.5) def test3dWeightedScalar(self): labels = np.array([ @@ -1073,7 +1073,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): ]) weight = 3.0 self._test_valid_weights( - labels, predictions, expected_loss=weight * 122.22222, + labels, predictions, expected_loss=weight * 137.5, weights=weight) def _test_invalid_weights( @@ -1124,7 +1124,7 @@ class MeanPairwiseSquaredErrorTest(test.TestCase): ]) self._test_valid_weights( # TODO(ptucker): This doesn't look right. - labels, predictions, expected_loss=9 * 122.22222, + labels, predictions, expected_loss=9 * 137.5, weights=np.ones((2, 3, 3))) def testLossWithAllZeroBatchSpecificWeights(self): diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 73563486e1..f84de3880a 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -547,12 +547,12 @@ def mean_pairwise_squared_error( num_present_per_batch = _num_present(diffs, weights, per_batch=True) term1 = 2.0 * _safe_div(sum_squares_diff_per_batch, - num_present_per_batch) + num_present_per_batch-1) sum_diff = math_ops.reduce_sum( diffs, reduction_indices=reduction_indices, keep_dims=True) term2 = 2.0 * _safe_div(math_ops.square(sum_diff), - math_ops.square(num_present_per_batch)) + math_ops.multiply(num_present_per_batch, num_present_per_batch-1)) weighted_losses = math_ops.multiply(term1 - term2, weights) loss = math_ops.reduce_sum(weighted_losses) -- GitLab From bd92b8dcf7cf6f10661ccfd52029ee8df103159b Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Thu, 1 Feb 2018 10:33:21 -0800 Subject: [PATCH 373/423] Removing the escape character. --- tensorflow/tools/docker/Dockerfile.devel | 2 +- tensorflow/tools/docker/Dockerfile.devel-cpu-mkl | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 26b136b091..d16761c367 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -70,7 +70,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1\.6 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.6 --depth=1 https://github.com/tensorflow/tensorflow.git . # TODO(craigcitro): Don't install the pip package, since it makes it # more difficult to experiment with local changes. Instead, just add diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl index 3f37390801..3690e7dfe5 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel LABEL maintainer="Clayne Robison" # These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1\.6 +ARG TF_BRANCH=r1.6 ARG WHL_DIR=/whl RUN apt-get update && apt-get install -y --no-install-recommends \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 4426a6e2c9..4ef37881bc 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -79,7 +79,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1\.6 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.6 --depth=1 https://github.com/tensorflow/tensorflow.git . # Configure the build for our CUDA configuration. ENV CI_BUILD_PYTHON python -- GitLab From c9dbbb898b3b8c3fcb113f5e43bc5a764ee81a8f Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 1 Feb 2018 10:48:44 -0800 Subject: [PATCH 374/423] compile libtensorflow on windows with AVX. (#16673) --- tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh | 2 +- tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh index fa28e3d79c..583d1d5f09 100755 --- a/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_cpu.sh @@ -41,7 +41,7 @@ run_configure_for_cpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt \ +bazel build -c opt --copt=/arch:AVX \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ diff --git a/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh b/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh index 573c926203..94276c6c5c 100644 --- a/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh +++ b/tensorflow/tools/ci_build/windows/libtensorflow_gpu.sh @@ -41,7 +41,7 @@ run_configure_for_gpu_build # build_libtensorflow_tarball in ../builds/libtensorflow.sh # cannot be used on Windows since it relies on pkg_tar rules. # So we do something special here -bazel build -c opt \ +bazel build -c opt --copt=/arch:AVX \ tensorflow:libtensorflow.so \ tensorflow/tools/lib_package:clicenses_generate \ tensorflow/java:libtensorflow_jni.so \ -- GitLab From 1bc391cb716e163712202c874f3431a5aa1a8f44 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Thu, 1 Feb 2018 10:54:55 -0800 Subject: [PATCH 375/423] Updating the cloud tpu version. --- tensorflow/contrib/tpu/profiler/pip_package/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py index 3dffebe668..cb61984799 100644 --- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py +++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py @@ -20,7 +20,7 @@ from __future__ import print_function from setuptools import setup -_VERSION = '1.5.0-rc1' +_VERSION = '1.6.0-rc0' CONSOLE_SCRIPTS = [ 'capture_tpu_profile=cloud_tpu_profiler.main:run_main', -- GitLab From b4cf62578ecbff2185f81987d1c41f3cd5cb9e19 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 1 Feb 2018 14:23:35 -0800 Subject: [PATCH 376/423] Fix linter error in losses_impl.py (#16675) --- tensorflow/python/ops/losses/losses_impl.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index f84de3880a..285e544047 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -551,8 +551,9 @@ def mean_pairwise_squared_error( sum_diff = math_ops.reduce_sum( diffs, reduction_indices=reduction_indices, keep_dims=True) - term2 = 2.0 * _safe_div(math_ops.square(sum_diff), - math_ops.multiply(num_present_per_batch, num_present_per_batch-1)) + term2 = 2.0 * _safe_div( + math_ops.square(sum_diff), + math_ops.multiply(num_present_per_batch, num_present_per_batch-1)) weighted_losses = math_ops.multiply(term1 - term2, weights) loss = math_ops.reduce_sum(weighted_losses) -- GitLab From 1c1aea213873a4f6865b619ee46c0d38e2458630 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Thu, 1 Feb 2018 14:31:04 -0800 Subject: [PATCH 377/423] added audio_ops.cc to fix the Op type not registered DecodeWav error - https://github.com/tensorflow/tensorflow/issues/15921 (#16537) --- tensorflow/contrib/makefile/tf_op_files.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt index 9a1ab50317..5a812af4e9 100644 --- a/tensorflow/contrib/makefile/tf_op_files.txt +++ b/tensorflow/contrib/makefile/tf_op_files.txt @@ -293,3 +293,4 @@ tensorflow/core/kernels/batchtospace_op.cc tensorflow/core/kernels/warn_about_ints.cc tensorflow/core/kernels/segment_reduction_ops.cc tensorflow/core/kernels/batch_util.cc +tensorflow/core/ops/audio_ops.cc -- GitLab From 474bf4e0458bb16f5d6882d3a179f072260168ce Mon Sep 17 00:00:00 2001 From: fo40225 Date: Fri, 2 Feb 2018 07:09:05 +0800 Subject: [PATCH 378/423] fix python 2.7 build break & import error on windows (#16649) --- tensorflow/contrib/cmake/python_modules.txt | 1 + tensorflow/contrib/cmake/tools/create_def_file.py | 5 +++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 914c4124c3..a7938f1f07 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -6,6 +6,7 @@ tensorflow/core/example tensorflow/core/framework tensorflow/core/lib tensorflow/core/lib/core +tensorflow/core/profiler tensorflow/core/protobuf tensorflow/core/util tensorflow/examples diff --git a/tensorflow/contrib/cmake/tools/create_def_file.py b/tensorflow/contrib/cmake/tools/create_def_file.py index f67698eb99..77ea914380 100644 --- a/tensorflow/contrib/cmake/tools/create_def_file.py +++ b/tensorflow/contrib/cmake/tools/create_def_file.py @@ -31,6 +31,7 @@ from __future__ import division from __future__ import print_function import argparse +import codecs import io import os import re @@ -103,7 +104,7 @@ def main(): for lib_path in args.input: proc = subprocess.Popen([DUMPBIN, "/nologo", "/linkermember:1", lib_path], stdout=subprocess.PIPE) - for line in io.TextIOWrapper(proc.stdout, encoding="utf-8"): + for line in codecs.getreader("utf-8")(proc.stdout): cols = line.split() if len(cols) < 2: continue @@ -131,7 +132,7 @@ def main(): # We compare on undname but use the decorated name from candidates. dupes = 0 proc = subprocess.Popen([UNDNAME, tmpfile.name], stdout=subprocess.PIPE) - for idx, line in enumerate(io.TextIOWrapper(proc.stdout, encoding="utf-8")): + for idx, line in enumerate(codecs.getreader("utf-8")(proc.stdout)): decorated = candidates[idx] if decorated in taken: # Symbol is already in output, done. -- GitLab From 087401a6a92c3d449c9564fff252d85ce736e5ff Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 1 Feb 2018 15:10:06 -0800 Subject: [PATCH 379/423] Improve shape function of NonMaxSuppression (#16664) * Improve shape function of NonMaxSuppression This fix tries to improve shape function of NonMaxSuppression. As was specified in the docs, the shapes of parameters of `tf.image.non_max_suppression` are clearly defined with: boxes: 2-D with shape [num_boxes, 4] scores: 1-D with shape [num_boxes] max_output_size: 0-D scalar iou_threshold: 0-D scalar However, there is no shape check in the shape function of NonMaxSuppression. This fix adds the shape check for NonMaxSuppression, and adds additinal test cases for it. Signed-off-by: Yong Tang * Add additional test cases for shape check of NonMaxSuppression. Signed-off-by: Yong Tang --- tensorflow/core/ops/image_ops.cc | 24 +++++++++++++++ tensorflow/python/ops/image_ops_test.py | 40 +++++++++++++++++++++++++ 2 files changed, 64 insertions(+) diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index ef2ac267cc..a62e2d782b 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -586,6 +586,17 @@ REGISTER_OP("NonMaxSuppression") .Output("selected_indices: int32") .Attr("iou_threshold: float = 0.5") .SetShapeFn([](InferenceContext* c) { + // Get inputs and validate ranks. + ShapeHandle boxes; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &boxes)); + ShapeHandle scores; + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &scores)); + ShapeHandle max_output_size; + TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &max_output_size)); + // The boxes is a 2-D float Tensor of shape [num_boxes, 4]. + DimensionHandle unused; + TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 1), 4, &unused)); + c->set_output(0, c->Vector(c->UnknownDim())); return Status::OK(); }); @@ -597,6 +608,19 @@ REGISTER_OP("NonMaxSuppressionV2") .Input("iou_threshold: float") .Output("selected_indices: int32") .SetShapeFn([](InferenceContext* c) { + // Get inputs and validate ranks. + ShapeHandle boxes; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &boxes)); + ShapeHandle scores; + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &scores)); + ShapeHandle max_output_size; + TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &max_output_size)); + ShapeHandle iou_threshold; + TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &iou_threshold)); + // The boxes is a 2-D float Tensor of shape [num_boxes, 4]. + DimensionHandle unused; + TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 1), 4, &unused)); + c->set_output(0, c->Vector(c->UnknownDim())); return Status::OK(); }); diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 6a516a9911..82b77ee8e3 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -3169,6 +3169,46 @@ class NonMaxSuppressionTest(test_util.TensorFlowTestCase): boxes, scores, max_output_size, iou_threshold).eval() self.assertAllClose(selected_indices, [3, 0, 5]) + def testInvalidShape(self): + # The boxes should be 2D of shape [num_boxes, 4]. + with self.assertRaisesRegexp( + ValueError, 'Shape must be rank 2 but is rank 1'): + boxes = constant_op.constant([0.0, 0.0, 1.0, 1.0]) + scores = constant_op.constant([0.9]) + selected_indices = image_ops.non_max_suppression( + boxes, scores, 3, 0.5) + + with self.assertRaisesRegexp( + ValueError, 'Dimension must be 4 but is 3'): + boxes = constant_op.constant([[0.0, 0.0, 1.0]]) + scores = constant_op.constant([0.9]) + selected_indices = image_ops.non_max_suppression( + boxes, scores, 3, 0.5) + + # The scores should be 1D of shape [num_boxes]. + with self.assertRaisesRegexp( + ValueError, 'Shape must be rank 1 but is rank 2'): + boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) + scores = constant_op.constant([[0.9]]) + selected_indices = image_ops.non_max_suppression( + boxes, scores, 3, 0.5) + + # The max_output_size should be a scaler (0-D). + with self.assertRaisesRegexp( + ValueError, 'Shape must be rank 0 but is rank 1'): + boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) + scores = constant_op.constant([0.9]) + selected_indices = image_ops.non_max_suppression( + boxes, scores, [3], 0.5) + + # The iou_threshold should be a scaler (0-D). + with self.assertRaisesRegexp( + ValueError, 'Shape must be rank 0 but is rank 2'): + boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]]) + scores = constant_op.constant([0.9]) + selected_indices = image_ops.non_max_suppression( + boxes, scores, 3, [[0.5]]) + if __name__ == "__main__": googletest.main() -- GitLab From f902b411106c4080e9c630b1c5118caaa702af7c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 20:20:18 -0800 Subject: [PATCH 380/423] Update ops-related pbtxt files. PiperOrigin-RevId: 184085402 --- .../core/ops/compat/ops_history.v1.pbtxt | 60 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 60 +++++++++++++++++++ 2 files changed, 120 insertions(+) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index 65ab81931a..0930eaa01f 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -17136,6 +17136,24 @@ op { type: DT_STRING } } +op { + name: "EnqueueInQueueDataset" + input_arg { + name: "queue" + type: DT_VARIANT + } + input_arg { + name: "components" + type_list_attr: "Tcomponents" + } + attr { + name: "Tcomponents" + type: "list(type)" + has_minimum: true + minimum: 1 + } + is_stateful: true +} op { name: "Enter" input_arg { @@ -32096,6 +32114,48 @@ op { minimum: 1 } } +op { + name: "PrependFromQueueAndPaddedBatchDataset" + input_arg { + name: "input_dataset" + type: DT_VARIANT + } + input_arg { + name: "batch_size" + type: DT_INT64 + } + input_arg { + name: "padded_shapes" + type: DT_INT64 + number_attr: "N" + } + input_arg { + name: "padding_values" + type_list_attr: "Toutput_types" + } + output_arg { + name: "handle" + type: DT_VARIANT + } + attr { + name: "Toutput_types" + type: "list(type)" + has_minimum: true + minimum: 1 + } + attr { + name: "output_shapes" + type: "list(shape)" + has_minimum: true + minimum: 1 + } + attr { + name: "N" + type: "int" + has_minimum: true + minimum: 1 + } +} op { name: "PreventGradient" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index b57206c9c4..36e0bab1b1 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -7644,6 +7644,24 @@ op { type: DT_STRING } } +op { + name: "EnqueueInQueueDataset" + input_arg { + name: "queue" + type: DT_VARIANT + } + input_arg { + name: "components" + type_list_attr: "Tcomponents" + } + attr { + name: "Tcomponents" + type: "list(type)" + has_minimum: true + minimum: 1 + } + is_stateful: true +} op { name: "Enter" input_arg { @@ -15926,6 +15944,48 @@ op { minimum: 1 } } +op { + name: "PrependFromQueueAndPaddedBatchDataset" + input_arg { + name: "input_dataset" + type: DT_VARIANT + } + input_arg { + name: "batch_size" + type: DT_INT64 + } + input_arg { + name: "padded_shapes" + type: DT_INT64 + number_attr: "N" + } + input_arg { + name: "padding_values" + type_list_attr: "Toutput_types" + } + output_arg { + name: "handle" + type: DT_VARIANT + } + attr { + name: "Toutput_types" + type: "list(type)" + has_minimum: true + minimum: 1 + } + attr { + name: "output_shapes" + type: "list(shape)" + has_minimum: true + minimum: 1 + } + attr { + name: "N" + type: "int" + has_minimum: true + minimum: 1 + } +} op { name: "PreventGradient" input_arg { -- GitLab From 6aae3000eac9ff06831284fdd9475463508d8408 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 31 Jan 2018 20:46:33 -0800 Subject: [PATCH 381/423] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 184086955 --- tensorflow/go/op/wrappers.go | 50 ++++++++++++++++++------------------ 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 5b19c90238..cb47651d7b 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -8729,31 +8729,6 @@ func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Out return op.Output(0) } -// Compute the pairwise cross product. -// -// `a` and `b` must be the same shape; they can either be simple 3-element vectors, -// or any shape where the innermost dimension is 3. In the latter case, each pair -// of corresponding 3-element vectors is cross-multiplied independently. -// -// Arguments: -// a: A tensor containing 3-element vectors. -// b: Another tensor, of same type and shape as `a`. -// -// Returns Pairwise cross product of the vectors in `a` and `b`. -func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Cross", - Input: []tf.Input{ - a, b, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Transforms a vector of brain.Example protos (as strings) into typed tensors. // // Arguments: @@ -21290,6 +21265,31 @@ func StatsAggregatorSummary(scope *Scope, iterator tf.Output) (summary tf.Output return op.Output(0) } +// Compute the pairwise cross product. +// +// `a` and `b` must be the same shape; they can either be simple 3-element vectors, +// or any shape where the innermost dimension is 3. In the latter case, each pair +// of corresponding 3-element vectors is cross-multiplied independently. +// +// Arguments: +// a: A tensor containing 3-element vectors. +// b: Another tensor, of same type and shape as `a`. +// +// Returns Pairwise cross product of the vectors in `a` and `b`. +func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Cross", + Input: []tf.Input{ + a, b, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Performs a padding as a preprocess during a convolution. // // Similar to FusedResizeAndPadConv2d, this op allows for an optimized -- GitLab From 56663eedd7296d85c9adb3e9537ddac521719445 Mon Sep 17 00:00:00 2001 From: Robin Richtsfeld Date: Fri, 2 Feb 2018 01:00:46 +0100 Subject: [PATCH 382/423] Remove BOM (#16505) --- tensorflow/core/kernels/mkl_cwise_ops_common.cc | 2 +- tensorflow/python/kernel_tests/io_ops_test.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/mkl_cwise_ops_common.cc b/tensorflow/core/kernels/mkl_cwise_ops_common.cc index c065724e0d..58f0c30f32 100644 --- a/tensorflow/core/kernels/mkl_cwise_ops_common.cc +++ b/tensorflow/core/kernels/mkl_cwise_ops_common.cc @@ -1,4 +1,4 @@ -/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. +/* 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. diff --git a/tensorflow/python/kernel_tests/io_ops_test.py b/tensorflow/python/kernel_tests/io_ops_test.py index f91875c6f0..61944f7e31 100644 --- a/tensorflow/python/kernel_tests/io_ops_test.py +++ b/tensorflow/python/kernel_tests/io_ops_test.py @@ -1,4 +1,4 @@ -# -*- coding: utf-8 -*- +# -*- coding: utf-8 -*- # Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); -- GitLab From 7d63461f14aa1b9c7204cb260df14d1f0422a5b4 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Wed, 31 Jan 2018 21:17:30 -0800 Subject: [PATCH 383/423] Internal change PiperOrigin-RevId: 184088913 --- .../lite/lib_package/create_ios_frameworks.sh | 81 +++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100755 tensorflow/contrib/lite/lib_package/create_ios_frameworks.sh diff --git a/tensorflow/contrib/lite/lib_package/create_ios_frameworks.sh b/tensorflow/contrib/lite/lib_package/create_ios_frameworks.sh new file mode 100755 index 0000000000..b58ae26601 --- /dev/null +++ b/tensorflow/contrib/lite/lib_package/create_ios_frameworks.sh @@ -0,0 +1,81 @@ +#!/bin/bash -x +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e + +echo "Starting" +TFLITE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/.." + +TMP_DIR=$(mktemp -d) +echo "Package dir: " $TMP_DIR +FW_DIR=$TMP_DIR/tensorflow_lite_ios_frameworks +FW_DIR_TFLITE=$FW_DIR/tensorflow_lite.framework +FW_DIR_TFLITE_HDRS=$FW_DIR_TFLITE/Headers + +echo "Creating target Headers directories" +mkdir -p $FW_DIR_TFLITE_HDRS + +echo "Headers, populating: TensorFlow Lite" +cd $TFLITE_DIR/../../.. + +find tensorflow/contrib/lite -name '*.h' \ + -not -path 'tensorflow/contrib/lite/downloads/*' \ + -not -path 'tensorflow/contrib/lite/examples/*' \ + -not -path 'tensorflow/contrib/lite/gen/*' \ + -not -path 'tensorflow/contrib/lite/toco/*' \ + -not -path 'tensorflow/contrib/lite/nnapi/*' \ + -not -path 'tensorflow/contrib/lite/java/*' \ + | tar -cf $FW_DIR_TFLITE_HDRS/tmp.tar -T - +cd $FW_DIR_TFLITE_HDRS +tar xf tmp.tar +rm -f tmp.tar + +echo "Headers, populating: Flatbuffer" +cd $TFLITE_DIR/downloads/flatbuffers/include/ +find . -name '*.h' | tar -cf $FW_DIR_TFLITE_HDRS/tmp.tar -T - +cd $FW_DIR_TFLITE_HDRS +tar xf tmp.tar +rm -f tmp.tar + +cd $TFLITE_DIR/../../.. +echo "Generate master LICENSE file and copy to target" +bazel build //tensorflow/tools/lib_package:clicenses_generate +cp $TFLITE_DIR/../../../bazel-genfiles/tensorflow/tools/lib_package/include/tensorflow/c/LICENSE \ + $FW_DIR_TFLITE + +echo "Copying static libraries" +cp $TFLITE_DIR/gen/lib/libtensorflow-lite.a \ + $FW_DIR_TFLITE/tensorflow_lite + +# This is required, otherwise they interfere with the documentation of the +# pod at cocoapods.org. +echo "Remove all README files" +cd $FW_DIR_TFLITE_HDRS +find . -type f -name README\* -exec rm -f {} \; +find . -type f -name readme\* -exec rm -f {} \; + +TARGET_GEN_LOCATION="$TFLITE_DIR/gen/ios_frameworks" +echo "Moving results to target: " $TARGET_GEN_LOCATION +cd $FW_DIR +zip -q -r tensorflow_lite.framework.zip tensorflow_lite.framework -x .DS_Store +rm -rf $TARGET_GEN_LOCATION +mkdir -p $TARGET_GEN_LOCATION +cp -r tensorflow_lite.framework.zip $TARGET_GEN_LOCATION + +echo "Cleaning up" +rm -rf $TMP_DIR + +echo "Finished" -- GitLab From baf490ba79acaacb458078370e4bad1c3fd17563 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 31 Jan 2018 23:05:26 -0800 Subject: [PATCH 384/423] [TF:XLA] Fix tfcompile OSS build - The @org_tensorflow package designation is unnecessary, and breaks the build when building without a sandbox. - The generated tests must use tf_cc_test, not cc_test. See the note in tensorflow/core/BUILD. Partially addresses #15338 PiperOrigin-RevId: 184095571 --- tensorflow/compiler/aot/tfcompile.bzl | 83 +++++++++++++++------------ 1 file changed, 45 insertions(+), 38 deletions(-) diff --git a/tensorflow/compiler/aot/tfcompile.bzl b/tensorflow/compiler/aot/tfcompile.bzl index 2b9c83ba14..58572fea3d 100644 --- a/tensorflow/compiler/aot/tfcompile.bzl +++ b/tensorflow/compiler/aot/tfcompile.bzl @@ -4,7 +4,7 @@ To use from your BUILD file, add the following line to load the macro: -load("@org_tensorflow//tensorflow/compiler/aot:tfcompile.bzl", "tf_library") +load("//tensorflow/compiler/aot:tfcompile.bzl", "tf_library") Then call the macro like this: @@ -16,14 +16,15 @@ tf_library( ) """ -load("@org_tensorflow//tensorflow:tensorflow.bzl", "if_android", "tf_copts") +load("//tensorflow:tensorflow.bzl", + "if_android", "tf_cc_test", "tf_copts") def tf_library(name, graph, config, freeze_checkpoint=None, freeze_saver=None, cpp_class=None, gen_test=True, gen_benchmark=True, visibility=None, testonly=None, tfcompile_flags=None, - tfcompile_tool="@org_tensorflow//tensorflow/compiler/aot:tfcompile", + tfcompile_tool="//tensorflow/compiler/aot:tfcompile", include_standard_runtime_deps=True, deps=None, tags=None): """Runs tfcompile to compile a TensorFlow graph into executable code. @@ -119,9 +120,9 @@ def tf_library(name, graph, config, out_nodes_file, ] + freeze_saver_srcs, outs=[freeze_file], - cmd=("$(location @org_tensorflow//tensorflow/python/tools:freeze_graph)" + + cmd=("$(location //tensorflow/python/tools:freeze_graph)" + freeze_args), - tools=["@org_tensorflow//tensorflow/python/tools:freeze_graph"], + tools=["//tensorflow/python/tools:freeze_graph"], tags=tags, ) tfcompile_graph = freeze_file @@ -213,22 +214,22 @@ def tf_library(name, graph, config, # These deps are required by all tf_library targets even if # include_standard_runtime_deps is False. Without them, the # generated code will fail to compile. - "@org_tensorflow//tensorflow/compiler/tf2xla:xla_compiled_cpu_function", - "@org_tensorflow//tensorflow/core:framework_lite", + "//tensorflow/compiler/tf2xla:xla_compiled_cpu_function", + "//tensorflow/core:framework_lite", ] + (need_xla_data_proto and [ # If we're generating the program shape, we must depend on the proto. - "@org_tensorflow//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla:xla_data_proto", ] or []) + (include_standard_runtime_deps and [ # TODO(cwhipkey): only depend on kernel code that the model actually needed. - "@org_tensorflow//tensorflow/compiler/tf2xla/kernels:index_ops_kernel_argmax_float_1d", - "@org_tensorflow//tensorflow/compiler/tf2xla/kernels:index_ops_kernel_argmax_float_2d", - "@org_tensorflow//tensorflow/compiler/xla/service/cpu:cpu_runtime_avx", - "@org_tensorflow//tensorflow/compiler/xla/service/cpu:cpu_runtime_neon", - "@org_tensorflow//tensorflow/compiler/xla/service/cpu:cpu_runtime_sse4_1", - "@org_tensorflow//tensorflow/compiler/xla/service/cpu:runtime_conv2d", - "@org_tensorflow//tensorflow/compiler/xla/service/cpu:runtime_matmul", - "@org_tensorflow//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_conv2d", - "@org_tensorflow//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_matmul", + "//tensorflow/compiler/tf2xla/kernels:index_ops_kernel_argmax_float_1d", + "//tensorflow/compiler/tf2xla/kernels:index_ops_kernel_argmax_float_2d", + "//tensorflow/compiler/xla/service/cpu:cpu_runtime_avx", + "//tensorflow/compiler/xla/service/cpu:cpu_runtime_neon", + "//tensorflow/compiler/xla/service/cpu:cpu_runtime_sse4_1", + "//tensorflow/compiler/xla/service/cpu:runtime_conv2d", + "//tensorflow/compiler/xla/service/cpu:runtime_matmul", + "//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_conv2d", + "//tensorflow/compiler/xla/service/cpu:runtime_single_threaded_matmul", "//third_party/eigen3", ] or []) + (deps or []), tags=tags, @@ -254,28 +255,32 @@ def tf_library(name, graph, config, name=("gen_" + test_name), testonly=1, srcs=[ - "@org_tensorflow//tensorflow/compiler/aot:test.cc", + "//tensorflow/compiler/aot:test.cc", header_file, ], outs=[test_file], cmd=("sed " + sed_replace + - " $(location @org_tensorflow//tensorflow/compiler/aot:test.cc) " + + " $(location //tensorflow/compiler/aot:test.cc) " + "> $(OUTS)"), tags=tags, ) - # The cc_test rule for the generated code. - native.cc_test( + # The cc_test rule for the generated code. To ensure that this works + # reliably across build configurations, we must use tf_cc_test instead of + # native.cc_test. This is related to how we build + # //tensorflow/core:lib -- see the note in tensorflow/core/BUILD + # for more details. + tf_cc_test( name=test_name, srcs=[test_file], deps=[ ":" + name, - "@org_tensorflow//tensorflow/compiler/aot:runtime", - "@org_tensorflow//tensorflow/compiler/aot:tf_library_test_main", - "@org_tensorflow//tensorflow/compiler/xla:executable_run_options", + "//tensorflow/compiler/aot:runtime", + "//tensorflow/compiler/aot:tf_library_test_main", + "//tensorflow/compiler/xla:executable_run_options", "//third_party/eigen3", - "@org_tensorflow//tensorflow/core:lib", - "@org_tensorflow//tensorflow/core:test", + "//tensorflow/core:lib", + "//tensorflow/core:test", ], tags=tags, ) @@ -283,7 +288,7 @@ def tf_library(name, graph, config, if gen_benchmark: benchmark_name = name + "_benchmark" benchmark_file = benchmark_name + ".cc" - benchmark_main = ("@org_tensorflow//tensorflow/compiler/aot:" + + benchmark_main = ("//tensorflow/compiler/aot:" + "benchmark_main.template") # Rule to rewrite benchmark.cc to produce the benchmark_file. @@ -301,7 +306,9 @@ def tf_library(name, graph, config, tags=tags, ) - # The cc_benchmark rule for the generated code. + # The cc_benchmark rule for the generated code. This does not need the + # tf_cc_binary since we (by deliberate design) do not depend on + # //tensorflow/core:lib. # # Note: to get smaller size on android for comparison, compile with: # --copt=-fvisibility=hidden @@ -315,12 +322,12 @@ def tf_library(name, graph, config, linkopts = if_android(["-pie", "-s"]), deps=[ ":" + name, - "@org_tensorflow//tensorflow/compiler/aot:benchmark", - "@org_tensorflow//tensorflow/compiler/aot:runtime", - "@org_tensorflow//tensorflow/compiler/xla:executable_run_options", + "//tensorflow/compiler/aot:benchmark", + "//tensorflow/compiler/aot:runtime", + "//tensorflow/compiler/xla:executable_run_options", "//third_party/eigen3", ] + if_android([ - "@org_tensorflow//tensorflow/compiler/aot:benchmark_extra_android", + "//tensorflow/compiler/aot:benchmark_extra_android", ]), tags=tags, ) @@ -330,11 +337,11 @@ def target_llvm_triple(): # TODO(toddw): Add target_triple for other targets. For details see: # http://llvm.org/docs/doxygen/html/Triple_8h_source.html return select({ - "@org_tensorflow//tensorflow:android_armeabi": "armv5-none-android", - "@org_tensorflow//tensorflow:android_arm": "armv7-none-android", - "@org_tensorflow//tensorflow:android_arm64": "aarch64-none-android", - "@org_tensorflow//tensorflow:android_x86": "i686-none-android", - "@org_tensorflow//tensorflow:linux_ppc64le": "ppc64le-ibm-linux-gnu", - "@org_tensorflow//tensorflow:darwin": "x86_64-none-darwin", + "//tensorflow:android_armeabi": "armv5-none-android", + "//tensorflow:android_arm": "armv7-none-android", + "//tensorflow:android_arm64": "aarch64-none-android", + "//tensorflow:android_x86": "i686-none-android", + "//tensorflow:linux_ppc64le": "ppc64le-ibm-linux-gnu", + "//tensorflow:darwin": "x86_64-none-darwin", "//conditions:default": "x86_64-pc-linux", }) -- GitLab From 47fcca75bc8ec9e3c9d484e055c94facef280e21 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Thu, 1 Feb 2018 06:47:06 -0800 Subject: [PATCH 385/423] [TF:XLA] Implement MatrixSetDiag and MatrixBandPart. Add support for int32 indices to the MatrixBandPart operator. PiperOrigin-RevId: 184133343 --- tensorflow/compiler/tests/BUILD | 13 +++ tensorflow/compiler/tests/binary_ops_test.py | 44 +++++++++ .../compiler/tests/matrix_band_part_test.py | 64 ++++++++++++ tensorflow/compiler/tf2xla/kernels/BUILD | 2 + .../tf2xla/kernels/matrix_band_part_op.cc | 98 +++++++++++++++++++ .../tf2xla/kernels/matrix_set_diag_op.cc | 93 ++++++++++++++++++ .../core/kernels/matrix_band_part_op.cc | 12 ++- tensorflow/core/ops/array_ops.cc | 5 +- .../kernel_tests/matrix_band_part_op_test.py | 11 ++- 9 files changed, 335 insertions(+), 7 deletions(-) create mode 100644 tensorflow/compiler/tests/matrix_band_part_test.py create mode 100644 tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc create mode 100644 tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 7277ba42ce..b0b038775f 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -353,6 +353,19 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "matrix_band_part_test", + size = "medium", + srcs = ["matrix_band_part_test.py"], + tags = ["optonly"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:platform_test", + ], +) + tf_xla_py_test( name = "momentum_test", size = "small", diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 16856bd736..9d34cdfe10 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -1181,6 +1181,50 @@ class BinaryOpsTest(XLATestCase): np.array([4, 5, 6], dtype=np.int32), expected=None) + def testMatrixSetDiag(self): + for dtype in self.numeric_types: + # Square + self._testBinary( + array_ops.matrix_set_diag, + np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 1.0]], + dtype=dtype), + np.array([1.0, 2.0, 3.0], dtype=dtype), + expected=np.array([[1.0, 1.0, 0.0], [1.0, 2.0, 1.0], [1.0, 1.0, 3.0]], + dtype=dtype)) + + self._testBinary( + array_ops.matrix_set_diag, + np.array([[[1.0, 0.0, 3.0], [0.0, 2.0, 0.0], [1.0, 0.0, 3.0]], + [[4.0, 0.0, 4.0], [0.0, 5.0, 0.0], [2.0, 0.0, 6.0]]], + dtype=dtype), + np.array([[-1.0, 0.0, -3.0], [-4.0, -5.0, -6.0]], dtype=dtype), + expected=np.array( + [[[-1.0, 0.0, 3.0], [0.0, 0.0, 0.0], [1.0, 0.0, -3.0]], + [[-4.0, 0.0, 4.0], [0.0, -5.0, 0.0], [2.0, 0.0, -6.0]]], + dtype=dtype)) + + # Rectangular + self._testBinary( + array_ops.matrix_set_diag, + np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 1.0]], dtype=dtype), + np.array([3.0, 4.0], dtype=dtype), + expected=np.array([[3.0, 1.0, 0.0], [1.0, 4.0, 1.0]], dtype=dtype)) + + self._testBinary( + array_ops.matrix_set_diag, + np.array([[0.0, 1.0], [1.0, 0.0], [1.0, 1.0]], dtype=dtype), + np.array([3.0, 4.0], dtype=dtype), + expected=np.array([[3.0, 1.0], [1.0, 4.0], [1.0, 1.0]], dtype=dtype)) + + self._testBinary( + array_ops.matrix_set_diag, + np.array([[[1.0, 0.0, 3.0], [0.0, 2.0, 0.0]], + [[4.0, 0.0, 4.0], [0.0, 5.0, 0.0]]], dtype=dtype), + np.array([[-1.0, -2.0], [-4.0, -5.0]], + dtype=dtype), + expected=np.array([[[-1.0, 0.0, 3.0], [0.0, -2.0, 0.0]], + [[-4.0, 0.0, 4.0], [0.0, -5.0, 0.0]]], + dtype=dtype)) if __name__ == "__main__": googletest.main() diff --git a/tensorflow/compiler/tests/matrix_band_part_test.py b/tensorflow/compiler/tests/matrix_band_part_test.py new file mode 100644 index 0000000000..29394f9ea5 --- /dev/null +++ b/tensorflow/compiler/tests/matrix_band_part_test.py @@ -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. +# ============================================================================== + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests.xla_test import XLATestCase +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test + + +class MatrixBandPartTest(XLATestCase): + + def _testMatrixBandPart(self, dtype, shape): + with self.test_session(): + batch_shape = shape[:-2] + mat = np.ones(shape).astype(dtype) + batch_mat = np.tile(mat, batch_shape + [1, 1]) + for lower in -1, 0, 1, shape[-2] - 1: + for upper in -1, 0, 1, shape[-1] - 1: + band_np = mat + if lower >= 0: + band_np = np.triu(band_np, -lower) + if upper >= 0: + band_np = np.tril(band_np, upper) + if batch_shape: + band_np = np.tile(band_np, batch_shape + [1, 1]) + + placeholder = array_ops.placeholder(dtype) + with self.test_scope(): + band = array_ops.matrix_band_part( + placeholder, + constant_op.constant(lower, dtype=dtypes.int32), + constant_op.constant(upper, dtype=dtypes.int32)) + feed_dict = {placeholder: batch_mat} + self.assertAllEqual(band_np, band.eval(feed_dict=feed_dict)) + + def testMatrixBandPart(self): + for dtype in self.float_types: + for batch_shape in [[], [2,], [1, 3, 2]]: + for rows in 1, 2, 7: + for cols in 1, 2, 7: + self._testMatrixBandPart(dtype, batch_shape + [rows, cols]) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 67be1a4ba6..e9be6f8476 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -44,6 +44,8 @@ tf_kernel_library( "l2loss_op.cc", "lrn_ops.cc", "matmul_op.cc", + "matrix_band_part_op.cc", + "matrix_set_diag_op.cc", "matrix_triangular_solve_op.cc", "mirror_pad_op.cc", "no_op.cc", diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc new file mode 100644 index 0000000000..faa415a97b --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc @@ -0,0 +1,98 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/core/framework/tensor_shape.h" + +namespace tensorflow { +namespace { + +class MatrixBandPartOp : public XlaOpKernel { + public: + explicit MatrixBandPartOp(OpKernelConstruction* context) + : XlaOpKernel(context) {} + + void Compile(XlaOpKernelContext* context) override { + const TensorShape input_shape = context->InputShape(0); + // Preliminary validation of sizes. + OP_REQUIRES(context, TensorShapeUtils::IsMatrixOrHigher(input_shape), + errors::InvalidArgument( + "input must be at least 2-dim, received shape: ", + input_shape.DebugString())); + + const TensorShape num_lower_in_shape = context->InputShape(1); + OP_REQUIRES(context, TensorShapeUtils::IsScalar(num_lower_in_shape), + errors::InvalidArgument("num_lower must be scalar, got shape ", + num_lower_in_shape.DebugString())); + + const TensorShape num_upper_in_shape = context->InputShape(2); + OP_REQUIRES(context, TensorShapeUtils::IsScalar(num_upper_in_shape), + errors::InvalidArgument("num_upper must be scalar, got shape ", + num_upper_in_shape.DebugString())); + + xla::ComputationBuilder* builder = context->builder(); + xla::ComputationDataHandle input = context->Input(0); + xla::ComputationDataHandle num_lower = context->Input(1); + xla::ComputationDataHandle num_upper = context->Input(2); + DataType input_type = context->input_type(0); + DataType index_type = context->input_type(1); + + TensorShape batch_shape = input_shape; + batch_shape.RemoveLastDims(2); + const int64 m = input_shape.dim_size(input_shape.dims() - 2); + const int64 n = input_shape.dim_size(input_shape.dims() - 1); + + // Compute 'offset', which is how many diagonals we are above/below the + // diagonal. + xla::ComputationDataHandle iota_m; + OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, index_type, m, &iota_m)); + + xla::ComputationDataHandle iota_n; + OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, index_type, n, &iota_n)); + + auto offset = builder->Sub(builder->Broadcast(iota_n, {m}), iota_m, + /*broadcast_dimensions=*/{0}); + + // If num_lower or num_upper are negative, include all lower/upper + // diagonals. + auto zero_index = XlaHelpers::Zero(builder, index_type); + num_lower = builder->Select( + builder->Lt(num_lower, zero_index), + XlaHelpers::IntegerLiteral(builder, index_type, m), num_lower); + num_upper = builder->Select( + builder->Lt(num_upper, zero_index), + XlaHelpers::IntegerLiteral(builder, index_type, n), num_upper); + + auto indicator = builder->And(builder->Le(builder->Neg(num_lower), offset), + builder->Le(offset, num_upper)); + indicator = builder->Broadcast(indicator, batch_shape.dim_sizes()); + + auto zero_input = XlaHelpers::Zero(builder, input_type); + auto output = builder->Select( + indicator, input, + builder->Broadcast(zero_input, input_shape.dim_sizes())); + + context->SetOutput(0, output); + } + + private: + TF_DISALLOW_COPY_AND_ASSIGN(MatrixBandPartOp); +}; +REGISTER_XLA_OP(Name("MatrixBandPart"), MatrixBandPartOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc new file mode 100644 index 0000000000..b2940bdcff --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc @@ -0,0 +1,93 @@ +/* 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/tf2xla/xla_helpers.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" + +namespace tensorflow { + +class MatrixSetDiagOp : public XlaOpKernel { + public: + explicit MatrixSetDiagOp(OpKernelConstruction* context) + : XlaOpKernel(context) {} + + void Compile(XlaOpKernelContext* context) override { + const TensorShape input_shape = context->InputShape(0); + const TensorShape diag_shape = context->InputShape(1); + + const int rank = input_shape.dims(); + + // Preliminary validation of sizes. + OP_REQUIRES(context, TensorShapeUtils::IsMatrixOrHigher(input_shape), + errors::InvalidArgument( + "input must be at least 2-dim, received shape: ", + input_shape.DebugString())); + + // Check to make sure the last dimension of diag is equal to the smaller of + // the last two dimensions of input. + const int64 m = input_shape.dim_size(rank - 2); + const int64 n = input_shape.dim_size(rank - 1); + const int64 min_dim = std::min(m, n); + + TensorShape batch_shape = input_shape; + batch_shape.RemoveLastDims(2); + + TensorShape expected_diag_shape = batch_shape; + expected_diag_shape.AddDim(min_dim); + OP_REQUIRES(context, expected_diag_shape == diag_shape, + errors::InvalidArgument( + "must have diagonal.shape == input.shape[:-2] + " + "min(input.shape[-2:]), but received input shape: ", + input_shape.DebugString(), + " and diagonal shape: ", diag_shape.DebugString())); + + xla::ComputationBuilder* builder = context->builder(); + xla::ComputationDataHandle input = context->Input(0); + xla::ComputationDataHandle diag = context->Input(1); + + auto zero = XlaHelpers::Zero(builder, context->input_type(0)); + + // Create an indicator tensor that is true only on the diagonal. + xla::ComputationDataHandle iota_m; + OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, DT_INT32, m, &iota_m)); + xla::ComputationDataHandle iota_n; + OP_REQUIRES_OK(context, XlaHelpers::Iota(builder, DT_INT32, n, &iota_n)); + auto indicator = builder->Eq(iota_m, + builder->Broadcast(iota_n, {m}), + /*broadcast_dimensions=*/{0}); + indicator = builder->Broadcast(indicator, batch_shape.dim_sizes()); + + // Broadcast diag up to the input shape. Use an implicit broadcast (Add) + // because we need to broadcast on the right. + std::vector diag_broadcast_dims(rank - 1); + std::iota(diag_broadcast_dims.begin(), diag_broadcast_dims.end(), 0); + if (min_dim != m) { + diag_broadcast_dims.back() = rank - 1; + } + diag = builder->Add(diag, builder->Broadcast(zero, input_shape.dim_sizes()), + /*broadcast_dimensions=*/diag_broadcast_dims); + + auto output = builder->Select(indicator, diag, input); + context->SetOutput(0, output); + } + + private: + TF_DISALLOW_COPY_AND_ASSIGN(MatrixSetDiagOp); +}; + +REGISTER_XLA_OP(Name("MatrixSetDiag"), MatrixSetDiagOp); + +} // namespace tensorflow diff --git a/tensorflow/core/kernels/matrix_band_part_op.cc b/tensorflow/core/kernels/matrix_band_part_op.cc index d7fff4bb0c..1439141f64 100644 --- a/tensorflow/core/kernels/matrix_band_part_op.cc +++ b/tensorflow/core/kernels/matrix_band_part_op.cc @@ -62,7 +62,15 @@ class MatrixBandPartOp : public OpKernel { OP_REQUIRES(context, TensorShapeUtils::IsScalar(num_lower_in.shape()), errors::InvalidArgument("num_lower must be scalar, got shape ", num_lower_in.shape().DebugString())); - const int64 num_lower = num_lower_in.scalar()(); + + auto as_int64_scalar = [](const Tensor& tensor) -> int64 { + if (tensor.dtype() == DT_INT32) { + return tensor.scalar()(); + } else { + return tensor.scalar()(); + } + }; + const int64 num_lower = as_int64_scalar(num_lower_in); OP_REQUIRES( context, num_lower <= input_reshaped.dimension(1), errors::InvalidArgument( @@ -73,7 +81,7 @@ class MatrixBandPartOp : public OpKernel { OP_REQUIRES(context, TensorShapeUtils::IsScalar(num_upper_in.shape()), errors::InvalidArgument("num_upper must be scalar, got shape ", num_upper_in.shape().DebugString())); - const int64 num_upper = num_upper_in.scalar()(); + const int64 num_upper = as_int64_scalar(num_upper_in); OP_REQUIRES(context, num_upper <= input_reshaped.dimension(2), errors::InvalidArgument("num_upper must be negative or less or " "equal to number of columns (", diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index fb9e8ad50c..87dfa77689 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -701,10 +701,11 @@ REGISTER_OP("MatrixDiagPart") // -------------------------------------------------------------------------- REGISTER_OP("MatrixBandPart") .Input("input: T") - .Input("num_lower: int64") - .Input("num_upper: int64") + .Input("num_lower: Tindex") + .Input("num_upper: Tindex") .Output("band: T") .Attr("T: type") + .Attr("Tindex: {int32, int64} = DT_INT64") .SetShapeFn(shape_inference::UnchangedShape); // -------------------------------------------------------------------------- diff --git a/tensorflow/python/kernel_tests/matrix_band_part_op_test.py b/tensorflow/python/kernel_tests/matrix_band_part_op_test.py index 317b8dc05b..68d626de2c 100644 --- a/tensorflow/python/kernel_tests/matrix_band_part_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_band_part_op_test.py @@ -21,6 +21,7 @@ import numpy as np from tensorflow.python.client import session from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -54,9 +55,13 @@ def _GetMatrixBandPartTest(dtype_, batch_shape_, shape_): band_np = np.tril(band_np, upper) if batch_shape_ is not (): band_np = np.tile(band_np, batch_shape_ + (1, 1)) - with self.test_session(use_gpu=False): - band = array_ops.matrix_band_part(batch_mat, lower, upper) - self.assertAllEqual(band_np, band.eval()) + for index_dtype in [dtypes_lib.int32, dtypes_lib.int64]: + with self.test_session(use_gpu=False): + band = array_ops.matrix_band_part( + batch_mat, + constant_op.constant(lower, index_dtype), + constant_op.constant(upper, index_dtype)) + self.assertAllEqual(band_np, band.eval()) return Test -- GitLab From fce33290a7b75003398761ea60b0bb6f1fdd3880 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 08:18:13 -0800 Subject: [PATCH 386/423] Update ops-related pbtxt files. PiperOrigin-RevId: 184141875 --- .../core/ops/compat/ops_history.v1.pbtxt | 36 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 17 +++++++-- 2 files changed, 51 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index 0930eaa01f..177561161e 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -24858,6 +24858,42 @@ op { type: "type" } } +op { + name: "MatrixBandPart" + input_arg { + name: "input" + type_attr: "T" + } + input_arg { + name: "num_lower" + type_attr: "Tindex" + } + input_arg { + name: "num_upper" + type_attr: "Tindex" + } + output_arg { + name: "band" + type_attr: "T" + } + attr { + name: "T" + type: "type" + } + attr { + name: "Tindex" + type: "type" + default_value { + type: DT_INT64 + } + allowed_values { + list { + type: DT_INT32 + type: DT_INT64 + } + } + } +} op { name: "MatrixDeterminant" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index 36e0bab1b1..2cd8d8a03b 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -12348,11 +12348,11 @@ op { } input_arg { name: "num_lower" - type: DT_INT64 + type_attr: "Tindex" } input_arg { name: "num_upper" - type: DT_INT64 + type_attr: "Tindex" } output_arg { name: "band" @@ -12362,6 +12362,19 @@ op { name: "T" type: "type" } + attr { + name: "Tindex" + type: "type" + default_value { + type: DT_INT64 + } + allowed_values { + list { + type: DT_INT32 + type: DT_INT64 + } + } + } } op { name: "MatrixDeterminant" -- GitLab From df344949494003ffa6eb8d9cb777558128436dc6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 09:55:53 -0800 Subject: [PATCH 387/423] Add shape inference for outside_compilation graph rewrite. Pull out enough of the graph to enable inference of the shape of a SendFromHost Op once the shape of corresponding RecvAtHost Ops are known. PiperOrigin-RevId: 184153187 --- .../jit/encapsulate_subgraphs_pass.cc | 647 ++++++++++++++--- .../jit/encapsulate_subgraphs_pass_test.cc | 682 ++++++++++++++---- tensorflow/core/framework/function.cc | 22 +- tensorflow/core/framework/function.h | 15 +- 4 files changed, 1125 insertions(+), 241 deletions(-) diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 0de163d3a8..8edae9fc9c 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -30,12 +30,14 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/optimization_registry.h" +#include "tensorflow/core/common_runtime/shape_refiner.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/graph_def_util.h" #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/graph/algorithm.h" #include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/graph/graph_def_builder.h" #include "tensorflow/core/graph/tensor_id.h" #include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/lib/gtl/map_util.h" @@ -141,8 +143,7 @@ struct NodeSlot { // everything to use it. static const char* const kArgOp = "_Arg"; static const char* const kRetValOp = "_Retval"; -static const char* const kSendToHostOp = "_XlaSendToHost"; -static const char* const kRecvFromHostOp = "_XlaRecvFromHost"; +static const char* const kHostComputeOp = "_XlaHostCompute"; static const char* const kSendFromHostOp = "_XlaSendFromHost"; static const char* const kRecvAtHostOp = "_XlaRecvAtHost"; @@ -171,7 +172,8 @@ class Encapsulator { // Write a copy of the input graph to 'graph_out', where the subgraphs are // replaced with calls to the new functions. - Status BuildOutputGraph(bool parallel_checking, Graph* graph_out); + Status BuildOutputGraph(bool parallel_checking, Graph* graph_out, + FunctionLibraryDefinition* library); private: // A subgraph of the input, all marked with a common 'group_attribute' @@ -201,21 +203,29 @@ class Encapsulator { // .. . // RAH --> C --> SFH // - // The compiled cluster is as follows. STH is a SendToHost node which is the - // source of a channel to the RAH node above. RFH is a RecvFromHost node which - // is the destination of a channel from the SFH node above. There is a control - // edge that ensures RFH follows STH, which is used in shape inference to - // ensure that the shapes on the STH host channel are known before the RFH - // channel is compiled. + // The compiled cluster is as follows. HC is a HostCompute node which is the + // source of a channel to the RAH node above and the destination of a channel + // from the SFH node above. // - // Arg --> B --> STH ..> RFH --> D --> Retval + // Arg --> B --> HC --> D --> Retval // - // The channels STH/RAH and SFH/RFH each transmit a tuple, so there is at most - // one RAH and SFH in each compiled cluster. This design is preferred over - // adding separate Arg/Retval nodes for each transmitted value because it - // simplifies the host code that would like to limit communication between - // host and device and, e.g., raise only one interrupt per channel rather than - // one per transmitted value. + // The channels HC/RAH and SFH/HC each transmit multiple tensors, so there is + // at most one RAH and SFH in each outside_compilation cluster. This design is + // preferred over adding separate Arg/Retval nodes for each transmitted value + // because it allows optimizations to the host code that would like to limit + // communication between host and device and, e.g., raise only one interrupt + // per channel rather than one per transmitted value. + // + // The shapes of the outputs from the HC node in general cannot be determined + // until the shapes of its inputs are known at compile time, since e.g., + // above, the shape of C's outputs aren't known until the shape of its inputs + // are known. If the shapes of the HC's outputs can be determined during the + // rewrite, they are stored in the node's 'shapes' attr. Otherwise a minimal + // graph is stored in the shape_inference_graph attr. This graph can be used + // when compiling the HC Op to determined the shape of the SFH inputs given + // the shapes of any ancestor RAH outputs. If it can be determined that the + // shape of the SFH inputs will not be inferrable even once the shapes of the + // RAH outputs are known, an error is returned by the rewriter. class Subgraph { public: // Creates a graph to build the subgraph in, if it doesn't already exist, @@ -246,6 +256,10 @@ class Encapsulator { const std::unordered_map& node_images, Graph* graph_out); + // Returns the names of all the outside_compilation subgraphs in this + // Subgraph. + void GetOutsideCompilationSubgraphNames(std::vector* names) const; + // Returns the Node that inputs to the function should be wired up to. Node* GetCallNodeForInputs() const; @@ -305,15 +319,9 @@ class Encapsulator { void RecordOutsideCompilationOutputOrControl( const string& outside_compilation_id, const Edge* edge); - // Adds the SendToHost nodes for each outside_compilation subgraph once the - // edges have all been recorded via RecordOutsideCompilationInputOrControl. - Status AddSendsToOutsideCompilation( - const std::unordered_map& node_images); - - // Adds the RecvFromHost nodes for each outside_compilation subgraph once - // the edges have all been recorded via - // RecordOutsideCompilationOutputOrControl. - Status AddRecvsFromOutsideCompilation( + // Adds the HostCompute nodes for each outside_compilation subgraph. + Status AddHostComputes( + const string& subgraph_name, const std::unordered_map& node_images); // Creates the sequencer node if it doesn't exist, adding it to graph_out. @@ -323,10 +331,16 @@ class Encapsulator { // all the downstream nodes of call_node_outputs. void ConnectSequencerToOutputs(Graph* graph_out); + Status AddShapeInferenceInfo( + const string& outside_compilation_subgraph_name, + const std::vector& shapes, GraphDef* inference_graph); + + Status ReplaceFunctionDef(FunctionLibraryDefinition* library); + private: struct OutsideCompilationSubgraph { // Map from source (producer node/slot) tensors in the original graph to - // input index (slot number in the SendToHost/RecvAtHost nodes that will + // input index (slot number in the HostCompute/RecvAtHost nodes that will // be created) for the outside_compilation subgraph. std::unordered_map inputs; @@ -335,14 +349,14 @@ class Encapsulator { // outside_compilation subgraph. These are recorded by // RecordOutsideCompilationInputOrControl while walking all the subgraph // edges, and lifted control edges within the subgraph are added by - // AddSendsToOutsideCompilation once the _SendToHost node has been + // AddSendsToOutsideCompilation once the _HostCompute node has been // created. The matching control edge from _RecvAtHost to the // destination is added by CopyEdgeToOutputGraph. std::unordered_set control_inputs; // Maps from source (producer node/slot) and destination (consumer // node/slot) tensors in the original graph to output index (slot number - // in the SendFromHost/RecvFromHost nodes that will be created) for the + // in the SendFromHost/HostCompute nodes that will be created) for the // outside_compilation subgraph. std::unordered_map outputs_by_src; std::unordered_map outputs_by_dst; @@ -352,13 +366,13 @@ class Encapsulator { // containing compiled subgraph. These are recorded by // RecordOutsideCompilationOutputOrControl while walking all the subgraph // edges, and lifted control edges within the subgraph are added by - // AddRecvsFromToOutsideCompilation once the _RecvFromHost node has been + // AddRecvsFromToOutsideCompilation once the _HostCompute node has been // created. The matching control edge from the source to _SendFromHost to // the destination is added by CopyEdgeToOutputGraph. std::unordered_set control_outputs; - // _SendToHost node in the subgraph. Not owned. - Node* send_to_host = nullptr; + // Name of the _HostCompute node in the subgraph. + string host_compute_name; // _RecvAtHost node in the output graph. Not owned. Node* recv_at_host = nullptr; @@ -516,6 +530,59 @@ class Encapsulator { const std::unordered_map& node_images, bool parallel_checking, Graph* graph_out); + // Constructs a minimal shape inference graph that can be used to determine + // the shape of send_node at the time that the subgraph is compiled. + // recv_at_host_nodes contains the names of all the recv_at_host nodes that + // send_node might depend on. These recv_at_host nodes have shapes that are + // not known during the rewrite pass, but will be known at compile time. + // + // If the shapes of all the inputs to send_node can be determined during the + // rewrite pass, on exit graphdef_out is empty and the shapes are returned in + // static_shape_out. Otherwise graphdef_out contains a graph that can be used + // for shape inference at compile time, where all the source nodes of the + // graph are either constants with known shapes, or nodes named in + // recv_at_host_nodes. + // + // A non-OK status is returned if neither of the above conditions can be + // satisfied, e.g., because send_node depends on a node that doesn't have a + // registered shape inference function. + Status DoStaticShapeInferenceForOutsideCompilationSend( + const Graph& graph_in, const ShapeRefiner& shape_refiner, + const std::unordered_set& recv_at_host_nodes, Node* send_node, + FunctionLibraryDefinition* library, + std::vector* static_shape_out, + std::unique_ptr* graphdef_out); + + // Makes a copy of graph containing only nodes that are ancestors of at least + // one node in send_from_host_nodes and store it in pruned_graph. On exit + // nodes_images contains a mapping from nodes in graph to nodes in + // pruned_graph. All functions in the copied graph are inlined. + Status MakePrunedGraphCopyAndInline( + const Graph& graph, const std::vector& sink_nodes, + std::unique_ptr* pruned_graph, + std::unordered_map* node_images, + FunctionLibraryDefinition* library); + + // Makes a copy of graph containing only nodes that are ancestors of a + // send_from_host node in an outside_compilation subgraph, and store it in + // pruned_graph. Also perform shape inference on the pruned graph, using + // shape_refiner. On exit node_images contains a mapping from nodes in graph + // to nodes in pruned_graph. + Status MakeGraphForOutsideCompilationSends( + const Graph& graph, std::unique_ptr* pruned_graph, + ShapeRefiner* shape_refiner, + std::unordered_map* node_images, + FunctionLibraryDefinition* library); + + // Performs static shape inference, as far as possible, for the send_from_host + // nodes in each outside_compilation subgraph. Where it is not possible to + // determine the shape statically, stores a serialized GraphDef in the + // HostCompute 'shape_inference_graph' attr, to be used at compile time for + // final inference. If the shapes are known statically they are stored in the + // HostCompute 'shapes' attr. + Status GetShapeInfoForOutsideCompilationSends( + Graph* graph_out, FunctionLibraryDefinition* library); + const string group_attribute_; const string outside_compilation_attribute_; const Graph* graph_in_; @@ -682,16 +749,20 @@ void Encapsulator::Subgraph::RecordOutsideCompilationOutputOrControl( } } -Status Encapsulator::Subgraph::AddSendsToOutsideCompilation( +Status Encapsulator::Subgraph::AddHostComputes( + const string& subgraph_name, const std::unordered_map& node_images) { for (auto& oc_subgraph_iter : outside_compilation_subgraphs_) { const string& oc_subgraph_name = oc_subgraph_iter.first; OutsideCompilationSubgraph& oc_subgraph = oc_subgraph_iter.second; - if (!oc_subgraph.inputs.empty() || !oc_subgraph.control_inputs.empty()) { - // Build a _SendToHost node sending all the args of the appropriate - // types. - std::vector dtypes(oc_subgraph.inputs.size(), DT_INVALID); + if (!oc_subgraph.inputs.empty() || !oc_subgraph.control_inputs.empty() || + !oc_subgraph.outputs_by_src.empty() || + !oc_subgraph.control_outputs.empty()) { + // Build a _HostCompute node. std::vector inputs(oc_subgraph.inputs.size()); + std::vector input_dtypes(oc_subgraph.inputs.size(), DT_INVALID); + std::vector output_dtypes(oc_subgraph.outputs_by_src.size(), + DT_INVALID); for (const auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; @@ -700,94 +771,64 @@ Status Encapsulator::Subgraph::AddSendsToOutsideCompilation( int input_index = input_src.second; DataType dtype = src_node->output_type(src_slot); - dtypes[input_index] = dtype; inputs[input_index].Reset(src_image->name(), src_slot, dtype); + input_dtypes[input_index] = dtype; } - NodeDef send_def; - NodeDefBuilder builder( - strings::StrCat("outside_compilation_", oc_subgraph_name, "_send"), - kSendToHostOp); - builder.Attr("dtypes", dtypes); + for (const auto& output : oc_subgraph.outputs_by_src) { + DataType dtype = output.first.dtype; + int output_index = output.second; + output_dtypes[output_index] = dtype; + } + + NodeDef host_compute_def; + NodeDefBuilder builder(strings::StrCat("outside_compilation_", + oc_subgraph_name, "_host_compute"), + kHostComputeOp); builder.Input(inputs); - Status s = builder.Finalize(&send_def); + builder.Attr("Tinputs", input_dtypes); + builder.Attr("Toutputs", output_dtypes); + builder.Attr("key", + strings::StrCat("host_compute_channel_", subgraph_name, "_", + oc_subgraph_name)); + Status s = builder.Finalize(&host_compute_def); if (!s.ok()) return s; - oc_subgraph.send_to_host = graph_->AddNode(send_def, &s); + Node* host_compute = graph_->AddNode(host_compute_def, &s); if (!s.ok()) return s; + oc_subgraph.host_compute_name = host_compute->name(); - // Connect the _SendToHost node to its producers in the subgraph. + // Connect the _HostCompute node to its producers in the subgraph. for (auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; Node* src_image = node_images.at(src_node); int src_slot = input_src.first.slot; int input_index = input_src.second; - graph_->AddEdge(src_image, src_slot, oc_subgraph.send_to_host, - input_index); + graph_->AddEdge(src_image, src_slot, host_compute, input_index); } - // Connect the _SendToHost node to its control edge producers in the + // Connect the _HostCompute node to its control edge producers in the // subgraph. for (const auto& src_node : oc_subgraph.control_inputs) { Node* src_image = node_images.at(src_node); - graph_->AddControlEdge(src_image, oc_subgraph.send_to_host); + graph_->AddControlEdge(src_image, host_compute); } - } - } - - return Status::OK(); -} - -Status Encapsulator::Subgraph::AddRecvsFromOutsideCompilation( - const std::unordered_map& node_images) { - for (auto& oc_subgraph_iter : outside_compilation_subgraphs_) { - const string& oc_subgraph_name = oc_subgraph_iter.first; - OutsideCompilationSubgraph& oc_subgraph = oc_subgraph_iter.second; - if (!oc_subgraph.outputs_by_src.empty() || - !oc_subgraph.control_outputs.empty()) { - // Build a _RecvFromHost node producing all the outputs of the appropriate - // types. - std::vector dtypes(oc_subgraph.outputs_by_src.size(), - DT_INVALID); - - for (const auto& output : oc_subgraph.outputs_by_src) { - DataType dtype = output.first.dtype; - int output_index = output.second; - dtypes[output_index] = dtype; - } - - NodeDef recv_def; - NodeDefBuilder builder( - strings::StrCat("outside_compilation_", oc_subgraph_name, "_recv"), - kRecvFromHostOp); - builder.Attr("dtypes", dtypes); - Status s = builder.Finalize(&recv_def); - if (!s.ok()) return s; - Node* recv = graph_->AddNode(recv_def, &s); - if (!s.ok()) return s; - - // Connect the consumers in the subgraph to the _RecvFromHost node. + // Connect the consumers in the subgraph to the _HostCompute node. for (const auto& output : oc_subgraph.outputs_by_dst) { const Node* dst_node = output.first.node; Node* dst_image = node_images.at(dst_node); int dst_slot = output.first.slot; int output_index = output.second; - graph_->AddEdge(recv, output_index, dst_image, dst_slot); + graph_->AddEdge(host_compute, output_index, dst_image, dst_slot); } - // Connect the control edge consumers in the subgraph to the _RecvFromHost + // Connect the control edge consumers in the subgraph to the _HostCompute // node. for (const auto& dst_node : oc_subgraph.control_outputs) { Node* dst_image = node_images.at(dst_node); - graph_->AddControlEdge(recv, dst_image); - } - - // Add a control edge in the subgraph so that the _SendToHost node, if - // any, is compiled before the _RecvFromHost node. - if (oc_subgraph.send_to_host != nullptr) { - graph_->AddControlEdge(oc_subgraph.send_to_host, recv); + graph_->AddControlEdge(host_compute, dst_image); } } } @@ -882,6 +923,63 @@ Status Encapsulator::Subgraph::BuildFunctionDef( return Status::OK(); } +Status Encapsulator::Subgraph::AddShapeInferenceInfo( + const string& outside_compilation_subgraph_name, + const std::vector& shapes, GraphDef* inference_graph) { + OutsideCompilationSubgraph& oc_subgraph = + outside_compilation_subgraphs_.at(outside_compilation_subgraph_name); + + Node* host_compute = nullptr; + for (Node* n : graph_->nodes()) { + if (n->name() == oc_subgraph.host_compute_name) { + host_compute = n; + break; + } + } + if (host_compute == nullptr) { + return errors::InvalidArgument( + "After rewriting subgraph ", outside_compilation_subgraph_name, + " there is no HostCompute Op for outside compilation subgraph ", + oc_subgraph.host_compute_name); + } + + if (inference_graph == nullptr) { + host_compute->AddAttr("shape_inference_graph", ""); + host_compute->AddAttr("shapes", shapes); + } else { + string serialized_graph; + if (!inference_graph->SerializeToString(&serialized_graph)) { + return errors::Internal( + "Failed to serialize graph for outside compilation subgraph ", + oc_subgraph.host_compute_name); + } + host_compute->AddAttr("shape_inference_graph", serialized_graph); + host_compute->AddAttr("shapes", std::vector()); + } + return Status::OK(); +} + +Status Encapsulator::Subgraph::ReplaceFunctionDef( + FunctionLibraryDefinition* library) { + const string& name = call_node_def_.name(); + + FunctionDef fdef; + TF_RETURN_IF_ERROR(GraphToFunctionDef(*graph_, name, &fdef)); + + if (VLOG_IS_ON(1)) { + VLOG(2) << "Replace function def " << name; + dump_graph::DumpGraphToFile( + strings::StrCat("replace_encapsulate_fdef_graph_", name), *graph_, + library); + dump_graph::DumpFunctionDefToFile( + strings::StrCat("replace_encapsulate_fdef_", name), fdef); + } + + TF_RETURN_IF_ERROR(library->RemoveFunction(name)); + TF_RETURN_IF_ERROR(library->AddFunctionDef(fdef)); + return Status::OK(); +} + Status Encapsulator::Subgraph::BuildParallelCheckOp( const std::unordered_map& node_images, Graph* graph_out) { @@ -980,7 +1078,9 @@ Status Encapsulator::Subgraph::AddRecvAtHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_recv"), kRecvAtHostOp); - builder.Attr("dtypes", dtypes); + builder.Attr("Toutputs", dtypes); + builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, + "_", oc_subgraph_name)); Status s = builder.Finalize(&recv_def); if (!s.ok()) return s; @@ -1020,7 +1120,9 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_send"), kSendFromHostOp); - builder.Attr("dtypes", dtypes); + builder.Attr("Tinputs", dtypes); + builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, + "_", oc_subgraph_name)); builder.Input(inputs); Status s = builder.Finalize(&send_def); if (!s.ok()) return s; @@ -1062,6 +1164,13 @@ Status Encapsulator::Subgraph::AddOutsideCompilationHostIONodes( return Status::OK(); } +void Encapsulator::Subgraph::GetOutsideCompilationSubgraphNames( + std::vector* names) const { + for (auto& entry : outside_compilation_subgraphs_) { + names->push_back(entry.first); + } +} + Status Encapsulator::GetFunctionNameAttr( Node const* node, string* attr, string* outside_compilation_attr) const { Status s = GetNodeAttr(node->attrs(), group_attribute_, attr); @@ -1220,8 +1329,7 @@ Status Encapsulator::SplitIntoSubgraphs() { // single input and output node for it. for (auto& entry : subgraphs_) { Subgraph& subgraph = entry.second; - TF_RETURN_IF_ERROR(subgraph.AddSendsToOutsideCompilation(node_images)); - TF_RETURN_IF_ERROR(subgraph.AddRecvsFromOutsideCompilation(node_images)); + TF_RETURN_IF_ERROR(subgraph.AddHostComputes(entry.first, node_images)); } MarkGuaranteedConstants(*graph_in_, src_arg_pairs); @@ -1509,8 +1617,344 @@ Status Encapsulator::AddEdgesToOutputGraph( return Status::OK(); } -Status Encapsulator::BuildOutputGraph(bool parallel_checking, - Graph* graph_out) { +namespace { + +// Adds a dummy Const node to graph_out. The "constant" has the type of +// data_type and the shape indicated in 'shape'. The dummy node is not a valid +// Const node because it does not have any value defined, but this doesn't +// matter because it will only be used subsequently for shape inference. (It +// would be possible to add a switch statement over data_type to create a value +// for the constant, but that would entail maintaining the logic as new types +// are added, and is not necessary.) +Node* AddDummyShapedNode(DataType data_type, const TensorShapeProto& shape, + Graph* graph_out) { + TensorProto dummy_proto; + dummy_proto.set_dtype(data_type); + *dummy_proto.mutable_tensor_shape() = shape; + // Don't set any value field in the proto, since it is only going to be used + // for shape inference. + + GraphDefBuilder::Options options(graph_out, /*status=*/nullptr); + NodeBuilder node_builder(options.GetNameForOp("KnownShape"), "Const", + options.op_registry()); + node_builder.Attr("dtype", data_type).Attr("value", dummy_proto); + return options.FinalizeBuilder(&node_builder); +} + +// Adds a copy of node_in to graph_out and adds the mapping to +// copied_node_images. +Status CopyShapeInferenceNodeToGraph( + Node* node_in, const Node* send_node, + const std::unordered_map& dummy_node_images, + FunctionLibraryDefinition* library, + std::unordered_map* copied_node_images, Graph* graph_out) { + // Once all the ancestor nodes have been added to graph_out, add this node + // and connect it to its ancestors. + Node* node_out = graph_out->CopyNode(node_in); + (*copied_node_images)[node_in] = node_out; + // Don't bother to build the shape inference graph if there's a node with no + // shape inference function, since it would just result in an error later at + // compile time. + const OpRegistrationData* op_reg_data; + TF_RETURN_IF_ERROR(library->LookUp(node_in->type_string(), &op_reg_data)); + if (op_reg_data->shape_inference_fn == nullptr) { + return errors::InvalidArgument( + "Shape inference is not possible for outside_compilation " + "SendFromHost node ", + send_node->name(), " because it depends on node ", node_in->name(), + " which does not have a shape inference function registered."); + } + // Add all the edges to the newly copied node. + for (const Edge* in_edge : node_in->in_edges()) { + if (!in_edge->IsControlEdge()) { + Node* src = in_edge->src(); + const auto iter = dummy_node_images.find(src); + if (iter == dummy_node_images.end()) { + // The src is a copied node so use the original output port. + graph_out->AddEdge((*copied_node_images)[in_edge->src()], + in_edge->src_output(), node_out, + in_edge->dst_input()); + } else { + // The src is a dummy node so use output port 0. + graph_out->AddEdge(iter->second, 0, node_out, in_edge->dst_input()); + } + } + } + return Status::OK(); +} + +} // namespace + +Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( + const Graph& graph_in, const ShapeRefiner& shape_refiner, + const std::unordered_set& recv_at_host_nodes, Node* send_node, + FunctionLibraryDefinition* library, + std::vector* static_shape_out, + std::unique_ptr* graphdef_out) { + // Maps from nodes in graph_in to nodes in graph_out. + // + // When an edge has fully defined shape the source node in graph_in is + // replaced in graph_out by a dummy constant node. The mapping from nodes + // in graph_in to dummy nodes is stored in dummy_node_images. + // + // When a node in graph_in has at least one ancestor that doesn't have fully + // defined shape, it is copied into graph_out. The mapping from nodes in + // graph_in to copied nodes is stored in copied_node_images. + // + // The two types of node are treated differently because, when adding edges to + // graph_out, an output from a dummy node always uses port 0, whereas an + // output from a copied node uses the same port that was used in graph_in. + std::unordered_map dummy_node_images; + std::unordered_map copied_node_images; + + std::unique_ptr graph_out(new Graph(graph_in.op_registry())); + graph_out->set_versions(graph_in.versions()); + static_shape_out->resize(send_node->num_inputs()); + + // We don't use the standard ReverseDFS because we want to cut off traversal + // whenever we find an output with fully defined shape. + // TODO(misard) make this work properly in the presence of control flow. + struct Work { + Node* node; + bool leave; // Are we entering or leaving node? + }; + std::vector stack({{send_node, false}}); + std::vector visited(graph_in.num_node_ids(), false); + while (!stack.empty()) { + Work w = stack.back(); + stack.pop_back(); + Node* n = w.node; + + if (w.leave) { + TF_RETURN_IF_ERROR(CopyShapeInferenceNodeToGraph( + n, send_node, dummy_node_images, library, &copied_node_images, + graph_out.get())); + } else { + if (visited[n->id()]) continue; + visited[n->id()] = true; + + // Arrange to revisit when all done with all inputs. + stack.push_back(Work{n, true}); + + bool has_parent_with_unknown_shape = false; + for (const Edge* in_edge : n->in_edges()) { + if (!in_edge->IsControlEdge()) { + Node* src_node = in_edge->src(); + int src_port = in_edge->src_output(); + shape_inference::InferenceContext* context = + shape_refiner.GetContext(src_node); + shape_inference::ShapeHandle shape = context->output(src_port); + if (context->FullyDefined(shape)) { + // This ancestor has known shape, so instead of adding it to the + // stack, add a dummy node with that shape to graph_out and + // continue. + TensorShapeProto proto; + context->ShapeHandleToProto(shape, &proto); + dummy_node_images[src_node] = AddDummyShapedNode( + src_node->output_type(src_port), proto, graph_out.get()); + if (n == send_node) { + (*static_shape_out)[in_edge->dst_input()] = proto; + } + } else { + if (!visited[src_node->id()]) { + has_parent_with_unknown_shape = true; + stack.push_back({src_node, false}); + } + } + } + } + if (!has_parent_with_unknown_shape) { + if (n == send_node) { + // The shapes of all the inputs to send_node are statically known. We + // won't have to do any inference at compile time so return now: the + // shapes were stored in static_shape_out above. + graphdef_out->reset(); + return Status::OK(); + } else { + // Any shape that is being processed is either the original send node + // or has at least one output with statically-unknown shape. If the + // latter and it doesn't have any inputs with statically-unknown + // shape, then check that it is of the recv nodes that we can fill in + // the shape of at run-time later. If it isn't one of those, then we + // won't have any additional knowledge at compile time, so we already + // know we won't be able to do shape inference and we can return an + // error now. + if (recv_at_host_nodes.find(n->name()) == recv_at_host_nodes.end()) { + return errors::InvalidArgument( + "Shape inference is not possible for outside_compilation " + "SendFromHost node ", + send_node->name(), " because shape of node ", n->name(), + " will not be known at compilation time."); + } + } + } + } + } + + graphdef_out->reset(new GraphDef()); + graph_out->ToGraphDef(graphdef_out->get()); + + return Status::OK(); +} + +Status Encapsulator::MakePrunedGraphCopyAndInline( + const Graph& graph, const std::vector& sink_nodes, + std::unique_ptr* pruned_graph, + std::unordered_map* node_images, + FunctionLibraryDefinition* library) { + // First copy all ancestor nodes of sink_nodes into a new graph. + pruned_graph->reset(new Graph(library)); + (*pruned_graph)->set_versions(graph.versions()); + ReverseDFSFrom(graph, sink_nodes, + /*enter=*/nullptr, + /*leave=*/[&](Node* n) { + if (!n->IsSource()) { + Node* copied = (*pruned_graph)->CopyNode(n); + node_images->emplace(n, copied); + } + }); + + // Add all the edges between copied nodes. + for (auto entry : *node_images) { + const Node* orig = entry.first; + Node* image = entry.second; + for (const Edge* out_edge : orig->out_edges()) { + auto iter = node_images->find(out_edge->dst()); + if (iter != node_images->end()) { + // The source and destination are both in the copied graph. + (*pruned_graph) + ->AddEdge(image, out_edge->src_output(), iter->second, + out_edge->dst_input()); + } + } + } + + // Find all the function call nodes, and inline them. + std::vector function_nodes; + for (auto node : (*pruned_graph)->nodes()) { + const OpRegistrationData* op_reg_data; + TF_RETURN_IF_ERROR(library->LookUp(node->type_string(), &op_reg_data)); + if (op_reg_data->is_function_op) { + function_nodes.push_back(node); + } + } + for (auto node : function_nodes) { + VLOG(2) << "Inlining function " << node->name(); + const FunctionDef* fdef = library->Find(node->type_string()); + if (fdef == nullptr) { + return errors::Internal("Failed to find function ", node->type_string(), + " in function library."); + } + FunctionBody* fbody = nullptr; + TF_RETURN_IF_ERROR( + FunctionDefToBodyHelper(*fdef, node->attrs(), library, + [library](const string& op, const OpDef** sig) { + return library->LookUpOpDef(op, sig); + }, + &fbody)); + InlineFunctionBody(*library, pruned_graph->get(), node, fbody); + delete fbody; + } + + return Status::OK(); +} + +Status Encapsulator::MakeGraphForOutsideCompilationSends( + const Graph& graph, std::unique_ptr* pruned_graph, + ShapeRefiner* shape_refiner, + std::unordered_map* node_images, + FunctionLibraryDefinition* library) { + // Find all the send_from_host nodes in all subgraphs, to use as roots for the + // pruning. + std::vector send_from_host_nodes; + for (auto& subgraph_entry : subgraphs_) { + Subgraph& subgraph = subgraph_entry.second; + std::vector outside_compilation_names; + subgraph.GetOutsideCompilationSubgraphNames(&outside_compilation_names); + for (const auto& name : outside_compilation_names) { + Node* send_node = subgraph.GetSendFromHostNode(name); + if (send_node != nullptr) { + send_from_host_nodes.push_back(send_node); + } + } + } + + // Make a copy of all the graph nodes needed to evaluate the send_from_host + // nodes, inlining any functions as needed. + TF_RETURN_IF_ERROR(MakePrunedGraphCopyAndInline( + graph, send_from_host_nodes, pruned_graph, node_images, library)); + + // Perform shape inference on the pruned graph. + shape_refiner->set_require_shape_inference_fns(false); + FixupSourceAndSinkEdges(pruned_graph->get()); + std::vector post_order; + GetReversePostOrder(*(*pruned_graph), &post_order); + for (auto node : post_order) { + // Ignore the status returned by the shape_refiner. At this point we want + // the best effort shapes, even if no shape function is registered for a + // node. + Status status = shape_refiner->AddNode(node); + VLOG_IF(1, !status.ok()) << "Shape inference failed for node: " << status; + } + + return Status::OK(); +} + +Status Encapsulator::GetShapeInfoForOutsideCompilationSends( + Graph* graph_out, FunctionLibraryDefinition* library) { + std::unique_ptr pruned_graph; + ShapeRefiner shape_refiner(graph_out->versions(), graph_out->op_registry()); + std::unordered_map node_images; + TF_RETURN_IF_ERROR(MakeGraphForOutsideCompilationSends( + *graph_out, &pruned_graph, &shape_refiner, &node_images, library)); + + for (auto& subgraph_entry : subgraphs_) { + Subgraph& subgraph = subgraph_entry.second; + // Find all the recv_at_host nodes in this subgraph. + std::vector outside_compilation_names; + subgraph.GetOutsideCompilationSubgraphNames(&outside_compilation_names); + std::unordered_set recv_at_host_names; + for (const auto& name : outside_compilation_names) { + Node* recv_node = subgraph.GetRecvAtHostNode(name); + if (recv_node != nullptr) { + recv_at_host_names.insert(recv_node->name()); + } + } + // For each send_from_host node, do as much shape inference as possible + // without knowing the shape of the recv_at_host nodes, and store the + // result, along with enough information to complete the job at compile time + // once the recv_at_host shapes are known. + for (const auto& name : outside_compilation_names) { + Node* send_node = subgraph.GetSendFromHostNode(name); + std::vector static_shape; + std::unique_ptr graphdef; + if (send_node != nullptr) { + TF_RETURN_IF_ERROR(DoStaticShapeInferenceForOutsideCompilationSend( + *pruned_graph, shape_refiner, recv_at_host_names, + node_images[send_node], library, &static_shape, &graphdef)); + if (graphdef == nullptr) { + VLOG(2) << "Send node " << send_node->name() << " shapes"; + for (int i = 0; i < static_shape.size(); ++i) { + VLOG(2) << static_shape[i].DebugString(); + } + } else { + VLOG(2) << "Send node " << send_node->name() << " graph\n" + << graphdef->DebugString(); + } + } + TF_RETURN_IF_ERROR( + subgraph.AddShapeInferenceInfo(name, static_shape, graphdef.get())); + } + if (!outside_compilation_names.empty()) { + TF_RETURN_IF_ERROR(subgraph.ReplaceFunctionDef(library)); + } + } + + return Status::OK(); +} + +Status Encapsulator::BuildOutputGraph(bool parallel_checking, Graph* graph_out, + FunctionLibraryDefinition* library) { // Map from nodes in the input graph to nodes in the output graph. std::unordered_map node_images; @@ -1522,6 +1966,9 @@ Status Encapsulator::BuildOutputGraph(bool parallel_checking, TF_RETURN_IF_ERROR( AddEdgesToOutputGraph(node_images, parallel_checking, graph_out)); + TF_RETURN_IF_ERROR( + GetShapeInfoForOutsideCompilationSends(graph_out, library)); + return Status::OK(); } @@ -1545,7 +1992,7 @@ Status EncapsulateSubgraphsInFunctions( std::unique_ptr out(new Graph(library)); out->set_versions(graph_in.versions()); TF_RETURN_IF_ERROR( - encapsulator.BuildOutputGraph(parallel_checking, out.get())); + encapsulator.BuildOutputGraph(parallel_checking, out.get(), library)); *graph_out = std::move(out); return Status::OK(); diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index b100861d5e..5032a0935d 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -29,17 +29,181 @@ limitations under the License. namespace tensorflow { namespace { +template +bool EqualProtoMap(const proto2::Map& a, + const proto2::Map& b, + const std::function& key_to_string, + const std::function& value_to_string, + const std::function& compare, + const string& map_name, string* diff) { + for (const auto& elt_a : a) { + const auto iter = b.find(elt_a.first); + if (iter == b.end()) { + if (diff) { + *diff = strings::StrCat( + map_name, " expected: contains element with key '", + key_to_string(elt_a.first), "' got: map has no such element"); + } + return false; + } + if (!compare(elt_a.first, elt_a.second, iter->second)) { + if (diff) { + *diff = strings::StrCat(map_name, " expected: element with key '", + key_to_string(elt_a.first), " has value '", + value_to_string(elt_a.second), "' got: '", + value_to_string(iter->second), "'"); + } + return false; + } + } + for (const auto& elt_b : b) { + const auto iter = a.find(elt_b.first); + if (iter == a.end()) { + if (diff) { + *diff = strings::StrCat(map_name, " got: contains element with key '", + key_to_string(elt_b.first), + "' expected: map has no such element"); + } + return false; + } + } + return true; +} + +bool EqualFunctionNodeDef(const NodeDef& a, const NodeDef& b, + const string& diff_preamble, string* diff) { + if (a.op() != b.op()) { + if (diff) { + *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), + ", expected op '", a.op(), "' got '", b.op()); + } + return false; + } + if (a.device() != b.device()) { + if (diff) { + *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), + ", expected device '", a.device(), "' got '", + b.device()); + } + return false; + } + if (a.input_size() != b.input_size()) { + if (diff) { + *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), + ", expected ", a.input_size(), " inputs got ", + b.input_size(), " expected:\n", a.DebugString(), + "\ngot:\n", b.DebugString()); + } + return false; + } + for (int i = 0; i < a.input_size(); ++i) { + if (a.input(i) != b.input(i)) { + if (diff) { + *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), + " input ", i, ", expected ", a.input(i), + " got ", b.input(i), " expected:\n", + a.DebugString(), "\ngot:\n", b.DebugString()); + } + return false; + } + } + return EqualProtoMap( + a.attr(), b.attr(), [](const string& s) { return s; }, + [](const AttrValue& v) { return v.DebugString(); }, + [](const string& key, const AttrValue& av, const AttrValue& bv) { + if (key == "shape_inference_graph") { + // Default serialization of GraphDef is unstable because maps don't + // serialize deterministically. Rather than go through the hoops to + // turn on deterministic serialization of this attr just for this + // test, add logic here to compare determinstically. + GraphDef ga; + if (!ga.ParseFromString(av.s())) { + return false; + } + GraphDef gb; + if (!gb.ParseFromString(bv.s())) { + return false; + } + return EqualGraphDef(ga, gb, nullptr); + } else { + return av.DebugString() == bv.DebugString(); + } + }, + strings::StrCat(diff_preamble, " attr mismatch for node ", a.name()), + diff); +} + bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, string* diff) { - // TODO(phawkins) use a more sophisticated equality test. - if (a.DebugString() != b.DebugString()) { + if (a.signature().DebugString() != b.signature().DebugString()) { if (diff) { - *diff = strings::StrCat("Definition mismatch for function ", + *diff = strings::StrCat("Signature mismatch for function ", a.signature().name(), ", expected:\n", - a.DebugString(), "\ngot:\n", b.DebugString()); + a.signature().DebugString(), "\ngot:\n", + b.signature().DebugString()); } return false; } + if (!EqualProtoMap( + a.attr(), b.attr(), [](const string& s) { return s; }, + [](const AttrValue& v) { return v.DebugString(); }, + [](const string& key, const AttrValue& av, const AttrValue& bv) { + return av.DebugString() == bv.DebugString(); + }, + strings::StrCat("attr mismatch for function ", a.signature().name()), + diff)) { + return false; + } + if (!EqualProtoMap( + a.ret(), b.ret(), [](const string& s) { return s; }, + [](const string& s) { return s; }, + [](const string& key, const string& av, const string& bv) { + return av == bv; + }, + strings::StrCat("ret mismatch for function ", a.signature().name()), + diff)) { + return false; + } + for (int i = 0; i < a.node_def_size(); ++i) { + bool found = false; + for (int j = 0; j < b.node_def_size(); ++j) { + if (a.node_def(i).name() == b.node_def(j).name()) { + if (!EqualFunctionNodeDef( + a.node_def(i), b.node_def(j), + strings::StrCat("Function ", a.signature().name()), diff)) { + return false; + } + found = true; + break; + } + } + if (!found) { + if (diff) { + *diff = strings::StrCat("Function ", a.signature().name(), + ", expected: has node '", a.node_def(i).name(), + "' got: no node of that name"); + } + return false; + } + } + for (int i = 0; i < b.node_def_size(); ++i) { + bool found = false; + for (int j = 0; j < a.node_def_size(); ++j) { + if (b.node_def(i).name() == a.node_def(j).name()) { + found = true; + break; + } + } + if (!found) { + if (diff) { + *diff = strings::StrCat("Function ", a.signature().name(), + ", got: has node '", b.node_def(i).name(), + "' expected: no node of that name"); + } + return false; + } + } return true; } @@ -84,29 +248,64 @@ bool EqualFunctionDefLibrary(const FunctionDefLibrary& expected, // TODO(misard): remove these fake registrations once there are real Ops to be // compiled. -REGISTER_OP("_XlaSendToHost") - .Input("input: dtypes") - .Attr("dtypes: list(type) >= 0"); - -REGISTER_OP("_XlaRecvFromHost") - .Output("output: dtypes") - .Attr("dtypes: list(type) >= 0"); +REGISTER_OP("_XlaHostCompute") + .Input("inputs: Tinputs") + .Output("outputs: Toutputs") + .Attr("Tinputs: list(type) >= 0") + .Attr("Toutputs: list(type) >= 0") + .Attr("key: string") + .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("_XlaSendFromHost") - .Input("input: dtypes") - .Attr("dtypes: list(type) >= 0"); + .Input("input: Tinputs") + .Attr("Tinputs: list(type) >= 0") + .Attr("key: string") + .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("_XlaRecvAtHost") - .Output("output: dtypes") - .Attr("dtypes: list(type) >= 0"); - -REGISTER_OP("InputTest").Output("o: float"); - -REGISTER_OP("UnaryTest").Input("a: float").Output("o: float"); + .Output("output: Toutputs") + .Attr("Toutputs: list(type) >= 0") + .Attr("key: string") + .SetShapeFn(::tensorflow::shape_inference::UnknownShape); + +REGISTER_OP("InputTest") + .Output("o: float") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->UnknownShape()); + return Status::OK(); + }); + +REGISTER_OP("InputTestShaped") + .Output("o: float") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->Vector(2)); + return Status::OK(); + }); + +REGISTER_OP("UnaryTest") + .Input("a: float") + .Output("o: float") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle o; + TF_RETURN_IF_ERROR(c->Merge(c->UnknownShape(), c->input(0), &o)); + c->set_output(0, o); + return Status::OK(); + }); REGISTER_OP("BinaryTest") .Input("a: float") .Input("b: float") - .Output("o: float"); + .Output("o: float") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + ::tensorflow::shape_inference::ShapeHandle o; + TF_RETURN_IF_ERROR(c->Merge(c->UnknownShape(), c->input(0), &o)); + c->set_output(0, o); + return Status::OK(); + }); +REGISTER_OP("BinaryTest2") + .Input("a: float") + .Input("b: float") + .Output("o: float") + .SetShapeFn(::tensorflow::shape_inference::UnknownShape); REGISTER_OP("AddNLikeTest") .Input("inputs: N * T") @@ -124,22 +323,48 @@ Node* Input(const GraphDefBuilder::Options& opts) { return ops::SourceOp("InputTest", opts); } -Node* RecvAtHost(const gtl::ArraySlice& dtypes, +Node* InputShaped(const GraphDefBuilder::Options& opts) { + return ops::SourceOp("InputTestShaped", opts); +} + +Node* KnownShape(const gtl::ArraySlice& shape, + const GraphDefBuilder::Options& opts) { + if (opts.HaveError()) return nullptr; + NodeBuilder node_builder(opts.GetNameForOp("Const"), "Const", + opts.op_registry()); + TensorProto value; + value.set_dtype(DT_FLOAT); + for (int dim : shape) { + value.mutable_tensor_shape()->add_dim()->set_size(dim); + } + return opts.WithAttr("value", value) + .WithAttr("dtype", DT_FLOAT) + .FinalizeBuilder(&node_builder); +} + +Node* RecvAtHost(const string& key, const gtl::ArraySlice& dtypes, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaRecvAtHost"), "_XlaRecvAtHost", opts.op_registry()); - return opts.WithAttr("dtypes", dtypes).FinalizeBuilder(&node_builder); + return opts.WithAttr("Toutputs", dtypes) + .WithAttr("key", key) + .FinalizeBuilder(&node_builder); } -Node* SendFromHost(const std::vector& inputs, - const gtl::ArraySlice& dtypes, +Node* SendFromHost(const string& key, const std::vector& inputs, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaSendFromHost"), "_XlaSendFromHost", opts.op_registry()); node_builder.Input(inputs); - return opts.WithAttr("dtypes", dtypes).FinalizeBuilder(&node_builder); + std::vector dtypes; + for (const auto& node : inputs) { + dtypes.push_back(node.dt); + } + return opts.WithAttr("key", key) + .WithAttr("Tinputs", dtypes) + .FinalizeBuilder(&node_builder); } Node* Unary(ops::NodeOut a, const GraphDefBuilder::Options& opts) { @@ -151,6 +376,11 @@ Node* Binary(ops::NodeOut a, ops::NodeOut b, return ops::BinaryOp("BinaryTest", std::move(a), std::move(b), opts); } +Node* BinaryUnknownShape(ops::NodeOut a, ops::NodeOut b, + const GraphDefBuilder::Options& opts) { + return ops::BinaryOp("BinaryTest2", std::move(a), std::move(b), opts); +} + Node* AddNLike(const std::vector& inputs, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; @@ -576,6 +806,21 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; + string shape_string_expected; + { + GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); + Node* recv = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + shape.opts().WithName("outside_compilation_F1_O1_recv")); + Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), + shape.opts().WithName("E")); + SendFromHost("host_compute_channel_F1_O1", {e}, + shape.opts().WithName("outside_compilation_F1_O1_send")); + GraphDef shape_graph; + TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); + EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); + } + *library_expected.add_function() = test::function::XTimesTwo(); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float"}, {}, @@ -584,19 +829,18 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { {{"c"}, "BinaryTest", {"b_0_arg", "C:o:0"}, {}, {"C"}}, {{"F"}, "BinaryTest", - {"C:o:0", "outside_compilation_O1_recv:output:0"}, + {"C:o:0", "outside_compilation_O1_host_compute:outputs:0"}, {}, - {"outside_compilation_O1_recv"}}, - {{"outside_compilation_O1_send"}, - "_XlaSendToHost", + {"outside_compilation_O1_host_compute"}}, + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", {"C:o:0", "c:o:0"}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", shape_string_expected}, + {"shapes", gtl::ArraySlice({})}}, {"c"}}, - {{"outside_compilation_O1_recv"}, - "_XlaRecvFromHost", - {}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, - {"outside_compilation_O1_send"}}, }, {{"f_0_retval", "F:o:0"}}); @@ -612,11 +856,11 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { Node* call = b2.opts().FinalizeBuilder(&node_builder); Node* recv = - RecvAtHost({DT_FLOAT, DT_FLOAT}, + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), b2.opts().WithName("E").WithControlInputs({recv, b})); - Node* send = SendFromHost({e}, {DT_FLOAT}, + Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -674,37 +918,71 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; + string shape_string_expected_1; + { + GraphDefBuilder shape1(GraphDefBuilder::kFailImmediately); + Node* recv = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + shape1.opts().WithName("outside_compilation_F1_O1_recv")); + Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), + shape1.opts().WithName("E")); + SendFromHost("host_compute_channel_F1_O1", {e}, + shape1.opts().WithName("outside_compilation_F1_O1_send")); + GraphDef shape1_graph; + TF_EXPECT_OK(shape1.ToGraphDef(&shape1_graph)); + EXPECT_TRUE(shape1_graph.SerializeToString(&shape_string_expected_1)); + } + + string shape_string_expected_2; + { + GraphDefBuilder shape2(GraphDefBuilder::kFailImmediately); + Node* recv1 = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + shape2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), + shape2.opts().WithName("E")); + Node* recv2 = + RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, + shape2.opts().WithName("outside_compilation_F1_O2_recv")); + Node* h = Binary(ops::NodeOut(recv2, 0), e, shape2.opts().WithName("H")); + SendFromHost("host_compute_channel_F1_O2", {h}, + shape2.opts().WithName("outside_compilation_F1_O2_send")); + GraphDef shape2_graph; + TF_EXPECT_OK(shape2.ToGraphDef(&shape2_graph)); + EXPECT_TRUE(shape2_graph.SerializeToString(&shape_string_expected_2)); + } + *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"i_0_retval:float"}, {}, { {{"C"}, "UnaryTest", {"a_0_arg"}}, {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}, {}}, - {{"I"}, "UnaryTest", {"outside_compilation_O2_recv:output:0"}}, + {{"I"}, + "UnaryTest", + {"outside_compilation_O2_host_compute:outputs:0"}}, {{"F"}, "BinaryTest", - {"C:o:0", "outside_compilation_O1_recv:output:0"}, + {"C:o:0", "outside_compilation_O1_host_compute:outputs:0"}, {}, - {"outside_compilation_O1_recv"}}, - {{"outside_compilation_O2_send"}, - "_XlaSendToHost", + {"outside_compilation_O1_host_compute"}}, + {{"outside_compilation_O2_host_compute"}, + "_XlaHostCompute", {"D:o:0", "F:o:0"}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F1_O2"}, + {"shape_inference_graph", shape_string_expected_2}, + {"shapes", gtl::ArraySlice({})}}, {"F"}}, - {{"outside_compilation_O1_send"}, - "_XlaSendToHost", + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", {"C:o:0", "D:o:0"}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", shape_string_expected_1}, + {"shapes", gtl::ArraySlice({})}}, {"D"}}, - {{"outside_compilation_O2_recv"}, - "_XlaRecvFromHost", - {}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, - {"outside_compilation_O2_send"}}, - {{"outside_compilation_O1_recv"}, - "_XlaRecvFromHost", - {}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, - {"outside_compilation_O1_send"}}, }, {{"i_0_retval", "I:o:0"}}); @@ -720,23 +998,24 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { Node* call = b2.opts().FinalizeBuilder(&node_builder); Node* recv1 = - RecvAtHost({DT_FLOAT, DT_FLOAT}, + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost({e}, {DT_FLOAT}, + Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); Node* recv2 = - RecvAtHost({DT_FLOAT, DT_FLOAT}, + RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O2_recv")); Node* g = Binary(e, ops::NodeOut(recv2, 1), b2.opts().WithName("G").WithControlInputs({recv2, e})); Node* h = Binary(ops::NodeOut(recv2, 0), e, b2.opts().WithName("H")); - Node* send2 = SendFromHost( - {h}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O2_send")); + Node* send2 = + SendFromHost("host_compute_channel_F1_O2", {h}, + b2.opts().WithName("outside_compilation_F1_O2_send")); Node* s = NoOp(b2.opts() .WithName("F1_sequencer") @@ -758,8 +1037,8 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { { GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); - Node* a = Input(b1.opts().WithName("A")); - Node* b = Input(b1.opts().WithName("B")); + Node* a = InputShaped(b1.opts().WithName("A")); + Node* b = InputShaped(b1.opts().WithName("B")); Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); Node* d = Binary(b, c, b1.opts().WithName("D").WithAttr("_encapsulate", "F1")); @@ -791,6 +1070,24 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; + string shape_string_expected; + { + GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); + Node* recv = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + shape.opts().WithName("outside_compilation_F1_O1_recv")); + Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), + shape.opts().WithName("E")); + SendFromHost("host_compute_channel_F1_O1", {e}, + shape.opts().WithName("outside_compilation_F1_O1_send")); + GraphDef shape_graph; + TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); + EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); + } + + TensorShapeProto shape_proto_expected; + shape_proto_expected.add_dim()->set_size(2); + *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float", "d_0_retval:float"}, {}, @@ -799,19 +1096,18 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "BinaryTest", - {"C:o:0", "outside_compilation_O1_recv:output:0"}, + {"C:o:0", "outside_compilation_O1_host_compute:outputs:0"}, {}, - {"outside_compilation_O1_recv"}}, - {{"outside_compilation_O1_send"}, - "_XlaSendToHost", + {"outside_compilation_O1_host_compute"}}, + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", {"C:o:0", "D:o:0"}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", shape_string_expected}, + {"shapes", gtl::ArraySlice({})}}, {"D"}}, - {{"outside_compilation_O1_recv"}, - "_XlaRecvFromHost", - {}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, - {"outside_compilation_O1_send"}}, }, {{"d_0_retval", "D:o:0"}, {"f_0_retval", "F:o:0"}}); @@ -822,16 +1118,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { {{"G"}, "BinaryTest", {"e_0_arg", "f_0_arg"}}, {{"I"}, "BinaryTest", - {"f_0_arg", "outside_compilation_O1_recv:output:0"}}, - {{"outside_compilation_O1_send"}, - "_XlaSendToHost", + {"f_0_arg", "outside_compilation_O1_host_compute:outputs:0"}}, + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", {"G:o:0"}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, - {{"outside_compilation_O1_recv"}, - "_XlaRecvFromHost", - {}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, - {"outside_compilation_O1_send"}}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F2_O1"}, + {"shape_inference_graph", ""}, + {"shapes", + gtl::ArraySlice({shape_proto_expected})}}}, }, {{"g_0_retval", "G:o:0"}, {"i_0_retval", "I:o:0"}}); @@ -839,15 +1135,15 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { std::unique_ptr lib_def( new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); - Node* a = Input(b2.opts().WithName("A")); - Node* b = Input(b2.opts().WithName("B")); + Node* a = InputShaped(b2.opts().WithName("A")); + Node* b = InputShaped(b2.opts().WithName("B")); Node* recv1 = - RecvAtHost({DT_FLOAT, DT_FLOAT}, + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost({e}, {DT_FLOAT}, + Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -857,12 +1153,14 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* s1 = NoOp( b2.opts().WithName("F1_sequencer").WithControlInputs({recv1, send1})); - Node* recv2 = RecvAtHost( - {DT_FLOAT}, b2.opts().WithName("outside_compilation_F2_O1_recv")); + Node* recv2 = + RecvAtHost("host_compute_channel_F2_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F2_O1_recv")); Node* h = Binary(ops::NodeOut(call1, 1), recv2, b2.opts().WithName("H").WithControlInput(s1)); - Node* send2 = SendFromHost( - {h}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F2_O1_send")); + Node* send2 = + SendFromHost("host_compute_channel_F2_O1", {h}, + b2.opts().WithName("outside_compilation_F2_O1_send")); NodeBuilder node_builder2("F2", "F2", lib_def.get()); node_builder2.Input(e).Input(call1); @@ -888,7 +1186,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { { GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); - Node* a = Input(b1.opts().WithName("A")); + Node* a = InputShaped(b1.opts().WithName("A")); Node* b = Input(b1.opts().WithName("B")); Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); Node* d = @@ -908,6 +1206,9 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; + TensorShapeProto shape_proto_expected; + shape_proto_expected.add_dim()->set_size(2); + *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float"}, {}, { @@ -915,11 +1216,16 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "BinaryTest", - {"D:o:0", "outside_compilation_O1_recv:output:0"}}, - {{"outside_compilation_O1_recv"}, - "_XlaRecvFromHost", + {"D:o:0", "outside_compilation_O1_host_compute:outputs:0"}}, + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", {}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, + {{"Tinputs", gtl::ArraySlice({})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", ""}, + {"shapes", + gtl::ArraySlice({shape_proto_expected})}}}, }, {{"f_0_retval", "F:o:0"}}); @@ -927,12 +1233,13 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { std::unique_ptr lib_def( new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); - Node* a = Input(b2.opts().WithName("A")); + Node* a = InputShaped(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); Node* e = Unary(a, b2.opts().WithName("E")); - Node* send1 = SendFromHost( - {e}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* send1 = + SendFromHost("host_compute_channel_F1_O1", {e}, + b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -954,7 +1261,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { { GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); - Node* a = Input(b1.opts().WithName("A")); + Node* a = InputShaped(b1.opts().WithName("A")); Node* b = Input(b1.opts().WithName("B")); Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); Node* d = @@ -975,6 +1282,9 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; + TensorShapeProto shape_proto_expected; + shape_proto_expected.add_dim()->set_size(2); + *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float"}, {}, { @@ -982,17 +1292,17 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "BinaryTest", - {"D:o:0", "outside_compilation_O1_recv:output:0"}}, - {{"outside_compilation_O1_send"}, - "_XlaSendToHost", + {"D:o:0", "outside_compilation_O1_host_compute:outputs:0"}}, + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", {}, - {{"dtypes", gtl::ArraySlice({})}}, + {{"Tinputs", gtl::ArraySlice({})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", ""}, + {"shapes", + gtl::ArraySlice({shape_proto_expected})}}, {"D"}}, - {{"outside_compilation_O1_recv"}, - "_XlaRecvFromHost", - {}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, - {"outside_compilation_O1_send"}}, }, {{"f_0_retval", "F:o:0"}}); @@ -1000,14 +1310,16 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { std::unique_ptr lib_def( new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); - Node* a = Input(b2.opts().WithName("A")); + Node* a = InputShaped(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); Node* recv1 = - RecvAtHost({}, b2.opts().WithName("outside_compilation_F1_O1_recv")); + RecvAtHost("host_compute_channel_F1_O1", {}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(a, b2.opts().WithName("E").WithControlInput(recv1)); - Node* send1 = SendFromHost( - {e}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* send1 = + SendFromHost("host_compute_channel_F1_O1", {e}, + b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -1055,10 +1367,14 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoOutputs) { {{"C"}, "UnaryTest", {"a_0_arg"}}, {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "UnaryTest", {"D:o:0"}}, - {{"outside_compilation_O1_send"}, - "_XlaSendToHost", + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", {"D:o:0"}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", ""}, + {"shapes", gtl::ArraySlice({})}}}, }, {{"f_0_retval", "F:o:0"}}); @@ -1069,8 +1385,9 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoOutputs) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = RecvAtHost( - {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* recv1 = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); @@ -1118,16 +1435,19 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlOutput) { { {{"C"}, "UnaryTest", {"a_0_arg"}}, {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, - {{"F"}, "UnaryTest", {"D:o:0"}, {}, {"outside_compilation_O1_recv"}}, - {{"outside_compilation_O1_send"}, - "_XlaSendToHost", + {{"F"}, + "UnaryTest", {"D:o:0"}, - {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, - {{"outside_compilation_O1_recv"}, - "_XlaRecvFromHost", {}, - {{"dtypes", gtl::ArraySlice({})}}, - {"outside_compilation_O1_send"}}, + {"outside_compilation_O1_host_compute"}}, + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", + {"D:o:0"}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", ""}, + {"shapes", gtl::ArraySlice({})}}}, }, {{"f_0_retval", "F:o:0"}}); @@ -1138,10 +1458,11 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlOutput) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = RecvAtHost( - {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* recv1 = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); - Node* send1 = SendFromHost({}, {}, + Node* send1 = SendFromHost("host_compute_channel_F1_O1", {}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -1215,5 +1536,110 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputsOrOutputs) { TF_EXPECT_FUNCTIONDEFLIBRARY_EQ(library_expected, library); } +// Test for shape inference of outside compilation. +TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { + FunctionDefLibrary library; + GraphDef graphdef; + + { + *library.add_function() = test::function::XTimesTwo(); + + GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); + Node* a = InputShaped(b1.opts().WithName("A")); + Node* b = Input(b1.opts().WithName("B")); + // Give nodes 'c' and 'd' names that collide after lowercasing. + Node* c = Unary(a, b1.opts().WithName("C")); + Node* d = Unary(b, b1.opts().WithName("c").WithControlInput(c).WithAttr( + "_encapsulate", "F1")); + Node* e = BinaryUnknownShape(c, d, + b1.opts() + .WithName("E") + .WithControlInputs({b, d}) + .WithAttr("_encapsulate", "F1") + .WithAttr("_outside", "O1")); + Node* f = Binary(c, e, + b1.opts().WithName("F").WithControlInput(e).WithAttr( + "_encapsulate", "F1")); + Binary(a, f, b1.opts().WithName("G").WithControlInput(e)); + TF_EXPECT_OK(b1.ToGraphDef(&graphdef)); + } + + TF_EXPECT_OK(Encapsulate(&graphdef, &library)); + + FunctionDefLibrary library_expected; + GraphDef graphdef_expected; + + string shape_string_expected; + { + GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); + Node* known = KnownShape({2}, shape.opts().WithName("KnownShape/_0")); + Node* recv = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, + shape.opts().WithName("outside_compilation_F1_O1_recv")); + Node* e = BinaryUnknownShape(known, recv, shape.opts().WithName("E")); + SendFromHost("host_compute_channel_F1_O1", {e}, + shape.opts().WithName("outside_compilation_F1_O1_send")); + GraphDef shape_graph; + TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); + EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); + } + + *library_expected.add_function() = test::function::XTimesTwo(); + *library_expected.add_function() = FunctionDefHelper::Create( + "F1", {"b_0_arg:float", "c_0_arg:float"}, {"f_0_retval:float"}, {}, + { + {{"c"}, "UnaryTest", {"b_0_arg"}, {}, {}}, + {{"F"}, + "BinaryTest", + {"c_0_arg", "outside_compilation_O1_host_compute:outputs:0"}, + {}, + {"outside_compilation_O1_host_compute"}}, + {{"outside_compilation_O1_host_compute"}, + "_XlaHostCompute", + {"c:o:0"}, + {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, + {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, + {"key", "host_compute_channel_F1_O1"}, + {"shape_inference_graph", shape_string_expected}, + {"shapes", gtl::ArraySlice({})}}, + {"c"}}, + }, + {{"f_0_retval", "F:o:0"}}); + + { + std::unique_ptr lib_def( + new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); + GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); + Node* a = InputShaped(b2.opts().WithName("A")); + Node* b = Input(b2.opts().WithName("B")); + Node* c = Unary(a, b2.opts().WithName("C")); + + NodeBuilder node_builder("F1", "F1", lib_def.get()); + node_builder.Input(b).Input(c); + Node* call = + b2.opts().WithControlInputs({c}).FinalizeBuilder(&node_builder); + + Node* recv = + RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, + b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* e = BinaryUnknownShape( + c, ops::NodeOut(recv, 0), + b2.opts().WithName("E").WithControlInputs({recv, b})); + Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, + b2.opts() + .WithName("outside_compilation_F1_O1_send") + .WithControlInput(e)); + + Node* s = NoOp( + b2.opts().WithName("F1_sequencer").WithControlInputs({recv, send})); + + Binary(a, call, b2.opts().WithName("G").WithControlInputs({s, e})); + TF_EXPECT_OK(b2.ToGraphDef(&graphdef_expected)); + } + + TF_EXPECT_GRAPH_EQ(graphdef_expected, graphdef); + TF_EXPECT_FUNCTIONDEFLIBRARY_EQ(library_expected, library); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc index d6b576166c..eae8e6c3c1 100644 --- a/tensorflow/core/framework/function.cc +++ b/tensorflow/core/framework/function.cc @@ -1064,26 +1064,36 @@ Status FunctionLibraryDefinition::AddLibrary( return Status::OK(); } -void FunctionLibraryDefinition::RemoveFunction(const string& func) { +Status FunctionLibraryDefinition::RemoveFunction(const string& func) { const auto& i = function_defs_.find(func); - DCHECK(i != function_defs_.end()); + if (i == function_defs_.end()) { + return errors::InvalidArgument("Tried to remove non-existent function ", + func); + } function_defs_.erase(i); + return Status::OK(); } -void FunctionLibraryDefinition::RemoveGradient(const string& func) { +Status FunctionLibraryDefinition::RemoveGradient(const string& func) { const auto& i = func_grad_.find(func); - DCHECK(i != func_grad_.end()); + if (i == func_grad_.end()) { + return errors::InvalidArgument("Tried to remove non-existent gradient ", + func); + } func_grad_.erase(i); + return Status::OK(); } void FunctionLibraryDefinition::Remove( const std::vector& funcs, const std::vector& funcs_with_grads) { for (const string& f : funcs) { - RemoveFunction(f); + Status s = RemoveFunction(f); + DCHECK(s.ok()); } for (const string& f : funcs_with_grads) { - RemoveGradient(f); + Status s = RemoveGradient(f); + DCHECK(s.ok()); } } diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h index b933ee0b0e..7d0e15641d 100644 --- a/tensorflow/core/framework/function.h +++ b/tensorflow/core/framework/function.h @@ -312,6 +312,14 @@ class FunctionLibraryDefinition : public OpRegistryInterface { // This operation is atomic. Status AddGradientDef(const GradientDef& grad); + // Remove function `func` from the library. Returns non-OK Status unless + // `func` is in the library. + Status RemoveFunction(const string& func); + + // Remove gradient of function `func` from the library. Returns non-OK Status + // unless `func` has a gradient. + Status RemoveGradient(const string& func); + // Adds the functions and gradients in 'other' to this function library. // Duplicate functions and gradients are ignored. // This operation is atomic. @@ -384,13 +392,6 @@ class FunctionLibraryDefinition : public OpRegistryInterface { // attr from. const FunctionDef* GetAttrImpl(const NodeDef& ndef) const; - // Remove function `func` from the library. `func` must be in the library. - void RemoveFunction(const string& func); - - // Remove gradient of function `func` from the library. `func` must have - // a gradient. - void RemoveGradient(const string& func); - // Remove all functions in `funcs` and all gradients of // functions in `funcs_with_grads` from this library. void Remove(const std::vector& funcs, -- GitLab From 69655f34611747e51fc2644d1888301ecfcc4c96 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 10:38:14 -0800 Subject: [PATCH 388/423] Fix nest bug with different dictionary key orderings. PiperOrigin-RevId: 184160009 --- tensorflow/python/data/util/nest.py | 4 ++-- tensorflow/python/data/util/nest_test.py | 4 ++++ tensorflow/python/util/nest.py | 4 ++-- tensorflow/python/util/nest_test.py | 4 ++++ 4 files changed, 12 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/data/util/nest.py b/tensorflow/python/data/util/nest.py index 2455395635..e387e35740 100644 --- a/tensorflow/python/data/util/nest.py +++ b/tensorflow/python/data/util/nest.py @@ -383,8 +383,8 @@ def assert_shallow_structure(shallow_tree, input_tree, check_types=True): "structure has keys %s, while shallow structure has keys %s." % (list(_six.iterkeys(input_tree)), list(_six.iterkeys(shallow_tree)))) - input_tree = list(_six.iteritems(input_tree)) - shallow_tree = list(_six.iteritems(shallow_tree)) + input_tree = list(sorted(_six.iteritems(input_tree))) + shallow_tree = list(sorted(_six.iteritems(shallow_tree))) for shallow_branch, input_branch in zip(shallow_tree, input_tree): assert_shallow_structure(shallow_branch, input_branch, diff --git a/tensorflow/python/data/util/nest_test.py b/tensorflow/python/data/util/nest_test.py index 90dd7dfe77..ff380815a4 100644 --- a/tensorflow/python/data/util/nest_test.py +++ b/tensorflow/python/data/util/nest_test.py @@ -277,6 +277,10 @@ class NestTest(test.TestCase): with self.assertRaisesRegexp(ValueError, expected_message): nest.assert_shallow_structure(inp_ab2, inp_ab1) + inp_ab = collections.OrderedDict([("a", 1), ("b", (2, 3))]) + inp_ba = collections.OrderedDict([("b", (2, 3)), ("a", 1)]) + nest.assert_shallow_structure(inp_ab, inp_ba) + def testFlattenUpTo(self): input_tree = (((2, 2), (3, 3)), ((4, 9), (5, 5))) shallow_tree = ((True, True), (False, True)) diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py index 874df3d108..c8525ed420 100644 --- a/tensorflow/python/util/nest.py +++ b/tensorflow/python/util/nest.py @@ -532,8 +532,8 @@ def assert_shallow_structure(shallow_tree, input_tree, check_types=True): (list(_six.iterkeys(input_tree)), list(_six.iterkeys(shallow_tree)))) - input_tree = list(_six.iteritems(input_tree)) - shallow_tree = list(_six.iteritems(shallow_tree)) + input_tree = list(sorted(_six.iteritems(input_tree))) + shallow_tree = list(sorted(_six.iteritems(shallow_tree))) for shallow_branch, input_branch in zip(shallow_tree, input_tree): assert_shallow_structure(shallow_branch, input_branch, diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py index 6bec397db5..8aaf799fd0 100644 --- a/tensorflow/python/util/nest_test.py +++ b/tensorflow/python/util/nest_test.py @@ -425,6 +425,10 @@ class NestTest(test.TestCase): with self.assertRaisesRegexp(ValueError, expected_message): nest.assert_shallow_structure(inp_ab2, inp_ab1) + inp_ab = collections.OrderedDict([("a", 1), ("b", (2, 3))]) + inp_ba = collections.OrderedDict([("b", (2, 3)), ("a", 1)]) + nest.assert_shallow_structure(inp_ab, inp_ba) + def testFlattenUpTo(self): # Shallow tree ends at scalar. input_tree = [[[2, 2], [3, 3]], [[4, 9], [5, 5]]] -- GitLab From a1a34b1440c4c4792f945275529e6c5b3c7aa2ca Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Thu, 1 Feb 2018 10:43:29 -0800 Subject: [PATCH 389/423] Add function paths to their signatures. fixes #16167 PiperOrigin-RevId: 184160925 --- tensorflow/tools/docs/pretty_docs.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/tensorflow/tools/docs/pretty_docs.py b/tensorflow/tools/docs/pretty_docs.py index c033c16ae9..ac04f566d0 100644 --- a/tensorflow/tools/docs/pretty_docs.py +++ b/tensorflow/tools/docs/pretty_docs.py @@ -162,7 +162,7 @@ def _build_class_page(page_info): parts.append(h3.format(**method_info.__dict__)) if method_info.signature is not None: - parts.append(_build_signature(method_info)) + parts.append(_build_signature(method_info, use_full_name=False)) parts.append(method_info.doc.docstring) parts.append(_build_function_details(method_info.doc.function_details)) @@ -259,14 +259,14 @@ def _build_module_page(page_info): return ''.join(parts) -def _build_signature(obj_info): +def _build_signature(obj_info, use_full_name=True): """Returns a md code block showing the function signature.""" # Special case tf.range, since it has an optional first argument if obj_info.full_name == 'tf.range': return ( '``` python\n' - "range(limit, delta=1, dtype=None, name='range')\n" - "range(start, limit, delta=1, dtype=None, name='range')\n" + "tf.range(limit, delta=1, dtype=None, name='range')\n" + "tf.range(start, limit, delta=1, dtype=None, name='range')\n" '```\n\n') parts = ['``` python'] @@ -281,7 +281,11 @@ def _build_signature(obj_info): sig = ',\n'.join(' %s' % sig_item for sig_item in obj_info.signature) sig = '\n'+sig+'\n' - parts.append(signature_template.format(name=obj_info.short_name, sig=sig)) + if use_full_name: + obj_name = obj_info.full_name + else: + obj_name = obj_info.short_name + parts.append(signature_template.format(name=obj_name, sig=sig)) parts.append('```\n\n') return '\n'.join(parts) -- GitLab From 14e0e7fe1eafd286f3813ba839b5f3236394a0a1 Mon Sep 17 00:00:00 2001 From: Anna R Date: Thu, 1 Feb 2018 11:06:53 -0800 Subject: [PATCH 390/423] Internal change. PiperOrigin-RevId: 184165180 --- tensorflow/contrib/lite/kernels/BUILD | 1 + tensorflow/contrib/lite/kernels/conv_test.cc | 1 + 2 files changed, 2 insertions(+) diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index d9051f3516..8c40adfae5 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -249,6 +249,7 @@ tf_cc_test( ":builtin_ops", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_absl//absl/memory", "@com_google_googletest//:gtest", ], ) diff --git a/tensorflow/contrib/lite/kernels/conv_test.cc b/tensorflow/contrib/lite/kernels/conv_test.cc index 461efffe39..7550f7cc0d 100644 --- a/tensorflow/contrib/lite/kernels/conv_test.cc +++ b/tensorflow/contrib/lite/kernels/conv_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include #include +#include "absl/memory/memory.h" #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/kernels/test_util.h" -- GitLab From 4adfae9fe10968063cf55cea1bfef9b405c407c0 Mon Sep 17 00:00:00 2001 From: Anna R Date: Thu, 1 Feb 2018 11:33:34 -0800 Subject: [PATCH 391/423] Automated g4 rollback of changelist 184153187 PiperOrigin-RevId: 184169668 --- .../jit/encapsulate_subgraphs_pass.cc | 647 +++-------------- .../jit/encapsulate_subgraphs_pass_test.cc | 682 ++++-------------- tensorflow/core/framework/function.cc | 22 +- tensorflow/core/framework/function.h | 15 +- 4 files changed, 241 insertions(+), 1125 deletions(-) diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 8edae9fc9c..0de163d3a8 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -30,14 +30,12 @@ limitations under the License. #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/optimization_registry.h" -#include "tensorflow/core/common_runtime/shape_refiner.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/graph_def_util.h" #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/graph/algorithm.h" #include "tensorflow/core/graph/graph.h" -#include "tensorflow/core/graph/graph_def_builder.h" #include "tensorflow/core/graph/tensor_id.h" #include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/lib/gtl/map_util.h" @@ -143,7 +141,8 @@ struct NodeSlot { // everything to use it. static const char* const kArgOp = "_Arg"; static const char* const kRetValOp = "_Retval"; -static const char* const kHostComputeOp = "_XlaHostCompute"; +static const char* const kSendToHostOp = "_XlaSendToHost"; +static const char* const kRecvFromHostOp = "_XlaRecvFromHost"; static const char* const kSendFromHostOp = "_XlaSendFromHost"; static const char* const kRecvAtHostOp = "_XlaRecvAtHost"; @@ -172,8 +171,7 @@ class Encapsulator { // Write a copy of the input graph to 'graph_out', where the subgraphs are // replaced with calls to the new functions. - Status BuildOutputGraph(bool parallel_checking, Graph* graph_out, - FunctionLibraryDefinition* library); + Status BuildOutputGraph(bool parallel_checking, Graph* graph_out); private: // A subgraph of the input, all marked with a common 'group_attribute' @@ -203,29 +201,21 @@ class Encapsulator { // .. . // RAH --> C --> SFH // - // The compiled cluster is as follows. HC is a HostCompute node which is the - // source of a channel to the RAH node above and the destination of a channel - // from the SFH node above. + // The compiled cluster is as follows. STH is a SendToHost node which is the + // source of a channel to the RAH node above. RFH is a RecvFromHost node which + // is the destination of a channel from the SFH node above. There is a control + // edge that ensures RFH follows STH, which is used in shape inference to + // ensure that the shapes on the STH host channel are known before the RFH + // channel is compiled. // - // Arg --> B --> HC --> D --> Retval + // Arg --> B --> STH ..> RFH --> D --> Retval // - // The channels HC/RAH and SFH/HC each transmit multiple tensors, so there is - // at most one RAH and SFH in each outside_compilation cluster. This design is - // preferred over adding separate Arg/Retval nodes for each transmitted value - // because it allows optimizations to the host code that would like to limit - // communication between host and device and, e.g., raise only one interrupt - // per channel rather than one per transmitted value. - // - // The shapes of the outputs from the HC node in general cannot be determined - // until the shapes of its inputs are known at compile time, since e.g., - // above, the shape of C's outputs aren't known until the shape of its inputs - // are known. If the shapes of the HC's outputs can be determined during the - // rewrite, they are stored in the node's 'shapes' attr. Otherwise a minimal - // graph is stored in the shape_inference_graph attr. This graph can be used - // when compiling the HC Op to determined the shape of the SFH inputs given - // the shapes of any ancestor RAH outputs. If it can be determined that the - // shape of the SFH inputs will not be inferrable even once the shapes of the - // RAH outputs are known, an error is returned by the rewriter. + // The channels STH/RAH and SFH/RFH each transmit a tuple, so there is at most + // one RAH and SFH in each compiled cluster. This design is preferred over + // adding separate Arg/Retval nodes for each transmitted value because it + // simplifies the host code that would like to limit communication between + // host and device and, e.g., raise only one interrupt per channel rather than + // one per transmitted value. class Subgraph { public: // Creates a graph to build the subgraph in, if it doesn't already exist, @@ -256,10 +246,6 @@ class Encapsulator { const std::unordered_map& node_images, Graph* graph_out); - // Returns the names of all the outside_compilation subgraphs in this - // Subgraph. - void GetOutsideCompilationSubgraphNames(std::vector* names) const; - // Returns the Node that inputs to the function should be wired up to. Node* GetCallNodeForInputs() const; @@ -319,9 +305,15 @@ class Encapsulator { void RecordOutsideCompilationOutputOrControl( const string& outside_compilation_id, const Edge* edge); - // Adds the HostCompute nodes for each outside_compilation subgraph. - Status AddHostComputes( - const string& subgraph_name, + // Adds the SendToHost nodes for each outside_compilation subgraph once the + // edges have all been recorded via RecordOutsideCompilationInputOrControl. + Status AddSendsToOutsideCompilation( + const std::unordered_map& node_images); + + // Adds the RecvFromHost nodes for each outside_compilation subgraph once + // the edges have all been recorded via + // RecordOutsideCompilationOutputOrControl. + Status AddRecvsFromOutsideCompilation( const std::unordered_map& node_images); // Creates the sequencer node if it doesn't exist, adding it to graph_out. @@ -331,16 +323,10 @@ class Encapsulator { // all the downstream nodes of call_node_outputs. void ConnectSequencerToOutputs(Graph* graph_out); - Status AddShapeInferenceInfo( - const string& outside_compilation_subgraph_name, - const std::vector& shapes, GraphDef* inference_graph); - - Status ReplaceFunctionDef(FunctionLibraryDefinition* library); - private: struct OutsideCompilationSubgraph { // Map from source (producer node/slot) tensors in the original graph to - // input index (slot number in the HostCompute/RecvAtHost nodes that will + // input index (slot number in the SendToHost/RecvAtHost nodes that will // be created) for the outside_compilation subgraph. std::unordered_map inputs; @@ -349,14 +335,14 @@ class Encapsulator { // outside_compilation subgraph. These are recorded by // RecordOutsideCompilationInputOrControl while walking all the subgraph // edges, and lifted control edges within the subgraph are added by - // AddSendsToOutsideCompilation once the _HostCompute node has been + // AddSendsToOutsideCompilation once the _SendToHost node has been // created. The matching control edge from _RecvAtHost to the // destination is added by CopyEdgeToOutputGraph. std::unordered_set control_inputs; // Maps from source (producer node/slot) and destination (consumer // node/slot) tensors in the original graph to output index (slot number - // in the SendFromHost/HostCompute nodes that will be created) for the + // in the SendFromHost/RecvFromHost nodes that will be created) for the // outside_compilation subgraph. std::unordered_map outputs_by_src; std::unordered_map outputs_by_dst; @@ -366,13 +352,13 @@ class Encapsulator { // containing compiled subgraph. These are recorded by // RecordOutsideCompilationOutputOrControl while walking all the subgraph // edges, and lifted control edges within the subgraph are added by - // AddRecvsFromToOutsideCompilation once the _HostCompute node has been + // AddRecvsFromToOutsideCompilation once the _RecvFromHost node has been // created. The matching control edge from the source to _SendFromHost to // the destination is added by CopyEdgeToOutputGraph. std::unordered_set control_outputs; - // Name of the _HostCompute node in the subgraph. - string host_compute_name; + // _SendToHost node in the subgraph. Not owned. + Node* send_to_host = nullptr; // _RecvAtHost node in the output graph. Not owned. Node* recv_at_host = nullptr; @@ -530,59 +516,6 @@ class Encapsulator { const std::unordered_map& node_images, bool parallel_checking, Graph* graph_out); - // Constructs a minimal shape inference graph that can be used to determine - // the shape of send_node at the time that the subgraph is compiled. - // recv_at_host_nodes contains the names of all the recv_at_host nodes that - // send_node might depend on. These recv_at_host nodes have shapes that are - // not known during the rewrite pass, but will be known at compile time. - // - // If the shapes of all the inputs to send_node can be determined during the - // rewrite pass, on exit graphdef_out is empty and the shapes are returned in - // static_shape_out. Otherwise graphdef_out contains a graph that can be used - // for shape inference at compile time, where all the source nodes of the - // graph are either constants with known shapes, or nodes named in - // recv_at_host_nodes. - // - // A non-OK status is returned if neither of the above conditions can be - // satisfied, e.g., because send_node depends on a node that doesn't have a - // registered shape inference function. - Status DoStaticShapeInferenceForOutsideCompilationSend( - const Graph& graph_in, const ShapeRefiner& shape_refiner, - const std::unordered_set& recv_at_host_nodes, Node* send_node, - FunctionLibraryDefinition* library, - std::vector* static_shape_out, - std::unique_ptr* graphdef_out); - - // Makes a copy of graph containing only nodes that are ancestors of at least - // one node in send_from_host_nodes and store it in pruned_graph. On exit - // nodes_images contains a mapping from nodes in graph to nodes in - // pruned_graph. All functions in the copied graph are inlined. - Status MakePrunedGraphCopyAndInline( - const Graph& graph, const std::vector& sink_nodes, - std::unique_ptr* pruned_graph, - std::unordered_map* node_images, - FunctionLibraryDefinition* library); - - // Makes a copy of graph containing only nodes that are ancestors of a - // send_from_host node in an outside_compilation subgraph, and store it in - // pruned_graph. Also perform shape inference on the pruned graph, using - // shape_refiner. On exit node_images contains a mapping from nodes in graph - // to nodes in pruned_graph. - Status MakeGraphForOutsideCompilationSends( - const Graph& graph, std::unique_ptr* pruned_graph, - ShapeRefiner* shape_refiner, - std::unordered_map* node_images, - FunctionLibraryDefinition* library); - - // Performs static shape inference, as far as possible, for the send_from_host - // nodes in each outside_compilation subgraph. Where it is not possible to - // determine the shape statically, stores a serialized GraphDef in the - // HostCompute 'shape_inference_graph' attr, to be used at compile time for - // final inference. If the shapes are known statically they are stored in the - // HostCompute 'shapes' attr. - Status GetShapeInfoForOutsideCompilationSends( - Graph* graph_out, FunctionLibraryDefinition* library); - const string group_attribute_; const string outside_compilation_attribute_; const Graph* graph_in_; @@ -749,20 +682,16 @@ void Encapsulator::Subgraph::RecordOutsideCompilationOutputOrControl( } } -Status Encapsulator::Subgraph::AddHostComputes( - const string& subgraph_name, +Status Encapsulator::Subgraph::AddSendsToOutsideCompilation( const std::unordered_map& node_images) { for (auto& oc_subgraph_iter : outside_compilation_subgraphs_) { const string& oc_subgraph_name = oc_subgraph_iter.first; OutsideCompilationSubgraph& oc_subgraph = oc_subgraph_iter.second; - if (!oc_subgraph.inputs.empty() || !oc_subgraph.control_inputs.empty() || - !oc_subgraph.outputs_by_src.empty() || - !oc_subgraph.control_outputs.empty()) { - // Build a _HostCompute node. + if (!oc_subgraph.inputs.empty() || !oc_subgraph.control_inputs.empty()) { + // Build a _SendToHost node sending all the args of the appropriate + // types. + std::vector dtypes(oc_subgraph.inputs.size(), DT_INVALID); std::vector inputs(oc_subgraph.inputs.size()); - std::vector input_dtypes(oc_subgraph.inputs.size(), DT_INVALID); - std::vector output_dtypes(oc_subgraph.outputs_by_src.size(), - DT_INVALID); for (const auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; @@ -771,64 +700,94 @@ Status Encapsulator::Subgraph::AddHostComputes( int input_index = input_src.second; DataType dtype = src_node->output_type(src_slot); + dtypes[input_index] = dtype; inputs[input_index].Reset(src_image->name(), src_slot, dtype); - input_dtypes[input_index] = dtype; } - for (const auto& output : oc_subgraph.outputs_by_src) { - DataType dtype = output.first.dtype; - int output_index = output.second; - output_dtypes[output_index] = dtype; - } - - NodeDef host_compute_def; - NodeDefBuilder builder(strings::StrCat("outside_compilation_", - oc_subgraph_name, "_host_compute"), - kHostComputeOp); + NodeDef send_def; + NodeDefBuilder builder( + strings::StrCat("outside_compilation_", oc_subgraph_name, "_send"), + kSendToHostOp); + builder.Attr("dtypes", dtypes); builder.Input(inputs); - builder.Attr("Tinputs", input_dtypes); - builder.Attr("Toutputs", output_dtypes); - builder.Attr("key", - strings::StrCat("host_compute_channel_", subgraph_name, "_", - oc_subgraph_name)); - Status s = builder.Finalize(&host_compute_def); + Status s = builder.Finalize(&send_def); if (!s.ok()) return s; - Node* host_compute = graph_->AddNode(host_compute_def, &s); + oc_subgraph.send_to_host = graph_->AddNode(send_def, &s); if (!s.ok()) return s; - oc_subgraph.host_compute_name = host_compute->name(); - // Connect the _HostCompute node to its producers in the subgraph. + // Connect the _SendToHost node to its producers in the subgraph. for (auto& input_src : oc_subgraph.inputs) { const Node* src_node = input_src.first.node; Node* src_image = node_images.at(src_node); int src_slot = input_src.first.slot; int input_index = input_src.second; - graph_->AddEdge(src_image, src_slot, host_compute, input_index); + graph_->AddEdge(src_image, src_slot, oc_subgraph.send_to_host, + input_index); } - // Connect the _HostCompute node to its control edge producers in the + // Connect the _SendToHost node to its control edge producers in the // subgraph. for (const auto& src_node : oc_subgraph.control_inputs) { Node* src_image = node_images.at(src_node); - graph_->AddControlEdge(src_image, host_compute); + graph_->AddControlEdge(src_image, oc_subgraph.send_to_host); } + } + } + + return Status::OK(); +} + +Status Encapsulator::Subgraph::AddRecvsFromOutsideCompilation( + const std::unordered_map& node_images) { + for (auto& oc_subgraph_iter : outside_compilation_subgraphs_) { + const string& oc_subgraph_name = oc_subgraph_iter.first; + OutsideCompilationSubgraph& oc_subgraph = oc_subgraph_iter.second; + if (!oc_subgraph.outputs_by_src.empty() || + !oc_subgraph.control_outputs.empty()) { + // Build a _RecvFromHost node producing all the outputs of the appropriate + // types. + std::vector dtypes(oc_subgraph.outputs_by_src.size(), + DT_INVALID); + + for (const auto& output : oc_subgraph.outputs_by_src) { + DataType dtype = output.first.dtype; + int output_index = output.second; + dtypes[output_index] = dtype; + } + + NodeDef recv_def; + NodeDefBuilder builder( + strings::StrCat("outside_compilation_", oc_subgraph_name, "_recv"), + kRecvFromHostOp); + builder.Attr("dtypes", dtypes); + Status s = builder.Finalize(&recv_def); + if (!s.ok()) return s; - // Connect the consumers in the subgraph to the _HostCompute node. + Node* recv = graph_->AddNode(recv_def, &s); + if (!s.ok()) return s; + + // Connect the consumers in the subgraph to the _RecvFromHost node. for (const auto& output : oc_subgraph.outputs_by_dst) { const Node* dst_node = output.first.node; Node* dst_image = node_images.at(dst_node); int dst_slot = output.first.slot; int output_index = output.second; - graph_->AddEdge(host_compute, output_index, dst_image, dst_slot); + graph_->AddEdge(recv, output_index, dst_image, dst_slot); } - // Connect the control edge consumers in the subgraph to the _HostCompute + // Connect the control edge consumers in the subgraph to the _RecvFromHost // node. for (const auto& dst_node : oc_subgraph.control_outputs) { Node* dst_image = node_images.at(dst_node); - graph_->AddControlEdge(host_compute, dst_image); + graph_->AddControlEdge(recv, dst_image); + } + + // Add a control edge in the subgraph so that the _SendToHost node, if + // any, is compiled before the _RecvFromHost node. + if (oc_subgraph.send_to_host != nullptr) { + graph_->AddControlEdge(oc_subgraph.send_to_host, recv); } } } @@ -923,63 +882,6 @@ Status Encapsulator::Subgraph::BuildFunctionDef( return Status::OK(); } -Status Encapsulator::Subgraph::AddShapeInferenceInfo( - const string& outside_compilation_subgraph_name, - const std::vector& shapes, GraphDef* inference_graph) { - OutsideCompilationSubgraph& oc_subgraph = - outside_compilation_subgraphs_.at(outside_compilation_subgraph_name); - - Node* host_compute = nullptr; - for (Node* n : graph_->nodes()) { - if (n->name() == oc_subgraph.host_compute_name) { - host_compute = n; - break; - } - } - if (host_compute == nullptr) { - return errors::InvalidArgument( - "After rewriting subgraph ", outside_compilation_subgraph_name, - " there is no HostCompute Op for outside compilation subgraph ", - oc_subgraph.host_compute_name); - } - - if (inference_graph == nullptr) { - host_compute->AddAttr("shape_inference_graph", ""); - host_compute->AddAttr("shapes", shapes); - } else { - string serialized_graph; - if (!inference_graph->SerializeToString(&serialized_graph)) { - return errors::Internal( - "Failed to serialize graph for outside compilation subgraph ", - oc_subgraph.host_compute_name); - } - host_compute->AddAttr("shape_inference_graph", serialized_graph); - host_compute->AddAttr("shapes", std::vector()); - } - return Status::OK(); -} - -Status Encapsulator::Subgraph::ReplaceFunctionDef( - FunctionLibraryDefinition* library) { - const string& name = call_node_def_.name(); - - FunctionDef fdef; - TF_RETURN_IF_ERROR(GraphToFunctionDef(*graph_, name, &fdef)); - - if (VLOG_IS_ON(1)) { - VLOG(2) << "Replace function def " << name; - dump_graph::DumpGraphToFile( - strings::StrCat("replace_encapsulate_fdef_graph_", name), *graph_, - library); - dump_graph::DumpFunctionDefToFile( - strings::StrCat("replace_encapsulate_fdef_", name), fdef); - } - - TF_RETURN_IF_ERROR(library->RemoveFunction(name)); - TF_RETURN_IF_ERROR(library->AddFunctionDef(fdef)); - return Status::OK(); -} - Status Encapsulator::Subgraph::BuildParallelCheckOp( const std::unordered_map& node_images, Graph* graph_out) { @@ -1078,9 +980,7 @@ Status Encapsulator::Subgraph::AddRecvAtHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_recv"), kRecvAtHostOp); - builder.Attr("Toutputs", dtypes); - builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, - "_", oc_subgraph_name)); + builder.Attr("dtypes", dtypes); Status s = builder.Finalize(&recv_def); if (!s.ok()) return s; @@ -1120,9 +1020,7 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, "_", oc_subgraph_name, "_send"), kSendFromHostOp); - builder.Attr("Tinputs", dtypes); - builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, - "_", oc_subgraph_name)); + builder.Attr("dtypes", dtypes); builder.Input(inputs); Status s = builder.Finalize(&send_def); if (!s.ok()) return s; @@ -1164,13 +1062,6 @@ Status Encapsulator::Subgraph::AddOutsideCompilationHostIONodes( return Status::OK(); } -void Encapsulator::Subgraph::GetOutsideCompilationSubgraphNames( - std::vector* names) const { - for (auto& entry : outside_compilation_subgraphs_) { - names->push_back(entry.first); - } -} - Status Encapsulator::GetFunctionNameAttr( Node const* node, string* attr, string* outside_compilation_attr) const { Status s = GetNodeAttr(node->attrs(), group_attribute_, attr); @@ -1329,7 +1220,8 @@ Status Encapsulator::SplitIntoSubgraphs() { // single input and output node for it. for (auto& entry : subgraphs_) { Subgraph& subgraph = entry.second; - TF_RETURN_IF_ERROR(subgraph.AddHostComputes(entry.first, node_images)); + TF_RETURN_IF_ERROR(subgraph.AddSendsToOutsideCompilation(node_images)); + TF_RETURN_IF_ERROR(subgraph.AddRecvsFromOutsideCompilation(node_images)); } MarkGuaranteedConstants(*graph_in_, src_arg_pairs); @@ -1617,344 +1509,8 @@ Status Encapsulator::AddEdgesToOutputGraph( return Status::OK(); } -namespace { - -// Adds a dummy Const node to graph_out. The "constant" has the type of -// data_type and the shape indicated in 'shape'. The dummy node is not a valid -// Const node because it does not have any value defined, but this doesn't -// matter because it will only be used subsequently for shape inference. (It -// would be possible to add a switch statement over data_type to create a value -// for the constant, but that would entail maintaining the logic as new types -// are added, and is not necessary.) -Node* AddDummyShapedNode(DataType data_type, const TensorShapeProto& shape, - Graph* graph_out) { - TensorProto dummy_proto; - dummy_proto.set_dtype(data_type); - *dummy_proto.mutable_tensor_shape() = shape; - // Don't set any value field in the proto, since it is only going to be used - // for shape inference. - - GraphDefBuilder::Options options(graph_out, /*status=*/nullptr); - NodeBuilder node_builder(options.GetNameForOp("KnownShape"), "Const", - options.op_registry()); - node_builder.Attr("dtype", data_type).Attr("value", dummy_proto); - return options.FinalizeBuilder(&node_builder); -} - -// Adds a copy of node_in to graph_out and adds the mapping to -// copied_node_images. -Status CopyShapeInferenceNodeToGraph( - Node* node_in, const Node* send_node, - const std::unordered_map& dummy_node_images, - FunctionLibraryDefinition* library, - std::unordered_map* copied_node_images, Graph* graph_out) { - // Once all the ancestor nodes have been added to graph_out, add this node - // and connect it to its ancestors. - Node* node_out = graph_out->CopyNode(node_in); - (*copied_node_images)[node_in] = node_out; - // Don't bother to build the shape inference graph if there's a node with no - // shape inference function, since it would just result in an error later at - // compile time. - const OpRegistrationData* op_reg_data; - TF_RETURN_IF_ERROR(library->LookUp(node_in->type_string(), &op_reg_data)); - if (op_reg_data->shape_inference_fn == nullptr) { - return errors::InvalidArgument( - "Shape inference is not possible for outside_compilation " - "SendFromHost node ", - send_node->name(), " because it depends on node ", node_in->name(), - " which does not have a shape inference function registered."); - } - // Add all the edges to the newly copied node. - for (const Edge* in_edge : node_in->in_edges()) { - if (!in_edge->IsControlEdge()) { - Node* src = in_edge->src(); - const auto iter = dummy_node_images.find(src); - if (iter == dummy_node_images.end()) { - // The src is a copied node so use the original output port. - graph_out->AddEdge((*copied_node_images)[in_edge->src()], - in_edge->src_output(), node_out, - in_edge->dst_input()); - } else { - // The src is a dummy node so use output port 0. - graph_out->AddEdge(iter->second, 0, node_out, in_edge->dst_input()); - } - } - } - return Status::OK(); -} - -} // namespace - -Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( - const Graph& graph_in, const ShapeRefiner& shape_refiner, - const std::unordered_set& recv_at_host_nodes, Node* send_node, - FunctionLibraryDefinition* library, - std::vector* static_shape_out, - std::unique_ptr* graphdef_out) { - // Maps from nodes in graph_in to nodes in graph_out. - // - // When an edge has fully defined shape the source node in graph_in is - // replaced in graph_out by a dummy constant node. The mapping from nodes - // in graph_in to dummy nodes is stored in dummy_node_images. - // - // When a node in graph_in has at least one ancestor that doesn't have fully - // defined shape, it is copied into graph_out. The mapping from nodes in - // graph_in to copied nodes is stored in copied_node_images. - // - // The two types of node are treated differently because, when adding edges to - // graph_out, an output from a dummy node always uses port 0, whereas an - // output from a copied node uses the same port that was used in graph_in. - std::unordered_map dummy_node_images; - std::unordered_map copied_node_images; - - std::unique_ptr graph_out(new Graph(graph_in.op_registry())); - graph_out->set_versions(graph_in.versions()); - static_shape_out->resize(send_node->num_inputs()); - - // We don't use the standard ReverseDFS because we want to cut off traversal - // whenever we find an output with fully defined shape. - // TODO(misard) make this work properly in the presence of control flow. - struct Work { - Node* node; - bool leave; // Are we entering or leaving node? - }; - std::vector stack({{send_node, false}}); - std::vector visited(graph_in.num_node_ids(), false); - while (!stack.empty()) { - Work w = stack.back(); - stack.pop_back(); - Node* n = w.node; - - if (w.leave) { - TF_RETURN_IF_ERROR(CopyShapeInferenceNodeToGraph( - n, send_node, dummy_node_images, library, &copied_node_images, - graph_out.get())); - } else { - if (visited[n->id()]) continue; - visited[n->id()] = true; - - // Arrange to revisit when all done with all inputs. - stack.push_back(Work{n, true}); - - bool has_parent_with_unknown_shape = false; - for (const Edge* in_edge : n->in_edges()) { - if (!in_edge->IsControlEdge()) { - Node* src_node = in_edge->src(); - int src_port = in_edge->src_output(); - shape_inference::InferenceContext* context = - shape_refiner.GetContext(src_node); - shape_inference::ShapeHandle shape = context->output(src_port); - if (context->FullyDefined(shape)) { - // This ancestor has known shape, so instead of adding it to the - // stack, add a dummy node with that shape to graph_out and - // continue. - TensorShapeProto proto; - context->ShapeHandleToProto(shape, &proto); - dummy_node_images[src_node] = AddDummyShapedNode( - src_node->output_type(src_port), proto, graph_out.get()); - if (n == send_node) { - (*static_shape_out)[in_edge->dst_input()] = proto; - } - } else { - if (!visited[src_node->id()]) { - has_parent_with_unknown_shape = true; - stack.push_back({src_node, false}); - } - } - } - } - if (!has_parent_with_unknown_shape) { - if (n == send_node) { - // The shapes of all the inputs to send_node are statically known. We - // won't have to do any inference at compile time so return now: the - // shapes were stored in static_shape_out above. - graphdef_out->reset(); - return Status::OK(); - } else { - // Any shape that is being processed is either the original send node - // or has at least one output with statically-unknown shape. If the - // latter and it doesn't have any inputs with statically-unknown - // shape, then check that it is of the recv nodes that we can fill in - // the shape of at run-time later. If it isn't one of those, then we - // won't have any additional knowledge at compile time, so we already - // know we won't be able to do shape inference and we can return an - // error now. - if (recv_at_host_nodes.find(n->name()) == recv_at_host_nodes.end()) { - return errors::InvalidArgument( - "Shape inference is not possible for outside_compilation " - "SendFromHost node ", - send_node->name(), " because shape of node ", n->name(), - " will not be known at compilation time."); - } - } - } - } - } - - graphdef_out->reset(new GraphDef()); - graph_out->ToGraphDef(graphdef_out->get()); - - return Status::OK(); -} - -Status Encapsulator::MakePrunedGraphCopyAndInline( - const Graph& graph, const std::vector& sink_nodes, - std::unique_ptr* pruned_graph, - std::unordered_map* node_images, - FunctionLibraryDefinition* library) { - // First copy all ancestor nodes of sink_nodes into a new graph. - pruned_graph->reset(new Graph(library)); - (*pruned_graph)->set_versions(graph.versions()); - ReverseDFSFrom(graph, sink_nodes, - /*enter=*/nullptr, - /*leave=*/[&](Node* n) { - if (!n->IsSource()) { - Node* copied = (*pruned_graph)->CopyNode(n); - node_images->emplace(n, copied); - } - }); - - // Add all the edges between copied nodes. - for (auto entry : *node_images) { - const Node* orig = entry.first; - Node* image = entry.second; - for (const Edge* out_edge : orig->out_edges()) { - auto iter = node_images->find(out_edge->dst()); - if (iter != node_images->end()) { - // The source and destination are both in the copied graph. - (*pruned_graph) - ->AddEdge(image, out_edge->src_output(), iter->second, - out_edge->dst_input()); - } - } - } - - // Find all the function call nodes, and inline them. - std::vector function_nodes; - for (auto node : (*pruned_graph)->nodes()) { - const OpRegistrationData* op_reg_data; - TF_RETURN_IF_ERROR(library->LookUp(node->type_string(), &op_reg_data)); - if (op_reg_data->is_function_op) { - function_nodes.push_back(node); - } - } - for (auto node : function_nodes) { - VLOG(2) << "Inlining function " << node->name(); - const FunctionDef* fdef = library->Find(node->type_string()); - if (fdef == nullptr) { - return errors::Internal("Failed to find function ", node->type_string(), - " in function library."); - } - FunctionBody* fbody = nullptr; - TF_RETURN_IF_ERROR( - FunctionDefToBodyHelper(*fdef, node->attrs(), library, - [library](const string& op, const OpDef** sig) { - return library->LookUpOpDef(op, sig); - }, - &fbody)); - InlineFunctionBody(*library, pruned_graph->get(), node, fbody); - delete fbody; - } - - return Status::OK(); -} - -Status Encapsulator::MakeGraphForOutsideCompilationSends( - const Graph& graph, std::unique_ptr* pruned_graph, - ShapeRefiner* shape_refiner, - std::unordered_map* node_images, - FunctionLibraryDefinition* library) { - // Find all the send_from_host nodes in all subgraphs, to use as roots for the - // pruning. - std::vector send_from_host_nodes; - for (auto& subgraph_entry : subgraphs_) { - Subgraph& subgraph = subgraph_entry.second; - std::vector outside_compilation_names; - subgraph.GetOutsideCompilationSubgraphNames(&outside_compilation_names); - for (const auto& name : outside_compilation_names) { - Node* send_node = subgraph.GetSendFromHostNode(name); - if (send_node != nullptr) { - send_from_host_nodes.push_back(send_node); - } - } - } - - // Make a copy of all the graph nodes needed to evaluate the send_from_host - // nodes, inlining any functions as needed. - TF_RETURN_IF_ERROR(MakePrunedGraphCopyAndInline( - graph, send_from_host_nodes, pruned_graph, node_images, library)); - - // Perform shape inference on the pruned graph. - shape_refiner->set_require_shape_inference_fns(false); - FixupSourceAndSinkEdges(pruned_graph->get()); - std::vector post_order; - GetReversePostOrder(*(*pruned_graph), &post_order); - for (auto node : post_order) { - // Ignore the status returned by the shape_refiner. At this point we want - // the best effort shapes, even if no shape function is registered for a - // node. - Status status = shape_refiner->AddNode(node); - VLOG_IF(1, !status.ok()) << "Shape inference failed for node: " << status; - } - - return Status::OK(); -} - -Status Encapsulator::GetShapeInfoForOutsideCompilationSends( - Graph* graph_out, FunctionLibraryDefinition* library) { - std::unique_ptr pruned_graph; - ShapeRefiner shape_refiner(graph_out->versions(), graph_out->op_registry()); - std::unordered_map node_images; - TF_RETURN_IF_ERROR(MakeGraphForOutsideCompilationSends( - *graph_out, &pruned_graph, &shape_refiner, &node_images, library)); - - for (auto& subgraph_entry : subgraphs_) { - Subgraph& subgraph = subgraph_entry.second; - // Find all the recv_at_host nodes in this subgraph. - std::vector outside_compilation_names; - subgraph.GetOutsideCompilationSubgraphNames(&outside_compilation_names); - std::unordered_set recv_at_host_names; - for (const auto& name : outside_compilation_names) { - Node* recv_node = subgraph.GetRecvAtHostNode(name); - if (recv_node != nullptr) { - recv_at_host_names.insert(recv_node->name()); - } - } - // For each send_from_host node, do as much shape inference as possible - // without knowing the shape of the recv_at_host nodes, and store the - // result, along with enough information to complete the job at compile time - // once the recv_at_host shapes are known. - for (const auto& name : outside_compilation_names) { - Node* send_node = subgraph.GetSendFromHostNode(name); - std::vector static_shape; - std::unique_ptr graphdef; - if (send_node != nullptr) { - TF_RETURN_IF_ERROR(DoStaticShapeInferenceForOutsideCompilationSend( - *pruned_graph, shape_refiner, recv_at_host_names, - node_images[send_node], library, &static_shape, &graphdef)); - if (graphdef == nullptr) { - VLOG(2) << "Send node " << send_node->name() << " shapes"; - for (int i = 0; i < static_shape.size(); ++i) { - VLOG(2) << static_shape[i].DebugString(); - } - } else { - VLOG(2) << "Send node " << send_node->name() << " graph\n" - << graphdef->DebugString(); - } - } - TF_RETURN_IF_ERROR( - subgraph.AddShapeInferenceInfo(name, static_shape, graphdef.get())); - } - if (!outside_compilation_names.empty()) { - TF_RETURN_IF_ERROR(subgraph.ReplaceFunctionDef(library)); - } - } - - return Status::OK(); -} - -Status Encapsulator::BuildOutputGraph(bool parallel_checking, Graph* graph_out, - FunctionLibraryDefinition* library) { +Status Encapsulator::BuildOutputGraph(bool parallel_checking, + Graph* graph_out) { // Map from nodes in the input graph to nodes in the output graph. std::unordered_map node_images; @@ -1966,9 +1522,6 @@ Status Encapsulator::BuildOutputGraph(bool parallel_checking, Graph* graph_out, TF_RETURN_IF_ERROR( AddEdgesToOutputGraph(node_images, parallel_checking, graph_out)); - TF_RETURN_IF_ERROR( - GetShapeInfoForOutsideCompilationSends(graph_out, library)); - return Status::OK(); } @@ -1992,7 +1545,7 @@ Status EncapsulateSubgraphsInFunctions( std::unique_ptr out(new Graph(library)); out->set_versions(graph_in.versions()); TF_RETURN_IF_ERROR( - encapsulator.BuildOutputGraph(parallel_checking, out.get(), library)); + encapsulator.BuildOutputGraph(parallel_checking, out.get())); *graph_out = std::move(out); return Status::OK(); diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index 5032a0935d..b100861d5e 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -29,181 +29,17 @@ limitations under the License. namespace tensorflow { namespace { -template -bool EqualProtoMap(const proto2::Map& a, - const proto2::Map& b, - const std::function& key_to_string, - const std::function& value_to_string, - const std::function& compare, - const string& map_name, string* diff) { - for (const auto& elt_a : a) { - const auto iter = b.find(elt_a.first); - if (iter == b.end()) { - if (diff) { - *diff = strings::StrCat( - map_name, " expected: contains element with key '", - key_to_string(elt_a.first), "' got: map has no such element"); - } - return false; - } - if (!compare(elt_a.first, elt_a.second, iter->second)) { - if (diff) { - *diff = strings::StrCat(map_name, " expected: element with key '", - key_to_string(elt_a.first), " has value '", - value_to_string(elt_a.second), "' got: '", - value_to_string(iter->second), "'"); - } - return false; - } - } - for (const auto& elt_b : b) { - const auto iter = a.find(elt_b.first); - if (iter == a.end()) { - if (diff) { - *diff = strings::StrCat(map_name, " got: contains element with key '", - key_to_string(elt_b.first), - "' expected: map has no such element"); - } - return false; - } - } - return true; -} - -bool EqualFunctionNodeDef(const NodeDef& a, const NodeDef& b, - const string& diff_preamble, string* diff) { - if (a.op() != b.op()) { - if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - ", expected op '", a.op(), "' got '", b.op()); - } - return false; - } - if (a.device() != b.device()) { - if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - ", expected device '", a.device(), "' got '", - b.device()); - } - return false; - } - if (a.input_size() != b.input_size()) { - if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - ", expected ", a.input_size(), " inputs got ", - b.input_size(), " expected:\n", a.DebugString(), - "\ngot:\n", b.DebugString()); - } - return false; - } - for (int i = 0; i < a.input_size(); ++i) { - if (a.input(i) != b.input(i)) { - if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - " input ", i, ", expected ", a.input(i), - " got ", b.input(i), " expected:\n", - a.DebugString(), "\ngot:\n", b.DebugString()); - } - return false; - } - } - return EqualProtoMap( - a.attr(), b.attr(), [](const string& s) { return s; }, - [](const AttrValue& v) { return v.DebugString(); }, - [](const string& key, const AttrValue& av, const AttrValue& bv) { - if (key == "shape_inference_graph") { - // Default serialization of GraphDef is unstable because maps don't - // serialize deterministically. Rather than go through the hoops to - // turn on deterministic serialization of this attr just for this - // test, add logic here to compare determinstically. - GraphDef ga; - if (!ga.ParseFromString(av.s())) { - return false; - } - GraphDef gb; - if (!gb.ParseFromString(bv.s())) { - return false; - } - return EqualGraphDef(ga, gb, nullptr); - } else { - return av.DebugString() == bv.DebugString(); - } - }, - strings::StrCat(diff_preamble, " attr mismatch for node ", a.name()), - diff); -} - bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, string* diff) { - if (a.signature().DebugString() != b.signature().DebugString()) { + // TODO(phawkins) use a more sophisticated equality test. + if (a.DebugString() != b.DebugString()) { if (diff) { - *diff = strings::StrCat("Signature mismatch for function ", + *diff = strings::StrCat("Definition mismatch for function ", a.signature().name(), ", expected:\n", - a.signature().DebugString(), "\ngot:\n", - b.signature().DebugString()); + a.DebugString(), "\ngot:\n", b.DebugString()); } return false; } - if (!EqualProtoMap( - a.attr(), b.attr(), [](const string& s) { return s; }, - [](const AttrValue& v) { return v.DebugString(); }, - [](const string& key, const AttrValue& av, const AttrValue& bv) { - return av.DebugString() == bv.DebugString(); - }, - strings::StrCat("attr mismatch for function ", a.signature().name()), - diff)) { - return false; - } - if (!EqualProtoMap( - a.ret(), b.ret(), [](const string& s) { return s; }, - [](const string& s) { return s; }, - [](const string& key, const string& av, const string& bv) { - return av == bv; - }, - strings::StrCat("ret mismatch for function ", a.signature().name()), - diff)) { - return false; - } - for (int i = 0; i < a.node_def_size(); ++i) { - bool found = false; - for (int j = 0; j < b.node_def_size(); ++j) { - if (a.node_def(i).name() == b.node_def(j).name()) { - if (!EqualFunctionNodeDef( - a.node_def(i), b.node_def(j), - strings::StrCat("Function ", a.signature().name()), diff)) { - return false; - } - found = true; - break; - } - } - if (!found) { - if (diff) { - *diff = strings::StrCat("Function ", a.signature().name(), - ", expected: has node '", a.node_def(i).name(), - "' got: no node of that name"); - } - return false; - } - } - for (int i = 0; i < b.node_def_size(); ++i) { - bool found = false; - for (int j = 0; j < a.node_def_size(); ++j) { - if (b.node_def(i).name() == a.node_def(j).name()) { - found = true; - break; - } - } - if (!found) { - if (diff) { - *diff = strings::StrCat("Function ", a.signature().name(), - ", got: has node '", b.node_def(i).name(), - "' expected: no node of that name"); - } - return false; - } - } return true; } @@ -248,64 +84,29 @@ bool EqualFunctionDefLibrary(const FunctionDefLibrary& expected, // TODO(misard): remove these fake registrations once there are real Ops to be // compiled. -REGISTER_OP("_XlaHostCompute") - .Input("inputs: Tinputs") - .Output("outputs: Toutputs") - .Attr("Tinputs: list(type) >= 0") - .Attr("Toutputs: list(type) >= 0") - .Attr("key: string") - .SetShapeFn(::tensorflow::shape_inference::UnknownShape); +REGISTER_OP("_XlaSendToHost") + .Input("input: dtypes") + .Attr("dtypes: list(type) >= 0"); + +REGISTER_OP("_XlaRecvFromHost") + .Output("output: dtypes") + .Attr("dtypes: list(type) >= 0"); REGISTER_OP("_XlaSendFromHost") - .Input("input: Tinputs") - .Attr("Tinputs: list(type) >= 0") - .Attr("key: string") - .SetShapeFn(::tensorflow::shape_inference::UnknownShape); + .Input("input: dtypes") + .Attr("dtypes: list(type) >= 0"); REGISTER_OP("_XlaRecvAtHost") - .Output("output: Toutputs") - .Attr("Toutputs: list(type) >= 0") - .Attr("key: string") - .SetShapeFn(::tensorflow::shape_inference::UnknownShape); - -REGISTER_OP("InputTest") - .Output("o: float") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->UnknownShape()); - return Status::OK(); - }); - -REGISTER_OP("InputTestShaped") - .Output("o: float") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->Vector(2)); - return Status::OK(); - }); - -REGISTER_OP("UnaryTest") - .Input("a: float") - .Output("o: float") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - ::tensorflow::shape_inference::ShapeHandle o; - TF_RETURN_IF_ERROR(c->Merge(c->UnknownShape(), c->input(0), &o)); - c->set_output(0, o); - return Status::OK(); - }); + .Output("output: dtypes") + .Attr("dtypes: list(type) >= 0"); + +REGISTER_OP("InputTest").Output("o: float"); + +REGISTER_OP("UnaryTest").Input("a: float").Output("o: float"); REGISTER_OP("BinaryTest") .Input("a: float") .Input("b: float") - .Output("o: float") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - ::tensorflow::shape_inference::ShapeHandle o; - TF_RETURN_IF_ERROR(c->Merge(c->UnknownShape(), c->input(0), &o)); - c->set_output(0, o); - return Status::OK(); - }); -REGISTER_OP("BinaryTest2") - .Input("a: float") - .Input("b: float") - .Output("o: float") - .SetShapeFn(::tensorflow::shape_inference::UnknownShape); + .Output("o: float"); REGISTER_OP("AddNLikeTest") .Input("inputs: N * T") @@ -323,48 +124,22 @@ Node* Input(const GraphDefBuilder::Options& opts) { return ops::SourceOp("InputTest", opts); } -Node* InputShaped(const GraphDefBuilder::Options& opts) { - return ops::SourceOp("InputTestShaped", opts); -} - -Node* KnownShape(const gtl::ArraySlice& shape, - const GraphDefBuilder::Options& opts) { - if (opts.HaveError()) return nullptr; - NodeBuilder node_builder(opts.GetNameForOp("Const"), "Const", - opts.op_registry()); - TensorProto value; - value.set_dtype(DT_FLOAT); - for (int dim : shape) { - value.mutable_tensor_shape()->add_dim()->set_size(dim); - } - return opts.WithAttr("value", value) - .WithAttr("dtype", DT_FLOAT) - .FinalizeBuilder(&node_builder); -} - -Node* RecvAtHost(const string& key, const gtl::ArraySlice& dtypes, +Node* RecvAtHost(const gtl::ArraySlice& dtypes, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaRecvAtHost"), "_XlaRecvAtHost", opts.op_registry()); - return opts.WithAttr("Toutputs", dtypes) - .WithAttr("key", key) - .FinalizeBuilder(&node_builder); + return opts.WithAttr("dtypes", dtypes).FinalizeBuilder(&node_builder); } -Node* SendFromHost(const string& key, const std::vector& inputs, +Node* SendFromHost(const std::vector& inputs, + const gtl::ArraySlice& dtypes, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; NodeBuilder node_builder(opts.GetNameForOp("_XlaSendFromHost"), "_XlaSendFromHost", opts.op_registry()); node_builder.Input(inputs); - std::vector dtypes; - for (const auto& node : inputs) { - dtypes.push_back(node.dt); - } - return opts.WithAttr("key", key) - .WithAttr("Tinputs", dtypes) - .FinalizeBuilder(&node_builder); + return opts.WithAttr("dtypes", dtypes).FinalizeBuilder(&node_builder); } Node* Unary(ops::NodeOut a, const GraphDefBuilder::Options& opts) { @@ -376,11 +151,6 @@ Node* Binary(ops::NodeOut a, ops::NodeOut b, return ops::BinaryOp("BinaryTest", std::move(a), std::move(b), opts); } -Node* BinaryUnknownShape(ops::NodeOut a, ops::NodeOut b, - const GraphDefBuilder::Options& opts) { - return ops::BinaryOp("BinaryTest2", std::move(a), std::move(b), opts); -} - Node* AddNLike(const std::vector& inputs, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; @@ -806,21 +576,6 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; - string shape_string_expected; - { - GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, - shape.opts().WithName("outside_compilation_F1_O1_recv")); - Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), - shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); - GraphDef shape_graph; - TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); - EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); - } - *library_expected.add_function() = test::function::XTimesTwo(); *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float"}, {}, @@ -829,18 +584,19 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { {{"c"}, "BinaryTest", {"b_0_arg", "C:o:0"}, {}, {"C"}}, {{"F"}, "BinaryTest", - {"C:o:0", "outside_compilation_O1_host_compute:outputs:0"}, + {"C:o:0", "outside_compilation_O1_recv:output:0"}, {}, - {"outside_compilation_O1_host_compute"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", + {"outside_compilation_O1_recv"}}, + {{"outside_compilation_O1_send"}, + "_XlaSendToHost", {"C:o:0", "c:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", shape_string_expected}, - {"shapes", gtl::ArraySlice({})}}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, {"c"}}, + {{"outside_compilation_O1_recv"}, + "_XlaRecvFromHost", + {}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, + {"outside_compilation_O1_send"}}, }, {{"f_0_retval", "F:o:0"}}); @@ -856,11 +612,11 @@ TEST(EncapsulateSubgraphsTest, OneFunctionOneOutside) { Node* call = b2.opts().FinalizeBuilder(&node_builder); Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost({DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), b2.opts().WithName("E").WithControlInputs({recv, b})); - Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send = SendFromHost({e}, {DT_FLOAT}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -918,71 +674,37 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; - string shape_string_expected_1; - { - GraphDefBuilder shape1(GraphDefBuilder::kFailImmediately); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, - shape1.opts().WithName("outside_compilation_F1_O1_recv")); - Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), - shape1.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape1.opts().WithName("outside_compilation_F1_O1_send")); - GraphDef shape1_graph; - TF_EXPECT_OK(shape1.ToGraphDef(&shape1_graph)); - EXPECT_TRUE(shape1_graph.SerializeToString(&shape_string_expected_1)); - } - - string shape_string_expected_2; - { - GraphDefBuilder shape2(GraphDefBuilder::kFailImmediately); - Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, - shape2.opts().WithName("outside_compilation_F1_O1_recv")); - Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), - shape2.opts().WithName("E")); - Node* recv2 = - RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, - shape2.opts().WithName("outside_compilation_F1_O2_recv")); - Node* h = Binary(ops::NodeOut(recv2, 0), e, shape2.opts().WithName("H")); - SendFromHost("host_compute_channel_F1_O2", {h}, - shape2.opts().WithName("outside_compilation_F1_O2_send")); - GraphDef shape2_graph; - TF_EXPECT_OK(shape2.ToGraphDef(&shape2_graph)); - EXPECT_TRUE(shape2_graph.SerializeToString(&shape_string_expected_2)); - } - *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"i_0_retval:float"}, {}, { {{"C"}, "UnaryTest", {"a_0_arg"}}, {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}, {}}, - {{"I"}, - "UnaryTest", - {"outside_compilation_O2_host_compute:outputs:0"}}, + {{"I"}, "UnaryTest", {"outside_compilation_O2_recv:output:0"}}, {{"F"}, "BinaryTest", - {"C:o:0", "outside_compilation_O1_host_compute:outputs:0"}, + {"C:o:0", "outside_compilation_O1_recv:output:0"}, {}, - {"outside_compilation_O1_host_compute"}}, - {{"outside_compilation_O2_host_compute"}, - "_XlaHostCompute", + {"outside_compilation_O1_recv"}}, + {{"outside_compilation_O2_send"}, + "_XlaSendToHost", {"D:o:0", "F:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F1_O2"}, - {"shape_inference_graph", shape_string_expected_2}, - {"shapes", gtl::ArraySlice({})}}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, {"F"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", + {{"outside_compilation_O1_send"}, + "_XlaSendToHost", {"C:o:0", "D:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", shape_string_expected_1}, - {"shapes", gtl::ArraySlice({})}}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, {"D"}}, + {{"outside_compilation_O2_recv"}, + "_XlaRecvFromHost", + {}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, + {"outside_compilation_O2_send"}}, + {{"outside_compilation_O1_recv"}, + "_XlaRecvFromHost", + {}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, + {"outside_compilation_O1_send"}}, }, {{"i_0_retval", "I:o:0"}}); @@ -998,24 +720,23 @@ TEST(EncapsulateSubgraphsTest, OneFunctionTwoOutside) { Node* call = b2.opts().FinalizeBuilder(&node_builder); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost({DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send1 = SendFromHost({e}, {DT_FLOAT}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); Node* recv2 = - RecvAtHost("host_compute_channel_F1_O2", {DT_FLOAT, DT_FLOAT}, + RecvAtHost({DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O2_recv")); Node* g = Binary(e, ops::NodeOut(recv2, 1), b2.opts().WithName("G").WithControlInputs({recv2, e})); Node* h = Binary(ops::NodeOut(recv2, 0), e, b2.opts().WithName("H")); - Node* send2 = - SendFromHost("host_compute_channel_F1_O2", {h}, - b2.opts().WithName("outside_compilation_F1_O2_send")); + Node* send2 = SendFromHost( + {h}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O2_send")); Node* s = NoOp(b2.opts() .WithName("F1_sequencer") @@ -1037,8 +758,8 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { { GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); - Node* a = InputShaped(b1.opts().WithName("A")); - Node* b = InputShaped(b1.opts().WithName("B")); + Node* a = Input(b1.opts().WithName("A")); + Node* b = Input(b1.opts().WithName("B")); Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); Node* d = Binary(b, c, b1.opts().WithName("D").WithAttr("_encapsulate", "F1")); @@ -1070,24 +791,6 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; - string shape_string_expected; - { - GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, - shape.opts().WithName("outside_compilation_F1_O1_recv")); - Node* e = Binary(ops::NodeOut(recv, 0), ops::NodeOut(recv, 1), - shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); - GraphDef shape_graph; - TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); - EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); - } - - TensorShapeProto shape_proto_expected; - shape_proto_expected.add_dim()->set_size(2); - *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float", "d_0_retval:float"}, {}, @@ -1096,18 +799,19 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "BinaryTest", - {"C:o:0", "outside_compilation_O1_host_compute:outputs:0"}, + {"C:o:0", "outside_compilation_O1_recv:output:0"}, {}, - {"outside_compilation_O1_host_compute"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", + {"outside_compilation_O1_recv"}}, + {{"outside_compilation_O1_send"}, + "_XlaSendToHost", {"C:o:0", "D:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", shape_string_expected}, - {"shapes", gtl::ArraySlice({})}}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT, DT_FLOAT})}}, {"D"}}, + {{"outside_compilation_O1_recv"}, + "_XlaRecvFromHost", + {}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, + {"outside_compilation_O1_send"}}, }, {{"d_0_retval", "D:o:0"}, {"f_0_retval", "F:o:0"}}); @@ -1118,16 +822,16 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { {{"G"}, "BinaryTest", {"e_0_arg", "f_0_arg"}}, {{"I"}, "BinaryTest", - {"f_0_arg", "outside_compilation_O1_host_compute:outputs:0"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", + {"f_0_arg", "outside_compilation_O1_recv:output:0"}}, + {{"outside_compilation_O1_send"}, + "_XlaSendToHost", {"G:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F2_O1"}, - {"shape_inference_graph", ""}, - {"shapes", - gtl::ArraySlice({shape_proto_expected})}}}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, + {{"outside_compilation_O1_recv"}, + "_XlaRecvFromHost", + {}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, + {"outside_compilation_O1_send"}}, }, {{"g_0_retval", "G:o:0"}, {"i_0_retval", "I:o:0"}}); @@ -1135,15 +839,15 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { std::unique_ptr lib_def( new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); - Node* a = InputShaped(b2.opts().WithName("A")); - Node* b = InputShaped(b2.opts().WithName("B")); + Node* a = Input(b2.opts().WithName("A")); + Node* b = Input(b2.opts().WithName("B")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT, DT_FLOAT}, + RecvAtHost({DT_FLOAT, DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1), b2.opts().WithName("E").WithControlInputs({recv1, b})); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {e}, + Node* send1 = SendFromHost({e}, {DT_FLOAT}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -1153,14 +857,12 @@ TEST(EncapsulateSubgraphsTest, TwoFunctionsTwoOutside) { Node* s1 = NoOp( b2.opts().WithName("F1_sequencer").WithControlInputs({recv1, send1})); - Node* recv2 = - RecvAtHost("host_compute_channel_F2_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F2_O1_recv")); + Node* recv2 = RecvAtHost( + {DT_FLOAT}, b2.opts().WithName("outside_compilation_F2_O1_recv")); Node* h = Binary(ops::NodeOut(call1, 1), recv2, b2.opts().WithName("H").WithControlInput(s1)); - Node* send2 = - SendFromHost("host_compute_channel_F2_O1", {h}, - b2.opts().WithName("outside_compilation_F2_O1_send")); + Node* send2 = SendFromHost( + {h}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F2_O1_send")); NodeBuilder node_builder2("F2", "F2", lib_def.get()); node_builder2.Input(e).Input(call1); @@ -1186,7 +888,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { { GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); - Node* a = InputShaped(b1.opts().WithName("A")); + Node* a = Input(b1.opts().WithName("A")); Node* b = Input(b1.opts().WithName("B")); Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); Node* d = @@ -1206,9 +908,6 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; - TensorShapeProto shape_proto_expected; - shape_proto_expected.add_dim()->set_size(2); - *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float"}, {}, { @@ -1216,16 +915,11 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "BinaryTest", - {"D:o:0", "outside_compilation_O1_host_compute:outputs:0"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", + {"D:o:0", "outside_compilation_O1_recv:output:0"}}, + {{"outside_compilation_O1_recv"}, + "_XlaRecvFromHost", {}, - {{"Tinputs", gtl::ArraySlice({})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, - {"shapes", - gtl::ArraySlice({shape_proto_expected})}}}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, }, {{"f_0_retval", "F:o:0"}}); @@ -1233,13 +927,12 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputs) { std::unique_ptr lib_def( new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); - Node* a = InputShaped(b2.opts().WithName("A")); + Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); Node* e = Unary(a, b2.opts().WithName("E")); - Node* send1 = - SendFromHost("host_compute_channel_F1_O1", {e}, - b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* send1 = SendFromHost( + {e}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -1261,7 +954,7 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { { GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); - Node* a = InputShaped(b1.opts().WithName("A")); + Node* a = Input(b1.opts().WithName("A")); Node* b = Input(b1.opts().WithName("B")); Node* c = Unary(a, b1.opts().WithName("C").WithAttr("_encapsulate", "F1")); Node* d = @@ -1282,9 +975,6 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { FunctionDefLibrary library_expected; GraphDef graphdef_expected; - TensorShapeProto shape_proto_expected; - shape_proto_expected.add_dim()->set_size(2); - *library_expected.add_function() = FunctionDefHelper::Create( "F1", {"a_0_arg:float", "b_0_arg:float"}, {"f_0_retval:float"}, {}, { @@ -1292,17 +982,17 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "BinaryTest", - {"D:o:0", "outside_compilation_O1_host_compute:outputs:0"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", + {"D:o:0", "outside_compilation_O1_recv:output:0"}}, + {{"outside_compilation_O1_send"}, + "_XlaSendToHost", {}, - {{"Tinputs", gtl::ArraySlice({})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, - {"shapes", - gtl::ArraySlice({shape_proto_expected})}}, + {{"dtypes", gtl::ArraySlice({})}}, {"D"}}, + {{"outside_compilation_O1_recv"}, + "_XlaRecvFromHost", + {}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}, + {"outside_compilation_O1_send"}}, }, {{"f_0_retval", "F:o:0"}}); @@ -1310,16 +1000,14 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlInput) { std::unique_ptr lib_def( new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); - Node* a = InputShaped(b2.opts().WithName("A")); + Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + RecvAtHost({}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(a, b2.opts().WithName("E").WithControlInput(recv1)); - Node* send1 = - SendFromHost("host_compute_channel_F1_O1", {e}, - b2.opts().WithName("outside_compilation_F1_O1_send")); + Node* send1 = SendFromHost( + {e}, {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_send")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); Node* call1 = b2.opts().FinalizeBuilder(&node_builder1); @@ -1367,14 +1055,10 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoOutputs) { {{"C"}, "UnaryTest", {"a_0_arg"}}, {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, {{"F"}, "UnaryTest", {"D:o:0"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", + {{"outside_compilation_O1_send"}, + "_XlaSendToHost", {"D:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, - {"shapes", gtl::ArraySlice({})}}}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, }, {{"f_0_retval", "F:o:0"}}); @@ -1385,9 +1069,8 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoOutputs) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* recv1 = RecvAtHost( + {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); NodeBuilder node_builder1("F1", "F1", lib_def.get()); node_builder1.Input(a).Input(b); @@ -1435,19 +1118,16 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlOutput) { { {{"C"}, "UnaryTest", {"a_0_arg"}}, {{"D"}, "BinaryTest", {"b_0_arg", "C:o:0"}}, - {{"F"}, - "UnaryTest", + {{"F"}, "UnaryTest", {"D:o:0"}, {}, {"outside_compilation_O1_recv"}}, + {{"outside_compilation_O1_send"}, + "_XlaSendToHost", {"D:o:0"}, + {{"dtypes", gtl::ArraySlice({DT_FLOAT})}}}, + {{"outside_compilation_O1_recv"}, + "_XlaRecvFromHost", {}, - {"outside_compilation_O1_host_compute"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", - {"D:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", ""}, - {"shapes", gtl::ArraySlice({})}}}, + {{"dtypes", gtl::ArraySlice({})}}, + {"outside_compilation_O1_send"}}, }, {{"f_0_retval", "F:o:0"}}); @@ -1458,11 +1138,10 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationControlOutput) { Node* a = Input(b2.opts().WithName("A")); Node* b = Input(b2.opts().WithName("B")); - Node* recv1 = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); + Node* recv1 = RecvAtHost( + {DT_FLOAT}, b2.opts().WithName("outside_compilation_F1_O1_recv")); Node* e = Unary(recv1, b2.opts().WithName("E")); - Node* send1 = SendFromHost("host_compute_channel_F1_O1", {}, + Node* send1 = SendFromHost({}, {}, b2.opts() .WithName("outside_compilation_F1_O1_send") .WithControlInput(e)); @@ -1536,110 +1215,5 @@ TEST(EncapsulateSubgraphsTest, OutsideCompilationNoInputsOrOutputs) { TF_EXPECT_FUNCTIONDEFLIBRARY_EQ(library_expected, library); } -// Test for shape inference of outside compilation. -TEST(EncapsulateSubgraphsTest, OutsideCompilationShapeInference) { - FunctionDefLibrary library; - GraphDef graphdef; - - { - *library.add_function() = test::function::XTimesTwo(); - - GraphDefBuilder b1(GraphDefBuilder::kFailImmediately); - Node* a = InputShaped(b1.opts().WithName("A")); - Node* b = Input(b1.opts().WithName("B")); - // Give nodes 'c' and 'd' names that collide after lowercasing. - Node* c = Unary(a, b1.opts().WithName("C")); - Node* d = Unary(b, b1.opts().WithName("c").WithControlInput(c).WithAttr( - "_encapsulate", "F1")); - Node* e = BinaryUnknownShape(c, d, - b1.opts() - .WithName("E") - .WithControlInputs({b, d}) - .WithAttr("_encapsulate", "F1") - .WithAttr("_outside", "O1")); - Node* f = Binary(c, e, - b1.opts().WithName("F").WithControlInput(e).WithAttr( - "_encapsulate", "F1")); - Binary(a, f, b1.opts().WithName("G").WithControlInput(e)); - TF_EXPECT_OK(b1.ToGraphDef(&graphdef)); - } - - TF_EXPECT_OK(Encapsulate(&graphdef, &library)); - - FunctionDefLibrary library_expected; - GraphDef graphdef_expected; - - string shape_string_expected; - { - GraphDefBuilder shape(GraphDefBuilder::kFailImmediately); - Node* known = KnownShape({2}, shape.opts().WithName("KnownShape/_0")); - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - shape.opts().WithName("outside_compilation_F1_O1_recv")); - Node* e = BinaryUnknownShape(known, recv, shape.opts().WithName("E")); - SendFromHost("host_compute_channel_F1_O1", {e}, - shape.opts().WithName("outside_compilation_F1_O1_send")); - GraphDef shape_graph; - TF_EXPECT_OK(shape.ToGraphDef(&shape_graph)); - EXPECT_TRUE(shape_graph.SerializeToString(&shape_string_expected)); - } - - *library_expected.add_function() = test::function::XTimesTwo(); - *library_expected.add_function() = FunctionDefHelper::Create( - "F1", {"b_0_arg:float", "c_0_arg:float"}, {"f_0_retval:float"}, {}, - { - {{"c"}, "UnaryTest", {"b_0_arg"}, {}, {}}, - {{"F"}, - "BinaryTest", - {"c_0_arg", "outside_compilation_O1_host_compute:outputs:0"}, - {}, - {"outside_compilation_O1_host_compute"}}, - {{"outside_compilation_O1_host_compute"}, - "_XlaHostCompute", - {"c:o:0"}, - {{"Tinputs", gtl::ArraySlice({DT_FLOAT})}, - {"Toutputs", gtl::ArraySlice({DT_FLOAT})}, - {"key", "host_compute_channel_F1_O1"}, - {"shape_inference_graph", shape_string_expected}, - {"shapes", gtl::ArraySlice({})}}, - {"c"}}, - }, - {{"f_0_retval", "F:o:0"}}); - - { - std::unique_ptr lib_def( - new FunctionLibraryDefinition(OpRegistry::Global(), library_expected)); - GraphDefBuilder b2(GraphDefBuilder::kFailImmediately, lib_def.get()); - Node* a = InputShaped(b2.opts().WithName("A")); - Node* b = Input(b2.opts().WithName("B")); - Node* c = Unary(a, b2.opts().WithName("C")); - - NodeBuilder node_builder("F1", "F1", lib_def.get()); - node_builder.Input(b).Input(c); - Node* call = - b2.opts().WithControlInputs({c}).FinalizeBuilder(&node_builder); - - Node* recv = - RecvAtHost("host_compute_channel_F1_O1", {DT_FLOAT}, - b2.opts().WithName("outside_compilation_F1_O1_recv")); - Node* e = BinaryUnknownShape( - c, ops::NodeOut(recv, 0), - b2.opts().WithName("E").WithControlInputs({recv, b})); - Node* send = SendFromHost("host_compute_channel_F1_O1", {e}, - b2.opts() - .WithName("outside_compilation_F1_O1_send") - .WithControlInput(e)); - - Node* s = NoOp( - b2.opts().WithName("F1_sequencer").WithControlInputs({recv, send})); - - Binary(a, call, b2.opts().WithName("G").WithControlInputs({s, e})); - TF_EXPECT_OK(b2.ToGraphDef(&graphdef_expected)); - } - - TF_EXPECT_GRAPH_EQ(graphdef_expected, graphdef); - TF_EXPECT_FUNCTIONDEFLIBRARY_EQ(library_expected, library); -} - } // namespace } // namespace tensorflow diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc index eae8e6c3c1..d6b576166c 100644 --- a/tensorflow/core/framework/function.cc +++ b/tensorflow/core/framework/function.cc @@ -1064,36 +1064,26 @@ Status FunctionLibraryDefinition::AddLibrary( return Status::OK(); } -Status FunctionLibraryDefinition::RemoveFunction(const string& func) { +void FunctionLibraryDefinition::RemoveFunction(const string& func) { const auto& i = function_defs_.find(func); - if (i == function_defs_.end()) { - return errors::InvalidArgument("Tried to remove non-existent function ", - func); - } + DCHECK(i != function_defs_.end()); function_defs_.erase(i); - return Status::OK(); } -Status FunctionLibraryDefinition::RemoveGradient(const string& func) { +void FunctionLibraryDefinition::RemoveGradient(const string& func) { const auto& i = func_grad_.find(func); - if (i == func_grad_.end()) { - return errors::InvalidArgument("Tried to remove non-existent gradient ", - func); - } + DCHECK(i != func_grad_.end()); func_grad_.erase(i); - return Status::OK(); } void FunctionLibraryDefinition::Remove( const std::vector& funcs, const std::vector& funcs_with_grads) { for (const string& f : funcs) { - Status s = RemoveFunction(f); - DCHECK(s.ok()); + RemoveFunction(f); } for (const string& f : funcs_with_grads) { - Status s = RemoveGradient(f); - DCHECK(s.ok()); + RemoveGradient(f); } } diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h index 7d0e15641d..b933ee0b0e 100644 --- a/tensorflow/core/framework/function.h +++ b/tensorflow/core/framework/function.h @@ -312,14 +312,6 @@ class FunctionLibraryDefinition : public OpRegistryInterface { // This operation is atomic. Status AddGradientDef(const GradientDef& grad); - // Remove function `func` from the library. Returns non-OK Status unless - // `func` is in the library. - Status RemoveFunction(const string& func); - - // Remove gradient of function `func` from the library. Returns non-OK Status - // unless `func` has a gradient. - Status RemoveGradient(const string& func); - // Adds the functions and gradients in 'other' to this function library. // Duplicate functions and gradients are ignored. // This operation is atomic. @@ -392,6 +384,13 @@ class FunctionLibraryDefinition : public OpRegistryInterface { // attr from. const FunctionDef* GetAttrImpl(const NodeDef& ndef) const; + // Remove function `func` from the library. `func` must be in the library. + void RemoveFunction(const string& func); + + // Remove gradient of function `func` from the library. `func` must have + // a gradient. + void RemoveGradient(const string& func); + // Remove all functions in `funcs` and all gradients of // functions in `funcs_with_grads` from this library. void Remove(const std::vector& funcs, -- GitLab From 997c209f9b8210f4bdc44a0172e0b64f5f7761c0 Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Thu, 1 Feb 2018 11:50:14 -0800 Subject: [PATCH 392/423] Added a utility to traverse the graph in reverse DFS order, identifying loops in the process. PiperOrigin-RevId: 184172483 --- tensorflow/core/grappler/graph_view.h | 1 + tensorflow/core/grappler/utils/BUILD | 26 +++++ tensorflow/core/grappler/utils/traversal.cc | 80 ++++++++++++++ tensorflow/core/grappler/utils/traversal.h | 39 +++++++ .../core/grappler/utils/traversal_test.cc | 101 ++++++++++++++++++ 5 files changed, 247 insertions(+) create mode 100644 tensorflow/core/grappler/utils/traversal.cc create mode 100644 tensorflow/core/grappler/utils/traversal.h create mode 100644 tensorflow/core/grappler/utils/traversal_test.cc diff --git a/tensorflow/core/grappler/graph_view.h b/tensorflow/core/grappler/graph_view.h index f4e2de75a6..173ce9c09c 100644 --- a/tensorflow/core/grappler/graph_view.h +++ b/tensorflow/core/grappler/graph_view.h @@ -46,6 +46,7 @@ class GraphView { }; explicit GraphView(GraphDef* graph); + GraphDef* GetGraph() const { return graph_; } NodeDef* GetNode(const string& node_name) const; // Get the specified input port. Note that the special '-1' port_id can be // used to access the controlling nodes (i.e. the nodes connected to node_name diff --git a/tensorflow/core/grappler/utils/BUILD b/tensorflow/core/grappler/utils/BUILD index 534f7a063f..137d51790d 100644 --- a/tensorflow/core/grappler/utils/BUILD +++ b/tensorflow/core/grappler/utils/BUILD @@ -99,3 +99,29 @@ tf_cc_test( "//tensorflow/core:test_main", ], ) + +cc_library( + name = "traversal", + srcs = ["traversal.cc"], + hdrs = ["traversal.h"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core/grappler:graph_view", + "//tensorflow/core/grappler:op_types", + "//tensorflow/core/grappler:utils", + ], +) + +tf_cc_test( + name = "traversal_test", + srcs = ["traversal_test.cc"], + deps = [ + ":traversal", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/core/grappler/utils/traversal.cc b/tensorflow/core/grappler/utils/traversal.cc new file mode 100644 index 0000000000..f44f53c4e6 --- /dev/null +++ b/tensorflow/core/grappler/utils/traversal.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/core/grappler/utils/traversal.h" +#include "tensorflow/core/framework/node_def.pb.h" + +namespace tensorflow { +namespace grappler { + +void ReverseDfs(const GraphView& graph_view, const std::vector& from, + const std::function& pre_order, + const std::function& post_order, + const std::function& on_back_edge) { + // Stack of work to do. + struct StackElem { + NodeDef* node; + bool children_visited; + NodeDef* src; + }; + std::vector stack; + + stack.reserve(from.size()); + for (NodeDef* node : from) { + stack.push_back(StackElem{node, false}); + } + + enum NodeState { NOT_VISITED = 0, VISITING = 1, DONE = 2 }; + std::unordered_map node_state; + while (!stack.empty()) { + StackElem w = stack.back(); + stack.pop_back(); + + if (w.children_visited) { + // We've processed all the children of this node + node_state[w.node] = DONE; + if (post_order) { + post_order(w.node); + } + continue; + } + + auto& rslt = node_state[w.node]; + if (rslt == DONE) { + continue; + } else if (rslt == VISITING) { + // Loop detected + if (on_back_edge) { + on_back_edge(w.src, w.node); + } + continue; + } + rslt = VISITING; + if (pre_order) { + pre_order(w.node); + } + + // Enqueue the node again with the children_visited flag set to true. + stack.push_back(StackElem{w.node, true, w.src}); + + // Now enqueu the node children. + for (const auto fanin : graph_view.GetFanins(*w.node, true)) { + stack.push_back(StackElem{fanin.node, false, w.node}); + } + } +} + +} // namespace grappler +} // namespace tensorflow diff --git a/tensorflow/core/grappler/utils/traversal.h b/tensorflow/core/grappler/utils/traversal.h new file mode 100644 index 0000000000..bb3fa090e8 --- /dev/null +++ b/tensorflow/core/grappler/utils/traversal.h @@ -0,0 +1,39 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CORE_GRAPPLER_UTILS_TRAVERSAL_H_ +#define TENSORFLOW_CORE_GRAPPLER_UTILS_TRAVERSAL_H_ + +#include +#include "tensorflow/core/grappler/graph_view.h" + +namespace tensorflow { +namespace grappler { + +// Traverse the graph in reverse dfs order, starting from the list of nodes +// specified in the 'from' argument. The pre_order and post_order functors will +// be called on each reachable node (including the 'from' nodes) in pre and post +// order. If loops are found, the on_back_edge functor will be called on the +// corresponding back edges. Moreover, the pre and post order will assume that +// these back edges will be cut. +void ReverseDfs(const GraphView& graph_view, const std::vector& from, + const std::function& pre_order, + const std::function& post_order, + const std::function& on_back_edge); + +} // namespace grappler +} // namespace tensorflow + +#endif // TENSORFLOW_CORE_GRAPPLER_UTILS_TRAVERSAL_H_ diff --git a/tensorflow/core/grappler/utils/traversal_test.cc b/tensorflow/core/grappler/utils/traversal_test.cc new file mode 100644 index 0000000000..cc68bd1a96 --- /dev/null +++ b/tensorflow/core/grappler/utils/traversal_test.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/core/grappler/utils/traversal.h" +//#include "tensorflow/core/framework/node_def.pb.h" +//#include "tensorflow/core/lib/core/status_test_util.h" +//#include "tensorflow/core/platform/protobuf.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace grappler { +namespace { + +class TraversalTest : public ::testing::Test { + protected: + static NodeDef CreateNode(const string& name, + const std::vector& inputs) { + return CreateNode(name, "", inputs); + } + static NodeDef CreateNode(const string& name, const string& op, + const std::vector& inputs) { + NodeDef node; + node.set_name(name); + if (!op.empty()) { + node.set_op(op); + } + for (const string& input : inputs) { + node.add_input(input); + } + return node; + } +}; + +TEST_F(TraversalTest, ReverseDfsNoLoop) { + GraphDef graph; + *graph.add_node() = CreateNode("2", {"5"}); + *graph.add_node() = CreateNode("0", {"5", "4"}); + *graph.add_node() = CreateNode("1", {"4", "3"}); + *graph.add_node() = CreateNode("3", {"2"}); + *graph.add_node() = CreateNode("5", {}); + *graph.add_node() = CreateNode("4", {}); + + std::vector start_nodes = {graph.mutable_node(1), + graph.mutable_node(2)}; + std::vector pre_order; + std::vector post_order; + bool found_back_edge = false; + ReverseDfs( + GraphView(&graph), start_nodes, + [&pre_order](NodeDef* n) { pre_order.push_back(n->name()); }, + [&post_order](NodeDef* n) { post_order.push_back(n->name()); }, + [&found_back_edge](NodeDef*, NodeDef*) { found_back_edge = true; }); + + EXPECT_EQ(std::vector({"1", "4", "3", "2", "5", "0"}), pre_order); + EXPECT_EQ(std::vector({"4", "5", "2", "3", "1", "0"}), post_order); + EXPECT_FALSE(found_back_edge); +} + +TEST_F(TraversalTest, ReverseDfsWithLoop) { + GraphDef graph; + // Create a loop + *graph.add_node() = CreateNode("2", "Merge", {"1", "5"}); + *graph.add_node() = CreateNode("3", "Switch", {"2"}); + *graph.add_node() = CreateNode("4", "Identity", {"3"}); + *graph.add_node() = CreateNode("5", "NextIteration", {"4"}); + *graph.add_node() = CreateNode("1", "Enter", {}); + *graph.add_node() = CreateNode("6", "Exit", {"3"}); + + std::vector start_nodes = {graph.mutable_node(5)}; + std::vector pre_order; + std::vector post_order; + std::vector back_edges; + ReverseDfs( + GraphView(&graph), start_nodes, + [&pre_order](NodeDef* n) { pre_order.push_back(n->name()); }, + [&post_order](NodeDef* n) { post_order.push_back(n->name()); }, + [&back_edges](NodeDef* src, NodeDef* dst) { + back_edges.push_back(strings::StrCat(src->name(), "->", dst->name())); + }); + + EXPECT_EQ(std::vector({"6", "3", "2", "1", "5", "4"}), pre_order); + EXPECT_EQ(std::vector({"1", "4", "5", "2", "3", "6"}), post_order); + EXPECT_EQ(std::vector({"4->3"}), back_edges); +} + +} // namespace +} // namespace grappler +} // namespace tensorflow -- GitLab From 77e6a452188e83ae4498cc3ae23e20e60061b367 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 1 Feb 2018 11:50:23 -0800 Subject: [PATCH 393/423] [tf.data] Fix bug where captured resources in shared iterators were invisible. This change ensures that a shared iterator (which requires a private FunctionLibraryRuntime that outlasts the calling op's runtime, because it can outlive a single session) uses the same Device as a non-shared iterator, and hence capturing resources from the creating graph will work as intended. Fixes #16481. PiperOrigin-RevId: 184172498 --- tensorflow/core/kernels/data/iterator_ops.cc | 30 ++++++++++++--- tensorflow/python/data/kernel_tests/BUILD | 3 ++ .../kernel_tests/iterator_ops_cluster_test.py | 37 +++++++++++++++++++ 3 files changed, 64 insertions(+), 6 deletions(-) diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index b37bd672ad..dd5f4a4554 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/graph_runner.h" +#include "tensorflow/core/common_runtime/renamed_device.h" #include "tensorflow/core/common_runtime/threadpool_device.h" #include "tensorflow/core/framework/iterator.pb.h" #include "tensorflow/core/framework/partial_tensor_shape.h" @@ -516,15 +517,32 @@ class IteratorHandleOp : public OpKernel { return Status::OK(); } + template // use like this: down_cast(foo); + static inline To down_cast(From* f) { // so we only accept pointers + static_assert( + (std::is_base_of::type>::value), + "target type not derived from source type"); + + // We skip the assert and hence the dynamic_cast if RTTI is disabled. +#if !defined(__GNUC__) || defined(__GXX_RTTI) + // Uses RTTI in dbg and fastbuild. asserts are disabled in opt builds. + assert(f == nullptr || dynamic_cast(f) != nullptr); +#endif // !defined(__GNUC__) || defined(__GXX_RTTI) + return static_cast(f); + } + FunctionLibraryRuntime* CreatePrivateFLR( OpKernelContext* ctx, std::unique_ptr* device_mgr, std::unique_ptr* flib_def, std::unique_ptr* pflr) { - Device* device = new ThreadPoolDevice( - SessionOptions(), ctx->device()->attributes().name(), Bytes(256 << 20), - DeviceLocality(), cpu_allocator()); - - device_mgr->reset(new DeviceMgr({device})); + // Wrap the existing device in order to see any captured resources + // in its resource manager. The existing device will outlive the + // IteratorResource, because we are storing the IteratorResource + // in that device's resourc manager. + Device* wrapped_device = RenamedDevice::NewRenamedDevice( + ctx->device()->name(), down_cast(ctx->device()), + false /* owns_underlying */, false /* isolate_session_state */); + device_mgr->reset(new DeviceMgr({wrapped_device})); flib_def->reset(new FunctionLibraryDefinition( *ctx->function_library()->GetFunctionLibraryDefinition())); pflr->reset(new ProcessFunctionLibraryRuntime( @@ -532,7 +550,7 @@ class IteratorHandleOp : public OpKernel { {} /* TODO(mrry): OptimizerOptions? */, nullptr /* TODO(mrry): ClusterFLR */)); - return (*pflr)->GetFLR(device->name()); + return (*pflr)->GetFLR(ctx->device()->name()); } mutex mu_; diff --git a/tensorflow/python/data/kernel_tests/BUILD b/tensorflow/python/data/kernel_tests/BUILD index 43cbde69d9..8b8adefa65 100644 --- a/tensorflow/python/data/kernel_tests/BUILD +++ b/tensorflow/python/data/kernel_tests/BUILD @@ -357,6 +357,9 @@ tf_py_test( "//tensorflow/python:session", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/ops:iterator_ops", + "//tensorflow/python:constant_op", + "//tensorflow/python:string_ops", + "//tensorflow/python:lookup_ops", ], grpc_enabled = True, tags = [ diff --git a/tensorflow/python/data/kernel_tests/iterator_ops_cluster_test.py b/tensorflow/python/data/kernel_tests/iterator_ops_cluster_test.py index 45dfa13720..2c65c49ebd 100644 --- a/tensorflow/python/data/kernel_tests/iterator_ops_cluster_test.py +++ b/tensorflow/python/data/kernel_tests/iterator_ops_cluster_test.py @@ -21,6 +21,7 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_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 function @@ -28,6 +29,8 @@ 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 functional_ops +from tensorflow.python.ops import lookup_ops +from tensorflow.python.ops import string_ops from tensorflow.python.platform import test @@ -103,6 +106,40 @@ class IteratorClusterTest(test.TestCase): "/job:worker/replica:0/task:1/cpu:0", workers[0].target) + def testCaptureHashTableInSharedIterator(self): + worker, _ = test_util.create_local_cluster(1, 1) + + # NOTE(mrry): We must use the V2 variants of `HashTable` + # etc. because these produce a `tf.resource`-typed output that is + # compatible with the in-graph function implementation. + default_val = -1 + keys = constant_op.constant(["brain", "salad", "surgery"]) + values = constant_op.constant([0, 1, 2], dtypes.int64) + table = lookup_ops.HashTable( + lookup_ops.KeyValueTensorInitializer(keys, values), + default_val, + shared_name="shared_table") + + input_sentences = dataset_ops.Dataset.from_tensor_slices( + ["brain brain tank salad surgery", "surgery brain"]) + + iterator = ( + input_sentences.map(lambda x: string_ops.string_split([x]).values).map( + table.lookup) + .make_initializable_iterator(shared_name="shared_iterator")) + init_op = iterator.initializer + get_next = iterator.get_next() + + with session.Session(worker[0].target) as sess: + sess.run(table.init) + sess.run(init_op) + self.assertAllEqual([0, 0, -1, 1, 2], sess.run(get_next)) + + with session.Session(worker[0].target) as sess: + self.assertAllEqual([2, 0], sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + if __name__ == "__main__": test.main() -- GitLab From ccedcbe14c798fb3b227030cf85b4fe89406f0d8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 11:54:13 -0800 Subject: [PATCH 394/423] Update deprecated API use PiperOrigin-RevId: 184173047 --- tensorflow/core/distributed_runtime/tensor_coding.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/distributed_runtime/tensor_coding.cc b/tensorflow/core/distributed_runtime/tensor_coding.cc index fe2d1a1293..34a4013547 100644 --- a/tensorflow/core/distributed_runtime/tensor_coding.cc +++ b/tensorflow/core/distributed_runtime/tensor_coding.cc @@ -81,7 +81,7 @@ void TensorResponse::InitPartial(const RecvTensorResponse& response) { Status TensorResponse::ParseFrom(Source* source) { if (!on_host_) { protobuf::io::CodedInputStream input(source->contents()); - input.SetTotalBytesLimit(INT_MAX, INT_MAX); // Unlimited + input.SetTotalBytesLimit(INT_MAX); // Unlimited // Pre-parse into local storage, then delegate to device. if (!meta_.ParseFromCodedStream(&input) || !input.ConsumedEntireMessage()) { @@ -217,7 +217,7 @@ bool TensorResponse::ParseTensorSubmessage( bool TensorResponse::ParseFast(Source* source) { protobuf::io::CodedInputStream input(source->contents()); - input.SetTotalBytesLimit(INT_MAX, INT_MAX); // Unlimited + input.SetTotalBytesLimit(INT_MAX); // Unlimited while (true) { auto p = input.ReadTagWithCutoff(127); int tag = GetTagFieldNumber(p.first); -- GitLab From 9ba944b79dff684954a4d4591e792d4fa1e858c2 Mon Sep 17 00:00:00 2001 From: Anna R Date: Thu, 1 Feb 2018 12:05:23 -0800 Subject: [PATCH 395/423] Internal change. PiperOrigin-RevId: 184174800 --- tensorflow/python/keras/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index a9dd8d8e9d..fdac22bb53 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -482,6 +482,7 @@ py_test( size = "small", srcs = ["_impl/keras/layers/normalization_test.py"], srcs_version = "PY2AND3", + tags = ["notsan"], deps = [ ":keras", "//tensorflow/python:client_testlib", -- GitLab From 6bca4c6c21e2602b707f22b2ea29ef8cee27ec9c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 12:17:08 -0800 Subject: [PATCH 396/423] Add a utility module that contains helper functions usable from within generated code. Add a helper for the control dependencies context manager. PiperOrigin-RevId: 184176409 --- tensorflow/BUILD | 1 + tensorflow/contrib/py2tf/BUILD | 1 + tensorflow/contrib/py2tf/__init__.py | 3 +- tensorflow/contrib/py2tf/converters/BUILD | 1 + .../py2tf/converters/side_effect_guards.py | 19 ++------ .../converters/side_effect_guards_test.py | 2 + tensorflow/contrib/py2tf/impl/config.py | 3 +- tensorflow/contrib/py2tf/utils/BUILD | 37 ++++++++++++++++ tensorflow/contrib/py2tf/utils/__init__.py | 21 +++++++++ .../contrib/py2tf/utils/context_managers.py | 41 ++++++++++++++++++ .../py2tf/utils/context_managers_test.py | 43 +++++++++++++++++++ 11 files changed, 155 insertions(+), 17 deletions(-) create mode 100644 tensorflow/contrib/py2tf/utils/BUILD create mode 100644 tensorflow/contrib/py2tf/utils/__init__.py create mode 100644 tensorflow/contrib/py2tf/utils/context_managers.py create mode 100644 tensorflow/contrib/py2tf/utils/context_managers_test.py diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 66a2ecd8b5..bda0a83af3 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -539,6 +539,7 @@ filegroup( "//tensorflow/contrib/py2tf/impl:all_files", "//tensorflow/contrib/py2tf/pyct:all_files", "//tensorflow/contrib/py2tf/pyct/static_analysis:all_files", + "//tensorflow/contrib/py2tf/utils:all_files", "//tensorflow/contrib/quantize:all_files", "//tensorflow/contrib/receptive_field:all_files", "//tensorflow/contrib/reduce_slice_ops:all_files", diff --git a/tensorflow/contrib/py2tf/BUILD b/tensorflow/contrib/py2tf/BUILD index cea3738499..479ea9beca 100644 --- a/tensorflow/contrib/py2tf/BUILD +++ b/tensorflow/contrib/py2tf/BUILD @@ -23,6 +23,7 @@ py_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/py2tf/impl", + "//tensorflow/contrib/py2tf/utils", "@gast_archive//:gast", "@six_archive//:six", ], diff --git a/tensorflow/contrib/py2tf/__init__.py b/tensorflow/contrib/py2tf/__init__.py index 878941b3a3..0d51bf0bf2 100644 --- a/tensorflow/contrib/py2tf/__init__.py +++ b/tensorflow/contrib/py2tf/__init__.py @@ -21,12 +21,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.impl.api import convert from tensorflow.contrib.py2tf.impl.api import graph_ready from tensorflow.contrib.py2tf.impl.api import to_code from tensorflow.contrib.py2tf.impl.api import to_graph from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ['to_graph', 'to_code', 'convert', 'graph_ready'] +_allowed_symbols = ['to_graph', 'to_code', 'convert', 'graph_ready', 'utils'] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD index b61fda3e91..3853c60f99 100644 --- a/tensorflow/contrib/py2tf/converters/BUILD +++ b/tensorflow/contrib/py2tf/converters/BUILD @@ -46,6 +46,7 @@ py_library( deps = [ ":converters", "//tensorflow/contrib/py2tf/pyct/static_analysis", + "//tensorflow/contrib/py2tf/utils", "@gast_archive//:gast", ], ) diff --git a/tensorflow/contrib/py2tf/converters/side_effect_guards.py b/tensorflow/contrib/py2tf/converters/side_effect_guards.py index 46a2269c20..ffca743542 100644 --- a/tensorflow/contrib/py2tf/converters/side_effect_guards.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards.py @@ -109,31 +109,20 @@ class SideEffectGuardTransformer(gast.NodeTransformer): # opt.minimize(loss) # or: # tf.py_func(...) - args_scope = anno.getanno(node.value, 'args_scope') - temp_name = self.namer.new_symbol('temp', args_scope.parent.referenced) - # TODO(mdan): Unsafe reference modification! - args_scope.mark_write(temp_name) template = """ - temp_result = call - if temp_result is not None: - if not isinstance(temp_result, (list, tuple)): - temp_result = (temp_result,) - ctx = tf.control_dependencies(temp_result) - else: - ctx = contextmanager(lambda: (yield))() - with ctx: - # TODO(mdan): Also insert ops to re-fetch if variables are involved. + with py2tf_utils.control_dependency_on_returns(tf, call): + # TODO(mdan): Also insert ops to re-fetch if variables are involved? pass # Will be removed below. """ # TODO(mdan): This is brittle. Reorganize the mechanism. - statements = templates.replace( - template, call=node.value, temp_result=temp_name) + statements = templates.replace(template, call=node.value) control_deps_guard = statements[-1] control_deps_guard.body = [] # First, attempt to gate future evaluation of args. If that's not # possible, gate all remaining statements (and that may fail too, see # _visit_and_reindent. + args_scope = anno.getanno(node.value, 'args_scope') guarded_args = tuple(args_scope.used & (args_scope.parent.modified | args_scope.parent.returned)) if guarded_args: diff --git a/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py b/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py index 5c56973dc2..452d7ab2be 100644 --- a/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py +++ b/tensorflow/contrib/py2tf/converters/side_effect_guards_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.contrib.py2tf import utils from tensorflow.contrib.py2tf.converters import converter_test_base from tensorflow.contrib.py2tf.converters import side_effect_guards from tensorflow.contrib.py2tf.pyct import compiler @@ -46,6 +47,7 @@ class SideEffectGuardsTest(converter_test_base.TestCase): node = side_effect_guards.transform(node, TestNamer()) result = compiler.ast_to_object(node) setattr(result, 'state_ops', state_ops) + setattr(result, 'py2tf_utils', utils) # TODO(mdan): Configure the namespaces instead of doing these hacks. ops.identity = array_ops.identity diff --git a/tensorflow/contrib/py2tf/impl/config.py b/tensorflow/contrib/py2tf/impl/config.py index 0892241983..6525806a09 100644 --- a/tensorflow/contrib/py2tf/impl/config.py +++ b/tensorflow/contrib/py2tf/impl/config.py @@ -36,4 +36,5 @@ NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',)) # TODO(mdan): Make sure copybara renames the reference below. COMPILED_IMPORT_STATEMENTS = ( 'import tensorflow as tf', -) + 'from tensorflow.contrib.py2tf import utils as ' + 'py2tf_utils') diff --git a/tensorflow/contrib/py2tf/utils/BUILD b/tensorflow/contrib/py2tf/utils/BUILD new file mode 100644 index 0000000000..01804aa883 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/BUILD @@ -0,0 +1,37 @@ +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_library( + name = "utils", + srcs = [ + "__init__.py", + "context_managers.py", + ], + srcs_version = "PY2AND3", + visibility = ["//tensorflow:__subpackages__"], + deps = [ + ], +) + +py_test( + name = "context_managers_test", + srcs = ["context_managers_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":utils", + "//tensorflow/python:client_testlib", + ], +) diff --git a/tensorflow/contrib/py2tf/utils/__init__.py b/tensorflow/contrib/py2tf/utils/__init__.py new file mode 100644 index 0000000000..bca33e89e9 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/__init__.py @@ -0,0 +1,21 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utility module that contains APIs usable in the generated code.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns diff --git a/tensorflow/contrib/py2tf/utils/context_managers.py b/tensorflow/contrib/py2tf/utils/context_managers.py new file mode 100644 index 0000000000..47d9839997 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/context_managers.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. +# ============================================================================== +"""Various context managers.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib + + +def control_dependency_on_returns(tf, return_value): + """Create a TF control dependency on the return values of a function. + + If the function had no return value, a no-op context is returned. + + Args: + tf: The TensorFlow module. + return_value: The return value to set as control dependency. + + Returns: + A context manager. + """ + if return_value is None: + return contextlib.contextmanager(lambda: (yield))() + # TODO(mdan): Filter to tensor objects. + if not isinstance(return_value, (list, tuple)): + return_value = (return_value,) + return tf.control_dependencies(return_value) diff --git a/tensorflow/contrib/py2tf/utils/context_managers_test.py b/tensorflow/contrib/py2tf/utils/context_managers_test.py new file mode 100644 index 0000000000..c903f08252 --- /dev/null +++ b/tensorflow/contrib/py2tf/utils/context_managers_test.py @@ -0,0 +1,43 @@ +# 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 context_managers module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.py2tf.utils import context_managers +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.platform import test + + +class ContextManagersTest(test.TestCase): + + def test_control_dependency_on_returns(self): + # Just dry run them. + with context_managers.control_dependency_on_returns(ops, None): + pass + with context_managers.control_dependency_on_returns( + ops, constant_op.constant(1)): + pass + with context_managers.control_dependency_on_returns( + ops, [constant_op.constant(1), + constant_op.constant(2)]): + pass + + +if __name__ == '__main__': + test.main() -- GitLab From 448f6c70fa9fac05ecf291e59b20cf2451c65a9f Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Thu, 1 Feb 2018 12:38:55 -0800 Subject: [PATCH 397/423] Made the addn optimization aware of the graph topology PiperOrigin-RevId: 184179246 --- tensorflow/core/grappler/optimizers/BUILD | 1 + .../grappler/optimizers/memory_optimizer.cc | 67 +++++++++++++++++-- 2 files changed, 63 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index 68de03e81c..8b9885e4c1 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -289,6 +289,7 @@ cc_library( "//tensorflow/core/grappler/costs:graph_memory", "//tensorflow/core/grappler/costs:graph_properties", "//tensorflow/core/grappler/utils:topological_sort", + "//tensorflow/core/grappler/utils:traversal", ], ) diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc index 6f95a00fa3..ffa03db262 100644 --- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc @@ -35,6 +35,7 @@ limitations under the License. #include "tensorflow/core/grappler/optimizers/static_schedule.h" #include "tensorflow/core/grappler/utils.h" #include "tensorflow/core/grappler/utils/topological_sort.h" +#include "tensorflow/core/grappler/utils/traversal.h" #include "tensorflow/core/protobuf/rewriter_config.pb.h" namespace tensorflow { @@ -497,7 +498,7 @@ bool SchedulingPass(Cluster* cluster, GrapplerItem* item) { if (!IsAddN(node)) { continue; } - // There is nothing to gain by optimizing nodes with 2 inputs of fewer. + // There is nothing to gain by optimizing nodes with 2 or fewer inputs. if (view.NumFanins(node, false) <= 2) { continue; } @@ -559,6 +560,54 @@ bool SchedulingPass(Cluster* cluster, GrapplerItem* item) { VLOG(1) << "Missing properties for " << node->name(); continue; } + + // Compute a topological ordering for the node fanin. + std::unordered_map topo_order; + ReverseDfs(view, {node}, nullptr, + [&topo_order](NodeDef* n) { + int topo_index = topo_order.size(); + topo_order[n] = topo_index; + }, + nullptr); + + std::vector input_topo_index; + + for (int i = 0; i < node->input_size(); ++i) { + const string& input = node->input(i); + const string node_name = NodeName(input); + NodeDef* node = view.GetNode(node_name); + input_topo_index.push_back(topo_order.at(node)); + } + int min_input_topo_index = INT_MAX; + int min_input_id = -1; + for (int i = 0; i < node->input_size(); ++i) { + if (IsControlInput(node->input(i))) { + // control inputs are always last. + break; + } + const int current = input_topo_index[i]; + if (current < min_input_topo_index) { + min_input_topo_index = current; + min_input_id = i; + } + } + CHECK_LE(0, min_input_id); + std::vector pre_ctrl_deps; + std::vector post_ctrl_deps; + for (int i = node->input_size() - 1; i >= 0; --i) { + if (!IsControlInput(node->input(i))) { + // control inputs are always last. + break; + } + if (input_topo_index[i] < min_input_topo_index) { + // These control dependencies can be executed before the node. + pre_ctrl_deps.push_back(node->input(i)); + } else { + // These control dependencies should be executed after the node. + post_ctrl_deps.push_back(node->input(i)); + } + } + const TensorShapeProto& shape = properties.GetOutputProperties(node->name())[0].shape(); DataType dtype = node->attr().at("T").type(); @@ -573,13 +622,19 @@ bool SchedulingPass(Cluster* cluster, GrapplerItem* item) { *(*tmp_var->mutable_attr())["shape"].mutable_shape() = shape; (*tmp_var->mutable_attr())["var_name"].set_s(tmp_var->name()); + for (const string& ctrl_dep : pre_ctrl_deps) { + *tmp_var->add_input() = ctrl_dep; + } + *tmp_var->add_input() = + AsControlDependency(NodeName(node->input(min_input_id))); + // Initialize it to zero NodeDef* zeros = item->graph.add_node(); zeros->set_name(strings::StrCat(node->name(), "/tmp_var_zeros")); zeros->set_op("ZerosLike"); zeros->set_device(device); (*zeros->mutable_attr())["T"].set_type(dtype); - *zeros->add_input() = node->input(0); + *zeros->add_input() = node->input(min_input_id); NodeDef* initialize = item->graph.add_node(); initialize->set_name(strings::StrCat(node->name(), "/tmp_var_initializer")); @@ -593,9 +648,7 @@ bool SchedulingPass(Cluster* cluster, GrapplerItem* item) { std::vector accumulates; for (int i = 0; i < node->input_size(); ++i) { const string& input = node->input(i); - if (IsControlInput(input)) { - *zeros->add_input() = input; - } else { + if (!IsControlInput(input)) { NodeDef* accumulate = item->graph.add_node(); accumulate->set_name( strings::StrCat(node->name(), "/tmp_var_accum_", i)); @@ -618,6 +671,10 @@ bool SchedulingPass(Cluster* cluster, GrapplerItem* item) { for (const NodeDef* accum : accumulates) { *node->add_input() = AsControlDependency(accum->name()); } + for (const string& ctrl_dep : post_ctrl_deps) { + *node->add_input() = ctrl_dep; + } + updated_graph = true; } -- GitLab From 1453d4c61178dbb4dea9e48790ea8fd7c58cd1d5 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 13:13:10 -0800 Subject: [PATCH 398/423] Verify tensor contents of tflite model PiperOrigin-RevId: 184183725 --- tensorflow/contrib/lite/tools/BUILD | 4 + tensorflow/contrib/lite/tools/verifier.cc | 170 ++++++++++++++- tensorflow/contrib/lite/tools/verifier.h | 4 +- .../contrib/lite/tools/verifier_test.cc | 200 +++++++++++++----- 4 files changed, 324 insertions(+), 54 deletions(-) diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index 1bffcfb987..4d3b553b22 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -99,8 +99,11 @@ cc_library( srcs = ["verifier.cc"], hdrs = ["verifier.h"], deps = [ + "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/schema:schema_fbs", + "@com_google_absl//absl/base:core_headers", ], ) @@ -112,6 +115,7 @@ cc_test( ":verifier", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/schema:schema_fbs", "//tensorflow/contrib/lite/testing:util", "@com_google_googletest//:gtest", diff --git a/tensorflow/contrib/lite/tools/verifier.cc b/tensorflow/contrib/lite/tools/verifier.cc index 95a0895379..726e2aaa31 100644 --- a/tensorflow/contrib/lite/tools/verifier.cc +++ b/tensorflow/contrib/lite/tools/verifier.cc @@ -14,13 +14,32 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/tools/verifier.h" +#include #include "tensorflow/contrib/lite/schema/schema_generated.h" +#include "tensorflow/contrib/lite/string_util.h" #include "tensorflow/contrib/lite/version.h" namespace tflite { namespace { +// Reports error message when the reporter is set. +void ReportError(ErrorReporter* error_reporter, const char* format, ...) { + if (error_reporter) { + va_list args; + va_start(args, format); + error_reporter->Report(format, args); + va_end(args); + } +} + +// Returns the int32_t value pointed by ptr. +const uint32_t* GetIntPtr(const char* ptr) { + return reinterpret_cast(ptr); +} + +// Verifies flatbuffer format of the model contents and returns the in-memory +// model. const Model* VerifyFlatbufferAndGetModel(const void* buf, size_t len) { ::flatbuffers::Verifier verifier(static_cast(buf), len); if (VerifyModelBuffer(verifier)) { @@ -30,14 +49,159 @@ const Model* VerifyFlatbufferAndGetModel(const void* buf, size_t len) { } } +const uint32_t kMaxNumString = UINT_MAX / sizeof(int32_t) - 2; + +// Verifies string tensor has legit buffer contents that follow the schema +// defined in lite/string_util.h +bool VerifyStringTensorBuffer(const Buffer& buffer, + ErrorReporter* error_reporter) { + uint32_t buffer_size = buffer.data()->size(); + const char* buffer_ptr = reinterpret_cast(buffer.data()->data()); + + uint32_t num_strings = *GetIntPtr(buffer_ptr); + if (num_strings > kMaxNumString) { + ReportError(error_reporter, + "String tensor has invalid num of string set: %d", num_strings); + return false; + } + uint32_t header_offsets = + static_cast(num_strings + 2) * sizeof(int32_t); + + if (buffer_size < header_offsets) { + ReportError(error_reporter, + "String tensor buffer requires at least %d bytes, but is " + "allocated with %d bytes", + header_offsets, buffer_size); + return false; + } + + uint32_t prev_ptr = header_offsets; + uint32_t offset = sizeof(int32_t); + + if (*GetIntPtr(buffer_ptr + offset) != header_offsets) { + ReportError(error_reporter, + "String tensor buffer initial offset must be: %d", + header_offsets); + return false; + } + offset += sizeof(int32_t); + for (int i = 1; i <= num_strings; i++, offset += sizeof(int32_t)) { + int string_offset = *GetIntPtr(buffer_ptr + offset); + if (string_offset < prev_ptr || string_offset > buffer_size) { + ReportError(error_reporter, "String tensor buffer is invalid: index %d", + i); + return false; + } + } + if (*GetIntPtr(buffer_ptr + offset - sizeof(int32_t)) != buffer_size) { + ReportError(error_reporter, "String tensor buffer last offset must be %d", + buffer_size); + return false; + } + return true; +} + +// Verifies numeric tensor has legit buffer. +bool VerifyNumericTensorBuffer(const Tensor& tensor, const Buffer& buffer, + ErrorReporter* error_reporter) { + uint64_t bytes_required = 1; + for (int dim : *tensor.shape()) { + bytes_required *= dim; + if (bytes_required > UINT_MAX) { + ReportError(error_reporter, "Tensor dimension overflow"); + return false; + } + } + switch (tensor.type()) { + case TensorType_FLOAT32: + bytes_required *= sizeof(float); + break; + case TensorType_INT32: + bytes_required *= sizeof(int32_t); + break; + case TensorType_UINT8: + bytes_required *= sizeof(uint8_t); + break; + case TensorType_INT64: + bytes_required *= sizeof(int64_t); + break; + case TensorType_FLOAT16: + // FALLTHROUGH_INTENDED; + default: + ReportError(error_reporter, "Invalid tensor type: %d", tensor.type()); + return false; + } + if (bytes_required > UINT_MAX) { + ReportError(error_reporter, "Tensor dimension overflow"); + return false; + } + + if (bytes_required != buffer.data()->size()) { + ReportError( + error_reporter, + "Tensor requires %d bytes, but is allocated with %d bytes buffer", + bytes_required, buffer.data()->size()); + return false; + } + return true; + + // TODO(yichengfan): verify quantized tensors. +} + +// Verifies tensors have valid properties and legit buffer if set. +bool VerifyTensors(const Model& model, ErrorReporter* error_reporter) { + if (!model.subgraphs()) { + return true; + } + for (const auto& subgraph : *model.subgraphs()) { + if (!subgraph->tensors()) { + return true; + } + for (const auto& tensor : *subgraph->tensors()) { + if (!tensor->buffer()) { + return true; + } + if (tensor->buffer() >= model.buffers()->size()) { + ReportError(error_reporter, "Invalid tensor buffer index: %d", + tensor->buffer()); + return false; + } + auto* buffer = model.buffers()->Get(tensor->buffer()); + if (!buffer || !buffer->data()) { + ReportError(error_reporter, "Tensor buffer %d not set", + tensor->buffer()); + return false; + } + + if (tensor->type() == TensorType_STRING) { + if (!VerifyStringTensorBuffer(*buffer, error_reporter)) { + return false; + } + } else { + if (!VerifyNumericTensorBuffer(*tensor, *buffer, error_reporter)) { + return false; + } + } + } + } + return true; +} + } // namespace -bool Verify(const void* buf, size_t len) { +bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter) { const Model* model = VerifyFlatbufferAndGetModel(buf, len); if (model == nullptr) { + ReportError(error_reporter, "Invalid flatbuffer format"); return false; } - - return model->version() == TFLITE_SCHEMA_VERSION; + if (model->version() != TFLITE_SCHEMA_VERSION) { + ReportError(error_reporter, "Invalid model version %d", model->version()); + return false; + } + if (!VerifyTensors(*model, error_reporter)) { + return false; + } + return true; } } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier.h b/tensorflow/contrib/lite/tools/verifier.h index 03e1f22b7e..d2bf3c91d5 100644 --- a/tensorflow/contrib/lite/tools/verifier.h +++ b/tensorflow/contrib/lite/tools/verifier.h @@ -18,13 +18,15 @@ limitations under the License. #include +#include "tensorflow/contrib/lite/error_reporter.h" + namespace tflite { // Verifies the integrity of a Tensorflow Lite flatbuffer model file. // Currently, it verifies: // * The file is following a legit flatbuffer schema. // * The model is in supported version. -bool Verify(const void* buf, size_t len); +bool Verify(const void* buf, size_t len, ErrorReporter* error_reporter); } // namespace tflite diff --git a/tensorflow/contrib/lite/tools/verifier_test.cc b/tensorflow/contrib/lite/tools/verifier_test.cc index 0481a55a78..244d4f0396 100644 --- a/tensorflow/contrib/lite/tools/verifier_test.cc +++ b/tensorflow/contrib/lite/tools/verifier_test.cc @@ -28,31 +28,62 @@ using flatbuffers::FlatBufferBuilder; using flatbuffers::Offset; using flatbuffers::Vector; -// Class that abstracts the list of buffers at the end of the TF Lite structure -class DeferredBufferWriter { +// Build single subgraph model. +class TfLiteFlatbufferModelBuilder { public: - DeferredBufferWriter() { - data_.push_back({}); // sentinel empty buffer. + TfLiteFlatbufferModelBuilder() { + buffers_.push_back( + CreateBuffer(builder_, builder_.CreateVector(std::vector{}))); } - Offset>> BuildBuffers(FlatBufferBuilder *builder) { - std::vector> buffer_vector; - for (const auto &vec : data_) { - auto data_buffer = builder->CreateVector(vec.data(), vec.size()); - buffer_vector.push_back(tflite::CreateBuffer(*builder, data_buffer)); + void AddTensor(const std::vector& shape, tflite::TensorType type, + const std::vector& buffer, const char* name) { + int buffer_index = 0; + if (!buffer.empty()) { + buffer_index = buffers_.size(); + buffers_.push_back(CreateBuffer(builder_, builder_.CreateVector(buffer))); } - return builder->CreateVector(buffer_vector); + tensors_.push_back(CreateTensorDirect(builder_, &shape, type, buffer_index, + name, /*quantization=*/0)); } - // Registers a buffer index and takes ownership of the data to write to it. - int Record(std::vector data) { - int buffer_index = data_.size(); - data_.emplace_back(std::move(data)); - return buffer_index; + void AddOperator(const std::vector& inputs, + const std::vector& outputs, + tflite::BuiltinOperator builtin_op, const char* custom_op) { + operator_codes_.push_back( + CreateOperatorCodeDirect(builder_, builtin_op, custom_op)); + operators_.push_back(CreateOperator( + builder_, operator_codes_.size() - 1, builder_.CreateVector(inputs), + builder_.CreateVector(outputs), BuiltinOptions_NONE, + /*builtin_options=*/0, + /*custom_options=*/0, tflite::CustomOptionsFormat_FLEXBUFFERS)); + } + + void FinishModel(const std::vector& inputs, + const std::vector& outputs) { + auto subgraph = std::vector>({CreateSubGraph( + builder_, builder_.CreateVector(tensors_), + builder_.CreateVector(inputs), builder_.CreateVector(outputs), + builder_.CreateVector(operators_), + builder_.CreateString("test_subgraph"))}); + auto result = CreateModel( + builder_, TFLITE_SCHEMA_VERSION, builder_.CreateVector(operator_codes_), + builder_.CreateVector(subgraph), builder_.CreateString("test_model"), + builder_.CreateVector(buffers_)); + tflite::FinishModelBuffer(builder_, result); + } + + bool Verify() { + return tflite::Verify(builder_.GetBufferPointer(), builder_.GetSize(), + DefaultErrorReporter()); } private: - std::vector> data_; + FlatBufferBuilder builder_; + std::vector> operators_; + std::vector> operator_codes_; + std::vector> tensors_; + std::vector> buffers_; }; TEST(VerifyModel, TestEmptyModel) { @@ -62,43 +93,26 @@ TEST(VerifyModel, TestEmptyModel) { /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); + ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize(), + DefaultErrorReporter())); } TEST(VerifyModel, TestSimpleModel) { - FlatBufferBuilder builder; - auto inputs = builder.CreateVector({0}); - auto outputs = builder.CreateVector({1}); - auto operator_codes = builder.CreateVector(std::vector>{ - CreateOperatorCodeDirect(builder, BuiltinOperator_CUSTOM, "test")}); - auto operators = - builder.CreateVector(std::vector>{CreateOperator( - builder, /*opcode_index=*/0, - /*inputs=*/builder.CreateVector({0}), - /*outputs=*/builder.CreateVector({1}), BuiltinOptions_NONE, - /*builtin_options=*/0, - /*custom_options=*/0, ::tflite::CustomOptionsFormat_FLEXBUFFERS)}); - std::vector shape; - auto tensors = builder.CreateVector(std::vector>{ - CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, - "input", /*quantization=*/0), - CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/0, - "output", /*quantization=*/0)}); - auto subgraph = std::vector>( - {CreateSubGraph(builder, tensors, inputs, outputs, operators, - builder.CreateString("Main"))}); - - auto model = CreateModel(builder, TFLITE_SCHEMA_VERSION, operator_codes, - builder.CreateVector(subgraph), - builder.CreateString("SmartReply"), /*buffers=*/0); - - ::tflite::FinishModelBuffer(builder, model); - ASSERT_TRUE(Verify(builder.GetBufferPointer(), builder.GetSize())); + TfLiteFlatbufferModelBuilder builder; + builder.AddOperator({0, 1}, {2}, BuiltinOperator_CUSTOM, "test"); + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4, 5, 6}, "input"); + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 19, 0, 0, 0, 'A', 'B', 'C'}, + "data"); + builder.AddTensor({2, 3}, TensorType_INT32, {}, "output"); + builder.FinishModel({0, 1}, {2}); + ASSERT_TRUE(builder.Verify()); } TEST(VerifyModel, TestCorruptedData) { string model = "123"; - ASSERT_FALSE(Verify(model.data(), model.size())); + ASSERT_FALSE(Verify(model.data(), model.size(), /*error_reporter=*/nullptr)); } TEST(VerifyModel, TestUnsupportedVersion) { @@ -106,7 +120,8 @@ TEST(VerifyModel, TestUnsupportedVersion) { auto model = CreateModel(builder, /*version=*/1, /*operator_codes=*/0, /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize())); + ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), + DefaultErrorReporter())); } TEST(VerifyModel, TestRandomModificationIsNotAllowed) { @@ -116,20 +131,105 @@ TEST(VerifyModel, TestRandomModificationIsNotAllowed) { /*subgraphs=*/0, /*description=*/0, /*buffers=*/0); ::tflite::FinishModelBuffer(builder, model); - string model_content(reinterpret_cast(builder.GetBufferPointer()), + string model_content(reinterpret_cast(builder.GetBufferPointer()), builder.GetSize()); for (int i = 0; i < model_content.size(); i++) { model_content[i] = (model_content[i] + 137) % 255; - EXPECT_FALSE(Verify(model_content.data(), model_content.size())) + EXPECT_FALSE(Verify(model_content.data(), model_content.size(), + DefaultErrorReporter())) << "Fail at position: " << i; } } +TEST(VerifyModel, TestIntTensorShapeIsGreaterThanBuffer) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor({2, 3}, TensorType_UINT8, {1, 2, 3, 4}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, TestIntTensorShapeIsSmallerThanBuffer) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor({2, 1}, TensorType_UINT8, {1, 2, 3, 4}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, TestIntTensorShapeOverflow) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor({1024, 2048, 4096}, TensorType_UINT8, {1, 2, 3, 4}, + "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, TensorBufferIsNotValid) { + FlatBufferBuilder builder; + std::vector shape = {2, 3}; + auto tensors = builder.CreateVector(std::vector>{ + CreateTensorDirect(builder, &shape, TensorType_INT32, /*buffer=*/2, + "input", /*quantization=*/0)}); + auto subgraph = std::vector>( + {CreateSubGraph(builder, tensors, /*inputs=*/0, /*outputs=*/0, + /*operators=*/0, builder.CreateString("Main"))}); + + auto buffers = builder.CreateVector(std::vector>{ + CreateBuffer(builder, + builder.CreateVector(std::vector{1, 2, 3, 4, 5, 6})), + }); + + auto model = CreateModel(builder, TFLITE_SCHEMA_VERSION, /*operator_codes=*/0, + builder.CreateVector(subgraph), + builder.CreateString("SmartReply"), buffers); + + ::tflite::FinishModelBuffer(builder, model); + ASSERT_FALSE(Verify(builder.GetBufferPointer(), builder.GetSize(), + DefaultErrorReporter())); +} + +TEST(VerifyModel, StringTensorHasInvalidNumString) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {0x00, 0x00, 0x00, 0x20, 16, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B'}, + "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, StringTensorOffsetTooSmall) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 12, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B'}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, StringTensorOffsetOutOfRange) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 22, 0, 0, 0, 'A', 'B'}, "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + +TEST(VerifyModel, StringTensorIsLargerThanRequired) { + TfLiteFlatbufferModelBuilder builder; + builder.AddTensor( + {2}, TensorType_STRING, + {2, 0, 0, 0, 16, 0, 0, 0, 17, 0, 0, 0, 18, 0, 0, 0, 'A', 'B', 'C'}, + "input"); + builder.FinishModel({}, {}); + ASSERT_FALSE(builder.Verify()); +} + // TODO(yichengfan): make up malicious files to test with. } // namespace tflite -int main(int argc, char **argv) { +int main(int argc, char** argv) { ::tflite::LogToStderr(); ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); -- GitLab From c460a245a25467a66d7319544afb92407057b424 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 13:13:12 -0800 Subject: [PATCH 399/423] Fix segfault when Softmax is first in graph PiperOrigin-RevId: 184183730 --- tensorflow/contrib/lite/toco/export_tensorflow.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index 529df3cd2e..4c70b01a9d 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -621,7 +621,8 @@ void ConvertSoftmaxOperator(const Model& model, const SoftmaxOperator& src_op, GraphDef* tensorflow_graph) { string softmax_input; Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]); - if (providing_op->type == OperatorType::kTensorFlowReshape) { + if (providing_op != nullptr && + providing_op->type == OperatorType::kTensorFlowReshape) { softmax_input = src_op.inputs[0]; } else { // Insert a reshape operator that reduces the dimensions down to the 2 that -- GitLab From 7092e612c1ec51b4aeafe9201706331dd4c3199e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 13:33:20 -0800 Subject: [PATCH 400/423] Fixes a type conversion bug in losses.compute_weighted_loss for reduction=SUM_OVER_BATCH_SIZE. PiperOrigin-RevId: 184186573 --- tensorflow/python/kernel_tests/losses_test.py | 28 +++++++++++++++++++ tensorflow/python/ops/losses/losses_impl.py | 2 +- 2 files changed, 29 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py index 81af3a0887..00c6706593 100644 --- a/tensorflow/python/kernel_tests/losses_test.py +++ b/tensorflow/python/kernel_tests/losses_test.py @@ -1345,6 +1345,34 @@ class ComputeWeightedLossTest(test.TestCase): self.assertAllClose( np.mean(self._raw_losses), unweighted_loss.eval()) + def testUnweightedFromPlaceholder(self): + for reduction in losses.Reduction.all(): + with ops.Graph().as_default() as g: + self.assertEqual(0, len(util.get_losses())) + raw_losses = array_ops.placeholder(dtype=dtypes.float32) + feed_dict = {raw_losses: self._raw_losses} + unweighted_losses = ( + losses.compute_weighted_loss(raw_losses, reduction=reduction), + losses.compute_weighted_loss( + raw_losses, weights=np.ones((1, 1, 1)), reduction=reduction), + losses.compute_weighted_loss( + raw_losses, weights=np.ones((1, 1, 4)), reduction=reduction), + ) + self.assertEqual(3, len(util.get_losses())) + with self.test_session(g): + for unweighted_loss in unweighted_losses: + if reduction == losses.Reduction.NONE: + self.assertAllClose( + self._raw_losses, unweighted_loss.eval(feed_dict)) + elif reduction == losses.Reduction.SUM: + self.assertAllClose( + np.sum(self._raw_losses), unweighted_loss.eval(feed_dict)) + else: + # reduction one of MEAN, SUM_OVER_NONZERO_WEIGHTS, + # SUM_BY_NONZERO_WEIGHTS or SUM_OVER_BATCH_SIZE. + self.assertAllClose( + np.mean(self._raw_losses), unweighted_loss.eval(feed_dict)) + def testScalarWeight(self): with ops.Graph().as_default(): self.assertEqual(0, len(util.get_losses())) diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 73563486e1..e75a9b22e4 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -151,7 +151,7 @@ def _num_present(losses, weights, per_batch=False): def _num_elements(losses): """Computes the number of elements in `losses` tensor.""" with ops.name_scope(None, "num_elements", values=[losses]) as scope: - return array_ops.size(losses, name=scope, out_type=losses.dtype) + return math_ops.cast(array_ops.size(losses, name=scope), dtype=losses.dtype) @tf_export("losses.compute_weighted_loss") -- GitLab From 44de67f366d37db2b5483734b0cbf9e312ca9d8e Mon Sep 17 00:00:00 2001 From: Noah Fiedel Date: Thu, 1 Feb 2018 13:48:13 -0800 Subject: [PATCH 401/423] Adding documentation on how to load & serve a model with the TensorFlow Serving Model Server. PiperOrigin-RevId: 184188752 --- .../docs_src/programmers_guide/saved_model.md | 26 ++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/programmers_guide/saved_model.md index 9f50be5b31..f27a658342 100644 --- a/tensorflow/docs_src/programmers_guide/saved_model.md +++ b/tensorflow/docs_src/programmers_guide/saved_model.md @@ -285,7 +285,7 @@ with tf.Session(graph=tf.Graph()) as sess: ``` -### Loading a Savedmodel in C++ +### Loading a SavedModel in C++ The C++ version of the SavedModel [loader](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/loader.h) @@ -303,6 +303,30 @@ LoadSavedModel(session_options, run_options, export_dir, {kSavedModelTagTrain}, &bundle); ``` +### Loading and Serving a SavedModel in TensorFlow Serving + +You can easily load and serve a SavedModel with the TensorFlow Serving Model +Server binary. See [instructions](https://www.tensorflow.org/serving/setup#installing_using_apt-get) +on how to install the server, or build it if you wish. + +Once you have the Model Server, run it with: +``` +tensorflow_model_server --port=port-numbers --model_name=your-model-name --model_base_path=your_model_base_path +``` +Set the port and model_name flags to values of your choosing. The +model_base_path flag expects to be to a base directory, with each version of +your model residing in a numerically named subdirectory. If you only have a +single version of your model, simply place it in a subdirectory like so: +* Place the model in /tmp/model/0001 +* Set model_base_path to /tmp/model + +Store different versions of your model in numerically named subdirectories of a +common base directory. For example, suppose the base directory is `/tmp/model`. +If you have only one version of your model, store it in `/tmp/model/0001`. If +you have two versions of your model, store the second version in +`/tmp/model/0002`, and so on. Set the `--model-base_path` flag to the base +directory (`/tmp/model`, in this example). TensorFlow Model Server will serve +the model in the highest numbered subdirectory of that base directory. ### Standard constants -- GitLab From 87aab43770cacbb73706ad6b11a28e9b19c1df0b Mon Sep 17 00:00:00 2001 From: Jacques Pienaar Date: Thu, 1 Feb 2018 13:48:33 -0800 Subject: [PATCH 402/423] [TFXLA] Use data flow to determine switch grouping. * Change how switch grouping works: - This is an intermediate step, next is combining DetermineBranchMapAndFrontier into one traversal. * Homogeneous the naming (switch_nodes -> switches); * Change graph dumping to be due to class member - currently still performed when vlog-level is sufficiently high; * Pass in correct library when dumping graphs; PiperOrigin-RevId: 184188816 --- .../tf2xla/functionalize_control_flow.cc | 279 +++++++++++++----- .../tf2xla/functionalize_control_flow_test.cc | 10 +- tensorflow/compiler/tf2xla/graph_compiler.cc | 2 +- 3 files changed, 209 insertions(+), 82 deletions(-) diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc index 1d9e0fb33e..7a4fa79078 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc @@ -285,7 +285,8 @@ Status BuildLoopBody(const Graph& graph, Frame* frame, Status FunctionalizeLoop(Graph* graph, Frame* frame, FunctionLibraryDefinition* library) { VLOG(2) << "Frame " << frame->name << " before: " - << dump_graph::DumpGraphToFile("functionalize_before", *graph); + << dump_graph::DumpGraphToFile("functionalize_before", *graph, + library); // Split loop-varying Enter nodes with multiple successors. If the same // Tensor is fed as input to multiple loop arguments, we may end up with a @@ -450,7 +451,7 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame, TF_RETURN_IF_ERROR(BuildLoopBody(*graph, frame, &arg_types, &body_graph)); VLOG(2) << "Frame " << frame->name << " condition: " - << dump_graph::DumpGraphToFile("loop_condition", *cond_graph) + << dump_graph::DumpGraphToFile("loop_condition", *cond_graph, library) << " body: " << dump_graph::DumpGraphToFile("loop_body", *body_graph); static std::atomic sequence_num(0LL); @@ -531,7 +532,8 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame, frame->parent->nodes.insert(while_node); VLOG(2) << "Frame " << frame->name << " after: " - << dump_graph::DumpGraphToFile("functionalize_after", *graph); + << dump_graph::DumpGraphToFile("functionalize_after", *graph, + library); return Status::OK(); } @@ -564,11 +566,11 @@ class FunctionalizeCond { explicit CondArgNode(Node* input) : input(input) {} string ToString() const { return strings::StrCat("input=", input->name(), - " switches=", NodesToString(switch_nodes)); + " switches=", NodesToString(switches)); } Node* input; - std::vector switch_nodes; + std::vector switches; }; using CondArgNodes = std::vector; @@ -582,15 +584,22 @@ class FunctionalizeCond { int count; }; - struct PredicateSwitches { - explicit PredicateSwitches(Node* predicate) : predicate(predicate) {} + // Group of switch nodes that will be part of the same XlaIf. + struct SwitchCluster { + explicit SwitchCluster(Node* predicate) : predicate(predicate) {} + string ToString() const { + return strings::StrCat(name, " predicate=", predicate->name(), + " switches=", NodesToString(switches)); + } + string name; Node* predicate; std::vector switches; }; - FunctionalizeCond(Graph* graph, FunctionLibraryDefinition* library) - : library_(library), graph_(graph) {} + FunctionalizeCond(Graph* graph, FunctionLibraryDefinition* library, + bool dump_graphs) + : library_(library), graph_(graph), dump_graphs_(dump_graphs) {} // Perform the actual cond functionalization. Iterate over groups of switch // nodes (linked by common predicate), from innermost to outermost, and @@ -601,27 +610,25 @@ class FunctionalizeCond { // frontier (the nodes where the cond ends). StatusOr, std::unordered_set>> - DetermineBranchMapAndFrontier(const std::vector& switches); + DetermineBranchMapAndFrontier(const SwitchCluster& switch_cluster); // Returns XlaIf node created from subgraph of merge and switch nodes. This // encapsulates the process of extracting the bodies needed for the then and // else branch, creates a XlaIf node, removing the nodes of the branches from // the graph and replacing the merge node with a XlaIf. StatusOr ConvertToXlaIf(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, - const std::vector& merge_nodes, - Node* predicate); + const SwitchCluster& switch_cluster, + const std::vector& switches); // Builds a XlaIfOp to replace the Switch-Graph-Merge cluster with. StatusOr BuildAndAddXlaIfOp(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, - const std::vector& merge_nodes, - Node* predicate); + const SwitchCluster& switch_cluster, + const std::vector& merge_nodes); // Extracts a function body corresponding to the given input edge of the merge // node. Status ExtractBody(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, + const std::vector& switches, const std::vector& merge_nodes, int input_edge, Graph* body); @@ -632,9 +639,9 @@ class FunctionalizeCond { // Adds all output edges from the `if_node`. Status AddOutputEdges(const std::vector& outputs, Node* if_node); - // Returns the switches of graph_ (along with grouping predicates) in - // postorder. Dead switch nodes are skipped and removed from the graph. - std::vector DeterminePredicateSwitchOrder(); + // Returns the switch clusters of graph_ in postorder. Dead switch nodes are + // skipped and removed from the graph. + std::vector DeterminePredicateSwitchOrder(); // Update the state for destination based on the state of source and the node // being updated. @@ -657,6 +664,7 @@ class FunctionalizeCond { FunctionLibraryDefinition* library_; Graph* graph_; + bool dump_graphs_; }; bool IsDeadSwitch(const Node* node) { @@ -704,10 +712,13 @@ Status FunctionalizeCond::ValidateFrontier( ") in both Else and Then branch should be in Both."); } } - if (pending[kBoth].empty() && pending[kThenBranch].empty() && - pending[kElseBranch].empty()) { - return errors::Internal("Unexpected empty frontier for switch nodes"); - } + // An empty frontier indicates a dead switch. Above we attempt to remove dead + // switch nodes, but not all are removed so don't treat it as an error yet. + // TODO(jpienaar): Find out why dead switch nodes remain. + // if (pending[kBoth].empty() && pending[kThenBranch].empty() && + // pending[kElseBranch].empty()) { + // return errors::Internal("Unexpected empty frontier for switch nodes"); + // } return Status::OK(); } @@ -734,33 +745,138 @@ Status FunctionalizeCond::Join(const ForwardFlowNode& src_state, return Status::OK(); } -std::vector +std::vector FunctionalizeCond::DeterminePredicateSwitchOrder() { + struct Cluster { + bool operator==(const Cluster& other) const { + return representative == other.representative; + } + int representative = -1; + }; + + // Perform a DFS over the graph and + // * Determine the reverse topological order of the nodes (there should be no + // cycles at this point so the post-order numbering corresponds to the + // reverse topological sorting); + // * Identify dead switches; + // * Initialize the cluster's representative; + std::vector> clusters(graph_->num_node_ids()); std::vector dead_switches; std::vector switch_order; - DFS(*graph_, nullptr, [this, &dead_switches, &switch_order](Node* n) { + std::vector rev_topo_sorted_nodes; + DFS(*graph_, nullptr, [&](Node* n) { + clusters[n->id()].Get().representative = n->id(); if (IsSwitch(n)) { if (IsDeadSwitch(n)) { dead_switches.push_back(n); } else { + rev_topo_sorted_nodes.push_back(n); switch_order.push_back(n); } + } else if (n->IsOp()) { + // Exclude src and sink nodes from further consideration. + rev_topo_sorted_nodes.push_back(n); } }); + std::vector switch_clusters; + // Return early if there are no switches in the graph. + if (switch_order.empty()) { + return switch_clusters; + } + // Remove all dead switch nodes. for (Node* n : dead_switches) { VLOG(2) << "Removing dead switch: " << n->DebugString(); graph_->RemoveNode(n); } - std::vector predicate_switch_order; - if (switch_order.empty()) { - return predicate_switch_order; + // Identify switch nodes that are part of the same control flow context by + // considering the operands of operations: an operation is part of the same + // control context as its operands unless the operation is a switch. Control + // dependencies are considered part of the same control flow context if the + // switch depth is the same (see comment below). + // TODO(jpienaar): This could be combined with DetermineBranchMapAndFrontier. + std::vector switch_depth(graph_->num_node_ids()); + // entry_cluster records the input cluster to a switch node. This is used when + // merging with a merge node where the dst's cluster is merged with the entry + // cluster of the merge node's cluster (which corresponds to a switch cluster + // and so has an entry cluster). + std::unordered_map*> entry_cluster; + for (auto it = rev_topo_sorted_nodes.rbegin(); + it != rev_topo_sorted_nodes.rend(); ++it) { + Node* n = *it; + + // Compute switch depth. + int new_switch_depth = 0; + for (const Edge* e : n->in_edges()) { + Node* src = e->src(); + new_switch_depth = std::max( + new_switch_depth, switch_depth[src->id()] + (IsSwitch(src) ? 1 : 0) - + (IsMerge(src) ? 1 : 0)); + } + switch_depth[n->id()] = new_switch_depth; + + // Only merge the input operands of a switch. The switch's clustering itself + // is determined by the interaction of the switch's outputs. + if (IsSwitch(n)) { + Node* input; + TF_CHECK_OK(n->input_node(0, &input)); + UnionFind& cluster = clusters[input->id()]; + entry_cluster[n->id()] = &cluster; + // Merge the inputs of the switch node with one another. This results in + // predicates and control input residing in the same cluster. + for (const Edge* e : n->in_edges()) { + Node* src = e->src(); + cluster.Merge(&clusters[src->id()]); + } + continue; + } + + for (const Edge* e : n->in_edges()) { + Node* src = e->src(); + if (!src->IsOp()) continue; + UnionFind* cluster = &clusters[src->id()]; + if (IsMerge(src)) { + cluster = entry_cluster.at(clusters[src->id()].Get().representative); + } + // Merge a node with its data operands and with its control operands if + // the src and dst are in the same ControlContext. The ControlContext is + // not explicitly available here, and instead the switch depth is used as + // a proxy here. Due to the invariant that control edges can only be from + // a containing scope to an inner scope or from the inner scope to its + // containing scope (for exit nodes), the switch depth will only match if + // the src and dst are in the same ControlContext. Control edges between + // ControlContexts are handled during the extraction. + if (!e->IsControlEdge() || + new_switch_depth == + switch_depth[src->id()] + (IsSwitch(src) ? 1 : 0)) { + cluster->Merge(&clusters[n->id()]); + } + } } + if (dump_graphs_) { + // Mark the switch cluster each node is part of. + for (Node* n : graph_->nodes()) { + n->ClearAttr("_XlaFunctionalizeSwitchGroup"); + n->AddAttr("_XlaFunctionalizeSwitchGroup", + clusters[n->id()].Get().representative); + } + LOG(INFO) << "FunctionalizeControlFlow (with_clusters): " + << dump_graph::DumpGraphToFile("functionalize_clustered", *graph_, + library_); + } + + struct Hash { + size_t operator()(const std::pair& item) const { + return Hash64Combine(hash()(item.first), + std::hash()(item.second.representative)); + } + }; + // Merge Switch nodes with common predicate. - std::unordered_map predicate_index; + std::unordered_map, int, Hash> predicate_index; // The nodes in switch_order are in reverse topological order, but the // clustered switches need not be (i.e., when considered as a cluster one // element of a cluster may be later in the topological order than another @@ -769,13 +885,19 @@ FunctionalizeCond::DeterminePredicateSwitchOrder() { for (auto it = switch_order.rbegin(); it != switch_order.rend(); ++it) { Node* pred; TF_CHECK_OK((*it)->input_node(1, &pred)); - if (predicate_index.find(pred) == predicate_index.end()) { - predicate_index[pred] = predicate_switch_order.size(); - predicate_switch_order.emplace_back(pred); + auto repr = std::make_pair(pred, clusters[(*it)->id()].Get()); + if (predicate_index.find(repr) == predicate_index.end()) { + predicate_index[repr] = switch_clusters.size(); + switch_clusters.emplace_back(pred); + // Generate a name by concating with the cluster representative as there + // could be multiple switch clusters with the same predicate. + switch_clusters[predicate_index[repr]].name = + strings::StrCat(pred->name(), "_", repr.second.representative, "_If"); } - predicate_switch_order[predicate_index[pred]].switches.push_back(*it); + switch_clusters[predicate_index[repr]].switches.push_back(*it); } - return predicate_switch_order; + + return switch_clusters; } StatusOr> @@ -823,10 +945,10 @@ StatusOr< std::pair, std::unordered_set>> FunctionalizeCond::DetermineBranchMapAndFrontier( - const std::vector& switches) { + const SwitchCluster& switch_cluster) { std::unordered_map branch_map; std::unordered_set frontier; - std::vector stack = switches; + std::vector stack = switch_cluster.switches; std::vector visited(graph_->num_node_ids(), false); while (!stack.empty()) { Node* n = stack.back(); @@ -868,7 +990,7 @@ FunctionalizeCond::DetermineBranchMapAndFrontier( } } - if (VLOG_IS_ON(2)) { + if (dump_graphs_) { for (const auto& kv : branch_map) { // Append attribute to the graph if running with logging to make the // changes clearer in the visualization. @@ -880,7 +1002,7 @@ FunctionalizeCond::DetermineBranchMapAndFrontier( } Status FunctionalizeCond::FunctionalizeInternal() { - std::vector predicate_switch_order = + std::vector predicate_switch_order = DeterminePredicateSwitchOrder(); // Iterate from innermost set of clustered switches to outermost, replacing @@ -894,10 +1016,12 @@ Status FunctionalizeCond::FunctionalizeInternal() { std::unordered_map branch_map; std::unordered_set frontier; TF_ASSIGN_OR_RETURN(std::tie(branch_map, frontier), - DetermineBranchMapAndFrontier(ps.switches)); + DetermineBranchMapAndFrontier(ps)); - VLOG(2) << "FunctionalizeControlFlow (before XlaIf conversion): " - << dump_graph::DumpGraphToFile("functionalize_bc", *graph_); + if (dump_graphs_) + LOG(INFO) << "FunctionalizeControlFlow (before XlaIf conversion): " + << dump_graph::DumpGraphToFile("functionalize_bc", *graph_, + library_); TF_RETURN_IF_ERROR(ValidateFrontier(branch_map, frontier)); // Sort the merge and switch nodes using NodeCmp. The switch-nodes are @@ -914,7 +1038,7 @@ Status FunctionalizeCond::FunctionalizeInternal() { input_index[in] = cond_arg_nodes.size(); cond_arg_nodes.emplace_back(in); } - cond_arg_nodes.at(input_index.at(in)).switch_nodes.push_back(switch_node); + cond_arg_nodes.at(input_index.at(in)).switches.push_back(switch_node); } std::vector merge_nodes(frontier.begin(), frontier.end()); std::sort(merge_nodes.begin(), merge_nodes.end(), NodeCmp()); @@ -923,9 +1047,8 @@ Status FunctionalizeCond::FunctionalizeInternal() { EnsureDominanceAndReturnNonDominatedControlNodes( branch_map, ps.switches)); - TF_ASSIGN_OR_RETURN( - Node * if_node, - ConvertToXlaIf(cond_arg_nodes, ps.switches, merge_nodes, ps.predicate)); + TF_ASSIGN_OR_RETURN(Node * if_node, + ConvertToXlaIf(cond_arg_nodes, ps, merge_nodes)); for (Node* old : old_control_nodes) { graph_->AddControlEdge(old, if_node); } @@ -934,25 +1057,26 @@ Status FunctionalizeCond::FunctionalizeInternal() { graph_->RemoveNode(del_kv.first); } for (auto& kv : cond_arg_nodes) { - for (Node* node : kv.switch_nodes) { + for (Node* node : kv.switches) { graph_->RemoveNode(node); } } - VLOG(2) << "FunctionalizeControlFlow (after XlaIf conversion): " - << dump_graph::DumpGraphToFile("functionalize_ac", *graph_); + if (dump_graphs_) + LOG(INFO) << "FunctionalizeControlFlow (after XlaIf conversion): " + << dump_graph::DumpGraphToFile("functionalize_ac", *graph_, + library_); } return Status::OK(); } StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( - const CondArgNodes& cond_arg_nodes, const std::vector& switch_nodes, - const std::vector& merge_nodes, Node* predicate) { - VLOG(2) << "Build if op for " << NodesToString(merge_nodes) << " with input " - << NodesToString(switch_nodes); + const CondArgNodes& cond_arg_nodes, const SwitchCluster& switch_cluster, + const std::vector& merge_nodes) { + VLOG(2) << "Build if op for " << switch_cluster.name; NodeDef if_def; // Create a new If node using the name of the merge node. - NodeDefBuilder builder(strings::StrCat(predicate->name(), "_If"), "XlaIf"); + NodeDefBuilder builder(switch_cluster.name, "XlaIf"); string branch[] = {"else_branch", "then_branch"}; for (int i = 0; i < 2; ++i) { static std::atomic sequence_num(0LL); @@ -962,12 +1086,9 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( body_name.set_name( strings::StrCat("_functionalize_if_", branch[i], "_", id)); auto body = xla::MakeUnique(graph_->op_registry()); - TF_RETURN_IF_ERROR( - ExtractBody(cond_arg_nodes, switch_nodes, merge_nodes, i, body.get())); + TF_RETURN_IF_ERROR(ExtractBody(cond_arg_nodes, switch_cluster.switches, + merge_nodes, i, body.get())); VLOG(3) << "Body " << branch[i] << ": " << DebugString(body.get()); - VLOG(4) << "FunctionalizeControlFlow (" << branch[i] << "): " - << dump_graph::DumpGraphToFile( - strings::StrCat("functionalize_", branch[i]), *body); FunctionDef body_fdef; TF_RETURN_IF_ERROR(GraphToFunctionDef(*body, body_name.name(), &body_fdef)); TF_RETURN_IF_ERROR(library_->AddFunctionDef(body_fdef)); @@ -979,7 +1100,7 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( DataTypeVector in_arg_types; for (auto& kv : cond_arg_nodes) { bool inserted = false; - for (const Node* arg : kv.switch_nodes) { + for (const Node* arg : kv.switches) { const Edge* in_edge; TF_RETURN_IF_ERROR(arg->input_edge(0, &in_edge)); if (in_edge->IsControlEdge()) { @@ -1006,10 +1127,11 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( builder.Attr("Tout", out_type); builder.Attr("Tcond", DT_BOOL); - builder.Device(predicate->assigned_device_name()); + builder.Device(switch_cluster.predicate->assigned_device_name()); // Conditional should be the first input ... builder.Input( - NodeDefBuilder::NodeOut(predicate->name(), 0, predicate->output_type(0))); + NodeDefBuilder::NodeOut(switch_cluster.predicate->name(), 0, + switch_cluster.predicate->output_type(0))); // ... followed by the other inputs. builder.Input(inputs); @@ -1019,7 +1141,7 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( } Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, - const std::vector& switch_nodes, + const std::vector& switches, const std::vector& merge_nodes, int input_edge, Graph* body) { VLOG(2) << "ExtractBody for " << NodesToString(merge_nodes) << " along edge " @@ -1029,7 +1151,7 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, int arg_count = 0; for (auto& kv : cond_arg_nodes) { Node* arg_node = nullptr; - for (const auto* arg : kv.switch_nodes) { + for (const auto* arg : kv.switches) { DataType dtype = arg->input_type(0); if (arg_node == nullptr) { TF_ASSIGN_OR_RETURN(arg_node, BuildArgNode(body, dtype, arg_count++)); @@ -1053,8 +1175,7 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, node_map.at(in->id()) = body->CopyNode(in); } - if (std::find(switch_nodes.begin(), switch_nodes.end(), in) == - switch_nodes.end()) { + if (std::find(switches.begin(), switches.end(), in) == switches.end()) { body->AddEdge(node_map.at(in->id()), in_edge->src_output(), node_map.at(node->id()), 0); } else { @@ -1076,7 +1197,7 @@ Status FunctionalizeCond::AddInputEdges(const CondArgNodes& cond_arg_nodes, graph_->AddEdge(predicate, 0, if_node, index++); for (auto& kv : cond_arg_nodes) { bool inserted = false; - for (const Node* arg : kv.switch_nodes) { + for (const Node* arg : kv.switches) { const Edge* in_edge; TF_RETURN_IF_ERROR(arg->input_edge(0, &in_edge)); if (in_edge->IsControlEdge()) { @@ -1119,16 +1240,17 @@ Status FunctionalizeCond::AddOutputEdges(const std::vector& outputs, } StatusOr FunctionalizeCond::ConvertToXlaIf( - const CondArgNodes& cond_arg_nodes, const std::vector& switch_nodes, - const std::vector& merge_nodes, Node* predicate) { - VLOG(1) << "ConvertToXlaIf for " << NodesToString(switch_nodes) << " -> " + const CondArgNodes& cond_arg_nodes, const SwitchCluster& switch_cluster, + const std::vector& merge_nodes) { + VLOG(1) << "ConvertToXlaIf for " << switch_cluster.ToString() << " -> " << NodesToString(merge_nodes); // Extract bodies and builds a If operator. TF_ASSIGN_OR_RETURN( Node * if_node, - BuildAndAddXlaIfOp(cond_arg_nodes, switch_nodes, merge_nodes, predicate)); - TF_RETURN_IF_ERROR(AddInputEdges(cond_arg_nodes, predicate, if_node)); + BuildAndAddXlaIfOp(cond_arg_nodes, switch_cluster, merge_nodes)); + TF_RETURN_IF_ERROR( + AddInputEdges(cond_arg_nodes, switch_cluster.predicate, if_node)); TF_RETURN_IF_ERROR(AddOutputEdges(merge_nodes, if_node)); return if_node; @@ -1137,18 +1259,19 @@ StatusOr FunctionalizeCond::ConvertToXlaIf( Status FunctionalizeCond::Functionalize(Graph* graph, FunctionLibraryDefinition* library) { VLOG(1) << "FunctionalizeCond::Functionalize"; - FunctionalizeCond fc(graph, library); + FunctionalizeCond fc(graph, library, /*dump_graphs=*/VLOG_IS_ON(2)); return fc.FunctionalizeInternal(); } } // namespace -// Transformation that converts Tensorflow's graph control flow constructs into +// Transformation that converts TensorFlow's graph control flow constructs into // functional equivalents. Status FunctionalizeControlFlow(Graph* graph, FunctionLibraryDefinition* library) { VLOG(2) << "FunctionalizeControlFlow (initial): " - << dump_graph::DumpGraphToFile("functionalize_initial", *graph); + << dump_graph::DumpGraphToFile("functionalize_initial", *graph, + library); // Note: BuildControlFlowInfo() requires that the graph's source node is // connected to all source nodes in the graph. Many graphs violate this // invariant. @@ -1160,7 +1283,8 @@ Status FunctionalizeControlFlow(Graph* graph, for (Node* node : graph->op_nodes()) { const ControlFlowInfo& cf = cf_info[node->id()]; - VLOG(2) << "node: " << node->name() << " frame_name: " << cf.frame_name + VLOG(2) << "node: " << node->name() << " (" << node->id() + << ") frame_name: " << cf.frame_name << " frame: " << (cf.frame ? cf.frame->name() : "---") << " parent_frame: " << (cf.parent_frame ? cf.parent_frame->name() : "---"); @@ -1228,7 +1352,8 @@ Status FunctionalizeControlFlow(Graph* graph, TF_RETURN_IF_ERROR(FunctionalizeCond::Functionalize(graph, library)); VLOG(2) << "FunctionalizeControlFlow (final): " - << dump_graph::DumpGraphToFile("functionalize_final", *graph); + << dump_graph::DumpGraphToFile("functionalize_final", *graph, + library); return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc index 71f12a1333..bc7276c3af 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc @@ -38,10 +38,11 @@ namespace { // Returns the names of the "then" and "else" functions for the XlaIf node in a // graph. -Status FindIfThenAndElse(const GraphDef& graph, NameAttrList* then_fn, - NameAttrList* else_fn) { +Status FindIfThenAndElse(const GraphDef& graph, string* op_name, + NameAttrList* then_fn, NameAttrList* else_fn) { for (const NodeDef& node : graph.node()) { if (node.op() == "XlaIf") { + *op_name = node.name(); const NameAttrList* result; TF_RETURN_IF_ERROR(GetNodeAttr(node, "then_branch", &result)); *then_fn = *result; @@ -96,9 +97,10 @@ TEST(FunctionalizeControlFlow, Conditional) { GraphDef graph_def; graph.ToGraphDef(&graph_def); + string op_name; NameAttrList then_fn; NameAttrList else_fn; - TF_EXPECT_OK(FindIfThenAndElse(graph_def, &then_fn, &else_fn)); + TF_EXPECT_OK(FindIfThenAndElse(graph_def, &op_name, &then_fn, &else_fn)); InstantiationResultForTest else_result; TF_EXPECT_OK( InstantiateFunctionForTest(else_fn.name(), library, &else_result)); @@ -109,7 +111,7 @@ TEST(FunctionalizeControlFlow, Conditional) { auto y = ops::Placeholder(scope.WithOpName("y"), DT_INT32); auto x = ops::Placeholder(scope.WithOpName("x"), DT_INT32); auto less = ops::Less(scope.WithOpName("cond/Less"), y, x); - auto if_op = ops::XlaIf(scope.WithOpName("cond/Less_If"), less, + auto if_op = ops::XlaIf(scope.WithOpName(op_name), less, std::initializer_list{less, y, x}, then_fn, else_fn, {DT_INT32}); GraphDef expected; diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index 02215b5112..c90ea09e17 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -136,7 +136,7 @@ Status GraphCompiler::Compile() { TF_RET_CHECK(src->id() < output_registry.size()); const NodeOutputs& src_outputs = output_registry[src->id()]; - tensor_inputs_[e->dst_input()] = src_outputs[e->src_output()]; + tensor_inputs_.at(e->dst_input()) = src_outputs.at(e->src_output()); } OpKernelContext op_context(¶ms, n->num_outputs()); -- GitLab From a964248ae9aaee99165594e80427152576e803fe Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Thu, 1 Feb 2018 14:11:08 -0800 Subject: [PATCH 403/423] Return an error instead of assertion when processing an ill-formed graph or an invalid set of fetch nodes PiperOrigin-RevId: 184192790 --- tensorflow/core/grappler/costs/virtual_scheduler.cc | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc index d7d07ee7a5..020492a3e9 100644 --- a/tensorflow/core/grappler/costs/virtual_scheduler.cc +++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc @@ -323,8 +323,13 @@ Status VirtualScheduler::Init() { } // Get the nodes that would run to output fetch_nodes. + bool ill_formed = false; std::vector nodes = - ComputeTransitiveFanin(graph, fetch_nodes); + ComputeTransitiveFanin(graph, fetch_nodes, &ill_formed); + if (ill_formed) { + return errors::InvalidArgument( + "Ill formed graph or invalid set of fetch nodes specified"); + } // TODO(dyoon): this is a bit inefficient as name_to_node is already built in // ComputeTransitiveFanin(). -- GitLab From f1f1d6d482e332f11452d9103a29149e2adc7125 Mon Sep 17 00:00:00 2001 From: Igor Saprykin Date: Thu, 1 Feb 2018 14:11:08 -0800 Subject: [PATCH 404/423] Throw an exception when the user's batch size isn't divisible by GPUs. The alternative to this is to have an adaptive approach that would unevenly split input into per-tower batches. The concern with that was that all towers will be as slow as the one with more input reducing the performance. Batch size seems to be commonly tailored to the available hardware. PiperOrigin-RevId: 184192793 --- .../python/estimator/replicate_model_fn.py | 10 +++ .../estimator/replicate_model_fn_test.py | 79 ++++++++++++++++++- 2 files changed, 85 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py index caa9dd8323..c9153c9352 100644 --- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py +++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py @@ -457,6 +457,13 @@ def _get_local_devices(device_type): def _split_batch(features, labels, number_of_shards, device): """Split input features and labes into batches.""" + def ensure_divisible_by_shards(sequence): + batch_size = ops_lib.convert_to_tensor(sequence).get_shape()[0] + if batch_size % number_of_shards != 0: + raise ValueError( + 'Batch size {} needs to be divisible by the number of GPUs, which ' + 'is {}.'.format(batch_size, number_of_shards)) + def split_dictionary(dictionary): """Split a dictionary into shards.""" shards = [{} for _ in range(number_of_shards)] @@ -467,6 +474,7 @@ def _split_batch(features, labels, number_of_shards, device): sp_input=tensor, num_split=number_of_shards, axis=0)): shards[i][name] = shard else: + ensure_divisible_by_shards(tensor) for i, shard in enumerate(array_ops.split(tensor, number_of_shards)): shards[i][name] = shard return shards @@ -476,6 +484,7 @@ def _split_batch(features, labels, number_of_shards, device): if isinstance(features, dict): feature_shards = split_dictionary(features) else: + ensure_divisible_by_shards(features) feature_shards = array_ops.split(features, number_of_shards) if labels is None: @@ -483,6 +492,7 @@ def _split_batch(features, labels, number_of_shards, device): elif isinstance(labels, dict): label_shards = split_dictionary(labels) else: + ensure_divisible_by_shards(labels) label_shards = array_ops.split(labels, number_of_shards) return feature_shards, label_shards diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py index 03d31226af..6936f8a131 100644 --- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py +++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py @@ -37,6 +37,7 @@ from tensorflow.python.feature_column import feature_column from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops as ops_lib +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 control_flow_ops @@ -433,6 +434,17 @@ class ReplicateModelTest(test_util.TensorFlowTestCase): 'probabilities': np.array([[0.1], [0.02]]) }, session.run(estimator_spec.predictions)) + def test_batch_size_that_is_not_divisible_by_the_number_of_gpus(self): + features = np.array([[1.0], [2.0], [3.0]]) + labels = np.array([[1.0], [2.0], [3.0]]) + + with self.assertRaisesRegexp( + ValueError, '.*Batch.+size.+needs.+to.+be.+divisible.+by.+GPUs.+'): + replicated_model_fn = replicate_model_fn.replicate_model_fn( + self.model_fn, devices=['/gpu:0', '/gpu:1']) + _ = replicated_model_fn( + features, labels, model_fn_lib.ModeKeys.TRAIN, self.params) + def test_unsupported_loss_reduction(self): with self.assertRaisesRegexp(ValueError, '.+none.+reduction.+is.+specified.+'): @@ -981,8 +993,13 @@ class SplitBatchTest(test_util.TensorFlowTestCase): return list(map(evaluate_items, first_list)), list( map(evaluate_items, second_list)) + def assertSparseValuesEqual(self, a, b): + self.assertAllEqual(a.indices, b.indices) + self.assertAllEqual(a.values, b.values) + self.assertAllEqual(a.dense_shape, b.dense_shape) + def test_simple_half_split(self): - with self.test_session() as session: # pylint: disable=unused-variable + with self.test_session(): features = [0.0, 1.0, 2.0, 3.0] labels = [10.0, 11.0, 12.0, 13.0] feature_shards, label_shards = replicate_model_fn._split_batch( @@ -995,7 +1012,7 @@ class SplitBatchTest(test_util.TensorFlowTestCase): self.assertAllEqual([[10.0, 11.0], [12.0, 13.0]], label_shards) def test_to_each_their_own(self): - with self.test_session() as session: # pylint: disable=unused-variable + with self.test_session(): features = [0.0, 1.0, 2.0, 3.0] labels = [10.0, 11.0, 12.0, 13.0] feature_shards, label_shards = replicate_model_fn._split_batch( @@ -1008,7 +1025,7 @@ class SplitBatchTest(test_util.TensorFlowTestCase): self.assertAllEqual([[10.0], [11.0], [12.0], [13.0]], label_shards) def test_one_batch(self): - with self.test_session() as session: # pylint: disable=unused-variable + with self.test_session(): features = [0.0, 1.0, 2.0, 3.0] labels = [10.0, 11.0, 12.0, 13.0] feature_shards, label_shards = replicate_model_fn._split_batch( @@ -1021,7 +1038,7 @@ class SplitBatchTest(test_util.TensorFlowTestCase): self.assertAllEqual([[10.0, 11.0, 12.0, 13.0]], label_shards) def test_half_split_in_dictionary(self): - with self.test_session() as session: # pylint: disable=unused-variable + with self.test_session(): features = {'first': [0.0, 1.0, 2.0, 3.0], 'second': [4.0, 5.0, 6.0, 7.0]} labels = [10.0, 11.0, 12.0, 13.0] @@ -1035,6 +1052,60 @@ class SplitBatchTest(test_util.TensorFlowTestCase): self.assertAllEqual([10.0, 11.0], label_shards[0].eval()) self.assertAllEqual([12.0, 13.0], label_shards[1].eval()) + def test_sparse_tensor_can_be_split_unevenly(self): + with self.test_session(): + features = { + 'x': + sparse_tensor.SparseTensor( + indices=[[0, 0], [1, 2], [2, 2]], + values=[1.0, 2.0, 3.0], + dense_shape=[3, 4]) + } + labels = np.array([[1.0], [2.0]]) + + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 2, device='/gpu:0') + + self.assertSparseValuesEqual( + sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 2]], values=[1., 2.], dense_shape=[2, 4]), + feature_shards[0]['x'].eval()) + self.assertSparseValuesEqual( + sparse_tensor.SparseTensorValue( + indices=[[0, 2]], values=[3.], dense_shape=[1, 4]), + feature_shards[1]['x'].eval()) + self.assertAllEqual([[1.0]], label_shards[0].eval()) + self.assertAllEqual([[2.0]], label_shards[1].eval()) + + def test_sparse_tensor_can_be_split_unevenly_repeated_row(self): + with self.test_session(): + features = { + 'x': + sparse_tensor.SparseTensor( + indices=[[0, 0], [1, 0], [1, 1]], + values=[1.0, 2.0, 3.0], + dense_shape=[3, 4]) + } + labels = np.array([[1.0], [2.0]]) + + feature_shards, label_shards = replicate_model_fn._split_batch( + features, labels, 2, device='/gpu:0') + + print(feature_shards[0]['x'].eval()) + print(feature_shards[1]['x'].eval()) + self.assertSparseValuesEqual( + sparse_tensor.SparseTensorValue( + indices=[[0, 0], [1, 0], [1, 1]], + values=[1., 2., 3.], + dense_shape=[2, 4]), feature_shards[0]['x'].eval()) + + second_batch = feature_shards[1]['x'].eval() + self.assertFalse(len(second_batch.indices)) + self.assertFalse(len(second_batch.values)) + self.assertAllEqual([1, 4], second_batch.dense_shape) + self.assertAllEqual([[1.0]], label_shards[0].eval()) + self.assertAllEqual([[2.0]], label_shards[1].eval()) + def test_one_batch_in_dictionary(self): with self.test_session() as session: # pylint: disable=unused-variable features = {'first': [0.0, 1.0, 2.0, 3.0], 'second': [4.0, 5.0, 6.0, 7.0]} -- GitLab From 31edddcc025b95fd6fec419d4372d3f3a4f89af8 Mon Sep 17 00:00:00 2001 From: Anna R Date: Thu, 1 Feb 2018 14:23:29 -0800 Subject: [PATCH 405/423] Internal change. PiperOrigin-RevId: 184194895 --- tensorflow/contrib/bayesflow/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD index 11c3c037c4..6e0f0a0572 100644 --- a/tensorflow/contrib/bayesflow/BUILD +++ b/tensorflow/contrib/bayesflow/BUILD @@ -217,6 +217,7 @@ cuda_py_test( "//tensorflow/python:platform_test", "//tensorflow/python:random_seed", ], + tags = ["notsan"], ) cuda_py_test( -- GitLab From 1f7352e0e5354f35e6f181071f791612257dc026 Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Thu, 1 Feb 2018 15:05:41 -0800 Subject: [PATCH 406/423] Revert TensorBoard entry point back to run_main PiperOrigin-RevId: 184201506 --- tensorflow/tools/pip_package/setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 1b1d60c4f3..3cd4d12100 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -80,13 +80,13 @@ CONSOLE_SCRIPTS = [ # is now declared by the tensorboard pip package. If we remove the # TensorBoard command, pip will inappropriately remove it during install, # even though the command is not removed, just moved to a different wheel. - 'tensorboard = tensorboard.main:main', + 'tensorboard = tensorboard.main:run_main', ] # pylint: enable=line-too-long # remove the tensorboard console script if building tf_nightly if 'tf_nightly' in project_name: - CONSOLE_SCRIPTS.remove('tensorboard = tensorboard.main:main') + CONSOLE_SCRIPTS.remove('tensorboard = tensorboard.main:run_main') TEST_PACKAGES = [ 'scipy >= 0.15.1', -- GitLab From 16ce8ed3f9c2eafb7e1f96ea620698da382e621c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 15:11:20 -0800 Subject: [PATCH 407/423] [XLA] add DotGeneral to the local Python XLA client. PiperOrigin-RevId: 184202425 --- .../xla/python/local_computation_builder.cc | 6 + .../xla/python/local_computation_builder.h | 4 + .../xla/python/local_computation_builder.i | 131 ++++++++++++++++++ tensorflow/compiler/xla/python/xla_client.py | 39 +++++- .../compiler/xla/python/xla_client_test.py | 24 ++++ 5 files changed, 203 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 9e3cf79383..8386acf0cd 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -404,6 +404,12 @@ ComputationDataHandle LocalComputationBuilder::Dot( return builder_.Dot(lhs, rhs); } +ComputationDataHandle LocalComputationBuilder::DotGeneral( + const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, + const DotDimensionNumbers& dimension_numbers) { + return builder_.DotGeneral(lhs, rhs, dimension_numbers); +} + ComputationDataHandle LocalComputationBuilder::ConvGeneralDilated( const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, tensorflow::gtl::ArraySlice window_strides, diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index ae5bbc03cb..f39d15cff7 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -192,6 +192,10 @@ class LocalComputationBuilder { ComputationDataHandle Dot(const ComputationDataHandle& lhs, const ComputationDataHandle& rhs); + ComputationDataHandle DotGeneral( + const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, + const DotDimensionNumbers& dimension_numbers); + ComputationDataHandle ConvGeneralDilated( const ComputationDataHandle& lhs, const ComputationDataHandle& rhs, tensorflow::gtl::ArraySlice window_strides, diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index fdd05692fc..5ea75550c9 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -34,6 +34,7 @@ limitations under the License. // ArraySlice> <- sequence of int pairs // PaddingConfig proto <- corresponding Python proto // ConvolutionDimensionNumbers proto <- corresponding Python proto +// DotDimensionNumbers proto <- corresponding Python proto // // Arrows indicate whether a conversion only ever occurs in one // direction, or whether it is maintained bidirectionally. @@ -511,6 +512,135 @@ tensorflow::ImportNumpy(); $1 = temps; } +// DotDimensionNumbers + +%typemap(in) const DotDimensionNumbers& + (DotDimensionNumbers dimension_numbers) { + int length; + + /* lhs_contracting_dimensions */ + PyObject* lhs_contracting_dimensions = PyObject_GetAttrString( + $input, "lhs_contracting_dimensions"); + if (!lhs_contracting_dimensions) { + return NULL; + } + + length = PySequence_Size(lhs_contracting_dimensions); + if (length == -1) { + Py_DECREF(lhs_contracting_dimensions); + return NULL; + } + + for (int i = 0; i < length; ++i) { + PyObject* item = PySequence_GetItem(lhs_contracting_dimensions, i); + if (!item) { + Py_DECREF(lhs_contracting_dimensions); + return NULL; + } + const int64 dimension = numpy::PyIntOrPyLongToLong(item); + if (dimension == -1 && PyErr_Occurred()) { + Py_DECREF(item); + Py_DECREF(lhs_contracting_dimensions); + return NULL; + } + dimension_numbers.add_lhs_contracting_dimensions(dimension); + Py_DECREF(item); + } + Py_DECREF(lhs_contracting_dimensions); + + /* rhs_contracting_dimensions */ + PyObject* rhs_contracting_dimensions = PyObject_GetAttrString( + $input, "rhs_contracting_dimensions"); + if (!lhs_contracting_dimensions) { + return NULL; + } + + length = PySequence_Size(rhs_contracting_dimensions); + if (length == -1) { + Py_DECREF(rhs_contracting_dimensions); + return NULL; + } + + for (int i = 0; i < length; ++i) { + PyObject* item = PySequence_GetItem(rhs_contracting_dimensions, i); + if (!item) { + Py_DECREF(rhs_contracting_dimensions); + return NULL; + } + const int64 dimension = numpy::PyIntOrPyLongToLong(item); + if (dimension == -1 && PyErr_Occurred()) { + Py_DECREF(item); + Py_DECREF(rhs_contracting_dimensions); + return NULL; + } + dimension_numbers.add_rhs_contracting_dimensions(dimension); + Py_DECREF(item); + } + Py_DECREF(rhs_contracting_dimensions); + + /* lhs_batch_dimensions */ + PyObject* lhs_batch_dimensions = PyObject_GetAttrString( + $input, "lhs_batch_dimensions"); + if (!lhs_batch_dimensions) { + return NULL; + } + + length = PySequence_Size(lhs_batch_dimensions); + if (length == -1) { + Py_DECREF(lhs_batch_dimensions); + return NULL; + } + + for (int i = 0; i < length; ++i) { + PyObject* item = PySequence_GetItem(lhs_batch_dimensions, i); + if (!item) { + Py_DECREF(lhs_batch_dimensions); + return NULL; + } + const int64 dimension = numpy::PyIntOrPyLongToLong(item); + if (dimension == -1 && PyErr_Occurred()) { + Py_DECREF(item); + Py_DECREF(lhs_batch_dimensions); + return NULL; + } + dimension_numbers.add_lhs_batch_dimensions(dimension); + Py_DECREF(item); + } + Py_DECREF(lhs_batch_dimensions); + + /* rhs_batch_dimensions */ + PyObject* rhs_batch_dimensions = PyObject_GetAttrString( + $input, "rhs_batch_dimensions"); + if (!rhs_batch_dimensions) { + return NULL; + } + + length = PySequence_Size(rhs_batch_dimensions); + if (length == -1) { + Py_DECREF(rhs_batch_dimensions); + return NULL; + } + + for (int i = 0; i < length; ++i) { + PyObject* item = PySequence_GetItem(rhs_batch_dimensions, i); + if (!item) { + Py_DECREF(rhs_batch_dimensions); + return NULL; + } + const int64 dimension = numpy::PyIntOrPyLongToLong(item); + if (dimension == -1 && PyErr_Occurred()) { + Py_DECREF(item); + Py_DECREF(rhs_batch_dimensions); + return NULL; + } + dimension_numbers.add_rhs_batch_dimensions(dimension); + Py_DECREF(item); + } + Py_DECREF(rhs_batch_dimensions); + + $1 = &dimension_numbers; +} + // PaddingConfig %typemap(in) const PaddingConfig& @@ -756,6 +886,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Lt; %unignore xla::swig::LocalComputationBuilder::Le; %unignore xla::swig::LocalComputationBuilder::Dot; +%unignore xla::swig::LocalComputationBuilder::DotGeneral; %unignore xla::swig::LocalComputationBuilder::ConvGeneralDilated; %unignore xla::swig::LocalComputationBuilder::Add; %unignore xla::swig::LocalComputationBuilder::Sub; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index bb42e8d703..b890980955 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -931,10 +931,37 @@ class ComputationBuilder(object): _unwrap_data_handle(init))) def Dot(self, lhs, rhs): - """Matrix multiplication between lhs and rhs.""" + """Enqueues a dot operation onto the computation. + + Args: + lhs: ComputationDataHandle for the rank 1 or rank 2 left-hand-side array. + rhs: ComputationDataHandle for the rank 1 or rank 2 right-hand-side array. + + Returns: a ComputationDataHandle representing the Dot operation. + """ return _wrap_data_handle( self._client.Dot(_unwrap_data_handle(lhs), _unwrap_data_handle(rhs))) + def DotGeneral(self, lhs, rhs, dimension_numbers): + """Enqueues a general dot operation onto the computation. + + Args: + lhs: ComputationDataHandle for the left-hand-side array. + rhs: ComputationDataHandle for the right-hand-side array. + dimension_numbers: either an xla_data_pb2.DotDimensionNumbers or a nested + tuple ((lhs_contract, rhs_contract), (lhs_batch, rhs_batch)) of lists of + integers representing the dimensions to treat as contracting dimensions + and batch dimensions on each input operand. + + Returns: a ComputationDataHandle representing the DotGeneral operation. + """ + if not isinstance(dimension_numbers, xla_data_pb2.DotDimensionNumbers): + dimension_numbers = GetDotDimensionsFromLists(dimension_numbers) + return _wrap_data_handle( + self._client.DotGeneral( + _unwrap_data_handle(lhs), _unwrap_data_handle(rhs), + dimension_numbers)) + def Conv(self, lhs, rhs, window_strides, padding): """Enqueues a Conv operation onto the computation. @@ -1071,3 +1098,13 @@ def GetPaddingConfigFromTriples(triples): dimension.edge_padding_high = hi dimension.interior_padding = interior return padding_config + + +def GetDotDimensionsFromLists(dimension_numbers): + (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers + dot_dims_proto = xla_data_pb2.DotDimensionNumbers() + dot_dims_proto.lhs_contracting_dimensions.extend(lhs_contract) + dot_dims_proto.rhs_contracting_dimensions.extend(rhs_contract) + dot_dims_proto.lhs_batch_dimensions.extend(lhs_batch) + dot_dims_proto.rhs_batch_dimensions.extend(rhs_batch) + return dot_dims_proto diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index 3b5bbfd786..421fba40e3 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -444,6 +444,30 @@ class SingleOpTest(LocalComputationTest): c.Dot(c.Constant(lhs), c.Constant(rhs)) self._ExecuteAndCompareClose(c, expected=np.dot(lhs, rhs)) + def testDotGeneral(self): + c = self._NewComputation() + rng = np.random.RandomState(0) + lhs = NumpyArrayF32(rng.randn(10, 3, 4)) + rhs = NumpyArrayF32(rng.randn(10, 4, 5)) + dimension_numbers = (([2], [1]), ([0], [0])) + c.DotGeneral(c.Constant(lhs), c.Constant(rhs), dimension_numbers) + self._ExecuteAndCompareClose(c, expected=np.matmul(lhs, rhs)) + + def testDotGeneralWithDotDimensionNumbersProto(self): + c = self._NewComputation() + rng = np.random.RandomState(0) + lhs = NumpyArrayF32(rng.randn(10, 3, 4)) + rhs = NumpyArrayF32(rng.randn(10, 4, 5)) + + dimension_numbers = xla_client.xla_data_pb2.DotDimensionNumbers() + dimension_numbers.lhs_contracting_dimensions.append(2) + dimension_numbers.rhs_contracting_dimensions.append(1) + dimension_numbers.lhs_batch_dimensions.append(0) + dimension_numbers.rhs_batch_dimensions.append(0) + + c.DotGeneral(c.Constant(lhs), c.Constant(rhs), dimension_numbers) + self._ExecuteAndCompareClose(c, expected=np.matmul(lhs, rhs)) + def testConvF32Same(self): c = self._NewComputation() a = lambda *dims: np.arange(np.prod(dims)).reshape(dims).astype("float32") -- GitLab From a7398af84e30eda2cf47496c82bdfe1c9e36381d Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Thu, 1 Feb 2018 15:11:35 -0800 Subject: [PATCH 408/423] Make jit_test.py work with C API enabled. PiperOrigin-RevId: 184202470 --- tensorflow/contrib/compiler/jit_test.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/compiler/jit_test.py b/tensorflow/contrib/compiler/jit_test.py index 2108e42bce..29a593f6bc 100644 --- a/tensorflow/contrib/compiler/jit_test.py +++ b/tensorflow/contrib/compiler/jit_test.py @@ -24,6 +24,7 @@ from tensorflow.python.framework import function from tensorflow.python.framework import op_def_registry from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed +from tensorflow.python.framework import test_util from tensorflow.python.ops import gradients from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops @@ -169,6 +170,7 @@ class JITTest(test.TestCase): self.assertEqual(b"jit_scope_0", func_attrs["_XlaScope"].s) +@test_util.with_c_api class CompilationEnabledInGradientTest(test.TestCase): def testCompilationInGradient(self): @@ -188,7 +190,7 @@ class CompilationEnabledInGradientTest(test.TestCase): for cg in c_grad_ops: self.assertTrue(cg.get_attr("_XlaCompile")) for ncg in nc_grad_ops: - with self.assertRaisesRegexp(ValueError, "No attr named"): + with self.assertRaisesRegexp(ValueError, "[Nn]o attr named"): ncg.get_attr("_XlaCompile") # d/dx (x ** 4) = 4 * (x ** 3) -- GitLab From d719238f1ddedf5569bfd0ca13fa3a29bfecdd78 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 1 Feb 2018 15:32:20 -0800 Subject: [PATCH 409/423] Add iterate_batches arg to Estimator.predict PiperOrigin-RevId: 184205196 --- .../contrib/learn/python/learn/estimators/estimator.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py index 8d59fe66d9..63d0f1e1d4 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py +++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py @@ -600,7 +600,8 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, input_fn=None, batch_size=None, outputs=None, - as_iterable=True): + as_iterable=True, + iterate_batches=False): """Returns predictions for given features. Args: @@ -616,6 +617,9 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, for each example until inputs are exhausted. Note: The inputs must terminate if you want the iterable to terminate (e.g. be sure to pass num_epochs=1 if you are using something like read_batch_features). + iterate_batches: If True, yield the whole batch at once instead of + decomposing the batch into individual samples. Only relevant when + as_iterable is True. Returns: A numpy array of predicted classes or regression values if the @@ -635,7 +639,8 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable, input_fn=input_fn, feed_fn=feed_fn, outputs=outputs, - as_iterable=as_iterable) + as_iterable=as_iterable, + iterate_batches=iterate_batches) def get_variable_value(self, name): """Returns value of the variable given by name. -- GitLab From e7cf813d810a8f0061d1f31d869a331687090904 Mon Sep 17 00:00:00 2001 From: Raghuraman Krishnamoorthi Date: Thu, 1 Feb 2018 16:14:51 -0800 Subject: [PATCH 410/423] Add functionality to fold batch norm (supporting both fused and unfused batch norm) to support quantized training. The weights are always now scaled by gamma/sigma, where sigma is the moving standard deviation for stability prior to quantization. For improved performance, the moving means and variances are frozen and the training graph modified accordingly. An additional parameter freeze_batch_norm_delay is added to foldbatchnorm function to set the delay at which training switches from regular batch norm to frozen mean and variances. Remove placement options within FoldBatchNorm as this causes folded training to place all ops on a single GPU. The modification now significantly speeds up distributed training. The tests for folding batch norms are also updated to reflect the additional topological changes to the graph. PiperOrigin-RevId: 184211434 --- tensorflow/contrib/quantize/BUILD | 4 + .../quantize/python/fold_batch_norms.py | 430 ++++++++++++++++-- .../quantize/python/fold_batch_norms_test.py | 97 ++-- .../contrib/quantize/python/quantize_graph.py | 12 +- 4 files changed, 467 insertions(+), 76 deletions(-) diff --git a/tensorflow/contrib/quantize/BUILD b/tensorflow/contrib/quantize/BUILD index 3c5b34a0a6..b7d525a1fa 100644 --- a/tensorflow/contrib/quantize/BUILD +++ b/tensorflow/contrib/quantize/BUILD @@ -77,9 +77,13 @@ py_library( "//tensorflow/contrib/graph_editor:graph_editor_py", "//tensorflow/python:array_ops", "//tensorflow/python:framework_ops", + "//tensorflow/python:layers", "//tensorflow/python:math_ops", "//tensorflow/python:nn", "//tensorflow/python:nn_ops", + "//tensorflow/python:ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", ], ) diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py index aa605e6caa..8ec5334a39 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py @@ -17,7 +17,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function - import re from tensorflow.contrib import graph_editor from tensorflow.contrib.quantize.python import common @@ -26,14 +25,16 @@ from tensorflow.contrib.quantize.python import input_to_ops from tensorflow.core.framework import attr_value_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.layers import utils 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.training import training_util from tensorflow.python.util import compat -def FoldBatchNorms(graph): +def FoldBatchNorms(graph, freeze_batch_norm_delay=None, is_training=True): """Finds batch norm layers and folds them into preceding layers. Folding only affects the following layers: Conv2D, fully connected, depthwise @@ -41,15 +42,25 @@ def FoldBatchNorms(graph): Args: graph: Graph to walk and modify. + freeze_batch_norm_delay: How many steps to wait before freezing + moving mean and variance and using them for batch normalization. This value + is used only when is_training is True. + is_training: Bool, true if training Raises: ValueError: When batch norm folding fails. """ - _FoldFusedBatchNorms(graph) - _FoldUnfusedBatchNorms(graph) + _FoldFusedBatchNorms( + graph, + freeze_batch_norm_delay=freeze_batch_norm_delay, + is_training=is_training) + _FoldUnfusedBatchNorms( + graph, + freeze_batch_norm_delay=freeze_batch_norm_delay, + is_training=is_training) -def _FoldFusedBatchNorms(graph): +def _FoldFusedBatchNorms(graph, freeze_batch_norm_delay, is_training): """Finds fused batch norm layers and folds them into preceding layers. Folding only affects the following layers: Conv2D, fully connected, depthwise @@ -57,6 +68,9 @@ def _FoldFusedBatchNorms(graph): Args: graph: Graph to walk and modify. + freeze_batch_norm_delay: How many steps to wait before freezing + moving mean and variance and using them for batch normalization + is_training: Bool, true if training Raises: ValueError: When batch norm folding fails. @@ -67,8 +81,7 @@ def _FoldFusedBatchNorms(graph): # `bn_op`. The '/' (i.e. `sep`) ensures that we reuse the existing scope # named `scope`. Otherwise, TF creates a unique scope whose name starts with # `scope`. - with graph.as_default(), graph.name_scope(scope + sep), ops.device( - match.bn_op.device): + with graph.as_default(), graph.name_scope(scope + sep): with graph.name_scope(scope + sep + 'BatchNorm_Fold' + sep): # new weights = old weights * gamma / sqrt(variance + epsilon) # new biases = -mean * gamma / sqrt(variance + epsilon) + beta @@ -79,9 +92,18 @@ def _FoldFusedBatchNorms(graph): match.mean_tensor * multiplier_tensor, name='bias') + correction_scale, correction_recip, correction_offset = None, None, None + if is_training: + correction_scale, correction_recip, correction_offset = ( + _ComputeBatchNormCorrections( + context='', + match=match, + freeze_batch_norm_delay=freeze_batch_norm_delay, + fused_batch_norm=True)) # The shape of depthwise weights is different, so we need to reshape the # multiplier_tensor to ensure that the scaled_weight_tensor has the # expected shape. + weights = match.weight_tensor if match.layer_op.type == 'DepthwiseConv2dNative': new_shape = [ match.weight_tensor.get_shape().as_list()[2], @@ -90,15 +112,29 @@ def _FoldFusedBatchNorms(graph): multiplier_tensor = array_ops.reshape( multiplier_tensor, new_shape, name='scale_reshape') + if correction_scale is not None: + correction_scale = array_ops.reshape( + correction_scale, new_shape, name='correction_reshape') + + if correction_scale is not None: + weights = math_ops.multiply( + correction_scale, weights, name='correction_mult') + # TODO(suharshs): This naming of the following ops needs to carefully # follow the naming expected by quantize.py. Generalize the quantize code # to not require these delicate naming conventions. scaled_weight_tensor = math_ops.multiply( - match.weight_tensor, multiplier_tensor, name='mul_fold') + weights, multiplier_tensor, name='mul_fold') new_layer_tensor = _CloneWithNewOperands( match.layer_op, match.input_tensor, scaled_weight_tensor) + if correction_recip is not None: + new_layer_tensor = math_ops.multiply( + correction_recip, new_layer_tensor, name='post_conv_mul') + new_layer_tensor = math_ops.add(new_layer_tensor, (correction_offset), + 'correction_add') + bias_add_tensor = math_ops.add( new_layer_tensor, bias_tensor, name='add_fold') @@ -165,6 +201,8 @@ def _FindFusedBatchNorms(graph): mean_pattern = graph_matcher.OpTypePattern('*') variance_pattern = graph_matcher.OpTypePattern('*') + moving_average_pattern = graph_matcher.OpTypePattern('*') + bn_decay_pattern = graph_matcher.OpTypePattern('*') conv_pattern = graph_matcher.OpTypePattern( 'Conv2D|DepthwiseConv2dNative', inputs=[input_pattern, weight_pattern]) # MatMul has a Reshape between it and FusedBatchNorm. @@ -180,6 +218,11 @@ def _FindFusedBatchNorms(graph): conv_pattern, gamma_pattern, beta_pattern, mean_pattern, variance_pattern ]) + conv_moving_average_sub_pattern = graph_matcher.OpTypePattern( + 'Sub', inputs=[moving_average_pattern, conv_batch_norm_pattern]) + # TODO(suharshs): Use a OneofPattern here when available + conv_moving_average_mul_pattern = graph_matcher.OpTypePattern( + 'Mul', inputs=[conv_moving_average_sub_pattern, bn_decay_pattern]) matmul_batch_norm_pattern = graph_matcher.OpTypePattern( 'FusedBatchNorm', inputs=[ @@ -191,8 +234,34 @@ def _FindFusedBatchNorms(graph): inputs=[matmul_batch_norm_pattern, graph_matcher.OpTypePattern('*')]) + matmul_moving_average_sub_pattern = graph_matcher.OpTypePattern( + 'Sub', inputs=[moving_average_pattern, matmul_batch_norm_pattern]) + matmul_moving_average_mul_pattern = graph_matcher.OpTypePattern( + 'Mul', inputs=[matmul_moving_average_sub_pattern, bn_decay_pattern]) + conv_matcher = graph_matcher.GraphMatcher(conv_batch_norm_pattern) matmul_matcher = graph_matcher.GraphMatcher(matmul_bn_output_reshape_pattern) + conv_moving_average_mul_matcher = graph_matcher.GraphMatcher( + conv_moving_average_mul_pattern) + matmul_moving_average_mul_matcher = graph_matcher.GraphMatcher( + matmul_moving_average_mul_pattern) + + def _GetMovingAverageTensors(graph, moving_avg_mul_matcher, + moving_avg_sub_pattern, bn_op): + """Gets the moving mean and variance tensors and the batch norm momentum.""" + for mul_match_result in moving_avg_mul_matcher.match_graph(graph): + sub_op = mul_match_result.get_op(moving_avg_sub_pattern) + + if sub_op.inputs[1].name == bn_op.outputs[1].name: + # During training: Batch Mean is bn_op.outputs[1] + moving_mean_tensor = sub_op.inputs[0] + bn_decay_mean_tensor = mul_match_result.get_tensor(bn_decay_pattern) + if sub_op.inputs[1].name == bn_op.outputs[2].name: + # During training: Batch Var is bn_op.outputs[2] + moving_variance_tensor = sub_op.inputs[0] + bn_decay_var_tensor = mul_match_result.get_tensor(bn_decay_pattern) + return (moving_mean_tensor, bn_decay_mean_tensor, moving_variance_tensor, + bn_decay_var_tensor) def _GetCommonTensors(match_result, bn_op, bn_input_tensor): """Gets tensors needed for FusedBatchNormMatch from match_result.""" @@ -222,10 +291,14 @@ def _FindFusedBatchNorms(graph): # calculation, the variance is corrected by the term N/N-1 (Bessel's # correction). The variance tensor read from FuseBatchNorm has bessel's # correction applied, so we undo it here. - n = math_ops.cast( - array_ops.size(bn_input_tensor) / array_ops.size(mean_tensor), - dtypes.float32) - variance_tensor = bn_op.outputs[2] * (n - 1) / n + scope, sep, _ = bn_op.name.rpartition('/') + g = ops.get_default_graph() + with g.as_default(), g.name_scope(scope + sep): + n = math_ops.cast( + array_ops.size(bn_input_tensor) / array_ops.size(mean_tensor), + dtypes.float32) + variance_tensor = math_ops.multiply( + bn_op.outputs[2], (n - 1) / n, name='Undo_Bessel_Correction') else: mean_tensor = match_result.get_tensor(mean_pattern) variance_tensor = match_result.get_tensor(variance_pattern) @@ -233,15 +306,30 @@ def _FindFusedBatchNorms(graph): variance_tensor) for match_result in conv_matcher.match_graph(graph): + moving_mean_tensor = None + moving_variance_tensor = None + bn_decay_mean_tensor = None + bn_decay_var_tensor = None layer_op = match_result.get_op(conv_pattern) layer_tensor = match_result.get_tensor(conv_pattern) bn_op = match_result.get_op(conv_batch_norm_pattern) - # In the case of convolution the output_tensor is the output of bn_op. - output_tensor = bn_op.outputs[0] + if bn_op.get_attr('is_training'): + (moving_mean_tensor, bn_decay_mean_tensor, moving_variance_tensor, + bn_decay_var_tensor) = _GetMovingAverageTensors( + graph, + moving_avg_mul_matcher=conv_moving_average_mul_matcher, + moving_avg_sub_pattern=conv_moving_average_sub_pattern, + bn_op=bn_op) + output_tensor = bn_op.outputs[0] + batch_epsilon_tensor = bn_op.get_attr('epsilon') (input_tensor, weight_tensor, gamma_tensor, beta_tensor, mean_tensor, - variance_tensor) = _GetCommonTensors(match_result, bn_op, layer_tensor) - yield _FusedBatchNormMatch( + variance_tensor) = _GetCommonTensors( + match_result, + bn_op, + layer_tensor, + ) + yield _BatchNormMatch( layer_op=layer_op, bn_op=bn_op, output_tensor=output_tensor, @@ -250,20 +338,38 @@ def _FindFusedBatchNorms(graph): gamma_tensor=gamma_tensor, beta_tensor=beta_tensor, mean_tensor=mean_tensor, - variance_tensor=variance_tensor) + variance_tensor=variance_tensor, + moving_mean_tensor=moving_mean_tensor, + moving_variance_tensor=moving_variance_tensor, + bn_decay_mean_tensor=bn_decay_mean_tensor, + bn_decay_var_tensor=bn_decay_var_tensor, + batch_epsilon_tensor=batch_epsilon_tensor) for match_result in matmul_matcher.match_graph(graph): + moving_mean_tensor = None + moving_variance_tensor = None + bn_decay_mean_tensor = None + bn_decay_var_tensor = None layer_op = match_result.get_op(matmul_pattern) layer_tensor = match_result.get_tensor(matmul_pattern) bn_op = match_result.get_op(matmul_batch_norm_pattern) + if bn_op.get_attr('is_training'): + (moving_mean_tensor, bn_decay_mean_tensor, moving_variance_tensor, + bn_decay_var_tensor) = _GetMovingAverageTensors( + graph, + moving_avg_mul_matcher=matmul_moving_average_mul_matcher, + moving_avg_sub_pattern=matmul_moving_average_sub_pattern, + bn_op=bn_op) + # In the MatMul case, the output of batch norm is reshaped back into a # 2D tensor, so the output_tensor is the output of the Reshape op. output_reshape_op = match_result.get_op(matmul_bn_output_reshape_pattern) output_tensor = output_reshape_op.outputs[0] + batch_epsilon_tensor = bn_op.get_attr('epsilon') (input_tensor, weight_tensor, gamma_tensor, beta_tensor, mean_tensor, variance_tensor) = _GetCommonTensors(match_result, bn_op, layer_tensor) - yield _FusedBatchNormMatch( + yield _BatchNormMatch( layer_op=layer_op, bn_op=bn_op, output_tensor=output_tensor, @@ -272,15 +378,21 @@ def _FindFusedBatchNorms(graph): gamma_tensor=gamma_tensor, beta_tensor=beta_tensor, mean_tensor=mean_tensor, - variance_tensor=variance_tensor) + variance_tensor=variance_tensor, + moving_mean_tensor=moving_mean_tensor, + moving_variance_tensor=moving_variance_tensor, + bn_decay_mean_tensor=bn_decay_mean_tensor, + bn_decay_var_tensor=bn_decay_var_tensor, + batch_epsilon_tensor=batch_epsilon_tensor) -class _FusedBatchNormMatch(object): - """Contains all information related to a found FusedBatchNorm.""" +class _BatchNormMatch(object): + """Contains all information related to a found Fused/UnfusedBatchNorm.""" def __init__(self, layer_op, bn_op, output_tensor, input_tensor, weight_tensor, gamma_tensor, beta_tensor, mean_tensor, - variance_tensor): + variance_tensor, moving_mean_tensor, moving_variance_tensor, + bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon_tensor): self._layer_op = layer_op self._bn_op = bn_op self._output_tensor = output_tensor @@ -290,6 +402,11 @@ class _FusedBatchNormMatch(object): self._beta_tensor = beta_tensor self._mean_tensor = mean_tensor self._variance_tensor = variance_tensor + self._moving_mean_tensor = moving_mean_tensor + self._moving_variance_tensor = moving_variance_tensor + self._bn_decay_mean_tensor = bn_decay_mean_tensor + self._bn_decay_var_tensor = bn_decay_var_tensor + self._batch_epsilon_tensor = batch_epsilon_tensor @property def layer_op(self): @@ -327,8 +444,28 @@ class _FusedBatchNormMatch(object): def variance_tensor(self): return self._variance_tensor + @property + def moving_mean_tensor(self): + return self._moving_mean_tensor + + @property + def moving_variance_tensor(self): + return self._moving_variance_tensor + + @property + def batch_epsilon_tensor(self): + return self._batch_epsilon_tensor + + @property + def bn_decay_mean_tensor(self): + return self._bn_decay_mean_tensor + + @property + def bn_decay_var_tensor(self): + return self._bn_decay_var_tensor + -def _FoldUnfusedBatchNorms(graph): +def _FoldUnfusedBatchNorms(graph, freeze_batch_norm_delay, is_training): """Finds unfused batch norm layers and folds them into preceding layers. Folding only affects the following layers: Conv2D, fully connected, depthwise @@ -336,6 +473,9 @@ def _FoldUnfusedBatchNorms(graph): Args: graph: Graph to walk and modify. + freeze_batch_norm_delay: How many steps to wait before freezing + moving mean and variance and using them for batch normalization + is_training: Bool, True if training Raises: ValueError: When batch norm folding fails. @@ -346,7 +486,12 @@ def _FoldUnfusedBatchNorms(graph): has_scaling = _HasScaling(graph, input_to_ops_map, bn) # The mangling code intimately depends on BatchNorm node's internals. - original_op, folded_op = _CreateFoldedOp(graph, bn, has_scaling=has_scaling) + original_op, folded_op = _CreateFoldedOp( + graph, + bn, + has_scaling=has_scaling, + freeze_batch_norm_delay=freeze_batch_norm_delay, + is_training=is_training) activation = common.GetEndpointActivationOp(graph, bn) if activation: @@ -407,7 +552,186 @@ def _HasScaling(graph, input_to_ops_map, bn): return sum(1 for op in rsqrt_consumers if op.type == 'Mul') == 1 -def _CreateFoldedOp(graph, context, has_scaling): +def _GetBatchNormParams(graph, context, has_scaling): + """Extracts relevant tensors for folding batch norms. + + Args: + graph: Graph to inspect. + context: The scope under which we look for batch norm params + has_scaling: Bool that specifies if scaling is done as part of batch + norm + + Returns: + _BatchNormMatch containing all required batch norm parameters + """ + gamma_tensor = None + batch_mean_tensor = None + batch_variance_tensor = None + moving_mean_tensor = None + moving_variance_tensor = None + batch_epsilon_tensor = None + bn_decay_mean_tensor = None + bn_decay_var_tensor = None + + split_context = context.split('/') + base_context = split_context[-1] + + oplist = graph.get_operations() + op_suffix_gamma = base_context + '/BatchNorm/gamma' + op_suffix_mean = base_context + '/BatchNorm/moments/Squeeze' + op_suffix_variance = base_context + '/BatchNorm/moments/Squeeze_1' + op_suffix_moving_variance = base_context + '/BatchNorm/moving_variance/read' + op_suffix_moving_mean = base_context + '/BatchNorm/moving_mean/read' + op_suffix_epsilon = base_context + '/BatchNorm/batchnorm/add/y' + op_suffix_bn_decay_mean = base_context + '/BatchNorm/AssignMovingAvg/decay' + op_suffix_bn_decay_var = base_context + '/BatchNorm/AssignMovingAvg_1/decay' + + # Parse through list of ops to find relevant ops + for op in oplist: + if op.name.endswith(op_suffix_mean): + # This is an efficient way to check for two things: + # Is batch norm present and is it training mode? + # Batch statistics are computed only during batch norm in training + batch_mean_tensor = graph.get_tensor_by_name(op.name + ':0') + if op.name.endswith(op_suffix_variance): + batch_variance_tensor = graph.get_tensor_by_name(op.name + ':0') + if op.name.endswith(op_suffix_moving_mean): + moving_mean_tensor = graph.get_tensor_by_name(op.name + ':0') + if op.name.endswith(op_suffix_moving_variance): + moving_variance_tensor = graph.get_tensor_by_name(op.name + ':0') + if op.name.endswith(op_suffix_epsilon): + batch_epsilon_tensor = graph.get_tensor_by_name(op.name + ':0') + if op.name.endswith(op_suffix_bn_decay_mean): + bn_decay_mean_tensor = graph.get_tensor_by_name(op.name + ':0') + if op.name.endswith(op_suffix_bn_decay_var): + bn_decay_var_tensor = graph.get_tensor_by_name(op.name + ':0') + if has_scaling: + if op.name.endswith(op_suffix_gamma): + gamma_tensor = graph.get_tensor_by_name(op.name + ':0') + + if not has_scaling: + gamma_tensor = array_ops.ones(batch_mean_tensor.shape) + + return _BatchNormMatch( + layer_op=None, + bn_op=None, + output_tensor=None, + input_tensor=None, + weight_tensor=None, + gamma_tensor=gamma_tensor, + beta_tensor=None, + mean_tensor=batch_mean_tensor, + variance_tensor=batch_variance_tensor, + moving_mean_tensor=moving_mean_tensor, + moving_variance_tensor=moving_variance_tensor, + bn_decay_mean_tensor=bn_decay_mean_tensor, + bn_decay_var_tensor=bn_decay_var_tensor, + batch_epsilon_tensor=batch_epsilon_tensor) + + +def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay, + fused_batch_norm): + """Computes batch norm correction params. + + Before batch normalization is frozen: + We use batch statistics for batch norm. + correction_scale = sigma_b/sigma_mv + correction_recip = 1/correction_scale + correction_offset = 0 + + After batch normalization is frozen: + correction_scale = sigma_b/sigma_mv + correction_recip = 1 + correction_offset = gamma*(mu_b/sigma_b-mu_mv/sigma_mv). + + Batch norm is frozen if global_step > bn_freeze_delay. + The corrections ensure that: + a) The weights are quantized after scaling by gamma/sigma_mv. This enables + smoother training as the scaling on the weights changes slowly, rather than + jump across mini-batches + b) Changing the values of the corrections allows for one to switch between + using batch statistics to using moving mean and average, without requiring + changes to batch_norm + + + Args: + context: The scope under which we look for batch norm params + match: Object containg required batch norm tensors for correction + computation + freeze_batch_norm_delay: Delay in steps at which computation switches + from regular batch norm to frozen mean and variance. + fused_batch_norm: Bool, true if fused batch norm is used + + Returns: + A tuple of correction_scale, correction_recip, correction_offset + """ + + g = ops.get_default_graph() + with g.name_scope(context + 'batch_norm_correction'): + recip_sigma_mv = math_ops.rsqrt( + match.moving_variance_tensor + match.batch_epsilon_tensor) + recip_sigma = math_ops.rsqrt( + match.variance_tensor + match.batch_epsilon_tensor) + correction_scale = math_ops.divide( + recip_sigma_mv, recip_sigma, name='scale_compute') + correction_scale = array_ops.identity( + correction_scale, name='correction_scale') + correction_recip = math_ops.reciprocal( + correction_scale, name='reciprocal_compute') + correction_offset = math_ops.multiply( + match.gamma_tensor, + match.mean_tensor * recip_sigma - + match.moving_mean_tensor * recip_sigma_mv, + name='offset_compute') + + if freeze_batch_norm_delay is not None: + use_mv_avg = math_ops.greater_equal( + training_util.get_or_create_global_step(), + freeze_batch_norm_delay, + name='use_moving_average') + else: + use_mv_avg = False + + bn_decay_zero = 0.0 + bn_decay_mean_consumers = list(match.bn_decay_mean_tensor.consumers()) + bn_decay_var_consumers = list(match.bn_decay_mean_tensor.consumers()) + + bn_decay_mean_out = utils.smart_cond( + use_mv_avg, + lambda: bn_decay_zero, + lambda: match.bn_decay_mean_tensor, + name='freeze_moving_mean') + graph_editor.reroute_ts( + [bn_decay_mean_out], [match.bn_decay_mean_tensor], + can_modify=bn_decay_mean_consumers) + + if fused_batch_norm is False: + bn_decay_var_consumers = list(match.bn_decay_var_tensor.consumers()) + bn_decay_var_out = utils.smart_cond( + use_mv_avg, + lambda: bn_decay_zero, + lambda: match.bn_decay_var_tensor, + name='freeze_moving_var') + graph_editor.reroute_ts( + [bn_decay_var_out], [match.bn_decay_var_tensor], + can_modify=bn_decay_var_consumers) + + correction_recip = utils.smart_cond( + use_mv_avg, + lambda: array_ops.ones(correction_scale.shape), + lambda: correction_recip, + name='correction_recip') + + correction_offset = utils.smart_cond( + use_mv_avg, + lambda: correction_offset, + lambda: array_ops.zeros(correction_offset.shape), + name='correction_offset') + return correction_scale, correction_recip, correction_offset + + +def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay, + is_training): """Folds in batch norm layer into preceding convolution or FC layer. Creates 3 new nodes, connects their inputs and adds them to the graph: @@ -419,6 +743,9 @@ def _CreateFoldedOp(graph, context, has_scaling): context: String, batch norm context, i.e. node into which BatchNorm is nested. has_scaling: Whether the batch norm has scaling enabled. + freeze_batch_norm_delay: How many steps to wait before freezing + moving mean and variance and using them for batch normalization + is_training: Bool, true if training Raises: ValueError: When operation type is not supported, or input and output tensor @@ -435,19 +762,43 @@ def _CreateFoldedOp(graph, context, has_scaling): mul_scale_name) op_below = mul_scale.inputs[0].op weights = op_below.inputs[1] - + match = _GetBatchNormParams( + graph=graph, context=context, has_scaling=has_scaling) + correction_scale, correction_recip, correction_offset = None, None, None + if is_training: + correction_scale, correction_recip, correction_offset = ( + _ComputeBatchNormCorrections( + context=context, + match=match, + freeze_batch_norm_delay=freeze_batch_norm_delay, + fused_batch_norm=False)) # Special handling for weights of depthwise convolution. if op_below.type == 'DepthwiseConv2dNative': - new_shape = [weights.get_shape().as_list()[2], - weights.get_shape().as_list()[3]] + new_shape = [ + weights.get_shape().as_list()[2], + weights.get_shape().as_list()[3] + ] scale_name = 'mul' if has_scaling else 'Rsqrt' - scale = graph.get_operation_by_name(context + '/BatchNorm/batchnorm/' + - scale_name) + scale = graph.get_operation_by_name( + context + '/BatchNorm/batchnorm/' + scale_name) scale = array_ops.reshape(scale.outputs[0], new_shape, context + '/scale_reshape') - mul_fold = _CloneOp(mul_scale, context + '/mul_fold', - [(0, weights), (1, scale)]) + + if correction_scale is not None: + correction_scale = array_ops.reshape(correction_scale, new_shape, + context + '/correction_reshape') + with ops.device(mul_scale.device): + weights = math_ops.multiply(correction_scale, weights, + context + '/correction_mult') + + mul_fold = _CloneOp(mul_scale, context + '/mul_fold', [(0, weights), + (1, scale)]) elif op_below.type in ['Conv2D', 'MatMul']: + + if correction_scale is not None: + with ops.device(mul_scale.device): + weights = math_ops.multiply(correction_scale, weights, + context + '/correction_mult') mul_fold = _CloneOp(mul_scale, context + '/mul_fold', [(0, weights)]) else: raise ValueError('Cannot handle operation of type: %s' % op_below.op) @@ -456,10 +807,17 @@ def _CreateFoldedOp(graph, context, has_scaling): conv_or_fc_folded = _CloneOp(op_below, op_below.name + '_Fold', [(1, mul_fold.outputs[0])]) - add_shift = graph.get_operation_by_name(context + - '/BatchNorm/batchnorm/add_1') - add_fold = _CloneOp(add_shift, context + '/add_fold', - [(0, conv_or_fc_folded.outputs[0])]) + add_shift = graph.get_operation_by_name( + context + '/BatchNorm/batchnorm/add_1') + + corrected_output = conv_or_fc_folded.outputs[0] + if correction_offset is not None: + with ops.device(conv_or_fc_folded.device): + corrected_output = math_ops.multiply(correction_recip, corrected_output, + context + '/post_conv_mul') + corrected_output = math_ops.add(corrected_output, (correction_offset), + context + '/correction_add') + add_fold = _CloneOp(add_shift, context + '/add_fold', [(0, corrected_output)]) _AssertShapesMatch('add_fold', add_fold.inputs[0], add_fold.outputs[0]) return add_shift, add_fold diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py index ecf321ff57..330bd8a647 100644 --- a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py +++ b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py @@ -46,26 +46,27 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): def _RunTestOverParameters(self, test_fn): parameters_list = [ - # (relu, relu_op_name, with_bypass, has_scaling, fused_batch_norm) - (nn_ops.relu6, 'Relu6', False, False, False), - (nn_ops.relu, 'Relu', False, False, False), - (nn_ops.relu6, 'Relu6', True, False, False), - (nn_ops.relu, 'Relu', True, False, False), - (nn_ops.relu6, 'Relu6', False, True, False), - (nn_ops.relu, 'Relu', False, True, False), - (nn_ops.relu6, 'Relu6', True, True, False), - (nn_ops.relu, 'Relu', True, True, False), + # (relu, relu_op_name, with_bypass, has_scaling, fused_batch_norm, + # freeze_batch_norm_delay) + (nn_ops.relu6, 'Relu6', False, False, False, 100), + (nn_ops.relu, 'Relu', False, False, False, None), + (nn_ops.relu6, 'Relu6', True, False, False, 100), + (nn_ops.relu, 'Relu', True, False, False, None), + (nn_ops.relu6, 'Relu6', False, True, False, 100), + (nn_ops.relu, 'Relu', False, True, False, None), + (nn_ops.relu6, 'Relu6', True, True, False, 100), + (nn_ops.relu, 'Relu', True, True, False, None), # Fused batch norm always has scaling enabled. - (nn_ops.relu6, 'Relu6', False, True, True), - (nn_ops.relu, 'Relu', False, True, True), - (nn_ops.relu6, 'Relu6', True, True, True), - (nn_ops.relu, 'Relu', True, True, True), + (nn_ops.relu6, 'Relu6', False, True, True, None), + (nn_ops.relu, 'Relu', False, True, True, 100), + (nn_ops.relu6, 'Relu6', True, True, True, None), + (nn_ops.relu, 'Relu', True, True, True, 100), ] for params in parameters_list: - test_fn(params[0], params[1], params[2], params[3], params[4]) + test_fn(params[0], params[1], params[2], params[3], params[4], params[5]) def _TestFoldConv2d(self, relu, relu_op_name, with_bypass, has_scaling, - fused_batch_norm): + fused_batch_norm, freeze_batch_norm_delay): """Tests folding cases: inputs -> Conv2d with batch norm -> Relu*. Args: @@ -75,6 +76,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): inputs to just before Relu*. has_scaling: Bool, when true the batch norm has scaling. fused_batch_norm: Bool, when true the batch norm is fused. + freeze_batch_norm_delay: None or the number of steps after which training + switches to using frozen mean and variance """ g = ops.Graph() with g.as_default(): @@ -99,12 +102,13 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): node = math_ops.add(inputs, node, name='test/Add') relu(node, name='test/' + relu_op_name) - fold_batch_norms.FoldBatchNorms(g) + fold_batch_norms.FoldBatchNorms( + g, is_training=True, freeze_batch_norm_delay=freeze_batch_norm_delay) folded_mul = g.get_operation_by_name(scope + '/mul_fold') self.assertEqual(folded_mul.type, 'Mul') self._AssertInputOpsAre(folded_mul, [ - scope + '/weights/read', + scope + '/correction_mult', self._BatchNormMultiplierName(scope, has_scaling, fused_batch_norm) ]) self._AssertOutputGoesToOps(folded_mul, g, [scope + '/Conv2D_Fold']) @@ -113,12 +117,12 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): self.assertEqual(folded_conv.type, 'Conv2D') self._AssertInputOpsAre(folded_conv, [scope + '/mul_fold', inputs.op.name]) - self._AssertOutputGoesToOps(folded_conv, g, [scope + '/add_fold']) + self._AssertOutputGoesToOps(folded_conv, g, [scope + '/post_conv_mul']) folded_add = g.get_operation_by_name(scope + '/add_fold') self.assertEqual(folded_add.type, 'Add') self._AssertInputOpsAre(folded_add, [ - scope + '/Conv2D_Fold', + scope + '/correction_add', self._BathNormBiasName(scope, fused_batch_norm) ]) output_op_names = ['test/Add' if with_bypass else 'test/' + relu_op_name] @@ -128,7 +132,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): self._RunTestOverParameters(self._TestFoldConv2d) def _TestFoldConv2dUnknownShape(self, relu, relu_op_name, with_bypass, - has_scaling, fused_batch_norm): + has_scaling, fused_batch_norm, + freeze_batch_norm_delay): """Tests folding cases: inputs -> Conv2d with batch norm -> Relu*. Tests that folding works even with an input shape where some dimensions are @@ -141,6 +146,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): inputs to just before Relu*. has_scaling: Bool, when true the batch norm has scaling. fused_batch_norm: Bool, when true the batch norm is fused. + freeze_batch_norm_delay: None or the number of steps after which training + switches to using frozen mean and variance """ g = ops.Graph() with g.as_default(): @@ -164,12 +171,13 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): node = math_ops.add(inputs, node, name='test/Add') relu(node, name='test/' + relu_op_name) - fold_batch_norms.FoldBatchNorms(g) + fold_batch_norms.FoldBatchNorms( + g, is_training=True, freeze_batch_norm_delay=freeze_batch_norm_delay) folded_mul = g.get_operation_by_name(scope + '/mul_fold') self.assertEqual(folded_mul.type, 'Mul') self._AssertInputOpsAre(folded_mul, [ - scope + '/weights/read', + scope + '/correction_mult', self._BatchNormMultiplierName(scope, has_scaling, fused_batch_norm) ]) self._AssertOutputGoesToOps(folded_mul, g, [scope + '/Conv2D_Fold']) @@ -177,12 +185,12 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): folded_conv = g.get_operation_by_name(scope + '/Conv2D_Fold') self.assertEqual(folded_conv.type, 'Conv2D') self._AssertInputOpsAre(folded_conv, [scope + '/mul_fold', inputs.op.name]) - self._AssertOutputGoesToOps(folded_conv, g, [scope + '/add_fold']) + self._AssertOutputGoesToOps(folded_conv, g, [scope + '/post_conv_mul']) folded_add = g.get_operation_by_name(scope + '/add_fold') self.assertEqual(folded_add.type, 'Add') self._AssertInputOpsAre(folded_add, [ - scope + '/Conv2D_Fold', + scope + '/correction_add', self._BathNormBiasName(scope, fused_batch_norm) ]) output_op_names = ['test/Add' if with_bypass else 'test/' + relu_op_name] @@ -192,7 +200,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): self._RunTestOverParameters(self._TestFoldConv2dUnknownShape) def _TestFoldFullyConnectedLayer(self, relu, relu_op_name, with_bypass, - has_scaling, fused_batch_norm): + has_scaling, fused_batch_norm, + freeze_batch_norm_delay): """Tests folding cases: inputs -> FC with batch norm -> Relu*. Args: @@ -202,6 +211,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): inputs to just before Relu*. has_scaling: Bool, when true the batch norm has scaling. fused_batch_norm: Bool, when true the batch norm is fused. + freeze_batch_norm_delay: None or the number of steps after which training + switches to using frozen mean and variance """ g = ops.Graph() with g.as_default(): @@ -223,12 +234,13 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): node = math_ops.add(inputs, node, name='test/Add') relu(node, name='test/' + relu_op_name) - fold_batch_norms.FoldBatchNorms(g) + fold_batch_norms.FoldBatchNorms( + g, is_training=True, freeze_batch_norm_delay=freeze_batch_norm_delay) folded_mul = g.get_operation_by_name(scope + '/mul_fold') self.assertEqual(folded_mul.type, 'Mul') self._AssertInputOpsAre(folded_mul, [ - scope + '/weights/read', + scope + '/correction_mult', self._BatchNormMultiplierName(scope, has_scaling, fused_batch_norm) ]) self._AssertOutputGoesToOps(folded_mul, g, [scope + '/MatMul_Fold']) @@ -237,12 +249,12 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): self.assertEqual(folded_conv.type, 'MatMul') self._AssertInputOpsAre(folded_conv, [scope + '/mul_fold', inputs.op.name]) - self._AssertOutputGoesToOps(folded_conv, g, [scope + '/add_fold']) + self._AssertOutputGoesToOps(folded_conv, g, [scope + '/post_conv_mul']) folded_add = g.get_operation_by_name(scope + '/add_fold') self.assertEqual(folded_add.type, 'Add') self._AssertInputOpsAre(folded_add, [ - scope + '/MatMul_Fold', + scope + '/correction_add', self._BathNormBiasName(scope, fused_batch_norm) ]) output_op_names = ['test/Add' if with_bypass else 'test/' + relu_op_name] @@ -252,7 +264,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): self._RunTestOverParameters(self._TestFoldFullyConnectedLayer) def _TestFoldDepthwiseConv2d(self, relu, relu_op_name, with_bypass, - has_scaling, fused_batch_norm): + has_scaling, fused_batch_norm, + freeze_batch_norm_delay): """Tests folding: inputs -> DepthwiseConv2d with batch norm -> Relu*. Args: @@ -262,6 +275,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): inputs to just before Relu*. has_scaling: Bool, when true the batch norm has scaling. fused_batch_norm: Bool, when true the batch norm is fused. + freeze_batch_norm_delay: None or the number of steps after which training + switches to using frozen mean and variance """ g = ops.Graph() with g.as_default(): @@ -286,7 +301,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): node = math_ops.add(inputs, node, name='test/Add') relu(node, name='test/' + relu_op_name) - fold_batch_norms.FoldBatchNorms(g) + fold_batch_norms.FoldBatchNorms( + g, is_training=True, freeze_batch_norm_delay=freeze_batch_norm_delay) folded_mul = g.get_operation_by_name(scope + '/mul_fold') self.assertEqual(folded_mul.type, 'Mul') @@ -295,8 +311,7 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): else: scale_reshape_op_name = scope + '/scale_reshape' self._AssertInputOpsAre(folded_mul, - [scope + '/depthwise_weights/read', - scale_reshape_op_name]) + [scope + '/correction_mult', scale_reshape_op_name]) self._AssertOutputGoesToOps(folded_mul, g, [scope + '/depthwise_Fold']) scale_reshape = g.get_operation_by_name(scale_reshape_op_name) @@ -311,12 +326,12 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): self.assertEqual(folded_conv.type, 'DepthwiseConv2dNative') self._AssertInputOpsAre(folded_conv, [scope + '/mul_fold', inputs.op.name]) - self._AssertOutputGoesToOps(folded_conv, g, [scope + '/add_fold']) + self._AssertOutputGoesToOps(folded_conv, g, [scope + '/post_conv_mul']) folded_add = g.get_operation_by_name(scope + '/add_fold') self.assertEqual(folded_add.type, 'Add') self._AssertInputOpsAre(folded_add, [ - scope + '/depthwise_Fold', + scope + '/correction_add', self._BathNormBiasName(scope, fused_batch_norm) ]) output_op_names = ['test/Add' if with_bypass else 'test/' + relu_op_name] @@ -326,7 +341,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): self._RunTestOverParameters(self._TestFoldDepthwiseConv2d) def _TestCompareFoldAndUnfolded(self, relu, relu_op_name, with_bypass, - has_scaling, fused_batch_norm): + has_scaling, fused_batch_norm, + freeze_batch_norm_delay): """Tests that running folded and unfolded BN returns the same results. Args: @@ -336,6 +352,8 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): inputs to just before Relu*. has_scaling: Bool, when true the batch norm has scaling. fused_batch_norm: Bool, when true the batch norm is fused. + freeze_batch_norm_delay: None or the number of steps after which training + switches to using frozen mean and variance """ random_seed.set_random_seed(1234) unfolded_g = ops.Graph() @@ -361,11 +379,12 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase): if with_bypass: node = math_ops.add(inputs, node, name='test/Add') relu_node = relu(node, name='test/' + relu_op_name) - folded_g = copy_graph.CopyGraph(unfolded_g) with folded_g.as_default(): - fold_batch_norms.FoldBatchNorms(folded_g) - + fold_batch_norms.FoldBatchNorms( + folded_g, + is_training=True, + freeze_batch_norm_delay=freeze_batch_norm_delay) with session.Session(graph=unfolded_g) as sess: sess.run(variables.global_variables_initializer()) grad_node = gradients.gradients(relu_node, inputs) diff --git a/tensorflow/contrib/quantize/python/quantize_graph.py b/tensorflow/contrib/quantize/python/quantize_graph.py index bbd9743d80..89b744c559 100644 --- a/tensorflow/contrib/quantize/python/quantize_graph.py +++ b/tensorflow/contrib/quantize/python/quantize_graph.py @@ -52,9 +52,19 @@ def _create_graph(input_graph, """ # TODO(suharshs): Describe the process in more detail in the doc string. g = copy_graph.CopyGraph(input_graph) + if is_training: + # TODO(raghuramank): Need to make freeze_batch_norm_delay + # a function of the batch size. For now setting this to 250 epochs + # This corresponds to 5 million steps at a batch size of 64. + freeze_batch_norm_delay = 5000000 + else: + freeze_batch_norm_delay = None with g.as_default(): with ops.device(device_name_or_function): - fold_batch_norms.FoldBatchNorms(g) + fold_batch_norms.FoldBatchNorms( + g, + freeze_batch_norm_delay=freeze_batch_norm_delay, + is_training=is_training) quantize.Quantize(g, is_training=is_training) if elements is None: return g -- GitLab From 922bc83735a5ce8f58245427efb17f87735c2848 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Thu, 1 Feb 2018 16:22:24 -0800 Subject: [PATCH 411/423] GCS Throttle: 1 token == 1 Kb Previously, 1 token was approximately 256 bytes. This is slightly less intuitive than 1 kb. PiperOrigin-RevId: 184212503 --- tensorflow/core/platform/cloud/gcs_throttle.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/platform/cloud/gcs_throttle.h b/tensorflow/core/platform/cloud/gcs_throttle.h index 8e46fca6ca..1a89daef08 100644 --- a/tensorflow/core/platform/cloud/gcs_throttle.h +++ b/tensorflow/core/platform/cloud/gcs_throttle.h @@ -126,7 +126,7 @@ class GcsThrottle { void UpdateState() EXCLUSIVE_LOCKS_REQUIRED(mu_); inline uint64 request_bytes_to_tokens(size_t num_bytes) { - return num_bytes >> 8; + return num_bytes >> 10; } mutex mu_; -- GitLab From c5e02bc8fd71d73c5d05f583ce5391f26ad937d7 Mon Sep 17 00:00:00 2001 From: Jacques Pienaar Date: Thu, 1 Feb 2018 16:29:30 -0800 Subject: [PATCH 412/423] Automated g4 rollback of changelist 184188816 PiperOrigin-RevId: 184213576 --- .../tf2xla/functionalize_control_flow.cc | 279 +++++------------- .../tf2xla/functionalize_control_flow_test.cc | 10 +- tensorflow/compiler/tf2xla/graph_compiler.cc | 2 +- 3 files changed, 82 insertions(+), 209 deletions(-) diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc index 7a4fa79078..1d9e0fb33e 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc @@ -285,8 +285,7 @@ Status BuildLoopBody(const Graph& graph, Frame* frame, Status FunctionalizeLoop(Graph* graph, Frame* frame, FunctionLibraryDefinition* library) { VLOG(2) << "Frame " << frame->name << " before: " - << dump_graph::DumpGraphToFile("functionalize_before", *graph, - library); + << dump_graph::DumpGraphToFile("functionalize_before", *graph); // Split loop-varying Enter nodes with multiple successors. If the same // Tensor is fed as input to multiple loop arguments, we may end up with a @@ -451,7 +450,7 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame, TF_RETURN_IF_ERROR(BuildLoopBody(*graph, frame, &arg_types, &body_graph)); VLOG(2) << "Frame " << frame->name << " condition: " - << dump_graph::DumpGraphToFile("loop_condition", *cond_graph, library) + << dump_graph::DumpGraphToFile("loop_condition", *cond_graph) << " body: " << dump_graph::DumpGraphToFile("loop_body", *body_graph); static std::atomic sequence_num(0LL); @@ -532,8 +531,7 @@ Status FunctionalizeLoop(Graph* graph, Frame* frame, frame->parent->nodes.insert(while_node); VLOG(2) << "Frame " << frame->name << " after: " - << dump_graph::DumpGraphToFile("functionalize_after", *graph, - library); + << dump_graph::DumpGraphToFile("functionalize_after", *graph); return Status::OK(); } @@ -566,11 +564,11 @@ class FunctionalizeCond { explicit CondArgNode(Node* input) : input(input) {} string ToString() const { return strings::StrCat("input=", input->name(), - " switches=", NodesToString(switches)); + " switches=", NodesToString(switch_nodes)); } Node* input; - std::vector switches; + std::vector switch_nodes; }; using CondArgNodes = std::vector; @@ -584,22 +582,15 @@ class FunctionalizeCond { int count; }; - // Group of switch nodes that will be part of the same XlaIf. - struct SwitchCluster { - explicit SwitchCluster(Node* predicate) : predicate(predicate) {} - string ToString() const { - return strings::StrCat(name, " predicate=", predicate->name(), - " switches=", NodesToString(switches)); - } + struct PredicateSwitches { + explicit PredicateSwitches(Node* predicate) : predicate(predicate) {} - string name; Node* predicate; std::vector switches; }; - FunctionalizeCond(Graph* graph, FunctionLibraryDefinition* library, - bool dump_graphs) - : library_(library), graph_(graph), dump_graphs_(dump_graphs) {} + FunctionalizeCond(Graph* graph, FunctionLibraryDefinition* library) + : library_(library), graph_(graph) {} // Perform the actual cond functionalization. Iterate over groups of switch // nodes (linked by common predicate), from innermost to outermost, and @@ -610,25 +601,27 @@ class FunctionalizeCond { // frontier (the nodes where the cond ends). StatusOr, std::unordered_set>> - DetermineBranchMapAndFrontier(const SwitchCluster& switch_cluster); + DetermineBranchMapAndFrontier(const std::vector& switches); // Returns XlaIf node created from subgraph of merge and switch nodes. This // encapsulates the process of extracting the bodies needed for the then and // else branch, creates a XlaIf node, removing the nodes of the branches from // the graph and replacing the merge node with a XlaIf. StatusOr ConvertToXlaIf(const CondArgNodes& cond_arg_nodes, - const SwitchCluster& switch_cluster, - const std::vector& switches); + const std::vector& switch_nodes, + const std::vector& merge_nodes, + Node* predicate); // Builds a XlaIfOp to replace the Switch-Graph-Merge cluster with. StatusOr BuildAndAddXlaIfOp(const CondArgNodes& cond_arg_nodes, - const SwitchCluster& switch_cluster, - const std::vector& merge_nodes); + const std::vector& switch_nodes, + const std::vector& merge_nodes, + Node* predicate); // Extracts a function body corresponding to the given input edge of the merge // node. Status ExtractBody(const CondArgNodes& cond_arg_nodes, - const std::vector& switches, + const std::vector& switch_nodes, const std::vector& merge_nodes, int input_edge, Graph* body); @@ -639,9 +632,9 @@ class FunctionalizeCond { // Adds all output edges from the `if_node`. Status AddOutputEdges(const std::vector& outputs, Node* if_node); - // Returns the switch clusters of graph_ in postorder. Dead switch nodes are - // skipped and removed from the graph. - std::vector DeterminePredicateSwitchOrder(); + // Returns the switches of graph_ (along with grouping predicates) in + // postorder. Dead switch nodes are skipped and removed from the graph. + std::vector DeterminePredicateSwitchOrder(); // Update the state for destination based on the state of source and the node // being updated. @@ -664,7 +657,6 @@ class FunctionalizeCond { FunctionLibraryDefinition* library_; Graph* graph_; - bool dump_graphs_; }; bool IsDeadSwitch(const Node* node) { @@ -712,13 +704,10 @@ Status FunctionalizeCond::ValidateFrontier( ") in both Else and Then branch should be in Both."); } } - // An empty frontier indicates a dead switch. Above we attempt to remove dead - // switch nodes, but not all are removed so don't treat it as an error yet. - // TODO(jpienaar): Find out why dead switch nodes remain. - // if (pending[kBoth].empty() && pending[kThenBranch].empty() && - // pending[kElseBranch].empty()) { - // return errors::Internal("Unexpected empty frontier for switch nodes"); - // } + if (pending[kBoth].empty() && pending[kThenBranch].empty() && + pending[kElseBranch].empty()) { + return errors::Internal("Unexpected empty frontier for switch nodes"); + } return Status::OK(); } @@ -745,138 +734,33 @@ Status FunctionalizeCond::Join(const ForwardFlowNode& src_state, return Status::OK(); } -std::vector +std::vector FunctionalizeCond::DeterminePredicateSwitchOrder() { - struct Cluster { - bool operator==(const Cluster& other) const { - return representative == other.representative; - } - int representative = -1; - }; - - // Perform a DFS over the graph and - // * Determine the reverse topological order of the nodes (there should be no - // cycles at this point so the post-order numbering corresponds to the - // reverse topological sorting); - // * Identify dead switches; - // * Initialize the cluster's representative; - std::vector> clusters(graph_->num_node_ids()); std::vector dead_switches; std::vector switch_order; - std::vector rev_topo_sorted_nodes; - DFS(*graph_, nullptr, [&](Node* n) { - clusters[n->id()].Get().representative = n->id(); + DFS(*graph_, nullptr, [this, &dead_switches, &switch_order](Node* n) { if (IsSwitch(n)) { if (IsDeadSwitch(n)) { dead_switches.push_back(n); } else { - rev_topo_sorted_nodes.push_back(n); switch_order.push_back(n); } - } else if (n->IsOp()) { - // Exclude src and sink nodes from further consideration. - rev_topo_sorted_nodes.push_back(n); } }); - std::vector switch_clusters; - // Return early if there are no switches in the graph. - if (switch_order.empty()) { - return switch_clusters; - } - // Remove all dead switch nodes. for (Node* n : dead_switches) { VLOG(2) << "Removing dead switch: " << n->DebugString(); graph_->RemoveNode(n); } - // Identify switch nodes that are part of the same control flow context by - // considering the operands of operations: an operation is part of the same - // control context as its operands unless the operation is a switch. Control - // dependencies are considered part of the same control flow context if the - // switch depth is the same (see comment below). - // TODO(jpienaar): This could be combined with DetermineBranchMapAndFrontier. - std::vector switch_depth(graph_->num_node_ids()); - // entry_cluster records the input cluster to a switch node. This is used when - // merging with a merge node where the dst's cluster is merged with the entry - // cluster of the merge node's cluster (which corresponds to a switch cluster - // and so has an entry cluster). - std::unordered_map*> entry_cluster; - for (auto it = rev_topo_sorted_nodes.rbegin(); - it != rev_topo_sorted_nodes.rend(); ++it) { - Node* n = *it; - - // Compute switch depth. - int new_switch_depth = 0; - for (const Edge* e : n->in_edges()) { - Node* src = e->src(); - new_switch_depth = std::max( - new_switch_depth, switch_depth[src->id()] + (IsSwitch(src) ? 1 : 0) - - (IsMerge(src) ? 1 : 0)); - } - switch_depth[n->id()] = new_switch_depth; - - // Only merge the input operands of a switch. The switch's clustering itself - // is determined by the interaction of the switch's outputs. - if (IsSwitch(n)) { - Node* input; - TF_CHECK_OK(n->input_node(0, &input)); - UnionFind& cluster = clusters[input->id()]; - entry_cluster[n->id()] = &cluster; - // Merge the inputs of the switch node with one another. This results in - // predicates and control input residing in the same cluster. - for (const Edge* e : n->in_edges()) { - Node* src = e->src(); - cluster.Merge(&clusters[src->id()]); - } - continue; - } - - for (const Edge* e : n->in_edges()) { - Node* src = e->src(); - if (!src->IsOp()) continue; - UnionFind* cluster = &clusters[src->id()]; - if (IsMerge(src)) { - cluster = entry_cluster.at(clusters[src->id()].Get().representative); - } - // Merge a node with its data operands and with its control operands if - // the src and dst are in the same ControlContext. The ControlContext is - // not explicitly available here, and instead the switch depth is used as - // a proxy here. Due to the invariant that control edges can only be from - // a containing scope to an inner scope or from the inner scope to its - // containing scope (for exit nodes), the switch depth will only match if - // the src and dst are in the same ControlContext. Control edges between - // ControlContexts are handled during the extraction. - if (!e->IsControlEdge() || - new_switch_depth == - switch_depth[src->id()] + (IsSwitch(src) ? 1 : 0)) { - cluster->Merge(&clusters[n->id()]); - } - } - } - - if (dump_graphs_) { - // Mark the switch cluster each node is part of. - for (Node* n : graph_->nodes()) { - n->ClearAttr("_XlaFunctionalizeSwitchGroup"); - n->AddAttr("_XlaFunctionalizeSwitchGroup", - clusters[n->id()].Get().representative); - } - LOG(INFO) << "FunctionalizeControlFlow (with_clusters): " - << dump_graph::DumpGraphToFile("functionalize_clustered", *graph_, - library_); + std::vector predicate_switch_order; + if (switch_order.empty()) { + return predicate_switch_order; } - struct Hash { - size_t operator()(const std::pair& item) const { - return Hash64Combine(hash()(item.first), - std::hash()(item.second.representative)); - } - }; - // Merge Switch nodes with common predicate. - std::unordered_map, int, Hash> predicate_index; + std::unordered_map predicate_index; // The nodes in switch_order are in reverse topological order, but the // clustered switches need not be (i.e., when considered as a cluster one // element of a cluster may be later in the topological order than another @@ -885,19 +769,13 @@ FunctionalizeCond::DeterminePredicateSwitchOrder() { for (auto it = switch_order.rbegin(); it != switch_order.rend(); ++it) { Node* pred; TF_CHECK_OK((*it)->input_node(1, &pred)); - auto repr = std::make_pair(pred, clusters[(*it)->id()].Get()); - if (predicate_index.find(repr) == predicate_index.end()) { - predicate_index[repr] = switch_clusters.size(); - switch_clusters.emplace_back(pred); - // Generate a name by concating with the cluster representative as there - // could be multiple switch clusters with the same predicate. - switch_clusters[predicate_index[repr]].name = - strings::StrCat(pred->name(), "_", repr.second.representative, "_If"); + if (predicate_index.find(pred) == predicate_index.end()) { + predicate_index[pred] = predicate_switch_order.size(); + predicate_switch_order.emplace_back(pred); } - switch_clusters[predicate_index[repr]].switches.push_back(*it); + predicate_switch_order[predicate_index[pred]].switches.push_back(*it); } - - return switch_clusters; + return predicate_switch_order; } StatusOr> @@ -945,10 +823,10 @@ StatusOr< std::pair, std::unordered_set>> FunctionalizeCond::DetermineBranchMapAndFrontier( - const SwitchCluster& switch_cluster) { + const std::vector& switches) { std::unordered_map branch_map; std::unordered_set frontier; - std::vector stack = switch_cluster.switches; + std::vector stack = switches; std::vector visited(graph_->num_node_ids(), false); while (!stack.empty()) { Node* n = stack.back(); @@ -990,7 +868,7 @@ FunctionalizeCond::DetermineBranchMapAndFrontier( } } - if (dump_graphs_) { + if (VLOG_IS_ON(2)) { for (const auto& kv : branch_map) { // Append attribute to the graph if running with logging to make the // changes clearer in the visualization. @@ -1002,7 +880,7 @@ FunctionalizeCond::DetermineBranchMapAndFrontier( } Status FunctionalizeCond::FunctionalizeInternal() { - std::vector predicate_switch_order = + std::vector predicate_switch_order = DeterminePredicateSwitchOrder(); // Iterate from innermost set of clustered switches to outermost, replacing @@ -1016,12 +894,10 @@ Status FunctionalizeCond::FunctionalizeInternal() { std::unordered_map branch_map; std::unordered_set frontier; TF_ASSIGN_OR_RETURN(std::tie(branch_map, frontier), - DetermineBranchMapAndFrontier(ps)); + DetermineBranchMapAndFrontier(ps.switches)); - if (dump_graphs_) - LOG(INFO) << "FunctionalizeControlFlow (before XlaIf conversion): " - << dump_graph::DumpGraphToFile("functionalize_bc", *graph_, - library_); + VLOG(2) << "FunctionalizeControlFlow (before XlaIf conversion): " + << dump_graph::DumpGraphToFile("functionalize_bc", *graph_); TF_RETURN_IF_ERROR(ValidateFrontier(branch_map, frontier)); // Sort the merge and switch nodes using NodeCmp. The switch-nodes are @@ -1038,7 +914,7 @@ Status FunctionalizeCond::FunctionalizeInternal() { input_index[in] = cond_arg_nodes.size(); cond_arg_nodes.emplace_back(in); } - cond_arg_nodes.at(input_index.at(in)).switches.push_back(switch_node); + cond_arg_nodes.at(input_index.at(in)).switch_nodes.push_back(switch_node); } std::vector merge_nodes(frontier.begin(), frontier.end()); std::sort(merge_nodes.begin(), merge_nodes.end(), NodeCmp()); @@ -1047,8 +923,9 @@ Status FunctionalizeCond::FunctionalizeInternal() { EnsureDominanceAndReturnNonDominatedControlNodes( branch_map, ps.switches)); - TF_ASSIGN_OR_RETURN(Node * if_node, - ConvertToXlaIf(cond_arg_nodes, ps, merge_nodes)); + TF_ASSIGN_OR_RETURN( + Node * if_node, + ConvertToXlaIf(cond_arg_nodes, ps.switches, merge_nodes, ps.predicate)); for (Node* old : old_control_nodes) { graph_->AddControlEdge(old, if_node); } @@ -1057,26 +934,25 @@ Status FunctionalizeCond::FunctionalizeInternal() { graph_->RemoveNode(del_kv.first); } for (auto& kv : cond_arg_nodes) { - for (Node* node : kv.switches) { + for (Node* node : kv.switch_nodes) { graph_->RemoveNode(node); } } - if (dump_graphs_) - LOG(INFO) << "FunctionalizeControlFlow (after XlaIf conversion): " - << dump_graph::DumpGraphToFile("functionalize_ac", *graph_, - library_); + VLOG(2) << "FunctionalizeControlFlow (after XlaIf conversion): " + << dump_graph::DumpGraphToFile("functionalize_ac", *graph_); } return Status::OK(); } StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( - const CondArgNodes& cond_arg_nodes, const SwitchCluster& switch_cluster, - const std::vector& merge_nodes) { - VLOG(2) << "Build if op for " << switch_cluster.name; + const CondArgNodes& cond_arg_nodes, const std::vector& switch_nodes, + const std::vector& merge_nodes, Node* predicate) { + VLOG(2) << "Build if op for " << NodesToString(merge_nodes) << " with input " + << NodesToString(switch_nodes); NodeDef if_def; // Create a new If node using the name of the merge node. - NodeDefBuilder builder(switch_cluster.name, "XlaIf"); + NodeDefBuilder builder(strings::StrCat(predicate->name(), "_If"), "XlaIf"); string branch[] = {"else_branch", "then_branch"}; for (int i = 0; i < 2; ++i) { static std::atomic sequence_num(0LL); @@ -1086,9 +962,12 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( body_name.set_name( strings::StrCat("_functionalize_if_", branch[i], "_", id)); auto body = xla::MakeUnique(graph_->op_registry()); - TF_RETURN_IF_ERROR(ExtractBody(cond_arg_nodes, switch_cluster.switches, - merge_nodes, i, body.get())); + TF_RETURN_IF_ERROR( + ExtractBody(cond_arg_nodes, switch_nodes, merge_nodes, i, body.get())); VLOG(3) << "Body " << branch[i] << ": " << DebugString(body.get()); + VLOG(4) << "FunctionalizeControlFlow (" << branch[i] << "): " + << dump_graph::DumpGraphToFile( + strings::StrCat("functionalize_", branch[i]), *body); FunctionDef body_fdef; TF_RETURN_IF_ERROR(GraphToFunctionDef(*body, body_name.name(), &body_fdef)); TF_RETURN_IF_ERROR(library_->AddFunctionDef(body_fdef)); @@ -1100,7 +979,7 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( DataTypeVector in_arg_types; for (auto& kv : cond_arg_nodes) { bool inserted = false; - for (const Node* arg : kv.switches) { + for (const Node* arg : kv.switch_nodes) { const Edge* in_edge; TF_RETURN_IF_ERROR(arg->input_edge(0, &in_edge)); if (in_edge->IsControlEdge()) { @@ -1127,11 +1006,10 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( builder.Attr("Tout", out_type); builder.Attr("Tcond", DT_BOOL); - builder.Device(switch_cluster.predicate->assigned_device_name()); + builder.Device(predicate->assigned_device_name()); // Conditional should be the first input ... builder.Input( - NodeDefBuilder::NodeOut(switch_cluster.predicate->name(), 0, - switch_cluster.predicate->output_type(0))); + NodeDefBuilder::NodeOut(predicate->name(), 0, predicate->output_type(0))); // ... followed by the other inputs. builder.Input(inputs); @@ -1141,7 +1019,7 @@ StatusOr FunctionalizeCond::BuildAndAddXlaIfOp( } Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, - const std::vector& switches, + const std::vector& switch_nodes, const std::vector& merge_nodes, int input_edge, Graph* body) { VLOG(2) << "ExtractBody for " << NodesToString(merge_nodes) << " along edge " @@ -1151,7 +1029,7 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, int arg_count = 0; for (auto& kv : cond_arg_nodes) { Node* arg_node = nullptr; - for (const auto* arg : kv.switches) { + for (const auto* arg : kv.switch_nodes) { DataType dtype = arg->input_type(0); if (arg_node == nullptr) { TF_ASSIGN_OR_RETURN(arg_node, BuildArgNode(body, dtype, arg_count++)); @@ -1175,7 +1053,8 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes, node_map.at(in->id()) = body->CopyNode(in); } - if (std::find(switches.begin(), switches.end(), in) == switches.end()) { + if (std::find(switch_nodes.begin(), switch_nodes.end(), in) == + switch_nodes.end()) { body->AddEdge(node_map.at(in->id()), in_edge->src_output(), node_map.at(node->id()), 0); } else { @@ -1197,7 +1076,7 @@ Status FunctionalizeCond::AddInputEdges(const CondArgNodes& cond_arg_nodes, graph_->AddEdge(predicate, 0, if_node, index++); for (auto& kv : cond_arg_nodes) { bool inserted = false; - for (const Node* arg : kv.switches) { + for (const Node* arg : kv.switch_nodes) { const Edge* in_edge; TF_RETURN_IF_ERROR(arg->input_edge(0, &in_edge)); if (in_edge->IsControlEdge()) { @@ -1240,17 +1119,16 @@ Status FunctionalizeCond::AddOutputEdges(const std::vector& outputs, } StatusOr FunctionalizeCond::ConvertToXlaIf( - const CondArgNodes& cond_arg_nodes, const SwitchCluster& switch_cluster, - const std::vector& merge_nodes) { - VLOG(1) << "ConvertToXlaIf for " << switch_cluster.ToString() << " -> " + const CondArgNodes& cond_arg_nodes, const std::vector& switch_nodes, + const std::vector& merge_nodes, Node* predicate) { + VLOG(1) << "ConvertToXlaIf for " << NodesToString(switch_nodes) << " -> " << NodesToString(merge_nodes); // Extract bodies and builds a If operator. TF_ASSIGN_OR_RETURN( Node * if_node, - BuildAndAddXlaIfOp(cond_arg_nodes, switch_cluster, merge_nodes)); - TF_RETURN_IF_ERROR( - AddInputEdges(cond_arg_nodes, switch_cluster.predicate, if_node)); + BuildAndAddXlaIfOp(cond_arg_nodes, switch_nodes, merge_nodes, predicate)); + TF_RETURN_IF_ERROR(AddInputEdges(cond_arg_nodes, predicate, if_node)); TF_RETURN_IF_ERROR(AddOutputEdges(merge_nodes, if_node)); return if_node; @@ -1259,19 +1137,18 @@ StatusOr FunctionalizeCond::ConvertToXlaIf( Status FunctionalizeCond::Functionalize(Graph* graph, FunctionLibraryDefinition* library) { VLOG(1) << "FunctionalizeCond::Functionalize"; - FunctionalizeCond fc(graph, library, /*dump_graphs=*/VLOG_IS_ON(2)); + FunctionalizeCond fc(graph, library); return fc.FunctionalizeInternal(); } } // namespace -// Transformation that converts TensorFlow's graph control flow constructs into +// Transformation that converts Tensorflow's graph control flow constructs into // functional equivalents. Status FunctionalizeControlFlow(Graph* graph, FunctionLibraryDefinition* library) { VLOG(2) << "FunctionalizeControlFlow (initial): " - << dump_graph::DumpGraphToFile("functionalize_initial", *graph, - library); + << dump_graph::DumpGraphToFile("functionalize_initial", *graph); // Note: BuildControlFlowInfo() requires that the graph's source node is // connected to all source nodes in the graph. Many graphs violate this // invariant. @@ -1283,8 +1160,7 @@ Status FunctionalizeControlFlow(Graph* graph, for (Node* node : graph->op_nodes()) { const ControlFlowInfo& cf = cf_info[node->id()]; - VLOG(2) << "node: " << node->name() << " (" << node->id() - << ") frame_name: " << cf.frame_name + VLOG(2) << "node: " << node->name() << " frame_name: " << cf.frame_name << " frame: " << (cf.frame ? cf.frame->name() : "---") << " parent_frame: " << (cf.parent_frame ? cf.parent_frame->name() : "---"); @@ -1352,8 +1228,7 @@ Status FunctionalizeControlFlow(Graph* graph, TF_RETURN_IF_ERROR(FunctionalizeCond::Functionalize(graph, library)); VLOG(2) << "FunctionalizeControlFlow (final): " - << dump_graph::DumpGraphToFile("functionalize_final", *graph, - library); + << dump_graph::DumpGraphToFile("functionalize_final", *graph); return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc index bc7276c3af..71f12a1333 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc @@ -38,11 +38,10 @@ namespace { // Returns the names of the "then" and "else" functions for the XlaIf node in a // graph. -Status FindIfThenAndElse(const GraphDef& graph, string* op_name, - NameAttrList* then_fn, NameAttrList* else_fn) { +Status FindIfThenAndElse(const GraphDef& graph, NameAttrList* then_fn, + NameAttrList* else_fn) { for (const NodeDef& node : graph.node()) { if (node.op() == "XlaIf") { - *op_name = node.name(); const NameAttrList* result; TF_RETURN_IF_ERROR(GetNodeAttr(node, "then_branch", &result)); *then_fn = *result; @@ -97,10 +96,9 @@ TEST(FunctionalizeControlFlow, Conditional) { GraphDef graph_def; graph.ToGraphDef(&graph_def); - string op_name; NameAttrList then_fn; NameAttrList else_fn; - TF_EXPECT_OK(FindIfThenAndElse(graph_def, &op_name, &then_fn, &else_fn)); + TF_EXPECT_OK(FindIfThenAndElse(graph_def, &then_fn, &else_fn)); InstantiationResultForTest else_result; TF_EXPECT_OK( InstantiateFunctionForTest(else_fn.name(), library, &else_result)); @@ -111,7 +109,7 @@ TEST(FunctionalizeControlFlow, Conditional) { auto y = ops::Placeholder(scope.WithOpName("y"), DT_INT32); auto x = ops::Placeholder(scope.WithOpName("x"), DT_INT32); auto less = ops::Less(scope.WithOpName("cond/Less"), y, x); - auto if_op = ops::XlaIf(scope.WithOpName(op_name), less, + auto if_op = ops::XlaIf(scope.WithOpName("cond/Less_If"), less, std::initializer_list{less, y, x}, then_fn, else_fn, {DT_INT32}); GraphDef expected; diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index c90ea09e17..02215b5112 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -136,7 +136,7 @@ Status GraphCompiler::Compile() { TF_RET_CHECK(src->id() < output_registry.size()); const NodeOutputs& src_outputs = output_registry[src->id()]; - tensor_inputs_.at(e->dst_input()) = src_outputs.at(e->src_output()); + tensor_inputs_[e->dst_input()] = src_outputs[e->src_output()]; } OpKernelContext op_context(¶ms, n->num_outputs()); -- GitLab From 1af63d6a6d738762c363ad05107d0b6959d53e76 Mon Sep 17 00:00:00 2001 From: Andrew Selle Date: Thu, 1 Feb 2018 16:51:59 -0800 Subject: [PATCH 413/423] Allow reordering of execution order of nodes with indirect execution_plan. Now whenever we want to operate in dependency order we use execution_plan. It begins as identity map (0, ..., nodes_size()) but can be changed in the future. This is the basis for more pluggable delegation. PiperOrigin-RevId: 184216885 --- tensorflow/contrib/lite/interpreter.cc | 68 +++++++---- tensorflow/contrib/lite/interpreter.h | 19 ++- tensorflow/contrib/lite/interpreter_test.cc | 127 ++++++++++++++++++++ 3 files changed, 190 insertions(+), 24 deletions(-) diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 5f5981e45a..a8db149eaa 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -36,6 +36,10 @@ constexpr const int kSlotsToReserve = 128; namespace tflite { // A trivial implementation of GraphInfo around the Interpreter. +// NOTE: this interpreter info represents the subset of the +// graph that is executed according to execution plan. Thus, +// the indices are execution plan indices rather than raw node +// indices. class InterpreterInfo : public GraphInfo { public: explicit InterpreterInfo(Interpreter* interpreter) @@ -45,9 +49,12 @@ class InterpreterInfo : public GraphInfo { TfLiteTensor* tensor(size_t index) override { return interpreter_->tensor(index); } - size_t num_nodes() const override { return interpreter_->nodes_size(); } + size_t num_nodes() const override { + return interpreter_->execution_plan().size(); + } const TfLiteNode& node(size_t index) const override { - return interpreter_->node_and_registration(index)->first; + int node_index = interpreter_->execution_plan()[index]; + return interpreter_->node_and_registration(node_index)->first; } const std::vector& inputs() const override { return interpreter_->inputs(); @@ -73,7 +80,7 @@ Interpreter::Interpreter(ErrorReporter* error_reporter) // Reserve some space for the tensors to avoid excessive resizing. tensors_.reserve(kSlotsToReserve); nodes_and_registration_.reserve(kSlotsToReserve); - next_node_to_prepare_ = 0; + next_execution_plan_index_to_prepare_ = 0; UseNNAPI(false); } @@ -160,7 +167,7 @@ TfLiteIntArray* convertVectorToTfLiteIntArray(const std::vector& x) { } // namespace TfLiteStatus Interpreter::AllocateTensors() { - next_node_to_prepare_ = 0; + next_execution_plan_index_to_prepare_ = 0; if (memory_planner_) { TF_LITE_ENSURE_STATUS(memory_planner_->ResetAllocations()); } @@ -190,7 +197,8 @@ TfLiteStatus Interpreter::AddNodeWithParameters( &context_, CheckTensorIndices("node outputs", outputs.data(), outputs.size())); - if (node_index) *node_index = nodes_and_registration_.size(); + int new_node_index = nodes_and_registration_.size(); + if (node_index) *node_index = new_node_index; nodes_and_registration_.resize(nodes_and_registration_.size() + 1); auto& node_and_reg = nodes_and_registration_.back(); TfLiteNode& node = node_and_reg.first; @@ -213,6 +221,7 @@ TfLiteStatus Interpreter::AddNodeWithParameters( } node.builtin_data = builtin_data_deleter.release(); node_and_reg.second = *registration; + execution_plan_.push_back(new_node_index); return kTfLiteOk; } @@ -240,16 +249,19 @@ bool HasDynamicTensor(const TfLiteContext& context, return false; } -TfLiteStatus Interpreter::PrepareOpsStartingAt(int first_node, - int* last_node_prepared) { - for (int i = first_node; i < nodes_and_registration_.size(); i++) { - TfLiteNode& node = nodes_and_registration_[i].first; - const TfLiteRegistration& registration = nodes_and_registration_[i].second; +TfLiteStatus Interpreter::PrepareOpsStartingAt( + int first_execution_plan_index, int* last_execution_plan_index_prepared) { + for (int execution_plan_index = first_execution_plan_index; + execution_plan_index < execution_plan_.size(); execution_plan_index++) { + int node_index = execution_plan_[execution_plan_index]; + TfLiteNode& node = nodes_and_registration_[node_index].first; + const TfLiteRegistration& registration = + nodes_and_registration_[node_index].second; if (OpPrepare(registration, &node) == kTfLiteError) { return kTfLiteError; } - *last_node_prepared = i; + *last_execution_plan_index_prepared = execution_plan_index; // Discontinue if the node has dynamic outputs. Note that we don't // stop for dynamic temporary tensors since they won't affect the @@ -268,14 +280,14 @@ TfLiteStatus Interpreter::PrepareOpsAndTensors() { memory_planner_->PlanAllocations(); } - int last_node_prepared = 0; + int last_exec_plan_index_prepared = 0; - TF_LITE_ENSURE_STATUS( - PrepareOpsStartingAt(next_node_to_prepare_, &last_node_prepared)); + TF_LITE_ENSURE_STATUS(PrepareOpsStartingAt( + next_execution_plan_index_to_prepare_, &last_exec_plan_index_prepared)); TF_LITE_ENSURE_STATUS(memory_planner_->ExecuteAllocations( - next_node_to_prepare_, last_node_prepared)); + next_execution_plan_index_to_prepare_, last_exec_plan_index_prepared)); - next_node_to_prepare_ = last_node_prepared + 1; + next_execution_plan_index_to_prepare_ = last_exec_plan_index_prepared + 1; return kTfLiteOk; } @@ -292,7 +304,7 @@ TfLiteStatus Interpreter::Invoke() { TfLiteStatus status = kTfLiteOk; if (nnapi_delegate_) { TF_LITE_ENSURE_STATUS(PrepareOpsAndTensors()); - if (next_node_to_prepare_ == nodes_and_registration_.size()) { + if (next_execution_plan_index_to_prepare_ == execution_plan_.size()) { TF_LITE_ENSURE_OK(&context_, nnapi_delegate_->Invoke(this)); return kTfLiteOk; } else { @@ -312,13 +324,17 @@ TfLiteStatus Interpreter::Invoke() { // TODO(b/71913981): we should force recalculation in the presence of dynamic // tensors, because they may have new value which in turn may affect shapes // and allocations. - for (int i = 0; i < nodes_and_registration_.size(); i++) { - if (i == next_node_to_prepare_) { + for (int execution_plan_index = 0; + execution_plan_index < execution_plan_.size(); execution_plan_index++) { + if (execution_plan_index == next_execution_plan_index_to_prepare_) { TF_LITE_ENSURE_STATUS(PrepareOpsAndTensors()); - TF_LITE_ENSURE(&context_, next_node_to_prepare_ >= i); + TF_LITE_ENSURE(&context_, next_execution_plan_index_to_prepare_ >= + execution_plan_index); } - TfLiteNode& node = nodes_and_registration_[i].first; - const TfLiteRegistration& registration = nodes_and_registration_[i].second; + int node_index = execution_plan_[execution_plan_index]; + TfLiteNode& node = nodes_and_registration_[node_index].first; + const TfLiteRegistration& registration = + nodes_and_registration_[node_index].second; if (OpInvoke(registration, &node) == kTfLiteError) { status = kTfLiteError; } @@ -422,6 +438,14 @@ TfLiteStatus Interpreter::SetTensorParametersReadWrite( return kTfLiteOk; } +TfLiteStatus Interpreter::SetExecutionPlan(const std::vector& new_plan) { + for (int node_index : new_plan) { + TF_LITE_ENSURE(&context_, node_index >= 0 && node_index < nodes_size()); + } + execution_plan_ = new_plan; + return kTfLiteOk; +} + TfLiteStatus Interpreter::ResizeTensorImpl(TfLiteTensor* tensor, TfLiteIntArray* new_size) { // Note that in theory we could resize kTfLiteArenaRwPersistent tensors too. diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 52e52df1b6..c822557d02 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -166,6 +166,13 @@ class Interpreter { // Return the number of ops in the model. int nodes_size() const { return nodes_and_registration_.size(); } + // WARNING: Experimental interface, subject to change + const std::vector& execution_plan() const { return execution_plan_; } + + // WARNING: Experimental interface, subject to change + // Overrides execution plan. This bounds checks indices sent in. + TfLiteStatus SetExecutionPlan(const std::vector& new_plan); + // Get a tensor data structure. // TODO(aselle): Create a safe ArrayHandle interface to avoid exposing this // read/write access to structure @@ -279,7 +286,8 @@ class Interpreter { // dynamic tensors is found or all ops have been prepared. Fill // 'last_node_prepared' with the id of the op containing dynamic tensors, or // the last in the graph. - TfLiteStatus PrepareOpsStartingAt(int first_node, int* last_node_prepared); + TfLiteStatus PrepareOpsStartingAt(int first_execution_plan_index, + int* last_execution_plan_index_prepared); // Tensors needed by the interpreter. Use `AddTensors` to add more blank // tensor entries. Note, `tensors_.data()` needs to be synchronized to the @@ -354,7 +362,14 @@ class Interpreter { // node id, and execute the node to generate the output tensor before continue // to allocate successors. This process repeats until all nodes are executed. // NOTE: this relies on the order of nodes that is in topological order. - int next_node_to_prepare_; + int next_execution_plan_index_to_prepare_; + + // WARNING: This is an experimental interface that is subject to change. + // This is a list of node indices (to index into nodes_and_registration). + // This represents a valid topological sort (dependency ordered) execution + // plan. In particular, it is valid for this ordering to contain only a + // subset of the node indices. + std::vector execution_plan_; // Whether to delegate to NN API std::unique_ptr nnapi_delegate_; diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index edff210943..2ab4bb6567 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -514,6 +514,133 @@ TEST(BasicInterpreter, TestCustomErrorReporter) { ASSERT_EQ(reporter.calls, 1); } +// Test fixture that allows playing with execution plans. It creates a two +// node graph that can be executed in either [0,1] order or [1,0] order. +// The CopyOp records when it is invoked in the class member run_order_ +// so we can test whether the execution plan was honored. +class TestExecutionPlan : public ::testing::Test { + // Encapsulates the node ids and provides them to a C primitive data type + // Allocatable with placement new, but never destructed, so make sure this + // doesn't own any heap allocated data. This is then is used as op local + // data to allow access to the test fixture data. + class CallReporting { + public: + CallReporting(int node_id, std::vector* run_order) + : node_id_(node_id), run_order_(run_order) {} + + void Record() { run_order_->push_back(node_id_); } + + private: + // The node id for this particular node + int node_id_; + // A pointer to the global run-order + std::vector* run_order_; + }; + + // Build a kernel registration for an op that copies its one input + // to an output + TfLiteRegistration CopyOpRegistration() { + TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; + + reg.prepare = [](TfLiteContext* context, TfLiteNode* node) { + // Set output size to input size + TfLiteTensor* tensor0 = &context->tensors[node->inputs->data[0]]; + TfLiteTensor* tensor1 = &context->tensors[node->outputs->data[0]]; + TfLiteIntArray* newSize = TfLiteIntArrayCopy(tensor0->dims); + return context->ResizeTensor(context, tensor1, newSize); + }; + + reg.invoke = [](TfLiteContext* context, TfLiteNode* node) { + CallReporting* call_reporting = + reinterpret_cast(node->builtin_data); + // Copy input data to output data. + TfLiteTensor* a0 = &context->tensors[node->inputs->data[0]]; + TfLiteTensor* a1 = &context->tensors[node->outputs->data[0]]; + int num = a0->dims->data[0]; + for (int i = 0; i < num; i++) { + a1->data.f[i] = a0->data.f[i]; + } + call_reporting->Record(); + return kTfLiteOk; + }; + return reg; + } + + // Adds a copy node going from tensor `input` to output tensor `output`. + // Note, input is used as the node_id. Inject run_order as op accessible + // data. Note: this is a little strange of a way to do this, but it is + // using op functionality to avoid static global variables. + void MakeCopyNode(int input, int output) { + // Ownership of call_reporting is taken by interpreter (malloc is used due + // to nodes being a C99 interface so free() is used). + TfLiteRegistration copy_op = CopyOpRegistration(); + CallReporting* call_reporting_1 = + reinterpret_cast(malloc(sizeof(CallReporting))); + new (call_reporting_1) CallReporting(input, &run_order_); + ASSERT_EQ(interpreter_.AddNodeWithParameters( + {0}, {2}, nullptr, 0, + reinterpret_cast(call_reporting_1), ©_op), + kTfLiteOk); + ASSERT_EQ(interpreter_.ResizeInputTensor(input, {3}), kTfLiteOk); + } + + void SetUp() final { + // Add two inputs and two outputs that don't depend on each other + ASSERT_EQ(interpreter_.AddTensors(4), kTfLiteOk); + interpreter_.SetInputs({0, 1}); + interpreter_.SetOutputs({2, 3}); + TfLiteQuantizationParams quantized; + for (int tensor_index = 0; tensor_index < 4; tensor_index++) { + ASSERT_EQ(interpreter_.SetTensorParametersReadWrite( + tensor_index, kTfLiteFloat32, "", {3}, quantized), + kTfLiteOk); + } + + // Define two copy functions that also use the user_data to report that + // they were called. + // i.e. tensor[2] = copy(tensor[0]); tensor[3] = copy(tensor[1]); + // thus we can reorder the two nodes arbitrary and still satisfy dependency + // order. + MakeCopyNode(0, 2); + MakeCopyNode(1, 3); + + ASSERT_EQ(interpreter_.AllocateTensors(), kTfLiteOk); + } + + protected: + Interpreter interpreter_; + + // list of node_ids that were run + std::vector run_order_; +}; + +TEST_F(TestExecutionPlan, DefaultExecutionPlan) { + // Check default order + ASSERT_EQ(interpreter_.Invoke(), kTfLiteOk); + ASSERT_EQ(run_order_, std::vector({0, 1})); +} + +TEST_F(TestExecutionPlan, ReversedExecutionPlan) { + // Check reversed order + interpreter_.SetExecutionPlan({1, 0}); + ASSERT_EQ(interpreter_.Invoke(), kTfLiteOk); + ASSERT_EQ(run_order_, std::vector({1, 0})); +} + +TEST_F(TestExecutionPlan, SubsetExecutionPlan) { + // Check running only node index 1 + interpreter_.SetExecutionPlan({1}); + ASSERT_EQ(interpreter_.Invoke(), kTfLiteOk); + ASSERT_EQ(run_order_, std::vector({1})); +} + +TEST_F(TestExecutionPlan, NullExecutionPlan) { + // Check nothing executed. + interpreter_.SetExecutionPlan({}); + ASSERT_EQ(interpreter_.Invoke(), kTfLiteOk); + ASSERT_EQ(run_order_, std::vector()); +} + } // namespace } // namespace tflite -- GitLab From c21282004f535edd7d1d56e2c72a17ca0880bcaf Mon Sep 17 00:00:00 2001 From: Benoit Steiner Date: Thu, 1 Feb 2018 17:19:32 -0800 Subject: [PATCH 414/423] Skip unknown devices since we can't optimize for them PiperOrigin-RevId: 184220515 --- tensorflow/core/common_runtime/graph_execution_state.cc | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/common_runtime/graph_execution_state.cc b/tensorflow/core/common_runtime/graph_execution_state.cc index 3b309e915c..33a5d60eb7 100644 --- a/tensorflow/core/common_runtime/graph_execution_state.cc +++ b/tensorflow/core/common_runtime/graph_execution_state.cc @@ -340,8 +340,11 @@ Status GraphExecutionState::OptimizeGraph( std::unordered_map device_map; Device* cpu_device = nullptr; for (const auto& device : device_set_->devices()) { - device_map[device->name()] = - grappler::GetDeviceInfo(device->parsed_name()); + DeviceProperties props = grappler::GetDeviceInfo(device->parsed_name()); + if (props.type() == "UNKNOWN") { + continue; + } + device_map[device->name()] = props; if (device->parsed_name().id == 0 && StringPiece(device->parsed_name().type) == "CPU" && device->GetAllocator(AllocatorAttributes()) != nullptr) { -- GitLab From ff81ca3d1303ec3ad178113a3398f8f1cac0304d Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Thu, 1 Feb 2018 17:20:11 -0800 Subject: [PATCH 415/423] Fix tests PiperOrigin-RevId: 184220615 --- tensorflow/core/platform/cloud/gcs_throttle_test.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/platform/cloud/gcs_throttle_test.cc b/tensorflow/core/platform/cloud/gcs_throttle_test.cc index a1e8167c27..694756022e 100644 --- a/tensorflow/core/platform/cloud/gcs_throttle_test.cc +++ b/tensorflow/core/platform/cloud/gcs_throttle_test.cc @@ -68,7 +68,7 @@ TEST_F(GcsThrottleTest, RejectRequest) { TEST_F(GcsThrottleTest, MarkResponses) { time_.AdvanceSeconds(1); EXPECT_TRUE(throttle_.AdmitRequest()); - throttle_.RecordResponse(32000000); // 32 MB response + throttle_.RecordResponse(128000000); // 128 MB response EXPECT_EQ(-25100, throttle_.available_tokens()); EXPECT_FALSE(throttle_.AdmitRequest()); time_.AdvanceSeconds(1); -- GitLab From ae805bd8ca3bb3e385b145ca8439e4150f4aae51 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 10:57:29 -0800 Subject: [PATCH 416/423] Fix sanity. --- tensorflow/tools/ci_build/ci_sanity.sh | 5 ++--- tensorflow/tools/pip_package/BUILD | 14 ++++++++++++++ third_party/flatbuffers/flatbuffers.BUILD | 2 ++ third_party/pcre.BUILD | 2 +- third_party/termcolor.BUILD | 2 +- 5 files changed, 20 insertions(+), 5 deletions(-) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 6e4b821463..c1612201a3 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -523,9 +523,8 @@ do_check_file_name_test() { } # Supply all sanity step commands and descriptions -SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check" "do_cmake_python_sanity" "do_check_file_name_test") -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") - +SANITY_STEPS=("do_pip_package_licenses_check") +SANITY_STEPS_DESC=("hihi") INCREMENTAL_FLAG="" DEFAULT_BAZEL_CONFIGS="--config=hdfs --config=gcp" diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index e4fa6694d8..6381b9144f 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -88,13 +88,23 @@ filegroup( "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", "//third_party/hadoop:LICENSE.txt", + "@absl_py//absl/flags:LICENSE", + "@arm_neon_2_x86_sse//:LICENSE", + "@astor_archive//:LICENSE", + "@aws//:LICENSE", + "@bazel_tools//third_party/def_parser:Copyright.txt", + "@bazel_tools//third_party/ijar:LICENSE", + "@bazel_tools//third_party/zlib:LICENSE.txt", "@boringssl//:LICENSE", + "@com_google_absl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", "@curl//:COPYING", "@eigen_archive//:COPYING.MPL2", "@farmhash_archive//:COPYING", "@fft2d//:fft/readme.txt", + "@flatbuffers//:LICENSE.txt", + "@gast_archive//:LICENSE", "@gemmlowp//:LICENSE", "@gif_archive//:COPYING", "@grpc//:LICENSE", @@ -105,11 +115,15 @@ filegroup( "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", "@grpc//third_party/nanopb:LICENSE.txt", + "@nasm//:LICENSE", "@nsync//:LICENSE", + "@pcre//:LICENCE", "@png_archive//:LICENSE", "@protobuf_archive//:LICENSE", "@six_archive//:LICENSE", "@snappy//:COPYING", + "@swig//:LICENSE", + "@termcolor_archive//:COPYING.txt", "@zlib_archive//:zlib.h", "@org_python_pypi_backports_weakref//:LICENSE", ] + if_mkl([ diff --git a/third_party/flatbuffers/flatbuffers.BUILD b/third_party/flatbuffers/flatbuffers.BUILD index f6b8e6ddb0..824c97be60 100644 --- a/third_party/flatbuffers/flatbuffers.BUILD +++ b/third_party/flatbuffers/flatbuffers.BUILD @@ -4,6 +4,8 @@ package( licenses(["notice"]) # Apache 2.0 +exports_files(["LICENSE.txt"]) + config_setting( name = "freebsd", values = {"cpu": "freebsd"}, diff --git a/third_party/pcre.BUILD b/third_party/pcre.BUILD index e2cdec4029..3a8e7a10b4 100644 --- a/third_party/pcre.BUILD +++ b/third_party/pcre.BUILD @@ -1,6 +1,6 @@ licenses(["notice"]) # BSD -exports_files(["COPYING"]) +exports_files(["LICENCE"]) cc_library( name = "pcre", diff --git a/third_party/termcolor.BUILD b/third_party/termcolor.BUILD index 6000e3289d..655d7cb85e 100644 --- a/third_party/termcolor.BUILD +++ b/third_party/termcolor.BUILD @@ -3,7 +3,7 @@ licenses(["notice"]) # MIT -exports_files(["LICENSE"]) +exports_files(["COPYING.txt"]) py_library( name = "termcolor", -- GitLab From d6c449fc8dbaa2a69107d74ef98b9850c799ab6e Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 10:59:16 -0800 Subject: [PATCH 417/423] Add back other sanity tests. --- tensorflow/tools/ci_build/ci_sanity.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index c1612201a3..f0d38db915 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -523,8 +523,8 @@ do_check_file_name_test() { } # Supply all sanity step commands and descriptions -SANITY_STEPS=("do_pip_package_licenses_check") -SANITY_STEPS_DESC=("hihi") +SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check" "do_cmake_python_sanity" "do_check_file_name_test") +SANITY_STEPS=("do_pip_package_licenses_check") +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" -- GitLab From 33d88a86ab0c97bcd1fe936057ea7a2dccec3e97 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 11:00:14 -0800 Subject: [PATCH 418/423] Add sanity test back. --- tensorflow/tools/ci_build/ci_sanity.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index f0d38db915..035b2d2546 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -523,7 +523,7 @@ do_check_file_name_test() { } # Supply all sanity step commands and descriptions -SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check" "do_cmake_python_sanity" "do_check_file_name_test") +SANITY_STEPS=("do_pip_package_licenses_check") +SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check" "do_cmake_python_sanity" "do_check_file_name_test") 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" -- GitLab From 8a5fc0e5ddcf0e19b14e78ba9f9c8dc783da4207 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 13:23:12 -0800 Subject: [PATCH 419/423] Fix build error. --- tensorflow/tools/pip_package/BUILD | 5 +---- tensorflow/workspace.bzl | 1 + third_party/com_google_absl.BUILD | 17 +++++++++++++++++ third_party/gast.BUILD | 2 +- 4 files changed, 20 insertions(+), 5 deletions(-) create mode 100644 third_party/com_google_absl.BUILD diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 6381b9144f..a9c4a8de42 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -92,9 +92,6 @@ filegroup( "@arm_neon_2_x86_sse//:LICENSE", "@astor_archive//:LICENSE", "@aws//:LICENSE", - "@bazel_tools//third_party/def_parser:Copyright.txt", - "@bazel_tools//third_party/ijar:LICENSE", - "@bazel_tools//third_party/zlib:LICENSE.txt", "@boringssl//:LICENSE", "@com_google_absl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", @@ -104,7 +101,7 @@ filegroup( "@farmhash_archive//:COPYING", "@fft2d//:fft/readme.txt", "@flatbuffers//:LICENSE.txt", - "@gast_archive//:LICENSE", + "@gast_archive//:PKG-INFO", "@gemmlowp//:LICENSE", "@gif_archive//:COPYING", "@grpc//:LICENSE", diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index f965bd696f..b6bba78401 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -114,6 +114,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): ], sha256 = "5996380e3e8b981f55d1c8d58e709c00dbb4806ba367be75d0925a68cc2f6478", strip_prefix = "abseil-cpp-720c017e30339fd1786ce4aac68bc8559736e53f", + build_file = str(Label("//third_party:com_google_absl.BUILD")), ) tf_http_archive( diff --git a/third_party/com_google_absl.BUILD b/third_party/com_google_absl.BUILD new file mode 100644 index 0000000000..0c8d327c1f --- /dev/null +++ b/third_party/com_google_absl.BUILD @@ -0,0 +1,17 @@ +package(default_visibility = ["//visibility:public"]) + +licenses(["notice"]) # Apache + +exports_files(["LICENSE"]) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/third_party/gast.BUILD b/third_party/gast.BUILD index 06db528ada..4866982e1f 100644 --- a/third_party/gast.BUILD +++ b/third_party/gast.BUILD @@ -3,7 +3,7 @@ licenses(["notice"]) # BSD 3-clause -exports_files(["LICENSE"]) +exports_files(["PKG-INFO"]) py_library( name = "gast", -- GitLab From 4b137b3510b0cdd266e2a87138ca7c16daac73ab Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 15:45:39 -0800 Subject: [PATCH 420/423] Add blacklist. --- tensorflow/tools/ci_build/ci_sanity.sh | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 035b2d2546..87ca159062 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -343,6 +343,13 @@ do_external_licenses_check(){ comm -2 -3 ${EXTERNAL_DEPENDENCIES_FILE} ${LICENSES_FILE} 2>&1 | tee ${MISSING_LICENSES_FILE} EXTERNAL_LICENSES_CHECK_END_TIME=$(date +'%s') + + + # Things okay to miss + echo ${MISSING_LICENSES_FILE} + grep -e "@bazel_tools//third_party/" -e "@com_google_absl//absl" -e "@org_tensorflow//" -v ${MISSING_LICENSES_FILE} > temp.txt + mv temp.txt ${MISSING_LICENSES_FILE} + echo echo "do_external_licenses_check took $((EXTERNAL_LICENSES_CHECK_END_TIME - EXTERNAL_LICENSES_CHECK_START_TIME)) s" @@ -525,6 +532,7 @@ do_check_file_name_test() { # Supply all sanity step commands and descriptions SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check" "do_cmake_python_sanity" "do_check_file_name_test") 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" -- GitLab From 73019bc43d81c781b591407f97f409b8570c6115 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 16:27:49 -0800 Subject: [PATCH 421/423] Add whitelist. --- tensorflow/tools/ci_build/ci_sanity.sh | 11 ++++++++--- tensorflow/tools/lib_package/BUILD | 4 ++++ 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 87ca159062..b3a8ff2ac7 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -343,13 +343,18 @@ do_external_licenses_check(){ comm -2 -3 ${EXTERNAL_DEPENDENCIES_FILE} ${LICENSES_FILE} 2>&1 | tee ${MISSING_LICENSES_FILE} EXTERNAL_LICENSES_CHECK_END_TIME=$(date +'%s') - - - # Things okay to miss + + # Blacklist echo ${MISSING_LICENSES_FILE} grep -e "@bazel_tools//third_party/" -e "@com_google_absl//absl" -e "@org_tensorflow//" -v ${MISSING_LICENSES_FILE} > temp.txt mv temp.txt ${MISSING_LICENSES_FILE} + # Whitelist + echo ${EXTRA_LICENSE_FILE} + grep -e "@bazel_tools//src/" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -v ${EXTRA_LICENSES_FILE} > temp.txt + mv temp.txt ${EXTRA_LICENSES_FILE} + + echo echo "do_external_licenses_check took $((EXTERNAL_LICENSES_CHECK_END_TIME - EXTERNAL_LICENSES_CHECK_START_TIME)) s" diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD index dbc81599de..7717d8d7de 100644 --- a/tensorflow/tools/lib_package/BUILD +++ b/tensorflow/tools/lib_package/BUILD @@ -99,6 +99,7 @@ genrule( "//third_party/hadoop:LICENSE.txt", "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", + "@aws//:LICENSE", "@boringssl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", @@ -114,6 +115,7 @@ genrule( "@libxsmm_archive//:LICENSE", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", + "@nasm//:LICENSE", "@nsync//:LICENSE", "@png_archive//:LICENSE", "@protobuf_archive//:LICENSE", @@ -134,6 +136,7 @@ genrule( "//third_party/hadoop:LICENSE.txt", "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", + "@aws//:LICENSE", "@boringssl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", "@cub_archive//:LICENSE.TXT", @@ -149,6 +152,7 @@ genrule( "@libxsmm_archive//:LICENSE", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", + "@nasm//:LICENSE", "@nsync//:LICENSE", "@png_archive//:LICENSE", "@protobuf_archive//:LICENSE", -- GitLab From b0d432dcc8c52177ebbaeb3a74d488f0af702f21 Mon Sep 17 00:00:00 2001 From: lissyx Date: Fri, 2 Feb 2018 03:03:31 +0100 Subject: [PATCH 422/423] Force sorting of CUDA and Python headers to avoid spurious rebuilds (#16586) If one does try to re-use Bazel cache of a TensorFlow CUDA-enabled or Python-enabled build, then it might happen that readdir() syscall behind the use of find in _read_dir() will generate a different ordering of the very same list of headers. This will make new genrules for symlinking the CUDA headers and in the end it will result in different actionKey computed by Bazel, hence invalidating the action cache. Fixes #16585 --- third_party/gpus/cuda_configure.bzl | 2 +- third_party/py/python_configure.bzl | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl index 8e1dd8a54f..255ae01190 100644 --- a/third_party/gpus/cuda_configure.bzl +++ b/third_party/gpus/cuda_configure.bzl @@ -826,7 +826,7 @@ def symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name, if src_dir != None: src_dir = _norm_path(src_dir) dest_dir = _norm_path(dest_dir) - files = _read_dir(repository_ctx, src_dir) + files = '\n'.join(sorted(_read_dir(repository_ctx, src_dir).splitlines())) # Create a list with the src_dir stripped to use for outputs. dest_files = files.replace(src_dir, '').splitlines() src_files = files.splitlines() diff --git a/third_party/py/python_configure.bzl b/third_party/py/python_configure.bzl index c16eb3a12a..954f21f5f8 100644 --- a/third_party/py/python_configure.bzl +++ b/third_party/py/python_configure.bzl @@ -118,7 +118,7 @@ def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name, if src_dir != None: src_dir = _norm_path(src_dir) dest_dir = _norm_path(dest_dir) - files = _read_dir(repository_ctx, src_dir) + files = '\n'.join(sorted(_read_dir(repository_ctx, src_dir).splitlines())) # Create a list with the src_dir stripped to use for outputs. dest_files = files.replace(src_dir, '').splitlines() src_files = files.splitlines() -- GitLab From dbe690c679138837f07f99e04e747cdcc1ce8fd2 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Thu, 1 Feb 2018 22:51:33 -0800 Subject: [PATCH 423/423] Fix sanity build (#16674) * Fix sanity. * Add back other sanity tests. * Add sanity test back. * Fix build error. * Add blacklist. * Add whitelist. -- GitLab
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
tensorflow-1.6.0rc0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.6.0rc0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.5.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A
tensorflow_gpu-1.5.0GPU3.5-3.6MSVC 2015 update 3Cmake v3.6.379
tensorflow-1.4.0CPU3.5-3.6MSVC 2015 update 3Cmake v3.6.3N/AN/A